{"meta":{"query_hash":"8f2164bc7fc5","filters":{"topic":"Indoor and Outdoor Localization Technologies"},"cohort_total":1429,"direct_labels_cover":0,"predictions_cover":1429,"exported":1429,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/8f2164bc7fc5","api":"https://metacan.xera.ac/api/v1/cohort?topic=Indoor+and+Outdoor+Localization+Technologies"},"results":[{"id":"W1035596560","doi":"10.1016/j.procs.2015.07.260","title":"Reducing Phase Cancellation Effect with ASK-PSK Modulated Stamp in Augmented UHF RFID Indoor Localization System","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Ultra high frequency; Ask price; Phase (matter); Telecommunications; Computer network; Physics","score_opus":0.008817598851492353,"score_gpt":0.2242047898534415,"score_spread":0.21538719100194914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1035596560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32640058,0.0013740037,0.66477764,0.0004918988,0.00021919118,0.00009261607,0.000073948424,0.0012946787,0.005275457],"genre_scores_gemma":[0.88072705,0.0004969242,0.11413105,0.00026449753,0.000099893885,0.00004319323,0.000087481356,0.000032836248,0.004117249],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954283,0.00008279453,0.000030151823,0.000072801195,0.00022553823,0.000045950266],"domain_scores_gemma":[0.9995485,0.00009918321,0.00012350945,0.000064904045,0.0001457165,0.000018051618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002472046,0.00047937056,0.00029242088,0.0003752541,0.00033030225,0.0004583846,0.00053961796,0.0005448685,0.0010835284],"category_scores_gemma":[0.00053760014,0.00016340891,0.00032678826,0.00038319424,0.00032776172,0.00094678835,0.00050430564,0.00042784528,0.0005932938],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000912064,0.0000961512,0.005405718,0.00039744898,0.000085809894,0.00068353186,0.00036261414,0.011840534,0.77468103,0.004015775,0.0010971198,0.20042227],"study_design_scores_gemma":[0.000106690706,0.0019040409,0.0048682545,0.00005008062,0.00019356074,0.003471045,0.00017916627,0.18585293,0.7856025,0.001416732,0.016259082,0.0000959479],"about_ca_topic_score_codex":0.00023053639,"about_ca_topic_score_gemma":0.00038029635,"teacher_disagreement_score":0.0010835284,"about_ca_system_score_codex":0.00023377402,"about_ca_system_score_gemma":0.00022174956,"threshold_uncertainty_score":0.0036247373},"labels":[],"label_agreement":null},{"id":"W1054521633","doi":"10.1007/s00034-015-0141-2","title":"Statistical Distribution of Difference of the Maximum-Likelihood Angle-of-Arrival Spectra","year":2015,"lang":"en","type":"article","venue":"Circuits Systems and Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Mathematics; Spectrum (functional analysis); Probability density function; Grid; Angle of arrival; Statistics; Spectral line; Function (biology); Algorithm; Mathematical analysis; Physics; Geometry; Computer science; Telecommunications","score_opus":0.019222281550765687,"score_gpt":0.21797497270872182,"score_spread":0.19875269115795613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1054521633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27693814,0.00082054146,0.71250373,0.00069498195,0.00010714444,0.00009613401,0.0009801261,0.0008398895,0.007019351],"genre_scores_gemma":[0.9600683,0.00041457382,0.034824125,0.000119032244,0.00011550763,0.000121053716,0.0015157901,0.00026260785,0.002559116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976648,0.00072954065,0.00011383825,0.0005232933,0.0006875448,0.00028104338],"domain_scores_gemma":[0.96181345,0.02986216,0.001837987,0.0022586195,0.0035852392,0.0006425479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061725653,0.0007814592,0.000761184,0.0017663663,0.00049489376,0.0019697524,0.0017146162,0.0013567726,0.006464246],"category_scores_gemma":[0.03315989,0.0006708007,0.00084914605,0.0011616051,0.0017578155,0.0034804163,0.0017610246,0.001545211,0.0013897556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040971586,0.00046885086,0.063371465,0.00096534,0.00039406685,0.0011681857,0.001337718,0.4403137,0.043660875,0.30421758,0.005894498,0.13411067],"study_design_scores_gemma":[0.00009131129,0.00023112961,0.029543174,0.00009768991,0.00006209809,0.0013075342,0.00027086856,0.885087,0.009622161,0.07099155,0.0025294858,0.00016598243],"about_ca_topic_score_codex":0.00058670586,"about_ca_topic_score_gemma":0.0004020797,"teacher_disagreement_score":0.006464246,"about_ca_system_score_codex":0.0008545439,"about_ca_system_score_gemma":0.00096743606,"threshold_uncertainty_score":0.032644033},"labels":[],"label_agreement":null},{"id":"W1068111385","doi":"10.1007/978-3-319-19662-6_7","title":"Localization of a Mobile Node in Shaded Areas","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Global Positioning System; Node (physics); Real-time computing; Probabilistic logic; Particle filter; Trajectory; Artificial intelligence; Telecommunications; Kalman filter","score_opus":0.015061487714176746,"score_gpt":0.22936498036517405,"score_spread":0.2143034926509973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1068111385","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08928636,0.0013337767,0.7618917,0.0007069928,0.0008554662,0.00023002963,0.0008954964,0.0036262658,0.14117387],"genre_scores_gemma":[0.6517205,0.002021235,0.24702404,0.00028919269,0.00018413384,0.00016605499,0.0011092155,0.0004486346,0.09703697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986017,0.000017694281,0.000004531595,0.000046026795,0.00003728743,0.000034208122],"domain_scores_gemma":[0.999785,0.000046868714,0.00002592338,0.00004299012,0.00006740829,0.000031754236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013464413,0.0005772243,0.00036009142,0.0005404959,0.0005441557,0.00071421417,0.00068705645,0.00067588926,0.008053911],"category_scores_gemma":[0.00058388617,0.00029142536,0.00023002458,0.000736291,0.0005065384,0.00097350666,0.0016278806,0.0004690163,0.004508472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009626621,0.00012959001,0.005270674,0.0010222697,0.000060701143,0.0034660432,0.002049751,0.1203287,0.21313913,0.12275315,0.036272656,0.49454466],"study_design_scores_gemma":[0.00011864604,0.00076859654,0.009984406,0.00051506807,0.00013460241,0.0038020958,0.003174444,0.3404192,0.1718405,0.10632426,0.36277816,0.00013993813],"about_ca_topic_score_codex":0.0019146978,"about_ca_topic_score_gemma":0.0020263975,"teacher_disagreement_score":0.008053911,"about_ca_system_score_codex":0.00025809646,"about_ca_system_score_gemma":0.00046599307,"threshold_uncertainty_score":0.026942968},"labels":[],"label_agreement":null},{"id":"W109900394","doi":"10.20868/upm.thesis.10798","title":"Nonparametric Message Passing Methods for Cooperative Localization and Tracking","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Stony Brook University; Deutsches Zentrum für Luft- und Raumfahrt; Aalborg Universitet; Politecnico di Torino; University of Ottawa","keywords":"Nonparametric statistics; Message passing; Tracking (education); Computer science; Theoretical computer science; Psychology; Mathematics; Statistics; Programming language; Pedagogy","score_opus":0.020298671182575907,"score_gpt":0.32570621202051203,"score_spread":0.30540754083793614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W109900394","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00053264847,0.00013489199,0.99885094,0.00004316315,0.000019252093,0.000009698801,0.000008635808,0.00008262983,0.00031801983],"genre_scores_gemma":[0.23073357,0.002018814,0.7591057,0.0001592279,0.0002784717,0.00057251676,0.00027439324,0.00014402476,0.0067133484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989942,0.00039539207,0.0000511067,0.0001536819,0.00034904614,0.00005666856],"domain_scores_gemma":[0.9983182,0.0009710376,0.00017504141,0.0001896776,0.00031184455,0.000034263878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018559466,0.0010567971,0.0010201168,0.0009302602,0.00043604482,0.00093242055,0.0015341903,0.0011177501,0.0013155086],"category_scores_gemma":[0.00478947,0.00044942598,0.00098927,0.0013404832,0.00091695256,0.0016246367,0.001019547,0.0017391514,0.000570862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004999642,0.000039306276,0.00035164884,0.00015936024,0.00006299037,0.00006114629,0.00012459932,0.75820357,0.002688535,0.122574806,0.0019777908,0.11370633],"study_design_scores_gemma":[0.000007901873,0.00001646631,0.000058715043,0.0000069340595,0.0000070281512,0.000013300976,0.000006236611,0.97779924,0.0004752695,0.019204844,0.0023971759,0.000006917989],"about_ca_topic_score_codex":0.0034406716,"about_ca_topic_score_gemma":0.0018483899,"teacher_disagreement_score":0.0034406716,"about_ca_system_score_codex":0.0008807174,"about_ca_system_score_gemma":0.0010295131,"threshold_uncertainty_score":0.009815276},"labels":[],"label_agreement":null},{"id":"W111456786","doi":"","title":"Assisting personal positioning in indoor environments using map matching","year":2011,"lang":"en","type":"article","venue":"Archives of Photogrammetry Cartography and Remote Sensing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Map matching; Computer science; Global Positioning System; Geospatial analysis; GNSS applications; Inertial navigation system; Real-time computing; Mobile mapping; USable; Computer vision; Matching (statistics); Position (finance); Artificial intelligence; Orientation (vector space); Remote sensing; Geography; Telecommunications","score_opus":0.01333861016533478,"score_gpt":0.20014540261042155,"score_spread":0.18680679244508677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W111456786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023896722,0.0001860996,0.96802974,0.000040479437,0.000060198905,0.00006294932,0.00015459585,0.0037055567,0.0038636287],"genre_scores_gemma":[0.4466058,0.00042327898,0.5467477,0.000048764807,0.000047114383,0.00009920715,0.00054346345,0.00019597549,0.005288758],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995359,0.000080325364,0.00002128662,0.000106466505,0.00020311555,0.000052883683],"domain_scores_gemma":[0.99969447,0.000063819236,0.000027980797,0.00008251486,0.00011024547,0.00002095533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041260337,0.00064229267,0.00075807126,0.0013761222,0.00037671562,0.000797675,0.0008389693,0.00054310507,0.0025888064],"category_scores_gemma":[0.0011054687,0.00026345652,0.0004193468,0.001826647,0.00019117283,0.0011869803,0.0012553505,0.0003404837,0.0019367648],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026016432,0.00009941037,0.0026878954,0.00018705672,0.000077712335,0.00033401878,0.00026607298,0.0436165,0.03608034,0.004140898,0.0040261834,0.9082236],"study_design_scores_gemma":[0.000051506846,0.00026796773,0.0067439945,0.000048321966,0.000105215346,0.0009906461,0.00040200565,0.88166475,0.06859912,0.008429213,0.032612346,0.00008496434],"about_ca_topic_score_codex":0.0018563485,"about_ca_topic_score_gemma":0.0015642815,"teacher_disagreement_score":0.0025888064,"about_ca_system_score_codex":0.00020118403,"about_ca_system_score_gemma":0.00039568773,"threshold_uncertainty_score":0.008660436},"labels":[],"label_agreement":null},{"id":"W126341972","doi":"","title":"Exploiting Smartphone Sensors for Indoor Positioning: A Survey","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Compass; Accelerometer; Gyroscope; Computer science; Context (archaeology); Real-time computing; Mobile device; Position (finance); Embedded system; Human–computer interaction; Engineering; Geography; World Wide Web","score_opus":0.0600438948853521,"score_gpt":0.21684355746522058,"score_spread":0.1567996625798685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W126341972","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027786994,0.8658677,0.07714255,0.001106517,0.0007105711,0.00025520596,0.0009247474,0.00056374626,0.025642077],"genre_scores_gemma":[0.11458411,0.8312258,0.039233368,0.00059214997,0.00073988363,0.00020350923,0.0016122246,0.00007941503,0.0117294965],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99906546,0.00017455175,0.00010570009,0.00016278097,0.00043157127,0.00005990994],"domain_scores_gemma":[0.997976,0.00079084473,0.00016252238,0.00011041888,0.0009045609,0.00005566185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007934247,0.00095521077,0.00079762744,0.0024380186,0.00023080847,0.0010460991,0.00091359636,0.0010209695,0.0025296058],"category_scores_gemma":[0.0017036898,0.00053868676,0.0006694662,0.0036513435,0.00022561179,0.0022119093,0.0004976767,0.000548353,0.0021578234],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001489295,0.00008914716,0.006522783,0.0063983444,0.0001118623,0.00026905967,0.000158521,0.001842566,0.010424482,0.0019029325,0.010231719,0.9618997],"study_design_scores_gemma":[0.00004051346,0.0013746505,0.0316815,0.004096468,0.0006284193,0.0064224782,0.0011437322,0.015072142,0.03064494,0.0023251127,0.9063347,0.00023524607],"about_ca_topic_score_codex":0.001477822,"about_ca_topic_score_gemma":0.00186035,"teacher_disagreement_score":0.0025296058,"about_ca_system_score_codex":0.00022876434,"about_ca_system_score_gemma":0.0003957353,"threshold_uncertainty_score":0.00846231},"labels":[],"label_agreement":null},{"id":"W126648389","doi":"","title":"Hybrid inference for sensor network localization using a mobile robot","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; University of British Columbia; McGill University","funders":"","keywords":"Computer science; Markov chain Monte Carlo; Inference; Extended Kalman filter; Kalman filter; Gaussian process; Particle filter; Convergence (economics); Simultaneous localization and mapping; Artificial intelligence; Machine learning; Mobile robot; Robot; Bayesian probability; Gaussian","score_opus":0.016788147271073344,"score_gpt":0.2620892161755435,"score_spread":0.24530106890447015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W126648389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041731074,0.00004585066,0.9952714,0.000025899348,0.000009880548,0.0000062347344,0.0000053489307,0.00022839241,0.00023392834],"genre_scores_gemma":[0.54819393,0.00014672105,0.44903496,0.00007457384,0.0000658496,0.00010111243,0.00006704298,0.000100275734,0.002215461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992988,0.00022740233,0.000028892357,0.000196906,0.00019575133,0.000052159026],"domain_scores_gemma":[0.9986119,0.0008907637,0.00015427615,0.0001731339,0.00013425185,0.00003556852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015516239,0.00065494276,0.0009173912,0.0005953106,0.000494472,0.0008349554,0.0014091448,0.0009300715,0.0015324951],"category_scores_gemma":[0.004099051,0.00060366624,0.0007592222,0.00050998246,0.0011657686,0.0018305759,0.0010630302,0.0009708516,0.00036487397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009692592,0.000023595536,0.00061280775,0.000038433103,0.000057326735,0.000051531148,0.000052991672,0.94023705,0.0026825694,0.012812525,0.00021278128,0.043121416],"study_design_scores_gemma":[0.0000061363635,0.000013410682,0.000058755664,0.0000019173376,0.0000047434833,0.000007067291,0.0000032326095,0.9959586,0.00048251808,0.003284799,0.00017481565,0.0000039999063],"about_ca_topic_score_codex":0.0043562623,"about_ca_topic_score_gemma":0.0048421593,"teacher_disagreement_score":0.0043562623,"about_ca_system_score_codex":0.0008038545,"about_ca_system_score_gemma":0.000702158,"threshold_uncertainty_score":0.008661807},"labels":[],"label_agreement":null},{"id":"W1270110300","doi":"10.1007/978-3-642-40238-8_11","title":"Smartphone Sensor Reliability for Augmented Reality Applications","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Compass; Augmented reality; Gyroscope; Global Positioning System; Android (operating system); Computer science; Mobile device; Reliability (semiconductor); Inertial measurement unit; Orientation (vector space); Accelerometer; Human–computer interaction; Real-time computing; Engineering; Computer vision; Geography; Telecommunications; Cartography","score_opus":0.01979339472094814,"score_gpt":0.23076154310049043,"score_spread":0.2109681483795423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1270110300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039164778,0.030355867,0.884994,0.0011839629,0.00106553,0.00013328565,0.0011134454,0.006766846,0.035222296],"genre_scores_gemma":[0.7609412,0.015200055,0.17060895,0.00030747656,0.0005615268,0.0001416342,0.0017339828,0.0014359383,0.049069226],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990355,0.00016298253,0.00006736109,0.00013908457,0.0005208894,0.000074144635],"domain_scores_gemma":[0.998142,0.0005826391,0.00013494365,0.00040963088,0.0006942089,0.000036598758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007559018,0.0013892336,0.00070720585,0.000929493,0.00023840847,0.0013261826,0.0016032234,0.0011301124,0.015248908],"category_scores_gemma":[0.003539739,0.00074836775,0.00050499954,0.0010498465,0.00037892623,0.0021217866,0.001171655,0.0008857357,0.0046340623],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005700925,0.00006739761,0.0017304304,0.0013436463,0.00006902091,0.00036112109,0.00036028156,0.017989097,0.08101308,0.010988941,0.029765604,0.85574144],"study_design_scores_gemma":[0.000074172414,0.0014757827,0.013846782,0.00092445087,0.00044372745,0.00583623,0.0007318924,0.4193349,0.2318113,0.03186093,0.29338846,0.0002715958],"about_ca_topic_score_codex":0.0009333676,"about_ca_topic_score_gemma":0.0012263971,"teacher_disagreement_score":0.015248908,"about_ca_system_score_codex":0.00038016567,"about_ca_system_score_gemma":0.00029156514,"threshold_uncertainty_score":0.051012635},"labels":[],"label_agreement":null},{"id":"W132351426","doi":"10.22260/isarc2013/0067","title":"Experimental Study for Efficient Use of RFID in Construction","year":2013,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.01601635894995589,"score_gpt":0.21999912643756017,"score_spread":0.20398276748760427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W132351426","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9837272,0.0000659587,0.013827984,0.000045752353,0.00003441969,0.00064786756,0.00017526397,0.000082450584,0.001393099],"genre_scores_gemma":[0.9703208,0.00017256642,0.024458244,0.000060549715,0.000019352907,0.0014496794,0.00035330144,0.0000266504,0.00313889],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982108,0.00060028274,0.00016607033,0.0002920833,0.00050554203,0.00022525893],"domain_scores_gemma":[0.9955727,0.00192971,0.0005453761,0.0007410736,0.00096849276,0.00024271087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017218015,0.0006090254,0.00043580378,0.0004945676,0.0004824879,0.00050162757,0.0010367587,0.00081166637,0.003314536],"category_scores_gemma":[0.002887576,0.00038332466,0.00052781263,0.00045898772,0.00069802516,0.0005757904,0.000828854,0.00063428294,0.00061959616],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008220327,0.027813016,0.050147753,0.002428644,0.000209684,0.0018160637,0.004629048,0.020934794,0.772245,0.0033535848,0.0021796525,0.10602241],"study_design_scores_gemma":[0.000715993,0.13218446,0.11120929,0.0002604753,0.00040031542,0.001230055,0.006952576,0.028201308,0.6966003,0.0014850722,0.020538628,0.00022146016],"about_ca_topic_score_codex":0.0003893495,"about_ca_topic_score_gemma":0.0006174357,"teacher_disagreement_score":0.003314536,"about_ca_system_score_codex":0.00037042334,"about_ca_system_score_gemma":0.00050260563,"threshold_uncertainty_score":0.011088192},"labels":[],"label_agreement":null},{"id":"W1480596045","doi":"10.1109/icc.2015.7249377","title":"Cost-effective and accurate nodes localization in heterogeneous wireless sensor networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; Institut National de la Recherche Scientifique","funders":"","keywords":"Wireless sensor network; Computer science; Transmission (telecommunications); Key distribution in wireless sensor networks; Wireless; Wireless network; Computer network; Distributed computing; Algorithm; Real-time computing; Telecommunications","score_opus":0.02312182784303023,"score_gpt":0.24733221376449455,"score_spread":0.2242103859214643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1480596045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019049473,0.0002504911,0.980037,0.00005261742,0.000019256355,0.00001260229,0.0000093095005,0.0002382365,0.00033087665],"genre_scores_gemma":[0.73459554,0.0004380903,0.26368377,0.000042801028,0.00003451606,0.000044157747,0.00006478004,0.000048157017,0.0010481084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995838,0.00012215217,0.000023783845,0.000077139644,0.00015520926,0.000037883907],"domain_scores_gemma":[0.99943703,0.0002246298,0.000105603656,0.00010742187,0.00010454437,0.000020828293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006291518,0.00051209633,0.0004590192,0.0005408334,0.00033151777,0.00049416005,0.0009395437,0.0004367847,0.00026539952],"category_scores_gemma":[0.0018587391,0.00019003442,0.00027448687,0.00051385927,0.0003479248,0.0011298372,0.00086791447,0.0003500518,0.0001651536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014471331,0.000044249868,0.0019263943,0.00013336039,0.000048582056,0.00027837456,0.00011921014,0.67858124,0.046356224,0.01282201,0.0008403422,0.25870526],"study_design_scores_gemma":[0.000010425953,0.000064013664,0.00059098186,0.0000064252163,0.000019810434,0.00010964139,0.000037995407,0.9805462,0.013198559,0.0037513613,0.0016508435,0.000013724485],"about_ca_topic_score_codex":0.0011015974,"about_ca_topic_score_gemma":0.0011897887,"teacher_disagreement_score":0.0011015974,"about_ca_system_score_codex":0.00033703924,"about_ca_system_score_gemma":0.00034385142,"threshold_uncertainty_score":0.00332731},"labels":[],"label_agreement":null},{"id":"W1483626562","doi":"10.1109/pimrc.2014.7136174","title":"Cooperative localization of mobile nodes in NLOS","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Non-line-of-sight propagation; Kalman filter; Computer science; Extended Kalman filter; Control theory (sociology); Covariance matrix; Quadratic programming; Covariance intersection; Square root; Mathematical optimization; Algorithm; Mathematics; Wireless; Artificial intelligence; Telecommunications","score_opus":0.004135334955454138,"score_gpt":0.19668518791457382,"score_spread":0.19254985295911967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1483626562","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023321409,0.00022093508,0.9752499,0.00006891657,0.000022666143,0.000018215398,0.000015219896,0.00015255332,0.00093016477],"genre_scores_gemma":[0.8995115,0.0003552815,0.09665348,0.000079854486,0.000051482362,0.000108503744,0.00009517492,0.00003745679,0.003107167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932754,0.00016022842,0.000029276069,0.00022753337,0.00017557149,0.00007987234],"domain_scores_gemma":[0.99934274,0.0002995043,0.00011432938,0.0000788327,0.00013239325,0.00003223274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006647941,0.00079494703,0.0008825332,0.00045011428,0.00057472725,0.0006476414,0.0010117298,0.0008896582,0.0005816818],"category_scores_gemma":[0.0018190572,0.00038874758,0.00050080084,0.0007484005,0.0008462314,0.0014032164,0.0014414507,0.0005553394,0.00025676127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017875544,0.00004608198,0.0019313394,0.00015540942,0.00008674801,0.00048012892,0.00041028496,0.90317994,0.013919171,0.008772835,0.00071430166,0.07012506],"study_design_scores_gemma":[0.000013805281,0.00005779243,0.00029576023,0.0000053345957,0.000014218944,0.000060191323,0.000058127895,0.9933693,0.0021551251,0.003084895,0.0008755669,0.000009923308],"about_ca_topic_score_codex":0.00498057,"about_ca_topic_score_gemma":0.00431987,"teacher_disagreement_score":0.00498057,"about_ca_system_score_codex":0.00048264037,"about_ca_system_score_gemma":0.00070417207,"threshold_uncertainty_score":0.009903133},"labels":[],"label_agreement":null},{"id":"W1487896116","doi":"10.1007/978-3-540-24606-0_2","title":"Matrix Pencil for Positioning in Wireless ad hoc Sensor Network","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Matrix pencil; Multipath propagation; Computer science; Pencil (optics); Wireless ad hoc network; Wireless sensor network; Triangulation; Wireless; Algorithm; Matrix (chemical analysis); Real-time computing; Engineering; Computer network; Mathematics; Telecommunications","score_opus":0.009817287662731804,"score_gpt":0.22320485790104852,"score_spread":0.21338757023831673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1487896116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011828083,0.0058941063,0.9678068,0.0004435493,0.0030369167,0.00006053654,0.0003134124,0.0017560885,0.019505732],"genre_scores_gemma":[0.079575464,0.015531778,0.7283224,0.0009783409,0.002805058,0.00052549614,0.001328957,0.0010250197,0.16990753],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995022,0.00016265658,0.0000312085,0.00008179525,0.00020115098,0.000021014192],"domain_scores_gemma":[0.999589,0.00016135952,0.000026504811,0.00009048136,0.00011498637,0.000017700537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003013369,0.0016988484,0.0007828328,0.0006070553,0.00030826783,0.0011281531,0.0011021203,0.0009206649,0.023639046],"category_scores_gemma":[0.0016351069,0.00027178097,0.00034601055,0.0016852451,0.00055556913,0.0012026357,0.0005557095,0.0010641002,0.012757884],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014469508,0.00004175692,0.00015308359,0.00090118847,0.000039130893,0.00036927694,0.00015333842,0.040479846,0.014393609,0.16541286,0.10625457,0.6716567],"study_design_scores_gemma":[0.000040499966,0.0003549146,0.00033250518,0.00026122524,0.000044632467,0.0015352218,0.00014097575,0.26979923,0.016367232,0.08971189,0.6213322,0.000079550795],"about_ca_topic_score_codex":0.0006727127,"about_ca_topic_score_gemma":0.0009548768,"teacher_disagreement_score":0.023639046,"about_ca_system_score_codex":0.00038693866,"about_ca_system_score_gemma":0.00026175895,"threshold_uncertainty_score":0.07908052},"labels":[],"label_agreement":null},{"id":"W1493185040","doi":"","title":"An optimal local map registration technique for wireless sensor network localization problems","year":2008,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Wireless sensor network; Affine transformation; Pairwise comparison; Rotation (mathematics); Global Map; Set (abstract data type); Local search (optimization); Artificial intelligence; Algorithm; Computer vision; Mathematics","score_opus":0.020309347113222218,"score_gpt":0.24305710201362818,"score_spread":0.22274775490040596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493185040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00086179894,0.000052676383,0.99863905,0.00003330544,0.000009609986,0.000009125586,0.0000049847304,0.00011357122,0.0002758448],"genre_scores_gemma":[0.116612844,0.00032974646,0.8809029,0.00005915325,0.000082957326,0.00019609922,0.00009509182,0.00015618879,0.0015650557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991653,0.000290913,0.000030727693,0.00015143031,0.0003159117,0.0000457843],"domain_scores_gemma":[0.9994386,0.00023701294,0.00007885116,0.00011176315,0.0001150442,0.000018631616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010357364,0.00089341035,0.0010123859,0.001063294,0.0006381046,0.000636499,0.0011874157,0.0008200739,0.0014925317],"category_scores_gemma":[0.0031617277,0.00050893694,0.0008777145,0.0013824155,0.0010040173,0.0020808943,0.0016027084,0.0013394419,0.0008388283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000146384,0.00006929855,0.00048168696,0.00019488731,0.00008915548,0.00017387727,0.0002670459,0.46586436,0.01922127,0.075523354,0.004192033,0.43377668],"study_design_scores_gemma":[0.000025272722,0.000115097195,0.00018194053,0.00001626041,0.00003092698,0.00022410761,0.000050084396,0.9540903,0.008795586,0.029370261,0.007069829,0.000030391833],"about_ca_topic_score_codex":0.000870212,"about_ca_topic_score_gemma":0.0007556065,"teacher_disagreement_score":0.0014925317,"about_ca_system_score_codex":0.00039848138,"about_ca_system_score_gemma":0.000811323,"threshold_uncertainty_score":0.0054775476},"labels":[],"label_agreement":null},{"id":"W1496157640","doi":"10.1007/978-3-642-03417-6_35","title":"Relative Span Weighted Localization of Uncooperative Nodes in Wireless Networks","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Span (engineering); Computer network; Wireless; Wireless network; Telecommunications; Structural engineering; Engineering","score_opus":0.007664853559296427,"score_gpt":0.20725111668557653,"score_spread":0.1995862631262801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496157640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03719558,0.0007228842,0.9592323,0.000058497324,0.00004834941,0.000016557986,0.00003179101,0.00014832527,0.002545778],"genre_scores_gemma":[0.7284525,0.0018775383,0.26002064,0.00007332225,0.00015061328,0.00009653803,0.00023979224,0.00013763848,0.008951444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935406,0.00018345694,0.000028110111,0.00015036871,0.00022519434,0.000058881582],"domain_scores_gemma":[0.9992859,0.00032128175,0.00008771944,0.00011845576,0.00015711752,0.000029588646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081285875,0.00085868535,0.0008223789,0.0009963656,0.00040137695,0.0007882212,0.0016274994,0.0007259522,0.0012080198],"category_scores_gemma":[0.0032752904,0.0004339315,0.00036603864,0.0019496499,0.0006619753,0.0022863213,0.002171251,0.00063835405,0.0004299265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026519175,0.00004873895,0.0011184576,0.00025680356,0.00007935853,0.00015838326,0.00033955547,0.7052132,0.016170233,0.050642647,0.0019033918,0.22380412],"study_design_scores_gemma":[0.000005435087,0.00009569239,0.0003690757,0.00001735864,0.00002045617,0.00014767457,0.00007428306,0.962632,0.003913487,0.031370506,0.0013402862,0.0000137725765],"about_ca_topic_score_codex":0.0006656665,"about_ca_topic_score_gemma":0.00086394575,"teacher_disagreement_score":0.0016274994,"about_ca_system_score_codex":0.00049757026,"about_ca_system_score_gemma":0.00028324965,"threshold_uncertainty_score":0.004298866},"labels":[],"label_agreement":null},{"id":"W1496761764","doi":"10.1109/icc.2015.7248825","title":"Optimum reference node deployment for TOA-based localization","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Software deployment; Computer science; Node (physics); Maxima and minima; Time of arrival; Wireless; Real-time computing; Mathematical optimization; Mathematics; Telecommunications; Engineering","score_opus":0.042798611985314684,"score_gpt":0.2540351624755274,"score_spread":0.2112365504902127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496761764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017652512,0.00044610683,0.9798687,0.00012013824,0.000038256883,0.000021402468,0.000020373143,0.00027603467,0.0015564555],"genre_scores_gemma":[0.72725904,0.0006680587,0.27043673,0.000066863926,0.00004231336,0.00007550348,0.00007754673,0.000058243553,0.0013156881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991498,0.00035628767,0.00002852784,0.00015580626,0.0002318261,0.00007771729],"domain_scores_gemma":[0.99932384,0.00019923605,0.0001411863,0.00011887537,0.00018224418,0.00003460335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071689097,0.0006807996,0.0006820396,0.0008453549,0.0006055514,0.00053970667,0.0008575022,0.0008624418,0.000614631],"category_scores_gemma":[0.0028887473,0.0003238203,0.00027688395,0.00093297637,0.0005739453,0.0010186557,0.00085511606,0.00046146705,0.000608853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032632612,0.0000691728,0.0018232507,0.00019810886,0.000052007927,0.00033038063,0.00035359486,0.76278347,0.062259767,0.030854326,0.0032511794,0.13769846],"study_design_scores_gemma":[0.0000438491,0.0002400764,0.0008605678,0.000026176904,0.00003145863,0.0002795227,0.00008728407,0.97305036,0.01351056,0.0062333797,0.00559263,0.000044132117],"about_ca_topic_score_codex":0.0016477848,"about_ca_topic_score_gemma":0.0032100405,"teacher_disagreement_score":0.0016477848,"about_ca_system_score_codex":0.0005993216,"about_ca_system_score_gemma":0.00057530444,"threshold_uncertainty_score":0.0043483973},"labels":[],"label_agreement":null},{"id":"W1497647118","doi":"10.1109/ccece.2015.7129304","title":"An efficient algorithm for localization using RSSI based on ZigBee","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Algorithm; Embedded system; Computer network; Real-time computing","score_opus":0.027514519543952846,"score_gpt":0.26309341013025905,"score_spread":0.2355788905863062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1497647118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019472693,0.00023151602,0.9958379,0.00004485776,0.00006014236,0.00003286142,0.000023068942,0.0011037536,0.00071855605],"genre_scores_gemma":[0.12617847,0.00093134015,0.8657616,0.00012219163,0.000094810115,0.00021946114,0.00039723332,0.00016501056,0.006129936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993693,0.000092311486,0.000050907343,0.00013625863,0.0003077798,0.000043486172],"domain_scores_gemma":[0.9996489,0.000057851696,0.000040050694,0.000051711693,0.00019001032,0.000011556158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038386806,0.00081143016,0.0008072262,0.0013214458,0.00056631,0.0006288952,0.0010695431,0.00071733625,0.0016688942],"category_scores_gemma":[0.00076928746,0.00038095273,0.00049398106,0.0012677093,0.0003626884,0.0012723386,0.00064676115,0.00064156525,0.0015398066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015321399,0.00007750495,0.0020360032,0.0003425621,0.00011800018,0.00020930367,0.00014566937,0.052111417,0.045553397,0.010856353,0.006625378,0.88177115],"study_design_scores_gemma":[0.0001736579,0.00036008225,0.003718654,0.000099586774,0.000157025,0.0017723413,0.00017087546,0.8288728,0.083712816,0.009902268,0.070873596,0.00018628978],"about_ca_topic_score_codex":0.0015172145,"about_ca_topic_score_gemma":0.0016840792,"teacher_disagreement_score":0.0016688942,"about_ca_system_score_codex":0.0003777758,"about_ca_system_score_gemma":0.00059070677,"threshold_uncertainty_score":0.005583048},"labels":[],"label_agreement":null},{"id":"W1497720255","doi":"10.5772/29446","title":"Emerging New Trends in Hybrid Vehicle Localization Systems","year":2012,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.01591533813962193,"score_gpt":0.221384054869319,"score_spread":0.20546871672969708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1497720255","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007798978,0.6950561,0.18775116,0.00872193,0.003535049,0.000048437516,0.00020372117,0.00082008156,0.09606454],"genre_scores_gemma":[0.116005324,0.6788988,0.096999265,0.0035834045,0.005482893,0.00013953909,0.00092263543,0.0002028571,0.09776525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996604,0.000064841515,0.000022030139,0.000073332536,0.0001504254,0.000029039269],"domain_scores_gemma":[0.99961424,0.00017216081,0.00002093577,0.000027978178,0.00013664197,0.000027976152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057959044,0.00052119396,0.0005471217,0.0009127229,0.00024303768,0.0018418194,0.0010258524,0.001481142,0.009904916],"category_scores_gemma":[0.0006812886,0.00034406604,0.00040376204,0.0020330893,0.00061767764,0.003763951,0.001016124,0.0014812767,0.00376936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006757704,0.000084757536,0.0004996405,0.0028167057,0.000047002988,0.00025742463,0.0002854871,0.008553381,0.0064451243,0.18899168,0.0410724,0.7508788],"study_design_scores_gemma":[0.000011180941,0.00016268369,0.0006545488,0.00047170298,0.000034601435,0.0009163725,0.00031456977,0.020588327,0.0021666845,0.064405285,0.9102314,0.000042654887],"about_ca_topic_score_codex":0.0007137004,"about_ca_topic_score_gemma":0.00068580534,"teacher_disagreement_score":0.009904916,"about_ca_system_score_codex":0.00061786955,"about_ca_system_score_gemma":0.0003993745,"threshold_uncertainty_score":0.033135235},"labels":[],"label_agreement":null},{"id":"W1500765646","doi":"10.1002/sec.877","title":"Mechanisms to locate noncooperative transmitters in wireless networks based on residual signal strengths","year":2013,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Transmitter; Computer science; Residual; Bounding overwatch; Position (finance); Algorithm; SIGNAL (programming language); Transmitter power output; Set (abstract data type); Wireless sensor network; Telecommunications; Mathematical optimization; Artificial intelligence; Computer network; Mathematics","score_opus":0.005577588613289634,"score_gpt":0.1969760233800704,"score_spread":0.19139843476678076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1500765646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085518666,0.00043457097,0.91211265,0.00013105093,0.000035805468,0.000075975746,0.000011734188,0.0004599309,0.0012195228],"genre_scores_gemma":[0.89680845,0.00027415494,0.10162347,0.000056896795,0.000027060572,0.00007982892,0.000027494054,0.000036823298,0.0010659052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769235,0.00079813803,0.00012829548,0.0003002349,0.0008613675,0.00021966702],"domain_scores_gemma":[0.98757964,0.006084411,0.0027036462,0.0018057874,0.0014305214,0.00039607086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041513857,0.0011597268,0.0013977581,0.0014315335,0.0005508796,0.0012671117,0.0027169848,0.0011589024,0.0009057037],"category_scores_gemma":[0.014856869,0.0004881439,0.0004491591,0.0006508919,0.001852658,0.0029217603,0.003918278,0.0008830442,0.0004019356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061622425,0.0001665087,0.0050999187,0.00025031055,0.0001750505,0.00036998655,0.0004085995,0.75281405,0.047967773,0.043697935,0.0007180747,0.14771554],"study_design_scores_gemma":[0.000051244762,0.0004011531,0.00074311456,0.000025880254,0.00004813651,0.00027934313,0.000087701104,0.97052044,0.019844458,0.0072122496,0.0007483306,0.00003795783],"about_ca_topic_score_codex":0.0006677019,"about_ca_topic_score_gemma":0.0005833474,"teacher_disagreement_score":0.0041513857,"about_ca_system_score_codex":0.00080893224,"about_ca_system_score_gemma":0.0007256655,"threshold_uncertainty_score":0.021954894},"labels":[],"label_agreement":null},{"id":"W1501435398","doi":"10.1007/978-3-642-14785-2_13","title":"iCCA-MAP Versus MCL and Dual MCL: Comparison of Mobile Node Localization Algorithms","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Node (physics); Probabilistic logic; Wireless sensor network; Algorithm; Real-time computing; Dual (grammatical number); Computer network; Artificial intelligence; Engineering","score_opus":0.013610512968757652,"score_gpt":0.25004378138182554,"score_spread":0.2364332684130679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501435398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15225907,0.014480462,0.79962885,0.0012616407,0.0011228138,0.00030669157,0.0016780145,0.009124429,0.020138014],"genre_scores_gemma":[0.575835,0.002961745,0.40953612,0.0002616514,0.00022107788,0.00020180797,0.002482696,0.0007411791,0.007758633],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971464,0.00089173263,0.00013167279,0.0003983453,0.0011322463,0.00029955883],"domain_scores_gemma":[0.9894493,0.0058324835,0.00047465347,0.001093852,0.002763393,0.00038629002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030435363,0.0012216892,0.0018831051,0.002603438,0.0006374187,0.0026678413,0.0027488403,0.0018492619,0.0057144053],"category_scores_gemma":[0.015697798,0.00044327465,0.0006127044,0.0038278585,0.00059699017,0.0027393587,0.0026657027,0.0011224567,0.0016851536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0065430473,0.00036208905,0.0046026227,0.0009819416,0.00047051455,0.00008138632,0.0001658314,0.17827979,0.0045218575,0.0072126137,0.015861137,0.78091705],"study_design_scores_gemma":[0.00023213461,0.0008548274,0.002632012,0.00008422108,0.00020544324,0.00037239835,0.00028591248,0.98121727,0.0049234373,0.0041260463,0.005006023,0.00006021685],"about_ca_topic_score_codex":0.006929005,"about_ca_topic_score_gemma":0.009589256,"teacher_disagreement_score":0.006929005,"about_ca_system_score_codex":0.00133333,"about_ca_system_score_gemma":0.0020656502,"threshold_uncertainty_score":0.01911664},"labels":[],"label_agreement":null},{"id":"W1502600815","doi":"10.1109/mwscas.1992.271167","title":"A simple approach to observer path design for bearings-only tracking","year":2003,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Observer (physics); Fisher information; Control theory (sociology); Computer science; Separation principle; Path (computing); Filter (signal processing); Tracking (education); Artificial intelligence; State observer; Mathematics; Computer vision; Nonlinear system; Control (management); Machine learning","score_opus":0.044432348527249886,"score_gpt":0.23500927621506462,"score_spread":0.19057692768781473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502600815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048549476,0.000020024463,0.9987915,0.000016318261,0.000012201675,0.00001989438,0.0000062814897,0.000086915716,0.0005614219],"genre_scores_gemma":[0.21933563,0.00027941316,0.77505237,0.000104711864,0.00006659293,0.0003377527,0.00008545526,0.0001055605,0.0046324306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949145,0.00011364074,0.00003143228,0.0001132613,0.00021415937,0.000036009453],"domain_scores_gemma":[0.99934965,0.00021241381,0.000108953725,0.00008811931,0.0002126475,0.000028097658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007719344,0.0008997794,0.0005891515,0.0005973433,0.00037397936,0.00080996886,0.00069506885,0.00092852855,0.0034917668],"category_scores_gemma":[0.002735412,0.00045479755,0.00056469813,0.00035274046,0.0006513499,0.0009211588,0.0007357642,0.001071594,0.0010339223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017946669,0.00011008602,0.0006531755,0.00030146816,0.00006142837,0.00018280593,0.00023237542,0.60028064,0.044196725,0.12736917,0.0026489778,0.2237837],"study_design_scores_gemma":[0.000043087613,0.00021366369,0.00024940417,0.000020863645,0.00002055102,0.00008807335,0.000015053913,0.9536824,0.009638501,0.026989585,0.0090088975,0.000029798903],"about_ca_topic_score_codex":0.001265698,"about_ca_topic_score_gemma":0.0017546888,"teacher_disagreement_score":0.0034917668,"about_ca_system_score_codex":0.0005781132,"about_ca_system_score_gemma":0.0010744915,"threshold_uncertainty_score":0.01168108},"labels":[],"label_agreement":null},{"id":"W1504008136","doi":"10.1155/2014/489289","title":"MND<sub>WSN</sub> for Helping People with Different Disabilities","year":2014,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Wheelchair; Dijkstra's algorithm; Baseline (sea); Assistive technology; Navigation system; Smart phone; Phone; Wireless sensor network; Human–computer interaction; Mobile phone; Mobile device; Path (computing); Disabled people; Real-time computing; Shortest path problem; Computer network; Graph; Telecommunications; World Wide Web; Physical medicine and rehabilitation; Theoretical computer science","score_opus":0.005979764738890683,"score_gpt":0.20385804718629008,"score_spread":0.1978782824473994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1504008136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19128719,0.015384845,0.68896765,0.0059538344,0.0021273,0.00063183403,0.003414547,0.011047111,0.08118576],"genre_scores_gemma":[0.6231187,0.0082374625,0.3076118,0.0012834778,0.00014622085,0.00049081346,0.002066656,0.00020661938,0.056838274],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99991965,0.0000141757355,0.000005817931,0.000015054027,0.000034192806,0.000011084224],"domain_scores_gemma":[0.99991524,0.000013096382,0.000009598648,0.000009065449,0.000040515042,0.000012362944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011227627,0.00032560373,0.00016326268,0.00031230602,0.00025049993,0.0001811645,0.00040814443,0.0002645358,0.005006036],"category_scores_gemma":[0.00029337817,0.0000678951,0.00017564758,0.00034525763,0.00009977502,0.00050107273,0.00073339004,0.00022020037,0.0012040617],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002993041,0.00011334137,0.0061086346,0.0009827001,0.000029596877,0.0006596208,0.00021847806,0.0027270499,0.06602158,0.005361823,0.040553685,0.8769242],"study_design_scores_gemma":[0.000181497,0.0011851576,0.033526585,0.0009607657,0.00031366208,0.007160872,0.0020311829,0.11012986,0.12423002,0.0075233695,0.7126306,0.00012642313],"about_ca_topic_score_codex":0.0024028702,"about_ca_topic_score_gemma":0.0047805123,"teacher_disagreement_score":0.005006036,"about_ca_system_score_codex":0.00025931397,"about_ca_system_score_gemma":0.00029967062,"threshold_uncertainty_score":0.016746879},"labels":[],"label_agreement":null},{"id":"W1504978873","doi":"10.3390/mi6060747","title":"WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices","year":2015,"lang":"en","type":"article","venue":"Micromachines","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"RSS; Computer science; Robustness (evolution); Real-time computing; Signal strength; Matching (statistics); Point (geometry); Simulation; Wireless; Telecommunications; Mathematics","score_opus":0.01233560259453802,"score_gpt":0.22201072550706535,"score_spread":0.20967512291252732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1504978873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04539714,0.00040867945,0.94707406,0.00007139411,0.00016574682,0.00008660308,0.0001476872,0.0029727498,0.003675955],"genre_scores_gemma":[0.62047505,0.00029187335,0.3735267,0.00015957371,0.000093623355,0.0001438246,0.0005243355,0.000108180044,0.0046769134],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994553,0.00008237225,0.000029527273,0.00009889044,0.00025281837,0.00008109572],"domain_scores_gemma":[0.99961674,0.0000759744,0.00005995729,0.00008001379,0.00014259473,0.000024716019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000405435,0.00090447126,0.000655922,0.00094318605,0.00042231925,0.00043967378,0.0013524875,0.00062916946,0.0022622547],"category_scores_gemma":[0.001947175,0.00026792768,0.0005082272,0.0010459148,0.00018426449,0.0006106677,0.00086326065,0.00041387774,0.0013112169],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005628711,0.000110664056,0.0071759126,0.0001923098,0.00009429092,0.00033847187,0.00011869962,0.044932574,0.026333325,0.0028818727,0.0051064785,0.9121524],"study_design_scores_gemma":[0.00012146746,0.00042427375,0.009534736,0.00005281902,0.0000991192,0.0016440983,0.00012606573,0.9225333,0.04430607,0.003932198,0.017152851,0.0000731495],"about_ca_topic_score_codex":0.005169256,"about_ca_topic_score_gemma":0.005548817,"teacher_disagreement_score":0.005169256,"about_ca_system_score_codex":0.00034047285,"about_ca_system_score_gemma":0.0006272012,"threshold_uncertainty_score":0.010278285},"labels":[],"label_agreement":null},{"id":"W1505034505","doi":"10.1109/icc.2015.7248813","title":"Energy-efficient dynamic event detection by participatory sensing","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Participatory sensing; Benchmark (surveying); Overhead (engineering); Event (particle physics); Energy consumption; TRACE (psycholinguistics); Beijing; Focus (optics); Real-time computing; Set (abstract data type); Distributed computing; Wireless sensor network; Energy (signal processing); Efficient energy use; Data mining; Data science; Computer network; Engineering","score_opus":0.013636627433594718,"score_gpt":0.22387070800041722,"score_spread":0.2102340805668225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1505034505","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014918345,0.000075403645,0.9835692,0.00008931555,0.000014062845,0.000036348178,0.000025213016,0.00019757649,0.0010745799],"genre_scores_gemma":[0.7149641,0.00014183733,0.28260133,0.00007091173,0.000029008957,0.0001736694,0.00011053741,0.00004670283,0.0018619067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998681,0.00038027632,0.000037240614,0.00037852224,0.00038458448,0.00013834759],"domain_scores_gemma":[0.9982705,0.0009503046,0.00022869863,0.00025443174,0.000214167,0.00008177865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001047636,0.0008979668,0.0010478097,0.00059953844,0.0006477806,0.0009651267,0.0020454866,0.00094646413,0.0008930413],"category_scores_gemma":[0.0031488787,0.0003865403,0.00066373055,0.00095227204,0.0008025193,0.001452015,0.00222359,0.0006977235,0.0002638381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030052522,0.00021559963,0.0030536351,0.00023345629,0.00010558676,0.0003570578,0.00050097826,0.6943026,0.035295777,0.019441329,0.0022568107,0.24393669],"study_design_scores_gemma":[0.000020834672,0.00008277954,0.0004442723,0.000008216946,0.000012479213,0.000116020106,0.00010278684,0.9848557,0.0037939665,0.009580359,0.00096801104,0.000014601622],"about_ca_topic_score_codex":0.002033543,"about_ca_topic_score_gemma":0.0027127785,"teacher_disagreement_score":0.0020454866,"about_ca_system_score_codex":0.00044703428,"about_ca_system_score_gemma":0.0008102699,"threshold_uncertainty_score":0.0055405498},"labels":[],"label_agreement":null},{"id":"W1514777247","doi":"10.1007/978-3-319-17885-1_629","title":"Indoor Positioning with Wireless Local Area Networks (WLAN)","year":2017,"lang":"en","type":"book-chapter","venue":"Encyclopedia of GIS","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Wi-Fi; Computer network; Wireless lan; Computer science; Wireless; Local area network; Wireless network; Telecommunications","score_opus":0.006512929367739671,"score_gpt":0.18555339175427948,"score_spread":0.1790404623865398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514777247","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027182633,0.07001901,0.5415939,0.0010270322,0.0032596425,0.000090082845,0.0005816776,0.0040255515,0.37668487],"genre_scores_gemma":[0.06873182,0.12886868,0.19898704,0.0011905662,0.0032315326,0.0001725042,0.00180908,0.0008477992,0.596161],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958426,0.000059893395,0.000022556273,0.00009961467,0.00020013054,0.00003357703],"domain_scores_gemma":[0.99984014,0.00004604365,0.000014890177,0.000034551438,0.000054585085,0.000009776156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021659838,0.0012107942,0.0006895235,0.0010288508,0.00038377935,0.0019051614,0.0011495501,0.00095575873,0.02725008],"category_scores_gemma":[0.00061663677,0.00041772216,0.00041869708,0.0032499107,0.00044919897,0.0020265607,0.0013192049,0.0011123122,0.02636627],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040022423,0.000029426124,0.00034991757,0.00067945814,0.00002184419,0.00017001638,0.00016391627,0.004428917,0.006724462,0.0358014,0.06764717,0.8839435],"study_design_scores_gemma":[0.000006065537,0.000091093716,0.0007074235,0.00035522232,0.00003961501,0.0011333586,0.00013817557,0.006256416,0.0047384417,0.0126205,0.9738776,0.00003611097],"about_ca_topic_score_codex":0.0010194586,"about_ca_topic_score_gemma":0.0013065925,"teacher_disagreement_score":0.02725008,"about_ca_system_score_codex":0.00042744173,"about_ca_system_score_gemma":0.00042769648,"threshold_uncertainty_score":0.091160595},"labels":[],"label_agreement":null},{"id":"W1516431056","doi":"10.1109/icdsp.2015.7251958","title":"Improved least-squares methods for source localization: An iterative Re-weighting approach","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Weighting; Range (aeronautics); Computer science; Least-squares function approximation; Algorithm; Wireless sensor network; Wireless; Iterative method; Mathematical optimization; Mathematics; Statistics; Telecommunications; Engineering; Computer network; Acoustics; Physics","score_opus":0.0444655855027627,"score_gpt":0.3155597802627209,"score_spread":0.2710941947599582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1516431056","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00080546475,0.00008724758,0.99867344,0.000023531597,0.000011978451,0.00000674524,0.0000057594057,0.00012308292,0.0002628207],"genre_scores_gemma":[0.053558122,0.00043533058,0.9416859,0.00007433351,0.00007421197,0.00009184025,0.00011641937,0.00022353958,0.003740256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990792,0.00031627936,0.000045657576,0.0001312845,0.0003821007,0.000045558412],"domain_scores_gemma":[0.9987978,0.00050552824,0.00009025535,0.00019590674,0.00038630204,0.000024244038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012567401,0.0010842661,0.0008418735,0.0008345485,0.0002606254,0.0005351193,0.0015320553,0.0009306711,0.0025153165],"category_scores_gemma":[0.0039624358,0.0005718711,0.0008077249,0.0012690049,0.0005452026,0.001442589,0.0013442176,0.0013544416,0.0016907444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014006195,0.00009763074,0.00065912004,0.00028270337,0.00013909423,0.000114719856,0.00022681111,0.4351642,0.047081903,0.022315415,0.0037940072,0.48998427],"study_design_scores_gemma":[0.000012448704,0.00004036994,0.00017224088,0.000010577721,0.000017235663,0.00006886971,0.000013227852,0.98584646,0.0052484004,0.0040542907,0.004496466,0.00001938538],"about_ca_topic_score_codex":0.0021392521,"about_ca_topic_score_gemma":0.002596784,"teacher_disagreement_score":0.0025153165,"about_ca_system_score_codex":0.00033992587,"about_ca_system_score_gemma":0.0006471228,"threshold_uncertainty_score":0.0084145665},"labels":[],"label_agreement":null},{"id":"W1519004125","doi":"10.1109/pimrc.2014.7136466","title":"3D localization in large-scale Wireless Sensor Networks: A micro-differential evolution approach","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Differential evolution; Wireless sensor network; Evolutionary algorithm; Optimization problem; Scale (ratio); Wireless; Population; Signal processing; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Digital signal processing","score_opus":0.004266549050937789,"score_gpt":0.18031947538043713,"score_spread":0.17605292632949934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519004125","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02376525,0.0005032938,0.97257704,0.00039941893,0.00003406956,0.000018953615,0.000016137798,0.000066869026,0.0026190283],"genre_scores_gemma":[0.81440395,0.0011736214,0.18005066,0.00016657401,0.0000536171,0.00016759541,0.00004998431,0.000048109843,0.0038860426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998957,0.00004114068,0.0000036592098,0.000016563614,0.000033767028,0.000009183746],"domain_scores_gemma":[0.9997478,0.00015722628,0.000034454337,0.000014843378,0.00003206772,0.000013628546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000323393,0.00030459647,0.00034413673,0.00035483326,0.0002848909,0.00046998612,0.0006948344,0.00074514194,0.00059186376],"category_scores_gemma":[0.0009285432,0.000254642,0.00048465686,0.00043697355,0.00069612917,0.00061336136,0.0006852621,0.00046570422,0.000092208364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069092507,0.000007276545,0.0003221876,0.000019817786,0.000014238811,0.00003569073,0.000035971338,0.9808576,0.0011205301,0.010891075,0.00011893189,0.006569835],"study_design_scores_gemma":[0.0000013653769,0.000004311016,0.000059888644,0.0000014429096,0.0000013838265,0.0000082308925,0.000004906297,0.99794966,0.00008421331,0.0016750764,0.00020749938,0.0000020186433],"about_ca_topic_score_codex":0.0027744581,"about_ca_topic_score_gemma":0.0018802511,"teacher_disagreement_score":0.0027744581,"about_ca_system_score_codex":0.0006493989,"about_ca_system_score_gemma":0.00035750397,"threshold_uncertainty_score":0.0055166483},"labels":[],"label_agreement":null},{"id":"W1523756459","doi":"10.1109/ccece.2001.933671","title":"Estimating position of mobile terminals from delay measurements with survey data","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Base station; Terminal (telecommunication); Position (finance); Computer science; Radio propagation; Mobile telephony; Radio propagation model; Mobile radio; Mobile station; Propagation delay; Telecommunications; Computer network","score_opus":0.07691659602792354,"score_gpt":0.25705827599155656,"score_spread":0.180141679963633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1523756459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4889586,0.00015423328,0.509185,0.00004817198,0.00001606806,0.000036927206,0.00030949598,0.00049320207,0.00079827214],"genre_scores_gemma":[0.9325116,0.00019320962,0.06628205,0.000008428336,0.0000109620205,0.000039817554,0.000440901,0.000021382388,0.0004915959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997546,0.000054783064,0.000014478296,0.00005463911,0.00008663969,0.000034886918],"domain_scores_gemma":[0.9989743,0.00040342577,0.0001521314,0.00013082579,0.00030251654,0.000036830934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040507864,0.000377,0.0004163629,0.001295831,0.0001798187,0.00043004146,0.00037078158,0.000426999,0.00033322666],"category_scores_gemma":[0.0045111566,0.00033679273,0.00024399004,0.0011740604,0.00020575104,0.0008186189,0.0004945012,0.00038367693,0.00037169782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004381,0.00012441688,0.18034469,0.00031253163,0.00014692475,0.0004906824,0.00052522734,0.45780557,0.052672748,0.005779565,0.001557209,0.29980236],"study_design_scores_gemma":[0.00002347254,0.00013465546,0.041831285,0.000022765687,0.000060400293,0.0002885362,0.00018514083,0.9383074,0.014700653,0.0027156142,0.0016918556,0.000038340037],"about_ca_topic_score_codex":0.005746649,"about_ca_topic_score_gemma":0.006463043,"teacher_disagreement_score":0.005746649,"about_ca_system_score_codex":0.00042172684,"about_ca_system_score_gemma":0.0004770624,"threshold_uncertainty_score":0.011426449},"labels":[],"label_agreement":null},{"id":"W1523792186","doi":"10.1109/icassp.1995.479556","title":"Correlation among time difference of arrival estimators and its effect on localization in a multipath environment","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Multilateration; Estimator; Multipath propagation; Covariance; Correlation; Statistics; Covariance matrix; Mathematics; Computer science; Algorithm; Azimuth","score_opus":0.005185630400796504,"score_gpt":0.1691728289899044,"score_spread":0.1639871985891079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1523792186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02681202,0.00029509273,0.9719497,0.00009834563,0.0000238067,0.000011339073,0.000025073623,0.00019898976,0.00058568333],"genre_scores_gemma":[0.7536756,0.001413215,0.24210219,0.00013511864,0.000095955926,0.00010523051,0.0002499397,0.00032653092,0.0018962674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980014,0.00070453295,0.00007874334,0.0002280812,0.000825514,0.00016163712],"domain_scores_gemma":[0.97607327,0.019255057,0.0014635561,0.0010839993,0.0020204904,0.00010356015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003182222,0.0008125513,0.0007190168,0.0007828518,0.00027587317,0.00073187816,0.0006585258,0.0007346061,0.0006201855],"category_scores_gemma":[0.022695499,0.0005507037,0.00065655145,0.0010990091,0.0010224773,0.0015433684,0.0011153951,0.0007812194,0.00030011634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028861855,0.000046122408,0.009996437,0.00026083493,0.00018735007,0.0009463199,0.00026914474,0.80069333,0.027777929,0.03254598,0.0010647764,0.12592316],"study_design_scores_gemma":[0.000014025107,0.00011550285,0.004999145,0.00005225466,0.00008017888,0.00073307264,0.000053164636,0.96865875,0.015453573,0.008457743,0.0013179618,0.000064639404],"about_ca_topic_score_codex":0.0017556989,"about_ca_topic_score_gemma":0.0017586456,"teacher_disagreement_score":0.003182222,"about_ca_system_score_codex":0.0006646867,"about_ca_system_score_gemma":0.0009381496,"threshold_uncertainty_score":0.016829371},"labels":[],"label_agreement":null},{"id":"W1529158540","doi":"10.1109/icdcs.2015.12","title":"Privacy-Preserving Compressive Sensing for Crowdsensing Based Trajectory Recovery","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Crowdsensing; Trajectory; Obfuscation; Encryption; Homomorphic encryption; Compressed sensing; Cloud computing; Metric (unit); Interpolation (computer graphics); Property (philosophy); Padding; Data mining; Computer vision; Computer security; Algorithm; Image (mathematics)","score_opus":0.03509442753615481,"score_gpt":0.2394178686735196,"score_spread":0.20432344113736478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529158540","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0231931,0.00032601788,0.9727664,0.00052223506,0.000072993265,0.0000611387,0.00013473103,0.00043279267,0.0024905805],"genre_scores_gemma":[0.87723714,0.00054631796,0.119346954,0.0003048086,0.00008311344,0.00013024529,0.00024888644,0.00003656641,0.0020659955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866855,0.0003406177,0.00006492357,0.00021567103,0.0005688084,0.00014147692],"domain_scores_gemma":[0.99829155,0.00079790415,0.00023794736,0.00039005186,0.00020772591,0.000074775555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008912715,0.0006475451,0.00058660936,0.0003800278,0.00060160714,0.0006058854,0.00093458936,0.0009239611,0.0010006961],"category_scores_gemma":[0.0042149513,0.00024722604,0.00047965648,0.00062492455,0.0011794796,0.0015119705,0.002024464,0.0012108549,0.00035629727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006830834,0.00014885959,0.0026435596,0.00041880814,0.00008826563,0.0008136465,0.000520096,0.6638153,0.058623184,0.070038304,0.005215956,0.19699098],"study_design_scores_gemma":[0.000026988337,0.00012289183,0.00039579443,0.000026471384,0.000013246368,0.00023881003,0.0000950593,0.96018374,0.0149477,0.020691508,0.0032284888,0.000029229368],"about_ca_topic_score_codex":0.001845792,"about_ca_topic_score_gemma":0.0015648409,"teacher_disagreement_score":0.001845792,"about_ca_system_score_codex":0.00060866534,"about_ca_system_score_gemma":0.0012248154,"threshold_uncertainty_score":0.0047135353},"labels":[],"label_agreement":null},{"id":"W1532003421","doi":"10.1109/pacrim.2003.1235923","title":"Data fusion for mobile terminal location","year":2004,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Terminal (telecommunication); Sensor fusion; Fuse (electrical); Cellular network; Real-time computing; Estimator; Covariance; Mobile telephony; Base station; Geolocation; Mobile radio; Telecommunications; Wireless; Artificial intelligence; Engineering; Statistics; Mathematics","score_opus":0.02233471370974286,"score_gpt":0.26127740593414983,"score_spread":0.23894269222440698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1532003421","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002856199,0.00057247805,0.9951989,0.00011567926,0.00007423608,0.00001469599,0.000041462044,0.00027573135,0.0008506981],"genre_scores_gemma":[0.5145013,0.0021911003,0.47856882,0.00024348217,0.00029587658,0.00017712623,0.00061739935,0.00009796955,0.0033068904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896705,0.00026925563,0.00005752153,0.00018676814,0.00045537073,0.00006405746],"domain_scores_gemma":[0.99890757,0.0003804393,0.000095432646,0.00026983928,0.00032162355,0.00002516725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012999332,0.0006093256,0.00095940585,0.00079212605,0.0005847914,0.00106001,0.00083619193,0.0012229477,0.0015597362],"category_scores_gemma":[0.004704781,0.00029286227,0.0007380413,0.0013786698,0.0005446585,0.0019744986,0.0016075975,0.0011345164,0.0011367823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044218768,0.00006424715,0.00096804905,0.00027565987,0.00013257649,0.00024462043,0.00021209741,0.32825857,0.031666327,0.06315529,0.0057853023,0.56879514],"study_design_scores_gemma":[0.00002120226,0.00010900222,0.0004969813,0.000030247182,0.000035387297,0.00016283126,0.000045476165,0.94122577,0.016843017,0.030192992,0.010800091,0.000036958674],"about_ca_topic_score_codex":0.0012016149,"about_ca_topic_score_gemma":0.00084629515,"teacher_disagreement_score":0.0015597362,"about_ca_system_score_codex":0.0005985741,"about_ca_system_score_gemma":0.00052009174,"threshold_uncertainty_score":0.0068747997},"labels":[],"label_agreement":null},{"id":"W1544876582","doi":"10.1109/jsen.2015.2438193","title":"Experimental Evaluation of Indoor Localization Using Wireless Sensor Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multilateration; Robustness (evolution); Wireless sensor network; Computer science; Real-time computing; Signal strength; Time of arrival; Wireless; Electronic engineering; Computer network; Engineering; Telecommunications","score_opus":0.05894580292779836,"score_gpt":0.2925175772523229,"score_spread":0.23357177432452456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1544876582","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9144382,0.00028088773,0.078150734,0.0001494482,0.00019516476,0.00013347366,0.0006561269,0.0015122592,0.0044836556],"genre_scores_gemma":[0.97968036,0.00017919477,0.01785142,0.000029952591,0.000016627842,0.00012085212,0.00058582897,0.00006615206,0.0014696338],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905354,0.0002661768,0.00007202447,0.0001646482,0.0003202689,0.00012337344],"domain_scores_gemma":[0.9975547,0.0008654643,0.0002798287,0.00034688352,0.00084472593,0.0001084917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008817095,0.0007973185,0.00036895878,0.000729641,0.0004051045,0.00031531302,0.00074675056,0.0005097662,0.0019747082],"category_scores_gemma":[0.0026958417,0.00015794704,0.00021114037,0.00077949866,0.00048702053,0.00058173505,0.0006251018,0.00029456508,0.00039933602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003922294,0.0023129764,0.016303744,0.0017327901,0.00020824569,0.00090116577,0.0006571184,0.23576355,0.5649211,0.0024601612,0.003006306,0.16781063],"study_design_scores_gemma":[0.00025919202,0.010775893,0.027517665,0.000118239914,0.00015506102,0.0006513599,0.00090772833,0.34490725,0.60717565,0.0010446111,0.006375467,0.00011182779],"about_ca_topic_score_codex":0.0011009827,"about_ca_topic_score_gemma":0.0013693718,"teacher_disagreement_score":0.0019747082,"about_ca_system_score_codex":0.00031038522,"about_ca_system_score_gemma":0.00025649383,"threshold_uncertainty_score":0.0066060424},"labels":[],"label_agreement":null},{"id":"W1546169424","doi":"10.1109/icc.2015.7248735","title":"Cosine similarity based fingerprinting algorithm in WLAN indoor positioning against device diversity","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"RSS; Euclidean distance; Computer science; Cosine similarity; Antenna diversity; Similarity (geometry); Signal strength; Fingerprint recognition; Trigonometric functions; Key (lock); Algorithm; Discrete cosine transform; Indoor positioning system; Fingerprint (computing); Computer vision; Artificial intelligence; Pattern recognition (psychology); Wireless; Mathematics; Telecommunications; Image (mathematics); Computer security","score_opus":0.025846087430937664,"score_gpt":0.22117957045256856,"score_spread":0.1953334830216309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546169424","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04269196,0.0013350449,0.9512328,0.00015412929,0.00023925367,0.000089149195,0.000111057314,0.0011443299,0.0030023062],"genre_scores_gemma":[0.55037767,0.0014613995,0.4405626,0.00014171572,0.0001540588,0.0001749128,0.0005118845,0.00009066251,0.0065250434],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99877363,0.00018240164,0.00009185298,0.00028586166,0.0005676451,0.00009853785],"domain_scores_gemma":[0.99916327,0.00015125608,0.00010300593,0.00016123774,0.00038052292,0.000040654657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006904194,0.00063232094,0.0010261593,0.0014240491,0.0005400751,0.0008641581,0.001394745,0.00077385077,0.0010616321],"category_scores_gemma":[0.0026173524,0.00029625182,0.00051489234,0.0024140875,0.0003934351,0.0013918676,0.000827019,0.00072464516,0.0008465072],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040117858,0.00011592396,0.0043706936,0.0002805297,0.00010555559,0.00035444426,0.00023228113,0.0713496,0.055969972,0.006851591,0.004206507,0.85576165],"study_design_scores_gemma":[0.00010352606,0.0004627714,0.0073804427,0.000058883954,0.00010391896,0.0021792613,0.00020222383,0.8931079,0.07405573,0.0042760377,0.017954174,0.00011513338],"about_ca_topic_score_codex":0.0029168772,"about_ca_topic_score_gemma":0.0014729958,"teacher_disagreement_score":0.0029168772,"about_ca_system_score_codex":0.00039800035,"about_ca_system_score_gemma":0.00086850463,"threshold_uncertainty_score":0.0057997704},"labels":[],"label_agreement":null},{"id":"W1547429778","doi":"10.1049/iet-com.2010.0480","title":"Lower bounds on mobile terminal localisation in an urban area","year":2011,"lang":"en","type":"article","venue":"IET Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"RSS; Estimator; Cramér–Rao bound; Upper and lower bounds; Computer science; Algorithm; Terminal (telecommunication); Mean squared error; Classification of discontinuities; Geolocation; Statistics; Mathematics; Telecommunications; Mathematical analysis","score_opus":0.04983564698988737,"score_gpt":0.2609106680542209,"score_spread":0.21107502106433354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1547429778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049233433,0.0036816648,0.9368355,0.00064637535,0.00009541455,0.000026326981,0.00022544866,0.0004324894,0.008823348],"genre_scores_gemma":[0.8864989,0.0032969275,0.10497536,0.00027170902,0.00019797758,0.00014867766,0.0007428933,0.00027748552,0.0035901146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9912879,0.0030723584,0.0004822786,0.00091282825,0.0033626084,0.0008819971],"domain_scores_gemma":[0.9011683,0.07846036,0.006063465,0.0045602694,0.0091663,0.00058134395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009601908,0.0013135844,0.002075816,0.0026592466,0.0007807081,0.003369167,0.0017417147,0.001947856,0.0028355238],"category_scores_gemma":[0.06625256,0.0007694058,0.00078361644,0.0025978351,0.0022925253,0.003574982,0.0030992017,0.0020944094,0.0011075073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002408162,0.000021370797,0.0025655306,0.0002908404,0.00007387971,0.0001699819,0.00014465551,0.93081325,0.0036110496,0.037721146,0.0010101303,0.023337424],"study_design_scores_gemma":[0.000016726,0.00015474223,0.0028250902,0.00016197927,0.000046514448,0.00036192458,0.00012014787,0.9639536,0.005350197,0.024086263,0.0028531624,0.00006962112],"about_ca_topic_score_codex":0.003801779,"about_ca_topic_score_gemma":0.0021202355,"teacher_disagreement_score":0.009601908,"about_ca_system_score_codex":0.0016337531,"about_ca_system_score_gemma":0.00093818613,"threshold_uncertainty_score":0.050780356},"labels":[],"label_agreement":null},{"id":"W1558204028","doi":"10.1109/ccece.2015.7129199","title":"Indoor positioning of mobile devices with agile iBeacon deployment","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Hybrid positioning system; Bluetooth Low Energy; Mobile device; Exploit; Agile software development; Bluetooth; Android (operating system); Software deployment; Global Positioning System; Laptop; Energy consumption; Location-based service; Embedded system; Wireless; Telecommunications; Computer security; Positioning system; Engineering; World Wide Web","score_opus":0.01034098013286524,"score_gpt":0.20781614655253663,"score_spread":0.1974751664196714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558204028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22417699,0.0020456437,0.7051115,0.0007278198,0.0003225993,0.0002623034,0.0004116094,0.00938883,0.057552673],"genre_scores_gemma":[0.87599427,0.0005224989,0.11642644,0.00021956973,0.00005529327,0.00012737935,0.00039264347,0.00012475725,0.0061371764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989261,0.00024222158,0.00004735506,0.00020587411,0.0003730637,0.00020533038],"domain_scores_gemma":[0.9991648,0.00012623459,0.00013048925,0.00024092467,0.00025132968,0.00008612959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005468725,0.00076529046,0.00063108915,0.0011517123,0.00047769694,0.0009933998,0.0010398879,0.000617695,0.0014469369],"category_scores_gemma":[0.0017253697,0.0002984796,0.00024979466,0.0011185431,0.00037503993,0.0008676032,0.0015810679,0.0005714976,0.0017686245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011028239,0.000262312,0.03003407,0.0003688856,0.00011376554,0.0019077745,0.0010464389,0.07930967,0.12165351,0.01433607,0.015565843,0.7342988],"study_design_scores_gemma":[0.00017004524,0.0017266963,0.050374158,0.00031456424,0.00021729627,0.0057606595,0.0014097361,0.6609075,0.12469798,0.0056836237,0.14852831,0.00020953214],"about_ca_topic_score_codex":0.0027249258,"about_ca_topic_score_gemma":0.003506238,"teacher_disagreement_score":0.0027249258,"about_ca_system_score_codex":0.00056512805,"about_ca_system_score_gemma":0.00036450406,"threshold_uncertainty_score":0.0054181814},"labels":[],"label_agreement":null},{"id":"W1558574741","doi":"10.1109/iccw.2015.7247270","title":"Sensor localization in NLOS environments with anchor uncertainty and unknown clock parameters","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Wireless sensor network; Convergence (economics); Time of arrival; Position (finance); Synchronization (alternating current); Node (physics); Algorithm; Identification (biology); Relaxation (psychology); Real-time computing; Wireless; Computer network; Telecommunications; Engineering","score_opus":0.012994384521108439,"score_gpt":0.19349617804591612,"score_spread":0.1805017935248077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558574741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01345206,0.00023347267,0.9856176,0.000056366218,0.000017308312,0.0000060404655,0.000011588003,0.00007328567,0.0005322479],"genre_scores_gemma":[0.8406677,0.0007878298,0.15649293,0.000060693288,0.000077703895,0.000056440716,0.00008749891,0.000054627482,0.0017145544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992061,0.00026975962,0.000038393937,0.00020362971,0.0002329182,0.00004933143],"domain_scores_gemma":[0.9985013,0.000956029,0.00027065948,0.00012256943,0.0001283821,0.000020976719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007999593,0.00053040235,0.0006539642,0.00032846397,0.0003654963,0.00054501346,0.00062934303,0.00065266166,0.00041650466],"category_scores_gemma":[0.004233115,0.00030863512,0.00029787238,0.00076141325,0.0008308023,0.0016048485,0.0009456313,0.00056468387,0.00018592911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020157588,0.000029301033,0.001866481,0.00023070646,0.000052537896,0.0003433031,0.00026173776,0.88807887,0.014737163,0.015915664,0.00059380825,0.07768881],"study_design_scores_gemma":[0.000009297471,0.00006139948,0.0004703809,0.00001024991,0.000009448893,0.00013972478,0.00006827648,0.986592,0.00406068,0.007339616,0.0012284629,0.000010503722],"about_ca_topic_score_codex":0.0011636978,"about_ca_topic_score_gemma":0.00088724814,"teacher_disagreement_score":0.0011636978,"about_ca_system_score_codex":0.0002979647,"about_ca_system_score_gemma":0.00038257954,"threshold_uncertainty_score":0.0042306185},"labels":[],"label_agreement":null},{"id":"W1576186752","doi":"10.1007/978-3-642-02085-8_13","title":"Compressed RF Tomography for Wireless Sensor Networks: Centralized and Decentralized Approaches","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Compressed sensing; Wireless sensor network; Inference; Radio frequency; Wireless; Key distribution in wireless sensor networks; Process (computing); Set (abstract data type); Wireless network; Real-time computing; Artificial intelligence; Computer network; Telecommunications","score_opus":0.017398818205520274,"score_gpt":0.2086868378523487,"score_spread":0.19128801964682843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576186752","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025490623,0.0010880724,0.99344796,0.00029563365,0.000049703165,0.000017369206,0.00003117148,0.00013815168,0.0023828791],"genre_scores_gemma":[0.5422471,0.0071202493,0.43958366,0.00030640754,0.0009997089,0.00021781708,0.00027681023,0.00024760913,0.009000638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991829,0.00030878535,0.000026818057,0.000111499205,0.00033002964,0.000039954368],"domain_scores_gemma":[0.9982864,0.0011108165,0.00013037775,0.00027330712,0.00017314368,0.000025793643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010915986,0.00073370605,0.0010912967,0.00052084954,0.00033795784,0.0009839527,0.0011889203,0.0010893771,0.0019483467],"category_scores_gemma":[0.0038397212,0.00051185547,0.00043471318,0.0012541077,0.0013826545,0.0024599372,0.001309317,0.0013547312,0.00031417367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017655763,0.00006730488,0.00040156383,0.00041117857,0.00007011647,0.00015476528,0.00018441826,0.54538804,0.013880918,0.17010501,0.008202624,0.26095748],"study_design_scores_gemma":[0.000018715204,0.000040257106,0.00022400288,0.000021241778,0.000018783418,0.00016038731,0.000026635766,0.9299946,0.0028512974,0.06388034,0.0027458444,0.000017920016],"about_ca_topic_score_codex":0.00066459394,"about_ca_topic_score_gemma":0.00097543735,"teacher_disagreement_score":0.0019483467,"about_ca_system_score_codex":0.00077433954,"about_ca_system_score_gemma":0.0006365269,"threshold_uncertainty_score":0.006517887},"labels":[],"label_agreement":null},{"id":"W1579773993","doi":"10.1002/acs.2471","title":"Least‐squares‐based adaptive target localization by mobile distance measurement sensors","year":2014,"lang":"en","type":"article","venue":"International Journal of Adaptive Control and Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convergence (economics); Algorithm; Recursive least squares filter; Noise (video); Computer science; Least-squares function approximation; Forgetting; Stability (learning theory); Control theory (sociology); Mathematics; Adaptive filter; Artificial intelligence; Statistics","score_opus":0.008628018849444856,"score_gpt":0.20705786827016273,"score_spread":0.19842984942071787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579773993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013732433,0.00022163679,0.9852188,0.000049847946,0.000037447604,0.00001021083,0.000010885026,0.00031499017,0.0004038198],"genre_scores_gemma":[0.61847156,0.00046610582,0.37808535,0.00008995892,0.00007448943,0.000057343124,0.000113917136,0.00006369614,0.002577649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996979,0.000050561288,0.00001728594,0.00009049346,0.00012258042,0.000021150234],"domain_scores_gemma":[0.9996642,0.00008823501,0.000061612176,0.000053263266,0.0001200027,0.000012745471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038683412,0.0004877569,0.0005200719,0.00035548385,0.00017738754,0.0004475549,0.0008857437,0.0005699258,0.00060429913],"category_scores_gemma":[0.001023545,0.00027939095,0.0004926956,0.00047837457,0.0004196981,0.00058386277,0.00044116483,0.0006702403,0.00036768452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027647376,0.00005668547,0.0016686236,0.00022918415,0.000109954846,0.00017450616,0.00014471816,0.5029609,0.11133803,0.010043214,0.0019947086,0.371003],"study_design_scores_gemma":[0.000013882112,0.000068484376,0.00036568742,0.000007285081,0.0000135198325,0.00008522985,0.000007181712,0.9831383,0.013784968,0.0008005623,0.0016996461,0.00001534346],"about_ca_topic_score_codex":0.0016153653,"about_ca_topic_score_gemma":0.0010651742,"teacher_disagreement_score":0.0016153653,"about_ca_system_score_codex":0.00029592155,"about_ca_system_score_gemma":0.00045257033,"threshold_uncertainty_score":0.0032119155},"labels":[],"label_agreement":null},{"id":"W1581015721","doi":"10.5281/zenodo.43736","title":"Primary Emitter Localization Using Smartly Initialized Metropolis-Hastings Algorithm","year":2013,"lang":"en","type":"article","venue":"INFM-OAR (INFN Catania)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Initialization; Interpolation (computer graphics); Algorithm; Computer science; Common emitter; Multilateration; Interference (communication); Cognitive radio; Reduction (mathematics); Node (physics); Artificial intelligence; Mathematics; Wireless; Telecommunications; Engineering; Channel (broadcasting); Electronic engineering; Motion (physics)","score_opus":0.011260849347989953,"score_gpt":0.20511103541179496,"score_spread":0.193850186063805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581015721","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049100807,0.0001284343,0.9928981,0.00008808626,0.000071652765,0.00003901646,0.000050620554,0.00085593027,0.0009580956],"genre_scores_gemma":[0.24172679,0.00016134992,0.75051016,0.00016563195,0.00014720416,0.00035909572,0.0005443634,0.00041750603,0.005967987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874675,0.00054617797,0.000063241394,0.0002587134,0.00026725905,0.00011779709],"domain_scores_gemma":[0.9962063,0.002497784,0.00015903047,0.0004372631,0.0005699305,0.00012967749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002003606,0.001216473,0.002651522,0.00092840125,0.0008267499,0.0015302685,0.0033701262,0.001833388,0.0054940246],"category_scores_gemma":[0.007544046,0.0014053248,0.0014009438,0.0016202489,0.0013038955,0.0017449977,0.0019861392,0.0024623955,0.0022663106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038017656,0.000081218226,0.00094129605,0.00011740851,0.00014937605,0.00013190792,0.00008547013,0.90497553,0.0023820335,0.013556978,0.002740838,0.074457824],"study_design_scores_gemma":[0.000017952418,0.000010545875,0.000038145175,0.0000026781327,0.0000062466393,0.000010211844,0.0000025351892,0.9973278,0.00039780934,0.0019663956,0.00021464328,0.0000050543085],"about_ca_topic_score_codex":0.011173542,"about_ca_topic_score_gemma":0.017191479,"teacher_disagreement_score":0.011173542,"about_ca_system_score_codex":0.0012640523,"about_ca_system_score_gemma":0.0020681394,"threshold_uncertainty_score":0.022217035},"labels":[],"label_agreement":null},{"id":"W1581957308","doi":"10.1109/aps.1987.1149950","title":"Polarization characterisitcs of backscatter from finite dielectric cylinders in the resonance region","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Dielectric; Polarization (electrochemistry); Backscatter (email); Materials science; Resonance (particle physics); Nuclear magnetic resonance; Optics; Condensed matter physics; Physics; Optoelectronics; Atomic physics; Computer science; Chemistry; Telecommunications","score_opus":0.009548003256320534,"score_gpt":0.19559843554257694,"score_spread":0.1860504322862564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581957308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89648104,0.00075093645,0.092420086,0.00008732662,0.000015807083,0.000017022887,0.0002152727,0.00016755129,0.009844968],"genre_scores_gemma":[0.9937921,0.0004798833,0.004922281,0.00001960935,0.000009195364,0.000010804409,0.0001495257,0.000027868646,0.000588663],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981576,0.000035870376,0.000004769284,0.000029318457,0.00008879179,0.000025466292],"domain_scores_gemma":[0.9991561,0.00041379064,0.000118170734,0.000060852868,0.00021498182,0.000036116144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026979533,0.00027292946,0.00019950187,0.00073308824,0.00020092963,0.00033733516,0.00014893203,0.00026190848,0.00076694624],"category_scores_gemma":[0.0008939352,0.00019875115,0.00012015538,0.0004600486,0.0008271568,0.00044941163,0.00020981167,0.00032078812,0.00043025575],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004688913,0.000085421976,0.009415463,0.00018411265,0.000027653597,0.0004519607,0.0007879809,0.01585248,0.9364144,0.011465388,0.00048428786,0.024362044],"study_design_scores_gemma":[0.000035448546,0.00051578315,0.05664136,0.000052715477,0.0000556427,0.0042380537,0.00088908593,0.1819037,0.73603135,0.016313398,0.00318024,0.00014328786],"about_ca_topic_score_codex":0.0001666833,"about_ca_topic_score_gemma":0.00013782918,"teacher_disagreement_score":0.00076694624,"about_ca_system_score_codex":0.00008288978,"about_ca_system_score_gemma":0.00007825642,"threshold_uncertainty_score":0.0025656223},"labels":[],"label_agreement":null},{"id":"W1589636315","doi":"10.1155/2015/280674","title":"Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor Networks","year":2015,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless sensor network; Granularity; Key distribution in wireless sensor networks; Node (physics); Software deployment; Sensor node; Wireless; Computer network; Distributed computing; Real-time computing; Wireless network; Telecommunications","score_opus":0.01296386451929276,"score_gpt":0.2221009929482773,"score_spread":0.20913712842898452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1589636315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07409638,0.0008541623,0.9233818,0.00019079178,0.000027761484,0.000036781672,0.00003086853,0.00037583377,0.0010055838],"genre_scores_gemma":[0.914126,0.0005563009,0.08467532,0.00006885275,0.00003376735,0.00004762518,0.00004999556,0.000024413992,0.00041773933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913955,0.00028809134,0.000045352255,0.00014916892,0.0002953922,0.00008244075],"domain_scores_gemma":[0.9991229,0.00033890843,0.0001861079,0.00023270321,0.000077808414,0.0000415709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010041255,0.00048253548,0.00059766823,0.00065239205,0.00044984152,0.000634373,0.0008695741,0.0006265679,0.00022925105],"category_scores_gemma":[0.0029382315,0.00035195207,0.0004516562,0.0010926254,0.0006937939,0.001919496,0.0016295376,0.00054328475,0.00010915391],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016770401,0.000057024416,0.0028362223,0.0001726955,0.00004467654,0.000363078,0.00030697,0.84086937,0.04134266,0.015306351,0.0009267707,0.09760644],"study_design_scores_gemma":[0.0000094244,0.000083560124,0.0007951869,0.000011825176,0.000017734501,0.00012663723,0.000088440174,0.9862011,0.0043554497,0.0070944093,0.0012003238,0.00001595996],"about_ca_topic_score_codex":0.0013809438,"about_ca_topic_score_gemma":0.001630319,"teacher_disagreement_score":0.0013809438,"about_ca_system_score_codex":0.0004787906,"about_ca_system_score_gemma":0.00025706453,"threshold_uncertainty_score":0.005310416},"labels":[],"label_agreement":null},{"id":"W1589711575","doi":"10.1109/ccece.2015.7129414","title":"An open source inertial sensor network with Bluetooth Smart","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bluetooth; Wireless sensor network; Android (operating system); Sensor node; Microcontroller; Computer science; Real-time computing; Inertial navigation system; Inertial measurement unit; Proximity sensor; Accelerometer; Embedded system; Key distribution in wireless sensor networks; Inertial frame of reference; Computer network; Telecommunications; Wireless; Artificial intelligence; Wireless network","score_opus":0.018105390330533013,"score_gpt":0.2299381338749807,"score_spread":0.21183274354444767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1589711575","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023475407,0.0031690588,0.90979743,0.0005022665,0.00092203164,0.0006486182,0.0009127743,0.0246935,0.035878893],"genre_scores_gemma":[0.46510336,0.003793932,0.4155248,0.0012039391,0.0009170706,0.00200677,0.0077448385,0.0014945767,0.10221072],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904114,0.0001528988,0.00007057288,0.00014854412,0.0005003203,0.00008642459],"domain_scores_gemma":[0.99945885,0.00007593951,0.000059579517,0.00009159092,0.00026319097,0.00005091394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000609838,0.0006631153,0.0004991593,0.0011368748,0.00044626856,0.0007197998,0.0015829591,0.0007828962,0.0062244236],"category_scores_gemma":[0.001128846,0.00025260932,0.0003305814,0.00087824994,0.0002194203,0.001692777,0.00133646,0.0006329351,0.0036094117],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010216417,0.00039214132,0.004157359,0.0008173468,0.00013158948,0.00097349857,0.0002634802,0.011744393,0.06973293,0.016146678,0.06253227,0.8320866],"study_design_scores_gemma":[0.00041953538,0.0014351305,0.0103625385,0.00023194645,0.00027233394,0.003465408,0.0001971508,0.24190569,0.077626124,0.00890916,0.6548737,0.00030132834],"about_ca_topic_score_codex":0.0010177075,"about_ca_topic_score_gemma":0.0011015786,"teacher_disagreement_score":0.0062244236,"about_ca_system_score_codex":0.00030121682,"about_ca_system_score_gemma":0.0004931457,"threshold_uncertainty_score":0.020822763},"labels":[],"label_agreement":null},{"id":"W1596639361","doi":"10.1109/icc.2015.7249381","title":"Localization in non-homogeneous one-dimensional wireless ad-hoc networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Wireless ad hoc network; Range (aeronautics); Independent and identically distributed random variables; Poisson distribution; Broadcasting (networking); Node (physics); Probability density function; Hop (telecommunications); Wireless; Wireless network; Computer network; Topology (electrical circuits); Algorithm; Mathematics; Random variable; Telecommunications; Statistics","score_opus":0.012726394772702545,"score_gpt":0.20752268041325772,"score_spread":0.19479628564055518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1596639361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08781452,0.0010653374,0.9071922,0.00023503587,0.00003807741,0.00003949148,0.00005790777,0.00014891168,0.003408618],"genre_scores_gemma":[0.95143765,0.0017322421,0.04357469,0.000107229156,0.00010331884,0.00008899619,0.00009410078,0.000046064357,0.0028156133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923575,0.00031572886,0.000035802143,0.00012370606,0.00021199863,0.0000770019],"domain_scores_gemma":[0.9957283,0.0030183308,0.00065276417,0.00017607362,0.00034420652,0.00008023517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013446772,0.0003939006,0.00056939345,0.0010113077,0.0004567589,0.0008789837,0.00083865726,0.0005118911,0.00044499672],"category_scores_gemma":[0.008114179,0.00035912497,0.0003996565,0.0009591897,0.00174691,0.0018938873,0.00089718937,0.00035718037,0.00016978351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029736124,0.000021188976,0.0025721132,0.00016311053,0.000037074955,0.00029004892,0.00021041643,0.9126413,0.0023524836,0.070797324,0.00033441733,0.01055087],"study_design_scores_gemma":[0.0000053175418,0.00002648514,0.00073163427,0.000007345734,0.000012705467,0.000089240246,0.00006365937,0.98026687,0.00065165653,0.017675241,0.0004577027,0.0000121946305],"about_ca_topic_score_codex":0.002764677,"about_ca_topic_score_gemma":0.0017716839,"teacher_disagreement_score":0.002764677,"about_ca_system_score_codex":0.0008630386,"about_ca_system_score_gemma":0.00041551102,"threshold_uncertainty_score":0.00711143},"labels":[],"label_agreement":null},{"id":"W1603033140","doi":"10.5772/34068","title":"The Application of Magnetic Sensors in Self-Contained Local Positioning","year":2012,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science","score_opus":0.005537706363818632,"score_gpt":0.19458114078531233,"score_spread":0.18904343442149368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603033140","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0124606285,0.3357814,0.46336234,0.0013492245,0.0027860925,0.00012534497,0.00021198254,0.0017646505,0.1821584],"genre_scores_gemma":[0.25094518,0.26854295,0.17854196,0.0012992189,0.0026678347,0.00021455376,0.00047857783,0.00027055485,0.29703906],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997855,0.000023061142,0.000007963449,0.000041328472,0.00012817484,0.000014047873],"domain_scores_gemma":[0.99991035,0.000045715482,0.0000072523726,0.000014096065,0.0000184993,0.0000041122394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014903717,0.0006194887,0.000653804,0.00066009664,0.00019589759,0.00086088834,0.0007374268,0.0010877723,0.0037384718],"category_scores_gemma":[0.00023598323,0.00037391804,0.00032659923,0.0013214792,0.00072353904,0.0011898715,0.00065529905,0.0008983248,0.0018161597],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012705826,0.00009211292,0.00033400598,0.0019731817,0.00006839672,0.00035434024,0.00039919376,0.013892902,0.09044637,0.09413292,0.032357197,0.7658223],"study_design_scores_gemma":[0.000043825086,0.00041531023,0.00266186,0.0008878466,0.000095156116,0.002966321,0.00021163978,0.04812665,0.058354206,0.079848275,0.8062706,0.00011826861],"about_ca_topic_score_codex":0.00023602777,"about_ca_topic_score_gemma":0.00039238407,"teacher_disagreement_score":0.0037384718,"about_ca_system_score_codex":0.0002624299,"about_ca_system_score_gemma":0.00016711328,"threshold_uncertainty_score":0.012506425},"labels":[],"label_agreement":null},{"id":"W1603578774","doi":"10.1109/wimob.2005.1512945","title":"Herecast:an open infrastructure for locationbased services using WiFi","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Architecture; Location-based service; Current (fluid); Positioning system; Telecommunications; Computer security; Engineering; Electrical engineering; Geography","score_opus":0.012546754599409191,"score_gpt":0.24952927918010417,"score_spread":0.236982524580695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603578774","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02119861,0.0012306833,0.8168767,0.0011665005,0.0015863355,0.0007654107,0.0016692965,0.100426726,0.055079766],"genre_scores_gemma":[0.51100004,0.0036378775,0.3251261,0.0015864089,0.0012356397,0.001221858,0.014088596,0.006783376,0.13532013],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99904937,0.00009920917,0.000051414383,0.00013311302,0.00046430624,0.00020251759],"domain_scores_gemma":[0.9988549,0.0001874155,0.000084585634,0.00035448253,0.00030795686,0.00021071351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010241991,0.00069434283,0.0007101975,0.001453087,0.001335902,0.002332377,0.0026305101,0.0014175976,0.015045018],"category_scores_gemma":[0.002784903,0.00066314446,0.00061579543,0.0010488479,0.0006368925,0.0040756403,0.003754602,0.002457009,0.0051544346],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017113909,0.0005191924,0.0044704694,0.0007595989,0.0002116443,0.0009309824,0.0010198299,0.01652858,0.038457103,0.10808931,0.1964724,0.63082945],"study_design_scores_gemma":[0.00036751153,0.00052276306,0.0026871236,0.00016876373,0.00017091777,0.0009019701,0.00027546796,0.13475446,0.035053276,0.02268791,0.80216277,0.00024706934],"about_ca_topic_score_codex":0.0071068993,"about_ca_topic_score_gemma":0.0065269694,"teacher_disagreement_score":0.015045018,"about_ca_system_score_codex":0.00115598,"about_ca_system_score_gemma":0.0014241928,"threshold_uncertainty_score":0.05033058},"labels":[],"label_agreement":null},{"id":"W1636013699","doi":"10.1109/plans.1994.303354","title":"GPS signal availability in an urban area-receiver performance analysis","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; SIGNAL (programming language); Multipath propagation; GPS signals; Computer science; Channel (broadcasting); Code (set theory); Real-time computing; Remote sensing; Assisted GPS; Electronic engineering; Telecommunications; Geography; Engineering","score_opus":0.016340945203527177,"score_gpt":0.19378534321445842,"score_spread":0.17744439801093126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1636013699","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9828797,0.00013337059,0.015981738,0.000020687721,0.000003694545,0.000007814224,0.00016149002,0.00025319454,0.0005583201],"genre_scores_gemma":[0.99763286,0.00006019482,0.0017760488,0.000008275047,0.000004218198,0.000007799902,0.00023527993,0.000016357164,0.0002590247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99916303,0.00025740906,0.000043837248,0.00016049673,0.00026241667,0.00011283097],"domain_scores_gemma":[0.99635446,0.00212543,0.00040873708,0.00024011187,0.000812951,0.00005842118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008533124,0.00032967463,0.00039831921,0.0005577865,0.00018018633,0.0003081121,0.00038018776,0.0004519771,0.00084461016],"category_scores_gemma":[0.003373945,0.00016842198,0.00019355594,0.0010016356,0.0002559037,0.0003818472,0.00025753674,0.00020767943,0.00031252732],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038800642,0.00017190952,0.17124295,0.0004933614,0.00042195633,0.0010472493,0.00062199816,0.55405647,0.1747208,0.0005865243,0.00063540234,0.09212122],"study_design_scores_gemma":[0.00007393289,0.0029421404,0.24270786,0.000038399685,0.0003633776,0.0019808833,0.0004159477,0.56726235,0.18235627,0.0003834756,0.0013766527,0.00009875837],"about_ca_topic_score_codex":0.0025107062,"about_ca_topic_score_gemma":0.001821659,"teacher_disagreement_score":0.0025107062,"about_ca_system_score_codex":0.00024555763,"about_ca_system_score_gemma":0.00016093251,"threshold_uncertainty_score":0.0049922466},"labels":[],"label_agreement":null},{"id":"W1644794749","doi":"","title":"Design and realization of a new antenna for localization with RFID","year":2011,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Realization (probability); Directivity; Bandwidth (computing); Computer science; Antenna (radio); Electronic engineering; Software; Antenna measurement; Reconfigurable antenna; Electrical engineering; Directional antenna; Omnidirectional antenna; Engineering; Antenna efficiency; Telecommunications; Mathematics","score_opus":0.01765112442733039,"score_gpt":0.2161359714816866,"score_spread":0.1984848470543562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1644794749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037032966,0.0004220089,0.9521235,0.00042395154,0.00046677547,0.00007103618,0.00008122418,0.001860393,0.0075181196],"genre_scores_gemma":[0.3595977,0.00042034683,0.614276,0.0004505173,0.00020146632,0.00016056675,0.00024160447,0.00023764175,0.024414094],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929655,0.00009352661,0.00004948168,0.0001608582,0.0002800915,0.000119518474],"domain_scores_gemma":[0.9990453,0.00013392384,0.00012060488,0.00022547993,0.00038892025,0.00008569883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047866124,0.0007458246,0.000818759,0.0004815455,0.0003591892,0.0013640521,0.0016211247,0.0019213147,0.0029853242],"category_scores_gemma":[0.00055513723,0.00058412825,0.0008766869,0.00048587567,0.00042513438,0.00095222035,0.0008365286,0.0007810583,0.0026628824],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040619305,0.000107062726,0.0013377565,0.00041442201,0.0001273304,0.00059116044,0.00033875045,0.006398101,0.86110735,0.012937893,0.0038862962,0.1123478],"study_design_scores_gemma":[0.00014457868,0.0011458824,0.0023461182,0.000057837067,0.00020623396,0.0032918495,0.00010829258,0.07556091,0.8060773,0.001865014,0.10908372,0.00011230264],"about_ca_topic_score_codex":0.00025665457,"about_ca_topic_score_gemma":0.00035846586,"teacher_disagreement_score":0.0029853242,"about_ca_system_score_codex":0.00070314575,"about_ca_system_score_gemma":0.00040004705,"threshold_uncertainty_score":0.009986937},"labels":[],"label_agreement":null},{"id":"W1686478837","doi":"10.1109/jsac.2015.2430280","title":"Localization and Location Verification in Non-Homogeneous One-Dimensional Wireless Ad-Hoc Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Wireless ad hoc network; Poisson distribution; Aloha; Hop (telecommunications); Node (physics); Fading; Independent and identically distributed random variables; Range (aeronautics); Probability density function; Wireless network; Wireless sensor network; Wireless; Computer network; Algorithm; Topology (electrical circuits); Random variable; Mathematics; Statistics; Telecommunications; Throughput","score_opus":0.02498286785264181,"score_gpt":0.2506566063966716,"score_spread":0.22567373854402978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1686478837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06610388,0.00062216085,0.9308926,0.0001208761,0.000028491131,0.00004699513,0.00002801552,0.000117462565,0.002039599],"genre_scores_gemma":[0.93527734,0.0008604931,0.062165067,0.000061380975,0.000058365385,0.000087774475,0.000056079098,0.000023952649,0.0014097679],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99803,0.000867466,0.00010230224,0.00028111515,0.0005857876,0.00013320261],"domain_scores_gemma":[0.99281424,0.0049798363,0.0010500031,0.0005305874,0.00054332893,0.00008195173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020863009,0.00037623275,0.00063630915,0.0007986085,0.0005845747,0.0010219155,0.00097424194,0.0006623441,0.00042205257],"category_scores_gemma":[0.011626673,0.00034698934,0.00039751068,0.0010527757,0.0018521476,0.0022783114,0.0009214981,0.0004063865,0.00013097882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007136799,0.000039351668,0.003102366,0.00018524942,0.00004775135,0.0003760424,0.00022487037,0.8933363,0.005428035,0.061660185,0.00033387024,0.03519472],"study_design_scores_gemma":[0.000006728086,0.000045991284,0.0005847693,0.0000057695097,0.000013278905,0.00009716894,0.00004195621,0.988427,0.0020446617,0.008328533,0.00039164122,0.000012444611],"about_ca_topic_score_codex":0.0025652228,"about_ca_topic_score_gemma":0.0014313598,"teacher_disagreement_score":0.0025652228,"about_ca_system_score_codex":0.0010417828,"about_ca_system_score_gemma":0.0007230224,"threshold_uncertainty_score":0.011033535},"labels":[],"label_agreement":null},{"id":"W1694723240","doi":"10.1002/wcm.2596","title":"An indoor radio propagation model considering angles for WLAN infrastructures","year":2015,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"RSS; Computer science; Radio propagation; Radio propagation model; Grid; Measure (data warehouse); Fingerprint (computing); Wireless; Attenuation; Wireless network; Wi-Fi; Signal strength; Real-time computing; Telecommunications; Data mining; Artificial intelligence; World Wide Web","score_opus":0.029767345283367188,"score_gpt":0.2742311346669789,"score_spread":0.2444637893836117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1694723240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068337575,0.0005728993,0.9207296,0.00032948353,0.00009782586,0.00006561676,0.00034244725,0.0007156612,0.00880888],"genre_scores_gemma":[0.96138906,0.0012074773,0.029189104,0.000073335286,0.00007530248,0.00014163883,0.000406116,0.00008270825,0.00743531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949527,0.00013312926,0.000017793498,0.00014786335,0.000099614925,0.000106341824],"domain_scores_gemma":[0.99958664,0.00013005114,0.00007816016,0.000045680157,0.0001332292,0.000026240714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046982063,0.0013807347,0.00066750083,0.0010195412,0.0004900904,0.0013238789,0.0016366823,0.0013391523,0.0016821248],"category_scores_gemma":[0.0011951927,0.0006235336,0.0011424245,0.0014377717,0.0006778341,0.001392069,0.0008521618,0.0009961255,0.00085874146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029325447,0.000019437563,0.0015491548,0.000021421303,0.000013272382,0.0001295239,0.000036354937,0.9876852,0.0011257455,0.0026044666,0.00037651893,0.006409612],"study_design_scores_gemma":[0.0000027185336,0.000010728276,0.00022508761,0.000002963507,0.000008318553,0.000021497424,0.0000119243605,0.9989423,0.00010444093,0.00045355086,0.00021002232,0.0000064654787],"about_ca_topic_score_codex":0.018661994,"about_ca_topic_score_gemma":0.008485945,"teacher_disagreement_score":0.018661994,"about_ca_system_score_codex":0.0009441629,"about_ca_system_score_gemma":0.00069559336,"threshold_uncertainty_score":0.037106752},"labels":[],"label_agreement":null},{"id":"W1703081249","doi":"10.1109/aps.2005.1552478","title":"Geolocation in Underground Mines Using Wireless Sensor Networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Geolocation; Wireless sensor network; Computer science; Wireless; Computer network; Real-time computing; Telecommunications; World Wide Web","score_opus":0.011619263523304273,"score_gpt":0.21708670804905555,"score_spread":0.20546744452575127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1703081249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09661417,0.0064693415,0.88965136,0.0006449789,0.00020609495,0.0000407192,0.00011085126,0.00059043453,0.0056720455],"genre_scores_gemma":[0.8962573,0.005381537,0.09467166,0.00011829353,0.00022359668,0.00006862699,0.00015831596,0.000037206664,0.0030834882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996389,0.00015780474,0.000019996263,0.00003980135,0.00012396635,0.000019572806],"domain_scores_gemma":[0.9996444,0.00016649991,0.0000739388,0.000045375647,0.000057611058,0.000012325977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039850568,0.00031413295,0.00033920072,0.0005380669,0.00023837006,0.00059190096,0.00042376964,0.0007015845,0.00053753506],"category_scores_gemma":[0.0013971493,0.00018951402,0.00021267959,0.0008005796,0.00046970596,0.001466191,0.00065126363,0.0002905207,0.0002528657],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003132167,0.00004582038,0.009443093,0.00042639291,0.00012179259,0.0010310899,0.0003995433,0.5922331,0.046647564,0.037287325,0.004350956,0.3077001],"study_design_scores_gemma":[0.000022783555,0.00021365043,0.0034691426,0.000086060856,0.000055064767,0.0012512853,0.0003205695,0.93664056,0.011328222,0.026720127,0.019837337,0.00005518863],"about_ca_topic_score_codex":0.00058118254,"about_ca_topic_score_gemma":0.00065707293,"teacher_disagreement_score":0.0007015845,"about_ca_system_score_codex":0.00014100807,"about_ca_system_score_gemma":0.00012595321,"threshold_uncertainty_score":0.002107501},"labels":[],"label_agreement":null},{"id":"W177600443","doi":"","title":"Emitter Location with LES-8/9 Using Differential Time-of-Arrival and Differential Doppler Shift","year":2000,"lang":"en","type":"article","venue":"Defense Technical Information Center (DTIC)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Common emitter; Doppler effect; Transmitter; Differential (mechanical device); Ultra high frequency; Multilateration; Geodesy; Telecommunications; Computer science; Remote sensing; Physics; Electrical engineering; Geology; Acoustics; Engineering","score_opus":0.008271995364450887,"score_gpt":0.1985931691828798,"score_spread":0.19032117381842892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W177600443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044357862,0.0006926434,0.94317764,0.00024416915,0.00014340568,0.00010571199,0.00016568947,0.0029226078,0.008190147],"genre_scores_gemma":[0.27308828,0.0005764636,0.71820724,0.00011084986,0.00006801768,0.00012909187,0.00041650978,0.000111111694,0.00729255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996081,0.000071199414,0.000030870106,0.000067551766,0.00018488233,0.000037428425],"domain_scores_gemma":[0.9991456,0.00022227404,0.00013008738,0.00020599026,0.00025693417,0.00003908544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076140126,0.00044051892,0.00028897237,0.0005698666,0.00023085358,0.00057443866,0.0005533279,0.00045078373,0.0019692725],"category_scores_gemma":[0.001232736,0.00028460016,0.0002525433,0.00036558043,0.0003828645,0.001172493,0.00092138845,0.00077184354,0.0012483204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068142096,0.00008632031,0.00954168,0.00036798412,0.00007140654,0.0005916619,0.0004985691,0.01160644,0.46047574,0.01789558,0.005109137,0.493074],"study_design_scores_gemma":[0.00018584059,0.0017034007,0.007979081,0.00018823019,0.00018158426,0.0053039426,0.00021775175,0.11862757,0.6740266,0.004167627,0.1872187,0.0001996943],"about_ca_topic_score_codex":0.0008261049,"about_ca_topic_score_gemma":0.0015547725,"teacher_disagreement_score":0.0019692725,"about_ca_system_score_codex":0.00023929936,"about_ca_system_score_gemma":0.0003351766,"threshold_uncertainty_score":0.006587863},"labels":[],"label_agreement":null},{"id":"W18036572","doi":"10.1016/j.bcp.2007.10.014","title":"Smart Localization in Underground Mines using Fingerprinting and ANNs: Strategies and Applications","year":2014,"lang":"en","type":"article","venue":"Biochemical Pharmacology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Institut national de la recherche scientifique","keywords":"Computer science; Mining engineering; Geology; Artificial intelligence; Computer security","score_opus":0.010694199254322398,"score_gpt":0.2543207830624998,"score_spread":0.24362658380817737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W18036572","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.098374054,0.0014398545,0.89553785,0.00029086013,0.00006135092,0.00004974507,0.00015043243,0.0014927685,0.002603094],"genre_scores_gemma":[0.7216699,0.0010277112,0.27402943,0.00011123399,0.0000545274,0.000084328305,0.00014855567,0.000047818514,0.0028263866],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983203,0.000035364304,0.000011783106,0.000058764184,0.00003670481,0.000025274596],"domain_scores_gemma":[0.99973506,0.00008396866,0.00007253781,0.00002406371,0.00006349891,0.000020744868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003586325,0.0006898919,0.00059053226,0.0007640047,0.00022702495,0.0005246586,0.00054573163,0.00071992184,0.00092574075],"category_scores_gemma":[0.00051396235,0.00030091888,0.0003739635,0.000706446,0.00027588947,0.0006858102,0.00043470217,0.00032049895,0.00037087649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003139326,0.00022075609,0.012468866,0.0002961347,0.0001335207,0.00031972484,0.00014609509,0.1829372,0.11471639,0.0026446455,0.0016014045,0.68420136],"study_design_scores_gemma":[0.00001796855,0.00017948008,0.0047123088,0.000033702825,0.000050880673,0.00019599093,0.000110404195,0.9678035,0.022148559,0.002704556,0.0020141236,0.000028436734],"about_ca_topic_score_codex":0.0018915018,"about_ca_topic_score_gemma":0.0028361618,"teacher_disagreement_score":0.0018915018,"about_ca_system_score_codex":0.0002825993,"about_ca_system_score_gemma":0.00033803357,"threshold_uncertainty_score":0.0037609935},"labels":[],"label_agreement":null},{"id":"W1835689522","doi":"10.5555/1771592.1771603","title":"An exploration of location error estimation","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Estimation; Visualization; Field (mathematics); Error analysis; Algorithm; Mean squared prediction error; Data mining; Error detection and correction; Statistics; Mathematics; Engineering","score_opus":0.026771647192810707,"score_gpt":0.27790382187158563,"score_spread":0.2511321746787749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1835689522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24347846,0.0017338195,0.7451226,0.0024124628,0.000072657866,0.00006111431,0.00024999987,0.00083657575,0.006032322],"genre_scores_gemma":[0.94066966,0.0004417556,0.058205545,0.00006648378,0.00003109676,0.00003658214,0.00009695175,0.000053841384,0.00039811898],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99467826,0.003695776,0.00014978052,0.00048612966,0.0008783266,0.000111864305],"domain_scores_gemma":[0.93430763,0.0572242,0.002524819,0.0036105127,0.0020498896,0.00028295178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005138141,0.0008300459,0.0005709741,0.0009685825,0.00041132647,0.0023334224,0.0011041441,0.0010171259,0.001912359],"category_scores_gemma":[0.05567375,0.00044551454,0.00044829873,0.0009745725,0.0015083805,0.003927947,0.0022672098,0.0012557912,0.00027861475],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024138447,0.0003285132,0.04708639,0.0013145341,0.0003990438,0.0010394735,0.006162628,0.402726,0.019800043,0.07113144,0.0037582074,0.44383985],"study_design_scores_gemma":[0.00009031786,0.0006520833,0.0103621315,0.00039106855,0.00013016866,0.0011381796,0.001942733,0.8709492,0.014598657,0.09189071,0.007729608,0.00012513243],"about_ca_topic_score_codex":0.0012600818,"about_ca_topic_score_gemma":0.0007987748,"teacher_disagreement_score":0.005138141,"about_ca_system_score_codex":0.00062957255,"about_ca_system_score_gemma":0.0007536745,"threshold_uncertainty_score":0.0271734},"labels":[],"label_agreement":null},{"id":"W1843335441","doi":"10.3390/s151027060","title":"An Accurate and Fault-Tolerant Target Positioning System for Buildings Using Laser Rangefinders and Low-Cost MEMS-Based MARG Sensors","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"China Scholarship Council","keywords":"Microelectromechanical systems; Laser scanning; Computer science; Fault tolerance; Fault (geology); Remote sensing; Laser; Geology; Engineering; Artificial intelligence; Materials science; Seismology; Physics; Optics; Nanotechnology; Distributed computing","score_opus":0.020501324291942326,"score_gpt":0.24763543339136562,"score_spread":0.2271341090994233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1843335441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1266635,0.00053003576,0.8645636,0.00018972902,0.00011317758,0.00007329943,0.00015968073,0.0055446317,0.0021624076],"genre_scores_gemma":[0.8659211,0.00022960632,0.13096544,0.00007009392,0.000047371323,0.00006967458,0.00022296718,0.000034175453,0.0024395671],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976736,0.000025978683,0.000013110114,0.000051244584,0.00011687704,0.000025441295],"domain_scores_gemma":[0.9998147,0.000019212865,0.000041725554,0.000031327207,0.00007790181,0.000015136457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019780833,0.0005854925,0.0004299593,0.00038280632,0.00044418834,0.0003010658,0.0005937452,0.0004195093,0.0007348536],"category_scores_gemma":[0.00032134124,0.00020744946,0.00023708308,0.0003138007,0.00017433625,0.00059598987,0.0004612008,0.00031130065,0.00051242905],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035770368,0.00011689362,0.01204899,0.00043321768,0.00009824195,0.00044297735,0.00035255923,0.054403022,0.38179213,0.0027280701,0.005416797,0.54180944],"study_design_scores_gemma":[0.00016956643,0.0013099798,0.023885163,0.00006422576,0.00028009724,0.0011544479,0.0002073486,0.7263595,0.22086649,0.0016631202,0.023883931,0.0001562371],"about_ca_topic_score_codex":0.0026823182,"about_ca_topic_score_gemma":0.0041208146,"teacher_disagreement_score":0.0026823182,"about_ca_system_score_codex":0.00033609523,"about_ca_system_score_gemma":0.0005707124,"threshold_uncertainty_score":0.0053334236},"labels":[],"label_agreement":null},{"id":"W1881529840","doi":"10.1109/cscwd.2015.7230981","title":"Indoor location based on WiFi","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Position (finance); Scheme (mathematics); Track (disk drive); Set (abstract data type); Real-time computing; Calibration; Location data; Computer network; Statistics; Mathematics","score_opus":0.018896741605914723,"score_gpt":0.21149363491547502,"score_spread":0.19259689330956029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1881529840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038708888,0.0019513911,0.93156564,0.00027451504,0.00048629078,0.00012237117,0.00090724195,0.002269124,0.023714539],"genre_scores_gemma":[0.87172365,0.0017916926,0.1151976,0.00014656443,0.0003434629,0.00011180977,0.0012694309,0.00007861348,0.009337185],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99853694,0.0003755014,0.000065491324,0.00032846874,0.00038866786,0.00030500986],"domain_scores_gemma":[0.9995229,0.00008391018,0.00007259301,0.00015006559,0.00014008864,0.000030443454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002915582,0.0010763468,0.0007857891,0.0012889619,0.0008715438,0.0012824037,0.0012842506,0.0010609633,0.0031723464],"category_scores_gemma":[0.0013850472,0.00027756533,0.00082797854,0.0022348366,0.00049824873,0.00205896,0.0016871489,0.0006149887,0.0023212214],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057910196,0.0001621707,0.021766143,0.00093277683,0.00033680498,0.0026839375,0.0005127815,0.28934604,0.032590024,0.07039231,0.018497063,0.5622008],"study_design_scores_gemma":[0.00006119098,0.0005682628,0.012138298,0.00019181038,0.0004163757,0.0052270433,0.00063585513,0.87254053,0.03206533,0.016368506,0.05953853,0.0002483243],"about_ca_topic_score_codex":0.006177604,"about_ca_topic_score_gemma":0.0061257705,"teacher_disagreement_score":0.006177604,"about_ca_system_score_codex":0.0004879483,"about_ca_system_score_gemma":0.0005837991,"threshold_uncertainty_score":0.012283266},"labels":[],"label_agreement":null},{"id":"W1881619528","doi":"10.1007/978-3-642-21538-4_3","title":"A Fuzzy Logic Approach for Indoor Mobile Robot Navigation Using UKF and Customized RFID Communication","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Real-time computing; Navigation system; RSS; Sensor fusion; Mobile robot; Mobile robot navigation; Robot; Encoder; Computer vision; Artificial intelligence; Embedded system; Robot control","score_opus":0.02679784763739915,"score_gpt":0.24373928417922827,"score_spread":0.21694143654182912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1881619528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030270019,0.00023851234,0.9921799,0.00005287488,0.000049167305,0.00001922544,0.000024464362,0.00009062849,0.004318309],"genre_scores_gemma":[0.3519006,0.00089359464,0.63378024,0.00018439807,0.00011596293,0.0001473594,0.00012482828,0.00003705889,0.012815959],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972814,0.000036661604,0.00001877774,0.00006924689,0.00011705394,0.000030030791],"domain_scores_gemma":[0.99988544,0.000035835466,0.000010703837,0.00000967378,0.000052785155,0.00000547396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028602305,0.0004895306,0.000624334,0.00061828544,0.000572132,0.0010027895,0.0012300437,0.00077357993,0.0024768158],"category_scores_gemma":[0.0005144942,0.00024562742,0.00094863673,0.00064599764,0.00038544266,0.00075037405,0.00042633893,0.0005889991,0.00055862847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012383975,0.0001534984,0.00069180387,0.00037106161,0.00010509492,0.0006466895,0.000386722,0.40525988,0.02882049,0.14826271,0.0036750694,0.41150317],"study_design_scores_gemma":[0.000010928539,0.000078223624,0.00024007964,0.000036737823,0.000043752407,0.00019058159,0.000068685964,0.9614252,0.0030803971,0.030279722,0.004515103,0.00003060443],"about_ca_topic_score_codex":0.0103067625,"about_ca_topic_score_gemma":0.0110441465,"teacher_disagreement_score":0.0103067625,"about_ca_system_score_codex":0.0009264174,"about_ca_system_score_gemma":0.00067557074,"threshold_uncertainty_score":0.020493567},"labels":[],"label_agreement":null},{"id":"W1885234456","doi":"10.1016/j.proeng.2015.10.085","title":"Enhanced Localization for Indoor Construction","year":2015,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Environmental science; Architectural engineering; Engineering","score_opus":0.011702966122655423,"score_gpt":0.20219663523559747,"score_spread":0.19049366911294205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1885234456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01828668,0.00045824167,0.97751105,0.000090792135,0.00004362732,0.000014905851,0.000053547443,0.0009106543,0.0026305094],"genre_scores_gemma":[0.8117016,0.0011241637,0.17805733,0.0000744808,0.00004180146,0.000060100778,0.00026137728,0.00009468597,0.008584419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966013,0.000088726425,0.000008233662,0.00006906701,0.00014012083,0.000033704964],"domain_scores_gemma":[0.99977416,0.0000737946,0.00003641091,0.0000503842,0.000057726887,0.000007515329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026020652,0.0005610647,0.00045930833,0.00043149246,0.00014096707,0.0004988919,0.00057110254,0.00058770936,0.0019145721],"category_scores_gemma":[0.0007186075,0.00022867361,0.0004398643,0.0005026883,0.0002850417,0.0007874494,0.0008027423,0.00033580602,0.0009708067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022713139,0.000089417874,0.004302018,0.0005353986,0.00007459723,0.00040942803,0.000283983,0.45577514,0.10729797,0.01480525,0.0031196647,0.41307995],"study_design_scores_gemma":[0.000026272997,0.00032405852,0.005290603,0.000055805736,0.000068277724,0.0006483199,0.00011359653,0.9371453,0.029314233,0.0052057584,0.021747928,0.000059816426],"about_ca_topic_score_codex":0.00206835,"about_ca_topic_score_gemma":0.0026592757,"teacher_disagreement_score":0.00206835,"about_ca_system_score_codex":0.0003615478,"about_ca_system_score_gemma":0.00036460572,"threshold_uncertainty_score":0.006404817},"labels":[],"label_agreement":null},{"id":"W189013490","doi":"10.1023/a:1023434702987","title":"Estimating Hop Counts in Position Based Routing Schemes for Ad Hoc Networks","year":2003,"lang":"en","type":"article","venue":"Telecommunication Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Scalability; Hop (telecommunications); Node (physics); Computer network; Wireless ad hoc network; Compass; Greedy algorithm; Routing (electronic design automation); Global Positioning System; Algorithm; Telecommunications; Wireless","score_opus":0.015835638832868083,"score_gpt":0.24578064244265807,"score_spread":0.22994500360978998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W189013490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22862434,0.0011188089,0.768362,0.00015846617,0.000093109935,0.00007904889,0.00010935047,0.0005746107,0.0008803307],"genre_scores_gemma":[0.7956191,0.00065612275,0.20201306,0.000022763841,0.00007661922,0.00006255864,0.00028473194,0.000041608626,0.0012235424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998784,0.00051879685,0.00008678561,0.00012388502,0.00038796567,0.000098630255],"domain_scores_gemma":[0.99335384,0.0049535967,0.00045941316,0.00049379584,0.0006365577,0.00010268418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017648394,0.0008625227,0.0008687344,0.0022003036,0.0008346901,0.00094873604,0.0012790121,0.0010434332,0.0004338113],"category_scores_gemma":[0.012284153,0.0008010791,0.0002966249,0.0018242543,0.000535202,0.0024127178,0.0010415874,0.0006801864,0.00023352382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050734024,0.00019398297,0.01322475,0.00014262774,0.00010132011,0.00012399917,0.000266196,0.70159644,0.00847013,0.005827587,0.00093968114,0.26860607],"study_design_scores_gemma":[0.000021856547,0.00014723661,0.0020008811,0.00001231482,0.00003162082,0.00010365259,0.00009489598,0.9878997,0.005016851,0.004183481,0.00046866233,0.000018780598],"about_ca_topic_score_codex":0.0027840051,"about_ca_topic_score_gemma":0.0039905114,"teacher_disagreement_score":0.0027840051,"about_ca_system_score_codex":0.0004952741,"about_ca_system_score_gemma":0.0005128285,"threshold_uncertainty_score":0.009333432},"labels":[],"label_agreement":null},{"id":"W1892841857","doi":"10.1111/tgis.12102","title":"Exploring Mobility Indoors: an Application of Sensor‐based and <scp>GIS</scp> Systems","year":2014,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Global Positioning System; Real-time computing; Computer science; Wireless sensor network; Context (archaeology); Accelerometer; Wireless; Tracking (education); Tracking system; Telecommunications; Computer network; Geography; Artificial intelligence","score_opus":0.029383577205175863,"score_gpt":0.226672217431225,"score_spread":0.19728864022604914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1892841857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7479784,0.00034830972,0.24562089,0.00041709587,0.00004614528,0.00015531539,0.0004838934,0.0009593516,0.003990585],"genre_scores_gemma":[0.91479254,0.0001481344,0.08451987,0.000017993996,0.000013437046,0.000039686,0.00009448216,0.000013286807,0.00036064733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996904,0.00014484237,0.000013003568,0.00006395461,0.00006391802,0.000023919685],"domain_scores_gemma":[0.9996049,0.00018292447,0.000047754893,0.00004634137,0.00008207998,0.000036040303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042335514,0.0003753146,0.00027691826,0.0012663032,0.00036076916,0.00042974704,0.00033633725,0.00027893926,0.00083477685],"category_scores_gemma":[0.0010416891,0.00016338211,0.00026271155,0.001041775,0.00026869358,0.00046584962,0.00065298856,0.00016283203,0.00015356751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006109731,0.00038272425,0.1508758,0.0006504053,0.00036868482,0.0020233218,0.0043984316,0.06990105,0.12851043,0.005283315,0.003281023,0.6337138],"study_design_scores_gemma":[0.00007869115,0.0009809785,0.145099,0.00010733201,0.00025178623,0.0020386332,0.004874572,0.78609705,0.044321667,0.004559518,0.011480843,0.0001100177],"about_ca_topic_score_codex":0.0038667524,"about_ca_topic_score_gemma":0.006379174,"teacher_disagreement_score":0.0038667524,"about_ca_system_score_codex":0.0002421585,"about_ca_system_score_gemma":0.00022254299,"threshold_uncertainty_score":0.0076885223},"labels":[],"label_agreement":null},{"id":"W1911164553","doi":"10.1109/pacrim.2001.953713","title":"Location of mobile terminals using time measurements and survey points","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Non-line-of-sight propagation; Multilateration; Microcell; Computer science; Base station; Terminal (telecommunication); Time of arrival; Algorithm; Mobile telephony; Radio propagation; Mobile radio; Real-time computing; Statistics; Mathematics; Telecommunications; Wireless","score_opus":0.05339498568724918,"score_gpt":0.24195065059091406,"score_spread":0.18855566490366488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1911164553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1203138,0.0006769255,0.87476677,0.00011348403,0.000049578448,0.000052401647,0.00032180728,0.0008579122,0.0028473944],"genre_scores_gemma":[0.7507296,0.0011890484,0.24519296,0.00002818642,0.00006349243,0.000075868695,0.0006420708,0.000033958157,0.0020447786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999534,0.00014798195,0.000024154924,0.00008712271,0.00018272547,0.000023924953],"domain_scores_gemma":[0.999185,0.0001861539,0.00015873797,0.0001512885,0.00028302555,0.00003568579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003739104,0.0004831172,0.0003863136,0.0017733164,0.00026273308,0.0008072008,0.0004553567,0.0006297955,0.00060283515],"category_scores_gemma":[0.002729357,0.00025217517,0.00031557737,0.002245115,0.00027090136,0.001381993,0.0007426918,0.00029393696,0.00076249003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043849094,0.00008343372,0.031034365,0.00036041738,0.00013271223,0.00045517835,0.0005447997,0.24294621,0.06456598,0.012189084,0.0021425472,0.64510673],"study_design_scores_gemma":[0.000052792046,0.000492271,0.0318523,0.000103050195,0.00012450371,0.0013492786,0.00054516696,0.8939319,0.045227986,0.008837241,0.017368415,0.00011506214],"about_ca_topic_score_codex":0.0016905054,"about_ca_topic_score_gemma":0.0021143428,"teacher_disagreement_score":0.0017733164,"about_ca_system_score_codex":0.00022439347,"about_ca_system_score_gemma":0.00023892114,"threshold_uncertainty_score":0.0033613443},"labels":[],"label_agreement":null},{"id":"W1918124526","doi":"10.1109/iccve.2014.7297658","title":"Dynamic base station DGPS for cooperative vehicle localization","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Pseudorange; Global Positioning System; Base station; Computer science; Dependency (UML); Position (finance); Assisted GPS; Real-time computing; Base (topology); Telecommunications; Artificial intelligence; GNSS applications; Mathematics","score_opus":0.006165039024648586,"score_gpt":0.21592468998275935,"score_spread":0.20975965095811078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1918124526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004419739,0.0009637495,0.99153894,0.000106576925,0.00007687019,0.000017134254,0.00004670846,0.00033949243,0.0024907796],"genre_scores_gemma":[0.71338046,0.003952497,0.27019316,0.00026820492,0.00023233658,0.00017446435,0.0004944578,0.00011359544,0.011190899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971,0.00007373809,0.0000076402375,0.000066626664,0.000112601236,0.00002928931],"domain_scores_gemma":[0.9997843,0.00006359355,0.000029575496,0.000048970247,0.000059949558,0.000013702587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023906653,0.00069322286,0.00043321273,0.00047218785,0.00031546576,0.0005383293,0.00094658625,0.00059642683,0.0015694704],"category_scores_gemma":[0.00066435675,0.00020635125,0.0003367346,0.0009766909,0.00043349614,0.0005600645,0.00088095875,0.00069830625,0.0010490507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022773737,0.000035015473,0.0015598249,0.00038733415,0.00011191953,0.0008205257,0.0002926485,0.3877767,0.064342424,0.09590232,0.008957979,0.43958557],"study_design_scores_gemma":[0.000036677502,0.00025003977,0.00085049006,0.000058550264,0.00008139953,0.0007336425,0.00013252666,0.87087035,0.01618752,0.028850863,0.081904076,0.000043816126],"about_ca_topic_score_codex":0.0024069473,"about_ca_topic_score_gemma":0.002109737,"teacher_disagreement_score":0.0024069473,"about_ca_system_score_codex":0.00047268157,"about_ca_system_score_gemma":0.0004041086,"threshold_uncertainty_score":0.005250454},"labels":[],"label_agreement":null},{"id":"W1918437315","doi":"10.1109/iri.2015.31","title":"Cooperative Multi-sensor Multi-vehicle Localization in Vehicular Adhoc Networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates - Technology Futures","keywords":"Global Positioning System; Computer science; Intelligent transportation system; Context (archaeology); Software deployment; Real-time computing; Vehicular ad hoc network; Position (finance); Particle filter; Filter (signal processing); Wireless ad hoc network; Transport engineering; Wireless; Telecommunications; Computer vision; Engineering","score_opus":0.028764709997403468,"score_gpt":0.2442336660119922,"score_spread":0.21546895601458874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1918437315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020500023,0.00057551527,0.9779806,0.00009330175,0.000027177484,0.000015777798,0.000013331397,0.00011659416,0.0006777393],"genre_scores_gemma":[0.9553542,0.00060065475,0.04256001,0.000041708194,0.000046254943,0.000049958035,0.00003999487,0.000014550657,0.0012926247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993699,0.00027440675,0.00002346665,0.00011378058,0.00014979712,0.00006872905],"domain_scores_gemma":[0.9991359,0.00051043974,0.000117836025,0.00006251042,0.00014144559,0.000031901538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091126777,0.00039641984,0.0004950841,0.00046720088,0.00037874098,0.00054738484,0.0008995082,0.0006476723,0.00028026817],"category_scores_gemma":[0.0022901446,0.00032782144,0.00034636585,0.00070831267,0.0006563059,0.0009026238,0.0007329552,0.00038666985,0.000117220305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057417983,0.000019495026,0.0010136743,0.000083847815,0.000033047298,0.0001301347,0.00012663504,0.9473744,0.0027636501,0.010686871,0.00038718444,0.037323657],"study_design_scores_gemma":[0.0000047576136,0.000037344955,0.00020675053,0.0000039647725,0.00000917978,0.000031533014,0.000028652652,0.9939812,0.00058865204,0.004501423,0.0006006183,0.0000058930164],"about_ca_topic_score_codex":0.006131902,"about_ca_topic_score_gemma":0.004355524,"teacher_disagreement_score":0.006131902,"about_ca_system_score_codex":0.00057888374,"about_ca_system_score_gemma":0.0005337379,"threshold_uncertainty_score":0.0121923685},"labels":[],"label_agreement":null},{"id":"W1933407121","doi":"10.1109/usnc-ursi.2015.7303502","title":"Localization of wireless devices in agricultural fields","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Scalability; Overhead (engineering); Wireless sensor network; Routing (electronic design automation); Wireless; Distributed computing; Computer network; Embedded system; Real-time computing; Telecommunications","score_opus":0.01331467372656232,"score_gpt":0.20948066434848753,"score_spread":0.19616599062192522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1933407121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11659662,0.0045175534,0.85753953,0.00061859767,0.00016042517,0.00010254685,0.00026351147,0.001469084,0.01873202],"genre_scores_gemma":[0.86458987,0.003790421,0.113527805,0.00013504861,0.00008276016,0.00010879717,0.00037031894,0.000071565395,0.017323375],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997323,0.000049863836,0.000011466549,0.00009690604,0.000078574485,0.000030877298],"domain_scores_gemma":[0.99986017,0.000039802075,0.000030505467,0.000024803914,0.000035740293,0.000008918499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026976937,0.0003306504,0.00041230198,0.00067815784,0.00047090344,0.0008699811,0.000580418,0.000590936,0.0012946221],"category_scores_gemma":[0.000691191,0.00025147817,0.00018279818,0.0009905584,0.000676349,0.0012803405,0.00086243724,0.00024852232,0.0010613332],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024584154,0.00010414301,0.014816254,0.0007107469,0.00005893869,0.0015754134,0.00082322053,0.3239039,0.12810305,0.04203178,0.008382552,0.47924414],"study_design_scores_gemma":[0.000076732664,0.00044915525,0.021108659,0.00032242222,0.000072049435,0.0017241344,0.0015943645,0.75152147,0.06848475,0.052394897,0.10214234,0.00010905015],"about_ca_topic_score_codex":0.0030625095,"about_ca_topic_score_gemma":0.002636754,"teacher_disagreement_score":0.0030625095,"about_ca_system_score_codex":0.000519754,"about_ca_system_score_gemma":0.00034714025,"threshold_uncertainty_score":0.0060893893},"labels":[],"label_agreement":null},{"id":"W1943340832","doi":"10.1155/2015/212657","title":"Towards Independency Using LMN4DISABLED System for Disabled","year":2015,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Wheelchair; Disabled people; Dijkstra's algorithm; Human–computer interaction; Wireless; Routing (electronic design automation); Embedded system; Shortest path problem; Physical medicine and rehabilitation; Telecommunications; World Wide Web; Theoretical computer science","score_opus":0.026816868582497214,"score_gpt":0.264175755297801,"score_spread":0.2373588867153038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1943340832","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6655553,0.0006353955,0.30790144,0.0004721494,0.0001824577,0.00022356506,0.00049021613,0.009040728,0.015498702],"genre_scores_gemma":[0.94888204,0.00015350257,0.04456189,0.000093569455,0.00001756819,0.00007902268,0.00036257628,0.00003429753,0.00581552],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996643,0.00007070984,0.00002548673,0.00008308754,0.00008737855,0.00006905465],"domain_scores_gemma":[0.9996903,0.00003261467,0.000031902444,0.000066525725,0.00012898834,0.000049759517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030148256,0.00048109156,0.00035109068,0.0005868054,0.00039701795,0.00041661048,0.00091145886,0.00037021452,0.00218211],"category_scores_gemma":[0.0006064666,0.00012763194,0.00019866212,0.00027924552,0.00019411097,0.0006941752,0.0011879174,0.00017575765,0.0009157337],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002378084,0.0005762774,0.047839276,0.00060953805,0.00013230169,0.0022468949,0.0009672539,0.023036897,0.26991746,0.0034273553,0.015362634,0.63350606],"study_design_scores_gemma":[0.0004708752,0.0027450302,0.058593027,0.00013893595,0.0004992161,0.004649358,0.0016608257,0.5714098,0.28392348,0.0034032788,0.07227096,0.00023513523],"about_ca_topic_score_codex":0.0042258007,"about_ca_topic_score_gemma":0.004887402,"teacher_disagreement_score":0.0042258007,"about_ca_system_score_codex":0.0003270778,"about_ca_system_score_gemma":0.00050313,"threshold_uncertainty_score":0.008402407},"labels":[],"label_agreement":null},{"id":"W1944600025","doi":"10.1007/11853565_14","title":"Practical Metropolitan-Scale Positioning for GSM Phones","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":270,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Metropolitan area; GSM; Downtown; Calibration; Computer science; Scale (ratio); Real-time computing; Set (abstract data type); Geography; Computer network; Statistics; Cartography; Mathematics","score_opus":0.01305806406679495,"score_gpt":0.2518605691543556,"score_spread":0.23880250508756068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1944600025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010288791,0.00068428856,0.9423998,0.00035810447,0.00017193997,0.000056619872,0.00023147427,0.0034628005,0.042346183],"genre_scores_gemma":[0.39060998,0.0015309425,0.5243194,0.00015436961,0.00021659773,0.00014738481,0.00074974773,0.00048194383,0.08178963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996866,0.00009068119,0.000012842331,0.00007454829,0.00009207189,0.000043227366],"domain_scores_gemma":[0.99973375,0.00006242167,0.000009329002,0.000114114584,0.00006723759,0.000013252017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002962939,0.00095321256,0.0003935255,0.0003752947,0.00059967756,0.0008198771,0.0009887386,0.0008699222,0.022238348],"category_scores_gemma":[0.0009297579,0.00044333993,0.0003119965,0.0007996675,0.00038233746,0.0011171151,0.0014725603,0.00053009274,0.011117504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022599212,0.000058740872,0.0023584396,0.00042086496,0.000041525323,0.0005913638,0.0004569801,0.06473385,0.062183324,0.07510703,0.029646121,0.7641758],"study_design_scores_gemma":[0.00008457083,0.00057368504,0.0043648737,0.00017923563,0.00011146181,0.003612699,0.0009124134,0.41732782,0.06478602,0.115086384,0.39284542,0.00011545591],"about_ca_topic_score_codex":0.001865981,"about_ca_topic_score_gemma":0.00652639,"teacher_disagreement_score":0.022238348,"about_ca_system_score_codex":0.00042186314,"about_ca_system_score_gemma":0.00044723676,"threshold_uncertainty_score":0.07439464},"labels":[],"label_agreement":null},{"id":"W195283163","doi":"10.22260/isarc2014/0109","title":"Experimental Study of Wireless Sensor Networks forIndoor Construction Operations","year":2014,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Wireless sensor network; Wireless site survey; Wireless; Computer science; Wireless network; Global Positioning System; Key distribution in wireless sensor networks; Telecommunications; Computer network","score_opus":0.007669032895765037,"score_gpt":0.20760029396786,"score_spread":0.19993126107209497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W195283163","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9872104,0.000079308,0.010987602,0.000056823596,0.00006121543,0.00009557133,0.0002691014,0.00009347209,0.0011466127],"genre_scores_gemma":[0.9926882,0.00013055497,0.0054705287,0.00002241068,0.000007800957,0.00014284362,0.0003967432,0.0000079388565,0.0011330156],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897087,0.00025533998,0.000108402906,0.00018187342,0.00033980762,0.00014381103],"domain_scores_gemma":[0.9982803,0.0007349156,0.00021866163,0.0002369352,0.00043554374,0.000093699986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095749233,0.00050876423,0.00031635337,0.0005313159,0.00039609402,0.00032209978,0.0005671089,0.0003675305,0.0014139089],"category_scores_gemma":[0.0015591779,0.00016539394,0.00034037436,0.00050067244,0.00042122803,0.0006637476,0.00042999003,0.00032907113,0.00019620714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042176167,0.0053275414,0.06066419,0.0013302232,0.0002549665,0.0013341904,0.0009979191,0.12284331,0.6500651,0.003228932,0.0026937933,0.14704226],"study_design_scores_gemma":[0.00019124482,0.019727845,0.08528705,0.00009133344,0.00020007815,0.0007661664,0.0021466266,0.30459037,0.5787751,0.0012315089,0.0068761557,0.00011641631],"about_ca_topic_score_codex":0.0009574216,"about_ca_topic_score_gemma":0.0013754482,"teacher_disagreement_score":0.0014139089,"about_ca_system_score_codex":0.00036678146,"about_ca_system_score_gemma":0.00032927227,"threshold_uncertainty_score":0.005063772},"labels":[],"label_agreement":null},{"id":"W1964089333","doi":"10.1108/14714171211215967","title":"RFID deployment protocols for indoor construction","year":2012,"lang":"en","type":"article","venue":"Construction Innovation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Software deployment; Computer science; Radio-frequency identification; System deployment; Identification (biology); Systems engineering; Range (aeronautics); Real-time computing; Protocol (science); Computer security; Engineering; Software engineering","score_opus":0.026411289466832355,"score_gpt":0.27210755256160923,"score_spread":0.24569626309477688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964089333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06989289,0.0004048669,0.9158152,0.00018264363,0.00008265748,0.0005931514,0.00022921144,0.001793267,0.011006058],"genre_scores_gemma":[0.73512876,0.00043709992,0.26002324,0.000057326724,0.000019607869,0.0005684801,0.00054579554,0.00010609339,0.003113556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984289,0.00051198417,0.00014367682,0.00022523553,0.0005626383,0.00012760115],"domain_scores_gemma":[0.9979095,0.00069915084,0.00038123896,0.000480636,0.00048569628,0.000043692617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013775509,0.0005273615,0.00038324832,0.00046103532,0.00045793058,0.0009782172,0.0013764683,0.0004919096,0.002462269],"category_scores_gemma":[0.0042434908,0.00028042664,0.0003856757,0.0006734174,0.0006010973,0.0010158869,0.0009735433,0.0004808396,0.0007841958],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000316069,0.00025316406,0.006730795,0.0008055672,0.00007154003,0.0004960173,0.0004296772,0.68460876,0.053981513,0.044711284,0.004251063,0.20334458],"study_design_scores_gemma":[0.000048930622,0.0005746975,0.0038344585,0.0001050845,0.00007362195,0.00059870863,0.00030218207,0.920372,0.038141288,0.010857649,0.025020752,0.000070680915],"about_ca_topic_score_codex":0.0026162844,"about_ca_topic_score_gemma":0.0022116979,"teacher_disagreement_score":0.0026162844,"about_ca_system_score_codex":0.001198153,"about_ca_system_score_gemma":0.0011568316,"threshold_uncertainty_score":0.008693278},"labels":[],"label_agreement":null},{"id":"W1964865687","doi":"10.1016/j.adhoc.2013.10.007","title":"Location error estimation in wireless ad hoc networks","year":2013,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Estimator; Cramér–Rao bound; Upper and lower bounds; Robustness (evolution); Computer science; Algorithm; Variance (accounting); Time of arrival; Mean squared error; Statistics; Probabilistic logic; RSS; Wireless ad hoc network; Mathematics; Wireless; Telecommunications","score_opus":0.006773266339124703,"score_gpt":0.20684962032450063,"score_spread":0.20007635398537593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964865687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01571537,0.0025626982,0.9804617,0.00019115701,0.00014122766,0.000014957518,0.000035458863,0.00019312075,0.00068428874],"genre_scores_gemma":[0.84841514,0.005875034,0.13768344,0.000115091985,0.0005799039,0.00007873624,0.00023253476,0.000067847766,0.0069522737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982906,0.00065682206,0.000115763076,0.0002604545,0.00054835697,0.00012794268],"domain_scores_gemma":[0.9964766,0.0022125153,0.00035115497,0.00028583212,0.00061816187,0.000055689536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017700671,0.00066140114,0.0009057689,0.0011504797,0.0004679352,0.0010295601,0.00093298167,0.0010011328,0.00063364126],"category_scores_gemma":[0.01033118,0.0004790417,0.00031815452,0.0019464717,0.0008641227,0.0022070264,0.0011360326,0.0007883008,0.0003113796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018315008,0.0000632189,0.003386371,0.00017952424,0.00009409094,0.00014964248,0.00012023401,0.78688544,0.0035493956,0.010105836,0.0017315653,0.19355145],"study_design_scores_gemma":[0.0000090833355,0.00004458605,0.00083795324,0.000023029905,0.000026248783,0.00011245561,0.00005167832,0.98926324,0.0022391647,0.0060757655,0.0013017465,0.00001501301],"about_ca_topic_score_codex":0.005140489,"about_ca_topic_score_gemma":0.0026724709,"teacher_disagreement_score":0.005140489,"about_ca_system_score_codex":0.00045528245,"about_ca_system_score_gemma":0.00061481964,"threshold_uncertainty_score":0.010221124},"labels":[],"label_agreement":null},{"id":"W1964966096","doi":"10.1155/2010/567040","title":"Centroid Localization of Uncooperative Nodes in Wireless Networks Using a Relative Span Weighting Method","year":2009,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"RSS; Computer science; Node (physics); Transmitter; Computer network; Weighting; Wireless network; Centroid; Wireless; Algorithm; Real-time computing; Telecommunications; Channel (broadcasting); Artificial intelligence","score_opus":0.025338382759668477,"score_gpt":0.2821900903984041,"score_spread":0.2568517076387356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964966096","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014738647,0.00013005128,0.9845703,0.00002814923,0.000014757464,0.000011143641,0.000007675101,0.00013362955,0.00036571082],"genre_scores_gemma":[0.5386722,0.00038257308,0.45844185,0.00004624953,0.00008072679,0.00010100757,0.00009188901,0.000063443,0.0021201011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991314,0.00027202308,0.000047903646,0.00016562679,0.00031278896,0.00007029853],"domain_scores_gemma":[0.9982889,0.0007114103,0.00027378544,0.00025606446,0.00040777886,0.00006190493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014401061,0.00064146984,0.00079565716,0.001217503,0.00034927973,0.0006359945,0.0014733718,0.0006126133,0.000901549],"category_scores_gemma":[0.006014504,0.00027839435,0.00042093216,0.0011604743,0.0005392655,0.0023048664,0.0016586998,0.0005497951,0.00044496532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031306036,0.000076530065,0.002347589,0.000121870784,0.000084927684,0.00015467092,0.00029991707,0.62198937,0.021290557,0.03089415,0.0011883582,0.32123908],"study_design_scores_gemma":[0.000014315443,0.0000931501,0.0003898378,0.0000067470887,0.000015801776,0.00011434452,0.00002833012,0.9888016,0.003747348,0.005836681,0.0009340038,0.000017893024],"about_ca_topic_score_codex":0.0012448231,"about_ca_topic_score_gemma":0.001099652,"teacher_disagreement_score":0.0014733718,"about_ca_system_score_codex":0.00051583495,"about_ca_system_score_gemma":0.00043664913,"threshold_uncertainty_score":0.0076161027},"labels":[],"label_agreement":null},{"id":"W1965919406","doi":"10.1016/j.dsp.2012.12.020","title":"Accurate and simple source localization using differential received signal strength","year":2013,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"RSS; Estimator; Algorithm; Cramér–Rao bound; Signal strength; Mathematics; Position (finance); Simple (philosophy); Differential (mechanical device); Gaussian; Computer science; SIGNAL (programming language); Variable (mathematics); Upper and lower bounds; Mathematical optimization; Statistics; Wireless; Telecommunications","score_opus":0.011572358851625926,"score_gpt":0.21205246634867106,"score_spread":0.20048010749704515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965919406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02074247,0.00035057875,0.97345823,0.00012831081,0.00014419126,0.00002584154,0.000099899415,0.0013720111,0.0036783812],"genre_scores_gemma":[0.5275695,0.00074190716,0.46141177,0.00024803213,0.00018621515,0.00007955674,0.00061375316,0.00015112945,0.008998126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993656,0.000101776786,0.00002847925,0.000108156346,0.0003549682,0.000040942865],"domain_scores_gemma":[0.9993917,0.00014550702,0.00006537246,0.00017063227,0.00020101944,0.00002573208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031013618,0.00096702366,0.0006271199,0.00085467775,0.00026225933,0.00087178586,0.00077769236,0.00083724514,0.0022214085],"category_scores_gemma":[0.0016047484,0.0004184812,0.00033007562,0.0008391125,0.00028484038,0.0015707173,0.0012030452,0.0006181084,0.0025590127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034860754,0.00008465712,0.0031797874,0.0003398274,0.00008554311,0.00029976826,0.00013543235,0.017658925,0.3310381,0.008729705,0.0038963766,0.63420326],"study_design_scores_gemma":[0.00015050756,0.0007060505,0.010239884,0.00015367159,0.00029800105,0.005078505,0.00020746903,0.4161537,0.5196525,0.0119746355,0.035215806,0.00016923732],"about_ca_topic_score_codex":0.00043530535,"about_ca_topic_score_gemma":0.00070288876,"teacher_disagreement_score":0.0022214085,"about_ca_system_score_codex":0.00021846191,"about_ca_system_score_gemma":0.00028981804,"threshold_uncertainty_score":0.0074313283},"labels":[],"label_agreement":null},{"id":"W1966000693","doi":"10.1109/glocom.2014.7036779","title":"Location information dissemination scheme for RFID-based distributed localization systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Dissemination; Scheme (mathematics); Leverage (statistics); Overhead (engineering); Computer network; Distributed computing; Mobile device; Context (archaeology); Information Dissemination; World Wide Web; Telecommunications; Artificial intelligence","score_opus":0.0044457393108072235,"score_gpt":0.2052587939625473,"score_spread":0.20081305465174007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966000693","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048498794,0.00067235506,0.94684494,0.0003105304,0.00012025955,0.00015309094,0.0000687133,0.0012486365,0.002082581],"genre_scores_gemma":[0.90637046,0.00031745894,0.08975754,0.000096809075,0.000046488763,0.00016190884,0.00013949975,0.00002051966,0.0030893213],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995141,0.0001287206,0.000052507483,0.00009632768,0.00015854978,0.00004965637],"domain_scores_gemma":[0.99912983,0.00021398085,0.00013165678,0.00021441432,0.00025523014,0.000054904176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007296664,0.00037669676,0.00051490654,0.0005930549,0.0006020646,0.0005779829,0.0014374371,0.00068723556,0.001052358],"category_scores_gemma":[0.002040774,0.00016251605,0.000250466,0.0006981336,0.00027782348,0.0012450366,0.0013355999,0.00048432391,0.00040117037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077431387,0.00026687625,0.0035735262,0.00058946933,0.00016351527,0.00080032117,0.0008633134,0.3130553,0.14874153,0.038479656,0.010049345,0.48264286],"study_design_scores_gemma":[0.00017041525,0.0004650085,0.0009124434,0.000026013995,0.000083201136,0.00061031926,0.00013902641,0.940944,0.030394798,0.005792762,0.020399349,0.0000625564],"about_ca_topic_score_codex":0.00078795425,"about_ca_topic_score_gemma":0.00058338715,"teacher_disagreement_score":0.0014374371,"about_ca_system_score_codex":0.0005706085,"about_ca_system_score_gemma":0.0004098338,"threshold_uncertainty_score":0.004140079},"labels":[],"label_agreement":null},{"id":"W1966356045","doi":"10.1109/glocom.2014.7036848","title":"An extended centroid localization algorithm based on error correction in WSN","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Centroid; Computer science; Reliability (semiconductor); Algorithm; Point (geometry); Feature (linguistics); Observational error; Wireless sensor network; Distance measurement; Expression (computer science); Scheme (mathematics); Measurement uncertainty; Artificial intelligence; Mathematics; Power (physics); Statistics","score_opus":0.005086147421726294,"score_gpt":0.21471111923741276,"score_spread":0.20962497181568646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966356045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047225486,0.00060415646,0.9931728,0.00007003293,0.00008922886,0.000023831255,0.00002815186,0.00048207192,0.00080715993],"genre_scores_gemma":[0.40588158,0.0019407711,0.58404076,0.00011142407,0.00022117562,0.0001834177,0.00030642105,0.00019230883,0.007122159],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990747,0.00015032079,0.000059985614,0.00024727284,0.00040337877,0.000064266605],"domain_scores_gemma":[0.99931145,0.0001512547,0.00010726332,0.000097689415,0.00030903047,0.000023402925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005762019,0.00075132673,0.0007468773,0.0011631919,0.00061035116,0.0007361181,0.0015543079,0.00074641337,0.0011598141],"category_scores_gemma":[0.0022688124,0.00024153404,0.00047777322,0.0019704378,0.00047191355,0.0019535823,0.0012487676,0.00075159426,0.00047387212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003267967,0.000041649808,0.0022010417,0.00042714208,0.00008992147,0.00039234644,0.00034600883,0.23887457,0.03706377,0.034637336,0.005574908,0.68002456],"study_design_scores_gemma":[0.00005680577,0.00021654437,0.0012364599,0.00003810509,0.000053365045,0.00071479584,0.0000729383,0.9426237,0.024676226,0.00819444,0.022027433,0.00008917544],"about_ca_topic_score_codex":0.0047246614,"about_ca_topic_score_gemma":0.0023047645,"teacher_disagreement_score":0.0047246614,"about_ca_system_score_codex":0.00067779666,"about_ca_system_score_gemma":0.0008377471,"threshold_uncertainty_score":0.009394288},"labels":[],"label_agreement":null},{"id":"W1966445758","doi":"10.1109/mfi.2010.5604469","title":"Radio-visual signal fusion for localization in cellular networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cellular network; Fuse (electrical); Weighting; Probabilistic logic; Base station; Sensor fusion; Computer vision; SIGNAL (programming language); Artificial intelligence; Particle filter; Real-time computing; Computer network; Engineering; Filter (signal processing)","score_opus":0.004618208698667337,"score_gpt":0.2033804505719876,"score_spread":0.19876224187332026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966445758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007897053,0.00050824985,0.990249,0.000059147485,0.000030243133,0.000013045835,0.000018406368,0.00025246383,0.0009724158],"genre_scores_gemma":[0.71697444,0.001015842,0.27963513,0.000090236106,0.00006450396,0.000057329667,0.00013667216,0.000035785233,0.0019899835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977833,0.00005568287,0.0000088359575,0.000041606978,0.00009334167,0.000022299473],"domain_scores_gemma":[0.99985015,0.000050726736,0.000017630702,0.00002117291,0.00005256202,0.000007769626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034664103,0.00033719107,0.0003321733,0.0005008839,0.00019676638,0.0004896333,0.0003651435,0.00048975466,0.0009918158],"category_scores_gemma":[0.0009290078,0.00014291453,0.00035425092,0.00055216305,0.00034037075,0.0005867455,0.0006354334,0.00032265022,0.00043283505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025810848,0.0000686661,0.0011142064,0.00025737155,0.000060453778,0.00019059514,0.00015784778,0.28716886,0.07730422,0.025510455,0.0025631033,0.60534614],"study_design_scores_gemma":[0.000009785853,0.00007494775,0.0006795608,0.000014382447,0.000019584673,0.000113461756,0.000026692494,0.97473735,0.013819257,0.007184685,0.003302401,0.000017974373],"about_ca_topic_score_codex":0.0014255974,"about_ca_topic_score_gemma":0.001377396,"teacher_disagreement_score":0.0014255974,"about_ca_system_score_codex":0.00036565503,"about_ca_system_score_gemma":0.00027102773,"threshold_uncertainty_score":0.0033179522},"labels":[],"label_agreement":null},{"id":"W1966542306","doi":"10.1155/2015/342428","title":"An Adaptive Linearized Method for Localizing Video Endoscopic Capsule Using Weighted Centroid Algorithm","year":2015,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Bangladesh University of Engineering and Technology; University of Saskatchewan","keywords":"Computer science; Path loss; Algorithm; Centroid; Path (computing); Capsule endoscopy; Minimum mean square error; Transmitter; Position (finance); Antenna (radio); Artificial intelligence; Mathematics; Wireless; Telecommunications; Statistics","score_opus":0.024178388615042,"score_gpt":0.2939191962976626,"score_spread":0.2697408076826206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966542306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003967653,0.00008964937,0.9951373,0.00003240939,0.00001535053,0.00001997197,0.000011868974,0.00033685545,0.0003889828],"genre_scores_gemma":[0.19191085,0.00028371572,0.80316246,0.000084441715,0.00005712986,0.00019606754,0.00020145797,0.0002134574,0.0038904636],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948645,0.000114028924,0.000023176855,0.00012886395,0.00020352528,0.000043909775],"domain_scores_gemma":[0.9995171,0.00013200493,0.00007104832,0.00004823429,0.00020947786,0.000022218255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005229067,0.00082468207,0.0007409842,0.0009684196,0.00041232439,0.0006450994,0.001664261,0.0008039549,0.0022236195],"category_scores_gemma":[0.001990328,0.00035903614,0.00073390815,0.0011010672,0.00042096106,0.0011555078,0.00097002136,0.00060552027,0.0011298733],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003069281,0.000083765546,0.0013435275,0.00019469703,0.000104951476,0.00017931919,0.00030792193,0.3783794,0.050785378,0.00974375,0.0038488503,0.55472153],"study_design_scores_gemma":[0.00001722327,0.000059825044,0.00031869815,0.000006326131,0.000017662593,0.00008770245,0.000026763453,0.99082196,0.0056940285,0.0011541311,0.0017742775,0.000021483474],"about_ca_topic_score_codex":0.006558664,"about_ca_topic_score_gemma":0.004483093,"teacher_disagreement_score":0.006558664,"about_ca_system_score_codex":0.00068862643,"about_ca_system_score_gemma":0.00089191925,"threshold_uncertainty_score":0.01304096},"labels":[],"label_agreement":null},{"id":"W1967232006","doi":"10.1109/glocom.2013.6831071","title":"Efficient range-free localization algorithm for randomly distributed wireless sensor networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Node (physics); Algorithm; Position (finance); Computer science; Wireless sensor network; Range (aeronautics); State (computer science); Topology (electrical circuits); Mathematics; Computer network; Combinatorics; Engineering","score_opus":0.0051552524900201875,"score_gpt":0.18841340391565872,"score_spread":0.18325815142563853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967232006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052117407,0.00029661367,0.99325114,0.00009416169,0.000037224254,0.000024347091,0.000014311347,0.0004890906,0.0005812267],"genre_scores_gemma":[0.39111155,0.00084403524,0.60289663,0.0002462309,0.00010271961,0.0003044241,0.00028400338,0.00012580227,0.0040845326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988716,0.00024215248,0.000053237334,0.00022470641,0.00052090856,0.00008734857],"domain_scores_gemma":[0.9991347,0.0003250678,0.00013218557,0.00013507991,0.00024349612,0.00002955459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006783854,0.00074042834,0.0008573719,0.0010073987,0.0006478676,0.00067873235,0.0016282836,0.0008841593,0.0010216783],"category_scores_gemma":[0.002933112,0.00040923533,0.0005445817,0.00092788826,0.0004732648,0.0018970013,0.0013620788,0.0007525363,0.0006454616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028889484,0.000094134244,0.0010584706,0.00022842434,0.00009127946,0.00020957537,0.00027784915,0.5720203,0.02267796,0.02300025,0.0041346233,0.37591827],"study_design_scores_gemma":[0.00003949926,0.0000796344,0.0002444634,0.000012252719,0.000019957171,0.00015874906,0.000028639966,0.98654383,0.004753184,0.005019159,0.0030776437,0.000022924956],"about_ca_topic_score_codex":0.0014636891,"about_ca_topic_score_gemma":0.0015968926,"teacher_disagreement_score":0.0016282836,"about_ca_system_score_codex":0.00058699277,"about_ca_system_score_gemma":0.0009578728,"threshold_uncertainty_score":0.0042589307},"labels":[],"label_agreement":null},{"id":"W1968446965","doi":"10.1109/icl-gnss.2014.6934170","title":"Estimation of heading misalignment between a pedestrian and a wearable device","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trusted Positioning (Canada)","funders":"","keywords":"Heading (navigation); Wearable computer; Computer science; Orientation (vector space); Inertial measurement unit; Wearable technology; Smart device; Real-time computing; Pedestrian; Inertial navigation system; Attitude and heading reference system; Human–computer interaction; Computer vision; Embedded system; Engineering; Transport engineering","score_opus":0.010668781759283923,"score_gpt":0.22134408393559232,"score_spread":0.2106753021763084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968446965","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60751104,0.0010094667,0.38538086,0.00010231396,0.00034221297,0.000082549566,0.00047024366,0.0016567942,0.0034444781],"genre_scores_gemma":[0.96046025,0.00035677679,0.03795437,0.000023371053,0.000049049322,0.000026310256,0.0003188173,0.000024836894,0.0007862725],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964356,0.000060113598,0.000027099484,0.00009486491,0.0001083785,0.00006609722],"domain_scores_gemma":[0.99946874,0.00009089821,0.00012753806,0.00006257145,0.00019802769,0.000052353498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027938109,0.0008177949,0.0006057474,0.0012164639,0.00025174662,0.00036567278,0.00029205618,0.00038185265,0.0007631294],"category_scores_gemma":[0.001311972,0.00021129988,0.0002442356,0.00076457026,0.0001569677,0.00036685195,0.0006445585,0.0003720298,0.0005498195],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030047263,0.0001942084,0.16454028,0.0007611308,0.00023142976,0.0021214022,0.00087088824,0.035503495,0.21921486,0.0013899909,0.005353246,0.56681436],"study_design_scores_gemma":[0.00013153309,0.001995533,0.3546667,0.00014671883,0.00028024288,0.0048146155,0.0017815928,0.50277144,0.12361086,0.0012308304,0.008408951,0.00016097032],"about_ca_topic_score_codex":0.0015668612,"about_ca_topic_score_gemma":0.0021638223,"teacher_disagreement_score":0.0015668612,"about_ca_system_score_codex":0.00017396397,"about_ca_system_score_gemma":0.0003598205,"threshold_uncertainty_score":0.0031155348},"labels":[],"label_agreement":null},{"id":"W1969082165","doi":"10.1155/2015/105682","title":"Accurate Nodes Localization in Anisotropic Wireless Sensor Networks","year":2015,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec en Abitibi-Témiscamingue; Université du Québec à Montréal","funders":"","keywords":"Computer science; Node (physics); Wireless sensor network; Position (finance); Algorithm; Wireless; Selection algorithm; Mechanism (biology); Power (physics); Selection (genetic algorithm); Computer network; Artificial intelligence; Telecommunications","score_opus":0.014497255305303639,"score_gpt":0.24128803327078163,"score_spread":0.226790777965478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969082165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016151352,0.000430176,0.9823291,0.00006499488,0.000025604424,0.000008739259,0.00001381263,0.00022533862,0.00075089524],"genre_scores_gemma":[0.6552298,0.0015636666,0.34078664,0.00005654749,0.00006014222,0.0000635917,0.00009513318,0.00006923245,0.0020752945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995338,0.0001296156,0.000026068174,0.00007080458,0.00020808222,0.000031746746],"domain_scores_gemma":[0.99948287,0.0001664557,0.00012951881,0.000111201734,0.000091499445,0.000018381852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041511407,0.00048618283,0.0004896613,0.0005596392,0.0002907626,0.0005948676,0.00058255985,0.00046047772,0.00024193831],"category_scores_gemma":[0.0018305881,0.00027061923,0.00026235485,0.0007212054,0.00046029186,0.0008594119,0.00091150915,0.00044774907,0.00021627375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013238717,0.00001638071,0.0020078414,0.00017692939,0.00003400078,0.0002281554,0.00021960541,0.71842915,0.048469003,0.0218382,0.0013891951,0.20705917],"study_design_scores_gemma":[0.0000070887468,0.000040661642,0.0004885215,0.000009386444,0.000011946203,0.00017619695,0.00003554834,0.98463017,0.005793944,0.004897754,0.003892262,0.000016604541],"about_ca_topic_score_codex":0.0014054846,"about_ca_topic_score_gemma":0.0012384369,"teacher_disagreement_score":0.0014054846,"about_ca_system_score_codex":0.00027717635,"about_ca_system_score_gemma":0.000328284,"threshold_uncertainty_score":0.0027945638},"labels":[],"label_agreement":null},{"id":"W1969404398","doi":"10.1109/pimrc.2011.6139689","title":"Improving the accuracy of connectivity-based positioning for mobile sensor networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Wireless sensor network; Range (aeronautics); Set (abstract data type); Node (physics); Real-time computing; Mobile radio; Algorithm; Computer network; Engineering","score_opus":0.014707757907479682,"score_gpt":0.21155162946130365,"score_spread":0.19684387155382396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969404398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10439832,0.0023449108,0.88793856,0.00046229528,0.00014677625,0.00003443832,0.00013380939,0.0015077378,0.0030330778],"genre_scores_gemma":[0.83405346,0.001186643,0.16312589,0.00007096877,0.000113780865,0.000041589687,0.0003280497,0.00016894517,0.00091061543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984837,0.00038721057,0.00009275567,0.00020815463,0.00073830306,0.00008994123],"domain_scores_gemma":[0.99363965,0.0037448974,0.00061260414,0.0011244061,0.00082297437,0.000055372446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013007146,0.00092390087,0.0006704473,0.0012243412,0.00055508694,0.0007483474,0.0013847214,0.0009580138,0.0007398454],"category_scores_gemma":[0.017945644,0.00040917916,0.00036223352,0.0014241387,0.0006678578,0.0022323541,0.0014479519,0.0005932686,0.0004002868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001718369,0.00003218682,0.0066781305,0.00020346422,0.00007783764,0.00013874445,0.0002392144,0.74088854,0.01239854,0.010083821,0.0013025989,0.22778511],"study_design_scores_gemma":[0.000028747447,0.00015213549,0.0032845538,0.000029002189,0.000049435832,0.00027390994,0.000052663177,0.97303975,0.01023742,0.009036116,0.0037829666,0.000033261927],"about_ca_topic_score_codex":0.0036894195,"about_ca_topic_score_gemma":0.003063727,"teacher_disagreement_score":0.0036894195,"about_ca_system_score_codex":0.00056648924,"about_ca_system_score_gemma":0.0005799941,"threshold_uncertainty_score":0.0073358417},"labels":[],"label_agreement":null},{"id":"W1969479488","doi":"10.1155/2009/765010","title":"Experimental Results on an Integrated GPS and Multisensor System for Land Vehicle Positioning","year":2009,"lang":"en","type":"article","venue":"International Journal of Navigation and Observation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Global Positioning System; Inertial measurement unit; Odometer; GPS/INS; Gyroscope; GPS signals; Computer science; Assisted GPS; Inertial navigation system; Kalman filter; Precision Lightweight GPS Receiver; Remote sensing; Real-time computing; Inertial frame of reference; Engineering; Artificial intelligence; Geography; Aerospace engineering; Telecommunications","score_opus":0.01907870148114766,"score_gpt":0.2725756046859313,"score_spread":0.25349690320478363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969479488","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80896527,0.00040640135,0.17613505,0.00027914313,0.00047164632,0.00048105876,0.00085395033,0.0035626274,0.00884482],"genre_scores_gemma":[0.95897347,0.00012599828,0.03566843,0.000058110996,0.00002044578,0.00016724599,0.00070397346,0.00004794818,0.004234321],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993986,0.00011757747,0.0000450731,0.00012895526,0.00021963617,0.00009015355],"domain_scores_gemma":[0.998961,0.000182712,0.00005122416,0.00013961401,0.0005842671,0.000081254446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008298632,0.00069889345,0.00068480475,0.000530938,0.00065390527,0.0004982941,0.0008382366,0.0009984956,0.006648258],"category_scores_gemma":[0.0016096781,0.00026118982,0.00037663,0.00054349273,0.0003889993,0.0007706544,0.0005582921,0.0003726416,0.0015399446],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006333318,0.002141443,0.012641907,0.001601193,0.000395122,0.0015316008,0.0014907732,0.12717177,0.5152892,0.0026123356,0.008697441,0.32009396],"study_design_scores_gemma":[0.00072000496,0.010526636,0.042062856,0.00008780285,0.00036853057,0.00077366683,0.0008824664,0.64212656,0.28761664,0.0010134227,0.013631397,0.00018996479],"about_ca_topic_score_codex":0.0047818213,"about_ca_topic_score_gemma":0.0054314802,"teacher_disagreement_score":0.006648258,"about_ca_system_score_codex":0.00042017308,"about_ca_system_score_gemma":0.000524883,"threshold_uncertainty_score":0.022240698},"labels":[],"label_agreement":null},{"id":"W1969578102","doi":"10.1016/j.aei.2006.09.002","title":"A proximity-based method for locating RFID tagged objects","year":2007,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Radio-frequency identification; Software deployment; Identification (biology); Computer science; Field (mathematics); Radio frequency; Tracking (education); Power (physics); Real-time computing; Electronic engineering; Engineering; Embedded system; Telecommunications; Software engineering; Computer security; Mathematics","score_opus":0.0054001043504273635,"score_gpt":0.23616664191981418,"score_spread":0.2307665375693868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969578102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009582814,0.0006072983,0.98513454,0.00005373155,0.00017279162,0.000056685294,0.000078645346,0.0012938897,0.0030195448],"genre_scores_gemma":[0.22904111,0.0010102813,0.751616,0.00015187272,0.00021748933,0.00022661014,0.00036983233,0.00012981024,0.017236963],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989802,0.0001610117,0.000042402306,0.00019210868,0.0005714603,0.00005276589],"domain_scores_gemma":[0.9990964,0.0002229907,0.00008667055,0.00018606588,0.0003669811,0.00004090963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004632015,0.0006869782,0.0007697006,0.001851654,0.0007530567,0.0009827577,0.001281491,0.0012245236,0.003886344],"category_scores_gemma":[0.0019660888,0.00042734455,0.0004955787,0.0020681492,0.0004336768,0.0012433395,0.0015337741,0.0006887814,0.0038541202],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061380747,0.00013360068,0.0013611566,0.0003681695,0.00007132549,0.000308964,0.00032931587,0.006625026,0.22652666,0.0068241144,0.0040779454,0.75276],"study_design_scores_gemma":[0.00024267897,0.0013260207,0.00857169,0.00016309004,0.00040917864,0.008824904,0.0005744636,0.49079555,0.3922225,0.009215075,0.087292105,0.0003627435],"about_ca_topic_score_codex":0.0015570602,"about_ca_topic_score_gemma":0.0016425109,"teacher_disagreement_score":0.003886344,"about_ca_system_score_codex":0.0003343162,"about_ca_system_score_gemma":0.00046595777,"threshold_uncertainty_score":0.013001084},"labels":[],"label_agreement":null},{"id":"W1969836417","doi":"10.1117/12.719068","title":"Two solutions to the localization using time difference of arrival problem","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Time of arrival; Arrival time; Algorithm; Telecommunications","score_opus":0.013315889742038395,"score_gpt":0.23018076008570143,"score_spread":0.21686487034366303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969836417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010325562,0.00015895138,0.99658304,0.00033738374,0.0000845863,0.000021888094,0.000018512093,0.00003491988,0.0017281676],"genre_scores_gemma":[0.096016504,0.0012058803,0.89074975,0.00048370133,0.00041318536,0.00043920396,0.00023076318,0.00010489581,0.010356153],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983406,0.00071430794,0.00009716695,0.00027107802,0.0004021678,0.0001747333],"domain_scores_gemma":[0.9983741,0.0008643783,0.00016118202,0.00013134695,0.00037210374,0.00009684862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024971766,0.0013226357,0.00097227196,0.0008245846,0.0004086566,0.0019097178,0.0016179379,0.0027805986,0.004355745],"category_scores_gemma":[0.006174713,0.00062493735,0.0011633631,0.00069851225,0.0011757616,0.0025639187,0.0024628255,0.0029232136,0.0013246988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018760681,0.00018061079,0.00045246974,0.0004961277,0.00007449418,0.00022488194,0.00036784585,0.32961646,0.005124546,0.5000779,0.011183272,0.15201376],"study_design_scores_gemma":[0.0001324587,0.00020029223,0.00019762242,0.00007537092,0.000029217492,0.00029847352,0.00010713236,0.8736971,0.0035894085,0.099607505,0.022004213,0.000061192375],"about_ca_topic_score_codex":0.00057924475,"about_ca_topic_score_gemma":0.00046518934,"teacher_disagreement_score":0.004355745,"about_ca_system_score_codex":0.000717909,"about_ca_system_score_gemma":0.0014353845,"threshold_uncertainty_score":0.014571488},"labels":[],"label_agreement":null},{"id":"W1970098652","doi":"10.1109/glocom.2011.6134367","title":"A Map Registration Localization Approach Based on Mobile Beacons for Wireless Sensor Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Beacon; Wireless sensor network; Computer science; Global Positioning System; Node (physics); Key distribution in wireless sensor networks; Sensor node; Mobile wireless sensor network; Real-time computing; Network topology; Computer network; Wireless; Wireless network; Engineering; Telecommunications","score_opus":0.01856589194606431,"score_gpt":0.20441563681814004,"score_spread":0.18584974487207573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970098652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016453969,0.00053130806,0.9961349,0.00008163265,0.00009551053,0.000022590702,0.000011043964,0.00062334584,0.0008544005],"genre_scores_gemma":[0.19550426,0.0033808064,0.79045844,0.00020781517,0.0003674537,0.0002571067,0.0002468471,0.0002458674,0.009331408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938524,0.00016846403,0.00002618958,0.000113226044,0.00026941253,0.00003748788],"domain_scores_gemma":[0.99958843,0.00012070739,0.000057157235,0.000071359456,0.0001386698,0.000023580373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049533404,0.0005856669,0.00065407326,0.0011324784,0.00048771183,0.0006406042,0.0014670925,0.00074979186,0.0015789624],"category_scores_gemma":[0.0011288535,0.00032946005,0.00060876366,0.0012615679,0.00053143164,0.0016581875,0.0009739905,0.00088572095,0.0011365126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033630262,0.000121771234,0.0012931551,0.0006875241,0.00013211463,0.00054419594,0.00042432777,0.05979895,0.07245674,0.05718952,0.0074673747,0.799548],"study_design_scores_gemma":[0.00012295501,0.0008685972,0.0014126665,0.000105437735,0.00021527562,0.0023043768,0.00026236795,0.72171193,0.07310851,0.023564782,0.17610954,0.00021348342],"about_ca_topic_score_codex":0.00090353546,"about_ca_topic_score_gemma":0.0009890534,"teacher_disagreement_score":0.0015789624,"about_ca_system_score_codex":0.00037921895,"about_ca_system_score_gemma":0.0005211192,"threshold_uncertainty_score":0.0052821636},"labels":[],"label_agreement":null},{"id":"W1971268196","doi":"10.1145/2594368.2602428","title":"Video","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Gyroscope; Phone; Dead reckoning; Computer vision; Reset (finance); Artificial intelligence; Real-time computing; Floorplan; Rotation (mathematics); Global Positioning System; Embedded system; Telecommunications; Engineering","score_opus":0.003213149618561131,"score_gpt":0.16398370044939045,"score_spread":0.16077055083082933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971268196","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029384184,0.0032754098,0.014283431,0.0038146419,0.011592707,0.0005970701,0.08078218,0.012671161,0.87004495],"genre_scores_gemma":[0.01936993,0.0029690063,0.012980969,0.0036272146,0.0028216348,0.000302309,0.07129861,0.0031332264,0.8834971],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99981004,0.000013968916,0.000008007946,0.00003302429,0.00010244943,0.000032532025],"domain_scores_gemma":[0.9991321,0.00015043987,0.000032177268,0.00007812208,0.0004373868,0.0001698657],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021746045,0.00090663356,0.0004507459,0.001347042,0.0009874193,0.0019090925,0.0011431952,0.0014433112,0.7340849],"category_scores_gemma":[0.0024074626,0.00024577408,0.00046853034,0.0011384635,0.00025195337,0.0021371127,0.0012245085,0.001237054,0.3888479],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007850936,0.000031847532,0.00015806447,0.0001478452,0.000004497259,0.00025764134,0.00003450706,0.00018555034,0.000675257,0.0011586599,0.9296211,0.06764666],"study_design_scores_gemma":[0.000016437694,0.000024337096,0.000564581,0.00008277489,0.0000042179595,0.0003955584,0.00006562004,0.00046072836,0.00040610656,0.0009770088,0.9969897,0.000012936655],"about_ca_topic_score_codex":0.01145738,"about_ca_topic_score_gemma":0.015110178,"teacher_disagreement_score":0.2659151,"about_ca_system_score_codex":0.0007825218,"about_ca_system_score_gemma":0.00072000554,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W1971410313","doi":"10.1016/j.aei.2013.07.001","title":"Localization of RFID-equipped assets during the operation phase of facilities","year":2013,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Radio-frequency identification; Real-time locating system; Computer science; Context (archaeology); Matching (statistics); Plan (archaeology); Real-time computing; Facility management; Computer security","score_opus":0.0045399949821440235,"score_gpt":0.2025752299799504,"score_spread":0.19803523499780637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971410313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.926142,0.000326172,0.06695722,0.000069686794,0.000038853104,0.00002108098,0.00038105855,0.00036255387,0.005701369],"genre_scores_gemma":[0.9958794,0.000048848764,0.0028970728,0.0000052429195,0.0000044001977,0.0000030980373,0.000134717,0.000009335789,0.0010179264],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997956,0.000034771896,0.000007726774,0.000040482777,0.000058606915,0.000062736],"domain_scores_gemma":[0.9996915,0.00007897001,0.0000767817,0.000027636721,0.00009403715,0.000031135893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016910976,0.00016202666,0.00016627375,0.00059113593,0.00020417004,0.00042430355,0.00022519293,0.000348984,0.0011504786],"category_scores_gemma":[0.0008624278,0.00009264225,0.00013005204,0.0004960201,0.00013635062,0.00034140178,0.00038076285,0.00020017257,0.0006353705],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032475835,0.00018999734,0.29867002,0.00039332037,0.00009853032,0.0029167393,0.003880567,0.04343775,0.3461654,0.0037076438,0.002977102,0.29431537],"study_design_scores_gemma":[0.000038764563,0.0014340379,0.5876605,0.00015121693,0.00016897693,0.0030032296,0.006138955,0.15791596,0.22845781,0.0017143108,0.013202433,0.00011380702],"about_ca_topic_score_codex":0.0018838743,"about_ca_topic_score_gemma":0.0030238202,"teacher_disagreement_score":0.0018838743,"about_ca_system_score_codex":0.0001744766,"about_ca_system_score_gemma":0.00022462402,"threshold_uncertainty_score":0.0038487911},"labels":[],"label_agreement":null},{"id":"W1971454239","doi":"10.1049/iet-com.2012.0265","title":"Range‐based localisation and tracking in non‐line‐of‐sight wireless channels with Gaussian scatterer distribution model","year":2013,"lang":"en","type":"article","venue":"IET Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Toronto","funders":"","keywords":"Line-of-sight; Range (aeronautics); Gaussian; Non-line-of-sight propagation; Tracking (education); Computer science; Wireless; Distribution (mathematics); Mathematics; Telecommunications; Physics; Mathematical analysis; Engineering; Aerospace engineering","score_opus":0.019935109636642995,"score_gpt":0.238712100562452,"score_spread":0.21877699092580902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971454239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0146439,0.00018950793,0.98316103,0.000050770726,0.00002745968,0.000018613704,0.000053622734,0.0004098565,0.0014451426],"genre_scores_gemma":[0.8409611,0.0011195664,0.1482039,0.0000967546,0.000061532235,0.000118877586,0.00035967692,0.00008807246,0.008990551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941087,0.00011172424,0.000027266558,0.00012797979,0.00022615607,0.00009597948],"domain_scores_gemma":[0.9994289,0.00023544738,0.000076810895,0.00007949514,0.00016157512,0.000017646787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059358083,0.000420649,0.00077778765,0.00047559896,0.00028538622,0.0007809258,0.001055384,0.0008492517,0.0007752676],"category_scores_gemma":[0.0017773508,0.0003193628,0.0005942845,0.001045355,0.00054783217,0.0011309163,0.0006604745,0.0005345721,0.0006418738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095850766,0.000036824575,0.0013266411,0.00008934342,0.00003930535,0.00016862038,0.00008786035,0.9263458,0.005617389,0.013072591,0.0008952778,0.05222457],"study_design_scores_gemma":[0.000006382328,0.00002989083,0.0004433526,0.0000044654607,0.000008408958,0.00007018884,0.000009663101,0.99564123,0.0012815442,0.0018487643,0.0006455539,0.000010460426],"about_ca_topic_score_codex":0.010380253,"about_ca_topic_score_gemma":0.0068393485,"teacher_disagreement_score":0.010380253,"about_ca_system_score_codex":0.0005850111,"about_ca_system_score_gemma":0.00093562825,"threshold_uncertainty_score":0.020639718},"labels":[],"label_agreement":null},{"id":"W1971502695","doi":"10.4028/www.scientific.net/amr.433-440.2656","title":"A Novel Wireless Location Algorithm Based on High Probability Measurements","year":2012,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Robustness (evolution); Algorithm; Wireless; Channel (broadcasting); Ultra-wideband; Line-of-sight; Time of arrival; Real-time computing; Computer network; Engineering; Telecommunications","score_opus":0.07961791860404022,"score_gpt":0.3260763806488265,"score_spread":0.24645846204478628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971502695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002708081,0.000075015174,0.996564,0.000031273692,0.00004289929,0.000012043924,0.000010058479,0.00025960908,0.0002970254],"genre_scores_gemma":[0.13528694,0.00022682018,0.8616172,0.00009675676,0.00013422742,0.00015235803,0.00014439419,0.00009277388,0.0022484225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988881,0.00019416593,0.000059625618,0.00028933672,0.0005177294,0.000051007304],"domain_scores_gemma":[0.99839693,0.0006886566,0.00020829261,0.00019056053,0.00046247226,0.000053007818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075740914,0.0006664209,0.0009386833,0.0011072219,0.00053906074,0.0010507897,0.0019174991,0.0010791915,0.0011463009],"category_scores_gemma":[0.0040875515,0.00043898218,0.00047544896,0.0011683934,0.00059495925,0.0024917924,0.0011971314,0.0011320809,0.0012077194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000262039,0.000104182516,0.0019044981,0.00012350621,0.00008742646,0.00010613819,0.00011833898,0.10213506,0.031165574,0.021845438,0.0028897643,0.83925813],"study_design_scores_gemma":[0.00007190899,0.00020645947,0.0009760303,0.000019637087,0.000034178418,0.0005909223,0.00003440699,0.9599995,0.019051071,0.01012301,0.0088340575,0.000058822083],"about_ca_topic_score_codex":0.00069446856,"about_ca_topic_score_gemma":0.00069860445,"teacher_disagreement_score":0.0019174991,"about_ca_system_score_codex":0.000512409,"about_ca_system_score_gemma":0.00087485486,"threshold_uncertainty_score":0.004005611},"labels":[],"label_agreement":null},{"id":"W1971546620","doi":"10.1109/jbhi.2013.2261997","title":"Compressive-Sampling-Based Positioning in Wireless Body Area Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Robustness (evolution); Compressed sensing; Fast Fourier transform; Feature extraction; Wireless; Wireless sensor network; Real-time computing; Segmentation; Body area network; Grid; Computer vision; Artificial intelligence; Feature vector; Data mining; Algorithm; Computer network; Telecommunications","score_opus":0.020531440166060934,"score_gpt":0.26159658915366224,"score_spread":0.2410651489876013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971546620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007711233,0.00035659302,0.99074566,0.00011792938,0.00003368868,0.00001622266,0.000021255131,0.00008138015,0.00091604557],"genre_scores_gemma":[0.700045,0.0022289802,0.29406613,0.00012571651,0.00020107957,0.00014273316,0.00016114081,0.000035497764,0.0029937492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995468,0.00018074233,0.000017552185,0.00005053162,0.00018167969,0.000022677788],"domain_scores_gemma":[0.9994319,0.00037358754,0.000065509834,0.000051265415,0.00006552989,0.000012114218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006206302,0.00041492973,0.0004013976,0.00027156158,0.00017128386,0.000393449,0.00047584157,0.0005877698,0.0005180558],"category_scores_gemma":[0.001695365,0.00021455341,0.00031295128,0.00047159384,0.0006441459,0.00067570055,0.0004895733,0.000530325,0.00018150306],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005317562,0.000014893381,0.00032124895,0.00007904578,0.000014437541,0.00005554648,0.000053338503,0.94013983,0.0056918934,0.016552432,0.00039946963,0.03662478],"study_design_scores_gemma":[0.0000028178092,0.000020699446,0.000108783854,0.000004924189,0.0000016772639,0.00001777537,0.0000050711037,0.99653256,0.0005664835,0.0023989237,0.0003360626,0.000004082993],"about_ca_topic_score_codex":0.0027937256,"about_ca_topic_score_gemma":0.0016996777,"teacher_disagreement_score":0.0027937256,"about_ca_system_score_codex":0.00042289917,"about_ca_system_score_gemma":0.00038752082,"threshold_uncertainty_score":0.0055549145},"labels":[],"label_agreement":null},{"id":"W1972890144","doi":"10.1007/s11633-007-0315-x","title":"New distributed positioning algorithm based on centroid of circular belt for wireless sensor networks","year":2007,"lang":"en","type":"article","venue":"International Journal of Automation and Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Node (physics); Wireless sensor network; Centroid; Algorithm; Computer science; Wireless; Key distribution in wireless sensor networks; Range (aeronautics); Position (finance); Wireless network; Computer network; Engineering; Artificial intelligence; Telecommunications","score_opus":0.0043937576097499535,"score_gpt":0.22758449723587293,"score_spread":0.22319073962612299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972890144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065616127,0.0005921736,0.99086386,0.00009157238,0.00022976089,0.000038049544,0.00003687048,0.00074140774,0.00084472087],"genre_scores_gemma":[0.23398015,0.00092052267,0.7592677,0.00010627873,0.00023881468,0.0002608487,0.00038649375,0.00009552911,0.0047437297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917954,0.0001592827,0.000047980895,0.00025087595,0.00030568152,0.000056657238],"domain_scores_gemma":[0.99935,0.00010143643,0.00007387248,0.000095527015,0.00034609853,0.000033080636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005956633,0.00096171355,0.0013884314,0.0014602127,0.0008624397,0.0008269992,0.002340129,0.0009513044,0.0016198285],"category_scores_gemma":[0.0015857046,0.00038847286,0.00055570126,0.00209425,0.000419859,0.0016068507,0.0009918691,0.00080192805,0.0007172454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064074196,0.00013005357,0.0020191974,0.00029337525,0.0001615673,0.00019031379,0.00030491134,0.11482353,0.04870188,0.019271575,0.011154652,0.80230826],"study_design_scores_gemma":[0.00013942349,0.0003010287,0.0012543418,0.000022626253,0.00008487407,0.000378189,0.000094628704,0.95956343,0.020453617,0.003948986,0.013691156,0.00006775171],"about_ca_topic_score_codex":0.0037942284,"about_ca_topic_score_gemma":0.0027638096,"teacher_disagreement_score":0.0037942284,"about_ca_system_score_codex":0.00081056415,"about_ca_system_score_gemma":0.0010512217,"threshold_uncertainty_score":0.007544279},"labels":[],"label_agreement":null},{"id":"W1973104709","doi":"10.1109/iccnc.2013.6504137","title":"Ambiguity resolution in RSS-based emitter geolocation","year":2013,"lang":"en","type":"article","venue":"2013 International Conference on Computing, Networking and Communications (ICNC)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Geolocation; RSS; Robustness (evolution); Common emitter; Computer science; Ambiguity resolution; Ambiguity; Remote sensing; Real-time computing; Electronic engineering; Telecommunications; Engineering; Geography; GNSS applications; Global Positioning System; World Wide Web","score_opus":0.03864977308457444,"score_gpt":0.2647509111545804,"score_spread":0.22610113807000592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973104709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020715848,0.0013884759,0.9752864,0.000183133,0.00008108186,0.000013514274,0.00003775953,0.000244341,0.0020493688],"genre_scores_gemma":[0.6733502,0.0018393933,0.32237598,0.00016542379,0.0002207055,0.000056141624,0.0001757032,0.000105471554,0.0017109596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984433,0.00051830045,0.00008310748,0.00024681084,0.0006061963,0.00010237028],"domain_scores_gemma":[0.9975176,0.0013154108,0.00034271664,0.00047321516,0.00030711782,0.000043997483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019183041,0.00075569615,0.00079811673,0.0012248602,0.000450631,0.0012243972,0.00094730087,0.0011437861,0.00062559795],"category_scores_gemma":[0.012101491,0.0005845772,0.00041336223,0.0018775773,0.0013533662,0.0020879733,0.0023005325,0.0010652168,0.0006578097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037013207,0.000049443133,0.0033161563,0.00032910856,0.000099516474,0.0007394502,0.0008438145,0.49665162,0.0388898,0.12070129,0.002423593,0.33558598],"study_design_scores_gemma":[0.000047566806,0.00013153345,0.0023103945,0.00008663125,0.00004259195,0.001418961,0.00025974002,0.8430338,0.03091865,0.1121781,0.009444465,0.00012763964],"about_ca_topic_score_codex":0.0005866991,"about_ca_topic_score_gemma":0.0003398155,"teacher_disagreement_score":0.0019183041,"about_ca_system_score_codex":0.00034256733,"about_ca_system_score_gemma":0.00040915262,"threshold_uncertainty_score":0.010145068},"labels":[],"label_agreement":null},{"id":"W1973378347","doi":"10.1117/12.778008","title":"Using received signal strength variation for surveillance in residential areas","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Signal strength; Wireless sensor network; Computer science; Wireless; Received signal strength indication; Ranging; SIGNAL (programming language); Interference (communication); Computer security; Real-time computing; Wireless network; Computer network; Telecommunications","score_opus":0.016582998434195664,"score_gpt":0.22892454242401558,"score_spread":0.21234154398981991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973378347","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73801565,0.0020525148,0.24378563,0.00046480735,0.00011569154,0.000079464204,0.00054308405,0.002597115,0.012345982],"genre_scores_gemma":[0.9691806,0.00030071335,0.029680844,0.00004996436,0.000030426836,0.000015161616,0.00015897035,0.000025060197,0.0005583879],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996947,0.0001186896,0.00001649522,0.000069055175,0.000073140116,0.000027974665],"domain_scores_gemma":[0.99922884,0.00027971942,0.00017404003,0.00006778266,0.00020335586,0.000046344354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034939696,0.00045203007,0.0002855781,0.0012176597,0.00018571313,0.00048012947,0.00035763998,0.0003825522,0.00083807245],"category_scores_gemma":[0.0013615931,0.00014036924,0.00018660526,0.00096976676,0.00017195659,0.00048669757,0.00024606456,0.00028358554,0.00044994484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009701777,0.00028204755,0.15548766,0.00037486493,0.00021533982,0.0013664068,0.00043656936,0.05538349,0.14830425,0.0022737652,0.0043064905,0.63059884],"study_design_scores_gemma":[0.00008105744,0.0014064691,0.24029388,0.00012360633,0.00041900988,0.0041538314,0.0007385571,0.5540923,0.18253103,0.0038824526,0.012097864,0.00017997637],"about_ca_topic_score_codex":0.00097143033,"about_ca_topic_score_gemma":0.00161782,"teacher_disagreement_score":0.0012176597,"about_ca_system_score_codex":0.00023552983,"about_ca_system_score_gemma":0.00015201868,"threshold_uncertainty_score":0.0028036237},"labels":[],"label_agreement":null},{"id":"W1973656737","doi":"10.1109/icwmc.2010.42","title":"Experiment Design for Distance Evaluation in Wireless Sensor Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Firmware; Wireless sensor network; Computer science; Node (physics); Wireless; Received signal strength indication; Signal strength; Sensor node; Key distribution in wireless sensor networks; Real-time computing; Wireless network; Embedded system; Computer network; Computer hardware; Engineering; Telecommunications","score_opus":0.02057362099931538,"score_gpt":0.25848014205619124,"score_spread":0.23790652105687587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973656737","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06370935,0.00023472901,0.9015638,0.00012338659,0.00052578084,0.029400233,0.00039216015,0.001425994,0.0026244668],"genre_scores_gemma":[0.13623255,0.0001234599,0.78169215,0.00019553195,0.0001190938,0.08007244,0.00034410853,0.0002400096,0.0009806653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9044476,0.07319771,0.0067380336,0.005487138,0.008725462,0.001404079],"domain_scores_gemma":[0.8531551,0.11187025,0.006784073,0.015361769,0.0118411,0.0009877653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05398256,0.0018746956,0.0016438281,0.0010803435,0.0013091804,0.0013875808,0.0017212852,0.0015990882,0.0058047413],"category_scores_gemma":[0.099245176,0.0008409166,0.0011749857,0.0010228144,0.0018009937,0.0014510765,0.0013791536,0.0017928744,0.0009354616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.06740015,0.016801868,0.03519881,0.012976906,0.0031129224,0.0009388116,0.0042790277,0.07202579,0.21442652,0.055850934,0.008080626,0.5089076],"study_design_scores_gemma":[0.026030548,0.18377027,0.065154135,0.0013080004,0.0029028472,0.001429164,0.0014266516,0.26605996,0.32234952,0.04905413,0.07959416,0.00092065486],"about_ca_topic_score_codex":0.00030390522,"about_ca_topic_score_gemma":0.00017871347,"teacher_disagreement_score":0.05398256,"about_ca_system_score_codex":0.0010167699,"about_ca_system_score_gemma":0.0013659924,"threshold_uncertainty_score":0.28549045},"labels":[],"label_agreement":null},{"id":"W1973832094","doi":"10.1109/ccece.2013.6567761","title":"Modeling the hop count distribution in wireless sensor networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wireless sensor network; Computer science; Poisson distribution; Flooding (psychology); Euclidean distance; Jump; Stochastic process; Computer network; Hop (telecommunications); Probability distribution; Mathematics; Topology (electrical circuits); Statistics; Combinatorics; Artificial intelligence","score_opus":0.0072562291312218965,"score_gpt":0.18689295782827695,"score_spread":0.17963672869705505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973832094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061069123,0.00056518754,0.93549335,0.0005471357,0.000061846535,0.00004370699,0.00018692786,0.00026828528,0.0017644855],"genre_scores_gemma":[0.93524134,0.0018988351,0.05829962,0.00013197896,0.0001907921,0.00020473143,0.00029059572,0.00012398443,0.0036181526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991554,0.00029933278,0.00003928017,0.00018721247,0.0002387313,0.00008003486],"domain_scores_gemma":[0.99574846,0.003222195,0.0005290516,0.00017801345,0.00023786305,0.00008447744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017667615,0.0008886115,0.0007200989,0.0013921693,0.0004914885,0.001319034,0.002523526,0.0016231571,0.0007174932],"category_scores_gemma":[0.009477285,0.00066121836,0.0006176588,0.0016264035,0.001889876,0.0033815359,0.0011565742,0.0011720497,0.00026601157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017406774,0.000016782933,0.0011024964,0.000036436737,0.000014679376,0.00009787013,0.00006209742,0.94532084,0.00058704836,0.048358526,0.00021921753,0.004166601],"study_design_scores_gemma":[0.0000034830437,0.000007677285,0.00013836198,0.000004235338,0.0000035037224,0.000017730847,0.00001059535,0.9785423,0.00010083919,0.02096823,0.0001970036,0.0000060368125],"about_ca_topic_score_codex":0.005288498,"about_ca_topic_score_gemma":0.0032666635,"teacher_disagreement_score":0.005288498,"about_ca_system_score_codex":0.0011406487,"about_ca_system_score_gemma":0.0006893996,"threshold_uncertainty_score":0.010515451},"labels":[],"label_agreement":null},{"id":"W1974853808","doi":"10.3390/s131115513","title":"Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Computer science; RSS; Kalman filter; Cross-correlation; Algorithm; Matching (statistics); Pattern recognition (psychology); Artificial intelligence; Map matching; Data mining; Global Positioning System; Mathematics; Statistics","score_opus":0.006698961570399669,"score_gpt":0.20897777991644004,"score_spread":0.20227881834604036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974853808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018831095,0.00019227527,0.9771993,0.0000529769,0.000065932705,0.000035139234,0.00007725756,0.0020802119,0.001465882],"genre_scores_gemma":[0.62438023,0.00028159985,0.3706552,0.000120684694,0.00007323835,0.00012364319,0.00037367287,0.00008023831,0.0039115385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993019,0.00008987649,0.0000369797,0.00017189182,0.00032570315,0.00007364047],"domain_scores_gemma":[0.9991221,0.0001703,0.00010932136,0.00015259378,0.00041954787,0.000026069718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005133256,0.0007145069,0.000802947,0.00092588493,0.0005396656,0.00065071206,0.001117534,0.00056902645,0.0012352619],"category_scores_gemma":[0.0017129423,0.0003626103,0.00039989775,0.0007837315,0.00029922667,0.0010730722,0.0005513277,0.00045395942,0.0008410111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046017184,0.00015060448,0.006364077,0.00018385539,0.00011337075,0.00021908728,0.00019229128,0.119501874,0.080255605,0.004575755,0.0044779046,0.7835055],"study_design_scores_gemma":[0.000033473723,0.00014003202,0.0041251085,0.000015592044,0.000057609064,0.00045264058,0.000027019987,0.938495,0.050928496,0.00080335786,0.004858925,0.00006268595],"about_ca_topic_score_codex":0.007948485,"about_ca_topic_score_gemma":0.008396327,"teacher_disagreement_score":0.007948485,"about_ca_system_score_codex":0.0006096425,"about_ca_system_score_gemma":0.00091158046,"threshold_uncertainty_score":0.01580447},"labels":[],"label_agreement":null},{"id":"W1975924107","doi":"10.1587/transcom.e98.b.502","title":"Indoor Fingerprinting Localization and Tracking System Using Particle Swarm Optimization and Kalman Filter","year":2015,"lang":"en","type":"article","venue":"IEICE Transactions on Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"National Natural Science Foundation of China; University of Houston","keywords":"Computer science; Particle swarm optimization; Kalman filter; Particle filter; Convergence (economics); Computational complexity theory; Mathematical optimization; Real-time computing; Algorithm; Artificial intelligence; Mathematics","score_opus":0.051516945886015506,"score_gpt":0.2606111230028237,"score_spread":0.20909417711680817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975924107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011533201,0.00023260429,0.98581934,0.00008166674,0.00004331732,0.000029214498,0.00002753558,0.0009107408,0.0013223055],"genre_scores_gemma":[0.71933943,0.0008113262,0.2749907,0.00011737208,0.0000934003,0.00018051999,0.00016959416,0.00004716067,0.0042504636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995408,0.00008042188,0.00003572261,0.00012121092,0.00017648298,0.000045343346],"domain_scores_gemma":[0.9997032,0.00007186459,0.00005771784,0.000040892024,0.00010854509,0.000017714161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046227916,0.0005674244,0.00077804335,0.00053131365,0.00044342488,0.0006120227,0.0006939565,0.0007273011,0.0008259175],"category_scores_gemma":[0.0010014719,0.00028739774,0.00051524653,0.00064661604,0.00023302872,0.0010783683,0.00059611996,0.0005556017,0.00038596516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031792646,0.00016791647,0.007491078,0.0002749795,0.00016715469,0.0003284511,0.00027879723,0.41727993,0.039870575,0.008489604,0.0039262967,0.52140737],"study_design_scores_gemma":[0.000021748792,0.000070412345,0.0008335563,0.000009221076,0.000031376494,0.00009442381,0.000016199412,0.99246794,0.0040986096,0.0008083197,0.0015289861,0.00001928373],"about_ca_topic_score_codex":0.0066124788,"about_ca_topic_score_gemma":0.002945468,"teacher_disagreement_score":0.0066124788,"about_ca_system_score_codex":0.00041698967,"about_ca_system_score_gemma":0.0005971273,"threshold_uncertainty_score":0.01314801},"labels":[],"label_agreement":null},{"id":"W1976533641","doi":"10.1504/ijsnet.2006.012032","title":"Ordinal MDS-based localisation for wireless sensor networks","year":2006,"lang":"en","type":"article","venue":"International Journal of Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless sensor network; Wireless; Computer network; Wireless network; Telecommunications","score_opus":0.007129906976830425,"score_gpt":0.2194846197992113,"score_spread":0.2123547128223809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976533641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026578235,0.0005549553,0.99534,0.00014349907,0.000059739497,0.000019044497,0.00006186184,0.0001986406,0.0009644643],"genre_scores_gemma":[0.3717158,0.002968517,0.6194543,0.00017730311,0.00023452732,0.00029455518,0.0005717828,0.000115133946,0.0044680038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986871,0.00055412087,0.00010883529,0.00015160313,0.0004513404,0.00004706561],"domain_scores_gemma":[0.997477,0.0011878876,0.00042673456,0.00030622416,0.0005226231,0.000079464065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012963223,0.0009485892,0.00078759535,0.0016615981,0.0006316623,0.0010357255,0.0011400633,0.0007620564,0.00196674],"category_scores_gemma":[0.0069062575,0.00035453268,0.00060552027,0.0020268057,0.0014505715,0.0019238528,0.0016844415,0.0008950014,0.00078676234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015765204,0.00001589295,0.0010033635,0.00044651487,0.00006394831,0.00021638119,0.000252563,0.66209847,0.0053298553,0.21749003,0.0035445706,0.1093808],"study_design_scores_gemma":[0.000013010638,0.000059136615,0.00021150812,0.000035985107,0.000014141825,0.00015524565,0.000052257456,0.89904255,0.0015038841,0.08934443,0.009532254,0.00003570909],"about_ca_topic_score_codex":0.001324606,"about_ca_topic_score_gemma":0.0010272225,"teacher_disagreement_score":0.00196674,"about_ca_system_score_codex":0.0010415572,"about_ca_system_score_gemma":0.00074459886,"threshold_uncertainty_score":0.007557094},"labels":[],"label_agreement":null},{"id":"W1977989201","doi":"10.5402/2012/503707","title":"A Hybrid RSS/TOA Method for 3D Positioning in an Indoor Environment","year":2012,"lang":"en","type":"article","venue":"ISRN Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Non-line-of-sight propagation; RSS; Multipath propagation; Computer science; Path loss; Nakagami distribution; Time of arrival; Real-time computing; Wireless; Fading; Telecommunications; Decoding methods","score_opus":0.01513369963839353,"score_gpt":0.2630037525629649,"score_spread":0.24787005292457134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977989201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00517272,0.00008000465,0.99265915,0.000027425122,0.000051671734,0.000022691125,0.000042127012,0.0011458143,0.0007984721],"genre_scores_gemma":[0.1287786,0.00016175381,0.8670867,0.00006503977,0.000070255046,0.00012057952,0.0002049713,0.00016694318,0.003345141],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929297,0.0001434995,0.000030831547,0.00013648179,0.00035616156,0.000040022765],"domain_scores_gemma":[0.9994947,0.000110974,0.00005662114,0.00010576163,0.00020846994,0.000023525065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037830326,0.00087507407,0.0007423862,0.0015395802,0.00040626992,0.00071618636,0.0011914304,0.000698454,0.0018995609],"category_scores_gemma":[0.0012885757,0.0004850395,0.0008750299,0.0013921559,0.00030187835,0.0007782751,0.00073093444,0.00052521226,0.002146179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002112435,0.000112818016,0.0016699068,0.00021227385,0.00017877945,0.00017824778,0.00021044242,0.06037498,0.12503783,0.004386543,0.0028824345,0.8045445],"study_design_scores_gemma":[0.00005338646,0.0003115343,0.0026544977,0.000027434515,0.000112234186,0.0011227868,0.000105551364,0.9159521,0.060599733,0.0020553558,0.01686177,0.0001435734],"about_ca_topic_score_codex":0.0014930789,"about_ca_topic_score_gemma":0.0024341051,"teacher_disagreement_score":0.0018995609,"about_ca_system_score_codex":0.00024352755,"about_ca_system_score_gemma":0.00049041474,"threshold_uncertainty_score":0.0063546896},"labels":[],"label_agreement":null},{"id":"W1978001812","doi":"10.1109/glocom.2001.965202","title":"Non-line-of-sight error mitigation in TDOA mobile location","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":193,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Non-line-of-sight propagation; Multilateration; Computer science; Base station; FDOA; Estimator; Residual; Identification (biology); Algorithm; Time of arrival; Real-time computing; Wireless; Statistics; Telecommunications; Mathematics","score_opus":0.01220195762137297,"score_gpt":0.21949649502429314,"score_spread":0.20729453740292017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978001812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044917352,0.00038344003,0.9537209,0.000056589997,0.00002542747,0.000015796724,0.000007878949,0.000115692776,0.0007568398],"genre_scores_gemma":[0.7697373,0.00068382005,0.22840542,0.00002861171,0.0000601871,0.000044766424,0.00003194172,0.000038130634,0.0009698289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933606,0.0002478757,0.000019653933,0.0000643962,0.00028694203,0.000044975346],"domain_scores_gemma":[0.99773115,0.0013167091,0.00034253905,0.00020145783,0.00038157898,0.000026557726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088503934,0.000491622,0.00032171584,0.00040518385,0.00019298578,0.00039400673,0.00069872825,0.00036879352,0.00032395462],"category_scores_gemma":[0.004828947,0.00020068322,0.0002973163,0.0006409581,0.00049415836,0.0010870228,0.0004812837,0.0003010948,0.00019752637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034335715,0.00008435452,0.0071108225,0.00035679704,0.000091123075,0.00027509467,0.00028361336,0.6952924,0.050181583,0.018207857,0.0004452343,0.22732776],"study_design_scores_gemma":[0.000016830663,0.00028376622,0.001102479,0.000013181467,0.000028426524,0.0002740368,0.000034314846,0.9762274,0.01896848,0.001968147,0.0010667932,0.000016149717],"about_ca_topic_score_codex":0.0015182189,"about_ca_topic_score_gemma":0.0014120364,"teacher_disagreement_score":0.0015182189,"about_ca_system_score_codex":0.00027062104,"about_ca_system_score_gemma":0.00035247827,"threshold_uncertainty_score":0.004680574},"labels":[],"label_agreement":null},{"id":"W1978165901","doi":"10.1109/icc.2014.6884046","title":"Two-step wireless positioning technique by exploitation of extended reference nodes","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Cramér–Rao bound; Computer science; Position (finance); Node (physics); Wireless; Hybrid positioning system; Wireless network; Real-time computing; Positioning system; Upper and lower bounds; Algorithm; Estimation theory; Telecommunications; Engineering; Mathematics","score_opus":0.007476385603471279,"score_gpt":0.22118992242682362,"score_spread":0.21371353682335234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978165901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015512943,0.00033983024,0.9822832,0.000045992772,0.000039806466,0.000019598727,0.000018475923,0.00035246921,0.0013877726],"genre_scores_gemma":[0.39243716,0.0005494452,0.6002681,0.00007944967,0.00009135657,0.00011071849,0.00013809897,0.000053709344,0.006271906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955136,0.00008590193,0.00001986519,0.00012861632,0.00018588857,0.000028375549],"domain_scores_gemma":[0.99955064,0.00010306719,0.000064782726,0.0001470511,0.00011343665,0.000020938172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004114143,0.00062471733,0.00054865936,0.0006201879,0.00032442564,0.00037196992,0.0012211836,0.000683126,0.0008648937],"category_scores_gemma":[0.0010044341,0.00030253697,0.00040471964,0.0008963387,0.00032176034,0.0011516731,0.0013653635,0.00052439084,0.00077889423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037279495,0.00008721826,0.002009548,0.0002747423,0.00010794295,0.00073279295,0.00043775514,0.093941644,0.26535493,0.02045364,0.0018454274,0.6143816],"study_design_scores_gemma":[0.00011047184,0.0012285833,0.0029305266,0.000056743327,0.00016187763,0.0033182856,0.00011810917,0.8200901,0.138004,0.0075082453,0.026342835,0.00013024648],"about_ca_topic_score_codex":0.00046027696,"about_ca_topic_score_gemma":0.00065232767,"teacher_disagreement_score":0.0012211836,"about_ca_system_score_codex":0.0001472327,"about_ca_system_score_gemma":0.00035373826,"threshold_uncertainty_score":0.0028933287},"labels":[],"label_agreement":null},{"id":"W1978459025","doi":"10.1109/noms.2014.6838325","title":"A case study for a secure and robust geo-fencing and access control framework","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Robustness (evolution); Fencing; Computer science; Access control; Computer security; Software; Focus (optics)","score_opus":0.017491600750331696,"score_gpt":0.24737098898965862,"score_spread":0.2298793882393269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978459025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42921814,0.0006003631,0.5069299,0.0051029753,0.00019398332,0.0012918471,0.00036530074,0.0012039834,0.05509352],"genre_scores_gemma":[0.86886495,0.0002246458,0.11799048,0.00022279283,0.000033956185,0.00027114362,0.000135154,0.00008230876,0.012174486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9967663,0.0012114561,0.00016768703,0.00034454314,0.00091954827,0.0005904149],"domain_scores_gemma":[0.9972938,0.00096493197,0.00024963683,0.0005618892,0.00040816664,0.00052157213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022274558,0.00057949685,0.0004686965,0.0007304292,0.0027180729,0.0024345857,0.0015889526,0.004328301,0.0042741173],"category_scores_gemma":[0.003662859,0.00025931545,0.0007365928,0.0005594555,0.0022697505,0.0024858213,0.0029816804,0.0015520902,0.0009855438],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012736849,0.0017188911,0.034838017,0.0007023758,0.00017892492,0.09461577,0.013437856,0.1983859,0.050075304,0.4554789,0.017431594,0.13186276],"study_design_scores_gemma":[0.00041844283,0.0025744126,0.012400343,0.00037474505,0.00018856577,0.04121211,0.018085094,0.55965847,0.054930452,0.064008854,0.24582161,0.0003269875],"about_ca_topic_score_codex":0.009852136,"about_ca_topic_score_gemma":0.008326327,"teacher_disagreement_score":0.009852136,"about_ca_system_score_codex":0.0015408269,"about_ca_system_score_gemma":0.0015155027,"threshold_uncertainty_score":0.019589603},"labels":[],"label_agreement":null},{"id":"W1979708692","doi":"10.4028/www.scientific.net/amr.433-440.4207","title":"Ultra Wideband Indoor Positioning Using Kalman Filters","year":2012,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Non-line-of-sight propagation; Multilateration; Kalman filter; Ultra-wideband; Computer science; Extended Kalman filter; Indoor positioning system; Electronic engineering; Real-time computing; Engineering; Wireless; Telecommunications; Artificial intelligence; Accelerometer","score_opus":0.0494512446991782,"score_gpt":0.33787810033014404,"score_spread":0.2884268556309658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979708692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024628616,0.00023176623,0.9961093,0.000024447932,0.000027657074,0.0000057853686,0.000019260851,0.0004685599,0.00065047457],"genre_scores_gemma":[0.4909997,0.001689098,0.5003003,0.00008154774,0.00011475162,0.00010465476,0.00031102824,0.00009498086,0.0063039465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963343,0.00007704893,0.000024053625,0.0001044042,0.00012725191,0.00003375125],"domain_scores_gemma":[0.9996673,0.000116444586,0.00005582785,0.00004115361,0.00011057152,0.000008810382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040921406,0.00058955624,0.0005787494,0.0003921898,0.0003411864,0.00073896215,0.00049728126,0.0005852166,0.0010948062],"category_scores_gemma":[0.001184249,0.0003466359,0.0004769178,0.0005887019,0.00022538862,0.00093701103,0.00048212215,0.00059949275,0.00078668405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014562215,0.000031157637,0.0015422443,0.00017462013,0.00014725301,0.000105614316,0.00015092481,0.50460917,0.019204842,0.010193466,0.0022959472,0.46139914],"study_design_scores_gemma":[0.00001629864,0.00006774121,0.00079836923,0.000030999785,0.00004987317,0.0000824764,0.000023859202,0.9840164,0.006612045,0.0026436946,0.005624909,0.000033273842],"about_ca_topic_score_codex":0.008391151,"about_ca_topic_score_gemma":0.0071431375,"teacher_disagreement_score":0.008391151,"about_ca_system_score_codex":0.0004227214,"about_ca_system_score_gemma":0.00057735335,"threshold_uncertainty_score":0.016684592},"labels":[],"label_agreement":null},{"id":"W1979983556","doi":"10.1109/ccnc.2013.6488464","title":"Selective context fusion utilizing an integrated RFID-WSN architecture","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Redundancy (engineering); Wireless sensor network; Context (archaeology); Sensor fusion; Computer network; Process (computing); Distributed computing; Artificial intelligence","score_opus":0.008379189871977877,"score_gpt":0.20426295650694007,"score_spread":0.19588376663496218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979983556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07421161,0.0004560305,0.9194425,0.00012457067,0.000045253837,0.0001227015,0.000038789694,0.0023740018,0.0031845716],"genre_scores_gemma":[0.65938395,0.0003678126,0.3357121,0.00012133709,0.00004010124,0.00010945826,0.00016125789,0.000056265828,0.004047782],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995679,0.00008507357,0.00003473435,0.0001380921,0.00013403379,0.00004014626],"domain_scores_gemma":[0.9997663,0.000058082063,0.000022190163,0.00006104912,0.000069558824,0.000022799424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064874935,0.00039591565,0.00039849788,0.00044200756,0.00042734636,0.00077492854,0.00092636317,0.0005834172,0.00078894076],"category_scores_gemma":[0.00063729263,0.00025732323,0.00039687305,0.00044638285,0.00025721922,0.0015009036,0.0013165816,0.00044796403,0.00040677722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008640139,0.000461936,0.0062144985,0.00033886486,0.00026786176,0.0008074505,0.00086854113,0.11994374,0.36995292,0.02123328,0.0022335134,0.47681338],"study_design_scores_gemma":[0.00004418165,0.0006124688,0.0025856674,0.000043466443,0.0002338444,0.0008313417,0.00018691842,0.88539064,0.08793643,0.007993103,0.014072046,0.00006984525],"about_ca_topic_score_codex":0.0011260343,"about_ca_topic_score_gemma":0.0017451331,"teacher_disagreement_score":0.0011260343,"about_ca_system_score_codex":0.00029402974,"about_ca_system_score_gemma":0.00039080053,"threshold_uncertainty_score":0.0034309626},"labels":[],"label_agreement":null},{"id":"W1980640720","doi":"10.1115/ipc2008-64646","title":"Performing a Comprehensive Single Pass Multiple Pipeline Survey","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Petroleum Technology Alliance Canada","funders":"","keywords":"Global Positioning System; Pipeline (software); Computer science; Synchronization (alternating current); Remote sensing; Waveform; Real-time computing; Attenuation; Interval (graph theory); Data stream; Interference (communication); Geology; Channel (broadcasting); Telecommunications","score_opus":0.04730085947884976,"score_gpt":0.2124927639104571,"score_spread":0.16519190443160733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980640720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40882695,0.00012624516,0.5786814,0.000065917084,0.000030755316,0.0012378559,0.00088864675,0.0025511426,0.007591071],"genre_scores_gemma":[0.51227015,0.0001597488,0.47851703,0.000047092584,0.000022015467,0.0006051155,0.0009937952,0.00017694321,0.0072080316],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988733,0.00010631326,0.000060267997,0.00023288147,0.0006099049,0.000117290954],"domain_scores_gemma":[0.99861264,0.000195775,0.00016906355,0.00026201663,0.0006582739,0.00010223104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000945351,0.0005384784,0.0008607649,0.0013843918,0.0007143959,0.00050403434,0.0004720483,0.00033223064,0.0026022554],"category_scores_gemma":[0.0016459158,0.00033717952,0.00036024078,0.0008388038,0.00020871486,0.0006813496,0.0010267352,0.00035714643,0.001091738],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022836961,0.00028010414,0.053317957,0.0003158064,0.000059442988,0.00040925384,0.001202119,0.018177832,0.3893183,0.00076569413,0.0021834336,0.5337418],"study_design_scores_gemma":[0.00006704935,0.0061276443,0.4668398,0.00011023449,0.00013199484,0.0018465479,0.0023742625,0.11805288,0.33991063,0.0017544313,0.062558636,0.00022588212],"about_ca_topic_score_codex":0.004166426,"about_ca_topic_score_gemma":0.018436346,"teacher_disagreement_score":0.004166426,"about_ca_system_score_codex":0.00032753497,"about_ca_system_score_gemma":0.0011539557,"threshold_uncertainty_score":0.008705378},"labels":[],"label_agreement":null},{"id":"W1981101964","doi":"10.1109/icindma.2010.5538059","title":"Reliable indoor location sensing technique using active RFID","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Mahalanobis distance; Received signal strength indication; Signal strength; Radio-frequency identification; Identification (biology); Real-time computing; Covariance; Artificial intelligence; Wireless; Telecommunications; Statistics; Mathematics; Computer security","score_opus":0.008093790207913008,"score_gpt":0.22046312847074945,"score_spread":0.21236933826283644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981101964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020324336,0.00053706387,0.97530127,0.000110099085,0.00008560439,0.00001494502,0.000029753626,0.0014348944,0.0021620612],"genre_scores_gemma":[0.66631,0.0007060104,0.32819107,0.00015174942,0.00018580395,0.000054225653,0.00016966565,0.00009978544,0.0041316883],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991541,0.00016806854,0.000034171517,0.0001653329,0.0004236605,0.000054673146],"domain_scores_gemma":[0.9989209,0.00022588467,0.00019510322,0.0003045454,0.00031949612,0.000034074907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040832974,0.0007812053,0.0006812136,0.001382486,0.00044217656,0.0007216793,0.0012337134,0.0008856332,0.0008500277],"category_scores_gemma":[0.0014298026,0.00042761737,0.00051615614,0.0010103273,0.0004086652,0.0013169609,0.0009182089,0.0006522242,0.0012746322],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030419987,0.00008456844,0.003582691,0.00038046096,0.00011289808,0.00055322226,0.000422243,0.042018916,0.2154664,0.008630437,0.003014861,0.72542906],"study_design_scores_gemma":[0.00009577833,0.00095157354,0.0054955436,0.000114407,0.00028342588,0.004831953,0.0002946733,0.64118284,0.2949072,0.008109113,0.043494582,0.00023897577],"about_ca_topic_score_codex":0.0005464299,"about_ca_topic_score_gemma":0.00071952783,"teacher_disagreement_score":0.001382486,"about_ca_system_score_codex":0.00022956406,"about_ca_system_score_gemma":0.00033415848,"threshold_uncertainty_score":0.0028436184},"labels":[],"label_agreement":null},{"id":"W1981119652","doi":"10.1016/j.actaastro.2008.11.012","title":"Ad hoc wireless sensor networks for exploration of Solar-system bodies","year":2009,"lang":"en","type":"article","venue":"Acta Astronautica","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Wireless ad hoc network; Wireless sensor network; Wireless; Computer science; Solar System; Computer network; Telecommunications; Astrobiology; Physics","score_opus":0.013771194700384536,"score_gpt":0.22004034237820388,"score_spread":0.20626914767781934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981119652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13243254,0.013287727,0.8360093,0.001604761,0.00051252224,0.00006633805,0.0002320888,0.0005520914,0.015302643],"genre_scores_gemma":[0.9034845,0.00740116,0.076798044,0.00008255515,0.0001506851,0.00006687133,0.00020648347,0.000033588418,0.011776103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999417,0.00002432014,0.0000025877287,0.000009292996,0.00001624331,0.000005878136],"domain_scores_gemma":[0.9998555,0.000079813995,0.000012606016,0.000014437645,0.000028215987,0.0000093417575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016419157,0.00018750901,0.00019835168,0.0002445418,0.00025391564,0.00031412157,0.00039133322,0.0002481002,0.000980956],"category_scores_gemma":[0.00046602305,0.000096336385,0.00011694432,0.000510745,0.00016572862,0.00068562594,0.0003024637,0.00028623067,0.00013579061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044176567,0.00010467759,0.004192208,0.0004898741,0.00009870511,0.00043098273,0.00030998501,0.4332014,0.03631683,0.038819544,0.010521008,0.47507298],"study_design_scores_gemma":[0.000026236006,0.00014355998,0.002110626,0.000043445456,0.000036912777,0.00023382607,0.00024381887,0.94362986,0.0075896024,0.020378359,0.025546541,0.00001719769],"about_ca_topic_score_codex":0.0009885762,"about_ca_topic_score_gemma":0.0017678905,"teacher_disagreement_score":0.0009885762,"about_ca_system_score_codex":0.00016923623,"about_ca_system_score_gemma":0.00019369775,"threshold_uncertainty_score":0.0032815933},"labels":[],"label_agreement":null},{"id":"W1981364162","doi":"10.1109/mobhoc.2006.278623","title":"Design and Implementation of a Sensor Network Based Location Determination Service for use in Home Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Middleware (distributed applications); Computer science; Context (archaeology); Wireless sensor network; Component (thermodynamics); Service (business); Home automation; Ubiquitous computing; Embedded system; Location-based service; Real-time computing; Scheme (mathematics); Computer network; Distributed computing; Telecommunications; Operating system","score_opus":0.013118430375087973,"score_gpt":0.23241940101571643,"score_spread":0.21930097064062845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981364162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079177365,0.00028865936,0.88830596,0.00060316804,0.00016041985,0.0011533861,0.00020809445,0.01886106,0.011241865],"genre_scores_gemma":[0.6405356,0.00027125445,0.34661746,0.00033445068,0.000052939133,0.00060614065,0.00073660945,0.00046114085,0.010384333],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993647,0.00009001904,0.00005289848,0.00009040413,0.00031330297,0.00008874082],"domain_scores_gemma":[0.99936,0.00006196172,0.000044013388,0.000093570554,0.00035077753,0.00008981702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007784802,0.0003361925,0.00046696086,0.000387798,0.00052855443,0.001081925,0.0014846786,0.00057560275,0.002306346],"category_scores_gemma":[0.0013240409,0.00035206685,0.00020350495,0.00041205995,0.00030728572,0.0007942754,0.0005774953,0.00058698433,0.0010413155],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015023001,0.00095567625,0.014144038,0.0008126087,0.00020726433,0.0014082533,0.001483019,0.06921493,0.30824274,0.042614643,0.03483314,0.52458143],"study_design_scores_gemma":[0.00023390433,0.0008338895,0.004518742,0.000058116824,0.00012499631,0.0008039645,0.00039211297,0.6717168,0.20522603,0.0032395723,0.1127674,0.00008440277],"about_ca_topic_score_codex":0.0034108448,"about_ca_topic_score_gemma":0.0027167068,"teacher_disagreement_score":0.0034108448,"about_ca_system_score_codex":0.0008524635,"about_ca_system_score_gemma":0.0013112231,"threshold_uncertainty_score":0.0077154636},"labels":[],"label_agreement":null},{"id":"W1983553030","doi":"10.1109/glocom.2012.6503131","title":"Multilateration localization in the presence of anchor location uncertainties","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Multilateration; Computer science; Least-squares function approximation; Algorithm; Non-line-of-sight propagation; Noise (video); FDOA; Wireless sensor network; Mathematical optimization; Wireless; Mathematics; Statistics; Telecommunications; Artificial intelligence; Computer network; Geometry","score_opus":0.012920908968910154,"score_gpt":0.22944799247981942,"score_spread":0.21652708351090927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983553030","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040160418,0.00010994229,0.9950246,0.00004544003,0.000014458058,0.0000060058087,0.000010026769,0.00009343646,0.0006800406],"genre_scores_gemma":[0.53497875,0.0009780943,0.4572658,0.000118933924,0.00010747695,0.000112002366,0.00015621886,0.000140794,0.006141925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993716,0.00017682945,0.000021534923,0.00015104283,0.00023391448,0.000045083787],"domain_scores_gemma":[0.9993913,0.00030676095,0.000116824056,0.000061189334,0.00010932204,0.000014441103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005135028,0.00068024185,0.0006956729,0.00044220267,0.00034655776,0.00069880515,0.00069889944,0.0009036849,0.00091926113],"category_scores_gemma":[0.0020930434,0.00031389165,0.0004889139,0.0008642734,0.000911332,0.0013713274,0.0017682204,0.0006913188,0.00031666266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006917046,0.000015625066,0.0004595838,0.00011941672,0.000039333703,0.00024139619,0.00016810579,0.87669885,0.017696932,0.031542875,0.0011720896,0.07177661],"study_design_scores_gemma":[0.000004569487,0.000034606488,0.00015080777,0.0000058576766,0.0000090178055,0.0000911823,0.00002399708,0.98543835,0.003845642,0.008377123,0.0020048136,0.000014020379],"about_ca_topic_score_codex":0.0019430713,"about_ca_topic_score_gemma":0.0018501856,"teacher_disagreement_score":0.0019430713,"about_ca_system_score_codex":0.00039212307,"about_ca_system_score_gemma":0.0005576881,"threshold_uncertainty_score":0.0038635135},"labels":[],"label_agreement":null},{"id":"W1984241264","doi":"10.1145/1577504.1577505","title":"Location aware computing for academic environments","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Terminal (telecommunication); Software deployment; Wireless; Wireless network; Mobile computing; Context (archaeology); Ubiquitous computing; Wireless site survey; Point (geometry); Field (mathematics); Location-based service; Computer network; Distributed computing; Wi-Fi array; Telecommunications; Geography; Human–computer interaction","score_opus":0.012075762722899779,"score_gpt":0.24692762808096935,"score_spread":0.23485186535806957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984241264","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009703846,0.012303392,0.83182424,0.015758162,0.0017803995,0.0001983828,0.0004708422,0.0065838224,0.121376984],"genre_scores_gemma":[0.33709282,0.016768968,0.5694936,0.0016021398,0.0014149719,0.0004088619,0.001169019,0.0006138523,0.07143571],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993389,0.00020368748,0.000051961633,0.000095711424,0.00023448488,0.00007521781],"domain_scores_gemma":[0.9992366,0.00016214722,0.000062638224,0.00026073586,0.00016863634,0.0001092205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070042134,0.000425832,0.00045935973,0.0008705478,0.0012288439,0.00373321,0.0010246021,0.0011980964,0.0105118165],"category_scores_gemma":[0.0026815212,0.00025875654,0.00033689453,0.0018966618,0.00076451345,0.0046225274,0.0024420111,0.0012071639,0.0067497957],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008641884,0.00007054626,0.0007914456,0.00032137506,0.000023729739,0.00023803119,0.0004548352,0.011732799,0.0035894283,0.4533888,0.07375678,0.4555457],"study_design_scores_gemma":[0.000027422853,0.000053689702,0.00072096044,0.00019662564,0.00003070675,0.00038180978,0.0005745616,0.07446837,0.0028137765,0.35514805,0.5655318,0.000052314554],"about_ca_topic_score_codex":0.0014072767,"about_ca_topic_score_gemma":0.0022488697,"teacher_disagreement_score":0.0105118165,"about_ca_system_score_codex":0.0008888071,"about_ca_system_score_gemma":0.0011395317,"threshold_uncertainty_score":0.03516555},"labels":[],"label_agreement":null},{"id":"W1984313423","doi":"10.1109/acssc.2013.6810660","title":"A joint localization and synchronization technique using time of arrival at multiple antenna receivers","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Cramér–Rao bound; Computer science; Upper and lower bounds; Algorithm; Position (finance); Asynchronous communication; Synchronization (alternating current); Time of arrival; Antenna (radio); Angle of arrival; Control theory (sociology); Estimation theory; Mathematics; Telecommunications; Wireless; Artificial intelligence","score_opus":0.008644304935583038,"score_gpt":0.18190698482666467,"score_spread":0.17326267989108163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984313423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055028903,0.00014162125,0.99362695,0.000040216313,0.000039640196,0.0000145076565,0.000008939332,0.00021036604,0.00041489533],"genre_scores_gemma":[0.3366528,0.00049112784,0.6582998,0.000093897295,0.0001792262,0.00012681099,0.000108286295,0.000057437963,0.00399057],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944645,0.00014091696,0.000035201734,0.00012591605,0.00019642097,0.000055042852],"domain_scores_gemma":[0.9994879,0.00011325539,0.00012277036,0.00011083215,0.00013938051,0.000025836216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000630451,0.0006412978,0.00085669867,0.0005410763,0.0004698031,0.0006046045,0.0010184568,0.0007757125,0.00082210905],"category_scores_gemma":[0.0013074597,0.00033006113,0.0005548328,0.0007864528,0.0003795495,0.0011228283,0.0010122316,0.0007700947,0.00074747036],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037743372,0.00012997194,0.0025125656,0.00031894175,0.00020670578,0.0003058665,0.00044204516,0.15440887,0.15566492,0.039855253,0.0027969105,0.6429804],"study_design_scores_gemma":[0.00009092139,0.0006463977,0.0010014458,0.000026057241,0.00014669134,0.0008514948,0.00006087286,0.9382519,0.041012,0.004630751,0.013211883,0.00006952389],"about_ca_topic_score_codex":0.0006410922,"about_ca_topic_score_gemma":0.00096515805,"teacher_disagreement_score":0.0010184568,"about_ca_system_score_codex":0.00025486108,"about_ca_system_score_gemma":0.00078038423,"threshold_uncertainty_score":0.0033342242},"labels":[],"label_agreement":null},{"id":"W1984750683","doi":"10.1682/jrrd.2008.09.0132","title":"Effect of mobility devices on orientation sensors that contain magnetometers","year":2009,"lang":"en","type":"article","venue":"The Journal of Rehabilitation Research and Development","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Orientation (vector space); Magnetometer; Computer science; Accelerometer; Simulation; Materials science; Acoustics; Magnetic field; Physics; Geometry; Mathematics","score_opus":0.019902442446975258,"score_gpt":0.31582864824704204,"score_spread":0.2959262058000668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984750683","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9605366,0.0018531333,0.03389513,0.000368542,0.00086779596,0.00033763805,0.00024979605,0.00033664776,0.0015547755],"genre_scores_gemma":[0.97092336,0.0008211966,0.02449912,0.00065177085,0.00009889377,0.00022472994,0.00031127044,0.00016474036,0.0023050476],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9933609,0.0018523207,0.0011327653,0.00094633776,0.0021700552,0.0005376952],"domain_scores_gemma":[0.97014445,0.01890575,0.0031337466,0.0025818127,0.004686998,0.00054722663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039163837,0.0023199054,0.0009803249,0.0009536131,0.00054568634,0.0009899536,0.0012727106,0.0022380457,0.0019203896],"category_scores_gemma":[0.039366595,0.001338098,0.00074492,0.00077999907,0.0010577268,0.0018645943,0.0019152045,0.0009892436,0.00063931476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011485473,0.0011382456,0.029260457,0.0022255967,0.00059412303,0.0017145096,0.0016049021,0.023582136,0.81400657,0.0008315719,0.0016046036,0.11195173],"study_design_scores_gemma":[0.000444331,0.029032974,0.067688,0.00068844965,0.0012100923,0.002054181,0.00074502936,0.029834645,0.8559717,0.00054779224,0.011526258,0.0002566388],"about_ca_topic_score_codex":0.0014531368,"about_ca_topic_score_gemma":0.0017996706,"teacher_disagreement_score":0.0039163837,"about_ca_system_score_codex":0.00043610114,"about_ca_system_score_gemma":0.00047059872,"threshold_uncertainty_score":0.020712078},"labels":[],"label_agreement":null},{"id":"W1986583221","doi":"10.1063/1.4792307","title":"Ear canal dynamic motion as a source of power for in-ear devices","year":2013,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Ear canal; Root canal; Power (physics); Temporomandibular joint; Deformation (meteorology); Acoustics; Joint (building); Middle ear; Inner ear; Computer science; Materials science; Physics; Engineering; Anatomy; Orthodontics; Structural engineering; Medicine","score_opus":0.004433457685046318,"score_gpt":0.20220270405037905,"score_spread":0.19776924636533272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986583221","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8838622,0.0016219784,0.108214796,0.00024410752,0.0000896689,0.00008004027,0.00025483122,0.00045668794,0.005175764],"genre_scores_gemma":[0.98951215,0.00036987683,0.008569926,0.00004056126,0.000033962388,0.000021059166,0.000051849805,0.00003738853,0.001363304],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998499,0.000036450536,0.0000069560165,0.000026379472,0.000056357898,0.000023887229],"domain_scores_gemma":[0.9997609,0.00011106413,0.00003974366,0.000033329983,0.00003940802,0.000015501048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014994145,0.00037968214,0.0003051164,0.0004001504,0.00020852828,0.0003127133,0.00043018314,0.00033312725,0.003239421],"category_scores_gemma":[0.00049011636,0.00014748365,0.0002104267,0.00028346496,0.00028431538,0.0004377947,0.0003897359,0.0001619996,0.00038611403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053255685,0.000072074756,0.0064564967,0.0002594913,0.000031909316,0.00047870385,0.00030687664,0.00096693553,0.92277753,0.00046952552,0.00031870964,0.06732922],"study_design_scores_gemma":[0.000092970724,0.0025499281,0.09234028,0.000068742884,0.0003528345,0.0077739754,0.0006776603,0.010445472,0.8697777,0.0014637783,0.014386581,0.00006998503],"about_ca_topic_score_codex":0.00019395075,"about_ca_topic_score_gemma":0.00042311085,"teacher_disagreement_score":0.003239421,"about_ca_system_score_codex":0.00013332095,"about_ca_system_score_gemma":0.00013437888,"threshold_uncertainty_score":0.010836959},"labels":[],"label_agreement":null},{"id":"W1987121138","doi":"10.1109/iccse.2014.6926459","title":"Particle Filtering-based tracking and localization on context-aware robotic system","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Particle filter; Computer science; Tracking (education); Computer vision; Artificial intelligence; Mobile robot; Entropy (arrow of time); Tracking system; Robot; Context (archaeology); Kalman filter","score_opus":0.010653466089776659,"score_gpt":0.1930401407917564,"score_spread":0.18238667470197975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987121138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071932157,0.00017701933,0.9914642,0.00004895789,0.000034376273,0.000016237445,0.000007398412,0.00027462014,0.00078394247],"genre_scores_gemma":[0.67217875,0.0007155321,0.32345355,0.00012273843,0.000089115296,0.00012382797,0.00007429533,0.000040805946,0.0032013215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972254,0.00005760375,0.000015796528,0.0000617114,0.000118375596,0.000023933822],"domain_scores_gemma":[0.999845,0.000052522213,0.000020414065,0.000021671716,0.00005126558,0.000009088774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003335429,0.00040125623,0.0005022922,0.0003379746,0.00040070942,0.00045708276,0.0004922844,0.00054141344,0.0004411955],"category_scores_gemma":[0.0006853545,0.00021497587,0.00039388053,0.0003089336,0.0002835371,0.00066308695,0.0004287,0.0004890129,0.00019993643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023292103,0.00009237101,0.0022377658,0.00023119748,0.00009933201,0.00023882135,0.00026142414,0.52809715,0.06100236,0.018580463,0.0019448617,0.3869813],"study_design_scores_gemma":[0.000010189578,0.00005388513,0.00059792027,0.000008626958,0.000015365025,0.000060707123,0.00001033624,0.9893618,0.0064654453,0.0015641119,0.0018388091,0.00001286464],"about_ca_topic_score_codex":0.005384254,"about_ca_topic_score_gemma":0.0029581715,"teacher_disagreement_score":0.005384254,"about_ca_system_score_codex":0.00036278923,"about_ca_system_score_gemma":0.00062454346,"threshold_uncertainty_score":0.010705829},"labels":[],"label_agreement":null},{"id":"W1988252124","doi":"10.1007/s00779-013-0693-8","title":"User-centric ambient information systems and applications","year":2013,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Human–computer interaction; Information system","score_opus":0.0049354909971054016,"score_gpt":0.18434761020679313,"score_spread":0.17941211920968772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988252124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038292922,0.0135815265,0.88756436,0.0012611985,0.0013668792,0.00017976406,0.00060644286,0.005415527,0.051731374],"genre_scores_gemma":[0.7459756,0.010336113,0.17063707,0.00092224666,0.0011371454,0.00023263753,0.001703811,0.0005910177,0.068464376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935526,0.00017384556,0.000046368692,0.00010793205,0.00024365021,0.00007297888],"domain_scores_gemma":[0.9993975,0.0001003884,0.000034170815,0.0001620359,0.00024345148,0.000062482504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007268307,0.0007145917,0.0006749266,0.00071051595,0.00056886504,0.0026238046,0.0009118449,0.00091983716,0.0049633374],"category_scores_gemma":[0.0012364998,0.0003885429,0.00031913727,0.0016069715,0.0004244749,0.0022893199,0.0017068775,0.00080422114,0.0020944492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043343334,0.00031998224,0.0048839,0.00075748423,0.0001648493,0.00050852273,0.0011062025,0.016584523,0.041014295,0.11993064,0.066018954,0.7482772],"study_design_scores_gemma":[0.000063051986,0.00072169706,0.014269183,0.00030304087,0.00032945105,0.0024223432,0.0013586455,0.21721584,0.051894285,0.08249107,0.62873906,0.00019228441],"about_ca_topic_score_codex":0.0014639255,"about_ca_topic_score_gemma":0.0029885792,"teacher_disagreement_score":0.0049633374,"about_ca_system_score_codex":0.00038761745,"about_ca_system_score_gemma":0.0006247191,"threshold_uncertainty_score":0.016604006},"labels":[],"label_agreement":null},{"id":"W1988435521","doi":"10.1109/issnip.2011.6146596","title":"Multi-target device-free tracking using radio frequency tomography","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Beijing University of Posts and Telecommunications","keywords":"Testbed; RSS; Tracking (education); Computer science; Metric (unit); Wireless sensor network; Attenuation; Real-time computing; Radio frequency; Tracking error; Artificial intelligence; SIGNAL (programming language); Computer vision; Algorithm; Computer network; Engineering; Telecommunications","score_opus":0.04826724765405522,"score_gpt":0.23136729073199938,"score_spread":0.18310004307794417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988435521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2499517,0.00047278943,0.74673164,0.00010058206,0.00003884602,0.000050910137,0.000030062858,0.00064613105,0.0019772486],"genre_scores_gemma":[0.88728493,0.00023836043,0.11146188,0.0000438502,0.00000903622,0.000035396355,0.00006198997,0.0000297489,0.00083490164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99940217,0.00018214068,0.000022936141,0.00012813426,0.00021504973,0.000049563],"domain_scores_gemma":[0.9988996,0.00055302813,0.00016781161,0.00022849863,0.00011416691,0.000036947968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072811317,0.0005076801,0.00052672304,0.00041992278,0.00031867207,0.0007879561,0.0007398031,0.0010456657,0.00032569686],"category_scores_gemma":[0.0028127735,0.00024263526,0.00037636602,0.00049256085,0.0005858327,0.0016236727,0.0008718694,0.00031982988,0.00016113688],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058704504,0.00018966182,0.013584267,0.00028792067,0.00016006289,0.0005659365,0.00042153182,0.5957194,0.1928803,0.0057432293,0.00054540415,0.18931529],"study_design_scores_gemma":[0.00003491501,0.00034616402,0.0061437795,0.000019915804,0.000047158625,0.0007563218,0.00006466643,0.9100925,0.07908191,0.0017448744,0.001621217,0.00004654613],"about_ca_topic_score_codex":0.0011988762,"about_ca_topic_score_gemma":0.0012195267,"teacher_disagreement_score":0.0011988762,"about_ca_system_score_codex":0.00037344248,"about_ca_system_score_gemma":0.00037905216,"threshold_uncertainty_score":0.0038506985},"labels":[],"label_agreement":null},{"id":"W1988995727","doi":"10.1016/j.adhoc.2014.12.001","title":"TOA-based joint synchronization and source localization with random errors in sensor positions and sensor clock biases","year":2014,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Cramér–Rao bound; Synchronization (alternating current); Position (finance); Computer science; Clock synchronization; Noise (video); Joint (building); Algorithm; Upper and lower bounds; Time of arrival; Wireless sensor network; Observational error; Real-time computing; Estimation theory; Mathematics; Statistics; Artificial intelligence; Telecommunications; Engineering; Wireless","score_opus":0.006884454109065977,"score_gpt":0.1886045590287481,"score_spread":0.1817201049196821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988995727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017496087,0.00036342366,0.9796504,0.00013406905,0.00019308778,0.000027400614,0.00008260124,0.0004403075,0.001612699],"genre_scores_gemma":[0.76866674,0.0007308571,0.22263058,0.00018293413,0.00027640228,0.00015602096,0.00046629747,0.00021799427,0.006672212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987355,0.0003866959,0.00007852512,0.00029999614,0.0003715493,0.00012771679],"domain_scores_gemma":[0.9978282,0.000998343,0.00025914217,0.00041403808,0.00043150032,0.000068712674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014719113,0.0009865715,0.0014045321,0.001050736,0.0007873834,0.0015472089,0.0011810046,0.001165283,0.0014690972],"category_scores_gemma":[0.009132867,0.00057996594,0.00075033365,0.0023440844,0.0009489521,0.0019774062,0.0019739415,0.0009286211,0.00096797175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007364249,0.00010444143,0.0020326013,0.00017508188,0.00020638877,0.00022288127,0.00033632602,0.7222894,0.020002522,0.036399197,0.0033638603,0.21413097],"study_design_scores_gemma":[0.000028685978,0.0000498912,0.0003475593,0.000008033998,0.000028712553,0.00010121316,0.000026374995,0.9885274,0.003277493,0.0066497214,0.0009376577,0.000017203209],"about_ca_topic_score_codex":0.0032132466,"about_ca_topic_score_gemma":0.003874892,"teacher_disagreement_score":0.0032132466,"about_ca_system_score_codex":0.00061677437,"about_ca_system_score_gemma":0.00149257,"threshold_uncertainty_score":0.007784307},"labels":[],"label_agreement":null},{"id":"W1989115430","doi":"10.1109/infocom.2014.6847958","title":"Electronic frog eye: Counting crowd using WiFi","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":405,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Scalability; Computer science; Metric (unit); Channel (broadcasting); Monotonic function; Channel state information; Code (set theory); Reliability (semiconductor); State (computer science); Artificial intelligence; Real-time computing; Wireless; Computer vision; Computer engineering; Algorithm; Computer network; Mathematics; Database; Telecommunications","score_opus":0.005721201997318561,"score_gpt":0.20645102612761693,"score_spread":0.20072982413029836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989115430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16161606,0.0011046513,0.81068087,0.0004451742,0.00031939396,0.00026154672,0.0005195982,0.011000791,0.014051848],"genre_scores_gemma":[0.8889406,0.00037993377,0.105784565,0.00022060524,0.000079639045,0.00012613201,0.00031576413,0.000096053685,0.004056625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940336,0.00012434927,0.000023565533,0.00011502007,0.00022407749,0.00010960041],"domain_scores_gemma":[0.99930704,0.00025042723,0.000096110736,0.0001227876,0.00015644144,0.000067187226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050724694,0.0008613536,0.0007064662,0.001601629,0.000624348,0.0006252495,0.0013111496,0.0007746262,0.0018440605],"category_scores_gemma":[0.002668553,0.00027364207,0.0003437616,0.00094011624,0.00050113007,0.0014296147,0.0018973091,0.00044078776,0.00069704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009418403,0.0002154592,0.016174912,0.00037239923,0.00018131566,0.0007296878,0.0007604258,0.14456962,0.036976516,0.009692567,0.014815615,0.7745696],"study_design_scores_gemma":[0.00006393792,0.00029486738,0.0046705194,0.000054382155,0.000054584692,0.000630869,0.00024142674,0.96038985,0.01876895,0.005924788,0.008804228,0.00010167077],"about_ca_topic_score_codex":0.00729507,"about_ca_topic_score_gemma":0.0070708697,"teacher_disagreement_score":0.00729507,"about_ca_system_score_codex":0.00053770747,"about_ca_system_score_gemma":0.00054721266,"threshold_uncertainty_score":0.014505208},"labels":[],"label_agreement":null},{"id":"W1989915270","doi":"10.1142/s0218126614500947","title":"RADIO PROPAGATION CHARACTERISTICS OF INDOOR LOCATION SYSTEM BASED ON RSSI AT 490 MHz","year":2014,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Ray tracing (physics); Signal strength; Radio propagation; Path loss; Reflection (computer programming); Radio signal; Computer science; SIGNAL (programming language); Tracking (education); Log-distance path loss model; Received signal strength indication; Logarithm; Real-time computing; Path (computing); Electronic engineering; Radio propagation model; Radio frequency; Simulation; Acoustics; Telecommunications; Engineering; Wireless; Optics; Physics; Computer network; Mathematics","score_opus":0.006339261420255958,"score_gpt":0.17511937323900098,"score_spread":0.16878011181874503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989915270","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7411041,0.0011249449,0.22457393,0.00031144993,0.00016275927,0.000071183305,0.0015495758,0.0057310616,0.025370961],"genre_scores_gemma":[0.99177086,0.00025892456,0.0049631046,0.00003567498,0.000020620844,0.00002288505,0.0005144605,0.000059645296,0.0023539565],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996935,0.000048611255,0.000014222287,0.00005365874,0.00014265043,0.000047396323],"domain_scores_gemma":[0.99948406,0.00013052272,0.00006595873,0.000048006183,0.00025352658,0.000017813198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022234808,0.0004244114,0.00031906486,0.00088571076,0.00018160172,0.00036535691,0.00030705935,0.00038231997,0.0024693604],"category_scores_gemma":[0.00058753684,0.00010047486,0.00015935588,0.0009401566,0.00014047664,0.0004225552,0.00015380872,0.00023256929,0.0012969428],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016695482,0.00019263804,0.08702719,0.00076985435,0.00019358593,0.0015684342,0.00094765885,0.12879878,0.4625982,0.007217757,0.009904121,0.29911217],"study_design_scores_gemma":[0.000066578905,0.002629622,0.14532389,0.0000972276,0.00035240597,0.0040174993,0.00068926974,0.56972736,0.2538443,0.0021560427,0.020832196,0.0002635831],"about_ca_topic_score_codex":0.0011709387,"about_ca_topic_score_gemma":0.0008143907,"teacher_disagreement_score":0.0024693604,"about_ca_system_score_codex":0.00020833233,"about_ca_system_score_gemma":0.00016772517,"threshold_uncertainty_score":0.008260846},"labels":[],"label_agreement":null},{"id":"W1990129270","doi":"10.1109/tits.2012.2213815","title":"GPS Localization Accuracy Classification: A Context-Based Approach","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":187,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Global Positioning System; Computer science; Process (computing); Context (archaeology); Scheme (mathematics); Location awareness; Accuracy and precision; Real-time computing; Artificial intelligence; Data mining; Geography; Mathematics; Telecommunications","score_opus":0.03994893135551321,"score_gpt":0.24910636274807268,"score_spread":0.2091574313925595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990129270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1337592,0.0024152675,0.85352826,0.00089429837,0.00031275206,0.0004494998,0.00091051235,0.0027381268,0.004992097],"genre_scores_gemma":[0.78298956,0.0007788379,0.2139562,0.0001603507,0.000253309,0.00015765919,0.00073737407,0.000065593755,0.00090106105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99729055,0.00050960534,0.00032614556,0.0005682533,0.0010786905,0.00022685746],"domain_scores_gemma":[0.99580485,0.0011106763,0.0005429036,0.0005772059,0.0017967055,0.00016778335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016358854,0.00108457,0.0013875061,0.0057878722,0.0010643054,0.0017832962,0.0015103243,0.0012265303,0.00074844866],"category_scores_gemma":[0.007056698,0.00036542956,0.0007930347,0.003240225,0.0006052323,0.0024454156,0.0017086904,0.0010833916,0.0005280189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008126091,0.0005899382,0.07690798,0.00065109984,0.0002893406,0.0008077334,0.000888187,0.056265775,0.03452561,0.010275374,0.0060816067,0.8119047],"study_design_scores_gemma":[0.000056295466,0.0005295093,0.041706234,0.00019384308,0.00041849344,0.0013876539,0.00076152984,0.91294354,0.020842347,0.008761022,0.012163939,0.0002356456],"about_ca_topic_score_codex":0.0064799874,"about_ca_topic_score_gemma":0.0090925265,"teacher_disagreement_score":0.0064799874,"about_ca_system_score_codex":0.00072394765,"about_ca_system_score_gemma":0.0008765131,"threshold_uncertainty_score":0.012884498},"labels":[],"label_agreement":null},{"id":"W1991031228","doi":"10.1007/s11276-013-0663-0","title":"Cooperative network solution and implementation for emergency applications with enhanced position estimation capability","year":2013,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Innovates - Technology Futures","keywords":"Computer science; Interoperability; Position (finance); Base station; Multipath propagation; Network topology; Protocol (science); Real-time computing; Computer network; Topology (electrical circuits); Distributed computing","score_opus":0.004464104463036264,"score_gpt":0.22838775706803982,"score_spread":0.22392365260500355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991031228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079967976,0.000111923015,0.9103132,0.00021415678,0.000072355055,0.00009318445,0.00004113818,0.0012516466,0.007934408],"genre_scores_gemma":[0.8831668,0.000076441036,0.10646884,0.0000769639,0.000031590676,0.00012868835,0.000089307956,0.000037892154,0.009923575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997085,0.00006376374,0.000010126106,0.000048654376,0.00009641655,0.00007257872],"domain_scores_gemma":[0.99959713,0.000061122664,0.000031770986,0.00007240244,0.0002084106,0.000029096725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042499762,0.0003736784,0.00022819836,0.00037215702,0.00043091472,0.00048928894,0.0008729563,0.00057513604,0.002491534],"category_scores_gemma":[0.0007875721,0.00017667084,0.00020078504,0.00029666675,0.0001747355,0.00080576184,0.0007413575,0.0003974881,0.0007675604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007107558,0.0006421168,0.005651849,0.00014286929,0.00009556109,0.0006158763,0.00065782893,0.20465113,0.2262491,0.022705898,0.010405143,0.5274719],"study_design_scores_gemma":[0.000049936065,0.000499439,0.0016149773,0.000011980678,0.000049592512,0.00026807387,0.00022682048,0.92512673,0.058005773,0.0025859738,0.01153435,0.000026322563],"about_ca_topic_score_codex":0.0026571152,"about_ca_topic_score_gemma":0.0046337675,"teacher_disagreement_score":0.0026571152,"about_ca_system_score_codex":0.0003511263,"about_ca_system_score_gemma":0.0006337862,"threshold_uncertainty_score":0.008334994},"labels":[],"label_agreement":null},{"id":"W1991347375","doi":"10.1109/ccece.2014.6900998","title":"Distributed collaborative localization for a heterogeneous multi-robot system","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Robot; Scalability; Computer science; Mobile robot; Scheme (mathematics); Sensor fusion; Monte Carlo method; Bandwidth (computing); Robot kinematics; Distributed computing; Artificial intelligence; Real-time computing; Computer network","score_opus":0.008763509947757608,"score_gpt":0.21381900992574746,"score_spread":0.20505549997798986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991347375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031171186,0.00033253236,0.96694416,0.00012641706,0.000026792206,0.000026733682,0.000009357256,0.0001920278,0.0011708678],"genre_scores_gemma":[0.91807336,0.0003089642,0.08011641,0.000043637447,0.0000430863,0.00006420624,0.000023615696,0.000016629761,0.00131018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993389,0.00022858276,0.000028207758,0.0001782928,0.00015803763,0.00006782454],"domain_scores_gemma":[0.9990534,0.00046876536,0.0001550009,0.00013735135,0.00012894305,0.00005658918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010979385,0.00043121408,0.0007128756,0.00041101876,0.00073780713,0.00072858034,0.0010751612,0.0008074293,0.00068025605],"category_scores_gemma":[0.0021968777,0.00026700457,0.0005193086,0.00044034029,0.00067247235,0.0012005292,0.001386052,0.00050170696,0.0001639808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009724516,0.000031266278,0.00077481964,0.00009579085,0.000050293806,0.00038712082,0.00015155859,0.940715,0.00910193,0.010682985,0.00044310943,0.037468858],"study_design_scores_gemma":[0.00001744014,0.00006064296,0.0002103519,0.0000046868554,0.000016696133,0.00005461545,0.000043551372,0.99489516,0.0011441162,0.0028595608,0.00068492995,0.000008171098],"about_ca_topic_score_codex":0.0025626486,"about_ca_topic_score_gemma":0.0018765392,"teacher_disagreement_score":0.0025626486,"about_ca_system_score_codex":0.0007298802,"about_ca_system_score_gemma":0.0005092538,"threshold_uncertainty_score":0.0058065057},"labels":[],"label_agreement":null},{"id":"W1991454737","doi":"10.1109/lwc.2015.2415788","title":"Accuracy Analysis of the Two-Reference-Node Angle-of-Arrival Localization System","year":2015,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Node (physics); Angle of arrival; Computer science; Ambiguity; Time of arrival; Algorithm; Physics; Telecommunications; Wireless; Acoustics","score_opus":0.038574097183394336,"score_gpt":0.26433810439480826,"score_spread":0.22576400721141393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991454737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12993337,0.0019421396,0.85835755,0.00031458173,0.00008377917,0.00004615144,0.0001569726,0.001115728,0.008049761],"genre_scores_gemma":[0.9599129,0.0005754343,0.037940186,0.00005677237,0.000031614527,0.0000292393,0.00020567208,0.00007260826,0.0011755972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715495,0.0005064689,0.00012509557,0.00042758428,0.0015139814,0.00027199733],"domain_scores_gemma":[0.99541324,0.0015367855,0.00036787862,0.00063226157,0.0019978406,0.000052108357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018156082,0.00049292535,0.0007425553,0.0011742667,0.00036930238,0.001032712,0.001145838,0.0008358971,0.0009314167],"category_scores_gemma":[0.009366455,0.00024120818,0.0004959033,0.0009963242,0.0005334739,0.001101042,0.0007688415,0.0004977861,0.0005116408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011898847,0.000061410836,0.016040633,0.0005721017,0.00020017722,0.00061043235,0.0005405035,0.6518661,0.09637074,0.026266724,0.002271497,0.20400977],"study_design_scores_gemma":[0.000016010217,0.00018560393,0.007009778,0.00003537371,0.00005527369,0.00045316748,0.00008163587,0.95408076,0.03344577,0.0021130845,0.0024657059,0.00005786151],"about_ca_topic_score_codex":0.0046733664,"about_ca_topic_score_gemma":0.0019317074,"teacher_disagreement_score":0.0046733664,"about_ca_system_score_codex":0.0009674625,"about_ca_system_score_gemma":0.000746205,"threshold_uncertainty_score":0.009601951},"labels":[],"label_agreement":null},{"id":"W1992308975","doi":"10.1109/ccece.2012.6334891","title":"Estimation of emitter power, location, and path loss exponent","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Royal Military College of Canada","funders":"","keywords":"Exponent; RSS; Algorithm; Computer science; Path (computing); Position (finance); Mathematics; Computer network","score_opus":0.005225885786639242,"score_gpt":0.199065362075315,"score_spread":0.19383947628867576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992308975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1537401,0.0012557884,0.8327662,0.00020760612,0.00010044385,0.0001804102,0.002082761,0.0027043484,0.006962282],"genre_scores_gemma":[0.6976432,0.0019934347,0.2897983,0.0000880061,0.00007350352,0.00023457999,0.00401711,0.00026542132,0.005886358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944407,0.00006029473,0.00004954887,0.0001563707,0.00024222981,0.000047526966],"domain_scores_gemma":[0.99806327,0.0005297401,0.00035106635,0.00026670683,0.00073874986,0.000050500523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069210527,0.0012394063,0.0005564688,0.0025882758,0.0002901213,0.0009329004,0.0007259194,0.0005304198,0.0016784118],"category_scores_gemma":[0.0055175857,0.0004633406,0.00043179322,0.0019273941,0.00021653208,0.0018082167,0.0005947483,0.0007332692,0.0020412693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040374385,0.00019090544,0.13638562,0.0005902432,0.00020938684,0.001110615,0.0003800651,0.19796754,0.105190285,0.0061171064,0.010735262,0.54071933],"study_design_scores_gemma":[0.000039261053,0.00023483958,0.12127781,0.00012551225,0.00014988851,0.0015172096,0.0002912423,0.7867072,0.068981044,0.0059576663,0.014510067,0.00020823101],"about_ca_topic_score_codex":0.0027130237,"about_ca_topic_score_gemma":0.0027304476,"teacher_disagreement_score":0.0027130237,"about_ca_system_score_codex":0.0004365364,"about_ca_system_score_gemma":0.00050484744,"threshold_uncertainty_score":0.005614817},"labels":[],"label_agreement":null},{"id":"W1992426331","doi":"10.5623/cig2013-052","title":"Pedestrian Navigation Services: Challenges and Current Trends","year":2013,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Popularity; Pedestrian; Computer science; Transport engineering; Human–computer interaction; Simulation; Engineering","score_opus":0.011684791726662407,"score_gpt":0.21241201674254145,"score_spread":0.20072722501587906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992426331","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033682767,0.68729776,0.06166928,0.14186707,0.004023135,0.00014669524,0.0014089089,0.0010823387,0.06882217],"genre_scores_gemma":[0.2979008,0.59667224,0.051564634,0.013861445,0.0068538883,0.00018859943,0.0029474732,0.00021629487,0.029794613],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99815506,0.00046431995,0.0001321483,0.00032960463,0.00058796635,0.0003308669],"domain_scores_gemma":[0.9921023,0.0030036117,0.00047889055,0.00016998939,0.0034294538,0.0008158028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053152423,0.0009141698,0.0010980577,0.0020530557,0.000795728,0.0057146386,0.0028979434,0.0035196303,0.014983075],"category_scores_gemma":[0.004645853,0.00039458144,0.00053706876,0.004252327,0.0014275074,0.008582807,0.0021810525,0.0026846537,0.004511786],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027584756,0.00021543141,0.007951549,0.0024705015,0.0000466579,0.0001244569,0.00038880156,0.0026725968,0.0014076218,0.03321509,0.07679766,0.8744338],"study_design_scores_gemma":[0.000065713764,0.00052214635,0.007648965,0.0029340808,0.0001577933,0.0011139568,0.008305804,0.024608988,0.001756402,0.03291946,0.9198555,0.00011118188],"about_ca_topic_score_codex":0.0071114763,"about_ca_topic_score_gemma":0.012768975,"teacher_disagreement_score":0.014983075,"about_ca_system_score_codex":0.0018979341,"about_ca_system_score_gemma":0.003684424,"threshold_uncertainty_score":0.050123394},"labels":[],"label_agreement":null},{"id":"W1992615411","doi":"10.1109/ursigass.2014.6929357","title":"On the Fingerprint-based position algorithm enhanced by Round Trip Time measurement in Radio Access System","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fingerprint (computing); Position (finance); Computer science; Matching (statistics); Key (lock); Metis; Algorithm; Fingerprint recognition; Real-time computing; Blossom algorithm; Distance measurement; Computer vision; Artificial intelligence; Mathematics","score_opus":0.008488722100787044,"score_gpt":0.19224102314332323,"score_spread":0.18375230104253618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992615411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039776783,0.0013830995,0.9549546,0.000116272204,0.00020845316,0.00005649038,0.00005681113,0.00108012,0.0023673757],"genre_scores_gemma":[0.5927217,0.0015561403,0.40110478,0.00011661495,0.00026567132,0.00009174055,0.00020714923,0.00006133103,0.0038748994],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992077,0.00016179097,0.0000448004,0.00014830106,0.00038184563,0.00005559693],"domain_scores_gemma":[0.99950135,0.00010057824,0.000055808152,0.00008738105,0.00023466012,0.000020302303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005800461,0.0005032694,0.00065130705,0.0010029132,0.00035007996,0.0006338717,0.0008609011,0.0005157148,0.00074263377],"category_scores_gemma":[0.0015739182,0.0001925768,0.0003611021,0.0013560833,0.00024369641,0.0013290421,0.000522143,0.00040752126,0.0005122938],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005709795,0.000090919784,0.0061246236,0.0002148033,0.00012887159,0.00031376496,0.00013375666,0.07606249,0.0665573,0.0069178557,0.0030411913,0.83984333],"study_design_scores_gemma":[0.00007319365,0.00056410424,0.00567102,0.000030835887,0.00011844638,0.0015193011,0.000047566144,0.934217,0.04580735,0.001407144,0.010464738,0.00007928421],"about_ca_topic_score_codex":0.0018881632,"about_ca_topic_score_gemma":0.0011769673,"teacher_disagreement_score":0.0018881632,"about_ca_system_score_codex":0.00028316758,"about_ca_system_score_gemma":0.00044577237,"threshold_uncertainty_score":0.0037543178},"labels":[],"label_agreement":null},{"id":"W1993428781","doi":"10.2193/2006-478","title":"Using Viewsheds to Determine Area Sampled by Ground‐Based Radiotelemetry","year":2008,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Telemetry; Viewshed analysis; Geographic information system; Geography; Remote sensing; Biotelemetry; Cartography; Environmental science; Fishery; Computer science; Telecommunications; Biology","score_opus":0.041249118421029636,"score_gpt":0.25034554741328585,"score_spread":0.20909642899225622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993428781","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8742865,0.00020871876,0.11057832,0.00012484602,0.000091432375,0.00017750832,0.0061891507,0.0015580138,0.006785622],"genre_scores_gemma":[0.8118316,0.00009239488,0.18289912,0.000058791793,0.000015257963,0.00014915099,0.0032360246,0.00008152343,0.0016360822],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993073,0.00017786275,0.000060518967,0.00013078381,0.00028645853,0.000037023794],"domain_scores_gemma":[0.99706537,0.00090967934,0.0004336968,0.00033045906,0.001159223,0.000101499725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008762856,0.0002635854,0.00025523093,0.001975255,0.0001588716,0.00056043186,0.0005492556,0.00033052085,0.0018991992],"category_scores_gemma":[0.002742234,0.00021496603,0.00033549036,0.0014606739,0.00017053992,0.0005404581,0.00052786747,0.00021431276,0.00074379676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007889965,0.00016321133,0.5039944,0.0002993191,0.00021283726,0.00023759453,0.0007003908,0.019212004,0.080780394,0.0009066064,0.005098764,0.38760552],"study_design_scores_gemma":[0.0001397107,0.001217993,0.6316214,0.00009257557,0.00022035323,0.0008728407,0.0010514791,0.17751764,0.17156677,0.000982602,0.014587285,0.00012925391],"about_ca_topic_score_codex":0.004331276,"about_ca_topic_score_gemma":0.008702926,"teacher_disagreement_score":0.004331276,"about_ca_system_score_codex":0.00029669673,"about_ca_system_score_gemma":0.00026116765,"threshold_uncertainty_score":0.008612156},"labels":[],"label_agreement":null},{"id":"W1994974710","doi":"10.1109/wamicon.2006.351901","title":"Application of Ad-hoc sensor networks for localization in underground mines","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Wireless sensor network; Wireless ad hoc network; Computer science; Safety monitoring; Coal mining; Mobile ad hoc network; Underground mining (soft rock); Node (physics); Constraint (computer-aided design); Computer network; Real-time computing; Wireless; Engineering; Telecommunications","score_opus":0.005367256129179569,"score_gpt":0.20328082154303634,"score_spread":0.19791356541385677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994974710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016310116,0.004474746,0.9749615,0.00046513847,0.00017128051,0.00002799434,0.00003575075,0.0002759445,0.0032775179],"genre_scores_gemma":[0.71743685,0.010055503,0.2668463,0.00016846658,0.00028784026,0.00007848451,0.00013471952,0.000054072596,0.004937821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999564,0.00021387244,0.000020987973,0.00006661439,0.00011677896,0.00001774104],"domain_scores_gemma":[0.9992046,0.00057336787,0.000053631116,0.000045519133,0.0001032165,0.00001963456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004355544,0.0005188541,0.00047202062,0.0006563841,0.00038701983,0.00053628196,0.0006033803,0.0009117081,0.0008530181],"category_scores_gemma":[0.0015268581,0.00025064277,0.00033120599,0.0010891006,0.00047990482,0.0010574829,0.0006781556,0.00040267134,0.00031787588],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011715907,0.000053365435,0.00431437,0.00045586732,0.00009827994,0.0011903658,0.0003137385,0.66825175,0.022641363,0.029979214,0.0033208127,0.26926377],"study_design_scores_gemma":[0.0000150558435,0.00011092407,0.000974619,0.00008088923,0.000033893226,0.0007687081,0.00013856559,0.95757794,0.0068242736,0.017070645,0.01637464,0.000029865003],"about_ca_topic_score_codex":0.0009961046,"about_ca_topic_score_gemma":0.0010417089,"teacher_disagreement_score":0.0009961046,"about_ca_system_score_codex":0.00025917974,"about_ca_system_score_gemma":0.00026473185,"threshold_uncertainty_score":0.0028536916},"labels":[],"label_agreement":null},{"id":"W1995145742","doi":"10.1109/icinfa.2014.6932827","title":"Wi-Fi positioning based on deep learning","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Hidden Markov model; Artificial intelligence; Deep learning; Coherence (philosophical gambling strategy); Artificial neural network; Wireless; Set (abstract data type); Deep neural networks; Pattern recognition (psychology); Markov process; Computer vision; Real-time computing; Machine learning; Telecommunications; Mathematics","score_opus":0.002873085814080363,"score_gpt":0.17200107159155492,"score_spread":0.16912798577747457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995145742","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009764944,0.00035589727,0.9869632,0.00010351948,0.000057355977,0.000014538891,0.000063412364,0.0011825927,0.0014944911],"genre_scores_gemma":[0.66606855,0.00076470646,0.32344407,0.00031038353,0.000105834566,0.00008450124,0.00053212116,0.00009823033,0.008591589],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998037,0.000023376928,0.000009647131,0.00005979102,0.00006604146,0.000037422797],"domain_scores_gemma":[0.99981505,0.000048203692,0.000026295265,0.000027562144,0.00006974523,0.000013098592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024098404,0.00064389146,0.0004738496,0.0004955043,0.00026868156,0.00042564247,0.0009703208,0.00067083683,0.0014009406],"category_scores_gemma":[0.0007739109,0.00029493743,0.0002911601,0.0007180869,0.00024395448,0.000810434,0.0007843284,0.00077336683,0.0007896921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100635516,0.00006704935,0.001734672,0.00010667351,0.0000725322,0.00011184535,0.000062866944,0.27817255,0.01972521,0.005567579,0.0033238512,0.6909545],"study_design_scores_gemma":[0.0000051304155,0.000025935899,0.00043549825,0.0000067720066,0.000010728945,0.00003391772,0.000006307631,0.99284714,0.0038014825,0.0016359892,0.0011827473,0.000008419904],"about_ca_topic_score_codex":0.0065250983,"about_ca_topic_score_gemma":0.0084843645,"teacher_disagreement_score":0.0065250983,"about_ca_system_score_codex":0.0004481596,"about_ca_system_score_gemma":0.0005611585,"threshold_uncertainty_score":0.012974262},"labels":[],"label_agreement":null},{"id":"W1996620237","doi":"10.4028/www.scientific.net/amm.220-223.1852","title":"Localization Technology Based on Quantum-Behaved Particle Swarm Optimization Algorithm for Wireless Sensor Network","year":2012,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"La Cité Collégiale","funders":"","keywords":"Particle swarm optimization; Wireless sensor network; Convergence (economics); Node (physics); Computer science; Swarm behaviour; Algorithm; Exploit; Position (finance); Multi-swarm optimization; Wireless; Mathematical optimization; Mathematics; Engineering; Computer network; Telecommunications","score_opus":0.009305750028275493,"score_gpt":0.20668990265254195,"score_spread":0.19738415262426645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996620237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028640404,0.0003600107,0.995408,0.000117981224,0.000041643274,0.000013560111,0.0000042844035,0.00007329604,0.0011171714],"genre_scores_gemma":[0.52256167,0.0024437117,0.46962032,0.0002179463,0.00013792397,0.00023109507,0.000077246536,0.000057768804,0.0046523553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996574,0.00011459159,0.000017126731,0.00006135436,0.00013158856,0.000017959208],"domain_scores_gemma":[0.9998349,0.000060070313,0.00002355355,0.00001648102,0.000057484984,0.0000075328207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049122085,0.00046277622,0.0005360219,0.00038616886,0.00039723032,0.0005448883,0.0007144177,0.0006791296,0.00059204403],"category_scores_gemma":[0.00082001026,0.00021720969,0.0005029102,0.0005831712,0.0007561521,0.0011538755,0.0006573623,0.0006738096,0.00015644888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057585246,0.000047828536,0.00084675243,0.00023908632,0.00008457262,0.00011601586,0.00018467742,0.75318444,0.0138971815,0.10874069,0.0020361622,0.12056502],"study_design_scores_gemma":[0.000009388702,0.000031946693,0.000098837074,0.0000065277522,0.000007865485,0.000024275456,0.000010895552,0.9898066,0.0011030395,0.0071680928,0.0017234139,0.0000091277325],"about_ca_topic_score_codex":0.0017938559,"about_ca_topic_score_gemma":0.00085965014,"teacher_disagreement_score":0.0017938559,"about_ca_system_score_codex":0.00053541193,"about_ca_system_score_gemma":0.0006369572,"threshold_uncertainty_score":0.0038847327},"labels":[],"label_agreement":null},{"id":"W1997426533","doi":"10.1155/2012/501679","title":"Effectiveness of GNSS Spoofing Countermeasure Based on Receiver CNR Measurements","year":2012,"lang":"en","type":"article","venue":"International Journal of Navigation and Observation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Spoofing attack; Non-line-of-sight propagation; Multipath propagation; Computer science; Transmitter; Galileo (satellite navigation); GNSS augmentation; Line-of-sight; Real-time computing; Global Positioning System; Computer security; Electronic engineering; Telecommunications; Remote sensing; Engineering; Wireless; Channel (broadcasting)","score_opus":0.03150816819409697,"score_gpt":0.26338475823142815,"score_spread":0.2318765900373312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997426533","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6663188,0.0011310313,0.3202538,0.0001964229,0.00020221826,0.00009663752,0.00009919715,0.0010762148,0.010625709],"genre_scores_gemma":[0.9695525,0.0002849265,0.029505072,0.000039345643,0.00003622182,0.00001787474,0.000056764544,0.000026826461,0.00048060197],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976675,0.00067381543,0.00010684774,0.0003557747,0.0010177012,0.00017844755],"domain_scores_gemma":[0.99402535,0.0034734167,0.0009833757,0.0006969146,0.00070435833,0.00011647717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015274893,0.0009054797,0.00067332847,0.0011472387,0.00032568292,0.00067518326,0.0006438267,0.0009392679,0.0007022688],"category_scores_gemma":[0.0109515125,0.00020788687,0.00022343341,0.00036278612,0.00070583157,0.0009995607,0.0007261459,0.00061200315,0.00033606833],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033221238,0.00031628585,0.029205536,0.000648509,0.00023314096,0.00062873546,0.0003438926,0.113129005,0.42230052,0.007445382,0.00087117456,0.42155567],"study_design_scores_gemma":[0.00008029939,0.002213493,0.03389693,0.00015224711,0.00021006874,0.002099576,0.00017883528,0.4768051,0.48042023,0.0014550601,0.002344662,0.00014358804],"about_ca_topic_score_codex":0.00035986476,"about_ca_topic_score_gemma":0.00045174966,"teacher_disagreement_score":0.0015274893,"about_ca_system_score_codex":0.0002560694,"about_ca_system_score_gemma":0.00047821042,"threshold_uncertainty_score":0.0080782175},"labels":[],"label_agreement":null},{"id":"W1997555679","doi":"10.1109/saci.2012.6250044","title":"Design and fabrication of a smart electronic guide for museums","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Object (grammar); Information transfer; Multimedia; Information exchange; Order (exchange); Controller (irrigation); World Wide Web; Work (physics); Human–computer interaction; Engineering; Telecommunications; Artificial intelligence","score_opus":0.012999008946329852,"score_gpt":0.22670644016596075,"score_spread":0.2137074312196309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997555679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15899664,0.0018954916,0.79897237,0.000565801,0.00084297353,0.0011806113,0.0008104301,0.004374501,0.032361194],"genre_scores_gemma":[0.27028114,0.00089156243,0.7004629,0.00023102477,0.000054420885,0.0007414165,0.00045317665,0.00013856206,0.026745869],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996117,0.0000326223,0.000028931681,0.000104395556,0.00017484782,0.000047604437],"domain_scores_gemma":[0.99967337,0.00004099733,0.000040685227,0.00006533609,0.00013938006,0.000040231447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032077378,0.00077716826,0.00052282,0.00069808966,0.0004102568,0.00073872274,0.0013348681,0.0012733757,0.0037247157],"category_scores_gemma":[0.00045923982,0.00053667167,0.0007037065,0.00041991542,0.0004359841,0.00069103326,0.00058995566,0.0003279105,0.0016012944],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019463268,0.00020299989,0.0031107003,0.0017639755,0.000115651615,0.002207277,0.0010177795,0.012535501,0.76277584,0.010249327,0.009924255,0.19590211],"study_design_scores_gemma":[0.00024813093,0.003183929,0.013351206,0.00028332145,0.00030980774,0.006339857,0.00077656325,0.061456677,0.5299358,0.0017349106,0.3819875,0.00039217898],"about_ca_topic_score_codex":0.0006018052,"about_ca_topic_score_gemma":0.0009730031,"teacher_disagreement_score":0.0037247157,"about_ca_system_score_codex":0.0002907069,"about_ca_system_score_gemma":0.0006368212,"threshold_uncertainty_score":0.01246047},"labels":[],"label_agreement":null},{"id":"W1998026332","doi":"10.1002/wcm.49","title":"Estimating position of mobile terminals from path loss measurements with survey data","year":2002,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Instituto de Telecomunicações","keywords":"Path loss; Computer science; Log-distance path loss model; Terminal (telecommunication); Position (finance); Radio propagation; Radio propagation model; Path (computing); Mobile telephony; Telecommunications; Probability density function; Mobile radio; Algorithm; Statistics; Computer network; Wireless; Mathematics","score_opus":0.06426223181379154,"score_gpt":0.27607575231706083,"score_spread":0.2118135205032693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998026332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4957781,0.00019159827,0.5019737,0.000068434296,0.000015081441,0.000031469015,0.00036289264,0.0008459447,0.000732797],"genre_scores_gemma":[0.93891937,0.00018619273,0.059736095,0.000014784272,0.000010498577,0.0000347064,0.0006406675,0.000020301311,0.0004373971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9996649,0.00011709621,0.00001991098,0.00006511658,0.00010435672,0.000028610735],"domain_scores_gemma":[0.998168,0.00070084917,0.00036478223,0.00030280382,0.00040262012,0.0000608551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005611505,0.00045882125,0.0004184812,0.0012064357,0.00016224619,0.0005720603,0.0003509506,0.0004849094,0.00035802665],"category_scores_gemma":[0.0051212986,0.00038540113,0.00024677295,0.0011160653,0.00023706975,0.0007539467,0.00063902757,0.0004150498,0.00055348093],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044949434,0.0001386565,0.16699448,0.0002964006,0.0002019422,0.00029984844,0.00032035742,0.50822526,0.04231287,0.0020319219,0.0019632124,0.27676556],"study_design_scores_gemma":[0.000018051494,0.0001504005,0.032496188,0.000022207318,0.000049481125,0.00024610636,0.0001013816,0.9542487,0.010207474,0.0014948384,0.00093950314,0.000025736554],"about_ca_topic_score_codex":0.0029346098,"about_ca_topic_score_gemma":0.003901312,"teacher_disagreement_score":0.0029346098,"about_ca_system_score_codex":0.00025855875,"about_ca_system_score_gemma":0.00029533164,"threshold_uncertainty_score":0.0058350563},"labels":[],"label_agreement":null},{"id":"W1998351569","doi":"10.1117/12.883306","title":"A novel density-based geolocation algorithm for a noncooperative radio emitter using power difference of arrival","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Defence Research and Development Canada","funders":"","keywords":"Geolocation; Common emitter; Multilateration; Algorithm; Computer science; Intersection (aeronautics); Position (finance); Transmitter; Direction finding; Radio frequency; Grid; Antenna (radio); Physics; Telecommunications; Mathematics; Optics; Geometry; Azimuth; Aerospace engineering","score_opus":0.01761110197118511,"score_gpt":0.220951310142422,"score_spread":0.20334020817123688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998351569","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037876603,0.0001020793,0.995256,0.000048168018,0.000036268357,0.000018319552,0.000011740748,0.00033861535,0.0004010854],"genre_scores_gemma":[0.1612242,0.00029320174,0.8349647,0.000097642565,0.00008611061,0.00019543502,0.00019584436,0.00008354822,0.0028592288],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928844,0.00009821439,0.00004134505,0.00015834493,0.00035885713,0.000054784894],"domain_scores_gemma":[0.9989849,0.0002985058,0.00014116064,0.000115673465,0.00040967585,0.000050130264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007165959,0.0007635189,0.0010424589,0.0013484387,0.0006253732,0.0007739113,0.0021286397,0.00083484646,0.001126649],"category_scores_gemma":[0.0029301972,0.00059021346,0.000569966,0.0012217928,0.0005012039,0.0017602921,0.001497809,0.0008683756,0.0011415342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023582832,0.0001356452,0.0027577109,0.0001703052,0.00009073541,0.00020832308,0.00034412605,0.24124737,0.021506978,0.017304752,0.0043732845,0.71162504],"study_design_scores_gemma":[0.000052322615,0.000071658265,0.0006674586,0.000013247106,0.000021851329,0.00030452805,0.000047684924,0.98127764,0.007893754,0.0047094827,0.0049046893,0.00003570067],"about_ca_topic_score_codex":0.002799268,"about_ca_topic_score_gemma":0.002573836,"teacher_disagreement_score":0.002799268,"about_ca_system_score_codex":0.0007971138,"about_ca_system_score_gemma":0.0011719263,"threshold_uncertainty_score":0.0057834387},"labels":[],"label_agreement":null},{"id":"W1998552131","doi":"10.1145/1236360.1236368","title":"Localization in wireless sensor networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":287,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer science; Range (aeronautics); Wireless; Upper and lower bounds; Measure (data warehouse); SIGNAL (programming language); Real-time computing; Algorithm; Computer network; Mathematics; Telecommunications; Engineering; Data mining","score_opus":0.005446138333896764,"score_gpt":0.20192027313614766,"score_spread":0.1964741348022509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998552131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033236835,0.01790915,0.9685099,0.0016974094,0.0007182484,0.00005910965,0.00010097691,0.00063036167,0.007051093],"genre_scores_gemma":[0.43003255,0.06172365,0.48714814,0.0015107429,0.0024622378,0.00055010756,0.00072019437,0.0002912804,0.0155610135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982424,0.0006482513,0.000117438976,0.00027374135,0.0006299267,0.00008817222],"domain_scores_gemma":[0.9987469,0.0007225492,0.00013410645,0.0001551123,0.00020431032,0.000037017224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013298374,0.00079767,0.0009640906,0.0008629851,0.0006493141,0.0015280762,0.0010379134,0.0015485274,0.0021906153],"category_scores_gemma":[0.006545209,0.00041969088,0.0003657753,0.0020913421,0.0010861383,0.0032233375,0.0013150771,0.0012316146,0.0012909534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010564476,0.0000384164,0.00089008396,0.0014936049,0.00008176957,0.0002508978,0.00017280268,0.28426054,0.004487023,0.18772961,0.018934535,0.501555],"study_design_scores_gemma":[0.00004068577,0.00015043339,0.00055432547,0.0002773206,0.000050232935,0.0006247743,0.00014347365,0.6499387,0.00353676,0.23873672,0.105888024,0.000058552767],"about_ca_topic_score_codex":0.0010156778,"about_ca_topic_score_gemma":0.0007809901,"teacher_disagreement_score":0.0021906153,"about_ca_system_score_codex":0.0006121507,"about_ca_system_score_gemma":0.0005600242,"threshold_uncertainty_score":0.0073283315},"labels":[],"label_agreement":null},{"id":"W1998782771","doi":"10.1109/ccece.2008.4564494","title":"Localization using multicarrier communication systems for Wireless Sensor Networks","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wireless sensor network; Computer science; Ranging; Wireless; Process (computing); Real-time computing; Computer network; Telecommunications","score_opus":0.021419088525485373,"score_gpt":0.19778955490338165,"score_spread":0.17637046637789627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998782771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034746015,0.012574636,0.97811675,0.00033109554,0.00031411744,0.000035999336,0.00001353936,0.00035573047,0.0047835275],"genre_scores_gemma":[0.3241131,0.026621,0.6339072,0.0006397151,0.001023567,0.00022539745,0.00012615166,0.00013311049,0.013210775],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992586,0.00023801549,0.00003363239,0.00011050123,0.0003238607,0.000035450154],"domain_scores_gemma":[0.99932146,0.00028462338,0.00010252542,0.000083754574,0.00019176933,0.00001585332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050271105,0.0005218954,0.00038077284,0.00071155035,0.0004187917,0.0009817217,0.0006620074,0.00087144086,0.0020935775],"category_scores_gemma":[0.0017954551,0.0002158039,0.00026666673,0.0013825018,0.00052722584,0.0010835737,0.00067987986,0.0011036184,0.0010141085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023230343,0.000058775066,0.00088158896,0.0011996843,0.00009317449,0.0004086632,0.00039907356,0.08290316,0.0913649,0.1674507,0.0067095263,0.64829844],"study_design_scores_gemma":[0.0000503182,0.000772735,0.0010475708,0.00045967175,0.00013648614,0.002992899,0.00020249654,0.6168233,0.07798199,0.06795505,0.23138328,0.00019421044],"about_ca_topic_score_codex":0.00086825853,"about_ca_topic_score_gemma":0.00077836553,"teacher_disagreement_score":0.0020935775,"about_ca_system_score_codex":0.0007284731,"about_ca_system_score_gemma":0.0004152174,"threshold_uncertainty_score":0.0070037246},"labels":[],"label_agreement":null},{"id":"W2001591522","doi":"10.1109/cicybs.2013.6597198","title":"Indoor geo-fencing and access control for wireless networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Moncton Hospital; Dalhousie University","funders":"National Institute for Materials Science; Mitacs; Dalhousie University","keywords":"Fencing; Computer science; Wireless; Wireless network; Computer network; Access control; Telecommunications","score_opus":0.007181789513374779,"score_gpt":0.20535991535821804,"score_spread":0.19817812584484326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001591522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02396099,0.001229646,0.96908206,0.00015270928,0.00008559804,0.000043366774,0.00003499461,0.0011530899,0.00425749],"genre_scores_gemma":[0.8635591,0.0009937972,0.12966849,0.000098058634,0.000081805665,0.000069003785,0.00008923778,0.00006557454,0.0053748074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916315,0.00025895637,0.00005235976,0.00014199209,0.0002740495,0.00010948215],"domain_scores_gemma":[0.9993005,0.00018824625,0.00011877831,0.00024395702,0.00011182094,0.00003662887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052041357,0.0004909079,0.00040927142,0.0006361127,0.0006349725,0.0009960476,0.0009314434,0.0006328506,0.0021175263],"category_scores_gemma":[0.001647747,0.0002056463,0.00037605848,0.00058970094,0.0009229427,0.0014109785,0.0010392047,0.0005134754,0.0007054221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042144945,0.00013547837,0.0072513213,0.00049141736,0.00009903977,0.0007867092,0.0007841078,0.14980468,0.06421984,0.08715619,0.004693094,0.6841567],"study_design_scores_gemma":[0.00004053921,0.0006167057,0.007522139,0.0002005299,0.00012485456,0.0020081585,0.00057291845,0.7893434,0.08403834,0.03878163,0.0766146,0.00013623691],"about_ca_topic_score_codex":0.0026461682,"about_ca_topic_score_gemma":0.0028903752,"teacher_disagreement_score":0.0026461682,"about_ca_system_score_codex":0.0004603362,"about_ca_system_score_gemma":0.0005079826,"threshold_uncertainty_score":0.0070837736},"labels":[],"label_agreement":null},{"id":"W2001710420","doi":"10.1109/memea.2013.6549706","title":"Correcting Smartphone orientation for accelerometer-based analysis","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"","keywords":"Accelerometer; Orientation (vector space); Quaternion; Rotation matrix; Computer science; Offset (computer science); Position (finance); Rotation (mathematics); Computer vision; Frame (networking); Acceleration; Gyroscope; Reference frame; Artificial intelligence; Mathematics; Engineering; Physics; Geometry","score_opus":0.010337970476723997,"score_gpt":0.21467119545317925,"score_spread":0.20433322497645526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001710420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032921627,0.00042535993,0.9578056,0.00014413343,0.00075773953,0.0001210821,0.0005898204,0.004101921,0.0031327812],"genre_scores_gemma":[0.32891357,0.0009248372,0.65975523,0.0002055579,0.00020677967,0.00028555206,0.001569966,0.0008156384,0.0073228916],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908817,0.00015434738,0.00006237952,0.0002110749,0.00040727796,0.000076717515],"domain_scores_gemma":[0.9984181,0.00016015986,0.00016231225,0.00030823582,0.00090989954,0.00004121126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068714976,0.0011796217,0.0005356508,0.001192915,0.00046286292,0.00091796834,0.0005182941,0.00046907619,0.00435145],"category_scores_gemma":[0.0058697024,0.00041689887,0.00035168996,0.0015614154,0.00021737316,0.0005998116,0.0007685426,0.0007208105,0.0047266795],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039085557,0.000083351544,0.017797127,0.00035451594,0.00011342308,0.00037848443,0.0004278257,0.012405797,0.17826998,0.004632751,0.018066527,0.76707935],"study_design_scores_gemma":[0.00010872662,0.0005835503,0.10044799,0.00030196222,0.00023334968,0.0025346687,0.00097221805,0.38993305,0.34811714,0.0076345336,0.14887215,0.00026069427],"about_ca_topic_score_codex":0.0036875901,"about_ca_topic_score_gemma":0.008975163,"teacher_disagreement_score":0.00435145,"about_ca_system_score_codex":0.00033555384,"about_ca_system_score_gemma":0.0008472569,"threshold_uncertainty_score":0.014557004},"labels":[],"label_agreement":null},{"id":"W2001831180","doi":"10.1109/upinlbs.2014.7033704","title":"Use of diversity techniques for weak GNSS signal tracking in fading environments","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Multipath propagation; Pseudorange; Computer science; Antenna diversity; Fading; Electronic engineering; SIGNAL (programming language); Antenna (radio); Multipath mitigation; Global Positioning System; Real-time computing; Telecommunications; Channel (broadcasting); Engineering","score_opus":0.03005205126517981,"score_gpt":0.22479560618453867,"score_spread":0.19474355491935885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001831180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13262911,0.0012236673,0.86217284,0.00014078883,0.0000666257,0.000030505502,0.000029194738,0.00046431247,0.0032429819],"genre_scores_gemma":[0.79871726,0.00064968166,0.1985506,0.000100220175,0.0000811244,0.000033870198,0.000058973626,0.00003311574,0.0017752089],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996119,0.000096396296,0.000013593323,0.00008186277,0.00015193668,0.000044290624],"domain_scores_gemma":[0.9991014,0.0004127592,0.00012360488,0.00014949389,0.00018066507,0.000032086005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041062047,0.00046458046,0.00037181834,0.000755483,0.0003638845,0.00055071554,0.000511359,0.0005977115,0.0006385944],"category_scores_gemma":[0.001617455,0.00020806416,0.00031104137,0.0006260316,0.00040124726,0.0007016129,0.0009196557,0.0004434103,0.00034199972],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039086878,0.000092166025,0.0059476257,0.0002004606,0.00014084151,0.00028547496,0.00025466806,0.06332116,0.35375035,0.005240146,0.00058585324,0.56979036],"study_design_scores_gemma":[0.00014696656,0.0017465758,0.01209022,0.000102476864,0.00021712361,0.0035142575,0.0001771356,0.60635406,0.35011464,0.009005052,0.016402027,0.00012948361],"about_ca_topic_score_codex":0.0003062647,"about_ca_topic_score_gemma":0.00066495157,"teacher_disagreement_score":0.000755483,"about_ca_system_score_codex":0.00022703882,"about_ca_system_score_gemma":0.00029162073,"threshold_uncertainty_score":0.002171576},"labels":[],"label_agreement":null},{"id":"W2003261109","doi":"10.1109/pimrc.2011.6139897","title":"Localization algorithm performance in ultra low power active RFID based patient tracking","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Tracking (education); Position (finance); Non-line-of-sight propagation; Radio channel; Algorithm; Shadow mapping; Line-of-sight; Line (geometry); Channel (broadcasting); Wireless; Computer vision; Location tracking; Power (physics); Range (aeronautics); Real-time computing; Artificial intelligence; Engineering; Telecommunications; Mathematics","score_opus":0.009106098949039812,"score_gpt":0.1788965868145147,"score_spread":0.1697904878654749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003261109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6672979,0.00063653267,0.3266586,0.00017911883,0.00005926506,0.000041346935,0.00007222879,0.0018966999,0.0031582029],"genre_scores_gemma":[0.95335406,0.00015916296,0.045191757,0.00006252545,0.000008445211,0.000023808198,0.000099600686,0.000041941912,0.0010587892],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992679,0.00021771628,0.000056986315,0.00013259739,0.00024053305,0.000084338135],"domain_scores_gemma":[0.9979091,0.0011704969,0.00020200307,0.000188615,0.000454787,0.00007497144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011660552,0.0003193759,0.0004234738,0.00060554594,0.0002673049,0.0010466459,0.0005594973,0.0009003287,0.00080761366],"category_scores_gemma":[0.0048501855,0.0001441254,0.00022717807,0.0004916307,0.00032561706,0.00065123284,0.00041914778,0.00029126008,0.00058834854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00468416,0.00040799432,0.036173876,0.00037025506,0.0002452332,0.00050903443,0.0007999752,0.2651946,0.12908395,0.0043093655,0.001992634,0.55622894],"study_design_scores_gemma":[0.00018127434,0.001382413,0.024800558,0.000041747327,0.0001577735,0.0015019523,0.00021504209,0.86180615,0.1058122,0.001292478,0.002734762,0.000073774514],"about_ca_topic_score_codex":0.001118346,"about_ca_topic_score_gemma":0.00067656947,"teacher_disagreement_score":0.0011660552,"about_ca_system_score_codex":0.00034781254,"about_ca_system_score_gemma":0.0003689171,"threshold_uncertainty_score":0.0061668158},"labels":[],"label_agreement":null},{"id":"W2003716713","doi":"10.1109/wpnc.2014.6843305","title":"RSSI-based indoor tracking using the extended Kalman filter and circularly polarized antennas","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Quattriuum (Canada); École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extended Kalman filter; Computer science; Multipath propagation; Kalman filter; Node (physics); Tracking (education); Trajectory; Multilateration; Position (finance); Control theory (sociology); Acoustics; Physics; Artificial intelligence; Telecommunications","score_opus":0.019106770847266585,"score_gpt":0.225848119600068,"score_spread":0.20674134875280142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003716713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01040198,0.00015273319,0.9880741,0.000021911083,0.000026501315,0.00001033641,0.000016178345,0.0005336012,0.0007625896],"genre_scores_gemma":[0.7252384,0.0007345075,0.27002668,0.000063901054,0.00004982688,0.00008313359,0.00015997133,0.00007052933,0.0035731078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963105,0.00007664348,0.000019095616,0.000115485316,0.00011484719,0.00004285907],"domain_scores_gemma":[0.99966633,0.000095037394,0.000063174666,0.000050928298,0.00011076202,0.000013890171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004177255,0.0007074572,0.0007488168,0.00049967674,0.00029799712,0.0005953489,0.0006102127,0.00061076926,0.00049481355],"category_scores_gemma":[0.0009420822,0.00036435467,0.000572036,0.0007035684,0.00031705506,0.0008429941,0.0004895175,0.00043763386,0.0005389947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040111216,0.00008934169,0.0062085106,0.00023864245,0.00022064998,0.00043036297,0.00026613413,0.55733895,0.04575213,0.0055296817,0.0013221204,0.3822024],"study_design_scores_gemma":[0.000015951402,0.00009538684,0.0013753158,0.000010515036,0.00005728537,0.00016749983,0.000026566368,0.98735994,0.008779423,0.00066650624,0.0014158783,0.000029757064],"about_ca_topic_score_codex":0.0048110904,"about_ca_topic_score_gemma":0.0038026713,"teacher_disagreement_score":0.0048110904,"about_ca_system_score_codex":0.0003201618,"about_ca_system_score_gemma":0.0006142322,"threshold_uncertainty_score":0.009566128},"labels":[],"label_agreement":null},{"id":"W2004172545","doi":"10.1007/s11036-008-0041-9","title":"Robust Range-Free Localization in Wireless Sensor Networks","year":2007,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada); University of Victoria","funders":"","keywords":"Computer science; Wireless sensor network; Key distribution in wireless sensor networks; Computation; Robustness (evolution); Range (aeronautics); Cryptography; Wireless; Computer network; Computer security; Wireless network; Algorithm; Telecommunications","score_opus":0.006840241135245116,"score_gpt":0.19957872743396837,"score_spread":0.19273848629872325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004172545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009032481,0.001873137,0.9868547,0.00022197366,0.00007204286,0.000014510947,0.000036511767,0.00047123677,0.0014235737],"genre_scores_gemma":[0.81834847,0.0034365281,0.17007676,0.0002093837,0.00040334812,0.00014489387,0.00025765866,0.00023270636,0.0068902243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981931,0.0005227518,0.000089069035,0.00033479452,0.0007319924,0.0001282265],"domain_scores_gemma":[0.99777156,0.0012702036,0.00034734694,0.0002854763,0.00028067082,0.000044663793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010806518,0.00071490434,0.0009288848,0.00091139064,0.00036900063,0.0008071578,0.0013707059,0.00096238905,0.0009404795],"category_scores_gemma":[0.0066864192,0.00048268476,0.00035114173,0.0012161551,0.000971664,0.0019871558,0.0012717661,0.0006828995,0.00057019474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025705615,0.000053069733,0.0005994262,0.0002950233,0.000090229725,0.00021932153,0.00015122717,0.7241485,0.018904014,0.038155,0.0036411777,0.21348591],"study_design_scores_gemma":[0.000023940776,0.00008362418,0.00048751628,0.000024708701,0.00003338063,0.00021947,0.000034964658,0.9571656,0.0066329846,0.031963177,0.0033003602,0.000030323174],"about_ca_topic_score_codex":0.0012955836,"about_ca_topic_score_gemma":0.0008693836,"teacher_disagreement_score":0.0013707059,"about_ca_system_score_codex":0.00047313535,"about_ca_system_score_gemma":0.00038918588,"threshold_uncertainty_score":0.005715072},"labels":[],"label_agreement":null},{"id":"W2005328778","doi":"10.1109/jstsp.2013.2291302","title":"Introduction to the Special Issue on Non-Cooperative Localization Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.005920381985443222,"score_gpt":0.21654183999035215,"score_spread":0.21062145800490892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005328778","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011080776,0.081093095,0.02911837,0.02167081,0.8214868,0.00014417896,0.0005789951,0.00042832957,0.044371452],"genre_scores_gemma":[0.009049273,0.09526141,0.008763657,0.01314375,0.7145572,0.00021763319,0.0015872904,0.00051437534,0.15690538],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990671,0.00010211422,0.000095877775,0.00025487773,0.00038518067,0.00009484819],"domain_scores_gemma":[0.99698085,0.0008761226,0.00017018056,0.00019865182,0.0012737933,0.00050036405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012290336,0.0012457821,0.0016412407,0.0021358512,0.00097124616,0.0029374745,0.0011785963,0.0022179424,0.028374474],"category_scores_gemma":[0.0030787867,0.0004669845,0.0009373696,0.0024127536,0.0006265416,0.0036294593,0.0015977427,0.0047639636,0.016954852],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004123617,0.000048244692,0.0002193524,0.00051601336,0.000028859506,0.00015843798,0.000027209138,0.0005889414,0.0009787022,0.004952644,0.90293854,0.089501746],"study_design_scores_gemma":[0.0000052806954,0.00005250368,0.000350931,0.00016187933,0.000018985564,0.000325414,0.000021556345,0.00084101997,0.00024814953,0.0031118486,0.9948467,0.00001569751],"about_ca_topic_score_codex":0.00041673478,"about_ca_topic_score_gemma":0.0009608782,"teacher_disagreement_score":0.028374474,"about_ca_system_score_codex":0.0008639087,"about_ca_system_score_gemma":0.0008709532,"threshold_uncertainty_score":0.094922066},"labels":[],"label_agreement":null},{"id":"W2005672442","doi":"10.1007/s10776-012-0194-0","title":"Anonymous Indoor Navigation System on Handheld Mobile Devices for Visually Impaired","year":2012,"lang":"en","type":"article","venue":"International Journal of Wireless Information Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CNIB Foundation; University of Toronto","funders":"","keywords":"Computer science; Navigation system; Real-time computing; Computer vision; Mobile device; Visually impaired; Kalman filter; Tracking system; Tracking (education); Wireless; Artificial intelligence; Location tracking; Embedded system; Human–computer interaction; Telecommunications","score_opus":0.006454158641832048,"score_gpt":0.23847921321851634,"score_spread":0.2320250545766843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005672442","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88135266,0.00033902467,0.09633001,0.00016268755,0.0002714345,0.00018320572,0.0008611466,0.00805492,0.012444873],"genre_scores_gemma":[0.9724689,0.00010201118,0.015065625,0.000077478595,0.000029164408,0.00007299044,0.00026070693,0.000052383362,0.011870726],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976104,0.00004850072,0.0000117535155,0.00005426365,0.00007589788,0.000048459427],"domain_scores_gemma":[0.99970007,0.000029001234,0.000022648777,0.000048880087,0.00016054731,0.00003887973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017871519,0.00043718418,0.0004962528,0.0003873993,0.00060118496,0.00038088975,0.0006727983,0.00047484206,0.0054372377],"category_scores_gemma":[0.00035535512,0.00012717745,0.00024415273,0.0002444588,0.00011516573,0.0003392712,0.0006451482,0.00022302306,0.0014372119],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005307045,0.0008627976,0.05620591,0.0004837041,0.00018050258,0.0043447125,0.0016530859,0.005817936,0.36077985,0.0023227553,0.026924238,0.53511745],"study_design_scores_gemma":[0.0009333201,0.0069310293,0.26243025,0.00029118673,0.0016430202,0.010103,0.003089404,0.248306,0.3829526,0.002626746,0.08019524,0.00049826107],"about_ca_topic_score_codex":0.005417015,"about_ca_topic_score_gemma":0.008033043,"teacher_disagreement_score":0.0054372377,"about_ca_system_score_codex":0.00029518892,"about_ca_system_score_gemma":0.0005858282,"threshold_uncertainty_score":0.018189311},"labels":[],"label_agreement":null},{"id":"W2005676902","doi":"10.1007/s10291-003-0080-4","title":"Effects of building materials on UHF ranging signals","year":2004,"lang":"en","type":"article","venue":"GPS Solutions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; Okanagan College; University of Calgary","funders":"Ministère de la Défense Nationale; Defence Research and Development Canada","keywords":"Pseudorange; Ranging; Transmitter; Ultra high frequency; Global Positioning System; Pseudorandom noise; Phase noise; Acoustics; Electronic engineering; Remote sensing; Computer science; Electrical engineering; Engineering; Telecommunications; Physics; GNSS applications; Spread spectrum; Code division multiple access; Geology","score_opus":0.008455763069976892,"score_gpt":0.21086659428655574,"score_spread":0.20241083121657885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005676902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9681686,0.00083629735,0.022365557,0.00009501906,0.00011770854,0.000019388943,0.00024008217,0.00037031152,0.0077870716],"genre_scores_gemma":[0.99639565,0.00024183621,0.0014274477,0.00003122279,0.000012779148,0.0000062552026,0.00015566661,0.00008099085,0.0016481514],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991365,0.00019205429,0.000031011776,0.00011399357,0.0003209575,0.00020553468],"domain_scores_gemma":[0.99262816,0.0056448528,0.0003474449,0.0002930113,0.00097443094,0.00011211931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006151748,0.00054229895,0.0004070121,0.0005351038,0.0003579084,0.0007865731,0.0004218587,0.00082799356,0.0062582516],"category_scores_gemma":[0.0044672433,0.00040891653,0.0003420085,0.00066932046,0.00042496802,0.00091798056,0.00044022442,0.00042229248,0.0012851708],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0054546627,0.00024385547,0.02017305,0.00060153345,0.00016570292,0.0010946922,0.00036956594,0.10382858,0.78402,0.0012201068,0.00061410764,0.082214095],"study_design_scores_gemma":[0.00009912607,0.001590537,0.08386831,0.00009763338,0.00053434033,0.0012608237,0.000697044,0.05877292,0.84953773,0.00065429014,0.0027899505,0.000097351025],"about_ca_topic_score_codex":0.0011304537,"about_ca_topic_score_gemma":0.0010524973,"teacher_disagreement_score":0.0062582516,"about_ca_system_score_codex":0.00036249173,"about_ca_system_score_gemma":0.0002122811,"threshold_uncertainty_score":0.020935953},"labels":[],"label_agreement":null},{"id":"W2005773356","doi":"10.2495/safe-v4-n2-135-142","title":"An application of internet of things in the field of urban building fire safety","year":2014,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fire safety; Field (mathematics); Architectural engineering; The Internet; Environmental science; Internet of Things; Computer science; Engineering; Computer security; Civil engineering; World Wide Web","score_opus":0.0026327664934894983,"score_gpt":0.21582294960169218,"score_spread":0.21319018310820267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005773356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064401284,0.012708275,0.8008713,0.0073135216,0.0022449607,0.0001684991,0.00019772966,0.0010751487,0.11101923],"genre_scores_gemma":[0.7348177,0.017213663,0.22282884,0.0012579726,0.0006239019,0.00007962446,0.00024593843,0.00007220708,0.022860201],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975544,0.000056840174,0.00001148277,0.00003694441,0.000120096316,0.000019238643],"domain_scores_gemma":[0.99977034,0.000062416395,0.000012928668,0.000036470858,0.00010134129,0.000016619097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036265387,0.0003305943,0.00018372745,0.0007600721,0.00050557463,0.0006338541,0.00036864096,0.0009221503,0.0016292637],"category_scores_gemma":[0.0005313263,0.00015422185,0.0005312306,0.0010023797,0.00038428823,0.001057351,0.00061195216,0.0004897712,0.00033904266],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020116438,0.0001959802,0.009589047,0.0008886634,0.00014460654,0.0037168013,0.00083349075,0.03954243,0.04577271,0.1318106,0.023465892,0.74383867],"study_design_scores_gemma":[0.00005653649,0.0007884865,0.013648114,0.0005586575,0.00024804016,0.0054912027,0.0024304541,0.40933096,0.039516177,0.14403263,0.38367754,0.0002211887],"about_ca_topic_score_codex":0.0011570498,"about_ca_topic_score_gemma":0.0012962106,"teacher_disagreement_score":0.0016292637,"about_ca_system_score_codex":0.00026268122,"about_ca_system_score_gemma":0.00043751806,"threshold_uncertainty_score":0.0054504275},"labels":[],"label_agreement":null},{"id":"W2005819591","doi":"10.1109/aina.2015.222","title":"On Data Fusion for Orientation Sensing in WBASNs Using Smart Phones","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sensor fusion; Gyroscope; Orientation (vector space); Accelerometer; Kalman filter; Computer science; Inertial measurement unit; Key (lock); Wireless sensor network; Wireless; Robot; Filter (signal processing); Real-time computing; Artificial intelligence; Computer vision; Engineering; Telecommunications; Computer security; Computer network","score_opus":0.0939958720373981,"score_gpt":0.3026587896577322,"score_spread":0.20866291762033407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005819591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035359986,0.0020099399,0.95901006,0.00033651717,0.00024982588,0.00007723091,0.0000519625,0.00033273973,0.0025716599],"genre_scores_gemma":[0.7551944,0.0029286796,0.2382076,0.00021489656,0.00017398622,0.00011477107,0.00020748177,0.000028535886,0.0029296628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991605,0.00020028585,0.0000668823,0.00015263719,0.0003326506,0.00008694211],"domain_scores_gemma":[0.9990133,0.00044619764,0.00005795198,0.00009409056,0.00036501038,0.000023329305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011285905,0.00057232124,0.0005558125,0.000697136,0.0005232889,0.0007444818,0.00056089676,0.00081775786,0.0011110959],"category_scores_gemma":[0.002315527,0.00023173193,0.00087218755,0.00076046254,0.00039719153,0.0018089897,0.00062251615,0.00066360214,0.0003255075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005829991,0.00017413894,0.0058730803,0.0004891588,0.0002609632,0.00039086468,0.0004955358,0.2696917,0.048394926,0.017070264,0.0028703434,0.653706],"study_design_scores_gemma":[0.00001643999,0.00047669304,0.0028687606,0.000057828544,0.000092988885,0.0002972893,0.00017725107,0.9523594,0.03287141,0.0037840114,0.0069467956,0.000051213516],"about_ca_topic_score_codex":0.0049080527,"about_ca_topic_score_gemma":0.0029045038,"teacher_disagreement_score":0.0049080527,"about_ca_system_score_codex":0.00054601487,"about_ca_system_score_gemma":0.00065248186,"threshold_uncertainty_score":0.009758949},"labels":[],"label_agreement":null},{"id":"W2005908327","doi":"10.1145/2077357.2077360","title":"A conceptual model of trust for indoor positioning systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Global Positioning System; Computer science; Context (archaeology); Positioning system; Conceptual framework; Hybrid positioning system; Conceptual model; Information system; Human–computer interaction; Location-based service; Point (geometry); Telecommunications; Engineering; Database; Geography","score_opus":0.04657866097625145,"score_gpt":0.207275057750142,"score_spread":0.16069639677389053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005908327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01794654,0.0011571611,0.8730964,0.013489876,0.00022277501,0.00025181993,0.00017840469,0.00028570477,0.09337145],"genre_scores_gemma":[0.83807945,0.0012690617,0.14653715,0.0012453836,0.00027582588,0.0004900654,0.0002494678,0.0001122741,0.011741251],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98556006,0.007083935,0.0012893818,0.001494633,0.0034450125,0.0011270394],"domain_scores_gemma":[0.9760206,0.012204736,0.0024286245,0.00321854,0.004324071,0.0018035219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0097857285,0.0009853429,0.00068199413,0.0019352556,0.0039100703,0.010815499,0.0027146742,0.006026077,0.005722542],"category_scores_gemma":[0.020804927,0.0009233262,0.0012394411,0.001943381,0.013108798,0.019504994,0.006155722,0.004866842,0.0016525569],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010158338,0.000011167056,0.00022500353,0.00004636564,0.000008745558,0.00012769178,0.0023119422,0.0017940642,0.0001459183,0.99167836,0.0006676792,0.0029730236],"study_design_scores_gemma":[0.000043313335,0.00012718314,0.00039649542,0.0002359119,0.0000461076,0.00076222577,0.0027995657,0.028117187,0.00066584005,0.89232975,0.07439484,0.00008151808],"about_ca_topic_score_codex":0.007927359,"about_ca_topic_score_gemma":0.002616119,"teacher_disagreement_score":0.010815499,"about_ca_system_score_codex":0.0055151004,"about_ca_system_score_gemma":0.0034797764,"threshold_uncertainty_score":0.051752508},"labels":[],"label_agreement":null},{"id":"W2006625273","doi":"10.1109/wpnc.2012.6268742","title":"Indoor positioning using particle filters with optimal importance function","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Particle filter; Degeneracy (biology); Function (biology); Range (aeronautics); Computer science; Mathematical optimization; Algorithm; Mathematics; Control theory (sociology); Artificial intelligence; Kalman filter; Engineering","score_opus":0.011839352839456616,"score_gpt":0.20166657883586483,"score_spread":0.18982722599640822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006625273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013499162,0.000061538616,0.998304,0.0000225623,0.00001895007,0.0000059505082,0.000004526196,0.000057027035,0.00017553692],"genre_scores_gemma":[0.33738646,0.00051130063,0.65929323,0.00008166557,0.00015649693,0.000121474026,0.00012993578,0.00008385046,0.002235576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990839,0.00030175183,0.000047366513,0.00015839105,0.0003251269,0.00008341083],"domain_scores_gemma":[0.9986827,0.00075430964,0.00014005568,0.00009889092,0.00028864839,0.000035448717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016059215,0.00083431596,0.0009780681,0.0011126758,0.00045938318,0.00095238094,0.0008649741,0.0012052446,0.0007136732],"category_scores_gemma":[0.0049169688,0.00060043304,0.001097685,0.0010501372,0.0006498769,0.001530168,0.0009963557,0.0011921524,0.0002682518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071630624,0.000037120317,0.0008434586,0.00008918639,0.00005553321,0.00007685371,0.00007733293,0.8656552,0.0034830545,0.023768894,0.0008905024,0.10495133],"study_design_scores_gemma":[0.0000069343278,0.000015706952,0.00015198888,0.0000046666983,0.0000069196685,0.000016201942,0.0000046034525,0.9953436,0.0007365257,0.0031737648,0.0005316673,0.0000072684297],"about_ca_topic_score_codex":0.0068966495,"about_ca_topic_score_gemma":0.0048028966,"teacher_disagreement_score":0.0068966495,"about_ca_system_score_codex":0.0009585411,"about_ca_system_score_gemma":0.001131897,"threshold_uncertainty_score":0.013713002},"labels":[],"label_agreement":null},{"id":"W2006960089","doi":"10.1109/icc.2010.5501947","title":"Wireless Sensor Network Localization with Spatially Correlated Shadowing","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Semidefinite programming; Wireless sensor network; Covariance; Shadow mapping; Gaussian; Computer science; Mathematical optimization; Estimator; Covariance matrix; Gaussian noise; Mathematics; Algorithm; Artificial intelligence; Statistics; Physics; Computer network","score_opus":0.0035000678979183355,"score_gpt":0.1718482069830698,"score_spread":0.16834813908515145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006960089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0122592375,0.0001586349,0.9863064,0.00011862252,0.000012878889,0.000006829966,0.000022208711,0.0000942143,0.0010209939],"genre_scores_gemma":[0.8300448,0.00097614893,0.16397789,0.000118418706,0.0000865307,0.00008575765,0.00015189713,0.000056592413,0.0045019602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914944,0.00039911122,0.000026369842,0.00014483675,0.00022870753,0.000051577677],"domain_scores_gemma":[0.99924254,0.0003726415,0.00013340134,0.000116149175,0.000120341545,0.000014967101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009487542,0.0005326579,0.00060975604,0.000246223,0.0001692919,0.00053154386,0.00063178304,0.0005273185,0.00054311],"category_scores_gemma":[0.0028955499,0.0003618311,0.00039539137,0.000663703,0.0007701717,0.0010093902,0.0007021871,0.00064001407,0.00018960547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029079303,0.000012465758,0.0002575113,0.000045675144,0.000014199217,0.00010551686,0.000046341313,0.96374476,0.0028608453,0.01801169,0.00047342665,0.014398403],"study_design_scores_gemma":[0.0000035330197,0.000011542906,0.00006853417,0.000002459432,0.000002039096,0.000025223735,0.000007053157,0.9936977,0.0005008248,0.0053817774,0.00029672947,0.000002628594],"about_ca_topic_score_codex":0.0017245035,"about_ca_topic_score_gemma":0.0015137331,"teacher_disagreement_score":0.0017245035,"about_ca_system_score_codex":0.00050484814,"about_ca_system_score_gemma":0.0006811708,"threshold_uncertainty_score":0.0050175786},"labels":[],"label_agreement":null},{"id":"W2007091679","doi":"10.1109/iecon.2011.6119649","title":"Directional performance of an algorithm used to locate microseismic events in underground mines","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Microseism; Weighting; Algorithm; Computer science; Azimuth; SIGNAL (programming language); Variance (accounting); Bandwidth (computing); Real-time computing; Event (particle physics); Seismology; Data mining; Acoustics; Geology; Mathematics; Telecommunications","score_opus":0.026414879510366713,"score_gpt":0.21944058365255648,"score_spread":0.19302570414218975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007091679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17521654,0.00018558683,0.8214299,0.00010350506,0.000067869914,0.000041445146,0.00005006285,0.001145246,0.0017599175],"genre_scores_gemma":[0.6024736,0.00012736581,0.39447486,0.000067757486,0.000030848314,0.00006384549,0.00023894908,0.00013166864,0.0023910475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952686,0.000092920694,0.00004351593,0.00011818798,0.0001674534,0.000051117568],"domain_scores_gemma":[0.99860364,0.00048012708,0.000117886295,0.00012747545,0.0006251071,0.000045799956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011313095,0.0005649498,0.00040293997,0.0005442452,0.00031850627,0.00081552955,0.0006460481,0.0010090825,0.00091815036],"category_scores_gemma":[0.0048155715,0.00019549568,0.0002628611,0.00046837202,0.00026968325,0.00061414903,0.00048677294,0.0003260619,0.0005206463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011945853,0.00015291478,0.009184369,0.000094318006,0.00014154235,0.00010908803,0.00015328184,0.35133848,0.06741315,0.0047422033,0.0015028937,0.5639732],"study_design_scores_gemma":[0.000036323323,0.00015422114,0.0019206757,0.0000059578197,0.000015108421,0.0001058715,0.000030841933,0.97833943,0.018078426,0.0007047268,0.0005937502,0.00001464741],"about_ca_topic_score_codex":0.0021777165,"about_ca_topic_score_gemma":0.001853923,"teacher_disagreement_score":0.0021777165,"about_ca_system_score_codex":0.00037610528,"about_ca_system_score_gemma":0.00083997723,"threshold_uncertainty_score":0.005982995},"labels":[],"label_agreement":null},{"id":"W2007215379","doi":"10.1145/2685553.2698994","title":"Shuriken","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Transfer (computing); Bluetooth; Mobile device; SwIPe; Bluetooth Low Energy; Computer network; Telecommunications; Wireless; World Wide Web","score_opus":0.021417750471715916,"score_gpt":0.1944889140854773,"score_spread":0.1730711636137614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007215379","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01625844,0.0009677844,0.9269584,0.00029537582,0.00034980252,0.000445143,0.0005797747,0.011847228,0.042298123],"genre_scores_gemma":[0.19917041,0.00090507657,0.69430095,0.0006001852,0.00014182374,0.00042313908,0.0027225928,0.0016645645,0.10007117],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980622,0.00028024326,0.000113142916,0.00064236665,0.0006978971,0.00020407078],"domain_scores_gemma":[0.9989581,0.00015197552,0.00005998651,0.00054903084,0.00021191465,0.000068856585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085610256,0.0011574726,0.0010462686,0.0015371024,0.001523499,0.002398356,0.0022533177,0.0010999957,0.023821669],"category_scores_gemma":[0.0027964422,0.0006398436,0.00095077883,0.001506594,0.0009777513,0.0032635645,0.004037742,0.0011015187,0.012874978],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039486625,0.00010884524,0.0026890507,0.0004547588,0.00010055898,0.00036556477,0.0013681462,0.012937625,0.01614266,0.041919343,0.025190895,0.89832765],"study_design_scores_gemma":[0.00017474471,0.0005437735,0.0050427895,0.00029919867,0.00018128166,0.0017964097,0.001475949,0.2532237,0.066637136,0.05359701,0.61675763,0.00027033285],"about_ca_topic_score_codex":0.0037737978,"about_ca_topic_score_gemma":0.0065324134,"teacher_disagreement_score":0.023821669,"about_ca_system_score_codex":0.0005942675,"about_ca_system_score_gemma":0.0012616838,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2008124902","doi":"10.4236/wsn.2010.211097","title":"Range-Based Localization in Wireless Networks Using Density-Based Outlier Detection","year":2010,"lang":"en","type":"article","venue":"Wireless Sensor Network","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Outlier; RSS; Range (aeronautics); Algorithm; Multilateration; Node (physics); Anomaly detection; Singular value decomposition; Artificial intelligence","score_opus":0.0076717458400508,"score_gpt":0.2032129263788885,"score_spread":0.19554118053883768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008124902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007292402,0.00022668163,0.99158233,0.00006368424,0.000026935404,0.00002036092,0.00001726635,0.00046638888,0.0003040469],"genre_scores_gemma":[0.4381018,0.0011284802,0.55870086,0.00009624031,0.00010706342,0.00015917906,0.0002459824,0.00010328072,0.0013571548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988249,0.00029884704,0.00007516435,0.00021550307,0.0005231947,0.00006245975],"domain_scores_gemma":[0.99761343,0.0011968511,0.0003974801,0.0002371498,0.0005051333,0.00004998597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011367379,0.00070535013,0.0012006555,0.0019894417,0.0004909432,0.0008119467,0.0012937518,0.00082937855,0.00040025395],"category_scores_gemma":[0.0058876127,0.00038938422,0.0006352717,0.0027408414,0.00063752563,0.0017814466,0.0013638753,0.00067429617,0.00038191272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001774921,0.0001012944,0.0058991914,0.00026353568,0.00014532894,0.00031413525,0.00027052572,0.45454535,0.015458184,0.011522729,0.0022983786,0.5090038],"study_design_scores_gemma":[0.000015992871,0.00006944595,0.00090361584,0.000017498473,0.000022598604,0.00031391444,0.000052841722,0.98489743,0.006562714,0.005283966,0.0018314379,0.000028543376],"about_ca_topic_score_codex":0.002128109,"about_ca_topic_score_gemma":0.001577706,"teacher_disagreement_score":0.002128109,"about_ca_system_score_codex":0.00054783135,"about_ca_system_score_gemma":0.00044909725,"threshold_uncertainty_score":0.0060117245},"labels":[],"label_agreement":null},{"id":"W2008301128","doi":"10.1109/wpnc.2011.5961014","title":"Second order cone programming for sensor network localization with anchor position uncertainty","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Second-order cone programming; Convexity; Node (physics); Convex optimization; Mathematical optimization; Position (finance); Relaxation (psychology); Computer science; Focus (optics); Computational complexity theory; Ranging; Wireless sensor network; Regular polygon; Algorithm; Mathematics; Engineering; Geometry","score_opus":0.012721440044372671,"score_gpt":0.19966412330969457,"score_spread":0.1869426832653219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008301128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001738891,0.00041967464,0.99486035,0.00021436559,0.000043323173,0.000022438117,0.00005053938,0.000056757694,0.0025936048],"genre_scores_gemma":[0.33630952,0.004285689,0.64393985,0.00043554275,0.00036734765,0.0006928476,0.0005866273,0.0002918305,0.013090686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888986,0.00049920107,0.000040289953,0.000118043055,0.00036723362,0.00008543581],"domain_scores_gemma":[0.9981207,0.0012194744,0.00016423457,0.000088753346,0.00031583913,0.00009103674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018809008,0.0014528753,0.0010243903,0.00058541156,0.000401628,0.001598703,0.001035001,0.00084887794,0.002283557],"category_scores_gemma":[0.004153314,0.00042770483,0.0008834873,0.0014332564,0.0010136976,0.0012305399,0.0010700016,0.0023796298,0.0005199108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045894318,0.000057338308,0.00027516665,0.00019248789,0.00003581958,0.00015158845,0.000096210584,0.8259285,0.001247161,0.13835268,0.0037888624,0.029828232],"study_design_scores_gemma":[0.000005615541,0.000025507226,0.000046449786,0.000012223255,0.0000029571509,0.000020989299,0.0000127027215,0.9670975,0.00019695077,0.031084338,0.0014888588,0.000005842798],"about_ca_topic_score_codex":0.0036147817,"about_ca_topic_score_gemma":0.002397205,"teacher_disagreement_score":0.0036147817,"about_ca_system_score_codex":0.0008754163,"about_ca_system_score_gemma":0.0015120157,"threshold_uncertainty_score":0.00994724},"labels":[],"label_agreement":null},{"id":"W2008465771","doi":"10.1007/s11277-011-0365-9","title":"Efficient Spectrum Allocation and Time of Arrival Based Localization in Cognitive Networks","year":2011,"lang":"en","type":"article","venue":"Wireless Personal Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Cognitive radio; Computer science; Orthogonal frequency-division multiplexing; Frequency allocation; Rectangle; Transmission (telecommunications); Spectrum (functional analysis); Cramér–Rao bound; Position (finance); Upper and lower bounds; Multilateration; Max-min fairness; Mathematical optimization; Topology (electrical circuits); Ranging; Resource allocation; Spectrum management; Algorithm; Telecommunications; Channel (broadcasting); Wireless; Computer network; Mathematics; Estimation theory; Physics","score_opus":0.023433855945980343,"score_gpt":0.2292890470854669,"score_spread":0.20585519113948655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008465771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031054687,0.0005939491,0.9654075,0.0001390376,0.000084508254,0.000016409473,0.00003084962,0.00018558203,0.0024875728],"genre_scores_gemma":[0.9291451,0.00040945606,0.0674221,0.00006436557,0.00011047219,0.000042734937,0.000037114805,0.00003568983,0.0027329605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991304,0.00027897258,0.000032132382,0.00011740297,0.00020951647,0.00023150236],"domain_scores_gemma":[0.9986156,0.00085500255,0.00011704093,0.00012818124,0.000221112,0.00006311946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093892304,0.00055154425,0.0008403275,0.00063692586,0.0005884781,0.0012253999,0.0012138047,0.00065834407,0.0010616665],"category_scores_gemma":[0.0036846655,0.00040629995,0.00034024182,0.001128438,0.0008063944,0.0012106928,0.0011424915,0.000588707,0.0002953874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004693214,0.00011938484,0.000692128,0.00009527579,0.00006165111,0.000107445994,0.00015291791,0.8228797,0.014964343,0.034907572,0.0018961646,0.12365415],"study_design_scores_gemma":[0.000016334961,0.000037371243,0.00017107803,0.0000039040115,0.00001642179,0.000054138935,0.000031295403,0.98676425,0.0019411664,0.010470963,0.0004815451,0.000011527087],"about_ca_topic_score_codex":0.0035140305,"about_ca_topic_score_gemma":0.0052595944,"teacher_disagreement_score":0.0035140305,"about_ca_system_score_codex":0.00081195217,"about_ca_system_score_gemma":0.0013196447,"threshold_uncertainty_score":0.0069871545},"labels":[],"label_agreement":null},{"id":"W2008562280","doi":"10.1109/ccece.2010.5575134","title":"Practical result of wireless indoor position estimation by using hybrid TDOA/RSS algorithm","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multilateration; RSS; Geolocation; Computer science; GNSS applications; FDOA; Wireless; Real-time computing; Algorithm; Reliability (semiconductor); Position (finance); Telecommunications; Global Positioning System; Mathematics; Azimuth","score_opus":0.01002493965364299,"score_gpt":0.26080439166190467,"score_spread":0.2507794520082617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008562280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2953021,0.00047445807,0.69339573,0.0002871245,0.00009248832,0.000059467795,0.00008828564,0.0020123883,0.008287924],"genre_scores_gemma":[0.8672148,0.00013949368,0.13093714,0.000030860567,0.000022018043,0.000025106121,0.000086686894,0.00003811887,0.0015057334],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991374,0.00036198448,0.000037997183,0.00010462882,0.00028948238,0.00006858166],"domain_scores_gemma":[0.9984309,0.0006732036,0.00006292785,0.00019446225,0.0006037892,0.00003461816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084079325,0.0005446842,0.00034836822,0.000421177,0.00028210424,0.00042345788,0.00031890426,0.0005602839,0.0019733047],"category_scores_gemma":[0.0024221186,0.00015268839,0.0001737675,0.000514581,0.00036079652,0.00062735693,0.00041610637,0.00019552624,0.0005762145],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024644288,0.00024677895,0.016961893,0.0004878764,0.00016237945,0.00068619894,0.00046024862,0.20545277,0.21687806,0.0058897813,0.0032466475,0.5470629],"study_design_scores_gemma":[0.000162383,0.0012167988,0.008636497,0.000030111289,0.00013533868,0.0013575291,0.00030464795,0.84279054,0.13817693,0.0016702446,0.0054573175,0.00006164222],"about_ca_topic_score_codex":0.0011168991,"about_ca_topic_score_gemma":0.0008684489,"teacher_disagreement_score":0.0019733047,"about_ca_system_score_codex":0.0001890228,"about_ca_system_score_gemma":0.000216946,"threshold_uncertainty_score":0.0066013336},"labels":[],"label_agreement":null},{"id":"W2009588365","doi":"10.1109/lcn.2011.6115533","title":"Scalability issues in localizing Things","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Trilateration; Computer science; Scalability; Internet of Things; Computer network; Node (physics); Wireless; Distributed computing; Wireless network; Retransmission; Computer security; Telecommunications; Engineering; Network packet","score_opus":0.018447079620099102,"score_gpt":0.2092457491249847,"score_spread":0.1907986695048856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009588365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20005159,0.016790384,0.7117316,0.010621347,0.00046297553,0.00032386158,0.0002519068,0.0022714897,0.057494845],"genre_scores_gemma":[0.9463426,0.0029858549,0.047033813,0.00039756153,0.00022323671,0.00017447716,0.000121838144,0.00016541487,0.0025552462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980444,0.00077149615,0.000096272124,0.00019381811,0.0007036007,0.0001903859],"domain_scores_gemma":[0.99162674,0.0058445376,0.0004850777,0.00098525,0.00090974424,0.00014863479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034418537,0.0006447883,0.00062114146,0.00062716444,0.0010095051,0.001190753,0.0010641416,0.0007085986,0.0020894124],"category_scores_gemma":[0.011278764,0.0003137268,0.00040953644,0.0008918296,0.0012871056,0.0048491405,0.0019639772,0.0008259318,0.00040221558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003655001,0.00017598415,0.012404772,0.0012890153,0.00017829546,0.0009422898,0.0015130355,0.39886492,0.07081442,0.19383821,0.012552857,0.3070607],"study_design_scores_gemma":[0.00010589971,0.00086572784,0.0079227155,0.00029728847,0.0002456409,0.0030216565,0.0027754076,0.7126356,0.04795512,0.1736294,0.05038524,0.00016032386],"about_ca_topic_score_codex":0.0015474859,"about_ca_topic_score_gemma":0.0019603325,"teacher_disagreement_score":0.0034418537,"about_ca_system_score_codex":0.0008267719,"about_ca_system_score_gemma":0.00054724375,"threshold_uncertainty_score":0.018202484},"labels":[],"label_agreement":null},{"id":"W2010234641","doi":"10.1109/iswcs.2006.4362361","title":"Mobile Station Location Estimation for MIMO Communication Systems","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"MIMO; Base station; Trilateration; Computer science; Multipath propagation; Multilateration; Mobile station; Cramér–Rao bound; Angle of arrival; Real-time computing; Wireless; Antenna array; Ranging; Estimation theory; Algorithm; Antenna (radio); Computer network; Telecommunications; Azimuth; Triangulation; Mathematics; Beamforming","score_opus":0.00727006224572358,"score_gpt":0.2232364224357468,"score_spread":0.21596636019002322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010234641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00217963,0.00076675246,0.99557626,0.000094761286,0.000060059443,0.000013760132,0.00006872053,0.0002463123,0.000993795],"genre_scores_gemma":[0.42801416,0.0048247697,0.5580251,0.00024054057,0.0005017273,0.00020109372,0.0008835644,0.000097721575,0.007211388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932814,0.00021073695,0.000025219686,0.00015551993,0.00022799075,0.000052333853],"domain_scores_gemma":[0.99928063,0.0003445912,0.00010279672,0.00008450096,0.00017071357,0.000016768116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004608435,0.000801362,0.00068398035,0.00057365815,0.00044176113,0.0006179694,0.00047897935,0.00060011324,0.0025155281],"category_scores_gemma":[0.00273231,0.00032106903,0.00039097824,0.0008663099,0.0003044124,0.0006399802,0.00065660966,0.00083974557,0.0015354712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001669137,0.00004442921,0.0019003126,0.00037648712,0.000100077144,0.0002425563,0.00013849606,0.58753943,0.022439398,0.04289128,0.007643233,0.33651733],"study_design_scores_gemma":[0.000016275922,0.00006974568,0.0011613271,0.000038510396,0.000026948632,0.0001993811,0.00003772853,0.96759385,0.0050620814,0.016380848,0.009374019,0.000039221435],"about_ca_topic_score_codex":0.0035206485,"about_ca_topic_score_gemma":0.0034334997,"teacher_disagreement_score":0.0035206485,"about_ca_system_score_codex":0.0005612674,"about_ca_system_score_gemma":0.0006590296,"threshold_uncertainty_score":0.008415282},"labels":[],"label_agreement":null},{"id":"W2010258063","doi":"10.1109/lcnw.2014.6927734","title":"Utilizing Sprouts WSN platform for equipment detection and localization in harsh environments","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Shovel; Crusher; Engineering; Computer science; Automotive engineering; Real-time computing; Mechanical engineering","score_opus":0.01203478584556476,"score_gpt":0.20575320813724993,"score_spread":0.19371842229168518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010258063","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27926847,0.00042200735,0.69325995,0.0003621274,0.0002007543,0.00023392768,0.0004993366,0.011296442,0.01445695],"genre_scores_gemma":[0.8533469,0.00048035948,0.12905292,0.00015347896,0.000045082903,0.00017298484,0.00078279537,0.00010744201,0.015858082],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982893,0.000019234314,0.000006103975,0.000040721956,0.00008498516,0.000019997738],"domain_scores_gemma":[0.9998838,0.000016898704,0.0000213309,0.000017330753,0.000043303073,0.000017334687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015433616,0.0004799002,0.00026127402,0.0004483855,0.00018109835,0.00028597616,0.00051600905,0.00022799503,0.0011567611],"category_scores_gemma":[0.00019781492,0.00015184656,0.00015189726,0.00023958726,0.0001450386,0.0005247789,0.00054156105,0.0002016107,0.00046209386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047729517,0.00017745729,0.011648326,0.00041973725,0.0000666385,0.0014778556,0.0004168618,0.067764774,0.5552945,0.0033806858,0.008747094,0.35012865],"study_design_scores_gemma":[0.00011596772,0.0012725866,0.024180302,0.00010142607,0.00012095845,0.0015069962,0.0007112927,0.5139176,0.361655,0.0024588443,0.09384889,0.000110131194],"about_ca_topic_score_codex":0.0033514968,"about_ca_topic_score_gemma":0.006924842,"teacher_disagreement_score":0.0033514968,"about_ca_system_score_codex":0.0002264251,"about_ca_system_score_gemma":0.00040278805,"threshold_uncertainty_score":0.006663978},"labels":[],"label_agreement":null},{"id":"W2010535462","doi":"10.1109/wcnc.2012.6214209","title":"Optimized access points deployment for WLAN indoor positioning system","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"RSS; Software deployment; Hybrid positioning system; Computer science; Wi-Fi; Signal strength; Indoor positioning system; Wireless; Euclidean distance; Real-time computing; Wireless lan; Positioning system; Computer network; Wireless network; Telecommunications; Engineering; Artificial intelligence; Accelerometer","score_opus":0.01912511270265123,"score_gpt":0.2558605395422799,"score_spread":0.23673542683962867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010535462","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05292128,0.0005199617,0.94334877,0.000082399834,0.000050504015,0.00005841285,0.00007768288,0.00089366385,0.0020472386],"genre_scores_gemma":[0.7856937,0.0005434035,0.21139267,0.00003716514,0.00004033557,0.00010341357,0.00018533989,0.000056216566,0.0019476716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935704,0.00024435946,0.000039926665,0.00009914701,0.00018709675,0.00007245827],"domain_scores_gemma":[0.99957436,0.00010893977,0.00007775202,0.00006622969,0.00014438923,0.000028259428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003505497,0.0008204979,0.00054108637,0.000772406,0.00032602742,0.0005027604,0.0006292912,0.00042292915,0.00085955317],"category_scores_gemma":[0.0012202436,0.00033553192,0.00025828034,0.00089280313,0.00020329977,0.000515819,0.0005309729,0.00028185343,0.00058290805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036190674,0.00007742321,0.004387397,0.00019246494,0.00008679112,0.00039908921,0.00013443678,0.63185936,0.07714837,0.005345494,0.0031582133,0.27684903],"study_design_scores_gemma":[0.00005192722,0.00029829663,0.004067489,0.000017651384,0.00008941016,0.00040210373,0.000065201675,0.96154577,0.02608581,0.001536914,0.005799996,0.000039459344],"about_ca_topic_score_codex":0.0023878475,"about_ca_topic_score_gemma":0.003241956,"teacher_disagreement_score":0.0023878475,"about_ca_system_score_codex":0.00044224528,"about_ca_system_score_gemma":0.00042105178,"threshold_uncertainty_score":0.0047478676},"labels":[],"label_agreement":null},{"id":"W2010990515","doi":"10.1109/lcn.2013.6761319","title":"A joint 3D localization and synchronization solution for Wireless Sensor Networks using UAV","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Wireless sensor network; Computer science; Synchronization (alternating current); Global Positioning System; Real-time computing; Broadcasting (networking); Position (finance); Key distribution in wireless sensor networks; Joint (building); Time synchronization; Software deployment; Wireless; Computer network; Wireless network; Telecommunications; Engineering","score_opus":0.013313156400414356,"score_gpt":0.20241056247459754,"score_spread":0.18909740607418318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010990515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012785086,0.00013730713,0.9857594,0.000053948632,0.000033295702,0.000013221614,0.000014320262,0.00032656174,0.0008768941],"genre_scores_gemma":[0.55113643,0.00031273675,0.44500616,0.00005882929,0.000043948297,0.00009717754,0.00010548702,0.000058257363,0.0031809974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997534,0.000050658793,0.000016174068,0.00006587189,0.000084303036,0.000029570296],"domain_scores_gemma":[0.99984,0.000029442932,0.000034787303,0.000035285757,0.000045082215,0.000015321404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024952806,0.00056643714,0.00045576209,0.00035327167,0.00039214717,0.00044765027,0.00063397107,0.0006260832,0.0008804817],"category_scores_gemma":[0.0006150836,0.00022502938,0.0005254318,0.0005244336,0.00025089574,0.0006641023,0.0012791643,0.00033386168,0.00032859712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015987852,0.000064059845,0.0014676127,0.00015757752,0.000082321574,0.00038835016,0.0003808853,0.56227493,0.06214062,0.014861312,0.0026749717,0.35534748],"study_design_scores_gemma":[0.000025271725,0.00016195135,0.00047719688,0.000012051126,0.000028835932,0.00017912658,0.000086247775,0.9833092,0.009068859,0.002546878,0.004082493,0.000021975937],"about_ca_topic_score_codex":0.0023969223,"about_ca_topic_score_gemma":0.0022793044,"teacher_disagreement_score":0.0023969223,"about_ca_system_score_codex":0.00022007359,"about_ca_system_score_gemma":0.00044288125,"threshold_uncertainty_score":0.0047659874},"labels":[],"label_agreement":null},{"id":"W2013581249","doi":"10.1155/2012/753206","title":"Particle-Filter-Based WiFi-Aided Reduced Inertial Sensors Navigation System for Indoor and GPS-Denied Environments","year":2012,"lang":"en","type":"article","venue":"International Journal of Navigation and Observation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Particle filter; Global Positioning System; Computer science; Inertial navigation system; Real-time computing; Multipath propagation; Unavailability; Inertial measurement unit; Indoor positioning system; GPS signals; Navigation system; Filter (signal processing); Assisted GPS; Accelerometer; Engineering; Inertial frame of reference; Telecommunications; Artificial intelligence; Computer vision; Channel (broadcasting)","score_opus":0.02424297123115796,"score_gpt":0.2501934233354913,"score_spread":0.22595045210433337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013581249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0246544,0.00015377524,0.9683661,0.0001220369,0.00018065836,0.000057262594,0.00013421127,0.0037777424,0.002553702],"genre_scores_gemma":[0.637657,0.00019766964,0.35371044,0.0001564137,0.00011391316,0.0001721319,0.000594442,0.000073898555,0.0073241154],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997273,0.00004663746,0.000017890612,0.000065695574,0.0001178744,0.000024659043],"domain_scores_gemma":[0.9996239,0.000057660334,0.000045175148,0.000067083274,0.00018796662,0.000018245519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037246422,0.0005246826,0.00070542574,0.00044830298,0.00034513473,0.0004429435,0.0009869523,0.0005911579,0.0020415734],"category_scores_gemma":[0.00083964225,0.00028482926,0.00036962592,0.00044010443,0.00018764439,0.0005259261,0.0005004816,0.00053003925,0.0017001219],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086482253,0.0002801791,0.007525513,0.0003308897,0.0001983146,0.00035894456,0.00036092036,0.103637755,0.11126254,0.0056441743,0.015061895,0.754474],"study_design_scores_gemma":[0.00010250192,0.00027771894,0.0044297646,0.000020598116,0.00009422805,0.00024348489,0.000025684054,0.9563978,0.027635882,0.00091054064,0.009819558,0.000042190168],"about_ca_topic_score_codex":0.0038550706,"about_ca_topic_score_gemma":0.006157519,"teacher_disagreement_score":0.0038550706,"about_ca_system_score_codex":0.00033372804,"about_ca_system_score_gemma":0.0007029206,"threshold_uncertainty_score":0.0076652765},"labels":[],"label_agreement":null},{"id":"W2014118384","doi":"10.1109/vtcfall.2012.6399003","title":"Localization in Wireless Networks Using Decision Trees and K-Means Clustering","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Singular value decomposition; Cluster analysis; Node (physics); Computer science; Wireless sensor network; Decision tree; Data mining; Routing (electronic design automation); Algorithm; Artificial intelligence; Mathematics; Engineering; Computer network","score_opus":0.01234118869794209,"score_gpt":0.22662117336687568,"score_spread":0.21427998466893358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014118384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008275306,0.00040699358,0.99045104,0.000119957534,0.000030368567,0.000032637094,0.00003812146,0.00026733297,0.00037820052],"genre_scores_gemma":[0.41515628,0.0011046164,0.5818193,0.000114211514,0.00012298461,0.00017583229,0.00028241673,0.000067105386,0.0011572142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832016,0.0006532866,0.0001265888,0.00026668696,0.00052969955,0.000103545746],"domain_scores_gemma":[0.9975387,0.0014549127,0.00031311254,0.00013490621,0.00049706077,0.00006132507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018309446,0.0008492096,0.0014054124,0.00185213,0.00095647434,0.0010535858,0.0013713842,0.0011771247,0.0007150968],"category_scores_gemma":[0.0050741527,0.00050685403,0.0008866455,0.002722695,0.0007159079,0.0019378435,0.0010731156,0.0009770972,0.00043561688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012071303,0.000065141474,0.0015526912,0.0001522727,0.000097573,0.000060407558,0.00012337395,0.80244076,0.0016959598,0.008900219,0.0018334244,0.18295753],"study_design_scores_gemma":[0.0000062232907,0.00002250738,0.00016656575,0.000007500568,0.0000071298955,0.000017861299,0.000019995372,0.99306196,0.0005403654,0.0057574185,0.00038387472,0.000008653377],"about_ca_topic_score_codex":0.006157837,"about_ca_topic_score_gemma":0.0042540375,"teacher_disagreement_score":0.006157837,"about_ca_system_score_codex":0.0008649419,"about_ca_system_score_gemma":0.00096686755,"threshold_uncertainty_score":0.012243986},"labels":[],"label_agreement":null},{"id":"W2014207266","doi":"10.1109/lcn.2011.6115541","title":"Wireless technology agnostic real-time localization in urban areas","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Trilateration; RSS; Signal strength; Computer science; Radio propagation; Transmitter; Wireless; Real-time computing; Shadow mapping; Transmitter power output; Wireless network; Node (physics); Telecommunications; Artificial intelligence; Engineering; Channel (broadcasting)","score_opus":0.008436536671369657,"score_gpt":0.18877585815005699,"score_spread":0.18033932147868734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014207266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18386072,0.00087057945,0.80881715,0.0001803814,0.000056535973,0.000038379127,0.00009264169,0.0016468858,0.004436713],"genre_scores_gemma":[0.9261977,0.00048086277,0.07093467,0.000034723398,0.000014772385,0.000033832966,0.00010306007,0.00004811648,0.0021522862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995183,0.00021801541,0.000018073708,0.000075803866,0.0001132342,0.00005657719],"domain_scores_gemma":[0.9992416,0.00032095026,0.00013314524,0.00015256561,0.0001304368,0.000021336378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064869685,0.0003391134,0.00034291844,0.00050151045,0.00029512562,0.00055293937,0.00048494682,0.00056912785,0.00075720524],"category_scores_gemma":[0.0017943188,0.00026824104,0.0002241093,0.0011054045,0.00049502274,0.0012891816,0.0006057387,0.00028840153,0.00041725172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004225059,0.000090023466,0.005115426,0.0002758672,0.000059230006,0.0005225049,0.0002904461,0.76313317,0.046937358,0.010914673,0.0024389056,0.16979992],"study_design_scores_gemma":[0.000022630862,0.00021240568,0.004569015,0.000013894194,0.000029508274,0.00035744655,0.00016904331,0.96325165,0.022561738,0.0044623907,0.004306549,0.00004370337],"about_ca_topic_score_codex":0.0029280668,"about_ca_topic_score_gemma":0.003673261,"teacher_disagreement_score":0.0029280668,"about_ca_system_score_codex":0.00029134616,"about_ca_system_score_gemma":0.00032053352,"threshold_uncertainty_score":0.0058220625},"labels":[],"label_agreement":null},{"id":"W2016103147","doi":"10.1145/2533810.2533816","title":"Quantitative comparison of indoor positioning on different densities of WiFi arrays in a single environment","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Canada Foundation for Innovation; University of Saskatchewan","keywords":"Hybrid positioning system; Global Positioning System; Computer science; Bluetooth; Positioning technology; Wireless; Location-based service; Positioning system; Mobile device; Real-time computing; Indoor positioning system; Consistency (knowledge bases); Embedded system; Point (geometry); Telecommunications; Artificial intelligence; World Wide Web","score_opus":0.020029648629139935,"score_gpt":0.2264132108783586,"score_spread":0.20638356224921867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016103147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.974878,0.00042977664,0.018066943,0.000053819076,0.000053474454,0.000043362703,0.0017182208,0.0005050213,0.00425139],"genre_scores_gemma":[0.993586,0.00017599615,0.0042570666,0.000017052707,0.00002087111,0.000034175824,0.0012088353,0.00003553818,0.0006645979],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981755,0.00026051392,0.000112280635,0.00042099762,0.00080780947,0.00022279727],"domain_scores_gemma":[0.9873281,0.006690695,0.0014667261,0.0008239409,0.0034085284,0.00028196882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000995855,0.0005766213,0.00052284764,0.0028101439,0.0003719981,0.0008058809,0.00071085023,0.00067761575,0.001950497],"category_scores_gemma":[0.007187381,0.0003497437,0.00034361583,0.0027136495,0.00053993776,0.0012949783,0.0009350075,0.00035529293,0.0006222723],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022533694,0.00038473035,0.533264,0.0011285401,0.0006723944,0.0011892153,0.0013626236,0.14685464,0.097392455,0.0016840685,0.0018843538,0.21192968],"study_design_scores_gemma":[0.000044575325,0.0012713354,0.8540734,0.00009142687,0.00032105745,0.0015157735,0.0021673352,0.09295024,0.044057272,0.0005980493,0.0027261686,0.00018337108],"about_ca_topic_score_codex":0.005238121,"about_ca_topic_score_gemma":0.0075746095,"teacher_disagreement_score":0.005238121,"about_ca_system_score_codex":0.0005245136,"about_ca_system_score_gemma":0.00027369166,"threshold_uncertainty_score":0.010415256},"labels":[],"label_agreement":null},{"id":"W2016314816","doi":"10.1109/acssc.2012.6489259","title":"Convergence properties of normalized random incremental gradient algorithms for least-squares source localization","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Division of Civil, Mechanical and Manufacturing Innovation; National Science Foundation","keywords":"Iterated function; Algorithm; Convergence (economics); Mathematics; Least-squares function approximation; Additive white Gaussian noise; Mathematical optimization; Gaussian; Sequence (biology); Gaussian noise; White noise; Applied mathematics; Statistics; Mathematical analysis","score_opus":0.022207801156806744,"score_gpt":0.2199707673315019,"score_spread":0.19776296617469516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016314816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006085171,0.00034468673,0.99196833,0.00012793855,0.000028495811,0.0000384818,0.000018137067,0.00015525584,0.0012335035],"genre_scores_gemma":[0.34720036,0.0011932045,0.6459222,0.00018196818,0.00012460427,0.0005000108,0.0003348155,0.00046916894,0.0040737134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998621,0.00069049356,0.00006232033,0.00014963305,0.0004026776,0.000073740914],"domain_scores_gemma":[0.99334043,0.0045494745,0.00037744225,0.0003188194,0.0012604888,0.0001533431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004315325,0.0011201452,0.00091725145,0.0012207533,0.00049801957,0.00097148586,0.0018604455,0.0012872454,0.0017439165],"category_scores_gemma":[0.023935273,0.00047632243,0.0006745684,0.000925941,0.00166195,0.0017277646,0.0015285499,0.0014611698,0.0006221419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015581104,0.00006905955,0.0011581494,0.0002484173,0.00008460481,0.00012492397,0.00020863388,0.8130456,0.0044840043,0.10726726,0.002070244,0.07108325],"study_design_scores_gemma":[0.000005965889,0.00002852256,0.000082551014,0.0000114072545,0.0000042271577,0.000023605136,0.000008818451,0.9882676,0.0006630871,0.010409802,0.00048639986,0.000007979588],"about_ca_topic_score_codex":0.0026759466,"about_ca_topic_score_gemma":0.0017629126,"teacher_disagreement_score":0.004315325,"about_ca_system_score_codex":0.000958557,"about_ca_system_score_gemma":0.0013454292,"threshold_uncertainty_score":0.022821903},"labels":[],"label_agreement":null},{"id":"W2016474489","doi":"10.1109/glocom.2013.6831058","title":"Does multi-hop communication enhance localization accuracy?","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer science; Hop (telecommunications); Node (physics); Key distribution in wireless sensor networks; Sensor node; Computer network; Wireless; Wireless network; Telecommunications; Engineering","score_opus":0.008894805445436582,"score_gpt":0.23546222766948052,"score_spread":0.22656742222404394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016474489","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5172407,0.032880615,0.40067983,0.015350961,0.0018863862,0.00014273355,0.00044819125,0.0018059283,0.029564774],"genre_scores_gemma":[0.9762256,0.0033431738,0.018380033,0.0003368509,0.00028580887,0.000026274298,0.00007713613,0.000098795324,0.0012262823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963676,0.0015350246,0.00019108583,0.00038084458,0.0011883626,0.0003371114],"domain_scores_gemma":[0.9403767,0.044567443,0.003887571,0.0051106736,0.005643244,0.00041442833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035316616,0.0006171403,0.00077888416,0.00083694485,0.000551366,0.0012697013,0.0010898988,0.0020598606,0.0025176362],"category_scores_gemma":[0.050004307,0.00040954893,0.0004189151,0.0014413313,0.00078312337,0.005097438,0.0012630348,0.00075318303,0.0010133364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015966614,0.00034699883,0.079120226,0.0027671827,0.0006469951,0.0009676102,0.001378105,0.19451958,0.03894808,0.02039907,0.008067908,0.6512416],"study_design_scores_gemma":[0.00036313332,0.0041757706,0.15165341,0.0012088814,0.0013994959,0.0066806083,0.0037286447,0.5736046,0.1358558,0.07034604,0.050540432,0.0004433036],"about_ca_topic_score_codex":0.0009525815,"about_ca_topic_score_gemma":0.0009809732,"teacher_disagreement_score":0.0035316616,"about_ca_system_score_codex":0.00048221083,"about_ca_system_score_gemma":0.00043392653,"threshold_uncertainty_score":0.018677473},"labels":[],"label_agreement":null},{"id":"W2016863885","doi":"10.1109/mownet.2013.6613810","title":"Indoor positioning using magnetic compass and accelerometer of smartphones","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"University of Windsor","keywords":"Compass; Global Positioning System; Accelerometer; Computer science; Tracking (education); Real-time computing; Indoor positioning system; Non-line-of-sight propagation; Positioning system; Software; Hybrid positioning system; Assisted GPS; Wireless; Acoustics; Telecommunications; Geography","score_opus":0.009373740846721657,"score_gpt":0.18729177166109712,"score_spread":0.17791803081437546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016863885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14257376,0.0059919367,0.8202045,0.00032425852,0.0006589,0.00018289883,0.00091990683,0.0067986897,0.022345142],"genre_scores_gemma":[0.83911765,0.0028350793,0.14569004,0.00016066675,0.00025688237,0.0001223983,0.0007654688,0.00005536879,0.010996487],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995179,0.000072037066,0.000032675474,0.00012658148,0.00020658193,0.000044165667],"domain_scores_gemma":[0.99969816,0.000028837409,0.00006461211,0.000051785555,0.00013863426,0.00001803854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001252914,0.0007279481,0.0005086377,0.0008000538,0.0002249749,0.00042898164,0.00059435534,0.00045816376,0.001713178],"category_scores_gemma":[0.0005366195,0.00024816266,0.00029032218,0.00083828456,0.00013244702,0.0005188034,0.00054929854,0.0002568734,0.0015885382],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004737444,0.00007999112,0.01813943,0.0011738328,0.00013783068,0.0009794144,0.00034551503,0.005214843,0.29939896,0.0027353286,0.006995162,0.66432595],"study_design_scores_gemma":[0.00019147874,0.0038512477,0.14562386,0.0007480777,0.00078034826,0.017400078,0.0009925492,0.20973058,0.41867787,0.003639198,0.19783783,0.00052687654],"about_ca_topic_score_codex":0.0016929044,"about_ca_topic_score_gemma":0.0022590507,"teacher_disagreement_score":0.001713178,"about_ca_system_score_codex":0.00013711254,"about_ca_system_score_gemma":0.00017469701,"threshold_uncertainty_score":0.0057311654},"labels":[],"label_agreement":null},{"id":"W2017163858","doi":"10.1007/s10776-013-0234-4","title":"Accurate Trilateration for Passive RFID Localization in Smart Homes","year":2013,"lang":"en","type":"article","venue":"International Journal of Wireless Information Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Université du Québec à Chicoutimi","keywords":"Trilateration; Computer science; Radio-frequency identification; Context (archaeology); Smart environment; Home automation; Fuzzy logic; Software deployment; Identification (biology); Computer security; Smart camera; Embedded system; Human–computer interaction; Real-time computing; Internet of Things; Artificial intelligence; Telecommunications; Software engineering","score_opus":0.004875365659693107,"score_gpt":0.21316589547723985,"score_spread":0.20829052981754675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017163858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030658273,0.00053436094,0.96452945,0.00012353128,0.00016498232,0.00002436536,0.000084304425,0.001177979,0.002702735],"genre_scores_gemma":[0.597585,0.0010402186,0.3928769,0.0002664941,0.0001611434,0.00009943173,0.00040137843,0.00019386546,0.0073755197],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99931836,0.00014672546,0.000038130624,0.00013602305,0.00029025142,0.0000705718],"domain_scores_gemma":[0.99948287,0.00012731335,0.00008033489,0.00012124066,0.0001706786,0.000017595441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004615038,0.00064670265,0.0007377913,0.00062461436,0.00049076707,0.00085682963,0.00080931385,0.0008447539,0.0024992158],"category_scores_gemma":[0.0015901232,0.00040469307,0.00038303554,0.0010267107,0.00038060936,0.001452773,0.0011924318,0.0009310694,0.0017909127],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012359708,0.00014296162,0.002322276,0.00041817973,0.00006356104,0.00033692867,0.00055489095,0.060492843,0.24079506,0.00947307,0.0065283845,0.6776358],"study_design_scores_gemma":[0.00009613262,0.0005902539,0.003431188,0.0001064619,0.00008560003,0.001545667,0.0003241399,0.8199529,0.15352854,0.0070327213,0.013212011,0.000094329465],"about_ca_topic_score_codex":0.0009887664,"about_ca_topic_score_gemma":0.0014547318,"teacher_disagreement_score":0.0024992158,"about_ca_system_score_codex":0.0002816901,"about_ca_system_score_gemma":0.0005022862,"threshold_uncertainty_score":0.0083607435},"labels":[],"label_agreement":null},{"id":"W2017422157","doi":"10.1109/wd.2011.6098183","title":"Burst mode symmetric double sided two way ranging","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Ranging; Computer science; Real-time computing; Position (finance); Mode (computer interface); Wireless; Burst mode (computing); Wireless sensor network; Electronic engineering; Computer network; Telecommunications; Engineering","score_opus":0.02543693781809348,"score_gpt":0.22887442182418752,"score_spread":0.20343748400609404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017422157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06819923,0.0006773441,0.92493856,0.00015285853,0.00013131266,0.00006774458,0.00008225537,0.0007170394,0.005033637],"genre_scores_gemma":[0.7334339,0.0007635802,0.2569249,0.00019156763,0.000105720464,0.00008855189,0.00022852243,0.000055047447,0.008208259],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959475,0.00006530317,0.000026388892,0.000090110465,0.00018737126,0.000036201054],"domain_scores_gemma":[0.9991192,0.00018344533,0.00017186286,0.0002361355,0.00024178876,0.000047504087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035115107,0.00042015905,0.0004573822,0.00050363695,0.0002409496,0.00038391346,0.00089098286,0.00046216723,0.0013257546],"category_scores_gemma":[0.0007578297,0.00023727033,0.00024225964,0.0005881461,0.00033871311,0.0008329081,0.00088438595,0.00039209917,0.00068278133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008874475,0.00009109688,0.0036440804,0.00045962018,0.00006390893,0.00057829457,0.0004722469,0.029208293,0.45155266,0.016146261,0.0033903327,0.49350584],"study_design_scores_gemma":[0.00023124724,0.0021734068,0.00445725,0.00009080812,0.000112714166,0.0073720245,0.0003162552,0.58176154,0.35334226,0.014567691,0.035418782,0.00015602885],"about_ca_topic_score_codex":0.00029672982,"about_ca_topic_score_gemma":0.00045446813,"teacher_disagreement_score":0.0013257546,"about_ca_system_score_codex":0.00018076815,"about_ca_system_score_gemma":0.0002851776,"threshold_uncertainty_score":0.004435122},"labels":[],"label_agreement":null},{"id":"W2017696693","doi":"10.1002/wcm.325","title":"Mobile location estimation for DS‐CDMA systems using self‐organizing maps","year":2006,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Code division multiple access; Robustness (evolution); Base station; Non-line-of-sight propagation; Cellular network; Real-time computing; Artificial neural network; Scalability; Algorithm; Artificial intelligence; Wireless; Telecommunications","score_opus":0.013064988015320597,"score_gpt":0.2434931125347183,"score_spread":0.2304281245193977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017696693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11448083,0.00022983523,0.88353384,0.00010473667,0.00003251008,0.000019849604,0.000027271635,0.00045750718,0.0011135995],"genre_scores_gemma":[0.9026378,0.000086344604,0.09620723,0.00001829955,0.000018249359,0.000024437695,0.000036837795,0.000011379605,0.00095950253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999828,0.000058412897,0.0000066282737,0.00002475198,0.000065516295,0.00001658628],"domain_scores_gemma":[0.99964535,0.00014714853,0.000039965616,0.000028220713,0.00012459591,0.000014695577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028225975,0.0002524918,0.00022534523,0.0005083816,0.00023883778,0.00032203068,0.00037381914,0.00026943136,0.0003845413],"category_scores_gemma":[0.0012293403,0.0001520961,0.00017758201,0.00032533376,0.00021425003,0.00051688676,0.0003334027,0.00023240347,0.0001513724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030580605,0.00008391821,0.0038990106,0.000075194104,0.00006423013,0.00012705068,0.00014052703,0.6351466,0.014814815,0.005484456,0.00158182,0.33827648],"study_design_scores_gemma":[0.0000037911047,0.000012896586,0.00030199855,0.0000015604281,0.000002928388,0.0000143916695,0.00001020279,0.9967399,0.0018078625,0.0008530357,0.00024830355,0.0000031230136],"about_ca_topic_score_codex":0.0027060169,"about_ca_topic_score_gemma":0.002612106,"teacher_disagreement_score":0.0027060169,"about_ca_system_score_codex":0.0003263745,"about_ca_system_score_gemma":0.00024923251,"threshold_uncertainty_score":0.0053804517},"labels":[],"label_agreement":null},{"id":"W2017716601","doi":"10.1109/vetecf.2011.6093084","title":"Impact of Emitter-Sensor Geometry on Accuracy of Received Signal Strength Based Geolocation","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Geolocation; Common emitter; RSS; Cramér–Rao bound; Dilution of precision; Signal strength; Upper and lower bounds; Computer science; SIGNAL (programming language); Algorithm; Multilateration; Mathematics; Geometry; Electronic engineering; Estimation theory; Telecommunications; Wireless; Engineering; Global Positioning System; Mathematical analysis; Azimuth","score_opus":0.02256152395856682,"score_gpt":0.24308447571710184,"score_spread":0.22052295175853504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017716601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46617487,0.0026423002,0.5217483,0.0010324671,0.0001614245,0.00007444305,0.0003922269,0.0009997576,0.006774233],"genre_scores_gemma":[0.9782452,0.00042063263,0.020827593,0.00008282527,0.000036372403,0.000016840379,0.00008944732,0.00007274998,0.00020836372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9920696,0.003470865,0.0003776751,0.0012517573,0.002373053,0.00045711067],"domain_scores_gemma":[0.9344856,0.048847217,0.005925354,0.007283731,0.0032068258,0.00025129216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004573567,0.0009984353,0.0008859953,0.00069405243,0.00034614405,0.0010818528,0.0010096576,0.0011706381,0.00050872815],"category_scores_gemma":[0.06688297,0.00071451097,0.00049531047,0.00092733064,0.0016835339,0.0017556888,0.0016047606,0.0006225695,0.0004930541],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009295164,0.000035742614,0.02193002,0.0002223043,0.00014803716,0.00059399736,0.00021165221,0.8859893,0.022131437,0.0053386246,0.00061628997,0.061853133],"study_design_scores_gemma":[0.0001744872,0.0013996655,0.04474402,0.00010882795,0.00026349895,0.0039233514,0.0002921102,0.8293769,0.103351295,0.012302089,0.003871967,0.00019190993],"about_ca_topic_score_codex":0.0017055495,"about_ca_topic_score_gemma":0.0010679511,"teacher_disagreement_score":0.004573567,"about_ca_system_score_codex":0.0009366634,"about_ca_system_score_gemma":0.00071866094,"threshold_uncertainty_score":0.024187624},"labels":[],"label_agreement":null},{"id":"W2017804511","doi":"10.1007/s00034-011-9366-x","title":"Fundamental Limit of OFDM Range Estimation in a Separable Multipath Environment","year":2011,"lang":"en","type":"article","venue":"Circuits Systems and Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Orthogonal frequency-division multiplexing; Cramér–Rao bound; Multipath propagation; Estimator; Channel (broadcasting); Delay spread; Algorithm; Separable space; Transmission (telecommunications); Upper and lower bounds; Mathematics; Range (aeronautics); Statistics; Computer science; Electronic engineering; Telecommunications; Mathematical analysis; Engineering","score_opus":0.026119801081797865,"score_gpt":0.2034043828766022,"score_spread":0.17728458179480433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017804511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056575883,0.004020287,0.9049384,0.0022414178,0.00023128756,0.00004075186,0.00024212852,0.00041252724,0.03129729],"genre_scores_gemma":[0.8584401,0.0034494828,0.12767322,0.0007124958,0.0007034895,0.00022765568,0.0003154964,0.0002319643,0.008246129],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99679226,0.0005375795,0.000100780926,0.00068015547,0.001532868,0.00035633624],"domain_scores_gemma":[0.98942155,0.008235441,0.00041882295,0.0008461678,0.00085255917,0.00022543632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023129012,0.00088115263,0.0012481563,0.0012772159,0.0011441016,0.0029740653,0.0014917672,0.0031323542,0.0032700917],"category_scores_gemma":[0.020598577,0.0007338805,0.00041944973,0.0012139439,0.0030035875,0.006322316,0.0041257804,0.0033873918,0.0010511911],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005959289,0.00011229822,0.002340274,0.00074583665,0.00011978151,0.0005441071,0.00081445085,0.050061855,0.038396742,0.81316036,0.0033226917,0.08978563],"study_design_scores_gemma":[0.000043979355,0.000107364714,0.0020267,0.00024837756,0.00006896341,0.0017171524,0.00020992503,0.5841307,0.024625698,0.38084644,0.0058550853,0.000119680604],"about_ca_topic_score_codex":0.00086557434,"about_ca_topic_score_gemma":0.000494422,"teacher_disagreement_score":0.0032700917,"about_ca_system_score_codex":0.0013717231,"about_ca_system_score_gemma":0.0009885696,"threshold_uncertainty_score":0.012231946},"labels":[],"label_agreement":null},{"id":"W2018517412","doi":"10.1016/j.procs.2013.06.045","title":"Energy Constrained Positioning in Mobile Wireless Ad hoc and Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Wireless sensor network; Wireless ad hoc network; Mobile ad hoc network; Wireless; Computer network; Energy (signal processing); Telecommunications","score_opus":0.0033340921202442597,"score_gpt":0.17669775311142488,"score_spread":0.1733636609911806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018517412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03141841,0.013435014,0.94650906,0.00045520443,0.00038516358,0.000115377516,0.00010758809,0.00040051877,0.00717361],"genre_scores_gemma":[0.77335113,0.017405389,0.19752075,0.00028804014,0.00048042802,0.00029605837,0.00031930406,0.000103832186,0.010235109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993586,0.00022663649,0.000031473748,0.00008899691,0.0002503599,0.00004394563],"domain_scores_gemma":[0.9992299,0.0004690228,0.000089935864,0.000095546595,0.00009760156,0.000018065597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005391591,0.00059332204,0.0007900609,0.0005030893,0.0003800673,0.0006247998,0.0006984813,0.00093647686,0.0013429634],"category_scores_gemma":[0.0022055663,0.0002923777,0.00024533813,0.0017414305,0.000573325,0.0011343176,0.0007884529,0.00053652795,0.0004714716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013146053,0.000069526875,0.0010298347,0.00043277224,0.00006311324,0.00025166763,0.00009575484,0.7706398,0.008367273,0.03418949,0.0029234607,0.1818059],"study_design_scores_gemma":[0.000024206229,0.0001817709,0.00088818034,0.000055040106,0.000023227787,0.0002476499,0.00006433957,0.9518019,0.0033136203,0.033205315,0.010162243,0.000032554286],"about_ca_topic_score_codex":0.0015545232,"about_ca_topic_score_gemma":0.001350455,"teacher_disagreement_score":0.0015545232,"about_ca_system_score_codex":0.0003436812,"about_ca_system_score_gemma":0.00033430057,"threshold_uncertainty_score":0.0044926405},"labels":[],"label_agreement":null},{"id":"W2018532493","doi":"10.1109/plans.2010.5507254","title":"A repeater-based localization system with wideband signalling","year":2010,"lang":"en","type":"article","venue":"IEEE/ION Position, Location and Navigation Symposium","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Repeater (horology); Computer science; Calibration; Estimator; Radio repeater; Scheme (mathematics); Electronic engineering; Real-time computing; Telecommunications; Transmitter; Artificial intelligence; Engineering; Mathematics; Statistics","score_opus":0.004139840787056587,"score_gpt":0.1909893288327385,"score_spread":0.1868494880456819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018532493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1939524,0.000509116,0.7911115,0.00037823425,0.00013658259,0.00013721526,0.00008199329,0.0039826916,0.00971029],"genre_scores_gemma":[0.8005711,0.00022636543,0.17607276,0.00028089003,0.00011096037,0.0001383785,0.00012878136,0.000053967517,0.02241674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994548,0.00011562384,0.00002486449,0.00015198208,0.00019159565,0.00006116626],"domain_scores_gemma":[0.99962366,0.00007137782,0.0000688853,0.000094597235,0.00009506653,0.00004643497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004135656,0.0004723731,0.0006254436,0.0004503976,0.00048462843,0.0007023193,0.001310813,0.0011051934,0.0026538344],"category_scores_gemma":[0.0006877561,0.0002963925,0.00026839046,0.00033791212,0.00037335616,0.0012577017,0.00095210335,0.00064761855,0.0019187124],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013688968,0.00030808215,0.0033037232,0.00023263445,0.00017197008,0.001030947,0.0008788949,0.051919155,0.55408937,0.01833648,0.0034145063,0.36494538],"study_design_scores_gemma":[0.00046529915,0.0034913218,0.0037896966,0.00007265535,0.00042941145,0.0028420622,0.00021562779,0.70671946,0.22904363,0.008140421,0.044530746,0.00025971406],"about_ca_topic_score_codex":0.0009087442,"about_ca_topic_score_gemma":0.0010080251,"teacher_disagreement_score":0.0026538344,"about_ca_system_score_codex":0.00035296349,"about_ca_system_score_gemma":0.00042672618,"threshold_uncertainty_score":0.008877993},"labels":[],"label_agreement":null},{"id":"W2019142848","doi":"10.1109/vetecf.2010.5594098","title":"A Gaussian Model for Dead-Reckoning Mobile Sensor Position Error","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Dead reckoning; Computer science; Wireless sensor network; Mobile robot; Position (finance); Real-time computing; Gaussian; Gaussian network model; Monte Carlo method; Chassis; Robot; Algorithm; Simulation; Artificial intelligence; Engineering; Computer network; Telecommunications; Global Positioning System; Mathematics; Statistics","score_opus":0.011138276214922695,"score_gpt":0.24144871709482196,"score_spread":0.23031044087989927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019142848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013673267,0.00038078966,0.9840056,0.00015577284,0.00006485327,0.000028655038,0.00014833189,0.0003213817,0.0012213761],"genre_scores_gemma":[0.91162485,0.001455877,0.07686722,0.00023963931,0.00014320249,0.0002028961,0.0006035302,0.00014891128,0.008713837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987728,0.00019057556,0.00006766456,0.00030632093,0.00049809687,0.00016461371],"domain_scores_gemma":[0.9974909,0.0010793132,0.00036697573,0.00027554343,0.0007236398,0.00006364451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015497399,0.0009370123,0.0012132013,0.00071794324,0.00052023627,0.0010654472,0.0030205087,0.0018015896,0.0012620252],"category_scores_gemma":[0.005285687,0.0005488717,0.00075922476,0.0015338828,0.0014228122,0.0021222227,0.00091390626,0.001375418,0.0009490923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097186145,0.000040935218,0.0016902133,0.00009098971,0.000032265532,0.00019480422,0.00016278675,0.9431166,0.002759479,0.03100934,0.0013021425,0.019503295],"study_design_scores_gemma":[0.000007352341,0.000032514727,0.00038575483,0.000007669265,0.000010404955,0.00005010188,0.0000105969775,0.9945182,0.0004062609,0.004049851,0.00050038635,0.000020864945],"about_ca_topic_score_codex":0.014943267,"about_ca_topic_score_gemma":0.008836289,"teacher_disagreement_score":0.014943267,"about_ca_system_score_codex":0.0010999689,"about_ca_system_score_gemma":0.0009124676,"threshold_uncertainty_score":0.029712558},"labels":[],"label_agreement":null},{"id":"W2019577451","doi":"10.3390/ijgi2030854","title":"An Improved Neural Network Training Algorithm for Wi-Fi Fingerprinting Positioning","year":2013,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"RSS; Computer science; Artificial neural network; Signal strength; Algorithm; Location-based service; Real-time computing; Mean squared error; Indoor positioning system; Data mining; Artificial intelligence; Computer network; Wireless sensor network; Accelerometer; Mathematics; Statistics","score_opus":0.005624948004334438,"score_gpt":0.22271903259288145,"score_spread":0.217094084588547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019577451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013301411,0.00020748163,0.9845679,0.00008520804,0.000048701768,0.000029860732,0.000031170406,0.00061042816,0.0011178644],"genre_scores_gemma":[0.4042059,0.00034121712,0.588123,0.0001664032,0.00007217965,0.0002473614,0.00022646518,0.00007620419,0.006541284],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963224,0.00006870114,0.00003055726,0.000101157646,0.00012688678,0.000040423965],"domain_scores_gemma":[0.99935347,0.00022737031,0.000049141006,0.000046253164,0.00030848783,0.00001535891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071079767,0.00058729696,0.0005779094,0.0004930821,0.00039113147,0.0005164822,0.00097239617,0.0010761766,0.0019600405],"category_scores_gemma":[0.0023159215,0.00028936937,0.0003278861,0.0007404749,0.00029599012,0.0008103789,0.00046634037,0.0010323053,0.0006025089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014652478,0.00006210127,0.0010193265,0.000057062705,0.000035178724,0.000064258296,0.000059897775,0.46217892,0.008492105,0.0022139344,0.0012651882,0.52440554],"study_design_scores_gemma":[0.0000053583594,0.000018600022,0.00022006629,0.0000042873294,0.0000051313095,0.000016074411,0.0000030075535,0.9974936,0.0016229558,0.00022894755,0.0003777368,0.000004309572],"about_ca_topic_score_codex":0.010919969,"about_ca_topic_score_gemma":0.0078834845,"teacher_disagreement_score":0.010919969,"about_ca_system_score_codex":0.00060427526,"about_ca_system_score_gemma":0.0007752454,"threshold_uncertainty_score":0.02171278},"labels":[],"label_agreement":null},{"id":"W2019796561","doi":"10.1109/infcom.2013.6566962","title":"Data loss and reconstruction in sensor networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":211,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Missing data; Wireless sensor network; Interpolation (computer graphics); Data mining; Compressed sensing; Stability (learning theory); Noise (video); Signal reconstruction; Data loss; Artificial intelligence; Machine learning; Signal processing","score_opus":0.012635467556933844,"score_gpt":0.1996841389948898,"score_spread":0.18704867143795595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019796561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019655354,0.0010523362,0.9779063,0.0004671304,0.000059474205,0.000023854782,0.00006387396,0.00014836901,0.00062326086],"genre_scores_gemma":[0.8085199,0.0029140709,0.1855203,0.00020752935,0.00027586933,0.00016376171,0.00029735538,0.00008729187,0.0020140356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838436,0.00056068937,0.0000945996,0.00021846368,0.00062680524,0.00011505499],"domain_scores_gemma":[0.9957618,0.0029226213,0.00037658343,0.00040189284,0.00046099877,0.00007611708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024313072,0.000773235,0.000756074,0.00073990214,0.0005629733,0.00092204177,0.0010592968,0.0011044961,0.0004660153],"category_scores_gemma":[0.009847634,0.00049227173,0.00046032408,0.001215455,0.0015559655,0.0021814893,0.0013156696,0.0012561495,0.00013413053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018228177,0.00003046173,0.0016449015,0.0001587375,0.00004718356,0.00016082505,0.00014072767,0.9090154,0.0033365386,0.019436734,0.0010956428,0.0647505],"study_design_scores_gemma":[0.0000061918663,0.000024970384,0.00028604467,0.000010704924,0.0000060191255,0.000049872448,0.000034779343,0.9849701,0.0014914523,0.012534214,0.0005779766,0.000007728857],"about_ca_topic_score_codex":0.00229761,"about_ca_topic_score_gemma":0.0011727018,"teacher_disagreement_score":0.0024313072,"about_ca_system_score_codex":0.0008069759,"about_ca_system_score_gemma":0.000783301,"threshold_uncertainty_score":0.012858093},"labels":[],"label_agreement":null},{"id":"W2020181805","doi":"10.1109/icc.2013.6654726","title":"Robust wireless multihop localization using mobile anchors","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer science; Node (physics); Key distribution in wireless sensor networks; Global Positioning System; Kalman filter; Computer network; Sensor node; Scheme (mathematics); Wireless; Position (finance); Real-time computing; Mobile wireless sensor network; Wireless network; Engineering; Artificial intelligence; Telecommunications; Mathematics","score_opus":0.017184465972129966,"score_gpt":0.20562526105434906,"score_spread":0.1884407950822191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020181805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018789327,0.00037984285,0.97949094,0.000047853617,0.000035193094,0.000012344259,0.000015915413,0.0004586136,0.0007700376],"genre_scores_gemma":[0.82549137,0.0005983535,0.1716919,0.000041587788,0.000060927083,0.000052175907,0.00008850689,0.000054404925,0.0019207848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942917,0.00015981586,0.00003183704,0.00014729808,0.00018557766,0.00004622712],"domain_scores_gemma":[0.9993457,0.0002292246,0.00012818322,0.00015808438,0.000120259545,0.00001861564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006476504,0.00047472215,0.0005961188,0.00059149775,0.00027292687,0.00042116558,0.00059410435,0.00059816544,0.00043936147],"category_scores_gemma":[0.0022252917,0.00027114584,0.00038405295,0.0006368989,0.00039384956,0.0011863506,0.0010184728,0.00040571223,0.00036885784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022682131,0.000049425456,0.0018748817,0.00018871174,0.00012810797,0.00035369725,0.00020995854,0.65345734,0.079478085,0.0202333,0.0014976161,0.24230209],"study_design_scores_gemma":[0.000020482243,0.00014307252,0.0005025717,0.000010323132,0.000024642415,0.0001282813,0.000027085624,0.9811809,0.011314861,0.0037014317,0.0029239268,0.000022377126],"about_ca_topic_score_codex":0.0011180732,"about_ca_topic_score_gemma":0.0005627533,"teacher_disagreement_score":0.0011180732,"about_ca_system_score_codex":0.00025246458,"about_ca_system_score_gemma":0.00021112166,"threshold_uncertainty_score":0.0034251213},"labels":[],"label_agreement":null},{"id":"W2020694399","doi":"10.1109/antem.2014.6887712","title":"A novel terrestrial local positioning technique using transmitting multi-frequency antenna array","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Transmitter; Computer science; Antenna (radio); Antenna array; Position (finance); Acoustics; Electronic engineering; Telecommunications; Engineering; Physics","score_opus":0.01751338568328634,"score_gpt":0.23165504820046529,"score_spread":0.21414166251717895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020694399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008435968,0.00068519963,0.98378754,0.00015025433,0.0001963141,0.000026644397,0.00006530322,0.00095121836,0.005701451],"genre_scores_gemma":[0.20259461,0.0015924778,0.7755255,0.00035648386,0.0003691535,0.00011459412,0.00041082528,0.000093401446,0.01894293],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997253,0.000046432335,0.000010887048,0.00007402352,0.00011895613,0.000024404011],"domain_scores_gemma":[0.99981827,0.000030983967,0.00003230308,0.00005002541,0.000051975447,0.000016493686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014506848,0.00041977936,0.0004965935,0.00052696385,0.0003864274,0.00039349849,0.0009323304,0.0006843977,0.002453797],"category_scores_gemma":[0.00029395576,0.00018798195,0.0004480153,0.0010728402,0.00022237966,0.0010483575,0.0007153425,0.0006742628,0.002137971],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017053964,0.00008407478,0.0014866393,0.0003514748,0.000089003384,0.0005877872,0.00023013182,0.01630311,0.2721993,0.012957021,0.0075698416,0.687971],"study_design_scores_gemma":[0.00012700756,0.0018504172,0.0048230803,0.000105443294,0.00029418018,0.0102585545,0.00036140255,0.56075984,0.23754154,0.006462651,0.17718637,0.00022958653],"about_ca_topic_score_codex":0.00032695403,"about_ca_topic_score_gemma":0.00067963597,"teacher_disagreement_score":0.002453797,"about_ca_system_score_codex":0.00020291013,"about_ca_system_score_gemma":0.00029238855,"threshold_uncertainty_score":0.008208811},"labels":[],"label_agreement":null},{"id":"W2020840586","doi":"10.1145/2641798.2641812","title":"Incorporating user motion information for indoor smartphone positioning in sparse Wi-Fi environments","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Accelerometer; Global Positioning System; Gyroscope; Real-time computing; Mobile device; Artificial intelligence; Computer vision; Position (finance); Telecommunications; Engineering","score_opus":0.004969416108977573,"score_gpt":0.17711299372417183,"score_spread":0.17214357761519425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020840586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071499035,0.00013970982,0.92672354,0.00012386143,0.000029981984,0.000023541861,0.00006418637,0.00076710776,0.00062895665],"genre_scores_gemma":[0.7850994,0.0002605977,0.2131851,0.00007212725,0.00003050468,0.000036373087,0.00020073041,0.000036684312,0.0010784748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997769,0.00007742802,0.000012713656,0.000035274712,0.00006585535,0.000031764128],"domain_scores_gemma":[0.99965346,0.000119315126,0.000048323018,0.00008430811,0.000075387725,0.00001907284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024726542,0.00053677143,0.0003876285,0.00030119298,0.00020729781,0.00024754918,0.00053181424,0.0005202732,0.00048597713],"category_scores_gemma":[0.0015288258,0.00024454456,0.00028629234,0.0004116281,0.00019740155,0.00069095485,0.0004490739,0.00032485678,0.00030731678],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003670702,0.0001650194,0.008232802,0.00022120947,0.000096611664,0.00054667564,0.0003309158,0.45794648,0.11284857,0.0033084415,0.0016392139,0.41429698],"study_design_scores_gemma":[0.000012998216,0.00013655655,0.0036179025,0.000012528667,0.000027038523,0.00025943606,0.000038840455,0.9813634,0.012191781,0.00085188716,0.0014626391,0.000025048554],"about_ca_topic_score_codex":0.0045758183,"about_ca_topic_score_gemma":0.011813621,"teacher_disagreement_score":0.0045758183,"about_ca_system_score_codex":0.00016532313,"about_ca_system_score_gemma":0.0002848966,"threshold_uncertainty_score":0.009098351},"labels":[],"label_agreement":null},{"id":"W2020853289","doi":"10.1016/j.comnet.2011.08.001","title":"Distributed optimal dynamic base station positioning in wireless sensor networks","year":2011,"lang":"en","type":"article","venue":"Computer Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Base station; Wireless sensor network; Wireless; Base transceiver station; Computer network; Wireless network; Base (topology); Real-time computing; Key distribution in wireless sensor networks; Telecommunications","score_opus":0.008136615971691531,"score_gpt":0.18842472745951563,"score_spread":0.18028811148782412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020853289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0182421,0.00069920515,0.9793513,0.00017730866,0.00008047491,0.00001614079,0.000038399772,0.00012359655,0.0012713821],"genre_scores_gemma":[0.9116152,0.0010192682,0.08329333,0.000060700477,0.00014476437,0.00008170853,0.000112413436,0.00003962117,0.0036330924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991604,0.0002862979,0.00003247079,0.00020116717,0.00021769619,0.00010184783],"domain_scores_gemma":[0.99916506,0.00049750326,0.00009227599,0.000096678305,0.00011668193,0.000031797863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009162552,0.0005743467,0.0012084007,0.0005532219,0.00045928577,0.0007918631,0.0011790504,0.0006340614,0.0007893047],"category_scores_gemma":[0.0032787665,0.0006683,0.0002597866,0.0013191485,0.0008566052,0.0013771442,0.0009759705,0.00051864696,0.00019520175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009969849,0.000023843013,0.00032234794,0.00003474445,0.000021419057,0.000023616169,0.000031636813,0.95750004,0.0017745749,0.007975894,0.00061156135,0.031580653],"study_design_scores_gemma":[0.000010692044,0.000021470865,0.00011543951,0.000002547756,0.0000060411144,0.000011541678,0.000010652016,0.9944148,0.00043627515,0.0046197767,0.00034692098,0.000003736935],"about_ca_topic_score_codex":0.0034008035,"about_ca_topic_score_gemma":0.0042299847,"teacher_disagreement_score":0.0034008035,"about_ca_system_score_codex":0.00089804543,"about_ca_system_score_gemma":0.0009242354,"threshold_uncertainty_score":0.0067620277},"labels":[],"label_agreement":null},{"id":"W2021108818","doi":"10.1109/wpnc.2014.6843290","title":"Distributed cooperative localization in wireless sensor networks without NLOS identification","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Non-line-of-sight propagation; Relaxation (psychology); Computer science; Wireless sensor network; Mean squared error; Outlier; Algorithm; Convex optimization; Iterative method; Convex function; Function (biology); A priori and a posteriori; Mathematical optimization; Mathematics; Regular polygon; Wireless; Statistics; Artificial intelligence; Telecommunications","score_opus":0.005895404975242134,"score_gpt":0.20637787140910283,"score_spread":0.2004824664338607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021108818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057733073,0.00015369673,0.9935967,0.000041507323,0.000010851433,0.000010189798,0.0000049720725,0.00013568322,0.00027308078],"genre_scores_gemma":[0.6990233,0.00041239674,0.2976465,0.00010289674,0.00006416953,0.00020245895,0.00007450209,0.000051070176,0.0024227689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991098,0.0002801324,0.000035396773,0.0002452435,0.00026601716,0.00006338353],"domain_scores_gemma":[0.9990006,0.00044985212,0.00018460177,0.0001415237,0.00019446468,0.000028939045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011733996,0.00077533745,0.0007701086,0.0004916342,0.00042815437,0.0005563097,0.0014471876,0.00101206,0.00034575717],"category_scores_gemma":[0.0026625595,0.0004093192,0.00050558354,0.0007414338,0.00084262673,0.0012610711,0.0012527317,0.00067182275,0.00022617674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000120108016,0.000040841165,0.00051812216,0.00009238469,0.00005055861,0.00010012018,0.00016578716,0.8804455,0.009591924,0.0083545,0.0007102289,0.09980995],"study_design_scores_gemma":[0.000009278771,0.000050922692,0.00009971254,0.000003010268,0.0000066271928,0.000023001016,0.000011252194,0.99604875,0.001440822,0.0017729742,0.00052784884,0.000005887808],"about_ca_topic_score_codex":0.0021962547,"about_ca_topic_score_gemma":0.0016677276,"teacher_disagreement_score":0.0021962547,"about_ca_system_score_codex":0.00052459486,"about_ca_system_score_gemma":0.00068545184,"threshold_uncertainty_score":0.0062056184},"labels":[],"label_agreement":null},{"id":"W2021564694","doi":"10.1109/iccwamtip.2013.6716612","title":"Study on distance and angle measurement for single-base-station UWB positioning system with circular antenna array","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Multipath propagation; Angle of arrival; Antenna (radio); Computer science; Acoustics; Antenna array; Time of arrival; Amplitude; Electronic engineering; Physics; Optics; Telecommunications; Engineering","score_opus":0.02243463971434445,"score_gpt":0.19618972330352705,"score_spread":0.1737550835891826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021564694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20170084,0.004596857,0.7844719,0.00025627032,0.00024248198,0.000041041552,0.000080608595,0.0006123816,0.007997592],"genre_scores_gemma":[0.9274025,0.002298932,0.06718149,0.000076207936,0.0000954945,0.00003104519,0.000115079216,0.000032094333,0.002767111],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914825,0.00013038234,0.000050472467,0.0002040158,0.0004167892,0.00005003753],"domain_scores_gemma":[0.9991866,0.00026973762,0.000095175594,0.00008149672,0.00033931585,0.000027701059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032337298,0.00038656243,0.00043241482,0.0005884444,0.0003294657,0.00052226614,0.00047994344,0.0005953401,0.00091845915],"category_scores_gemma":[0.0012529949,0.00022784814,0.00026905633,0.0009970441,0.00022626581,0.0012905188,0.00025709491,0.00029120676,0.00035948877],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073523936,0.00008246545,0.02419461,0.0010600125,0.0001873876,0.0007506786,0.0006077763,0.06713204,0.34635156,0.012308123,0.0017647013,0.54482543],"study_design_scores_gemma":[0.0000668925,0.001861416,0.027722908,0.000092611284,0.00030739108,0.004542363,0.0005411897,0.65009356,0.289257,0.0030774348,0.022225073,0.00021207587],"about_ca_topic_score_codex":0.000973913,"about_ca_topic_score_gemma":0.00064678804,"teacher_disagreement_score":0.000973913,"about_ca_system_score_codex":0.00038327134,"about_ca_system_score_gemma":0.00032043696,"threshold_uncertainty_score":0.0030725002},"labels":[],"label_agreement":null},{"id":"W2022203402","doi":"10.3390/s150101292","title":"Received Signal Strength Recovery in Green WLAN Indoor Positioning System Using Singular Value Thresholding","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Heilongjiang Province; University of Toronto; National Natural Science Foundation of China","keywords":"RSS; Computer science; Thresholding; Real-time computing; Indoor positioning system; Compressed sensing; Computer network; Algorithm; Artificial intelligence","score_opus":0.021279958852903867,"score_gpt":0.22441092850389144,"score_spread":0.20313096965098756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022203402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070235334,0.0001239365,0.9274382,0.0001130305,0.000033951055,0.000018816869,0.00002716776,0.0007340085,0.0012754505],"genre_scores_gemma":[0.8004556,0.0001379801,0.19726248,0.00009514004,0.00003074251,0.00003635886,0.00010991011,0.00003573626,0.0018359699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960667,0.00007896174,0.000018960505,0.00008837276,0.00016663388,0.00004050285],"domain_scores_gemma":[0.9997533,0.000055699693,0.000046595902,0.000042646243,0.00008279609,0.00001890839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035781373,0.00033870697,0.00053824566,0.00038957284,0.00022970176,0.00043140116,0.00048364408,0.00053326547,0.00049223535],"category_scores_gemma":[0.00093593594,0.0001611707,0.0002847394,0.00057395047,0.00032377525,0.0004985185,0.0004752163,0.00033387917,0.00031319217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005925976,0.00011996942,0.003933639,0.00015518063,0.00008209607,0.0005688069,0.00030175442,0.18766905,0.25085148,0.0060755997,0.0019493335,0.5477004],"study_design_scores_gemma":[0.000023331208,0.00013863134,0.0016621932,0.0000086072205,0.000022433467,0.0002479733,0.000039809063,0.9586756,0.036536332,0.0015575545,0.0010628965,0.000024688796],"about_ca_topic_score_codex":0.0011781697,"about_ca_topic_score_gemma":0.00091569626,"teacher_disagreement_score":0.0011781697,"about_ca_system_score_codex":0.00024195031,"about_ca_system_score_gemma":0.0003493923,"threshold_uncertainty_score":0.0023425817},"labels":[],"label_agreement":null},{"id":"W2022276020","doi":"10.1109/syscon.2012.6189530","title":"Experimental design and analysis in kinematic-based localization in wireless mobile platform network","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Wireless sensor network; Simultaneous localization and mapping; Mobile robot; Kinematics; Sensor fusion; Wireless; Inertial measurement unit; Set (abstract data type); Real-time computing; Embedded system; Artificial intelligence; Robot; Computer network; Telecommunications","score_opus":0.01317822033476885,"score_gpt":0.23041554825124413,"score_spread":0.2172373279164753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022276020","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5450965,0.00013333783,0.44352347,0.00013669407,0.000120929035,0.00053391367,0.0005351769,0.00096336805,0.008956571],"genre_scores_gemma":[0.9514085,0.000114564915,0.044888053,0.000028438024,0.000011905296,0.0008203862,0.00043520902,0.00004940263,0.0022435107],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930084,0.00018467133,0.000030010284,0.0001223554,0.00024695168,0.00011514248],"domain_scores_gemma":[0.999102,0.00027347743,0.000095667754,0.00017131938,0.0003133016,0.00004422871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009084858,0.00045289315,0.00022080187,0.00048309396,0.0004878427,0.00041400184,0.00061058445,0.00040628738,0.0040117144],"category_scores_gemma":[0.002072178,0.00017586794,0.00023799126,0.00044241833,0.0005244796,0.0007194922,0.0004259924,0.00034956154,0.0005875477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00394328,0.0023307947,0.017248573,0.0008297788,0.0001249533,0.00067398284,0.000491879,0.23556362,0.5358541,0.03033859,0.0022554218,0.17034514],"study_design_scores_gemma":[0.00038285248,0.0075491695,0.020794775,0.00006643244,0.00016474929,0.0004006893,0.000506146,0.5983795,0.35681275,0.007190236,0.00767157,0.00008121088],"about_ca_topic_score_codex":0.001708997,"about_ca_topic_score_gemma":0.00079884793,"teacher_disagreement_score":0.0040117144,"about_ca_system_score_codex":0.00046553422,"about_ca_system_score_gemma":0.00047310666,"threshold_uncertainty_score":0.013420522},"labels":[],"label_agreement":null},{"id":"W2023192896","doi":"10.1109/glocomw.2013.6825187","title":"Second order cone programming for robust localization in mobile sensor networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Second-order cone programming; Computer science; Heuristic; Focus (optics); Wireless sensor network; Tracking (education); Convex optimization; Mobile telephony; Robustness (evolution); Mathematical optimization; Regular polygon; Dynamic programming; Algorithm; Artificial intelligence; Mobile radio; Mathematics; Computer network","score_opus":0.008394377145994935,"score_gpt":0.2021735062360695,"score_spread":0.19377912909007455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023192896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015857223,0.00030339436,0.9965843,0.00010666082,0.00003303946,0.000016717127,0.000023030398,0.000045812514,0.0013012914],"genre_scores_gemma":[0.43812922,0.0025753055,0.5506551,0.00032476147,0.00027207524,0.0004423205,0.00027898853,0.0002173933,0.007104895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989813,0.00045123385,0.0000373303,0.00010270127,0.0003598001,0.00006774333],"domain_scores_gemma":[0.9988949,0.0006580968,0.000106731975,0.000057141715,0.00023338989,0.00004982701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017463212,0.0011084962,0.00085955317,0.00048517802,0.00029067116,0.0010663784,0.00082885,0.0006973463,0.0011971485],"category_scores_gemma":[0.0030072953,0.00031025446,0.0006482793,0.0009014807,0.0009667644,0.00093130226,0.0007826053,0.001739077,0.0003221007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040725852,0.00003524989,0.00020874247,0.00014102472,0.000026444015,0.00007879493,0.000054576474,0.87047124,0.0017407347,0.101715066,0.0019286975,0.023558656],"study_design_scores_gemma":[0.0000035211242,0.000023093451,0.000025132384,0.000006859753,0.0000017285633,0.000009530311,0.0000054768593,0.98676,0.00024400912,0.012141591,0.00077495293,0.0000041038566],"about_ca_topic_score_codex":0.0027793015,"about_ca_topic_score_gemma":0.0016269394,"teacher_disagreement_score":0.0027793015,"about_ca_system_score_codex":0.0008565436,"about_ca_system_score_gemma":0.0011555485,"threshold_uncertainty_score":0.009235561},"labels":[],"label_agreement":null},{"id":"W2023235084","doi":"10.1155/2008/142803","title":"Location Estimation in a Smart Home: System Implementation and Evaluation Using Experimental Data","year":2008,"lang":"en","type":"article","venue":"International Journal of Telemedicine and Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Université de Sherbrooke","keywords":"Robustness (evolution); Computer science; Home automation; Set (abstract data type); Noise (video); Context (archaeology); Autonomy; Artificial intelligence; Population; Real-time computing; Simulation; Human–computer interaction; Medicine; Telecommunications","score_opus":0.041233824402043755,"score_gpt":0.3537826906516847,"score_spread":0.31254886624964096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023235084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94402593,0.0001548213,0.05238457,0.00013095204,0.000063956075,0.0005135624,0.0003260119,0.0015515655,0.0008486393],"genre_scores_gemma":[0.9377955,0.00012507914,0.060050506,0.00004923042,0.000023482275,0.000402344,0.00076789834,0.00004635893,0.0007395896],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99880064,0.00063857815,0.000113448106,0.0001816005,0.00018031138,0.00008538616],"domain_scores_gemma":[0.99642366,0.0017205632,0.00017065297,0.0004746909,0.0010538481,0.00015647458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026919076,0.0006037108,0.0009757729,0.00047562143,0.00033969665,0.00048749897,0.001163085,0.0012818219,0.0017648132],"category_scores_gemma":[0.006514989,0.00026018816,0.00026175997,0.0005183897,0.00050887896,0.00073716894,0.00064858096,0.00034128365,0.0005922153],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01162575,0.011701963,0.06555224,0.0027385508,0.00083866005,0.0015504585,0.0031994742,0.23952474,0.15878205,0.0016434031,0.0056781117,0.49716464],"study_design_scores_gemma":[0.0019027712,0.013643399,0.054320887,0.00010062092,0.00041670076,0.0007198467,0.0010060726,0.846232,0.07701141,0.0007092646,0.0037596212,0.00017735183],"about_ca_topic_score_codex":0.004392059,"about_ca_topic_score_gemma":0.0037035893,"teacher_disagreement_score":0.004392059,"about_ca_system_score_codex":0.00037225068,"about_ca_system_score_gemma":0.0004281815,"threshold_uncertainty_score":0.014236331},"labels":[],"label_agreement":null},{"id":"W2023405532","doi":"10.1109/ainaw.2007.108","title":"Bayesian Filtering and Anonymous Sensors for Localization in a Smart Home","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Context (archaeology); Computer science; Set (abstract data type); Autonomy; Home automation; Bayesian probability; Population; Motion (physics); Human–computer interaction; Artificial intelligence; Certainty; Quality (philosophy); Machine learning; Telecommunications; Mathematics","score_opus":0.00661453195862826,"score_gpt":0.21061646121123365,"score_spread":0.20400192925260538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023405532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034773793,0.00018359764,0.96377224,0.00017232522,0.000025002195,0.000022029708,0.000025853948,0.00030241595,0.0007227729],"genre_scores_gemma":[0.6837664,0.00041999418,0.31340525,0.00014632296,0.000069188965,0.000083320876,0.00008133798,0.0000332825,0.0019949467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984365,0.00094328466,0.00005257678,0.00016775605,0.00032003268,0.00007982],"domain_scores_gemma":[0.9984773,0.0008533369,0.00017406161,0.00023731623,0.0002099872,0.000048138823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021342735,0.00036888738,0.00050142134,0.0006598174,0.00041978352,0.0005225634,0.0005547474,0.00088102417,0.0005039131],"category_scores_gemma":[0.004329754,0.00032664312,0.00036204277,0.0005716272,0.0006569977,0.0013944302,0.0006195094,0.00040292478,0.00021657275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010455045,0.00026892312,0.009354741,0.00026398036,0.00013566558,0.00042135696,0.0009709781,0.43746138,0.045048174,0.06166019,0.0030877257,0.44028133],"study_design_scores_gemma":[0.000054324304,0.00025230055,0.0035260178,0.00002688579,0.000058199763,0.00022576186,0.0001456304,0.94868964,0.014035912,0.027343495,0.0055758185,0.00006598767],"about_ca_topic_score_codex":0.0025815794,"about_ca_topic_score_gemma":0.0036507729,"teacher_disagreement_score":0.0025815794,"about_ca_system_score_codex":0.00042544657,"about_ca_system_score_gemma":0.0004475208,"threshold_uncertainty_score":0.011287272},"labels":[],"label_agreement":null},{"id":"W2023839579","doi":"10.1109/cscwd.2013.6581045","title":"Indoor localization of ubiquitous heterogeneous devices","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Calibration; Signal strength; Wireless; Real-time computing; Interval (graph theory); Data mining; Telecommunications; Statistics","score_opus":0.005919289246964165,"score_gpt":0.18763927549254097,"score_spread":0.1817199862455768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023839579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46895158,0.00032388265,0.52653265,0.00006999285,0.00004927633,0.000074277916,0.0000993269,0.00076023676,0.0031388386],"genre_scores_gemma":[0.9747215,0.000110810586,0.024381353,0.000020680784,0.000015086955,0.000023652276,0.000053624222,0.00001560469,0.00065780355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990409,0.00032206182,0.00003153542,0.00022083729,0.00019747022,0.00018710803],"domain_scores_gemma":[0.9988882,0.000439881,0.000109410175,0.00029466214,0.00020863692,0.00005925874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066845986,0.0006494026,0.00054935535,0.00057859556,0.00044755705,0.00069266674,0.0006799113,0.00043821163,0.0008284106],"category_scores_gemma":[0.0027111587,0.00019114166,0.0003305319,0.00080514053,0.00040034862,0.0010108463,0.0011453638,0.00025310085,0.00029837803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013406158,0.00037762738,0.030732177,0.0006578337,0.00021806367,0.004178143,0.000827856,0.3997282,0.1779057,0.017951172,0.0023114423,0.3637711],"study_design_scores_gemma":[0.00008921633,0.001899817,0.027014261,0.00007231618,0.00026681475,0.0022940934,0.00079851213,0.81715703,0.1344347,0.006229421,0.009653843,0.00008995809],"about_ca_topic_score_codex":0.0021872325,"about_ca_topic_score_gemma":0.0019916156,"teacher_disagreement_score":0.0021872325,"about_ca_system_score_codex":0.00035948242,"about_ca_system_score_gemma":0.00023484275,"threshold_uncertainty_score":0.004349053},"labels":[],"label_agreement":null},{"id":"W2024503785","doi":"10.1109/ecc.2014.6862597","title":"Pairwise observable relative localization in ground aerial multi-robot networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Observability; Pairwise comparison; Robot; Computer science; Supervisory control; Observable; Orientation (vector space); Control (management); Artificial intelligence; Range (aeronautics); Nonlinear system; Control theory (sociology); Engineering; Mathematics","score_opus":0.015657508667314034,"score_gpt":0.2087864082792797,"score_spread":0.19312889961196567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024503785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072317846,0.00030659902,0.925244,0.00012525868,0.00001151373,0.000015382773,0.000036636546,0.00011557454,0.0018272236],"genre_scores_gemma":[0.97615564,0.00028351732,0.022311805,0.000022782506,0.00001866194,0.00003802482,0.00005794466,0.000018363487,0.0010932909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948883,0.00017171685,0.000018750952,0.00011262583,0.00015287867,0.000055209643],"domain_scores_gemma":[0.9983821,0.0009975277,0.00038983993,0.000061484825,0.00011950023,0.000049476635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055744423,0.0005280119,0.00038384285,0.0005359315,0.00033372198,0.00060694275,0.00057103776,0.00054911873,0.0007984952],"category_scores_gemma":[0.0035348497,0.0002811433,0.00031450993,0.000564893,0.0009039819,0.0013324458,0.0011283444,0.0005542559,0.00007936923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004214973,0.0000113496635,0.0012152422,0.00006201007,0.000020185125,0.00026698993,0.00014122494,0.94172156,0.0030254342,0.04032297,0.00019243754,0.01297849],"study_design_scores_gemma":[0.000004838691,0.000031361287,0.00047540467,0.00000697441,0.000006694119,0.00005302217,0.00006763955,0.9690897,0.00067153346,0.029254613,0.0003315378,0.000006786734],"about_ca_topic_score_codex":0.0023041265,"about_ca_topic_score_gemma":0.00144579,"teacher_disagreement_score":0.0023041265,"about_ca_system_score_codex":0.00057984015,"about_ca_system_score_gemma":0.00029898543,"threshold_uncertainty_score":0.0045814514},"labels":[],"label_agreement":null},{"id":"W2024865852","doi":"10.1049/el.2010.1979","title":"Multipath mitigation for LOS TBRE using NDB OFDM transmission and phase correlation","year":2010,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Orthogonal frequency-division multiplexing; Multipath propagation; Electronic engineering; Transmission (telecommunications); Computer science; Phase (matter); Correlation; Channel (broadcasting); Telecommunications; Physics; Mathematics; Engineering","score_opus":0.0056913689012426305,"score_gpt":0.23441185298214828,"score_spread":0.22872048408090564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024865852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05115595,0.0003355289,0.94666195,0.00010321213,0.000036223955,0.000030635718,0.000019549541,0.00036215235,0.0012948597],"genre_scores_gemma":[0.49102756,0.0005958635,0.5064256,0.00010191855,0.00008646095,0.0000732533,0.0000837171,0.000047857175,0.0015577694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995447,0.00012845376,0.000018392722,0.00005424467,0.00020463446,0.000049659735],"domain_scores_gemma":[0.9990613,0.0004266626,0.00020482675,0.00009750025,0.00017942039,0.000030200308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005453827,0.00066150445,0.0003939947,0.0006000243,0.00031085312,0.00037714862,0.0006170695,0.00048299358,0.0006712238],"category_scores_gemma":[0.0019745342,0.00032006108,0.00029651745,0.00081484014,0.00036901896,0.0008756567,0.000658377,0.00046092013,0.00037135056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007815053,0.00015850858,0.00621868,0.00033781194,0.00013431413,0.00053606404,0.0002754344,0.056127794,0.41617087,0.0122177405,0.0014125494,0.5056287],"study_design_scores_gemma":[0.00012719946,0.00072926073,0.004453511,0.00006625082,0.00013294395,0.002041034,0.00009916559,0.70877767,0.27471095,0.0020168957,0.006724775,0.0001202112],"about_ca_topic_score_codex":0.0005006703,"about_ca_topic_score_gemma":0.0013303908,"teacher_disagreement_score":0.0006712238,"about_ca_system_score_codex":0.0002301334,"about_ca_system_score_gemma":0.00053557096,"threshold_uncertainty_score":0.0028842688},"labels":[],"label_agreement":null},{"id":"W2024943762","doi":"10.1145/1996461.1996537","title":"TREC","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Orientation (vector space); Sensor fusion; Abstraction; Augmented reality; Tracking (education); Information retrieval; Data mining; Artificial intelligence","score_opus":0.021939544831159313,"score_gpt":0.1558509205647725,"score_spread":0.1339113757336132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024943762","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037240807,0.0010896891,0.54729384,0.0011694037,0.0018794412,0.000978794,0.018403983,0.18200377,0.24345699],"genre_scores_gemma":[0.06411493,0.0017867532,0.4818257,0.0019883194,0.0008933216,0.001522899,0.10945813,0.042177048,0.29623288],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965224,0.0003667268,0.00021851445,0.0005674746,0.0019254228,0.0003995021],"domain_scores_gemma":[0.99377596,0.00043909624,0.00016841873,0.0017879121,0.0034995573,0.0003290417],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027905167,0.0015742682,0.0010529602,0.0023569972,0.0013512515,0.0035361012,0.004068128,0.0020789653,0.15528746],"category_scores_gemma":[0.0067050196,0.0006822467,0.0016990433,0.0015879687,0.00060779834,0.003474722,0.00299098,0.002170528,0.14015919],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040318363,0.0001567628,0.00071903947,0.000443209,0.00006103549,0.00037633601,0.00018547321,0.0045299158,0.013316609,0.04358769,0.57858115,0.35763967],"study_design_scores_gemma":[0.000065570806,0.000099162135,0.0005452685,0.00009766183,0.000027507984,0.0005807188,0.00005225522,0.016490301,0.011632483,0.0066992147,0.96362734,0.000082425126],"about_ca_topic_score_codex":0.009416085,"about_ca_topic_score_gemma":0.0080913035,"teacher_disagreement_score":0.84471256,"about_ca_system_score_codex":0.0014137418,"about_ca_system_score_gemma":0.002735079,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2024947222","doi":"10.1145/2700271","title":"GreenLocs","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"RSS; Computer science; Inference; Profiling (computer programming); Nonparametric statistics; Bayesian inference; Efficient energy use; Data mining; Accelerometer; Bayesian probability; Mobile device; Real-time computing; Artificial intelligence; Econometrics; World Wide Web","score_opus":0.02599916013742729,"score_gpt":0.22370325876911473,"score_spread":0.19770409863168745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024947222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03298951,0.0011193187,0.81542885,0.0009124547,0.00052831793,0.00037700654,0.015216815,0.10105367,0.03237404],"genre_scores_gemma":[0.49031442,0.0013186442,0.41249356,0.0016326635,0.0003562226,0.0006665722,0.03511783,0.006711275,0.051388826],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992524,0.00011701865,0.000030424564,0.00023239646,0.00028549484,0.000082303704],"domain_scores_gemma":[0.99863714,0.00031283998,0.0001397307,0.00047830597,0.00029212335,0.00013974415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007487715,0.000981884,0.0008317445,0.0011568349,0.00049789855,0.0012437879,0.0020885693,0.0010571581,0.017371122],"category_scores_gemma":[0.0039259847,0.00048100075,0.00069422513,0.000975653,0.0004889057,0.0024307428,0.003030041,0.00091202045,0.013097735],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015117627,0.00025924083,0.01669997,0.00075912825,0.00021494852,0.0006317405,0.0008928021,0.05092929,0.01592742,0.027568292,0.16702804,0.71757734],"study_design_scores_gemma":[0.00019948519,0.00043478195,0.011358381,0.00021996914,0.0001150336,0.0011157593,0.00042679237,0.5621325,0.022433158,0.056461718,0.34487393,0.00022853471],"about_ca_topic_score_codex":0.003882768,"about_ca_topic_score_gemma":0.007530088,"teacher_disagreement_score":0.017371122,"about_ca_system_score_codex":0.000504599,"about_ca_system_score_gemma":0.00080365804,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2025416284","doi":"10.1109/tmc.2012.206","title":"Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Beijing University of Posts and Telecommunications","keywords":"RSS; Computer science; Background subtraction; Calibration; Baseline (sea); Artificial intelligence; Pixel; Computer vision; Mathematics","score_opus":0.020730493715700477,"score_gpt":0.2583825845539969,"score_spread":0.2376520908382964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025416284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025075153,0.00018559807,0.9727711,0.00003650129,0.000035866804,0.000017213039,0.000025174972,0.000869894,0.000983511],"genre_scores_gemma":[0.5262368,0.00048410424,0.47053537,0.000082514736,0.000041818468,0.00007290718,0.00026839948,0.00029050204,0.0019876088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940455,0.00012896882,0.000023394885,0.00014571441,0.00023370636,0.00006370571],"domain_scores_gemma":[0.99915683,0.00036802623,0.00008665323,0.0001436026,0.00020790516,0.000036886286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086400704,0.00071215315,0.0006951613,0.0008993033,0.0004602337,0.0007017944,0.0010839228,0.00062582066,0.0010007767],"category_scores_gemma":[0.003335236,0.00033456873,0.0004412988,0.0010993965,0.0005019545,0.0012822523,0.00086107256,0.0006968098,0.00071311946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000522751,0.00020075188,0.0035655715,0.00018036723,0.00007113,0.00037737505,0.00027537756,0.27316266,0.1319898,0.009671333,0.0019717272,0.57801116],"study_design_scores_gemma":[0.000011621774,0.00009104319,0.0025666421,0.000013584834,0.000022436192,0.0002669159,0.000049048675,0.9318028,0.0586723,0.003562993,0.00291077,0.000029689694],"about_ca_topic_score_codex":0.0022684475,"about_ca_topic_score_gemma":0.002620374,"teacher_disagreement_score":0.0022684475,"about_ca_system_score_codex":0.0006172559,"about_ca_system_score_gemma":0.00055866153,"threshold_uncertainty_score":0.0045694113},"labels":[],"label_agreement":null},{"id":"W2025797281","doi":"10.3390/s130201539","title":"Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":224,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Western Economic Diversification Canada; Ministry of Advanced Education, Government of Alberta","keywords":"Computer science; Accelerometer; Algorithm; Mobile phone; Microelectromechanical systems; Inertial measurement unit; Step detection; Global Positioning System; Real-time computing; Mobile device; Artificial intelligence; Computer vision; Telecommunications","score_opus":0.015808631122497162,"score_gpt":0.21772004089192365,"score_spread":0.20191140976942648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025797281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36702496,0.00053354295,0.62764263,0.00010361485,0.00005396836,0.00015625962,0.00041280268,0.0024472014,0.001625033],"genre_scores_gemma":[0.73585254,0.00030302734,0.258973,0.00006638965,0.000037531725,0.0001914033,0.0008788147,0.00004940807,0.0036478473],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999068,0.000014127749,0.000010312431,0.000027577003,0.000025349778,0.00001577332],"domain_scores_gemma":[0.99975735,0.00009606231,0.000028444043,0.000022170841,0.00007650286,0.00001947361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001587441,0.0003298809,0.0003167034,0.00083520403,0.00014372343,0.0002780694,0.0003377575,0.00039210217,0.0015229174],"category_scores_gemma":[0.0009084153,0.00010745854,0.0002721956,0.00032999663,0.00008483159,0.00024601366,0.00017487335,0.00025805342,0.0009133588],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006085391,0.00020742851,0.016407011,0.00009431927,0.000045022385,0.00027814475,0.00007466807,0.020215724,0.07126705,0.0009563553,0.0028442855,0.88700145],"study_design_scores_gemma":[0.000027086795,0.00029517082,0.025509004,0.000017682234,0.000040149218,0.00061485794,0.000084722844,0.92644846,0.043646377,0.0010709976,0.0022150292,0.000030564835],"about_ca_topic_score_codex":0.0013298444,"about_ca_topic_score_gemma":0.0014625096,"teacher_disagreement_score":0.0015229174,"about_ca_system_score_codex":0.00013436799,"about_ca_system_score_gemma":0.00024595505,"threshold_uncertainty_score":0.005094707},"labels":[],"label_agreement":null},{"id":"W2026422590","doi":"10.1109/vtcfall.2014.6966167","title":"RSS-Based Localization in Obstructed Environment with Unknown Path Loss Exponent","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"RSS; Transmitter; Signal strength; Path loss; Computer science; Path (computing); SIGNAL (programming language); Ranging; Algorithm; Transmitter power output; Radio propagation; Exponent; Real-time computing; Telecommunications; Wireless; Computer network","score_opus":0.00309258633743159,"score_gpt":0.15325950440560027,"score_spread":0.15016691806816868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026422590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06313881,0.00071971543,0.93349177,0.00009471688,0.00004054359,0.000020257756,0.000037981485,0.0012309968,0.0012252736],"genre_scores_gemma":[0.82182556,0.00077999174,0.1747427,0.000057667778,0.000052509815,0.000041613737,0.00011935751,0.00005884018,0.002321658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994066,0.00016856405,0.00003283335,0.00011380146,0.00023412444,0.00004405895],"domain_scores_gemma":[0.99932086,0.00023195006,0.00015221785,0.00011012159,0.00016290895,0.000021886188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046179202,0.0005628346,0.00071617746,0.00084854674,0.00022205274,0.0003926539,0.0006296631,0.00047755375,0.00045727365],"category_scores_gemma":[0.0017777737,0.00024677717,0.00026585994,0.0009010994,0.00042929768,0.001042378,0.0007721095,0.00027068064,0.0004160459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048196787,0.000095174655,0.012381592,0.00041211804,0.00015251437,0.001429228,0.0003769245,0.3391531,0.109327376,0.007346694,0.0026153843,0.5262279],"study_design_scores_gemma":[0.000039789014,0.00021437182,0.0048562614,0.0000245079,0.000056670422,0.0010199591,0.00009532844,0.9530601,0.033951245,0.0025891555,0.0040370035,0.00005572642],"about_ca_topic_score_codex":0.0007026296,"about_ca_topic_score_gemma":0.00075931195,"teacher_disagreement_score":0.00084854674,"about_ca_system_score_codex":0.0001775942,"about_ca_system_score_gemma":0.00025027574,"threshold_uncertainty_score":0.002442181},"labels":[],"label_agreement":null},{"id":"W2027527173","doi":"10.1007/s00779-006-0094-3","title":"Pedestrian navigation with high sensitivity GPS receivers and MEMS","year":2006,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Global Positioning System; Computer science; Multipath mitigation; Pedestrian; Dead reckoning; Real-time computing; Satellite system; Multipath propagation; GPS signals; GNSS augmentation; Remote sensing; Terrain; Satellite; Satellite navigation; Simulation; Assisted GPS; Telecommunications; Transport engineering; Engineering; Geography; Aerospace engineering","score_opus":0.004705776907245582,"score_gpt":0.17694995623556103,"score_spread":0.17224417932831546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027527173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1773379,0.0017081475,0.79798627,0.00034590598,0.0005668242,0.00008154472,0.00033749518,0.00312138,0.018514508],"genre_scores_gemma":[0.81739986,0.0006960319,0.15999474,0.00025062452,0.0001841589,0.00006387967,0.0002783805,0.000100843696,0.021031413],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948597,0.00012535762,0.0000148834415,0.0000934034,0.00021762513,0.000062655236],"domain_scores_gemma":[0.9996556,0.00008324228,0.00004490117,0.000054165597,0.00014033604,0.000021655587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037556782,0.00082612265,0.000583077,0.00055087934,0.00042214998,0.0006618501,0.0007025462,0.0007963686,0.002147507],"category_scores_gemma":[0.0008285645,0.0006022953,0.00039843807,0.00063395384,0.00019175484,0.0005980021,0.0006989824,0.00053189613,0.0011908602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019219771,0.00023698417,0.032868825,0.0005245949,0.0003278304,0.0011210387,0.00058124977,0.017207107,0.5776351,0.011766508,0.010381403,0.3454273],"study_design_scores_gemma":[0.00022637348,0.0030628904,0.057113543,0.00017782973,0.0011310205,0.005846754,0.00044524833,0.25958753,0.60012484,0.0047348645,0.067248486,0.00030063608],"about_ca_topic_score_codex":0.0019009382,"about_ca_topic_score_gemma":0.0045911935,"teacher_disagreement_score":0.002147507,"about_ca_system_score_codex":0.00033057318,"about_ca_system_score_gemma":0.0003717262,"threshold_uncertainty_score":0.007184088},"labels":[],"label_agreement":null},{"id":"W2029320761","doi":"10.1007/s11276-011-0333-z","title":"Use of flip ambiguity probabilities in robust sensor network localization","year":2011,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ambiguity; Computer science; Wireless sensor network; Set (abstract data type); Algorithm; Expression (computer science); Graph; Data mining; Theoretical computer science; Computer network","score_opus":0.04435432677390935,"score_gpt":0.19279704349912277,"score_spread":0.14844271672521342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029320761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010408189,0.00025208705,0.988,0.000102813545,0.00004029005,0.000013068807,0.000023533748,0.00014290209,0.0010170637],"genre_scores_gemma":[0.7863943,0.0007444454,0.2106322,0.0001506605,0.00015622829,0.000077459066,0.00012260019,0.00016088065,0.0015611717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984212,0.000658471,0.000074682066,0.000245741,0.00047600485,0.00012402346],"domain_scores_gemma":[0.9881175,0.00856286,0.0008331044,0.0014313783,0.00086914195,0.0001858411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032328963,0.0009424377,0.0010387577,0.0014233042,0.00074759685,0.0014573379,0.0009202176,0.0012812958,0.0016447745],"category_scores_gemma":[0.02782808,0.0007432796,0.00057760545,0.0013539257,0.0019241129,0.0041869255,0.0027681473,0.0013262661,0.00030167235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045081694,0.000053293683,0.0027297207,0.00012481505,0.00008300182,0.00021532134,0.000107652,0.72491384,0.0058574113,0.10913746,0.000933106,0.15539353],"study_design_scores_gemma":[0.000020490468,0.00006334072,0.0005550755,0.000022773756,0.000034357283,0.00017250632,0.000022052334,0.93391913,0.0049002827,0.059430648,0.00082638586,0.000033026554],"about_ca_topic_score_codex":0.0007410252,"about_ca_topic_score_gemma":0.0007778273,"teacher_disagreement_score":0.0032328963,"about_ca_system_score_codex":0.00057403115,"about_ca_system_score_gemma":0.0006480993,"threshold_uncertainty_score":0.017097414},"labels":[],"label_agreement":null},{"id":"W2029427472","doi":"10.1109/qbsc.2014.6841185","title":"Optimization of non-convex multiband cooperative sensing","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematical optimization; Computer science; Set (abstract data type); Regular polygon; Convex optimization; Algorithm; Solution set; Mathematics","score_opus":0.004916199845386626,"score_gpt":0.19555074459540828,"score_spread":0.19063454475002165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029427472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007432053,0.000084838866,0.9912505,0.00010131084,0.000012270221,0.000016664362,0.00001363988,0.00005997182,0.0010288295],"genre_scores_gemma":[0.6355386,0.00030196685,0.35981035,0.0001743297,0.000050801635,0.00026285622,0.00008924676,0.00008765288,0.0036841827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993037,0.0002842673,0.00002436201,0.00013159482,0.00020149307,0.000054521264],"domain_scores_gemma":[0.99903035,0.00063997897,0.00010294166,0.000073516625,0.00012506018,0.000028235214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011374517,0.0008095542,0.0009131277,0.00033597494,0.00027482124,0.0006579997,0.0009240228,0.0009774957,0.000838708],"category_scores_gemma":[0.0031175725,0.0004463488,0.00047923808,0.0005761687,0.0008613105,0.0008751487,0.0010476321,0.0006545968,0.00022527303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036088844,0.000014010756,0.0001265043,0.00003086595,0.000014512733,0.000028699307,0.000025869474,0.9768777,0.0014068712,0.005770502,0.000318776,0.015349605],"study_design_scores_gemma":[0.0000035449584,0.000010128556,0.000025540332,0.0000014675009,0.0000013435424,0.000005799696,0.0000031669886,0.9977785,0.00027121932,0.0017957311,0.00010207612,0.0000014530996],"about_ca_topic_score_codex":0.0021387262,"about_ca_topic_score_gemma":0.0014653262,"teacher_disagreement_score":0.0021387262,"about_ca_system_score_codex":0.00062088016,"about_ca_system_score_gemma":0.0007372164,"threshold_uncertainty_score":0.0060154796},"labels":[],"label_agreement":null},{"id":"W2029726047","doi":"10.4108/icst.collaboratecom.2012.250437","title":"A Collaborative Bluetooth-Based Approach to Localization of Mobile Devices","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Bluetooth; Computer science; Mobile device; Mobile computing; Computer network; Wireless; Telecommunications; World Wide Web","score_opus":0.0070769243090152605,"score_gpt":0.21969838958772944,"score_spread":0.21262146527871417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029726047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016749691,0.00019389916,0.99597,0.00004887786,0.0000486389,0.000029316525,0.000013550634,0.00037250534,0.0016483251],"genre_scores_gemma":[0.20195562,0.00072744273,0.7872861,0.00016956324,0.00023619986,0.00023268565,0.00015294385,0.00013816876,0.009101307],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974734,0.000631996,0.00011568487,0.00062272,0.0010077908,0.00014834524],"domain_scores_gemma":[0.9981687,0.00044839195,0.0001841612,0.00066580524,0.00044248434,0.000090348825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010975704,0.0009402345,0.0009357295,0.002314669,0.0011838435,0.0015036939,0.0029700666,0.0016628332,0.001918726],"category_scores_gemma":[0.003726988,0.00059727556,0.0011799656,0.001978404,0.00082489394,0.0022715665,0.003246261,0.0010784977,0.0015984465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002906111,0.0002675007,0.0027648169,0.00055560534,0.00033190288,0.0008827267,0.0012807868,0.099821635,0.05277833,0.0568402,0.0072933314,0.7768926],"study_design_scores_gemma":[0.00016002126,0.00070847577,0.003719625,0.00023270567,0.00030141533,0.004581146,0.0005868437,0.80557895,0.056119032,0.04120215,0.08652372,0.00028600477],"about_ca_topic_score_codex":0.0014327536,"about_ca_topic_score_gemma":0.0023801778,"teacher_disagreement_score":0.0029700666,"about_ca_system_score_codex":0.0004202514,"about_ca_system_score_gemma":0.0006495406,"threshold_uncertainty_score":0.006418824},"labels":[],"label_agreement":null},{"id":"W2032215574","doi":"10.1155/2014/269596","title":"RFID Localization Using Angle of Arrival Cluster Forming","year":2014,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Plan for Science, Technology and Innovation; King Saud University","keywords":"Computer science; Received signal strength indication; Radio-frequency identification; Transmission (telecommunications); Angle of arrival; SIGNAL (programming language); Communication source; Power (physics); Real-time computing; Identification (biology); Cluster (spacecraft); Transmitter; Time of arrival; Wireless; Telecommunications; Computer network; Antenna (radio)","score_opus":0.008365022555796561,"score_gpt":0.2269436732619908,"score_spread":0.21857865070619426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032215574","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036655072,0.00026500793,0.95799387,0.00008925992,0.000079572696,0.00014006846,0.00005777802,0.0016341223,0.0030852496],"genre_scores_gemma":[0.5967723,0.000406324,0.3996964,0.00009151279,0.00004484976,0.00015019429,0.0001719026,0.000077778706,0.0025886965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976935,0.0006233752,0.00008734678,0.00042393312,0.0009833948,0.0001884964],"domain_scores_gemma":[0.99751854,0.0005778826,0.0003483561,0.000487415,0.00096931,0.0000986449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010625817,0.0007513486,0.00092952,0.0015905504,0.00080028456,0.0009292544,0.001791749,0.00093331025,0.0010106345],"category_scores_gemma":[0.0035259626,0.000353899,0.0006082788,0.002812589,0.00060629065,0.0012738724,0.0012190981,0.00054425834,0.0006389475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010496471,0.0002775828,0.0065886164,0.00028236271,0.0001393608,0.00029239524,0.00049917115,0.49009162,0.06317925,0.015423478,0.004212353,0.41796416],"study_design_scores_gemma":[0.00008241716,0.000555238,0.0025878665,0.00001902355,0.000057843332,0.0005814542,0.000159873,0.93075603,0.052485514,0.0029762024,0.009630527,0.000108027656],"about_ca_topic_score_codex":0.006389195,"about_ca_topic_score_gemma":0.0034617304,"teacher_disagreement_score":0.006389195,"about_ca_system_score_codex":0.00112854,"about_ca_system_score_gemma":0.001029361,"threshold_uncertainty_score":0.012704015},"labels":[],"label_agreement":null},{"id":"W2033364470","doi":"10.1109/icnsc.2014.6819620","title":"Indoor localization for mobile devices","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"New York Institute of Technology","funders":"National Science Foundation","keywords":"Computer science; Signal strength; Cluster analysis; Floor plan; Naive Bayes classifier; Mobile device; Data mining; Classifier (UML); Real-time computing; Mobile computing; SIGNAL (programming language); Artificial intelligence; Pattern recognition (psychology); Wireless; Computer network; Telecommunications; Engineering","score_opus":0.005805776445037355,"score_gpt":0.20981739008144284,"score_spread":0.2040116136364055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033364470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037540314,0.0036986673,0.96663773,0.0005747851,0.000540179,0.00010819985,0.0006617124,0.0121694375,0.011855345],"genre_scores_gemma":[0.39029574,0.007538944,0.55672705,0.0012091753,0.0007341868,0.00049834413,0.004306911,0.00079149683,0.037898216],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99871993,0.00025466018,0.00007811175,0.00032760005,0.000497825,0.000121960955],"domain_scores_gemma":[0.9992636,0.00013242672,0.000067899666,0.0002709524,0.00023550005,0.000029639416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000625977,0.0010803847,0.000747672,0.0008427387,0.00080980145,0.0017844655,0.0016469486,0.0012834368,0.007817013],"category_scores_gemma":[0.0024683564,0.00039880973,0.0007877965,0.0011316006,0.000439665,0.0018716067,0.0018336367,0.0010168939,0.007674669],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020643667,0.00007477361,0.0031920772,0.001029051,0.0001835853,0.0005969537,0.00029981096,0.042328175,0.019102953,0.04751524,0.060461298,0.8250097],"study_design_scores_gemma":[0.00006377197,0.00032040686,0.0051503503,0.00060741603,0.00019821632,0.0020622,0.00035250236,0.4561061,0.027914636,0.042599484,0.4644456,0.0001793303],"about_ca_topic_score_codex":0.0047333464,"about_ca_topic_score_gemma":0.0052236207,"teacher_disagreement_score":0.007817013,"about_ca_system_score_codex":0.00074186985,"about_ca_system_score_gemma":0.00056204334,"threshold_uncertainty_score":0.026150465},"labels":[],"label_agreement":null},{"id":"W2034561078","doi":"10.1007/s11063-012-9255-8","title":"Using Laplacian Eigenmap as Heuristic Information to Solve Nonlinear Constraints Defined on a Graph and Its Application in Distributed Range-Free Localization of Wireless Sensor Networks","year":2012,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Wireless sensor network; Heuristic; Computer science; Artificial neural network; Nonlinear system; Computational intelligence; Graph; Graph theory; Mathematical optimization; Convergence (economics); Mathematics; Algorithm; Artificial intelligence; Theoretical computer science; Combinatorics","score_opus":0.011895579397461695,"score_gpt":0.2282511541850169,"score_spread":0.2163555747875552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034561078","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009123319,0.00006257509,0.9899852,0.00009975859,0.000015992595,0.00001416914,0.000012212558,0.00010245939,0.00058436394],"genre_scores_gemma":[0.445504,0.0002340964,0.55079705,0.00013973602,0.000046657253,0.00017200006,0.00012674922,0.00013674046,0.0028429793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973804,0.00011735029,0.0000121173625,0.000039849776,0.00006734614,0.000025271354],"domain_scores_gemma":[0.99886155,0.0008010886,0.000060547267,0.000060109724,0.00018560304,0.000031190433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075591006,0.0006793238,0.00064073637,0.0007666109,0.00035966773,0.000646931,0.00074574037,0.0011405858,0.001315111],"category_scores_gemma":[0.0035858045,0.00046559723,0.00054965337,0.0008927253,0.00068287307,0.0013654903,0.0008852615,0.00073891814,0.00018528508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046748402,0.000052145697,0.00018014996,0.000043510314,0.000029896817,0.00003621505,0.00005601645,0.92876834,0.0027986574,0.010620308,0.0009340849,0.056434013],"study_design_scores_gemma":[0.0000024681258,0.000006321271,0.000019730258,0.0000011146848,0.0000014207222,0.0000031422499,0.0000029026853,0.9978707,0.00019738324,0.001825602,0.000066410525,0.0000027605977],"about_ca_topic_score_codex":0.0050934907,"about_ca_topic_score_gemma":0.0054102736,"teacher_disagreement_score":0.0050934907,"about_ca_system_score_codex":0.0004304266,"about_ca_system_score_gemma":0.0008788619,"threshold_uncertainty_score":0.010127664},"labels":[],"label_agreement":null},{"id":"W2035029323","doi":"10.1145/1280940.1281042","title":"HA-A2L","year":2007,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Position (finance); Wireless sensor network; Node (physics); Tracking (education); Degree (music); Computer vision; Real-time computing; Algorithm; Artificial intelligence; Computer network; Physics; Acoustics","score_opus":0.01295991180357716,"score_gpt":0.22687897081172723,"score_spread":0.21391905900815006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035029323","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01542262,0.00054267666,0.96725476,0.00030285816,0.0002575798,0.00013019408,0.00039127326,0.0055133426,0.010184751],"genre_scores_gemma":[0.32219288,0.00042134788,0.6581745,0.00053688104,0.00019803268,0.00031202874,0.0012590959,0.00045285432,0.016452216],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991347,0.00023164027,0.000040695308,0.00019349693,0.00031377174,0.000085683125],"domain_scores_gemma":[0.9981183,0.0004895252,0.00014449148,0.0006314485,0.00049624563,0.000120009696],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009122929,0.00065518817,0.00048454019,0.00090917427,0.0007630745,0.0010889081,0.0015390993,0.0009798085,0.009190433],"category_scores_gemma":[0.0027625088,0.0002551572,0.0004826818,0.0009180496,0.00054515584,0.001490368,0.001959303,0.00069797516,0.00500322],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005104556,0.00019867951,0.0036745532,0.0003816362,0.00010324574,0.0002607245,0.00017581158,0.055428952,0.023654472,0.033121604,0.035424948,0.847065],"study_design_scores_gemma":[0.00016602871,0.00057693745,0.0017622358,0.000041620347,0.0000470359,0.0012836687,0.0001244613,0.8439628,0.02779436,0.027836595,0.0963206,0.00008365663],"about_ca_topic_score_codex":0.002091763,"about_ca_topic_score_gemma":0.0028650612,"teacher_disagreement_score":0.99080956,"about_ca_system_score_codex":0.00036588465,"about_ca_system_score_gemma":0.0008454722,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2036267567","doi":"10.1109/wcnc.2014.6952893","title":"Range-free localization algorithm for heterogeneous Wireless Sensor Networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec en Abitibi-Témiscamingue; Université du Québec à Montréal","funders":"","keywords":"Wireless sensor network; Computer science; Range (aeronautics); Algorithm; Node (physics); Transmission (telecommunications); Key distribution in wireless sensor networks; Heterogeneous network; Wireless; Wireless network; Computer network; Telecommunications; Engineering","score_opus":0.0061458142564929765,"score_gpt":0.19442341357345297,"score_spread":0.18827759931696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036267567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002973929,0.0001255929,0.9961469,0.00003334472,0.000020203777,0.000014951487,0.000009369929,0.00025418244,0.00042144905],"genre_scores_gemma":[0.30776528,0.0004438507,0.6875961,0.00012113183,0.000064348584,0.00017992908,0.00026738015,0.0001530984,0.003408884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994137,0.00011427928,0.000032853197,0.00013304072,0.0002617895,0.000044372056],"domain_scores_gemma":[0.9993412,0.00024127211,0.00008630885,0.00013693179,0.00016933847,0.00002507148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006179149,0.00058398326,0.00060405297,0.0010016031,0.00052217057,0.000577432,0.0015659014,0.0005884834,0.0010534598],"category_scores_gemma":[0.0022785673,0.00025843258,0.00048802057,0.00076385296,0.0004251844,0.0016159096,0.0014745278,0.00069284864,0.0005664128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016943224,0.000051345032,0.0009936105,0.00013794172,0.00008892549,0.00021341049,0.00022105781,0.4520845,0.023668831,0.03549495,0.0028204795,0.48405537],"study_design_scores_gemma":[0.000026080594,0.0000558111,0.00028134612,0.0000116170495,0.000024086563,0.00014004801,0.000027831118,0.97770405,0.0068702865,0.010096092,0.0047402806,0.000022443546],"about_ca_topic_score_codex":0.0012532425,"about_ca_topic_score_gemma":0.001088976,"teacher_disagreement_score":0.0015659014,"about_ca_system_score_codex":0.0005147907,"about_ca_system_score_gemma":0.0005388403,"threshold_uncertainty_score":0.0037350655},"labels":[],"label_agreement":null},{"id":"W2036695734","doi":"10.1109/ipin.2013.6817886","title":"Enhancing Cluster-based RFID Tag Localization using artificial neural networks and virtual reference tags","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cluster analysis; Bilinear interpolation; Artificial neural network; Data mining; Mobile device; Radio-frequency identification; Identification (biology); Interpolation (computer graphics); Similarity (geometry); Artificial intelligence; Pattern recognition (psychology); Real-time computing; Computer vision; Image (mathematics)","score_opus":0.015153485670841883,"score_gpt":0.21613947698905792,"score_spread":0.20098599131821604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036695734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06376295,0.00021761587,0.9333428,0.00006869349,0.00005188249,0.000024667543,0.000018762908,0.0008485673,0.0016639266],"genre_scores_gemma":[0.8261959,0.00023002774,0.17100185,0.0000642413,0.000034244385,0.00005019649,0.00007671326,0.00005105251,0.0022957423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996892,0.000077070355,0.00001714818,0.00007267574,0.00010579797,0.00003809555],"domain_scores_gemma":[0.9993383,0.0002361296,0.00009528533,0.000057128986,0.0002532963,0.000019860274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006097398,0.00057626475,0.0004995236,0.0006915148,0.00033363738,0.0006028443,0.00090849097,0.0006378085,0.00062140025],"category_scores_gemma":[0.0017060044,0.00027324972,0.0004496128,0.0008577429,0.00031253535,0.0009760639,0.0006586868,0.00034997353,0.00028801107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023841302,0.000094571966,0.0018171916,0.000091252616,0.00006018461,0.000101147496,0.00012111671,0.76327,0.014706686,0.0018063118,0.0006555611,0.2170375],"study_design_scores_gemma":[0.0000044577373,0.000031877968,0.0003169776,0.0000030820388,0.0000117178015,0.000020382082,0.000015207844,0.9952651,0.0036882272,0.0003478205,0.00028750207,0.00000757884],"about_ca_topic_score_codex":0.006395249,"about_ca_topic_score_gemma":0.0059229806,"teacher_disagreement_score":0.006395249,"about_ca_system_score_codex":0.0006028391,"about_ca_system_score_gemma":0.0004043737,"threshold_uncertainty_score":0.012716055},"labels":[],"label_agreement":null},{"id":"W2037219995","doi":"10.1109/mass.2014.114","title":"Methods for Node Localization in Wireless Sensor Networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Wireless sensor network; Node (physics); Wireless ad hoc network; Computer science; Computer network; Key distribution in wireless sensor networks; Wireless; Sensor node; Wireless network; Telecommunications; Engineering","score_opus":0.010302145239477605,"score_gpt":0.27264045771578227,"score_spread":0.2623383124763047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037219995","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041871014,0.013939733,0.98078567,0.00024842581,0.0005256394,0.000082001476,0.00009675395,0.00040933228,0.0034936185],"genre_scores_gemma":[0.032577876,0.049751274,0.9017094,0.00046492432,0.0013470831,0.0008053169,0.0004919633,0.0003115123,0.012540622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979334,0.00054007675,0.00013367507,0.0002980972,0.0010251487,0.000069611575],"domain_scores_gemma":[0.9992162,0.00041062137,0.000072061346,0.00012364503,0.00015691416,0.000020530668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011382316,0.0012744276,0.0010177027,0.0023623956,0.0007501778,0.0013672105,0.0019768407,0.0015772269,0.00375838],"category_scores_gemma":[0.0031929275,0.00056665804,0.00093335734,0.0029398033,0.0013806702,0.0020912953,0.0016880328,0.0018689638,0.003437591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077179415,0.00005280131,0.00064925017,0.0026514,0.00012794141,0.00034510237,0.00032722516,0.053875346,0.0083049005,0.26114735,0.021427635,0.651014],"study_design_scores_gemma":[0.00006155611,0.00017785895,0.000665333,0.0009499041,0.00010172248,0.0015876876,0.0001972522,0.23073031,0.009193257,0.28092033,0.47526297,0.00015179778],"about_ca_topic_score_codex":0.0011175617,"about_ca_topic_score_gemma":0.0009139168,"teacher_disagreement_score":0.00375838,"about_ca_system_score_codex":0.00060782215,"about_ca_system_score_gemma":0.0006405654,"threshold_uncertainty_score":0.012573004},"labels":[],"label_agreement":null},{"id":"W2038034075","doi":"10.1080/10095020.2013.817110","title":"Ubiquitous indoor vision navigation using a smart device","year":2013,"lang":"en","type":"article","venue":"Geo-spatial Information Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Computer science; Real-time computing; Hybrid positioning system; Indoor positioning system; Floor plan; Geodetic datum; Accelerometer; Reliability (semiconductor); Position (finance); Positioning system; Computer vision; Artificial intelligence; Telecommunications; Engineering; Geography","score_opus":0.010369063156145147,"score_gpt":0.24409340712460798,"score_spread":0.23372434396846284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038034075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048321135,0.001932404,0.90984005,0.00014833624,0.00036990884,0.00013145825,0.0007255951,0.01627786,0.022253202],"genre_scores_gemma":[0.6600009,0.0010492047,0.31784728,0.0003918454,0.000084006366,0.0001739766,0.0013015387,0.00014251599,0.01900863],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997019,0.00003344088,0.000013135663,0.00008958004,0.00013371148,0.000028221637],"domain_scores_gemma":[0.9998933,0.0000103678,0.000013511887,0.000026597516,0.00004303773,0.000013239289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001354598,0.0003390929,0.00045041923,0.00048789987,0.00017781396,0.00053925754,0.00065497245,0.000559335,0.0035067843],"category_scores_gemma":[0.00030600815,0.00013647886,0.00023820964,0.00049104594,0.00012790217,0.00070749637,0.00075168017,0.00022083257,0.0018837875],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003469497,0.00013721918,0.0029424215,0.000524739,0.00006929144,0.0006573919,0.0002685289,0.006953094,0.1467456,0.0066952566,0.022518048,0.81214136],"study_design_scores_gemma":[0.00017243522,0.001201439,0.023809329,0.0002608991,0.00027153443,0.004589972,0.0004269592,0.5287185,0.18376672,0.005516853,0.25097856,0.00028688696],"about_ca_topic_score_codex":0.003745008,"about_ca_topic_score_gemma":0.003641775,"teacher_disagreement_score":0.003745008,"about_ca_system_score_codex":0.00023854559,"about_ca_system_score_gemma":0.0003388943,"threshold_uncertainty_score":0.011731327},"labels":[],"label_agreement":null},{"id":"W2039082810","doi":"10.1109/isspit.2007.4458094","title":"A Directional Routing Protocol for Ad Hoc Networks with Angle-of-Arrival Estimation","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Computer network; Wireless ad hoc network; Wireless Routing Protocol; Optimized Link State Routing Protocol; Routing protocol; Dynamic Source Routing; Zone Routing Protocol; Directional antenna; Transmission (telecommunications); Global Positioning System; Routing (electronic design automation); Wireless; Topology (electrical circuits); Real-time computing; Antenna (radio); Telecommunications; Engineering; Electrical engineering","score_opus":0.011876563750000384,"score_gpt":0.25992335540448136,"score_spread":0.24804679165448099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039082810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023356704,0.0014452685,0.9882372,0.00049166667,0.00057370565,0.000388098,0.00016575916,0.0014937617,0.004868777],"genre_scores_gemma":[0.08605659,0.0036523498,0.89169234,0.0007614554,0.00038269418,0.0017756018,0.0010409177,0.00020679108,0.0144313285],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988865,0.00029839587,0.00014600143,0.00011385186,0.0005005781,0.000054642504],"domain_scores_gemma":[0.9990476,0.00023242661,0.00015204205,0.00021757199,0.00029319673,0.000057104247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014429732,0.00079737825,0.00055843015,0.00088755705,0.0008420941,0.0010703141,0.0014466846,0.0008257889,0.0021518245],"category_scores_gemma":[0.002316598,0.00035759396,0.00048704722,0.0011695342,0.00061084784,0.0013813088,0.0011275504,0.0015583631,0.001460005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020102569,0.00019199996,0.0008069752,0.0009648505,0.00018001499,0.000436427,0.00037265712,0.032200832,0.06548625,0.203641,0.02765011,0.6678678],"study_design_scores_gemma":[0.00030756413,0.0011985924,0.0011343451,0.00018898434,0.000401189,0.0027154493,0.00022253997,0.3134952,0.052244354,0.075039156,0.5527522,0.00030046346],"about_ca_topic_score_codex":0.0006057157,"about_ca_topic_score_gemma":0.0012877206,"teacher_disagreement_score":0.0021518245,"about_ca_system_score_codex":0.0004269504,"about_ca_system_score_gemma":0.0008350099,"threshold_uncertainty_score":0.0076312423},"labels":[],"label_agreement":null},{"id":"W2039087548","doi":"10.1145/1464420.1464421","title":"Cooperative node localization using nonlinear data projection","year":2009,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":135,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Communications Research Centre Canada","funders":"","keywords":"Computer science; Node (physics); Position (finance); Range (aeronautics); Nonlinear system; Multidimensional scaling; Projection (relational algebra); Algorithm; Curvilinear coordinates; Scaling; Mathematics; Machine learning","score_opus":0.038340845918192805,"score_gpt":0.2737769495478779,"score_spread":0.2354361036296851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039087548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014443697,0.00003833661,0.9845615,0.00005058372,0.000008447553,0.0000142174285,0.000012186167,0.00020996763,0.0006609323],"genre_scores_gemma":[0.62160707,0.00017279807,0.3744984,0.000045580553,0.00002014789,0.00009600772,0.00011218661,0.000052612053,0.003395118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961853,0.000109076725,0.000013885005,0.00007876217,0.00015230183,0.000027376145],"domain_scores_gemma":[0.9993647,0.00023464393,0.00007128873,0.00013749665,0.0001725942,0.000019321198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048325906,0.0004803597,0.0004073816,0.00043774527,0.00031519882,0.00046083867,0.0006727874,0.00036563253,0.0007084811],"category_scores_gemma":[0.0018696088,0.00023225616,0.0003147511,0.0008361035,0.00067708996,0.0011497445,0.0014674678,0.00056374114,0.00031444037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001756658,0.00005926406,0.0014471272,0.00008783839,0.00005168117,0.00009716149,0.00026514288,0.6013343,0.032117493,0.017639082,0.0012691381,0.34545618],"study_design_scores_gemma":[0.0000051080942,0.000028906337,0.00020719088,0.0000019013104,0.0000039261085,0.000028889197,0.000018559715,0.9915868,0.005113778,0.0021953436,0.0008010135,0.000008644026],"about_ca_topic_score_codex":0.0040268362,"about_ca_topic_score_gemma":0.003731561,"teacher_disagreement_score":0.0040268362,"about_ca_system_score_codex":0.00042453822,"about_ca_system_score_gemma":0.0006639716,"threshold_uncertainty_score":0.008006811},"labels":[],"label_agreement":null},{"id":"W2039465987","doi":"10.1145/2442616.2442621","title":"Potential risks of WiFi-based indoor positioning and progress on improving localization functionality","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Canada Foundation for Innovation","keywords":"Computer science; Risk analysis (engineering); Business","score_opus":0.018062630501110857,"score_gpt":0.24922903492029497,"score_spread":0.23116640441918412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039465987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20489973,0.029363329,0.69736814,0.0085211,0.0013617678,0.00034267962,0.0007444929,0.005692895,0.051705856],"genre_scores_gemma":[0.76342136,0.014736786,0.19060099,0.0019252783,0.0003595328,0.00023938858,0.0006932073,0.00033534906,0.027688146],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968272,0.00080993754,0.00019316973,0.0004082456,0.0013448992,0.00041645873],"domain_scores_gemma":[0.99024606,0.0029691842,0.0006725322,0.0014728765,0.004473545,0.00016590965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038602839,0.0013016248,0.00051831605,0.0011770143,0.0006972147,0.0014210863,0.0023549665,0.0017137629,0.0063662934],"category_scores_gemma":[0.009433642,0.0004449649,0.0006244498,0.0017280846,0.0009782005,0.0025913948,0.0017004067,0.0011726951,0.0022414182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041755053,0.00009079168,0.029961722,0.0022763095,0.00016371065,0.0006758299,0.00068861357,0.01832922,0.043682683,0.0133816255,0.0077431803,0.88258874],"study_design_scores_gemma":[0.0001542136,0.0042008827,0.09357079,0.0029459647,0.0010456046,0.013442301,0.002679212,0.09065061,0.32345852,0.011839649,0.45548436,0.0005279094],"about_ca_topic_score_codex":0.015277073,"about_ca_topic_score_gemma":0.012380501,"teacher_disagreement_score":0.015277073,"about_ca_system_score_codex":0.0013460751,"about_ca_system_score_gemma":0.0011719698,"threshold_uncertainty_score":0.030376315},"labels":[],"label_agreement":null},{"id":"W2039844146","doi":"10.1109/ccece.2014.6901032","title":"Relative localization with symmetry preserving observers","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Control theory (sociology); Extended Kalman filter; Robustness (evolution); Invariant extended Kalman filter; Computer science; Initialization; Estimator; Kalman filter; Nonlinear system; Observer (physics); Mathematics; Artificial intelligence","score_opus":0.006271543894497721,"score_gpt":0.17768564348918095,"score_spread":0.17141409959468323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039844146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012881532,0.000022157708,0.99808705,0.000010332415,0.00000931854,0.000007165592,0.000003749855,0.00007380707,0.0004981568],"genre_scores_gemma":[0.5191456,0.00030384137,0.4745284,0.000074391464,0.00007459024,0.00015845467,0.00009970974,0.00007363958,0.005541387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999283,0.00019010478,0.00003734328,0.00013457354,0.00031307281,0.000042022235],"domain_scores_gemma":[0.999318,0.00017517312,0.00015942179,0.00015539069,0.00017490135,0.000017192926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092677324,0.00043284253,0.0005070775,0.00034830827,0.00020587957,0.0006845235,0.0007213452,0.00061841897,0.0014618057],"category_scores_gemma":[0.0018741606,0.00027019822,0.0007113938,0.0003126164,0.0007478772,0.0012078005,0.0009590234,0.00079584785,0.0005581176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017102143,0.00006469252,0.00097612164,0.00024572105,0.000097981545,0.00021093598,0.0003154996,0.4093296,0.06860447,0.30778575,0.0016078537,0.21059035],"study_design_scores_gemma":[0.000019830408,0.00018513584,0.00021635264,0.000012963269,0.000014341151,0.00007287199,0.000020491329,0.96258867,0.011510044,0.020585775,0.0047540287,0.000019468655],"about_ca_topic_score_codex":0.0007164467,"about_ca_topic_score_gemma":0.0006142884,"teacher_disagreement_score":0.0014618057,"about_ca_system_score_codex":0.00041270134,"about_ca_system_score_gemma":0.00060673343,"threshold_uncertainty_score":0.0049013495},"labels":[],"label_agreement":null},{"id":"W2039873131","doi":"10.1109/icwits.2012.6417818","title":"A modified fingerprinting technique for an indoor, range-free, localization system with dynamic radio map annealing over time","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Fingerprint (computing); RSS; Fingerprint recognition; Computer science; Signal strength; Real-time computing; Calibration; Wireless; Reduction (mathematics); Artificial intelligence; Telecommunications; Mathematics; Statistics","score_opus":0.007643496238651717,"score_gpt":0.21208733271577873,"score_spread":0.20444383647712702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039873131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016910644,0.00022415188,0.9809269,0.00006488942,0.00008219322,0.00005012812,0.000027277407,0.0007212012,0.0009927313],"genre_scores_gemma":[0.20328519,0.0002503189,0.79173416,0.000099314406,0.00004699143,0.00009294646,0.00007296021,0.00012642719,0.004291596],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995061,0.00007960689,0.000026566888,0.00012988313,0.00022082888,0.000036979603],"domain_scores_gemma":[0.9994821,0.00010689029,0.00006730367,0.0001907802,0.00013284721,0.00002005846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037115236,0.0004018103,0.0004997139,0.0004524752,0.00032840075,0.00041243428,0.0009658948,0.0007580099,0.0017674943],"category_scores_gemma":[0.0011898533,0.0002953325,0.00048402435,0.00069090025,0.0003333709,0.00076181826,0.00036467056,0.0006967132,0.0006883119],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036952383,0.000102304584,0.00083373766,0.00022325938,0.000075922195,0.00025998853,0.00024262803,0.021768263,0.54987824,0.005066478,0.0013533944,0.4198262],"study_design_scores_gemma":[0.00008885123,0.0010925372,0.0073517165,0.00005441383,0.0002286487,0.0037256668,0.0000909666,0.45002922,0.48047075,0.0022699886,0.05438775,0.00020946894],"about_ca_topic_score_codex":0.000725127,"about_ca_topic_score_gemma":0.0010681069,"teacher_disagreement_score":0.0017674943,"about_ca_system_score_codex":0.00031200453,"about_ca_system_score_gemma":0.00032001807,"threshold_uncertainty_score":0.0059128404},"labels":[],"label_agreement":null},{"id":"W2039945376","doi":"10.1109/icarcv.2012.6485178","title":"Improved low cost GPS localization by using communicative vehicles","year":2012,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Robustness (evolution); Global Positioning System; Computer science; Position (finance); Real-time computing; Divergence (linguistics); Iterative method; Algorithm; Telecommunications","score_opus":0.02219761474767134,"score_gpt":0.2524134374520628,"score_spread":0.23021582270439145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039945376","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008051044,0.0004051548,0.989335,0.00007759885,0.00008683791,0.000015419711,0.000017094424,0.0008286869,0.0011831792],"genre_scores_gemma":[0.45631704,0.00093424437,0.53154844,0.00011562459,0.00021759835,0.000119136086,0.00022090744,0.00016897179,0.010358063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991148,0.00024341232,0.00002977296,0.0001859804,0.00035310388,0.00007283681],"domain_scores_gemma":[0.99944454,0.00012261445,0.000044571447,0.0001458843,0.00020771728,0.000034725956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051470485,0.0011143814,0.0007557451,0.0009775739,0.0005776599,0.0009602897,0.0017691761,0.0013125133,0.0016376979],"category_scores_gemma":[0.001302447,0.00042730218,0.00066048553,0.0008035467,0.00050413,0.0015115004,0.0017005088,0.00082048465,0.002075017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005161524,0.000094677314,0.0022984429,0.00063568103,0.0001710749,0.0008506726,0.00072127994,0.14720996,0.15139964,0.044075258,0.006734051,0.6452931],"study_design_scores_gemma":[0.00010263967,0.0003491964,0.0008462179,0.000045655535,0.0001476032,0.0009550082,0.00012536842,0.89016443,0.0654661,0.005965094,0.035706244,0.0001264913],"about_ca_topic_score_codex":0.0020411361,"about_ca_topic_score_gemma":0.0020433296,"teacher_disagreement_score":0.0020411361,"about_ca_system_score_codex":0.0004007134,"about_ca_system_score_gemma":0.0006206441,"threshold_uncertainty_score":0.0054786205},"labels":[],"label_agreement":null},{"id":"W2040501041","doi":"10.1109/icif.2010.5711942","title":"Bimodal localization in cellular networks utilizing particle filters","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cellular network; Computer science; Fuse (electrical); Particle filter; Probabilistic logic; Base station; SIGNAL (programming language); Wireless network; Wireless; Artificial intelligence; Computer vision; Real-time computing; Computer network; Telecommunications; Engineering; Filter (signal processing)","score_opus":0.0063842006751338425,"score_gpt":0.19134904698168212,"score_spread":0.18496484630654828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040501041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005401969,0.00008558835,0.9940059,0.000037923648,0.000015936354,0.000006584582,0.000009038496,0.00011209284,0.00032494025],"genre_scores_gemma":[0.732468,0.00051164086,0.2633536,0.00007711784,0.00006762485,0.00008053797,0.00010163627,0.00003804889,0.0033018538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973446,0.00007231426,0.000011032062,0.00005987703,0.000084187996,0.000038093858],"domain_scores_gemma":[0.9995704,0.00022075552,0.000052367264,0.000038651622,0.000093408446,0.000024408564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055587635,0.0005097669,0.00053591514,0.0005656333,0.00027359097,0.00070305826,0.0004776669,0.00069816416,0.0006432894],"category_scores_gemma":[0.0015336163,0.00030701162,0.0004279634,0.0006717437,0.00064425054,0.00078918174,0.00084610283,0.00055684306,0.00022947039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013025363,0.00003082668,0.0014844651,0.000055569097,0.00004172584,0.00009577044,0.000098769255,0.8722129,0.008193313,0.024749149,0.00087697705,0.09203023],"study_design_scores_gemma":[0.0000044292306,0.000013527722,0.00012481227,0.0000019573636,0.0000033856415,0.000012634839,0.000007387245,0.9960104,0.0006132184,0.002839899,0.00036367486,0.000004523948],"about_ca_topic_score_codex":0.0059167794,"about_ca_topic_score_gemma":0.003377041,"teacher_disagreement_score":0.0059167794,"about_ca_system_score_codex":0.00067630224,"about_ca_system_score_gemma":0.00051069853,"threshold_uncertainty_score":0.011764646},"labels":[],"label_agreement":null},{"id":"W2040516528","doi":"10.1109/ipin.2012.6418915","title":"Mitigation of attitude and gyro errors through vision aiding","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Nokia Foundation; Else Kröner-Fresenius-Stiftung","keywords":"Computer vision; Gyroscope; Artificial intelligence; Computer science; Vanishing point; Inertial navigation system; Intersection (aeronautics); Attitude and heading reference system; Point (geometry); Process (computing); Inertial measurement unit; Orientation (vector space); Mathematics; Engineering; Image (mathematics)","score_opus":0.0098052678247654,"score_gpt":0.2431710922613972,"score_spread":0.23336582443663179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040516528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1665303,0.0012578547,0.8180925,0.00018692206,0.00029036662,0.000097261,0.000107500535,0.0053388057,0.008098509],"genre_scores_gemma":[0.7891524,0.0006129181,0.20437777,0.00012459207,0.00005296551,0.000049050817,0.00016267171,0.00007527558,0.0053923083],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997969,0.000023889812,0.000007269657,0.000041089017,0.0000890587,0.00004177849],"domain_scores_gemma":[0.99981505,0.000026294138,0.000027641165,0.000027776037,0.00008997179,0.000013303825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001751021,0.00069114164,0.0004814627,0.00044342555,0.00023841437,0.0006102148,0.00061593455,0.00064212625,0.0013488401],"category_scores_gemma":[0.0006163072,0.00016758977,0.00029568662,0.00027493632,0.00020960752,0.00047832396,0.0008356519,0.00039458464,0.0009057179],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005489372,0.0002678076,0.0020433736,0.0003125688,0.00006099895,0.00072892336,0.00024578688,0.06695528,0.34895194,0.0027047866,0.0045325323,0.5726471],"study_design_scores_gemma":[0.00006805194,0.0013581966,0.009457208,0.00008223894,0.00011113768,0.0012417551,0.000218372,0.7393369,0.22623652,0.00239679,0.01940051,0.00009231888],"about_ca_topic_score_codex":0.0026690278,"about_ca_topic_score_gemma":0.0022853678,"teacher_disagreement_score":0.0026690278,"about_ca_system_score_codex":0.00015192578,"about_ca_system_score_gemma":0.00050234125,"threshold_uncertainty_score":0.0053070188},"labels":[],"label_agreement":null},{"id":"W2040937658","doi":"10.4028/www.scientific.net/amr.756-759.3946","title":"Simple and Robust RSSI Estimation Using M-Estimator","year":2013,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Estimator; Computer science; Simple (philosophy); Non-line-of-sight propagation; Kalman filter; Fading; Signal strength; Algorithm; Smoothing; Wireless; Real-time computing; Statistics; Mathematics; Artificial intelligence; Telecommunications","score_opus":0.04678990080761825,"score_gpt":0.3286030618464191,"score_spread":0.28181316103880083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040937658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032457327,0.00012272366,0.9958125,0.000021488642,0.000020803287,0.000011405371,0.0000132047735,0.00039186646,0.00036025108],"genre_scores_gemma":[0.25918108,0.0003734041,0.73855835,0.00005959834,0.000078202895,0.00007590578,0.00013066654,0.00010018646,0.0014425894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900633,0.00022785582,0.00007421024,0.00024420148,0.0004045022,0.000042867177],"domain_scores_gemma":[0.9988914,0.0004108263,0.00021500501,0.00022264238,0.00023834729,0.00002172004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000860961,0.00090125046,0.0010291375,0.0011026884,0.00027921572,0.0006425887,0.0011766203,0.0010560619,0.00075544446],"category_scores_gemma":[0.0039645554,0.0004129404,0.00079608185,0.0009550925,0.00041836457,0.0012610693,0.00083850016,0.0007666624,0.00086191524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023321697,0.00009993271,0.0039202906,0.00033576565,0.00021001992,0.00018238495,0.00011093595,0.3528851,0.070919156,0.016884556,0.002556069,0.55166256],"study_design_scores_gemma":[0.00002084489,0.00010541471,0.0013449498,0.000018106191,0.000036240835,0.00027658336,0.000017540473,0.9681161,0.022502411,0.0035969184,0.0039100875,0.000054674558],"about_ca_topic_score_codex":0.0010095051,"about_ca_topic_score_gemma":0.0008624286,"teacher_disagreement_score":0.0011766203,"about_ca_system_score_codex":0.00030762458,"about_ca_system_score_gemma":0.00047945327,"threshold_uncertainty_score":0.0045532584},"labels":[],"label_agreement":null},{"id":"W2040941369","doi":"10.1109/netwks.2012.6381674","title":"A wireless sensor network deployment model with target localization constraints","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Software deployment; Wireless sensor network; Computer science; Computer network; Wireless network; Wireless; Key distribution in wireless sensor networks; Telecommunications","score_opus":0.009827004787262263,"score_gpt":0.19789916649731445,"score_spread":0.1880721617100522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040941369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02741448,0.0016310372,0.941753,0.0021572176,0.00019984535,0.00015278228,0.0008334672,0.00030853273,0.025549637],"genre_scores_gemma":[0.7692349,0.0054376554,0.17735623,0.00063756085,0.00038466032,0.00082043564,0.0010920317,0.00016258862,0.044873904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992823,0.0002589559,0.000028452661,0.00021493557,0.00014259719,0.000072763614],"domain_scores_gemma":[0.999446,0.00028694046,0.00010370701,0.000032202086,0.0000905277,0.000040718398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067214714,0.0014720543,0.0008099484,0.0007020577,0.00044433036,0.0013815996,0.002282864,0.0021757155,0.0033593231],"category_scores_gemma":[0.0015977839,0.00066366285,0.0007192954,0.0022555087,0.00072977797,0.0024015664,0.0009716307,0.001521255,0.0009746459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004274392,0.00004240489,0.0002723128,0.00013188284,0.000026005355,0.0003206228,0.000046160323,0.9499909,0.0015045248,0.037345268,0.0019441129,0.008333039],"study_design_scores_gemma":[0.000012430415,0.000042580243,0.00012812047,0.000009854559,0.000011249023,0.000073900956,0.00001710808,0.9915503,0.00018256939,0.0057829195,0.0021809505,0.0000079792935],"about_ca_topic_score_codex":0.0052531,"about_ca_topic_score_gemma":0.004322818,"teacher_disagreement_score":0.0052531,"about_ca_system_score_codex":0.001078166,"about_ca_system_score_gemma":0.00071908534,"threshold_uncertainty_score":0.011238039},"labels":[],"label_agreement":null},{"id":"W2041487242","doi":"10.1007/s10514-006-9720-1","title":"Locating sensor nodes on construction projects","year":2006,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Wireless sensor network; Scalability; Flexibility (engineering); Distributed computing; Triangulation; Key distribution in wireless sensor networks; Wireless; Range (aeronautics); Wireless ad hoc network; Field (mathematics); Real-time computing; Computer network; Wireless network; Telecommunications","score_opus":0.008481927521194286,"score_gpt":0.19324896473775172,"score_spread":0.18476703721655743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041487242","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7688516,0.00036125112,0.21518508,0.00019387205,0.000047126905,0.000075652446,0.00033165776,0.0003138459,0.014640003],"genre_scores_gemma":[0.96574104,0.00027427016,0.0295126,0.0000064534834,0.00001013917,0.00003653185,0.00021672485,0.000015020891,0.004187341],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962723,0.000118930744,0.0000113635415,0.0000592038,0.00013754693,0.000045803154],"domain_scores_gemma":[0.9996314,0.00014065797,0.00005568611,0.000044631553,0.00009255045,0.000035068944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033491914,0.00029395882,0.0003129119,0.00084590854,0.000610334,0.0006040297,0.0006206169,0.00062811706,0.0016514079],"category_scores_gemma":[0.0017186408,0.00033973213,0.00020912763,0.001626323,0.00038749145,0.0009031417,0.00094581145,0.00023422469,0.0004110415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007350142,0.00010918626,0.03363146,0.00023346378,0.00004721919,0.0011089052,0.0008010892,0.70453084,0.024192438,0.017834384,0.003300822,0.21347518],"study_design_scores_gemma":[0.000024530138,0.00021493864,0.023581116,0.000046421457,0.00003143424,0.00028948463,0.0012790115,0.9502946,0.008463515,0.0106033785,0.005146641,0.000024862453],"about_ca_topic_score_codex":0.006372809,"about_ca_topic_score_gemma":0.0108338855,"teacher_disagreement_score":0.006372809,"about_ca_system_score_codex":0.0003967968,"about_ca_system_score_gemma":0.00049043156,"threshold_uncertainty_score":0.012671411},"labels":[],"label_agreement":null},{"id":"W2042262247","doi":"10.1109/rose.2013.6698429","title":"Simulator based study of robot alignment and localization","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"USable; Computer science; Robot; Mobile robot; Node (physics); Wireless; Key (lock); Resource (disambiguation); Real-time computing; Simulation; Artificial intelligence; Computer network; Engineering; Telecommunications","score_opus":0.0071972979338920115,"score_gpt":0.19738685941764333,"score_spread":0.19018956148375132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042262247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24283765,0.0009561756,0.72425747,0.0005941099,0.00015381092,0.00018151653,0.00038473765,0.0005209411,0.030113619],"genre_scores_gemma":[0.94211257,0.0007700213,0.050702598,0.000051574585,0.000040214352,0.0001634942,0.00022213442,0.000104790364,0.005832638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966526,0.00015897366,0.0000103327975,0.000033010252,0.00009844079,0.00003394352],"domain_scores_gemma":[0.9984004,0.0011456456,0.00015664421,0.000072947754,0.00017762763,0.000046762267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037185947,0.00046455016,0.0005452961,0.00048475873,0.000250676,0.00062626763,0.0007708189,0.0008465621,0.0022413703],"category_scores_gemma":[0.0029583347,0.00028641193,0.00039182435,0.00047868883,0.00057366607,0.00052554224,0.0005122752,0.00048619375,0.00022808266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022276487,0.000024648842,0.0005911766,0.00005892465,0.000017483364,0.0000717664,0.000049020076,0.9815248,0.0019213699,0.012058645,0.00017578235,0.0034840293],"study_design_scores_gemma":[0.000004378572,0.000026184165,0.00014789336,0.0000045077168,0.0000033187043,0.00002025794,0.000015008035,0.99745625,0.00052679016,0.0010045791,0.00078721007,0.0000036926876],"about_ca_topic_score_codex":0.005062659,"about_ca_topic_score_gemma":0.0021571284,"teacher_disagreement_score":0.005062659,"about_ca_system_score_codex":0.00051433704,"about_ca_system_score_gemma":0.0005084925,"threshold_uncertainty_score":0.01006639},"labels":[],"label_agreement":null},{"id":"W2042414722","doi":"10.4304/jnw.10.3.141-151","title":"Wi-Fi-based Positioning in a Complex Underground Environment","year":2015,"lang":"en","type":"article","venue":"Journal of Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Computer science; Real-time computing; Computer security; Human–computer interaction","score_opus":0.02883959241326816,"score_gpt":0.2149274203378151,"score_spread":0.18608782792454692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042414722","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7504834,0.0001541555,0.2439859,0.000063126914,0.000040702173,0.000047170026,0.0001985118,0.000836794,0.004190271],"genre_scores_gemma":[0.95660317,0.000095998075,0.041805882,0.000014309762,0.0000072815856,0.000019885318,0.00011204436,0.000014935716,0.0013264364],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980253,0.00004176558,0.000008721674,0.000042035183,0.00007602773,0.000028966944],"domain_scores_gemma":[0.99988365,0.000035335248,0.000017052238,0.000029563264,0.000026858168,0.0000075140447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017972819,0.0002948126,0.0002554739,0.0002132636,0.00020327709,0.0002931251,0.0003180611,0.00033893774,0.0005767552],"category_scores_gemma":[0.0005438114,0.00010303539,0.00009587102,0.0003085386,0.0002992207,0.00033534886,0.0004636578,0.00013990424,0.00034863933],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007179558,0.00009265594,0.021999449,0.0003592567,0.000083780425,0.0012279995,0.0009197461,0.13381363,0.51861453,0.002954266,0.0011035706,0.31811315],"study_design_scores_gemma":[0.00007526376,0.0013578605,0.09994675,0.000059664213,0.00013788906,0.00224666,0.0008230023,0.57831144,0.30196893,0.0024181355,0.01253516,0.00011926944],"about_ca_topic_score_codex":0.0025002954,"about_ca_topic_score_gemma":0.003320731,"teacher_disagreement_score":0.0025002954,"about_ca_system_score_codex":0.00015548579,"about_ca_system_score_gemma":0.00018133993,"threshold_uncertainty_score":0.004971504},"labels":[],"label_agreement":null},{"id":"W2042486336","doi":"10.1007/s10291-014-0417-1","title":"Cycling dead reckoning for enhanced portable device navigation on multi-gear bicycles","year":2014,"lang":"en","type":"article","venue":"GPS Solutions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Dead reckoning; Inertial navigation system; Computer science; Orientation (vector space); Inertial measurement unit; Real-time computing; Microelectromechanical systems; Simulation; Embedded system; Engineering; Global Positioning System; Artificial intelligence; Telecommunications","score_opus":0.033231944730505766,"score_gpt":0.2676863422115147,"score_spread":0.23445439748100894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042486336","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7137057,0.0004653179,0.26744097,0.00012081601,0.00017529108,0.000085520274,0.00035402508,0.0029454653,0.0147069255],"genre_scores_gemma":[0.97326463,0.00008850154,0.021291133,0.000026311154,0.00001040465,0.000019903857,0.0001550403,0.00004710683,0.0050969324],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985003,0.000020912312,0.0000060516477,0.000031219275,0.000059722497,0.000032043514],"domain_scores_gemma":[0.9998739,0.000019776526,0.0000088420875,0.00002090248,0.00006501855,0.000011607805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009854776,0.00046566606,0.0003922324,0.00033609683,0.00027211726,0.0003620987,0.0006158283,0.00035415162,0.003471992],"category_scores_gemma":[0.0003891998,0.00014562174,0.00017355077,0.00025531516,0.00008880884,0.00032313782,0.0005315456,0.00017942119,0.0009925488],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017723754,0.0002011747,0.009460479,0.00030256747,0.00006056869,0.00077198417,0.00048143367,0.029004762,0.21776526,0.00095614675,0.004478407,0.73474497],"study_design_scores_gemma":[0.00015217502,0.0021558304,0.074136965,0.00015321419,0.00027511225,0.0021266986,0.0011124433,0.7497963,0.14651662,0.0013282648,0.022124873,0.00012156147],"about_ca_topic_score_codex":0.004032874,"about_ca_topic_score_gemma":0.009667518,"teacher_disagreement_score":0.004032874,"about_ca_system_score_codex":0.00013054791,"about_ca_system_score_gemma":0.00031311423,"threshold_uncertainty_score":0.011614978},"labels":[],"label_agreement":null},{"id":"W2043253127","doi":"10.1016/j.tcs.2011.08.040","title":"The minimum positional error incurred by any connectivity-based positioning algorithm for mobile wireless systems","year":2011,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Node (physics); Wireless sensor network; Algorithm; Wireless network; Wireless; Computer network; Geographic routing; Routing (electronic design automation); Real-time computing; Distributed computing; Routing protocol; Engineering","score_opus":0.008877635359583953,"score_gpt":0.22069445401295212,"score_spread":0.21181681865336818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043253127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06532588,0.0009966441,0.92875993,0.0006932516,0.0003425723,0.00004088763,0.00020528078,0.00038699468,0.0032485295],"genre_scores_gemma":[0.80256027,0.00091348734,0.18983772,0.000114059934,0.00021472605,0.000103414626,0.00033829958,0.00024149276,0.0056766174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99782,0.0005287177,0.00014608775,0.0003022837,0.000983407,0.00021954309],"domain_scores_gemma":[0.9951344,0.0028853605,0.00032862942,0.0005167237,0.0010121765,0.00012276342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012550122,0.0011572664,0.0009166228,0.00072010333,0.00084002677,0.0012619218,0.0011234034,0.0014257792,0.0020411888],"category_scores_gemma":[0.015508168,0.00036284197,0.00035648077,0.0014266233,0.00081748434,0.0019347601,0.0015446013,0.00109921,0.0005908301],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007395596,0.000060356004,0.0025955539,0.00039662558,0.00009521156,0.00025612663,0.00015956862,0.79875803,0.022909014,0.026373435,0.0022682662,0.14538826],"study_design_scores_gemma":[0.0000361526,0.00038517234,0.0027130113,0.00005707936,0.00006308487,0.0005462429,0.00010573478,0.9581594,0.020159299,0.015016396,0.0027199776,0.000038441183],"about_ca_topic_score_codex":0.0025535054,"about_ca_topic_score_gemma":0.0021543067,"teacher_disagreement_score":0.0025535054,"about_ca_system_score_codex":0.0009200651,"about_ca_system_score_gemma":0.0011883617,"threshold_uncertainty_score":0.006828487},"labels":[],"label_agreement":null},{"id":"W2044699738","doi":"10.1109/vtcfall.2014.6966175","title":"Sensing in Mobile Sensor Networks with Noisy Mobility Knowledge","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Computer science; Mobility model; Markov process; Imperfect; Mobile computing; Mobile telephony; Real-time computing; Computer network; Mobile radio","score_opus":0.004740444960630152,"score_gpt":0.19713746073830124,"score_spread":0.1923970157776711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044699738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46807885,0.002901582,0.5237925,0.0016452823,0.000084668245,0.000080879356,0.00014496548,0.00027400075,0.0029972917],"genre_scores_gemma":[0.98976725,0.00042184675,0.009349807,0.00006029634,0.000041111158,0.00002867286,0.000033363583,0.000015844204,0.00028176108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99666363,0.0014646784,0.00018401667,0.00042239917,0.00084338646,0.00042174594],"domain_scores_gemma":[0.9704004,0.024377633,0.002703773,0.0012247539,0.0009789732,0.00031435658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004357431,0.001177796,0.0013493649,0.00070963457,0.0009016208,0.0012719454,0.0012353784,0.0016275032,0.00041946952],"category_scores_gemma":[0.030788166,0.0007158806,0.0004768519,0.0010186213,0.0025365248,0.0041168537,0.0014738286,0.0008441677,0.000116016876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017336632,0.000027691247,0.0018572479,0.000072584044,0.0000438309,0.00016478174,0.00007631345,0.9875207,0.0018391972,0.003780095,0.00010547318,0.0043388563],"study_design_scores_gemma":[0.000013635449,0.000084146566,0.0010396617,0.000010890097,0.00002354175,0.00012208972,0.0000461902,0.9912168,0.0011739783,0.006127839,0.00012872479,0.000012516362],"about_ca_topic_score_codex":0.0034899323,"about_ca_topic_score_gemma":0.0022613616,"teacher_disagreement_score":0.004357431,"about_ca_system_score_codex":0.0017014756,"about_ca_system_score_gemma":0.00091834273,"threshold_uncertainty_score":0.023044527},"labels":[],"label_agreement":null},{"id":"W2044702072","doi":"10.1109/secon.2010.5508243","title":"Scheduling for Scalable Energy-Efficient Localization in Mobile Ad Hoc Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Correctness; Scalability; Wireless ad hoc network; Mobile ad hoc network; Wireless sensor network; Scheduling (production processes); Distributed computing; Upper and lower bounds; Efficient energy use; Latency (audio); Computation; Real-time computing; Algorithm; Computer network; Wireless; Mathematical optimization; Mathematics","score_opus":0.004847942038162415,"score_gpt":0.20412491280137945,"score_spread":0.19927697076321704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044702072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032582916,0.0010671727,0.96296316,0.00021157876,0.0001576231,0.00013338763,0.00006100655,0.00071107264,0.0021120673],"genre_scores_gemma":[0.78556514,0.00074418756,0.21125437,0.00008961087,0.00014895551,0.00019496099,0.00014487634,0.000091198075,0.0017666466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993901,0.00022736585,0.000045698886,0.000084429266,0.00017652505,0.000075706295],"domain_scores_gemma":[0.99844223,0.0008745953,0.00016324919,0.00021185896,0.00021415399,0.00009392635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013879621,0.0005934425,0.00077645906,0.0005145216,0.00089023175,0.00068356364,0.0011446663,0.00042081362,0.0011262777],"category_scores_gemma":[0.003532657,0.00032443338,0.00024751332,0.000832885,0.0004996674,0.0010233334,0.00085045834,0.0004551586,0.00031315393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028297585,0.00009425601,0.00072526117,0.0001915052,0.000035461606,0.00015894063,0.00017810745,0.82529867,0.009795607,0.023432491,0.0036864749,0.1361203],"study_design_scores_gemma":[0.000032240412,0.00006588055,0.00012074191,0.0000075517833,0.000008349254,0.000034756562,0.000031079606,0.9901338,0.0013291272,0.006577769,0.0016507423,0.0000079142155],"about_ca_topic_score_codex":0.0023299106,"about_ca_topic_score_gemma":0.0033474935,"teacher_disagreement_score":0.0023299106,"about_ca_system_score_codex":0.00076889317,"about_ca_system_score_gemma":0.001191755,"threshold_uncertainty_score":0.0073403716},"labels":[],"label_agreement":null},{"id":"W2045012981","doi":"10.1016/j.sigpro.2013.04.004","title":"A new constrained weighted least squares algorithm for TDOA-based localization","year":2013,"lang":"en","type":"article","venue":"Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":132,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"University of Chinese Academy of Sciences; City University of Hong Kong","keywords":"Mathematics; Multilateration; Linear least squares; Algorithm; Least-squares function approximation; Estimator; Non-linear least squares; Total least squares; Matrix (chemical analysis); Quadratic equation; Generalized least squares; Gaussian elimination; Gaussian; Mathematical optimization; Applied mathematics; Statistics; Estimation theory; Geometry","score_opus":0.008601640146762396,"score_gpt":0.21421704556929702,"score_spread":0.20561540542253462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045012981","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00065735367,0.0000708899,0.99876285,0.0000247757,0.00004609275,0.000010223018,0.000014333155,0.00014868694,0.00026483674],"genre_scores_gemma":[0.01948788,0.000231762,0.9768183,0.00008900088,0.00006243736,0.000107056716,0.00015634375,0.00012929796,0.0029179123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990845,0.00014458087,0.000050288018,0.0002036888,0.00047425283,0.000042689797],"domain_scores_gemma":[0.99920636,0.00018524865,0.00006096932,0.000097811055,0.00041523555,0.000034438108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006116524,0.0010573852,0.00096971635,0.0007894506,0.00046444716,0.00081583153,0.0015045626,0.0010266345,0.002994951],"category_scores_gemma":[0.002718802,0.00057346903,0.00072955375,0.0019144454,0.0004502568,0.0016626733,0.0014668006,0.0012821552,0.0023162002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001821412,0.00009581421,0.0005047083,0.00022360503,0.00014221825,0.00008658784,0.00012429162,0.12773508,0.059309628,0.012064411,0.006826942,0.79270464],"study_design_scores_gemma":[0.000027338036,0.000062618754,0.00031471034,0.000020120255,0.00003835931,0.00016385873,0.000020116877,0.97266227,0.010881182,0.00321693,0.012553868,0.000038652466],"about_ca_topic_score_codex":0.003890622,"about_ca_topic_score_gemma":0.006189475,"teacher_disagreement_score":0.003890622,"about_ca_system_score_codex":0.0003602049,"about_ca_system_score_gemma":0.0015341108,"threshold_uncertainty_score":0.010019124},"labels":[],"label_agreement":null},{"id":"W2045323798","doi":"10.1145/1247721.1247724","title":"Determining precise distance in a highly mobile environment using imprecise GPS measurements","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Global Positioning System; Computer science; Remote sensing; Geodesy; Geology; Telecommunications","score_opus":0.023347848024984685,"score_gpt":0.23774552301427324,"score_spread":0.21439767498928855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045323798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20724525,0.0028218075,0.781878,0.0002495551,0.00023855949,0.000050062372,0.001081812,0.0016123503,0.0048225094],"genre_scores_gemma":[0.8428448,0.0018156854,0.15088303,0.00006610859,0.00009177523,0.000059583723,0.0010174668,0.000099812416,0.0031218654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912244,0.0002165902,0.000042022013,0.0001799465,0.00036651187,0.00007245377],"domain_scores_gemma":[0.998456,0.0007074011,0.00019164327,0.0003328056,0.00025684305,0.000055302273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004051435,0.00094723894,0.00091691327,0.0011228347,0.00049462117,0.000901607,0.0008928304,0.0010638777,0.0021208418],"category_scores_gemma":[0.0036625476,0.00052541326,0.00027767415,0.0016179333,0.0005254483,0.001423111,0.0013067214,0.00069886015,0.0019166879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016725515,0.00010342238,0.02484117,0.0011963948,0.00017303348,0.0013899263,0.00071552256,0.124625884,0.20740324,0.0044010486,0.0047883517,0.6286894],"study_design_scores_gemma":[0.00021085415,0.0022868288,0.0862654,0.0004968713,0.00047718364,0.00675371,0.0016409502,0.6110419,0.23348746,0.024262609,0.032603957,0.00047230037],"about_ca_topic_score_codex":0.002016239,"about_ca_topic_score_gemma":0.003002717,"teacher_disagreement_score":0.0021208418,"about_ca_system_score_codex":0.00020199765,"about_ca_system_score_gemma":0.00031653498,"threshold_uncertainty_score":0.0070949793},"labels":[],"label_agreement":null},{"id":"W2047138769","doi":"10.1109/itsc.2012.6338896","title":"A new base stations placement approach for enhanced vehicle position estimation in parking lot","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Base station; Computer science; Range (aeronautics); Real-time computing; Position (finance); Wireless; Intelligent transportation system; Base (topology); Transport engineering; Telecommunications; Engineering; Mathematics","score_opus":0.014907578022773088,"score_gpt":0.24152816186024695,"score_spread":0.22662058383747385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047138769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005862771,0.000099690675,0.99288213,0.000030118465,0.00003370966,0.000019705923,0.000025279944,0.00031637127,0.00073018426],"genre_scores_gemma":[0.26922324,0.00031842725,0.7254252,0.00007810304,0.000104459206,0.00011831994,0.0001861395,0.00006386602,0.004482271],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996117,0.00009286851,0.000019396324,0.00009663835,0.0001404635,0.000038940783],"domain_scores_gemma":[0.999746,0.00005350427,0.000026087739,0.000038552458,0.00011877587,0.000017010505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032803623,0.0007940317,0.00061820145,0.0008003078,0.00039460626,0.0005016178,0.0011330389,0.00073870725,0.0012815539],"category_scores_gemma":[0.0006446335,0.00035230594,0.0005296751,0.00093832304,0.0002743597,0.00061934855,0.0006496267,0.0003810034,0.0008710004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029040276,0.000111513,0.0022394184,0.00018665851,0.00012898016,0.00049061066,0.00024253369,0.39574227,0.06437364,0.010995774,0.0035550075,0.52164316],"study_design_scores_gemma":[0.000024259693,0.00013933066,0.0006876357,0.000008247887,0.00003651821,0.0003573623,0.000033380664,0.9799492,0.011439613,0.00213439,0.005161371,0.00002863238],"about_ca_topic_score_codex":0.0029142478,"about_ca_topic_score_gemma":0.0035643291,"teacher_disagreement_score":0.0029142478,"about_ca_system_score_codex":0.00033876978,"about_ca_system_score_gemma":0.00062882743,"threshold_uncertainty_score":0.0057945848},"labels":[],"label_agreement":null},{"id":"W2047273371","doi":"10.1109/wimob.2011.6085407","title":"Anchor node placement for localization in wireless sensor networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Wireless sensor network; Computer science; Node (physics); Focus (optics); Curvilinear coordinates; Set (abstract data type); Class (philosophy); Computer network; Key distribution in wireless sensor networks; Position (finance); Protocol (science); Wireless network; Wireless; Distributed computing; Artificial intelligence; Mathematics; Engineering; Telecommunications","score_opus":0.01882167935211168,"score_gpt":0.20993047606095291,"score_spread":0.19110879670884123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047273371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016069959,0.0037133286,0.9911862,0.00023909794,0.0003184847,0.000047642465,0.00003520693,0.00028881314,0.0025641422],"genre_scores_gemma":[0.24432638,0.018781444,0.7261363,0.0002937838,0.00096756034,0.0005118651,0.0004379688,0.00030413512,0.00824048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824035,0.00068176165,0.00008663982,0.0002606715,0.0006872785,0.000043356962],"domain_scores_gemma":[0.99898475,0.0004900826,0.00015495729,0.00014798909,0.00019247466,0.000029789333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011365872,0.0010670949,0.00067112176,0.00090380025,0.0007591803,0.0010826379,0.0012882947,0.0015966478,0.0022932705],"category_scores_gemma":[0.006147859,0.00039791537,0.0004536069,0.0024120696,0.0014876258,0.0015201704,0.0011725789,0.0012836679,0.0020768584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001603317,0.000041233485,0.0008078335,0.0009042657,0.000067787405,0.0005718959,0.00028838767,0.5216679,0.015447639,0.15276983,0.011374606,0.29589835],"study_design_scores_gemma":[0.000042556912,0.0002198351,0.00041329412,0.0002240703,0.000056948473,0.00064238464,0.00010092583,0.7736142,0.008383683,0.15809159,0.05814244,0.00006796756],"about_ca_topic_score_codex":0.0010677452,"about_ca_topic_score_gemma":0.0009042396,"teacher_disagreement_score":0.0022932705,"about_ca_system_score_codex":0.0005941123,"about_ca_system_score_gemma":0.00059264275,"threshold_uncertainty_score":0.007671714},"labels":[],"label_agreement":null},{"id":"W2048198391","doi":"10.1109/tsp.2012.2222402","title":"A Maximum Likelihood Time Delay Estimator in a Multipath Environment Using Importance Sampling","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Multipath propagation; Estimator; Algorithm; Computer science; Initialization; Mathematical optimization; Estimation theory; Dimension (graph theory); Iterative method; Convergence (economics); Maximization; Computational complexity theory; Mathematics; Expectation–maximization algorithm; Maximum likelihood; Statistics","score_opus":0.020923431025993595,"score_gpt":0.2396263874622118,"score_spread":0.2187029564362182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048198391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023242675,0.00005715623,0.99736077,0.00002198397,0.0000106340085,0.000006373487,0.0000063946195,0.00006520982,0.00014715876],"genre_scores_gemma":[0.14327678,0.00030614177,0.854675,0.00005474584,0.000102255486,0.00006374469,0.00012171522,0.00007585443,0.0013237804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994992,0.0001476081,0.000018849505,0.00007424926,0.00023052967,0.000029577499],"domain_scores_gemma":[0.9988431,0.00075618847,0.00008843478,0.00008490597,0.00018831789,0.00003901295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008880863,0.00053159986,0.0006555839,0.0005642347,0.00020302799,0.0005577488,0.00077573024,0.00065922126,0.0008245387],"category_scores_gemma":[0.004769465,0.00035750124,0.00041049192,0.0007611963,0.0004753411,0.0012944161,0.00088909554,0.00088871096,0.0003934216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024923787,0.00008379427,0.0023167662,0.00023631327,0.00009702856,0.0002309745,0.0001257654,0.5287093,0.04735061,0.04812973,0.0017001675,0.37077028],"study_design_scores_gemma":[0.00001581255,0.000037023572,0.00023619697,0.000008397485,0.00000880923,0.00009816667,0.0000061312344,0.9895314,0.004671706,0.0042023463,0.0011715536,0.000012424522],"about_ca_topic_score_codex":0.00090421195,"about_ca_topic_score_gemma":0.0008902484,"teacher_disagreement_score":0.00090421195,"about_ca_system_score_codex":0.00035840608,"about_ca_system_score_gemma":0.00079714,"threshold_uncertainty_score":0.004696667},"labels":[],"label_agreement":null},{"id":"W2050672002","doi":"10.1145/2632048.2636062","title":"Car-level congestion and position estimation for railway trips using mobile phones","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Canadian Pain Society","keywords":"Bluetooth; Computer science; Train; Real-time computing; Estimator; Doors; TRIPS architecture; Transport engineering; Simulation; Telecommunications; Engineering; Statistics; Wireless; Mathematics; Geography","score_opus":0.01802198936191922,"score_gpt":0.2371765924715764,"score_spread":0.21915460310965718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050672002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14132372,0.00025301366,0.8551905,0.00009364139,0.000038806633,0.000054665874,0.00021596272,0.0013334427,0.0014963225],"genre_scores_gemma":[0.811273,0.00015223412,0.18617772,0.000045450422,0.000055241966,0.000076928445,0.00041979263,0.000056537487,0.0017431005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967074,0.000050209146,0.000018628674,0.000108196386,0.000113443784,0.000038848197],"domain_scores_gemma":[0.9995295,0.0001355056,0.00010880853,0.000051989435,0.00014525752,0.00002898915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003433018,0.0006498301,0.00053426926,0.0014252687,0.00028146914,0.00047703035,0.00084250624,0.00052175164,0.0009090806],"category_scores_gemma":[0.0015368413,0.00035272734,0.00050292193,0.0007889934,0.00019241932,0.00069533184,0.00054984563,0.00041904827,0.00056102267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002885319,0.00027171473,0.06797739,0.00022689153,0.00026766246,0.00032070154,0.00037684338,0.25500208,0.04296788,0.0018860579,0.002290317,0.62812394],"study_design_scores_gemma":[0.000025204316,0.00013776303,0.026456434,0.000015485544,0.000052574156,0.0002886688,0.000096088,0.96259993,0.007891143,0.00084274745,0.0015457409,0.000048220194],"about_ca_topic_score_codex":0.0052138427,"about_ca_topic_score_gemma":0.006977537,"teacher_disagreement_score":0.0052138427,"about_ca_system_score_codex":0.00026896116,"about_ca_system_score_gemma":0.00041208314,"threshold_uncertainty_score":0.010367036},"labels":[],"label_agreement":null},{"id":"W2050792882","doi":"10.1109/vtcfall.2014.6966221","title":"Unknown Transmit Power Energy-Based Source Localization in Wireless Sensor Networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Cramér–Rao bound; Wireless sensor network; Transmitter power output; Upper and lower bounds; Signal-to-noise ratio (imaging); Computer science; Energy (signal processing); SIGNAL (programming language); Power (physics); Wireless; Algorithm; Estimation theory; Mathematics; Telecommunications; Statistics; Physics; Channel (broadcasting); Computer network","score_opus":0.00295813960426247,"score_gpt":0.1644540000694359,"score_spread":0.16149586046517342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050792882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010375616,0.0011341848,0.9872091,0.00010690002,0.000042391915,0.000009845965,0.00001306916,0.00022173196,0.0008870902],"genre_scores_gemma":[0.750731,0.0050859475,0.23881547,0.00013582005,0.00016645275,0.00010647287,0.00013474497,0.000111007874,0.004713014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999634,0.00013329297,0.00001530601,0.000059197544,0.00014677797,0.000011345687],"domain_scores_gemma":[0.9996221,0.00021349461,0.000060281665,0.000031726726,0.000065999186,0.0000062811396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036724488,0.00049115997,0.00044561602,0.000453853,0.00021804759,0.0006015277,0.0005308269,0.00058928714,0.00054630515],"category_scores_gemma":[0.0017519697,0.00028106797,0.00021408587,0.0007832223,0.00057198503,0.001061895,0.00062285736,0.0003604383,0.0003413813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016294367,0.000043164368,0.00087185577,0.00045816984,0.00006296872,0.00020184377,0.00015670931,0.63957536,0.044857796,0.018467046,0.001547235,0.29359493],"study_design_scores_gemma":[0.000009890646,0.00008501306,0.0003098391,0.000021668171,0.000014259055,0.00012749771,0.000022563823,0.98180264,0.010636464,0.0051460266,0.0018059222,0.000018212304],"about_ca_topic_score_codex":0.0006008513,"about_ca_topic_score_gemma":0.000586483,"teacher_disagreement_score":0.0006015277,"about_ca_system_score_codex":0.00026106782,"about_ca_system_score_gemma":0.00021693732,"threshold_uncertainty_score":0.0019421577},"labels":[],"label_agreement":null},{"id":"W2050795796","doi":"10.1109/chinacom.2008.4685105","title":"RadLoco: A rapid and low cost indoor location-sensing system","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"RSS; Computer science; Real-time computing; Wireless sensor network; Kernel (algebra); Indoor positioning system; Radio propagation; Computer network; Telecommunications; Accelerometer","score_opus":0.009659967217846012,"score_gpt":0.18302303789510085,"score_spread":0.17336307067725484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050795796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056718875,0.0011022146,0.75308484,0.0004008211,0.0003665032,0.0008247541,0.0030918224,0.15660548,0.027804703],"genre_scores_gemma":[0.46526307,0.0006135799,0.48765385,0.0010578557,0.00024208565,0.00088191684,0.0074135917,0.0017679698,0.035105973],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991115,0.00011977655,0.000053418935,0.00022573199,0.00038271153,0.00010680471],"domain_scores_gemma":[0.9990509,0.00013042883,0.000117109594,0.0002960722,0.00026505667,0.00014037373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007668673,0.00069968816,0.0011264248,0.0015252045,0.000431121,0.000832743,0.001992701,0.00087877316,0.008478587],"category_scores_gemma":[0.0013493116,0.0003658911,0.0002937448,0.0007192588,0.00035113067,0.0013290517,0.0021064347,0.0006390772,0.006592123],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022381805,0.0005984334,0.007421558,0.0007393519,0.000114619696,0.0008708422,0.00050929235,0.008696343,0.15219137,0.007842391,0.09777349,0.7210041],"study_design_scores_gemma":[0.000954931,0.0025853459,0.018311784,0.00018716061,0.00031999883,0.0043062544,0.00035342696,0.37989956,0.16411714,0.0033905634,0.42485952,0.00071439537],"about_ca_topic_score_codex":0.0015344284,"about_ca_topic_score_gemma":0.0017155462,"teacher_disagreement_score":0.008478587,"about_ca_system_score_codex":0.0004104181,"about_ca_system_score_gemma":0.0005892757,"threshold_uncertainty_score":0.028363705},"labels":[],"label_agreement":null},{"id":"W2051036170","doi":"10.1109/iembs.2011.6091315","title":"Indoor waypoint navigation via magnetic anomalies","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ASTER","funders":"","keywords":"Waypoint; Computer science; Real-time computing; Navigation system; Radio navigation; Magnetometer; Embedded system; Simulation; Telecommunications; Global Positioning System; Magnetic field","score_opus":0.012302068292154094,"score_gpt":0.17146212187163298,"score_spread":0.15916005357947888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051036170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3012955,0.0004279209,0.6785315,0.0001433994,0.00012849693,0.00012762116,0.00066622684,0.009698315,0.008980996],"genre_scores_gemma":[0.8737157,0.00017408816,0.12230961,0.000033466324,0.000025109981,0.000063159234,0.00035760738,0.000077404766,0.0032438673],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979705,0.000028058761,0.00000543071,0.000053697062,0.000087198096,0.00002847226],"domain_scores_gemma":[0.99975604,0.00006215196,0.00004502017,0.000036819318,0.00008324508,0.000016765183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014852814,0.00030716037,0.00031813298,0.00087391876,0.00021462543,0.00049788173,0.00047584274,0.00034180903,0.0012471273],"category_scores_gemma":[0.00072886574,0.00016509631,0.00019406823,0.00058597454,0.00018444395,0.00042750163,0.0007976256,0.00023439738,0.0005796208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006635182,0.00008887169,0.020900311,0.00030219142,0.00007600018,0.0006553497,0.0012849044,0.031116392,0.23396589,0.0040413234,0.005135529,0.7017697],"study_design_scores_gemma":[0.00013258462,0.00088281627,0.08096244,0.0001684616,0.0001874896,0.0025447193,0.0009669144,0.632352,0.21416062,0.0063278624,0.061092883,0.00022118702],"about_ca_topic_score_codex":0.0040256963,"about_ca_topic_score_gemma":0.0066258796,"teacher_disagreement_score":0.0040256963,"about_ca_system_score_codex":0.00022694512,"about_ca_system_score_gemma":0.0002912912,"threshold_uncertainty_score":0.008004546},"labels":[],"label_agreement":null},{"id":"W2051305419","doi":"10.1007/s11235-009-9223-4","title":"Neural-based approach for localization of sensors in indoor environment","year":2009,"lang":"en","type":"article","venue":"Telecommunication Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Norleaf Networks (Canada); University of Ottawa","funders":"","keywords":"Computer science; Node (physics); Gradient descent; Artificial neural network; Received signal strength indication; Signal strength; Wireless sensor network; Algorithm; Position (finance); SIGNAL (programming language); Real-time computing; Transmitter; Wireless; Artificial intelligence; Computer network; Telecommunications; Acoustics","score_opus":0.01718536148226756,"score_gpt":0.2235563385749642,"score_spread":0.20637097709269664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051305419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011740678,0.00034678014,0.98504144,0.00008383888,0.0000582435,0.000016999571,0.000036771708,0.00046648618,0.0022087686],"genre_scores_gemma":[0.7263127,0.00068812433,0.26084435,0.00018407812,0.00012768255,0.000115952236,0.00021527956,0.00005709685,0.01145483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977547,0.000044302607,0.000012246085,0.000057407116,0.00007692169,0.000033715478],"domain_scores_gemma":[0.99977773,0.000064246175,0.000021761984,0.000017619073,0.00010973122,0.000008804425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033439748,0.00054962456,0.00048738648,0.0005681785,0.00029984378,0.0005950142,0.0010245513,0.0010458132,0.0015931398],"category_scores_gemma":[0.0009240738,0.00026606533,0.000424722,0.0008002322,0.00029217007,0.0006257697,0.0005654673,0.00059310184,0.00052599557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015189394,0.000101083075,0.0012361575,0.00012255595,0.00009103686,0.000127569,0.00007883243,0.5844547,0.016082361,0.0062746913,0.0017433015,0.38953584],"study_design_scores_gemma":[0.0000037788598,0.000019101493,0.00025768604,0.000004650977,0.000011742774,0.000023379433,0.000007552615,0.99637467,0.0018984178,0.001033278,0.00036013804,0.0000055381724],"about_ca_topic_score_codex":0.0083461,"about_ca_topic_score_gemma":0.00956392,"teacher_disagreement_score":0.0083461,"about_ca_system_score_codex":0.00050868985,"about_ca_system_score_gemma":0.0005646252,"threshold_uncertainty_score":0.016595066},"labels":[],"label_agreement":null},{"id":"W2052211117","doi":"10.1109/dcoss.2013.27","title":"Predictive Filtering for Adjacency-Based Localization in MANET","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Adjacency list; Mobile ad hoc network; Kalman filter; Node (physics); Adjacency matrix; Noise (video); Bandwidth (computing); Artificial intelligence; Data mining; Algorithm; Computer network; Theoretical computer science; Telecommunications; Wireless","score_opus":0.006479159783030338,"score_gpt":0.1891460185717011,"score_spread":0.18266685878867076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052211117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015432834,0.00014274754,0.983364,0.00010039933,0.000035863646,0.00001307023,0.000028599183,0.00029313893,0.0005893634],"genre_scores_gemma":[0.8854741,0.00045394906,0.11218377,0.00007251197,0.00006976395,0.00006672622,0.00012971136,0.000043307384,0.0015061363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969625,0.00008698151,0.000017160095,0.000059775244,0.00010871351,0.000031138596],"domain_scores_gemma":[0.9987381,0.0008155547,0.00011681174,0.00011275448,0.00018671446,0.000030119334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007473205,0.00034563968,0.000580493,0.000708181,0.0004816763,0.00060490106,0.00070366217,0.0005198335,0.0006997825],"category_scores_gemma":[0.004395046,0.00026617266,0.00032603083,0.000852684,0.00053419085,0.0009107279,0.00050588965,0.00058843015,0.00015749152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042858013,0.000014392511,0.0005848314,0.00003237846,0.000014975138,0.00004848905,0.000060562375,0.9508957,0.0018298462,0.009084402,0.00046277582,0.036928784],"study_design_scores_gemma":[0.0000016935306,0.0000070975407,0.00009641408,0.00000196002,0.0000027188369,0.0000058661108,0.0000049050036,0.99727374,0.00028666435,0.0021608276,0.00015471078,0.0000034285351],"about_ca_topic_score_codex":0.012931351,"about_ca_topic_score_gemma":0.0103000235,"teacher_disagreement_score":0.012931351,"about_ca_system_score_codex":0.0005885069,"about_ca_system_score_gemma":0.0005702763,"threshold_uncertainty_score":0.025712132},"labels":[],"label_agreement":null},{"id":"W2052248394","doi":"10.1587/transcom.e97.b.1728","title":"Efficient Indoor Fingerprinting Localization Technique Using Regional Propagation Model","year":2014,"lang":"en","type":"article","venue":"IEICE Transactions on Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Trilateration; Fingerprint (computing); Fingerprint recognition; RSS; Data mining; Cluster analysis; Affinity propagation; Triangulation; Algorithm; Artificial intelligence; Pattern recognition (psychology); Real-time computing; Fuzzy clustering","score_opus":0.030573232870359575,"score_gpt":0.2566712372566859,"score_spread":0.22609800438632632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052248394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007982855,0.00009726201,0.9906522,0.00004049777,0.000011838174,0.000011516707,0.000028895844,0.00062805973,0.0005468532],"genre_scores_gemma":[0.66447026,0.0006311686,0.33121413,0.00007511732,0.00004797597,0.00008397294,0.0002750934,0.00010950183,0.0030928005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940336,0.00012866939,0.000022915006,0.0001453499,0.00023466418,0.00006507868],"domain_scores_gemma":[0.99946564,0.00012406312,0.00007242226,0.00015058197,0.00016939397,0.000017832554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044523514,0.0007214913,0.0007856297,0.0008366199,0.00036474338,0.0005598977,0.0013398278,0.00066369737,0.0010348049],"category_scores_gemma":[0.0012748186,0.00033854102,0.0006366501,0.00120048,0.00031438042,0.0015068098,0.00076910306,0.0006701726,0.00085335376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022765831,0.00007931481,0.00338528,0.00014475723,0.00008605281,0.00023602802,0.00017106155,0.5265107,0.04886216,0.00928876,0.0024871454,0.4085211],"study_design_scores_gemma":[0.000009144543,0.000057229914,0.00048242934,0.000004636081,0.000021716744,0.00020182025,0.00001919366,0.9898267,0.0068616364,0.0012259663,0.0012705679,0.000018911789],"about_ca_topic_score_codex":0.004736664,"about_ca_topic_score_gemma":0.0039195437,"teacher_disagreement_score":0.004736664,"about_ca_system_score_codex":0.0004687682,"about_ca_system_score_gemma":0.0006394939,"threshold_uncertainty_score":0.0094181895},"labels":[],"label_agreement":null},{"id":"W2053702202","doi":"10.1155/asp/2006/42737","title":"A New Position Location System Using DTV Transmitter Identification Watermark Signals","year":2006,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Communications Research Centre Canada","funders":"","keywords":"Transmitter; Global Positioning System; Computer science; SIGNAL (programming language); Synchronization (alternating current); Digital television; Position (finance); Digital watermarking; Positioning system; Watermark; Electronic engineering; Telecommunications; Acoustics; Computer vision; Engineering; Physics","score_opus":0.00791523779757921,"score_gpt":0.2410611767967255,"score_spread":0.2331459389991463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053702202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0246045,0.00069506525,0.9688401,0.00024414447,0.00045277653,0.00008190366,0.00008401788,0.0022415563,0.0027559388],"genre_scores_gemma":[0.4234839,0.0010844024,0.5542774,0.00039270858,0.00056395977,0.00018248089,0.00037134232,0.00008693924,0.01955686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995055,0.000061241226,0.00004090258,0.00012353172,0.00023011226,0.000038765396],"domain_scores_gemma":[0.99942577,0.0000729644,0.0001327007,0.000117103824,0.00020252974,0.000048972393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036077833,0.00047448638,0.00067550415,0.0006274505,0.0003271371,0.0008495171,0.0013184701,0.0010819755,0.0019300415],"category_scores_gemma":[0.0008283554,0.00030343275,0.00025929319,0.0006395442,0.00031773894,0.0018090844,0.0010662718,0.0008039672,0.0016112614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055870996,0.00011492713,0.0016988895,0.00050244346,0.00007974363,0.00060108776,0.0003122959,0.008491629,0.44153398,0.017084531,0.005227616,0.5237942],"study_design_scores_gemma":[0.0004566667,0.0025296288,0.0027506116,0.000109059394,0.0003188011,0.0045651714,0.00011325402,0.42971364,0.43440494,0.0038064232,0.12101088,0.0002208814],"about_ca_topic_score_codex":0.0002712676,"about_ca_topic_score_gemma":0.00035812412,"teacher_disagreement_score":0.0019300415,"about_ca_system_score_codex":0.00026442963,"about_ca_system_score_gemma":0.00038016244,"threshold_uncertainty_score":0.0064566135},"labels":[],"label_agreement":null},{"id":"W2054754702","doi":"10.1109/saconet.2014.6867779","title":"Exploiting dual-antenna diversity for phase cancellation in augmented RFID system","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"China Scholarship Council","keywords":"Ultra high frequency; Radio-frequency identification; Computer science; Antenna (radio); Antenna diversity; Dual (grammatical number); SIGNAL (programming language); Electronic engineering; Phase (matter); Radio frequency; Telecommunications; Engineering; Physics","score_opus":0.01476352345538238,"score_gpt":0.22241718993305348,"score_spread":0.2076536664776711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054754702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073494345,0.000383405,0.92180896,0.000115373114,0.000066016815,0.000016771863,0.000018364,0.0004213847,0.0036754333],"genre_scores_gemma":[0.844544,0.00027591566,0.15226369,0.00012342157,0.00006092252,0.000028690236,0.000038867525,0.000027505534,0.0026369756],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995542,0.00013130245,0.000021259986,0.000077203906,0.00016302317,0.00005303152],"domain_scores_gemma":[0.99956256,0.00015632871,0.000083787716,0.00008820459,0.00008443047,0.000024593923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024911066,0.00047924012,0.00044949364,0.00030693584,0.0003557883,0.0006228173,0.0006540773,0.00066561037,0.0009370177],"category_scores_gemma":[0.0006280599,0.00027632856,0.00055070006,0.00037366984,0.00040216363,0.0009724526,0.00082302565,0.0004730523,0.0006700002],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086808077,0.000122142,0.004208533,0.0003219489,0.00016423702,0.0010142284,0.0004280056,0.13610084,0.5896474,0.021561954,0.0009781293,0.24458447],"study_design_scores_gemma":[0.00010359683,0.0009884841,0.0014467093,0.000034464785,0.00016956251,0.0035530883,0.00009813264,0.7454412,0.22709881,0.007133741,0.013807167,0.00012503364],"about_ca_topic_score_codex":0.00018913258,"about_ca_topic_score_gemma":0.00021336936,"teacher_disagreement_score":0.0009370177,"about_ca_system_score_codex":0.000222414,"about_ca_system_score_gemma":0.00021049034,"threshold_uncertainty_score":0.0031346083},"labels":[],"label_agreement":null},{"id":"W2055198680","doi":"10.1049/ip-com:20050142","title":"Mobile positioning based on relaying capability of mobile stations in hybrid wireless networks","year":2006,"lang":"en","type":"article","venue":"IEE Proceedings - Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer network; Mobile wireless; Mobile ad hoc network; Flooding (psychology); Scheme (mathematics); Wireless; Cellular network; Wireless network; Real-time computing; Telecommunications; Network packet","score_opus":0.007573176544881515,"score_gpt":0.22649566510988545,"score_spread":0.21892248856500393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055198680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16666783,0.00085269933,0.8302582,0.00009242635,0.000039654806,0.00003613532,0.000025432375,0.00046370254,0.0015639882],"genre_scores_gemma":[0.9231294,0.00032471906,0.07557984,0.000021775058,0.00004134454,0.00003238826,0.000033269338,0.000012350024,0.000824873],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997009,0.00010214802,0.000019854997,0.000050377916,0.00009684276,0.000029809295],"domain_scores_gemma":[0.99931836,0.00033225134,0.00009202375,0.00013020806,0.000106084684,0.000021072274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042924532,0.00033312663,0.0003587455,0.0004123639,0.00020577242,0.00035593947,0.000714966,0.00058997923,0.00024823364],"category_scores_gemma":[0.0011837273,0.0002185265,0.00022899108,0.0003271876,0.00048033957,0.0009154214,0.00053184817,0.00023354878,0.00018693971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005330488,0.00007193606,0.0030363505,0.0003229958,0.00012805218,0.0013134715,0.00059930683,0.25299948,0.38439888,0.039718803,0.0011219892,0.31575572],"study_design_scores_gemma":[0.00007287876,0.0008911959,0.0014578492,0.000018002154,0.00012000285,0.0014319333,0.00008429899,0.9137299,0.06753244,0.009495073,0.0051020356,0.00006442603],"about_ca_topic_score_codex":0.00045690284,"about_ca_topic_score_gemma":0.0005340198,"teacher_disagreement_score":0.000714966,"about_ca_system_score_codex":0.00018562374,"about_ca_system_score_gemma":0.00015043796,"threshold_uncertainty_score":0.0022701025},"labels":[],"label_agreement":null},{"id":"W2055387155","doi":"10.1080/07408170490257853","title":"An improved algorithm for solving a multi-period facility location problem","year":2004,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computation; Pruning; Feature (linguistics); Computer science; Algorithm; Mathematical optimization; Facility location problem; Period (music); Mathematics; Data mining","score_opus":0.01243745283278811,"score_gpt":0.23637301878444245,"score_spread":0.22393556595165434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055387155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029212544,0.00007713314,0.9952572,0.00004575338,0.000030451503,0.000042770254,0.000045840166,0.00040860678,0.0011710515],"genre_scores_gemma":[0.029924009,0.00007439041,0.9682745,0.000037007238,0.000028624097,0.00010339992,0.0001726501,0.000084753236,0.0013007368],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992499,0.0001652176,0.00005524436,0.0001542636,0.0002829457,0.00009238508],"domain_scores_gemma":[0.9990619,0.0004777089,0.00006970718,0.00015647736,0.00020110897,0.00003313676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010125798,0.0006608334,0.0010490393,0.0009893526,0.00056810357,0.0008579723,0.0023169178,0.0013279814,0.0065099197],"category_scores_gemma":[0.0032636453,0.00043805392,0.0007950765,0.0012668057,0.00034128316,0.001600207,0.0010047699,0.0014596477,0.0016532699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019963848,0.00015057495,0.0007989409,0.0002458255,0.00007183944,0.00020444569,0.00011798954,0.4314284,0.007734929,0.029387424,0.008669574,0.52099043],"study_design_scores_gemma":[0.00006062539,0.000049896345,0.00015379903,0.000014176111,0.000015084061,0.0001157297,0.000015460868,0.984806,0.0015609801,0.007908845,0.005288711,0.000010551724],"about_ca_topic_score_codex":0.0032631177,"about_ca_topic_score_gemma":0.004559136,"teacher_disagreement_score":0.0065099197,"about_ca_system_score_codex":0.0005103168,"about_ca_system_score_gemma":0.001385416,"threshold_uncertainty_score":0.021777868},"labels":[],"label_agreement":null},{"id":"W2055797184","doi":"10.1007/s11277-012-0850-9","title":"Range Estimation in a Time Varying Multipath Channel","year":2012,"lang":"en","type":"article","venue":"Wireless Personal Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; New York Institute of Technology","funders":"","keywords":"Multipath propagation; Cramér–Rao bound; Estimator; Channel (broadcasting); Computer science; Algorithm; Channel state information; Doppler effect; Delay spread; Range (aeronautics); SIGNAL (programming language); Estimation theory; Statistics; Telecommunications; Mathematics; Physics; Wireless","score_opus":0.027368783474348596,"score_gpt":0.25688657737660964,"score_spread":0.22951779390226104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055797184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12241847,0.00037550254,0.87534785,0.00008808554,0.00003790481,0.00001222061,0.000048393646,0.00032766693,0.00134387],"genre_scores_gemma":[0.85214967,0.0006542739,0.1430407,0.0000516336,0.00008843072,0.000026374379,0.00014920729,0.00006509165,0.003774631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995616,0.00009852741,0.000015991927,0.00009305562,0.00014284246,0.00008800604],"domain_scores_gemma":[0.9989666,0.0006599966,0.00010432311,0.00007478068,0.00016048245,0.00003377956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037489264,0.0005288696,0.0006341185,0.0005722138,0.00033491783,0.00053948374,0.0005751753,0.00083086966,0.0005790129],"category_scores_gemma":[0.002756137,0.00041119882,0.00036627168,0.0009215522,0.000487968,0.0011681987,0.0008058421,0.0005559707,0.00033052117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058506883,0.00007392335,0.0043924376,0.0001680968,0.0001384739,0.00078161847,0.00019315652,0.7740795,0.06268172,0.00502089,0.00075474725,0.15113045],"study_design_scores_gemma":[0.000021433489,0.00008966583,0.0017442812,0.0000069860025,0.000041532825,0.00037988237,0.000049906645,0.9862059,0.009941723,0.0011325072,0.00036353196,0.000022653005],"about_ca_topic_score_codex":0.003357947,"about_ca_topic_score_gemma":0.0028232906,"teacher_disagreement_score":0.003357947,"about_ca_system_score_codex":0.00024263689,"about_ca_system_score_gemma":0.00045491155,"threshold_uncertainty_score":0.006676793},"labels":[],"label_agreement":null},{"id":"W2056645936","doi":"10.1109/glocomw.2013.6825138","title":"Angular diversity approach to indoor positioning using visible light","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Collimated light; Light-emitting diode; Optics; Detector; Computer science; Computer vision; Materials science; Artificial intelligence; Physics","score_opus":0.011745354161780549,"score_gpt":0.19240173263769325,"score_spread":0.1806563784759127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056645936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058183976,0.00023164145,0.93651444,0.00009016287,0.000024099845,0.000012275329,0.000026522019,0.00045866362,0.0044581722],"genre_scores_gemma":[0.89848924,0.00016132952,0.09914869,0.000026249509,0.000021691614,0.000024275554,0.000035212124,0.000030024978,0.002063367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997112,0.00007904037,0.0000061586443,0.0000490755,0.00011045833,0.000043926604],"domain_scores_gemma":[0.99972576,0.00010726799,0.000047343096,0.000042591255,0.00005726314,0.000019791518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002023782,0.00039179836,0.00030757912,0.0003791313,0.00026756088,0.00047499101,0.0005205356,0.00034641326,0.0010713623],"category_scores_gemma":[0.0004417716,0.00019168883,0.000329374,0.00033031203,0.0004008787,0.00041104227,0.0005781681,0.00032969099,0.00032279667],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040269707,0.00006769555,0.0028255065,0.00013972679,0.00007937163,0.00029281527,0.00024823088,0.63921714,0.16490014,0.02643368,0.0007644172,0.16462861],"study_design_scores_gemma":[0.00002388972,0.00023907234,0.0011353759,0.000010187601,0.000024694695,0.00023774343,0.000042407268,0.94911784,0.04246675,0.0039793937,0.0026725584,0.0000501668],"about_ca_topic_score_codex":0.0013305196,"about_ca_topic_score_gemma":0.001869428,"teacher_disagreement_score":0.0013305196,"about_ca_system_score_codex":0.0005143395,"about_ca_system_score_gemma":0.00026358038,"threshold_uncertainty_score":0.0037318468},"labels":[],"label_agreement":null},{"id":"W2056995207","doi":"10.1109/joe.2013.2279421","title":"A Machine Learning Approach for Dead-Reckoning Navigation at Sea Using a Single Accelerometer","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Oceanic Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Acceleration; Accelerometer; Dead reckoning; Heading (navigation); Orientation (vector space); Global Positioning System; Computer science; Coordinate system; Inertial navigation system; Gyroscope; Computer vision; Pitch angle; Artificial intelligence; Geodesy; Acoustics; Engineering; Geology; Physics; Aerospace engineering; Mathematics","score_opus":0.021531843286885666,"score_gpt":0.21591025501467617,"score_spread":0.1943784117277905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056995207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007311745,0.00031619423,0.99137115,0.000077783916,0.000052326795,0.000014918611,0.000027538672,0.00041877743,0.00040966814],"genre_scores_gemma":[0.42814517,0.0005883105,0.56557775,0.00018909144,0.00023000073,0.0001956838,0.00034856942,0.00009270304,0.0046327277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995229,0.0000953192,0.000044019387,0.00016947833,0.000116929616,0.000051377618],"domain_scores_gemma":[0.9992506,0.000322272,0.0000916301,0.000065213855,0.00024260987,0.000027591857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073861855,0.0008464673,0.0010707593,0.0008267742,0.0004523286,0.00063781644,0.0013107979,0.0010390324,0.0009532883],"category_scores_gemma":[0.001866736,0.00045403384,0.00064668665,0.0011705072,0.000364092,0.00076177646,0.0006419301,0.0011304494,0.00061496435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007214399,0.00008217569,0.0012941515,0.000071600305,0.0000806437,0.000073626565,0.000070738,0.43465036,0.0028252746,0.0026118858,0.0016550035,0.5565125],"study_design_scores_gemma":[0.0000031336403,0.000013497911,0.00013929975,0.0000037813627,0.0000035875184,0.000010695194,0.0000039790802,0.99847335,0.00032440524,0.0007613447,0.00025822636,0.000004621571],"about_ca_topic_score_codex":0.0057917642,"about_ca_topic_score_gemma":0.0049459836,"teacher_disagreement_score":0.0057917642,"about_ca_system_score_codex":0.0004405187,"about_ca_system_score_gemma":0.000768836,"threshold_uncertainty_score":0.011516094},"labels":[],"label_agreement":null},{"id":"W2057319337","doi":"10.1007/s00779-008-0195-2","title":"Mobile map interactions during a rendezvous: exploring the implications of automation","year":2008,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rendezvous; Computer science; Automation; Context (archaeology); Task (project management); Process (computing); Human–computer interaction; Systems engineering; Engineering","score_opus":0.02453846724915231,"score_gpt":0.23127950532861916,"score_spread":0.20674103807946684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057319337","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9738981,0.00016604822,0.006883493,0.00029323052,0.000016190692,0.000016567676,0.000060019564,0.000068801746,0.018597657],"genre_scores_gemma":[0.9985061,0.00003705308,0.0007719716,0.00001120812,0.0000034074124,0.0000042800934,0.000012881386,0.000008775718,0.0006441458],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996749,0.00014381758,0.000004119655,0.000051010204,0.00006167928,0.00006432969],"domain_scores_gemma":[0.99897873,0.00064159796,0.00011824331,0.00007456531,0.000103038095,0.000083808845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025037024,0.00022107422,0.00016104779,0.00032681786,0.00079074374,0.0017237255,0.0004034967,0.0007242516,0.0029975662],"category_scores_gemma":[0.003979081,0.0001738101,0.0001647717,0.00030998955,0.00066846056,0.0013434426,0.0006891993,0.00031352974,0.00036766368],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005909178,0.0008174846,0.28622058,0.0007094644,0.0003468807,0.006559668,0.20894328,0.031536542,0.13869837,0.049653064,0.0078027397,0.26280278],"study_design_scores_gemma":[0.00012604667,0.0010511992,0.64045954,0.00022660072,0.00027512357,0.0026147224,0.17529668,0.09126789,0.015540795,0.03403829,0.03891438,0.00018872284],"about_ca_topic_score_codex":0.00787275,"about_ca_topic_score_gemma":0.0121802,"teacher_disagreement_score":0.00787275,"about_ca_system_score_codex":0.00029934454,"about_ca_system_score_gemma":0.00035831315,"threshold_uncertainty_score":0.015653849},"labels":[],"label_agreement":null},{"id":"W2058534978","doi":"10.5555/1365562.1365564","title":"Localization applying an efficient neural network mapping","year":2007,"lang":"en","type":"article","venue":"Autonomic Computing and Communication Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Communications Research Centre Canada","funders":"","keywords":"Node (physics); Computer science; Position (finance); Curvilinear coordinates; Range (aeronautics); Artificial neural network; Scheme (mathematics); Projection (relational algebra); Data mining; Artificial intelligence; Algorithm; Mathematics; Engineering","score_opus":0.015128328281942301,"score_gpt":0.23168618094457433,"score_spread":0.21655785266263203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058534978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013338449,0.000056777757,0.9846762,0.000094224044,0.000017852255,0.000019479867,0.0000105716645,0.000349514,0.0014368952],"genre_scores_gemma":[0.61621195,0.00017782401,0.38023847,0.00006355579,0.00003902939,0.00010168281,0.00005202035,0.00005873366,0.00305674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997751,0.00006373237,0.000008624004,0.000045317312,0.00008721156,0.000020031992],"domain_scores_gemma":[0.99956244,0.0001756255,0.0000551857,0.000086389235,0.00010810153,0.000012250858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043539188,0.0004302012,0.00030377862,0.00043960378,0.0003365194,0.00042320887,0.00059485994,0.00045346553,0.0010475622],"category_scores_gemma":[0.0021447171,0.00016472144,0.00022793528,0.00053662766,0.0004892708,0.00083800335,0.000947997,0.00044418834,0.0003091213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099979414,0.00004303573,0.0011021892,0.00005774898,0.00003148366,0.000068672336,0.00009563542,0.6921128,0.021037601,0.017555531,0.0010556562,0.26673967],"study_design_scores_gemma":[0.0000044082135,0.00001821746,0.00015102685,0.0000022217191,0.0000030350855,0.000022551776,0.000006291651,0.9945569,0.0021931652,0.0023272643,0.00071029284,0.000004678988],"about_ca_topic_score_codex":0.004030695,"about_ca_topic_score_gemma":0.0039570024,"teacher_disagreement_score":0.004030695,"about_ca_system_score_codex":0.00048690196,"about_ca_system_score_gemma":0.0006305694,"threshold_uncertainty_score":0.0080145},"labels":[],"label_agreement":null},{"id":"W2058552223","doi":"10.1109/icuwb.2015.7324418","title":"Accurate Sensors Localization in Underground Mines or Tunnels","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; Institut National de la Recherche Scientifique","funders":"","keywords":"Position (finance); Computer science; Data mining; Scheme (mathematics); Node (physics); Algorithm; Wireless sensor network; Distance measurement; Underground mining (soft rock); Real-time computing; Artificial intelligence; Engineering; Coal mining; Mathematics; Computer network","score_opus":0.04443297562802166,"score_gpt":0.25870447459700624,"score_spread":0.21427149896898456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058552223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095345855,0.00065350626,0.9010251,0.00011637988,0.00005517396,0.000021797297,0.00008883121,0.00082805677,0.0018652346],"genre_scores_gemma":[0.8406683,0.0005680032,0.15602264,0.000038467493,0.000022699132,0.000033401542,0.00016848715,0.00005838769,0.0024195178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994804,0.0001445218,0.000026868314,0.00011249597,0.00018602487,0.000049673454],"domain_scores_gemma":[0.99948335,0.00013360806,0.00013999776,0.00011078388,0.000108747176,0.000023450188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004976981,0.00043130308,0.00060790236,0.0005690633,0.00031448924,0.00057855475,0.0006603685,0.0006272466,0.0005393172],"category_scores_gemma":[0.0014679506,0.00030376532,0.00026053545,0.0006851938,0.0004810693,0.0010819843,0.0012119302,0.00042591483,0.00039574775],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047737206,0.000049438735,0.011958335,0.0004466454,0.00008865395,0.0010999081,0.0004701634,0.57054985,0.10346726,0.01844687,0.0035672882,0.28937823],"study_design_scores_gemma":[0.000025064417,0.00017353248,0.0041302433,0.000051516028,0.000022591012,0.00082557026,0.00024339846,0.9552698,0.021503732,0.009597674,0.008114682,0.000042164535],"about_ca_topic_score_codex":0.0009304016,"about_ca_topic_score_gemma":0.0011460515,"teacher_disagreement_score":0.0009304016,"about_ca_system_score_codex":0.00020037919,"about_ca_system_score_gemma":0.00042321451,"threshold_uncertainty_score":0.002632141},"labels":[],"label_agreement":null},{"id":"W2058681739","doi":"10.1155/2009/128679","title":"Probabilistic Localization and Tracking of Malicious Insiders Using Hyperbolic Position Bounding in Vehicular Networks","year":2009,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; RSS; Spoofing attack; Computer security; Probabilistic logic; Computer network; Bounding overwatch; Position (finance); Real-time computing; Artificial intelligence","score_opus":0.02436456755000413,"score_gpt":0.2535891183691861,"score_spread":0.22922455081918194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058681739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084692605,0.00036596047,0.91330343,0.00013393103,0.000020326182,0.000034359942,0.000028452838,0.00037550353,0.0010455133],"genre_scores_gemma":[0.90659904,0.0002700414,0.09240295,0.000027669908,0.0000143327725,0.000035655168,0.000057016096,0.000019281097,0.00057403336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879396,0.0005602172,0.000057343703,0.00016995889,0.00031457303,0.00010386138],"domain_scores_gemma":[0.99570256,0.0023400101,0.0008392379,0.00057172024,0.00044997787,0.000096501164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020727501,0.00056915305,0.00077523023,0.001124872,0.0004296602,0.0009812387,0.0012508967,0.0006247998,0.0003091207],"category_scores_gemma":[0.008226113,0.0005001117,0.0003582694,0.0009989229,0.0011254621,0.0015748566,0.0018082778,0.00042284897,0.00020095223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013271677,0.000018227,0.0037770213,0.000040614374,0.000028202165,0.000100280246,0.0001455607,0.92338824,0.0039858534,0.009245909,0.00028762204,0.058849722],"study_design_scores_gemma":[0.000005071793,0.00002663021,0.00038996158,0.000004936629,0.000005864919,0.00004963653,0.000018500505,0.9953401,0.0015210919,0.0023981105,0.0002306338,0.0000093676545],"about_ca_topic_score_codex":0.005496774,"about_ca_topic_score_gemma":0.0028335415,"teacher_disagreement_score":0.005496774,"about_ca_system_score_codex":0.0008758469,"about_ca_system_score_gemma":0.0006920498,"threshold_uncertainty_score":0.01096189},"labels":[],"label_agreement":null},{"id":"W2058846534","doi":"10.1002/j.2161-4296.2011.tb02586.x","title":"Multi-Magnetometer Based Perturbation Mitigation for Indoor Orientation Estimation","year":2011,"lang":"en","type":"article","venue":"NAVIGATION Journal of the Institute of Navigation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Magnetometer; Orientation (vector space); Magnetic field; Computer science; Perturbation (astronomy); Earth's magnetic field; Remote sensing; Environmental science; Physics; Geography; Mathematics","score_opus":0.02384705618952011,"score_gpt":0.2503594995500136,"score_spread":0.22651244336049348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058846534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08642848,0.0004531528,0.9093546,0.00015277756,0.00022204002,0.000046325575,0.000082596234,0.0015068218,0.0017532144],"genre_scores_gemma":[0.7998395,0.00026164966,0.1975894,0.00006800083,0.000081038124,0.000040751485,0.00015186536,0.000039234445,0.0019285663],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997478,0.00006218784,0.000010982001,0.000051365412,0.00009963344,0.00002808479],"domain_scores_gemma":[0.99964666,0.00007606759,0.000056956942,0.000050820945,0.00015055668,0.000018974071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022712933,0.00069387583,0.000522548,0.00045531307,0.00027505087,0.000286522,0.00056619506,0.0005089018,0.00079286133],"category_scores_gemma":[0.00066921016,0.00022939841,0.00030841745,0.00039294295,0.00013901379,0.00029219876,0.0003493077,0.00038970026,0.00060976937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009663308,0.00020712563,0.005481548,0.00029484674,0.00013130547,0.0003322391,0.00012325529,0.08175558,0.49622628,0.0011318446,0.0030337402,0.41031578],"study_design_scores_gemma":[0.000048008256,0.00068926986,0.009075147,0.000033624143,0.00010428385,0.0006923412,0.00006899594,0.7629163,0.21953811,0.00049723807,0.0062752813,0.00006137796],"about_ca_topic_score_codex":0.0013330488,"about_ca_topic_score_gemma":0.0018271087,"teacher_disagreement_score":0.0013330488,"about_ca_system_score_codex":0.00022673099,"about_ca_system_score_gemma":0.0003364381,"threshold_uncertainty_score":0.0026524067},"labels":[],"label_agreement":null},{"id":"W2058905010","doi":"10.1109/glocom.2012.6503132","title":"Indoor positioning and distance-aware graph-based semi-supervised learning method","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Computer science; Scheme (mathematics); Software deployment; Artificial intelligence; Graph; Wireless; Machine learning; Location awareness; Data mining; Radio propagation; Pattern recognition (psychology); Computer network; Theoretical computer science; Mathematics; Telecommunications","score_opus":0.007950386208427215,"score_gpt":0.2270222220860546,"score_spread":0.21907183587762738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058905010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015416311,0.000067466186,0.98316115,0.00009003804,0.000014935182,0.000019928271,0.000033095104,0.0005942644,0.0006029424],"genre_scores_gemma":[0.65498346,0.00013405106,0.3412222,0.00012819597,0.000063549305,0.00010084082,0.00028534,0.00010423571,0.0029782266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992508,0.000312665,0.000026350346,0.00016649182,0.0001850104,0.00005866655],"domain_scores_gemma":[0.9978465,0.0010347372,0.00027804024,0.0003222433,0.00044753027,0.000071068855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090653636,0.00047693326,0.00083620625,0.0008124324,0.0003451469,0.0005189167,0.0019207131,0.0008715492,0.0011379762],"category_scores_gemma":[0.0029333518,0.00029486913,0.00051160133,0.00080164423,0.00070028123,0.0010619694,0.0008095549,0.00071313686,0.00054811995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018849592,0.00016228425,0.0014466915,0.000115807015,0.000063954045,0.00009245108,0.00013125545,0.7505878,0.00557347,0.00794588,0.0025336393,0.23115823],"study_design_scores_gemma":[0.0000036616009,0.000012098325,0.000085746,0.0000013919583,0.0000022634626,0.000012265031,0.0000040665577,0.99836916,0.00047709598,0.0009029125,0.00012629126,0.0000031098252],"about_ca_topic_score_codex":0.004174074,"about_ca_topic_score_gemma":0.0038530258,"teacher_disagreement_score":0.004174074,"about_ca_system_score_codex":0.0005751005,"about_ca_system_score_gemma":0.0007490152,"threshold_uncertainty_score":0.008299589},"labels":[],"label_agreement":null},{"id":"W2059774582","doi":"10.1145/1568199.1568209","title":"WHLocator","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Wireless; Real-time computing; Telecommunications","score_opus":0.0031717838712462636,"score_gpt":0.17319561494961863,"score_spread":0.17002383107837238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059774582","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063645974,0.0011853544,0.62692404,0.0013692854,0.0021112375,0.0013854234,0.009359884,0.08786052,0.20615824],"genre_scores_gemma":[0.3056575,0.0012055406,0.2549949,0.0020013133,0.00039036295,0.0013856078,0.01562482,0.0057644914,0.41297552],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994407,0.000077303215,0.000024596646,0.00016201865,0.00020941092,0.00008594658],"domain_scores_gemma":[0.99885213,0.00021316951,0.00008067002,0.00037453786,0.00031866351,0.0001609005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054457365,0.00069106714,0.0004994565,0.0006601583,0.0006822242,0.00085308956,0.0014578063,0.0011056319,0.06755887],"category_scores_gemma":[0.0018105884,0.00031496873,0.00043119685,0.0004910673,0.00036327547,0.0014821994,0.00245755,0.0005894293,0.038548302],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017471355,0.0001880754,0.008937958,0.0010096743,0.00011415087,0.001020608,0.0015023428,0.0026472064,0.07796004,0.01382308,0.25284287,0.6382069],"study_design_scores_gemma":[0.00013506184,0.0005448409,0.0052250004,0.000124849,0.00008961793,0.0017345897,0.00041433578,0.015593548,0.050610263,0.002096547,0.9233056,0.00012584034],"about_ca_topic_score_codex":0.0013347308,"about_ca_topic_score_gemma":0.0015402114,"teacher_disagreement_score":0.06755887,"about_ca_system_score_codex":0.00021519796,"about_ca_system_score_gemma":0.0005117803,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2060204554","doi":"10.1145/2370216.2370388","title":"Precise passive RFID localization for service delivery in smart home","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Home automation; Computer science; Service (business); Granularity; Service delivery framework; Everyday life; Computer security; Cognitive impairment; Internet privacy; Human–computer interaction; Cognition; Telecommunications; Business; Psychology; Marketing","score_opus":0.011030795532154445,"score_gpt":0.20842537532928251,"score_spread":0.19739457979712807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060204554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039653897,0.001352843,0.9530172,0.0003216423,0.00016344184,0.00004902227,0.000037463964,0.0025265156,0.0028779637],"genre_scores_gemma":[0.76305723,0.0011681946,0.22917183,0.00026478418,0.00013809274,0.000060959977,0.00010384832,0.000089473855,0.005945671],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99931455,0.0002740581,0.000046944246,0.00011742235,0.00018724373,0.000059795402],"domain_scores_gemma":[0.9995603,0.00015632104,0.00006863491,0.0001226903,0.00007368073,0.000018428282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006925189,0.00037426845,0.00053154613,0.0006292879,0.00035213784,0.000728243,0.0007455563,0.0008312791,0.0009744272],"category_scores_gemma":[0.0013472416,0.00036834693,0.00028975337,0.00048331986,0.0005065287,0.0017899342,0.00092023954,0.0003858649,0.00089377],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075276126,0.0001280537,0.0039129164,0.0004983269,0.000066999455,0.00071990956,0.0010062574,0.04214019,0.29448968,0.032145705,0.0071746963,0.6169644],"study_design_scores_gemma":[0.00016686348,0.0013695194,0.005622639,0.00015071122,0.00026433647,0.0033834388,0.00063885085,0.66821486,0.2217685,0.022684893,0.07548341,0.00025200116],"about_ca_topic_score_codex":0.00060971046,"about_ca_topic_score_gemma":0.0006226277,"teacher_disagreement_score":0.0009744272,"about_ca_system_score_codex":0.00032108737,"about_ca_system_score_gemma":0.00023062013,"threshold_uncertainty_score":0.0036624074},"labels":[],"label_agreement":null},{"id":"W2060238489","doi":"10.1016/j.procs.2014.07.041","title":"WiLoVe: A WiFi-coverage based Location Verification System in LBS","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Location-based service; Embedded system; Computer network; Real-time computing; Computer security","score_opus":0.00466476254274714,"score_gpt":0.18267203983048508,"score_spread":0.17800727728773794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060238489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13887027,0.0006482201,0.7950603,0.00034626067,0.00021244673,0.0005864813,0.00040600303,0.057520397,0.0063496083],"genre_scores_gemma":[0.9139217,0.000117698415,0.08009425,0.00023351029,0.000048772843,0.00017633273,0.0006030226,0.00024195998,0.0045626233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988788,0.00025873628,0.000097768076,0.00023705621,0.00033032717,0.000197358],"domain_scores_gemma":[0.9988783,0.00019531156,0.00017064842,0.0004236464,0.00021458104,0.00011746841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007437861,0.0006357405,0.000800015,0.0007983939,0.00055742136,0.0008086008,0.002120517,0.0009237274,0.004486033],"category_scores_gemma":[0.0022802535,0.0003358478,0.00038394093,0.00043066405,0.000507122,0.002220822,0.0024075543,0.0007459423,0.0014814868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004243047,0.0009308293,0.017000044,0.0008346598,0.0002912525,0.0045747953,0.0009121562,0.04459956,0.19161408,0.02241169,0.035831835,0.6767561],"study_design_scores_gemma":[0.0004605939,0.0013686223,0.006387753,0.00009516389,0.00014245998,0.0020772994,0.00017059465,0.83292234,0.116001755,0.0054520597,0.03472067,0.00020072018],"about_ca_topic_score_codex":0.0017153199,"about_ca_topic_score_gemma":0.0011261582,"teacher_disagreement_score":0.004486033,"about_ca_system_score_codex":0.00045316195,"about_ca_system_score_gemma":0.0006740331,"threshold_uncertainty_score":0.015007257},"labels":[],"label_agreement":null},{"id":"W2060918376","doi":"10.1109/icc.2012.6364358","title":"Received signal strength calibration for handset localization in WLAN","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"University of Victoria","keywords":"RSS; Handset; Calibration; Computer science; Transformation (genetics); Affine transformation; Wireless; Laptop; Signal strength; Key (lock); Real-time computing; SIGNAL (programming language); Non-line-of-sight propagation; Artificial intelligence; Telecommunications; Mathematics; Statistics","score_opus":0.01266039791728463,"score_gpt":0.22044306675405126,"score_spread":0.20778266883676663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060918376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03671757,0.00014237044,0.9590496,0.00005597106,0.000049631373,0.000055803168,0.000041595187,0.00189214,0.0019953232],"genre_scores_gemma":[0.73575616,0.00021284081,0.26134866,0.000079848876,0.00003294602,0.00010722751,0.00016750209,0.00021821259,0.0020766268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846196,0.00050600496,0.000051164207,0.00021051995,0.0006987235,0.00007164091],"domain_scores_gemma":[0.9985734,0.00044567377,0.00020496837,0.00036949804,0.00037855055,0.00002784696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009449405,0.00074265996,0.00045510998,0.0007808015,0.00032765084,0.00056905247,0.00090918597,0.0006247572,0.0025113523],"category_scores_gemma":[0.0046111476,0.00036650064,0.00035164738,0.00087983924,0.00038628743,0.0007118993,0.00079726154,0.00067888724,0.0013700892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039694025,0.00019764029,0.0064616306,0.00021168793,0.000108692366,0.00026979105,0.00026648876,0.16888106,0.1951597,0.0043615215,0.001981799,0.621703],"study_design_scores_gemma":[0.00006610317,0.00047713218,0.010512192,0.000050633593,0.00006366023,0.0007439552,0.00009610583,0.7550244,0.21908316,0.0029396957,0.010845719,0.000097111915],"about_ca_topic_score_codex":0.001294018,"about_ca_topic_score_gemma":0.0010347792,"teacher_disagreement_score":0.0025113523,"about_ca_system_score_codex":0.00045532978,"about_ca_system_score_gemma":0.00045771414,"threshold_uncertainty_score":0.008401334},"labels":[],"label_agreement":null},{"id":"W2061378448","doi":"10.1117/12.486710","title":"Mitigation of NLOS error in angle-of-arrival wireless location","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Multipath propagation; Angle of arrival; Wireless; Time of arrival; Real-time computing; Channel (broadcasting); Telecommunications; Antenna (radio)","score_opus":0.010038380047493788,"score_gpt":0.21845820176374042,"score_spread":0.20841982171624662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061378448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02917942,0.0004479814,0.9684654,0.00014026058,0.00008578522,0.00001559245,0.000030605115,0.00043725164,0.0011976851],"genre_scores_gemma":[0.60516965,0.00093795947,0.38995007,0.00011540564,0.00026439579,0.00008535146,0.00020593303,0.0001012646,0.0031699953],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991253,0.00024483458,0.00006002379,0.0001368171,0.00034522172,0.00008790104],"domain_scores_gemma":[0.99765563,0.0009116713,0.00042637912,0.00035547555,0.000595902,0.000054896253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008967697,0.00077688135,0.0005448597,0.0007589875,0.00046911443,0.00048950076,0.0006623729,0.0006985852,0.00062221393],"category_scores_gemma":[0.0060475506,0.00035705182,0.00029416181,0.00076496607,0.0005488777,0.0016023451,0.0012861437,0.0006104032,0.0008159112],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059338,0.00008980332,0.005294721,0.00029982065,0.00014319413,0.0003176101,0.00039864823,0.2380051,0.108239196,0.010221958,0.0017303745,0.63466626],"study_design_scores_gemma":[0.000114167415,0.000664009,0.0065588574,0.000100931466,0.00016024637,0.0014236164,0.000251065,0.8401097,0.12276284,0.011805471,0.015944604,0.0001045956],"about_ca_topic_score_codex":0.00092658395,"about_ca_topic_score_gemma":0.0014374227,"teacher_disagreement_score":0.00092658395,"about_ca_system_score_codex":0.00018759358,"about_ca_system_score_gemma":0.00055796915,"threshold_uncertainty_score":0.0047426224},"labels":[],"label_agreement":null},{"id":"W2062486942","doi":"10.1109/glocom.2014.7036847","title":"A feature scaling based k-nearest neighbor algorithm for indoor positioning system","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"k-nearest neighbors algorithm; Computer science; Scaling; Feature (linguistics); Pattern recognition (psychology); Best bin first; Artificial intelligence; Algorithm; Mathematics","score_opus":0.004202595201716645,"score_gpt":0.18720624971619934,"score_spread":0.1830036545144827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062486942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009127705,0.00042957044,0.9886154,0.00005942231,0.00007372677,0.000045181754,0.00005584994,0.0007149634,0.00087820186],"genre_scores_gemma":[0.33754396,0.0005452877,0.6578348,0.00010197025,0.000085139625,0.00024125908,0.00045703558,0.000076250944,0.003114198],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891925,0.00017188647,0.00006458443,0.00029389755,0.00048039996,0.00007011723],"domain_scores_gemma":[0.9995964,0.00008152307,0.00003830527,0.00006558783,0.00020265939,0.000015589309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004510805,0.0006530754,0.0011054781,0.0007515788,0.00071836857,0.00049005303,0.0013677081,0.0008126542,0.0012525992],"category_scores_gemma":[0.0018281536,0.00027928015,0.0006003764,0.0012924906,0.00031381028,0.0011389556,0.0007373534,0.0007013458,0.00096366165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017103742,0.00008585215,0.001612373,0.00012229977,0.00007529182,0.00013123253,0.00013028529,0.22941099,0.012090663,0.0045072697,0.004389655,0.7472729],"study_design_scores_gemma":[0.0000116917745,0.00005154291,0.0006096631,0.000006387182,0.000011045928,0.00016213833,0.000028691642,0.99225736,0.0025779984,0.0017362543,0.0025250441,0.000022277984],"about_ca_topic_score_codex":0.0069270036,"about_ca_topic_score_gemma":0.0047524315,"teacher_disagreement_score":0.0069270036,"about_ca_system_score_codex":0.00045008512,"about_ca_system_score_gemma":0.0006784323,"threshold_uncertainty_score":0.013773382},"labels":[],"label_agreement":null},{"id":"W2062604482","doi":"10.1109/smc.2014.6974515","title":"Adaptive multi-model and entropy-based localization on context-aware robotic system","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Entropy (arrow of time); Mobile robot; Artificial intelligence; Robot; Particle filter; Computer vision; Kalman filter","score_opus":0.014178999836380908,"score_gpt":0.2003878532082574,"score_spread":0.18620885337187648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062604482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014431606,0.00012481697,0.9844811,0.00004225535,0.000020762038,0.000012115276,0.000006150377,0.0002500317,0.00063118455],"genre_scores_gemma":[0.7953296,0.00021025259,0.20255087,0.000060452294,0.000042646174,0.000057766887,0.000037022848,0.00003522017,0.0016761272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978644,0.000039655133,0.00001152119,0.000047174744,0.00009497824,0.000020141408],"domain_scores_gemma":[0.9998425,0.000053060576,0.000023603492,0.000026114205,0.000043032916,0.000011667484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024330475,0.0003824559,0.0004563292,0.00033093613,0.00032816353,0.00035270583,0.00051781634,0.00040547305,0.0004132907],"category_scores_gemma":[0.00061035756,0.00018922836,0.00037839686,0.00021792954,0.00028318565,0.00081762613,0.0006753214,0.00040382092,0.00013130071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018527023,0.00007333455,0.0018997997,0.00012957603,0.00007503069,0.00025422746,0.00024520128,0.62477607,0.06163612,0.016093438,0.00094268785,0.2936892],"study_design_scores_gemma":[0.000005871844,0.000042040585,0.0003654952,0.000003820204,0.00000892056,0.00006232881,0.00000855568,0.99192625,0.005352094,0.0014804872,0.0007340838,0.00001005376],"about_ca_topic_score_codex":0.0016774023,"about_ca_topic_score_gemma":0.0014268468,"teacher_disagreement_score":0.0016774023,"about_ca_system_score_codex":0.00027599157,"about_ca_system_score_gemma":0.00037538027,"threshold_uncertainty_score":0.0033352375},"labels":[],"label_agreement":null},{"id":"W2062709195","doi":"10.1145/2639108.2642905","title":"Poster","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Computer science; Wireless network; Field (mathematics); Square (algebra); Wireless; Geometry; Mathematics; Telecommunications","score_opus":0.0036552754056748227,"score_gpt":0.16711962411349524,"score_spread":0.16346434870782042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062709195","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006150288,0.005021907,0.021042826,0.014688518,0.036052614,0.00059933786,0.007889625,0.0033454373,0.90520936],"genre_scores_gemma":[0.025951339,0.0025819945,0.008830677,0.00320751,0.0041679684,0.00020576445,0.008322381,0.00081984705,0.9459125],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99902177,0.00013740516,0.00006447708,0.00032432197,0.00030937814,0.00014263752],"domain_scores_gemma":[0.9981887,0.0001933279,0.00006329871,0.00027261424,0.00078886887,0.00049311214],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001036648,0.00092399196,0.0007055578,0.0014170578,0.0020565328,0.0037099677,0.0017188468,0.0021778904,0.5939102],"category_scores_gemma":[0.002444075,0.00036954842,0.0008669218,0.00093192764,0.00052348245,0.0025100878,0.0030548498,0.0020011228,0.3772394],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028408054,0.00015096125,0.0009195655,0.00030802996,0.000024287447,0.00039701824,0.00017341068,0.0004433354,0.002077465,0.015447537,0.81435484,0.16541958],"study_design_scores_gemma":[0.000021870079,0.000044180502,0.0005536641,0.00008007635,0.000009696703,0.00030542281,0.00011713408,0.0003237385,0.0007655421,0.004684912,0.99308276,0.000011021788],"about_ca_topic_score_codex":0.0012970287,"about_ca_topic_score_gemma":0.0023379494,"teacher_disagreement_score":0.40608978,"about_ca_system_score_codex":0.0011656655,"about_ca_system_score_gemma":0.0013609456,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2062813200","doi":"10.1109/lcn.2014.6925771","title":"A cooperative localization scheme using RFID crowdsourcing and time-shifted multilateration","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multilateration; Crowdsourcing; Computer science; Real-time computing; Scheme (mathematics); Leverage (statistics); Internet of Things; Real-time locating system; Mobile device; Object (grammar); Embedded system; Computer network; Artificial intelligence; Engineering","score_opus":0.0072458654673379416,"score_gpt":0.20095379764167026,"score_spread":0.19370793217433233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062813200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030552598,0.00022500234,0.9656314,0.00016823459,0.000114672424,0.00009757242,0.000026551339,0.00069566106,0.0024883018],"genre_scores_gemma":[0.87124723,0.00015332141,0.12405404,0.00016834038,0.00006909868,0.0001809097,0.00005803015,0.000038453083,0.0040306733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987627,0.00026705262,0.000054591823,0.00029938584,0.0004417814,0.00017434466],"domain_scores_gemma":[0.9985386,0.0003550013,0.00021424147,0.00035546735,0.0003944585,0.00014217506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008136437,0.0009853345,0.0010186,0.0010128032,0.0011563005,0.0007008471,0.002461145,0.0011113976,0.0008800623],"category_scores_gemma":[0.0023874387,0.0003370349,0.00071747164,0.0012389648,0.0009144678,0.0014723942,0.0030730446,0.0007055666,0.00057702075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008847856,0.00033782524,0.002818551,0.00036473348,0.00022700921,0.0022452383,0.0013556308,0.40512928,0.14629918,0.034402374,0.0055731493,0.40036228],"study_design_scores_gemma":[0.00008731563,0.00035315214,0.00040514042,0.000012408143,0.000056469322,0.000624457,0.00015058141,0.96753925,0.019755919,0.0053445017,0.0055928202,0.00007806297],"about_ca_topic_score_codex":0.0022321471,"about_ca_topic_score_gemma":0.0018376547,"teacher_disagreement_score":0.002461145,"about_ca_system_score_codex":0.00070672645,"about_ca_system_score_gemma":0.00084857625,"threshold_uncertainty_score":0.005127728},"labels":[],"label_agreement":null},{"id":"W2063019515","doi":"10.1109/icc.2012.6364564","title":"3D compressive sensing for nodes localization in WNs based on RSS","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Compressed sensing; RSS; Computer science; Algorithm; Position (finance); Nyquist–Shannon sampling theorem; Wireless sensor network; Base station; Noise (video); Nyquist rate; Signal reconstruction; Sampling (signal processing); Real-time computing; Signal processing; Artificial intelligence; Computer vision; Telecommunications; Computer network","score_opus":0.01350739132567633,"score_gpt":0.2307157686526658,"score_spread":0.21720837732698947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063019515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010128615,0.00013866056,0.9883228,0.0001083804,0.000025511377,0.000012929843,0.000028956683,0.00012961357,0.0011044636],"genre_scores_gemma":[0.61977226,0.0010150623,0.37557465,0.00013016471,0.0000789559,0.00016833116,0.00025643167,0.000054653923,0.0029494606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974805,0.00007843766,0.000010976171,0.00003836131,0.00010517151,0.000019045216],"domain_scores_gemma":[0.99973804,0.00013708392,0.00003003169,0.00003065437,0.000053195825,0.000011067983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003885373,0.00044156556,0.00036025365,0.0002833812,0.00021576132,0.00041051712,0.00046404137,0.00043816504,0.0007981802],"category_scores_gemma":[0.0009685151,0.00021287963,0.0004973261,0.000440034,0.0004896552,0.0007360956,0.0007564757,0.000589545,0.0002485979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013501685,0.000031953823,0.0007863406,0.00012811684,0.000033724416,0.00014362589,0.0001361372,0.82765025,0.03110589,0.029315166,0.0015175756,0.1090161],"study_design_scores_gemma":[0.0000033949295,0.000021364873,0.00010334576,0.000004488904,0.0000033210335,0.000025711843,0.000014002517,0.99498665,0.0020498799,0.0021854732,0.00059653714,0.000005805567],"about_ca_topic_score_codex":0.0022128208,"about_ca_topic_score_gemma":0.0018033655,"teacher_disagreement_score":0.0022128208,"about_ca_system_score_codex":0.00029531223,"about_ca_system_score_gemma":0.0005252543,"threshold_uncertainty_score":0.0043998957},"labels":[],"label_agreement":null},{"id":"W2063261157","doi":"10.1088/0031-9155/59/22/6797","title":"Standardized accuracy assessment of the calypso wireless transponder tracking system","year":2014,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Cancer Research UK","keywords":"Transponder (aeronautics); Tracking (education); Computer science; Wireless; Telecommunications; Tracking system; Medical physics; Aeronautics; Artificial intelligence; Engineering; Medicine; Aerospace engineering; Psychology; Kalman filter","score_opus":0.08284348796703263,"score_gpt":0.34945735166087766,"score_spread":0.26661386369384504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063261157","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7104629,0.0020807178,0.26768285,0.00021110509,0.00042397162,0.010353698,0.0017108931,0.0008376204,0.0062361704],"genre_scores_gemma":[0.75653553,0.0014117231,0.22441307,0.00023905485,0.00009185587,0.0115807885,0.0027587975,0.00022834215,0.0027408495],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9861178,0.0049424144,0.002608709,0.0010566647,0.0049488735,0.00032548662],"domain_scores_gemma":[0.97427964,0.0043357713,0.0028594853,0.0031529015,0.015024807,0.00034742962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010565976,0.0009631577,0.0007394038,0.0018731476,0.00057149946,0.0008359999,0.0010730027,0.0009913553,0.0011747299],"category_scores_gemma":[0.023279453,0.000331284,0.000798886,0.0009899008,0.0011261286,0.00045691352,0.0015258451,0.00057186757,0.000625527],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007129161,0.0031097867,0.20716561,0.002668733,0.0007301673,0.0009565202,0.0041758553,0.023523938,0.27003673,0.0038727324,0.007759179,0.4688716],"study_design_scores_gemma":[0.0008421677,0.061085768,0.4168924,0.00062590203,0.0016131707,0.0040272023,0.0027642178,0.047273792,0.41432816,0.0019476989,0.04803315,0.00056646037],"about_ca_topic_score_codex":0.0010999301,"about_ca_topic_score_gemma":0.0015701856,"teacher_disagreement_score":0.010565976,"about_ca_system_score_codex":0.0007007842,"about_ca_system_score_gemma":0.0014007183,"threshold_uncertainty_score":0.055878937},"labels":[],"label_agreement":null},{"id":"W2063662985","doi":"10.1109/smc.2014.6974027","title":"Comparing alternative cluster management approaches for mobile node tracking in a factory Wireless Sensor Network","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wireless sensor network; Computer science; Distributed computing; Robustness (evolution); Wireless; Key distribution in wireless sensor networks; Computer network; Factory (object-oriented programming); Wireless network; Node (physics); Real-time computing; Engineering; Telecommunications","score_opus":0.035989911785965265,"score_gpt":0.23473396508163252,"score_spread":0.19874405329566724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063662985","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7732591,0.0014270497,0.21304825,0.00037041906,0.0001481056,0.0003957853,0.00015890048,0.0014696852,0.009722651],"genre_scores_gemma":[0.9413348,0.00041861896,0.05670425,0.00004110712,0.000016985643,0.00013134122,0.00014359485,0.000042978,0.00116642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985868,0.00048084708,0.00011034987,0.00020595221,0.00042757983,0.00018848946],"domain_scores_gemma":[0.9974015,0.0012157024,0.00016779533,0.00043111475,0.0006129322,0.0001710521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022309197,0.0005078766,0.0005263351,0.0010425459,0.0007543165,0.0010331268,0.0018107487,0.0007930517,0.0009957417],"category_scores_gemma":[0.0037129952,0.00023051506,0.00037844683,0.0011211738,0.00051097997,0.001878017,0.0007508279,0.00038546673,0.00022203602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028619822,0.00074555527,0.010545894,0.00049094786,0.00030203917,0.00014456874,0.00054708944,0.71728873,0.018227853,0.009699219,0.0023567204,0.23678944],"study_design_scores_gemma":[0.00014989896,0.0010566391,0.006135974,0.00002416749,0.00012257765,0.00008028549,0.0005357039,0.97740775,0.00963166,0.0018789299,0.0029286707,0.000047740457],"about_ca_topic_score_codex":0.005714511,"about_ca_topic_score_gemma":0.0066450937,"teacher_disagreement_score":0.005714511,"about_ca_system_score_codex":0.0015271112,"about_ca_system_score_gemma":0.00092431204,"threshold_uncertainty_score":0.011798322},"labels":[],"label_agreement":null},{"id":"W2064256022","doi":"10.1109/iros.2012.6386015","title":"Development of a relative localization scheme for ground-aerial multi-robot systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Robot; Sensor fusion; Computer science; Computer vision; Extended Kalman filter; Kalman filter; Scheme (mathematics); Artificial intelligence; Global Positioning System; Kinematics; Mobile robot; Mathematics; Physics; Telecommunications","score_opus":0.03845244532973859,"score_gpt":0.2556912543468695,"score_spread":0.2172388090171309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064256022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031768233,0.00004161652,0.99612135,0.000020048787,0.00001285116,0.00001975457,0.000004097884,0.0002345986,0.00036882647],"genre_scores_gemma":[0.26525393,0.00014730125,0.73207474,0.00003064132,0.000024567305,0.00012710968,0.000045414035,0.00006185038,0.002234511],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946374,0.00012330644,0.00003604673,0.00009863715,0.00024866435,0.000029595974],"domain_scores_gemma":[0.9996406,0.00007569552,0.00005940832,0.00007651479,0.00012550897,0.000022212227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072361453,0.0004023575,0.00035866807,0.00032215044,0.00033784396,0.00047138956,0.0010568389,0.00058669545,0.001994574],"category_scores_gemma":[0.0012348696,0.00028990203,0.0003520484,0.00023999235,0.00039996675,0.0012430092,0.001074772,0.0007268788,0.0007718321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012352504,0.00008547953,0.0006411891,0.00039883857,0.00006703768,0.0002471699,0.00066704187,0.34658435,0.1500046,0.06777661,0.0015905288,0.4318137],"study_design_scores_gemma":[0.000026888447,0.00034485484,0.00024852823,0.000027241096,0.000019680676,0.00014509469,0.000057770576,0.95819664,0.02587679,0.0051020687,0.009929044,0.000025412886],"about_ca_topic_score_codex":0.0007316404,"about_ca_topic_score_gemma":0.0006128971,"teacher_disagreement_score":0.001994574,"about_ca_system_score_codex":0.00030164645,"about_ca_system_score_gemma":0.00041765903,"threshold_uncertainty_score":0.006672561},"labels":[],"label_agreement":null},{"id":"W2065133500","doi":"10.1186/1687-6180-2011-94","title":"Advances in angle-of-arrival and multidimensional signal processing for localization and communications","year":2011,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Angle of arrival; SIGNAL (programming language); Computer science; Signal processing; Direction of arrival; Antenna array; Antenna (radio); Telecommunications","score_opus":0.025390338989777723,"score_gpt":0.28260221887654136,"score_spread":0.25721187988676364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065133500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034077468,0.022520734,0.9608675,0.0013793698,0.00087557785,0.00003992463,0.0001487074,0.00060193316,0.010158524],"genre_scores_gemma":[0.096937016,0.06408265,0.823947,0.0009486387,0.0023943454,0.00017601065,0.0006086526,0.00022420984,0.010681433],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99855393,0.0003760823,0.000108902175,0.00024338637,0.00065385626,0.00006382996],"domain_scores_gemma":[0.995968,0.0018831841,0.00030363558,0.0004480479,0.0013153127,0.000081881706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014870479,0.0011806784,0.00087007723,0.0017857067,0.00031141672,0.0016863438,0.0009004445,0.0013315533,0.0051843785],"category_scores_gemma":[0.004754112,0.0004039006,0.00062896445,0.0034717415,0.0008084036,0.0029443074,0.0015414738,0.0023231767,0.004042101],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016034153,0.00011673108,0.0014405617,0.0008918722,0.00007832814,0.0001240228,0.00016200509,0.022417331,0.04903068,0.060926847,0.010509289,0.8541419],"study_design_scores_gemma":[0.00009669746,0.0007524872,0.0048595215,0.0005349579,0.00017666495,0.0022055043,0.0003913511,0.33969623,0.06860847,0.09631224,0.48598814,0.0003777924],"about_ca_topic_score_codex":0.00083926076,"about_ca_topic_score_gemma":0.00087488483,"teacher_disagreement_score":0.0051843785,"about_ca_system_score_codex":0.00041147918,"about_ca_system_score_gemma":0.0005644907,"threshold_uncertainty_score":0.017343462},"labels":[],"label_agreement":null},{"id":"W2065781837","doi":"10.1109/upinlbs.2014.7033737","title":"Fast WiFi access point localization and autonomous crowdsourcing","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"","keywords":"Crowdsourcing; Computer science; Real-time computing; Point (geometry); Field (mathematics); Path (computing); Estimation; Propagation delay; Computer network; Engineering; Mathematics","score_opus":0.006618709233957285,"score_gpt":0.20656887403019875,"score_spread":0.19995016479624145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065781837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04411936,0.00028818412,0.9469759,0.00016822171,0.00010916403,0.00016857407,0.00028965328,0.003723908,0.0041569513],"genre_scores_gemma":[0.8391659,0.00027051865,0.1543753,0.000111817244,0.00013868093,0.0002931603,0.0006140308,0.00011861297,0.0049120216],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986307,0.00023778815,0.000057283774,0.00030665903,0.000633423,0.00013413304],"domain_scores_gemma":[0.9989237,0.00025677402,0.0001485683,0.00031305518,0.00029710587,0.00006074329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006383942,0.0009015928,0.00096053135,0.0014041582,0.0007054584,0.0008608986,0.0018560446,0.00089468044,0.00120991],"category_scores_gemma":[0.002757966,0.0004114296,0.0005699079,0.0012661598,0.00047589806,0.0012844531,0.002286391,0.0005328542,0.0010927942],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068673893,0.00017007515,0.0076400302,0.00038971155,0.00017924584,0.0012562994,0.0007164113,0.17881066,0.06146873,0.008946993,0.008587651,0.7311474],"study_design_scores_gemma":[0.00010053857,0.0002157678,0.0048356205,0.000037396047,0.000059553102,0.000883225,0.0002692089,0.95015794,0.020739257,0.009124516,0.013449681,0.00012727617],"about_ca_topic_score_codex":0.0082131615,"about_ca_topic_score_gemma":0.004904263,"teacher_disagreement_score":0.0082131615,"about_ca_system_score_codex":0.0005043816,"about_ca_system_score_gemma":0.00080450095,"threshold_uncertainty_score":0.016330719},"labels":[],"label_agreement":null},{"id":"W2066631219","doi":"10.1109/jsen.2013.2257731","title":"DuRT: Dual RSSI Trend Based Localization for Wireless Sensor Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université de Montréal","funders":"","keywords":"Wireless sensor network; Beacon; Computer science; Received signal strength indication; Unavailability; Trajectory; Real-time computing; Range (aeronautics); Position (finance); Wireless; Computer network; Telecommunications; Engineering; Mathematics; Statistics","score_opus":0.009449484005863634,"score_gpt":0.20773061486178146,"score_spread":0.19828113085591784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066631219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066729533,0.00051274797,0.9844087,0.00017934277,0.0001851877,0.00006724552,0.0002282523,0.0051192543,0.0026263853],"genre_scores_gemma":[0.5001409,0.0017967478,0.47893843,0.00031024122,0.00021425873,0.00044643518,0.0021955646,0.00061264186,0.015344777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994099,0.00008826845,0.00003389878,0.000115539406,0.00030333037,0.00004903124],"domain_scores_gemma":[0.99956435,0.000066088076,0.00007176078,0.0000958272,0.00017203062,0.000029985551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006210589,0.000851119,0.00053236657,0.0012149347,0.00036126722,0.00068774464,0.001322249,0.00068295246,0.0021518592],"category_scores_gemma":[0.0018153025,0.00021623829,0.00045365572,0.0014091147,0.00038264977,0.0013267512,0.0010346732,0.00069933117,0.0018410648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000482165,0.00011615469,0.00299087,0.00050371996,0.00007216632,0.0007073747,0.0002457126,0.12444083,0.058376838,0.023984458,0.020832205,0.7672475],"study_design_scores_gemma":[0.000063529595,0.00032178735,0.001241437,0.000037246464,0.000029400662,0.0007413875,0.00008030167,0.93902236,0.018448977,0.006663807,0.03329181,0.000057876085],"about_ca_topic_score_codex":0.0020756174,"about_ca_topic_score_gemma":0.0020612874,"teacher_disagreement_score":0.0021518592,"about_ca_system_score_codex":0.00064926554,"about_ca_system_score_gemma":0.0005117999,"threshold_uncertainty_score":0.0071986318},"labels":[],"label_agreement":null},{"id":"W2066969414","doi":"10.1109/vtcfall.2014.6965956","title":"Efficient RSSD-Based Source Positioning with System Parameter Uncertainties","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Estimator; Least-squares function approximation; Computer science; SIGNAL (programming language); Algorithm; Matrix (chemical analysis); Cramér–Rao bound; Estimation theory; Mathematics; Mathematical optimization; Control theory (sociology); Statistics; Artificial intelligence","score_opus":0.0036245489045729907,"score_gpt":0.16391305494776182,"score_spread":0.16028850604318884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066969414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003665701,0.00020342023,0.9950465,0.000044895954,0.000018271976,0.0000071636564,0.000026982965,0.00047601113,0.00051115314],"genre_scores_gemma":[0.37902713,0.0008230675,0.6151536,0.00010628443,0.000090021014,0.00009755179,0.00043215769,0.00015220739,0.0041179694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991773,0.00020410112,0.000040670155,0.00014661215,0.00039761828,0.000033733693],"domain_scores_gemma":[0.99933857,0.0002683709,0.000084009356,0.0001464751,0.00014956988,0.000012968617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005548378,0.0008918534,0.0011169119,0.00076636724,0.0002455656,0.0007107103,0.00087002246,0.00067231536,0.00085147633],"category_scores_gemma":[0.002218768,0.0005241942,0.0005232802,0.0016208042,0.00037923196,0.0010170266,0.0012085951,0.00056108285,0.0010577096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018918744,0.000036665657,0.0011976599,0.00034866857,0.00013754837,0.0001856353,0.00013099283,0.4103897,0.038298775,0.009471311,0.0021540206,0.53745985],"study_design_scores_gemma":[0.000023115655,0.000066328816,0.0005625238,0.000019062898,0.000024764902,0.00022754146,0.000022053013,0.97623515,0.01347174,0.0044856663,0.0048389444,0.000023060016],"about_ca_topic_score_codex":0.0009427521,"about_ca_topic_score_gemma":0.0010269124,"teacher_disagreement_score":0.0011169119,"about_ca_system_score_codex":0.00028830805,"about_ca_system_score_gemma":0.00054873887,"threshold_uncertainty_score":0.0029343367},"labels":[],"label_agreement":null},{"id":"W2067053438","doi":"10.1145/2750858.2807540","title":"IDyLL","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Council","keywords":"Idyll; Inertial measurement unit; Computer science; Computer vision; Dead reckoning; Silhouette; Artificial intelligence; Ambiguity; Geography; Remote sensing; Telecommunications; Global Positioning System; Art","score_opus":0.022023088150023122,"score_gpt":0.19541200016263685,"score_spread":0.17338891201261372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067053438","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021965155,0.0040053977,0.50871235,0.0027061393,0.002757539,0.0005073859,0.016872661,0.075565584,0.36690784],"genre_scores_gemma":[0.23355857,0.0040020267,0.31520313,0.0026481187,0.00074007706,0.0006230894,0.048186883,0.0050145127,0.3900236],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991322,0.0001332216,0.000054519187,0.00021015863,0.00034299094,0.00012686079],"domain_scores_gemma":[0.99878985,0.00014067847,0.000072282324,0.0004071045,0.00042527227,0.00016485453],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008422173,0.0008085481,0.00057117845,0.0011678581,0.00069345575,0.0016504115,0.0013439411,0.0010564167,0.062215284],"category_scores_gemma":[0.0022015949,0.00026351656,0.00043667312,0.0010287067,0.00034449913,0.0015307742,0.0033275778,0.0009012247,0.06286917],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004791568,0.000089520196,0.004225426,0.0004857794,0.00003582787,0.00035276497,0.00026273736,0.002598478,0.011935742,0.029858725,0.24663217,0.70304364],"study_design_scores_gemma":[0.000040418923,0.00011023988,0.0017323621,0.000077937744,0.000019444396,0.0006045214,0.00009585391,0.0107019795,0.0109928865,0.0043658703,0.97121555,0.000042936605],"about_ca_topic_score_codex":0.002224666,"about_ca_topic_score_gemma":0.002637918,"teacher_disagreement_score":0.93778473,"about_ca_system_score_codex":0.00081550545,"about_ca_system_score_gemma":0.001218744,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2068655606","doi":"10.1109/nesea.2012.6474010","title":"Accurate passive RFID localization system for smart homes","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec","keywords":"Trilateration; Computer science; Robustness (evolution); Home automation; Radio-frequency identification; Software deployment; Smart environment; Computer security; Context (archaeology); Fuzzy logic; Focus (optics); Ubiquitous computing; Smart camera; Identification (biology); Embedded system; Real-time computing; Human–computer interaction; Internet of Things; Artificial intelligence; Telecommunications; Engineering; Software engineering","score_opus":0.01157165997124111,"score_gpt":0.2200608972304532,"score_spread":0.2084892372592121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068655606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028304225,0.00033882665,0.9682538,0.00009451739,0.00007171773,0.000032097287,0.000020374162,0.001465104,0.0014192523],"genre_scores_gemma":[0.6675966,0.00047137652,0.3258741,0.00013937637,0.00007167956,0.00007166125,0.00013077611,0.00004731914,0.005597129],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995678,0.00013278692,0.00003054632,0.00009331507,0.00014403797,0.000031512987],"domain_scores_gemma":[0.9997112,0.00007061738,0.000038965576,0.000053576852,0.000113095244,0.000012497585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044316016,0.00036869297,0.00047513068,0.00042966573,0.00031085018,0.0005072129,0.0007335187,0.00071537663,0.0015065962],"category_scores_gemma":[0.0008485952,0.00019806821,0.00025241598,0.00027867476,0.00028696758,0.0012559574,0.00043911315,0.00029184102,0.00096956536],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009057678,0.00011683744,0.002508135,0.00043181173,0.000046532376,0.00042960842,0.000502589,0.049932323,0.321804,0.012240738,0.0041501964,0.60693145],"study_design_scores_gemma":[0.00018817965,0.0011151695,0.0030009898,0.000081127626,0.00017877934,0.0016971594,0.00021268487,0.74647826,0.21242619,0.0053612427,0.029147523,0.00011267431],"about_ca_topic_score_codex":0.00047599015,"about_ca_topic_score_gemma":0.00037797933,"teacher_disagreement_score":0.0015065962,"about_ca_system_score_codex":0.00027703657,"about_ca_system_score_gemma":0.00024659326,"threshold_uncertainty_score":0.0050400496},"labels":[],"label_agreement":null},{"id":"W2069370482","doi":"10.1016/j.adhoc.2012.07.005","title":"Accurate time synchronization of ultrasonic TOF measurements in IEEE 802.15.4 based wireless sensor networks","year":2012,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Information Technology Research Centre; Junta de Andalucía","keywords":"Pseudorange; Time division multiple access; Computer science; Synchronization (alternating current); Real-time computing; Wireless sensor network; Clock synchronization; Ultrasonic sensor; Wireless; Global Positioning System; Computer network; Telecommunications","score_opus":0.014155252885613338,"score_gpt":0.21592044897934018,"score_spread":0.20176519609372684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069370482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113465,0.001450468,0.8807226,0.00019876329,0.00034259082,0.000034694054,0.00013723962,0.00083906547,0.002809535],"genre_scores_gemma":[0.9225239,0.00075343106,0.074372225,0.00004899706,0.00015322633,0.000051123956,0.00019264911,0.000055188524,0.0018493272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888974,0.000287125,0.00008727263,0.00018230232,0.0004618215,0.000091823546],"domain_scores_gemma":[0.9987159,0.00045331402,0.00026002235,0.00020601106,0.0003261448,0.000038750877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011435759,0.0004950301,0.0004976153,0.0009510801,0.00036681572,0.00068092253,0.0007014952,0.00059719017,0.00081171206],"category_scores_gemma":[0.0059881443,0.00030767207,0.00013589606,0.0010862295,0.00031111075,0.0020524696,0.0010001322,0.0006311078,0.00032642332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012433943,0.00011953792,0.006525597,0.00030653577,0.00008112001,0.0003016926,0.00039250148,0.29457498,0.097000256,0.014374007,0.004215941,0.58086437],"study_design_scores_gemma":[0.000046717767,0.0003012386,0.0040071066,0.000047813424,0.000046237863,0.00033840045,0.00016320261,0.93361133,0.047474086,0.006960877,0.006962474,0.00004038993],"about_ca_topic_score_codex":0.0011824179,"about_ca_topic_score_gemma":0.0012122523,"teacher_disagreement_score":0.0011824179,"about_ca_system_score_codex":0.0003442439,"about_ca_system_score_gemma":0.0006724278,"threshold_uncertainty_score":0.006047845},"labels":[],"label_agreement":null},{"id":"W2069916231","doi":"10.1109/jsen.2012.2218100","title":"Path Planning Algorithm for Mobile Anchor-Based Localization in Wireless Sensor Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wireless sensor network; Trajectory; Motion planning; Computer science; Node (physics); Path (computing); Real-time computing; Obstacle; Key distribution in wireless sensor networks; Scheme (mathematics); Wireless; Mobile wireless sensor network; Algorithm; Computer network; Wireless network; Engineering; Artificial intelligence; Mathematics; Telecommunications; Geography","score_opus":0.012539801636413628,"score_gpt":0.2436897132067676,"score_spread":0.23114991157035397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069916231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029215792,0.00026014482,0.9954058,0.00006627341,0.000028371898,0.000051558287,0.00003565104,0.00042571954,0.0008049598],"genre_scores_gemma":[0.20134315,0.001160094,0.7934317,0.000058081027,0.00003005633,0.00039446016,0.00034855248,0.00008904259,0.0031448987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978,0.000057429443,0.000014269168,0.000043341788,0.000087348,0.00001759],"domain_scores_gemma":[0.9997111,0.00012841243,0.000036949077,0.000025918944,0.000085513064,0.000011983215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000362381,0.0006043121,0.00044718286,0.0005575157,0.0005915042,0.00037230641,0.0008294612,0.000445371,0.0017827937],"category_scores_gemma":[0.0011989572,0.00024390632,0.00031347174,0.0009964601,0.00038627436,0.0006554761,0.00064175454,0.00069794565,0.0004861574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091947055,0.000037611295,0.00054049457,0.00017962045,0.000034619687,0.00013345353,0.00016314948,0.72699577,0.00645864,0.016934821,0.00381538,0.24461445],"study_design_scores_gemma":[0.000019697152,0.00006599466,0.0001268791,0.000015001976,0.000012786757,0.0000934574,0.000027443715,0.9861492,0.0026859432,0.0053368118,0.005453282,0.00001341309],"about_ca_topic_score_codex":0.003954363,"about_ca_topic_score_gemma":0.0037774225,"teacher_disagreement_score":0.003954363,"about_ca_system_score_codex":0.0005096265,"about_ca_system_score_gemma":0.001264151,"threshold_uncertainty_score":0.007862687},"labels":[],"label_agreement":null},{"id":"W2071776010","doi":"10.1109/giis.2014.6934255","title":"Distributed relative cooperative positioning in Vehicular Ad-Hoc Networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Wireless ad hoc network; Robustness (evolution); Scalability; Global Positioning System; Vehicular ad hoc network; Real-time computing; Computer network; GNSS applications; Relative velocity; Distributed computing; Telecommunications; Wireless","score_opus":0.004057370141641101,"score_gpt":0.18806174370545564,"score_spread":0.18400437356381455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071776010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021243166,0.0007305309,0.9763362,0.00006569096,0.00005607546,0.000024841009,0.000013961852,0.0003831861,0.0011463921],"genre_scores_gemma":[0.83049935,0.00081372436,0.16624343,0.000042994074,0.000069271126,0.00009692921,0.00008323514,0.000028546125,0.0021225265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916744,0.0002943139,0.00003405596,0.00016346345,0.00028143605,0.000059301987],"domain_scores_gemma":[0.9994147,0.00018260299,0.00008676797,0.000112759015,0.00017659439,0.000026693315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088628446,0.0005575837,0.00060611067,0.00054737466,0.00044111436,0.0005330154,0.0012776557,0.0005894321,0.00029400788],"category_scores_gemma":[0.0015822878,0.00027570146,0.00032417616,0.00086125877,0.00046936178,0.0010969463,0.0009027205,0.00033612264,0.00020962072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010111125,0.000038420825,0.0012051145,0.00010846506,0.000060381644,0.00016263631,0.00020509005,0.8240343,0.01217829,0.014423572,0.001210362,0.14627227],"study_design_scores_gemma":[0.00002078433,0.00014946805,0.0002902229,0.000006716559,0.00002303061,0.00011121439,0.000043935015,0.9889115,0.0035326995,0.0040332344,0.0028603845,0.000016766042],"about_ca_topic_score_codex":0.002881407,"about_ca_topic_score_gemma":0.0020273235,"teacher_disagreement_score":0.002881407,"about_ca_system_score_codex":0.0004011155,"about_ca_system_score_gemma":0.00052580883,"threshold_uncertainty_score":0.005729258},"labels":[],"label_agreement":null},{"id":"W2071814311","doi":"10.1049/el:20000960","title":"Robust estimation of mobile terminal position","year":2000,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Non-line-of-sight propagation; Terminal (telecommunication); Position (finance); Computer science; Estimation; Mobile telephony; Line-of-sight; Electronic engineering; Real-time computing; Mobile radio; Engineering; Wireless; Telecommunications; Aerospace engineering","score_opus":0.003928694807028837,"score_gpt":0.1843396898177406,"score_spread":0.18041099501071176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071814311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013493592,0.00021326357,0.9847728,0.00005337791,0.00003761017,0.00001090869,0.000079370475,0.00067469757,0.0006643248],"genre_scores_gemma":[0.72800606,0.0006465402,0.26783377,0.000081275204,0.00018362349,0.00009629852,0.00073207007,0.00018324648,0.0022370687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922705,0.00014517494,0.000041399206,0.00022601616,0.0002824041,0.000077838275],"domain_scores_gemma":[0.9987507,0.00042191482,0.00023108665,0.00024804063,0.00031526425,0.000032996035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006392974,0.0009680765,0.00093287375,0.00086620223,0.00024792308,0.0010338766,0.0008174341,0.0010745499,0.00096596475],"category_scores_gemma":[0.005291175,0.00037657004,0.0005957241,0.00068687677,0.00038396087,0.0011811609,0.0010331894,0.0009608063,0.0011343808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031797908,0.000039191757,0.0015954706,0.00015337784,0.0001285156,0.00018361968,0.00006552682,0.66039264,0.07193473,0.007901452,0.0020291833,0.25525826],"study_design_scores_gemma":[0.000011689717,0.00009222707,0.0013625346,0.00001236562,0.000026181016,0.00013526631,0.000013532004,0.97217166,0.021828046,0.0028999292,0.0014151124,0.00003149924],"about_ca_topic_score_codex":0.0013330284,"about_ca_topic_score_gemma":0.0010722689,"teacher_disagreement_score":0.0013330284,"about_ca_system_score_codex":0.00037088763,"about_ca_system_score_gemma":0.0005486131,"threshold_uncertainty_score":0.0033810139},"labels":[],"label_agreement":null},{"id":"W2072158021","doi":"10.1016/j.pmcj.2015.02.001","title":"Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing","year":2015,"lang":"en","type":"article","venue":"Pervasive and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":110,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Mitacs","keywords":"Computer science; Crowdsourcing; Process (computing); Real-time computing; Inertial measurement unit; Positioning system; Indoor positioning system; Embedded system; Accelerometer; Simulation; Artificial intelligence; Operating system; Point (geometry); World Wide Web","score_opus":0.017786317021805692,"score_gpt":0.24352418698060022,"score_spread":0.22573786995879452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072158021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2593683,0.00091872044,0.7148255,0.00039116733,0.00078274845,0.00028634412,0.00082553696,0.00942948,0.013172159],"genre_scores_gemma":[0.94094896,0.00016575259,0.053226072,0.00010448542,0.00011427949,0.00009489131,0.00040372965,0.00004705675,0.0048947986],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995233,0.000056293084,0.0000220462,0.00013879917,0.00017602523,0.00008359264],"domain_scores_gemma":[0.9997099,0.00003317673,0.00003083629,0.00006025316,0.00012679659,0.000039008693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021362565,0.0008669482,0.00083410734,0.00091351586,0.0005689524,0.0006185993,0.0010587666,0.0006593576,0.0012720592],"category_scores_gemma":[0.00066604314,0.00034511663,0.00044539798,0.00082495523,0.00021723124,0.000617156,0.0010373727,0.00039328754,0.0013067793],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010813269,0.00035278115,0.024559483,0.00044845557,0.00025553495,0.0012866497,0.00060876406,0.037145626,0.22810015,0.0033178343,0.0140949935,0.68874836],"study_design_scores_gemma":[0.00022328836,0.00076817,0.02762024,0.000059553517,0.00031584088,0.0017085199,0.00045514427,0.8833522,0.064152054,0.003755725,0.01739268,0.00019651886],"about_ca_topic_score_codex":0.006601107,"about_ca_topic_score_gemma":0.009420939,"teacher_disagreement_score":0.006601107,"about_ca_system_score_codex":0.00028894204,"about_ca_system_score_gemma":0.000650557,"threshold_uncertainty_score":0.01312536},"labels":[],"label_agreement":null},{"id":"W2073515309","doi":"10.1016/j.autcon.2012.10.005","title":"Accuracy assessment of Ultra-Wide Band technology in tracking static resources in indoor construction scenarios","year":2012,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Tracking (education); Computer science; Ultra-wideband; Metre; Real-time computing; Mode (computer interface); Tracking system; Ranging; Key (lock); Simulation; Telecommunications; Artificial intelligence; Kalman filter; Computer security","score_opus":0.009143858103549913,"score_gpt":0.2565653241378385,"score_spread":0.24742146603428858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073515309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8327714,0.00063489104,0.16188599,0.00007173079,0.000053346936,0.000025121686,0.00013853582,0.00037785864,0.0040411917],"genre_scores_gemma":[0.9909719,0.00014140927,0.008265015,0.000014387242,0.0000059849303,0.0000064953356,0.00010113084,0.00001206789,0.0004814871],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99894685,0.00030923044,0.000039598035,0.0001468187,0.00040285452,0.00015457727],"domain_scores_gemma":[0.99761605,0.0011769101,0.00022726375,0.00023885435,0.00068549544,0.000055438064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009855448,0.0004638554,0.00040517925,0.00095652894,0.00030959703,0.00077777205,0.0006853378,0.0010956033,0.00054182974],"category_scores_gemma":[0.004242214,0.00016897114,0.00029875463,0.00083332835,0.0003002128,0.00092277525,0.0006701592,0.00025480738,0.00031892193],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034935744,0.00031037297,0.17641535,0.0005016769,0.00037388798,0.0007646921,0.00064035767,0.38796443,0.11681993,0.0030458423,0.0012550873,0.30841476],"study_design_scores_gemma":[0.000042548003,0.0013323454,0.08797476,0.00006311998,0.00032528973,0.000903518,0.00055220735,0.82591045,0.07978625,0.0011969092,0.0018379905,0.00007457593],"about_ca_topic_score_codex":0.003114613,"about_ca_topic_score_gemma":0.0024831044,"teacher_disagreement_score":0.003114613,"about_ca_system_score_codex":0.00038190707,"about_ca_system_score_gemma":0.00028402224,"threshold_uncertainty_score":0.006192982},"labels":[],"label_agreement":null},{"id":"W2074060156","doi":"10.1109/ipin.2011.6071947","title":"Magnetic field based heading estimation for pedestrian navigation environments","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Heading (navigation); Gyroscope; Computer science; Orientation (vector space); Accelerometer; Dead reckoning; Field (mathematics); Attitude and heading reference system; Global Positioning System; Step detection; Remote sensing; Real-time computing; Artificial intelligence; Engineering; Telecommunications; Aerospace engineering; Geography","score_opus":0.017921120738029813,"score_gpt":0.21035436301933946,"score_spread":0.19243324228130965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074060156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07958548,0.001028504,0.91354096,0.00012184395,0.00021700162,0.000054983884,0.00027287885,0.0023068134,0.0028715848],"genre_scores_gemma":[0.8381795,0.0009643246,0.15638584,0.00011264177,0.00017039636,0.00006536144,0.0007362032,0.00007192873,0.0033139193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983644,0.000026818303,0.0000089526975,0.000035231293,0.000070385715,0.000022067246],"domain_scores_gemma":[0.99962294,0.00009009685,0.00006885451,0.000026300746,0.00016980301,0.000021905045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022088336,0.00043836096,0.00046374733,0.0007720617,0.00018058778,0.00039248058,0.00029000264,0.00037929343,0.00075250294],"category_scores_gemma":[0.0012597098,0.00013808846,0.00020732549,0.00047173322,0.0001677231,0.00029527448,0.00031127958,0.00031916253,0.00076624827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008141352,0.0001620003,0.016910357,0.00047785082,0.00008342577,0.00045927038,0.0001388673,0.10702201,0.11860288,0.004145602,0.008314123,0.74286956],"study_design_scores_gemma":[0.00004339361,0.00045955757,0.015168763,0.000051593048,0.00006430007,0.0006066932,0.0000965973,0.92525655,0.047480173,0.002416557,0.008292921,0.00006285694],"about_ca_topic_score_codex":0.0016675633,"about_ca_topic_score_gemma":0.0017602067,"teacher_disagreement_score":0.0016675633,"about_ca_system_score_codex":0.00021529317,"about_ca_system_score_gemma":0.0003730395,"threshold_uncertainty_score":0.0033156872},"labels":[],"label_agreement":null},{"id":"W2075515059","doi":"10.1007/s00779-013-0692-9","title":"Efficient and accurate sensor network localization","year":2013,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless sensor network; Ranging; Node (physics); Distributed computing; Key distribution in wireless sensor networks; Computer network; Range (aeronautics); Wireless; Real-time computing; Wireless network; Telecommunications","score_opus":0.007190663608170996,"score_gpt":0.19502342965134029,"score_spread":0.18783276604316929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075515059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010924,0.0007038188,0.9831976,0.00021698038,0.00011572132,0.000019309118,0.00010418759,0.001345669,0.0033726771],"genre_scores_gemma":[0.66415757,0.0017876117,0.31850228,0.00013368377,0.00017750518,0.000076729266,0.0007090957,0.0001826575,0.014272888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912244,0.00017302265,0.000042843956,0.00015660732,0.0004191649,0.000085885295],"domain_scores_gemma":[0.9992374,0.00020766136,0.00009211833,0.00020959867,0.00023190858,0.000021299675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038269447,0.0008084645,0.0006748626,0.00088686566,0.00038935902,0.001066666,0.0007941797,0.0006285117,0.0015574486],"category_scores_gemma":[0.002212922,0.00046157942,0.00027497587,0.0012181903,0.0003194228,0.0022045695,0.0013744412,0.0006206908,0.0013280046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002065122,0.00008693729,0.0024078658,0.00020256038,0.000065336644,0.00019139377,0.000111062545,0.3675213,0.062496208,0.017944772,0.012441669,0.5363244],"study_design_scores_gemma":[0.0000133658405,0.000056374727,0.001593332,0.000019728184,0.000028985118,0.0003008667,0.00008097123,0.95309407,0.024186214,0.008001541,0.012603742,0.000020779411],"about_ca_topic_score_codex":0.0027389028,"about_ca_topic_score_gemma":0.0052893613,"teacher_disagreement_score":0.0027389028,"about_ca_system_score_codex":0.00051476003,"about_ca_system_score_gemma":0.0007282242,"threshold_uncertainty_score":0.0054458976},"labels":[],"label_agreement":null},{"id":"W2076045012","doi":"10.1109/tap.2013.2272715","title":"An Ultra-Wideband Spatial Filter for Time-of-Arrival Localization in Tunnels","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Multipath propagation; Filter (signal processing); Spatial filter; Computer science; Wideband; Rake; Acoustics; Noise (video); Time of arrival; SIGNAL (programming language); Filter design; Matched filter; Electronic engineering; Direction of arrival; Signal processing; Antenna (radio); Telecommunications; Physics; Engineering; Artificial intelligence; Wireless; Computer vision; Channel (broadcasting); Radar","score_opus":0.008583900513678487,"score_gpt":0.21203164918123876,"score_spread":0.20344774866756027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076045012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025368262,0.00023479364,0.9727664,0.00006697338,0.000033406264,0.000013460214,0.000018392562,0.00028099216,0.0012173896],"genre_scores_gemma":[0.556064,0.00050560303,0.43830475,0.00013842747,0.000054988333,0.0000616138,0.00009373861,0.000057983514,0.004718844],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997805,0.000054202126,0.00001098766,0.000031583935,0.00009558056,0.000027161],"domain_scores_gemma":[0.9995394,0.00012324829,0.000086031905,0.000055249722,0.00017571759,0.000020335163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034211678,0.00041204505,0.00028510258,0.000396462,0.00027923784,0.00045622673,0.0005763727,0.0005555996,0.0010445124],"category_scores_gemma":[0.00063679996,0.0001699134,0.00027562235,0.0004276995,0.00029436295,0.000632014,0.00036846177,0.0003488589,0.0005330426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003845974,0.00009364738,0.0027037738,0.00028578576,0.0000597387,0.0002620836,0.00017982483,0.07003682,0.56750476,0.02129506,0.001648356,0.3355455],"study_design_scores_gemma":[0.000048503585,0.00094523886,0.0022684522,0.00006372581,0.0000826958,0.0014245178,0.00016422044,0.65412164,0.31305227,0.005590856,0.02215272,0.00008508115],"about_ca_topic_score_codex":0.0004691527,"about_ca_topic_score_gemma":0.00087620044,"teacher_disagreement_score":0.0010445124,"about_ca_system_score_codex":0.00026432757,"about_ca_system_score_gemma":0.00043932354,"threshold_uncertainty_score":0.0034942627},"labels":[],"label_agreement":null},{"id":"W2076051056","doi":"10.1504/ijhvs.2014.057828","title":"On message filtering for cooperative localisation of vehicles in an urban environment","year":2013,"lang":"en","type":"article","venue":"International Journal of Heavy Vehicle Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Dedicated short-range communications; Position (finance); Range (aeronautics); Degenerate energy levels; Measure (data warehouse); Engineering; Filter (signal processing); Computer science; Wireless; Real-time computing; Transport engineering; Simulation; Telecommunications; Data mining; Electrical engineering; Aerospace engineering","score_opus":0.01281651091063156,"score_gpt":0.23429515844431195,"score_spread":0.2214786475336804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076051056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00930915,0.0001419978,0.98995084,0.00005066778,0.00002165428,0.000012635865,0.0000068379068,0.00011763827,0.00038847205],"genre_scores_gemma":[0.6392853,0.0007293597,0.3549687,0.00015288692,0.00020239937,0.00013003836,0.00014995098,0.0000820108,0.0042993072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988949,0.00034299743,0.000059181082,0.00018353589,0.0004142786,0.00010513869],"domain_scores_gemma":[0.9966382,0.0024409578,0.00027322923,0.00023983074,0.00035613356,0.00005155344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015275497,0.00075799006,0.0007494946,0.0012540652,0.0006291823,0.0006726028,0.00094163883,0.0011238824,0.0006906083],"category_scores_gemma":[0.006497823,0.0003084998,0.00065846334,0.0012996341,0.00088913366,0.0014018307,0.0009527939,0.00057349855,0.0003996649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003050938,0.00007900549,0.0024803937,0.00019191093,0.00010911651,0.00026233768,0.0004974187,0.6028229,0.019627813,0.023233306,0.0011516211,0.34923905],"study_design_scores_gemma":[0.0000116925785,0.00010958689,0.00058851193,0.00001110297,0.000020301097,0.00010031029,0.000039469305,0.9861378,0.005065012,0.006312021,0.0015844262,0.0000196856],"about_ca_topic_score_codex":0.0035572825,"about_ca_topic_score_gemma":0.0030525199,"teacher_disagreement_score":0.0035572825,"about_ca_system_score_codex":0.00064899545,"about_ca_system_score_gemma":0.00053668034,"threshold_uncertainty_score":0.0080785155},"labels":[],"label_agreement":null},{"id":"W2076367212","doi":"10.1109/tvt.2014.2339734","title":"Mobile Localization in Non-Line-of-Sight Using Constrained Square-Root Unscented Kalman Filter","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariance intersection; Unscented transform; Control theory (sociology); Kalman filter; Quadratic programming; Covariance matrix; Extended Kalman filter; Convex optimization; Cholesky decomposition","score_opus":0.008627087780360566,"score_gpt":0.2263486633386372,"score_spread":0.21772157555827665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076367212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062248223,0.000101134276,0.99301976,0.00003521767,0.000015070651,0.000010463415,0.000016437418,0.00011665155,0.0004603422],"genre_scores_gemma":[0.6916766,0.0007531589,0.30313182,0.00010590297,0.00006607662,0.00013791752,0.0002490388,0.000065328044,0.0038140982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942964,0.00013157388,0.000031444753,0.00016233206,0.00018856149,0.00005637559],"domain_scores_gemma":[0.9994537,0.00023178697,0.00010837249,0.000056014986,0.00012953197,0.000020584135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049696164,0.0007290276,0.0008370776,0.00040278462,0.00032000832,0.0006454576,0.0008678537,0.00075417134,0.0007488785],"category_scores_gemma":[0.0017151224,0.0003673881,0.000719996,0.0008218036,0.0005585204,0.0014020556,0.00093005766,0.0006426828,0.00031694776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012316258,0.000043862026,0.0011669805,0.00014392176,0.00007093863,0.00020573751,0.0001280974,0.8962937,0.013367082,0.009812544,0.0008742902,0.07776966],"study_design_scores_gemma":[0.000007640437,0.00003706074,0.00022013338,0.0000051651264,0.0000078991125,0.00003223651,0.000011348414,0.99628204,0.0014959336,0.0013047734,0.000587486,0.000008213722],"about_ca_topic_score_codex":0.009557503,"about_ca_topic_score_gemma":0.008058381,"teacher_disagreement_score":0.009557503,"about_ca_system_score_codex":0.0004990161,"about_ca_system_score_gemma":0.0011694975,"threshold_uncertainty_score":0.019003749},"labels":[],"label_agreement":null},{"id":"W2077532359","doi":"10.1109/icsens.2012.6411521","title":"Energy-efficient location tracking with smartphones for IoT","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Global Positioning System; Computer science; Accelerometer; Real-time computing; Assisted GPS; Tracking (education); Orientation (vector space); Mobile device; Internet of Things; Wireless sensor network; Location tracking; Process (computing); Embedded system; Computer network; Telecommunications","score_opus":0.010934874102120264,"score_gpt":0.20114010238832145,"score_spread":0.19020522828620118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077532359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09289378,0.0029308172,0.88875866,0.00062213466,0.00026329304,0.00009322018,0.00045238863,0.007771187,0.0062145363],"genre_scores_gemma":[0.83629346,0.0011931286,0.15774107,0.00019059567,0.00006840706,0.00006857035,0.0005359977,0.00009849564,0.003810209],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998023,0.00004020004,0.000013127727,0.000036250032,0.00008470883,0.000023425277],"domain_scores_gemma":[0.9996803,0.00007238016,0.00004081605,0.000100768884,0.000087626286,0.000018265213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020461009,0.00041999327,0.0003699113,0.00031627616,0.00026299272,0.00043670787,0.0005314395,0.0004758768,0.0013630424],"category_scores_gemma":[0.0011399926,0.00020784306,0.0002217244,0.0005501382,0.00015902348,0.00070044573,0.0005992899,0.0004009064,0.00078922795],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005495232,0.00016625466,0.012598137,0.00037873647,0.00010103192,0.0006493066,0.00029276128,0.10605135,0.10174806,0.007888839,0.022274932,0.7473011],"study_design_scores_gemma":[0.000041578638,0.0002597,0.0053343675,0.00006704423,0.00006192635,0.0007126881,0.00011884413,0.92941386,0.03734459,0.0050337734,0.021558952,0.000052571148],"about_ca_topic_score_codex":0.0025332998,"about_ca_topic_score_gemma":0.0049061365,"teacher_disagreement_score":0.0025332998,"about_ca_system_score_codex":0.00020329202,"about_ca_system_score_gemma":0.00027052403,"threshold_uncertainty_score":0.005037129},"labels":[],"label_agreement":null},{"id":"W2078174931","doi":"10.1016/j.compind.2012.08.017","title":"Evaluating alternative approaches to mobile object localization in wireless sensor networks with passive architecture","year":2012,"lang":"en","type":"article","venue":"Computers in Industry","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Trilateration; Wireless sensor network; Real-time computing; Computer science; Kalman filter; Key distribution in wireless sensor networks; Mobile wireless sensor network; Wireless; Wireless network; Node (physics); Computer network; Engineering; Artificial intelligence; Telecommunications","score_opus":0.05490122736423183,"score_gpt":0.2632087997271447,"score_spread":0.2083075723629129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078174931","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.701763,0.0038466707,0.2846805,0.0006186448,0.00019081295,0.00046064644,0.00019664278,0.00032188205,0.007921236],"genre_scores_gemma":[0.928857,0.0009269113,0.06862728,0.000059595954,0.000058357942,0.0001664938,0.00013751932,0.00003749011,0.0011294058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99225134,0.004897889,0.00032595595,0.00043191251,0.0017446592,0.0003481593],"domain_scores_gemma":[0.9739058,0.021759422,0.000782909,0.0010297585,0.0023000047,0.00022209292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069835223,0.0014719323,0.0009798206,0.002731032,0.00075203343,0.0018318467,0.002066295,0.0018418088,0.0014815313],"category_scores_gemma":[0.020795172,0.00047045742,0.001168192,0.0020213549,0.0015450898,0.0037221047,0.0015724817,0.0006623009,0.00018271733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030012075,0.0005805859,0.0054348344,0.0006881638,0.00040944974,0.00007677245,0.00013296804,0.8690869,0.004474388,0.0073267133,0.0002646752,0.108523265],"study_design_scores_gemma":[0.00019227393,0.0025706398,0.0020661806,0.000033500073,0.00029402692,0.00006514218,0.00021906605,0.98767287,0.002634742,0.0038169213,0.00040033108,0.00003427704],"about_ca_topic_score_codex":0.006858943,"about_ca_topic_score_gemma":0.010239734,"teacher_disagreement_score":0.0069835223,"about_ca_system_score_codex":0.0038240221,"about_ca_system_score_gemma":0.001375971,"threshold_uncertainty_score":0.036932826},"labels":[],"label_agreement":null},{"id":"W2078248583","doi":"10.1007/s10291-002-0029-z","title":"Performance analysis of a stand-alone high-sensitivity receiver","year":2002,"lang":"en","type":"article","venue":"GPS Solutions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sensitivity (control systems); Remote sensing; Environmental science; Ranging; SIGNAL (programming language); Global Positioning System; Range (aeronautics); Non-line-of-sight propagation; Satellite; Acoustics; Wireless; Telecommunications; Computer science; Engineering; Geography; Aerospace engineering; Electronic engineering; Physics","score_opus":0.01958336710732017,"score_gpt":0.1935936826218537,"score_spread":0.17401031551453353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078248583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6708642,0.0013626275,0.3074866,0.00045456423,0.00015799842,0.00012230533,0.00038225303,0.0040071486,0.015162202],"genre_scores_gemma":[0.97749525,0.00020044333,0.015625147,0.00015389481,0.00005634521,0.000031803513,0.00023313894,0.00012776154,0.006076123],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99826306,0.00041191682,0.00004460507,0.00028595323,0.0007013054,0.00029320156],"domain_scores_gemma":[0.9973355,0.0012384122,0.00016993744,0.00021544354,0.00091995846,0.00012070865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008984314,0.0010117876,0.0010512222,0.0006775767,0.0007050044,0.0012733744,0.0011927548,0.0018430961,0.0053177816],"category_scores_gemma":[0.0020851367,0.00049147464,0.00062573183,0.0006465889,0.00039064648,0.0010960955,0.0007328874,0.0006834727,0.0024046963],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0076758177,0.00050126156,0.022348277,0.0008300788,0.0008835435,0.0012282408,0.00053386757,0.27815852,0.54435754,0.004986808,0.00310846,0.13538767],"study_design_scores_gemma":[0.00015440401,0.0038871788,0.0153792845,0.000038698607,0.0006113597,0.0021978652,0.00016473365,0.7975054,0.17506813,0.00072891376,0.0041559297,0.000108153414],"about_ca_topic_score_codex":0.0034748476,"about_ca_topic_score_gemma":0.0028354162,"teacher_disagreement_score":0.0053177816,"about_ca_system_score_codex":0.00090954814,"about_ca_system_score_gemma":0.0008838927,"threshold_uncertainty_score":0.017789721},"labels":[],"label_agreement":null},{"id":"W2078432885","doi":"10.2316/journal.201.2010.1.201-2167","title":"SENSOR GRAPHS FOR GUARANTEED COOPERATIVE LOCALIZATION PERFORMANCE","year":2010,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Computer network","score_opus":0.007601705999963265,"score_gpt":0.20346348740346154,"score_spread":0.19586178140349828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078432885","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03823658,0.0003491112,0.95587456,0.00040332816,0.000048938942,0.000046583107,0.00014511764,0.0008072262,0.004088474],"genre_scores_gemma":[0.92556965,0.00051076035,0.07068793,0.00018720426,0.00006555838,0.00020374524,0.00026192938,0.00015912384,0.0023540684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818844,0.0005818281,0.000079267935,0.0003797266,0.00055888854,0.00021186935],"domain_scores_gemma":[0.9890599,0.007225165,0.0011320343,0.0012595919,0.0009882285,0.00033503652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018070121,0.0012091479,0.000942246,0.00094325515,0.0006621785,0.0013346386,0.001295383,0.0013192168,0.0022024289],"category_scores_gemma":[0.015837124,0.000518323,0.0004622822,0.0011926304,0.001442291,0.0027789671,0.002306345,0.001456328,0.00058167375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024036535,0.000048078848,0.00051107776,0.00013348066,0.000033830598,0.00018145338,0.00014189011,0.78556406,0.0039493144,0.1711128,0.0024168163,0.035666835],"study_design_scores_gemma":[0.000028282973,0.00009738796,0.00027507008,0.000019219999,0.000009751188,0.0000732523,0.000029358693,0.8476446,0.0014349985,0.14879462,0.0015758189,0.000017624752],"about_ca_topic_score_codex":0.0014128314,"about_ca_topic_score_gemma":0.0010285503,"teacher_disagreement_score":0.0022024289,"about_ca_system_score_codex":0.0011403773,"about_ca_system_score_gemma":0.0011151194,"threshold_uncertainty_score":0.009556532},"labels":[],"label_agreement":null},{"id":"W2078433651","doi":"10.1109/plans.2014.6851508","title":"Vision-based context and height estimation for 3D indoor location","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"","keywords":"GNSS applications; Computer science; Inertial navigation system; Inertial measurement unit; Context (archaeology); Mobile device; Accelerometer; Inertial frame of reference; Global Positioning System; Barometer; Real-time computing; Computer vision; Artificial intelligence; Telecommunications; Geography","score_opus":0.004896732297773826,"score_gpt":0.21342155232508392,"score_spread":0.2085248200273101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078433651","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057863202,0.0046499847,0.9206639,0.00021418816,0.0005737061,0.00029396068,0.0023210812,0.0075165853,0.005903531],"genre_scores_gemma":[0.53417444,0.002995918,0.4522831,0.0002508315,0.00027864185,0.00025149857,0.0045803087,0.00022093201,0.0049643097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958426,0.000042485644,0.000015864933,0.000117388874,0.0001657581,0.00007426635],"domain_scores_gemma":[0.9997967,0.000034508474,0.000018005787,0.000052025356,0.000078200144,0.000020665128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020500357,0.0010432071,0.0010334192,0.0014973361,0.0003309289,0.0007662282,0.0009568713,0.00062063936,0.0034076727],"category_scores_gemma":[0.001019381,0.00032367095,0.0010177395,0.0015797985,0.00017688508,0.0009604339,0.0013421087,0.00076361495,0.002320422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004473216,0.00024880675,0.0027769017,0.000366468,0.00016143188,0.00029236497,0.00012728656,0.04234534,0.04879293,0.002712029,0.010281923,0.89144707],"study_design_scores_gemma":[0.000055319488,0.00026767666,0.008217829,0.00011207289,0.00012233737,0.000534189,0.00020708647,0.94959337,0.024070278,0.0040266155,0.012716503,0.00007677568],"about_ca_topic_score_codex":0.015105459,"about_ca_topic_score_gemma":0.026765576,"teacher_disagreement_score":0.015105459,"about_ca_system_score_codex":0.0003121292,"about_ca_system_score_gemma":0.0007136297,"threshold_uncertainty_score":0.030035079},"labels":[],"label_agreement":null},{"id":"W2079959981","doi":"10.1109/qbsc.2014.6841203","title":"A reduced complexity iterative grid search for RSS-based emitter localization","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"RSS; Computer science; Grid; Computational complexity theory; Algorithm; Iterative method; Search algorithm; Cognitive radio; Mathematical optimization; Mathematics; Wireless; Telecommunications","score_opus":0.028794270528296487,"score_gpt":0.2617401610795916,"score_spread":0.2329458905512951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079959981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005080942,0.00007471863,0.99385685,0.000025419215,0.00001216999,0.000015253732,0.000017342056,0.0002970627,0.0006203594],"genre_scores_gemma":[0.28350237,0.00013861242,0.7143525,0.000056844714,0.000025334768,0.00013314509,0.00019174101,0.00010620386,0.0014931316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958426,0.00014780997,0.000019144392,0.000051174255,0.00016098695,0.00003665252],"domain_scores_gemma":[0.99942964,0.00029441444,0.000045691282,0.000085890075,0.00012207347,0.000022161883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046129792,0.00040509255,0.0006362964,0.0004758586,0.0002286311,0.00047588308,0.0012031782,0.00048273077,0.0015367694],"category_scores_gemma":[0.0024030085,0.00027606575,0.0003566123,0.0006971642,0.0003616111,0.00065016275,0.0008919681,0.00050599186,0.0007817746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002941646,0.00006721805,0.0011904178,0.00008711446,0.00006189216,0.00012319906,0.00012628663,0.75095516,0.009808419,0.010700104,0.002354262,0.22423178],"study_design_scores_gemma":[0.000022517494,0.000028802591,0.000109734334,0.0000032076546,0.0000037957814,0.000040827883,0.000006375753,0.9962255,0.0011660218,0.0016657028,0.00072242203,0.0000050665444],"about_ca_topic_score_codex":0.003281994,"about_ca_topic_score_gemma":0.0030269427,"teacher_disagreement_score":0.003281994,"about_ca_system_score_codex":0.00030638298,"about_ca_system_score_gemma":0.000742325,"threshold_uncertainty_score":0.006525755},"labels":[],"label_agreement":null},{"id":"W2080339829","doi":"10.1016/j.future.2012.10.001","title":"An efficient closed-form solution for joint synchronization and localization using TOA","year":2012,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Jiangnan University","keywords":"Cramér–Rao bound; Computer science; Synchronization (alternating current); Estimator; Node (physics); Algorithm; Upper and lower bounds; Key (lock); Joint (building); Wireless sensor network; Noise (video); Signal-to-noise ratio (imaging); Position (finance); Estimation theory; Channel (broadcasting); Telecommunications; Artificial intelligence; Mathematics; Statistics; Computer network","score_opus":0.021459127931572817,"score_gpt":0.22518390039644542,"score_spread":0.2037247724648726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080339829","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00100165,0.00008311113,0.9967404,0.00007624524,0.000076709686,0.00003237738,0.00002667238,0.0002281028,0.0017348793],"genre_scores_gemma":[0.16981275,0.00045109147,0.8179168,0.00021460942,0.00015573076,0.00038401267,0.00028173946,0.00018754286,0.010595667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993666,0.00014578184,0.00003563412,0.00013391479,0.0002528521,0.000065326254],"domain_scores_gemma":[0.99918896,0.00034922158,0.000060003116,0.00009063675,0.00028404122,0.000027011756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008995064,0.0013736928,0.0011488578,0.0005177776,0.00078001287,0.0014853624,0.0012133694,0.001736435,0.008131125],"category_scores_gemma":[0.0033831273,0.0004954587,0.00091083074,0.000966501,0.00069387816,0.0011704686,0.0014268013,0.0015712513,0.003359114],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021497744,0.000107767344,0.0005775793,0.00035026914,0.00011999522,0.00023657533,0.00036743277,0.6294947,0.014993175,0.07583161,0.010880582,0.26682535],"study_design_scores_gemma":[0.000034198794,0.00004281671,0.000080519116,0.000024120376,0.000017878545,0.00008473014,0.00004880849,0.98307264,0.0019063973,0.009831095,0.0048403684,0.000016471962],"about_ca_topic_score_codex":0.0043510823,"about_ca_topic_score_gemma":0.0070089106,"teacher_disagreement_score":0.008131125,"about_ca_system_score_codex":0.0006704626,"about_ca_system_score_gemma":0.0018941488,"threshold_uncertainty_score":0.027201295},"labels":[],"label_agreement":null},{"id":"W2081111364","doi":"10.4236/iim.2015.72006","title":"Inferring Locations of Mobile Devices from Wi-Fi Data","year":2015,"lang":"en","type":"article","venue":"Intelligent Information Management","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristics; Location-based service; Android (operating system); Mobile device; Service provider; Computer network; Service (business); World Wide Web","score_opus":0.04406115261989905,"score_gpt":0.26399367223042447,"score_spread":0.21993251961052543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081111364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45547062,0.00060092774,0.5341607,0.0002900293,0.00008799489,0.0002037457,0.004377162,0.0022221538,0.0025867221],"genre_scores_gemma":[0.8525052,0.00028280332,0.14378278,0.00004640385,0.000040813975,0.000083979416,0.0025182222,0.000052161813,0.00068761565],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995296,0.00008670325,0.00004748654,0.00016818286,0.000101418584,0.00006659159],"domain_scores_gemma":[0.998723,0.0005381571,0.0001981632,0.00024683174,0.00023500391,0.000058906215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034048437,0.00078269705,0.0004924257,0.0024401902,0.00039221504,0.0010673632,0.00067167653,0.0007002425,0.0005298781],"category_scores_gemma":[0.005134966,0.00037958354,0.00047053682,0.0015439587,0.00033385245,0.0012376043,0.0006788371,0.0006227775,0.0007646413],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069251267,0.0003577717,0.20872608,0.0006107412,0.00028394532,0.0016832752,0.0011414158,0.30027187,0.07052682,0.010989284,0.0054941787,0.39922205],"study_design_scores_gemma":[0.0000332559,0.00019575818,0.059237592,0.00007494776,0.00013119215,0.0007425981,0.0005749114,0.8942827,0.028877309,0.00943647,0.0063395565,0.000073659954],"about_ca_topic_score_codex":0.0057259286,"about_ca_topic_score_gemma":0.010873718,"teacher_disagreement_score":0.0057259286,"about_ca_system_score_codex":0.00040329716,"about_ca_system_score_gemma":0.0006070637,"threshold_uncertainty_score":0.011385202},"labels":[],"label_agreement":null},{"id":"W2083501029","doi":"10.4236/pos.2013.43023","title":"Map Aided Pedestrian Dead Reckoning Using Buildings Information for Indoor Navigation Applications","year":2013,"lang":"en","type":"article","venue":"Positioning","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Dead reckoning; Map matching; Computer science; GNSS applications; Turn-by-turn navigation; Inertial navigation system; Navigation system; Mobile robot navigation; Real-time computing; Geospatial analysis; Air navigation; GNSS augmentation; Satellite navigation; Mobile mapping; Global Positioning System; Artificial intelligence; Remote sensing; Telecommunications; Geography; Mobile robot; Orientation (vector space)","score_opus":0.009373178067123707,"score_gpt":0.22514304486578018,"score_spread":0.21576986679865648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083501029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07975228,0.00033458875,0.90884256,0.000048882237,0.00009226036,0.000057097153,0.00032158374,0.006415178,0.0041356524],"genre_scores_gemma":[0.7765999,0.00031481264,0.21781382,0.000030236484,0.00002401814,0.000051255705,0.0005899377,0.000101011996,0.004475158],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998486,0.000025019604,0.0000061237565,0.00003005567,0.00007258501,0.000017619783],"domain_scores_gemma":[0.999845,0.000019893307,0.00001778564,0.0000364946,0.00007108085,0.000009783087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011411182,0.00055544765,0.00035568845,0.0006143595,0.0002059585,0.00037887134,0.00055199256,0.0003276808,0.0017377282],"category_scores_gemma":[0.0003833212,0.000174869,0.00028257316,0.0006341448,0.00008898125,0.0004814675,0.00045348282,0.00020407351,0.0012219022],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043432773,0.00013657771,0.0059920712,0.00032612815,0.00008679529,0.0006932573,0.00040648525,0.06688991,0.10266748,0.0021977664,0.005343092,0.8148262],"study_design_scores_gemma":[0.000050016675,0.0005145623,0.0123095,0.00005987936,0.00015575861,0.0010600205,0.00032493545,0.83437294,0.117532775,0.002245458,0.031264834,0.00010929315],"about_ca_topic_score_codex":0.0031385573,"about_ca_topic_score_gemma":0.004273659,"teacher_disagreement_score":0.0031385573,"about_ca_system_score_codex":0.00013105928,"about_ca_system_score_gemma":0.00028946457,"threshold_uncertainty_score":0.0062406063},"labels":[],"label_agreement":null},{"id":"W2083821388","doi":"10.1088/0957-0233/19/7/075202","title":"Foot mounted inertial system for pedestrian navigation","year":2008,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":228,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"AUTO21 Network of Centres of Excellence","keywords":"Inertial measurement unit; Global Positioning System; Computer science; Inertial navigation system; Sensitivity (control systems); Dead reckoning; Bridge (graph theory); Focus (optics); Simulation; SIGNAL (programming language); Pedestrian; Real-time computing; Inertial frame of reference; Artificial intelligence; Engineering; Telecommunications; Electronic engineering; Physics","score_opus":0.027330631494599175,"score_gpt":0.22690761090286704,"score_spread":0.19957697940826785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083821388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04133463,0.002465238,0.9248177,0.0003286821,0.0007191425,0.00020022519,0.00063919055,0.0058367853,0.023658462],"genre_scores_gemma":[0.6478911,0.0019488515,0.32287258,0.0003838025,0.00041143576,0.00029408978,0.0014607086,0.00011227772,0.02462508],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998067,0.000040210074,0.000008193944,0.000032101005,0.00009204072,0.00002076615],"domain_scores_gemma":[0.9998698,0.000014651181,0.000015657484,0.000021367005,0.00006673373,0.000011743142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020187482,0.00045244332,0.0003773333,0.00039071465,0.00032455433,0.00038675888,0.00045850343,0.00039266454,0.006022337],"category_scores_gemma":[0.00037165816,0.00012701747,0.00017937638,0.00040340138,0.0001135395,0.00029317627,0.00044558718,0.0003398805,0.0028107492],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048802263,0.00007569423,0.009174933,0.0004581138,0.00007366804,0.00042784808,0.00026216081,0.008521476,0.16896476,0.012621874,0.022621365,0.7763101],"study_design_scores_gemma":[0.00025629805,0.0037588088,0.034583423,0.00037401196,0.00046597066,0.002715695,0.00036075496,0.21330178,0.13919866,0.0065242783,0.5982675,0.0001929353],"about_ca_topic_score_codex":0.001474583,"about_ca_topic_score_gemma":0.0029458965,"teacher_disagreement_score":0.006022337,"about_ca_system_score_codex":0.00022929412,"about_ca_system_score_gemma":0.0004345698,"threshold_uncertainty_score":0.020146728},"labels":[],"label_agreement":null},{"id":"W2084678651","doi":"10.1109/vetecf.2010.5594102","title":"A Fuzzy Logic Approach to Angle of Arrival Averaging","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Fuzzy logic; Fuzzy number; Angle of arrival; Fuzzy set operations; Computer science; Mathematics; Defuzzification; Algorithm; Fuzzy set; Artificial intelligence","score_opus":0.009183004842390267,"score_gpt":0.20389148354843883,"score_spread":0.19470847870604857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084678651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00099026,0.00021954306,0.9957457,0.00008860475,0.000039615992,0.000008322109,0.000016852891,0.000051429626,0.0028396938],"genre_scores_gemma":[0.3277391,0.0017903389,0.66395015,0.00035060506,0.00057434035,0.000103414444,0.00011311352,0.000062571016,0.0053163306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999226,0.00017555383,0.000047686466,0.00014648584,0.00036414227,0.00004016223],"domain_scores_gemma":[0.9994742,0.00025391488,0.00005082546,0.000031582513,0.00017107508,0.000018316981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008582498,0.00044973625,0.0005346579,0.0012238333,0.0005054443,0.0011255607,0.00095534866,0.0006400302,0.0015113199],"category_scores_gemma":[0.0020112807,0.00021606745,0.0007397474,0.0008613426,0.0006216268,0.0011807166,0.00056769245,0.0010436221,0.00039037157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005473519,0.000062797015,0.0005613718,0.00018015623,0.00011487402,0.00029237487,0.00031178325,0.34445503,0.014443926,0.41384837,0.0021850201,0.22348945],"study_design_scores_gemma":[0.000012456932,0.00007480463,0.00038057208,0.00005199594,0.000046519686,0.0002161033,0.000038279373,0.81741375,0.0042024474,0.16506353,0.012442584,0.00005705416],"about_ca_topic_score_codex":0.0028791663,"about_ca_topic_score_gemma":0.0020219302,"teacher_disagreement_score":0.0028791663,"about_ca_system_score_codex":0.0009911956,"about_ca_system_score_gemma":0.0006460623,"threshold_uncertainty_score":0.007191658},"labels":[],"label_agreement":null},{"id":"W2084730219","doi":"10.1155/2013/912029","title":"GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks","year":2013,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Path loss; Microcell; ISM band; Bluetooth; Radio propagation; Global Positioning System; GPS signals; Real-time computing; Radio propagation model; Telecommunications; Electronic engineering; Simulation; Antenna (radio); Wireless; Assisted GPS","score_opus":0.008191555374917543,"score_gpt":0.22738570955446355,"score_spread":0.21919415417954602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084730219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021616835,0.00018167672,0.977225,0.000027510529,0.000028842527,0.000011742258,0.000022799895,0.00045592268,0.00042959547],"genre_scores_gemma":[0.6322059,0.00050825597,0.36510554,0.000030988853,0.000066578,0.0000817897,0.00018067169,0.00009930563,0.0017210591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997248,0.000085949934,0.000016316848,0.000046754285,0.0001069031,0.00001927956],"domain_scores_gemma":[0.9995384,0.00019403976,0.00007150977,0.000075974895,0.000109745975,0.0000103659795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039744738,0.0006519659,0.0002726626,0.0006486301,0.00021317965,0.00034024214,0.0005471702,0.0003193981,0.0004938356],"category_scores_gemma":[0.0022765712,0.00030313,0.00026956576,0.00057990075,0.00019779985,0.0007543374,0.0003830781,0.00041886617,0.00033521108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010370307,0.00004554772,0.0060276138,0.00008542087,0.000054343487,0.00016001942,0.000103434206,0.6482744,0.023039436,0.0042198077,0.0013985088,0.3164878],"study_design_scores_gemma":[0.000005286135,0.00003470369,0.0015379182,0.0000058494775,0.0000091604725,0.00007047817,0.000010653793,0.99233526,0.0040824353,0.00086599577,0.0010286195,0.000013546025],"about_ca_topic_score_codex":0.001967094,"about_ca_topic_score_gemma":0.0030353651,"teacher_disagreement_score":0.001967094,"about_ca_system_score_codex":0.0003076099,"about_ca_system_score_gemma":0.00027028198,"threshold_uncertainty_score":0.003911257},"labels":[],"label_agreement":null},{"id":"W2085470892","doi":"10.3390/jsan1010036","title":"Localization in Wireless Sensor Networks and Anchor Placement","year":2012,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Wireless sensor network; Computer science; Node (physics); Global Positioning System; Protocol (science); Set (abstract data type); Class (philosophy); Computer network; Position (finance); Curvilinear coordinates; Distributed computing; Artificial intelligence; Mathematics; Engineering; Telecommunications","score_opus":0.006988768717321359,"score_gpt":0.20516151355927248,"score_spread":0.19817274484195113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085470892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004679185,0.04457089,0.9187535,0.0025905522,0.0014824788,0.000108133325,0.0001016492,0.00044754197,0.027266165],"genre_scores_gemma":[0.3838694,0.1430584,0.438631,0.0016500492,0.004881508,0.00066226587,0.0005106418,0.00027516784,0.026461598],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981352,0.0007143421,0.0000951492,0.00025523856,0.00072521507,0.00007477162],"domain_scores_gemma":[0.99869233,0.00074024196,0.00019518565,0.00013640561,0.00020249464,0.000033367527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001194668,0.0011268042,0.0007888734,0.001079918,0.00061603234,0.0016616795,0.001043214,0.0020107669,0.0028171488],"category_scores_gemma":[0.004367557,0.00039794232,0.0004908347,0.0029661085,0.0024028986,0.002767688,0.0013888387,0.001546396,0.0012987934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006472786,0.00005316068,0.0010494187,0.0011935133,0.00006603245,0.00046942753,0.00025807266,0.27601573,0.004950656,0.42203423,0.014366475,0.2794785],"study_design_scores_gemma":[0.00003761337,0.00032806932,0.001274892,0.00053783663,0.00006025935,0.0013128349,0.00030852857,0.33570346,0.0037618806,0.4869998,0.16957113,0.00010363479],"about_ca_topic_score_codex":0.0012109984,"about_ca_topic_score_gemma":0.0008969372,"teacher_disagreement_score":0.0028171488,"about_ca_system_score_codex":0.00083888735,"about_ca_system_score_gemma":0.0007163993,"threshold_uncertainty_score":0.009424329},"labels":[],"label_agreement":null},{"id":"W2086412156","doi":"10.1109/plans.2010.5507301","title":"New method for magnetometers based orientation estimation","year":2010,"lang":"en","type":"article","venue":"IEEE/ION Position, Location and Navigation Symposium","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Magnetometer; Heading (navigation); Calibration; Orientation (vector space); Computer science; Computation; Computer vision; Constant (computer programming); Work (physics); Artificial intelligence; Magnetic field; Algorithm; Mathematics; Engineering; Geodesy; Physics; Geography; Statistics","score_opus":0.005607235286357691,"score_gpt":0.2640098015129965,"score_spread":0.2584025662266388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086412156","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011846139,0.0001590399,0.996599,0.0000317408,0.00016084404,0.000030477098,0.000050278817,0.00089924206,0.0008847634],"genre_scores_gemma":[0.074314915,0.0005858172,0.9152755,0.00009800862,0.00020895309,0.00019243274,0.00040270042,0.00019714463,0.008724507],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992355,0.00008919733,0.000034405693,0.00019335521,0.00040835122,0.000039245053],"domain_scores_gemma":[0.9995259,0.00006419601,0.000053076867,0.00006419968,0.0002719193,0.000020831098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033736505,0.0010150524,0.00075240113,0.0014876626,0.00044183966,0.00075419183,0.0010556369,0.00085538824,0.0036279336],"category_scores_gemma":[0.0014322433,0.00048256078,0.0005526198,0.0011463854,0.0003366226,0.00091804744,0.0008689198,0.0010684711,0.0031118914],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001493402,0.000056169298,0.0016486867,0.00026047928,0.00008609549,0.00012707658,0.0001117325,0.023618124,0.07363192,0.010711739,0.009565327,0.8800333],"study_design_scores_gemma":[0.00008820173,0.00016955262,0.004115591,0.0000740705,0.00010447169,0.0013004445,0.00010471986,0.81385154,0.07617244,0.0070588035,0.096771985,0.00018813711],"about_ca_topic_score_codex":0.0018313166,"about_ca_topic_score_gemma":0.0024416079,"teacher_disagreement_score":0.0036279336,"about_ca_system_score_codex":0.0003799003,"about_ca_system_score_gemma":0.0007270724,"threshold_uncertainty_score":0.012136638},"labels":[],"label_agreement":null},{"id":"W2086469330","doi":"10.1109/plans.2012.6236838","title":"Characterization of the impact of indoor Doppler errors on Pedestrian Dead Reckoning","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Else Kröner-Fresenius-Stiftung; Ministry of Advanced Education, Government of Alberta","keywords":"Doppler effect; GNSS applications; Computer science; Dead reckoning; Kalman filter; Narrowband; Remote sensing; Computer vision; Artificial intelligence; Global Positioning System; Geography; Telecommunications; Physics","score_opus":0.016102365772609782,"score_gpt":0.24382071931551713,"score_spread":0.22771835354290734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086469330","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9630966,0.0000850558,0.035908174,0.000011566324,0.00001273104,0.00000941934,0.000060060807,0.00014658322,0.00066982425],"genre_scores_gemma":[0.9981761,0.000024915302,0.0016285577,0.0000031149518,0.0000015780179,0.0000024325852,0.000036693356,0.000009813908,0.00011682542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961984,0.000058031146,0.000017687718,0.000059228212,0.0001612513,0.000084006715],"domain_scores_gemma":[0.99832577,0.00086257054,0.00022889437,0.0001692066,0.00034676812,0.00006682885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004981974,0.0003546121,0.0004532734,0.00043852752,0.00017621087,0.00033774044,0.0002495043,0.00031848377,0.000401451],"category_scores_gemma":[0.002600896,0.00017358651,0.000225012,0.00037279387,0.00028627762,0.0003303413,0.00035397307,0.00024431408,0.00008765801],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008524352,0.00013801995,0.05130706,0.00020145721,0.00011066507,0.00077601377,0.00031592182,0.84148955,0.050901618,0.0007506675,0.0002725178,0.05288406],"study_design_scores_gemma":[0.000021371156,0.0007823416,0.09775335,0.00002634904,0.000090087546,0.00029806292,0.00020800825,0.8437172,0.05628635,0.00029337307,0.00047377258,0.000049712366],"about_ca_topic_score_codex":0.0039169607,"about_ca_topic_score_gemma":0.003613228,"teacher_disagreement_score":0.0039169607,"about_ca_system_score_codex":0.00021764028,"about_ca_system_score_gemma":0.0002592067,"threshold_uncertainty_score":0.0077883005},"labels":[],"label_agreement":null},{"id":"W2086630782","doi":"10.1109/ccece.2014.6900968","title":"Hybrid localization of an emitter by combining angle-of-arrival and received signal strength measurements","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Royal Military College of Canada","funders":"","keywords":"RSS; Angle of arrival; Signal strength; Common emitter; Computer science; Multilateration; Algorithm; SIGNAL (programming language); Time of arrival; Measure (data warehouse); Set (abstract data type); Noise (video); Signal-to-noise ratio (imaging); Noise measurement; Electronic engineering; Acoustics; Artificial intelligence; Telecommunications; Engineering; Physics; Wireless; Noise reduction; Data mining; Antenna (radio)","score_opus":0.01150104968677372,"score_gpt":0.20467795819078763,"score_spread":0.19317690850401392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086630782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076591517,0.000120823315,0.9913032,0.000036501122,0.000020859508,0.000016005944,0.000012874613,0.000244522,0.00058598403],"genre_scores_gemma":[0.37113294,0.0006085869,0.62370557,0.000101843194,0.00008174812,0.00012260604,0.00010049448,0.00008998814,0.0040562074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932885,0.00015579615,0.000047453465,0.00016227762,0.00026634661,0.000039338844],"domain_scores_gemma":[0.9995461,0.00017218998,0.000086244756,0.00006432845,0.00011706913,0.000014085972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077023776,0.0009480651,0.0008920901,0.0008096679,0.00020784404,0.00091180624,0.00106619,0.0008049698,0.0009116451],"category_scores_gemma":[0.0015806471,0.0006525241,0.0007363773,0.0010312939,0.00044695294,0.0017184616,0.0010475832,0.0005683275,0.00067889405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018442747,0.00014180518,0.0034728027,0.00041241045,0.00022550672,0.00018177782,0.00026123584,0.35491735,0.13642795,0.011015709,0.0010599889,0.49169904],"study_design_scores_gemma":[0.000028722669,0.00024604273,0.0012260138,0.000028216404,0.000095054165,0.00029716766,0.000042925654,0.9495651,0.040091775,0.0034936357,0.0048225624,0.000062820254],"about_ca_topic_score_codex":0.0010988535,"about_ca_topic_score_gemma":0.0020319885,"teacher_disagreement_score":0.0010988535,"about_ca_system_score_codex":0.0003060846,"about_ca_system_score_gemma":0.00042829744,"threshold_uncertainty_score":0.0040735006},"labels":[],"label_agreement":null},{"id":"W2088860979","doi":"10.1109/ais.2010.5547016","title":"Indoor robot navigation through intelligent processing of RFID signal measurements","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"RSS; Mobile robot; Mobile robot navigation; Robot; Computer science; Real-time computing; Radio navigation; Navigation system; Embedded system; Artificial intelligence; Robot control; Global Positioning System; Telecommunications","score_opus":0.028065638741841654,"score_gpt":0.25395326763303644,"score_spread":0.2258876288911948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088860979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040454872,0.00018921889,0.95623916,0.000030492893,0.000049219583,0.000020976235,0.000017009055,0.0011082421,0.0018909109],"genre_scores_gemma":[0.57408816,0.00033489565,0.42171887,0.00006748328,0.000040744708,0.000046745325,0.00008734101,0.00005479491,0.0035610432],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999001,0.000017620017,0.000005535963,0.000024900111,0.000043575208,0.00000823402],"domain_scores_gemma":[0.99992156,0.000019403335,0.00002016139,0.0000148363615,0.000020806381,0.000003210939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074605654,0.00034691326,0.0002649826,0.00013799212,0.00011843124,0.00020327864,0.00037572664,0.0002580662,0.00058843684],"category_scores_gemma":[0.00024653308,0.00012422453,0.00022758302,0.00012741561,0.00014872201,0.0002847326,0.0002045808,0.00014060581,0.0003607797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020649862,0.000068255766,0.00166371,0.00028159335,0.000051509873,0.00050270866,0.00016121149,0.08678355,0.5616747,0.0045586657,0.0013405143,0.3427071],"study_design_scores_gemma":[0.00006535556,0.00079380727,0.0061105145,0.000037729314,0.00014905592,0.0020581442,0.00008096213,0.6437283,0.3192578,0.002423963,0.025210882,0.00008354903],"about_ca_topic_score_codex":0.00031142295,"about_ca_topic_score_gemma":0.00058683724,"teacher_disagreement_score":0.00058843684,"about_ca_system_score_codex":0.00008736693,"about_ca_system_score_gemma":0.00016051458,"threshold_uncertainty_score":0.0019685626},"labels":[],"label_agreement":null},{"id":"W2089144876","doi":"10.1504/ijaacs.2014.058014","title":"CCA-MAP and iCCA-MAP: stationary and mobile WSN localisation algorithms","year":2013,"lang":"en","type":"article","venue":"International Journal of Autonomous and Adaptive Communications Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Testbed; Wireless sensor network; Algorithm; Node (physics); Real-time computing; Computer network","score_opus":0.014861479252318517,"score_gpt":0.23938711260820106,"score_spread":0.22452563335588255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089144876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004332817,0.00079591613,0.9904006,0.0002272829,0.00015768113,0.00006582337,0.00009214887,0.0011090373,0.0028187754],"genre_scores_gemma":[0.18839039,0.0017687383,0.79894817,0.00027106082,0.00022802841,0.00044827416,0.00074437086,0.00043062156,0.008770307],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993069,0.00016545456,0.00003621894,0.0001302339,0.0002901241,0.00007113364],"domain_scores_gemma":[0.9986431,0.00043150736,0.0001535201,0.00023731038,0.0004590324,0.0000755155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008819565,0.0009997598,0.0008889409,0.0015120439,0.00086059235,0.0011759856,0.001872445,0.001330282,0.0027622315],"category_scores_gemma":[0.0044035423,0.00038282585,0.0009038983,0.0020539048,0.0010281957,0.0019362192,0.0021415846,0.0014533431,0.0019972879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033147362,0.00010004397,0.0026607518,0.00045849185,0.0001362816,0.0003399259,0.0003535894,0.33711204,0.012067069,0.0705661,0.021087749,0.5547864],"study_design_scores_gemma":[0.00005943914,0.00016044696,0.0011190103,0.00006719542,0.000040567,0.00066551706,0.00014979125,0.9195762,0.008991402,0.02663317,0.042453326,0.000083927735],"about_ca_topic_score_codex":0.0034534512,"about_ca_topic_score_gemma":0.003457062,"teacher_disagreement_score":0.0034534512,"about_ca_system_score_codex":0.0006625383,"about_ca_system_score_gemma":0.0014598005,"threshold_uncertainty_score":0.009240568},"labels":[],"label_agreement":null},{"id":"W2089946312","doi":"10.1109/tmc.2013.143","title":"Reducing the Positional Error of Connectivity-Based Positioning Algorithms Through Cooperation Between Neighbors","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Wireless sensor network; Algorithm; Probabilistic logic; Range (aeronautics); Set (abstract data type); Brooks–Iyengar algorithm; Distributed algorithm; Real-time computing; Distributed computing; Key distribution in wireless sensor networks; Computer network; Wireless; Wireless network; Artificial intelligence; Telecommunications","score_opus":0.013968955136682141,"score_gpt":0.24610582933579284,"score_spread":0.2321368741991107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089946312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04579984,0.00020278398,0.95211333,0.00008513194,0.000021673692,0.00002339798,0.000018370698,0.00041319392,0.001322249],"genre_scores_gemma":[0.73366463,0.00026469174,0.26418468,0.000048063797,0.000034554345,0.00007236704,0.00012230698,0.00007345865,0.0015352712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891996,0.00031883808,0.00005552688,0.00014725406,0.00049165275,0.000066826164],"domain_scores_gemma":[0.9975358,0.0012522804,0.0002500668,0.00056630885,0.00034934908,0.00004613902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090839533,0.0006605938,0.0006685978,0.00075074524,0.0005321524,0.0005226917,0.0011124559,0.00069257105,0.0006804986],"category_scores_gemma":[0.0058918004,0.0003999233,0.00038913504,0.0007146754,0.00056165026,0.0012981447,0.0014121641,0.0004430157,0.00024156747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013354748,0.00003150021,0.0017237532,0.00006182011,0.00004859327,0.000093594004,0.00017882253,0.7819641,0.0125681395,0.008314586,0.00061850686,0.19426297],"study_design_scores_gemma":[0.000024258898,0.00015692916,0.00083659607,0.0000094976385,0.000022491555,0.00015290319,0.000035487326,0.9875141,0.00513557,0.004576105,0.001518262,0.000017968963],"about_ca_topic_score_codex":0.0029661409,"about_ca_topic_score_gemma":0.002873704,"teacher_disagreement_score":0.0029661409,"about_ca_system_score_codex":0.00038356462,"about_ca_system_score_gemma":0.0009071879,"threshold_uncertainty_score":0.0058977604},"labels":[],"label_agreement":null},{"id":"W2090104800","doi":"10.1109/icosp.2010.5656675","title":"Joint estimation of time of arrival and power profile for UWB localization","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Time of arrival; Estimator; Computer science; Ultra-wideband; Algorithm; Joint (building); Maximum likelihood sequence estimation; Nyquist rate; Estimation theory; Sampling (signal processing); A priori and a posteriori; Statistics; Telecommunications; Mathematics; Wireless; Engineering","score_opus":0.004998522221709625,"score_gpt":0.2001280071094557,"score_spread":0.19512948488774606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090104800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041292394,0.00012395778,0.99536324,0.000024314802,0.00000827069,0.000005457222,0.00000940408,0.00013732434,0.00019874188],"genre_scores_gemma":[0.505984,0.00082321296,0.4911628,0.000058061152,0.000070017086,0.000086872984,0.00016884782,0.00008858775,0.001557509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950063,0.00020591669,0.000022383818,0.00006470564,0.00017785664,0.00002848231],"domain_scores_gemma":[0.9990977,0.00048117343,0.00012795003,0.000119483404,0.00014864803,0.000025137615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000700039,0.0005315446,0.0006252045,0.00055326044,0.00021387104,0.0005690766,0.00051221123,0.00058285915,0.00058187975],"category_scores_gemma":[0.0040134504,0.0003830569,0.00041442714,0.00076597044,0.00032618007,0.0012649592,0.0007759764,0.0005805469,0.00044124556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016647796,0.000066482324,0.0018449208,0.00016674965,0.000084599276,0.00017087668,0.00013849646,0.5566113,0.038024027,0.020453542,0.0010813951,0.38119107],"study_design_scores_gemma":[0.0000055316273,0.000025872287,0.00027640382,0.0000061674286,0.000009002949,0.00009196715,0.000010648721,0.9907285,0.0037645646,0.004265704,0.00080384186,0.0000118128755],"about_ca_topic_score_codex":0.0006951142,"about_ca_topic_score_gemma":0.0006568932,"teacher_disagreement_score":0.000700039,"about_ca_system_score_codex":0.0002463949,"about_ca_system_score_gemma":0.000545574,"threshold_uncertainty_score":0.0037022233},"labels":[],"label_agreement":null},{"id":"W2090178842","doi":"10.1109/tim.2011.2181912","title":"An RFID-Based Position and Orientation Measurement System for Mobile Objects in Intelligent Environments","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Orientation (vector space); Radio-frequency identification; Ambient intelligence; Real-time computing; Context (archaeology); Identification (biology); Location awareness; Intelligent sensor; Location-based service; Position (finance); Positioning system; Mobile device; Mobile computing; Ubiquitous computing; Mobile telephony; Human–computer interaction; Mobile radio; Wireless sensor network; Telecommunications; Engineering; Computer security; Computer network","score_opus":0.023936570519831155,"score_gpt":0.23434357297963795,"score_spread":0.2104070024598068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090178842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09698711,0.0010688461,0.8834893,0.00019388895,0.00040670237,0.00026946605,0.00032431513,0.012294989,0.0049654082],"genre_scores_gemma":[0.62808883,0.0004574145,0.3618685,0.0004471644,0.00016816525,0.00025721616,0.0006455046,0.00013046549,0.00793675],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993185,0.00013584831,0.000054798245,0.00017440441,0.0002512221,0.00006520573],"domain_scores_gemma":[0.9994404,0.00006826948,0.00010201753,0.00012623775,0.00020800722,0.00005508495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065076473,0.0006419751,0.0009656542,0.0007834493,0.0005069009,0.0005504778,0.0014332872,0.0011657092,0.002048282],"category_scores_gemma":[0.0009666728,0.00031235663,0.0002867857,0.0008103028,0.0002837781,0.0009874579,0.00069739146,0.0005689573,0.002634994],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009075201,0.00036831707,0.0069075697,0.00052142935,0.0001410892,0.00059325027,0.00034744156,0.0055621434,0.49889156,0.0021848439,0.0065485933,0.47702634],"study_design_scores_gemma":[0.00053179637,0.004995834,0.032247994,0.00014535121,0.0007502199,0.005624702,0.00024138596,0.21282327,0.64808875,0.0013777498,0.09263346,0.000539531],"about_ca_topic_score_codex":0.00082993,"about_ca_topic_score_gemma":0.0010644053,"teacher_disagreement_score":0.002048282,"about_ca_system_score_codex":0.00033925424,"about_ca_system_score_gemma":0.0005365934,"threshold_uncertainty_score":0.0068522096},"labels":[],"label_agreement":null},{"id":"W2090350516","doi":"10.1109/glocom.2013.6831061","title":"GOSSIPY: A distributed localization system for Internet of Things using RFID technology","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Scalability; Internet of Things; Key (lock); Interleaving; Context (archaeology); Distributed computing; Mobile computing; The Internet; Mobile device; Computer network; Interrogation; Computer security; World Wide Web; Database","score_opus":0.008579402624977047,"score_gpt":0.20438458614618493,"score_spread":0.19580518352120788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090350516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057525344,0.00065932784,0.91112643,0.0003316482,0.0002445277,0.00023702129,0.00018415018,0.02499856,0.0046930984],"genre_scores_gemma":[0.76873493,0.00041493768,0.22170871,0.00029085716,0.00007797742,0.00030089635,0.00049304025,0.0003601835,0.0076184077],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997769,0.00005294715,0.000018251618,0.000042742693,0.00007982021,0.000029337903],"domain_scores_gemma":[0.99959654,0.00008903406,0.000060420265,0.00008155146,0.000100535166,0.00007189496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004654353,0.0004529011,0.00052534766,0.0005467484,0.0006210467,0.0004611698,0.0011530251,0.00060890237,0.0019991163],"category_scores_gemma":[0.00092712697,0.0001917945,0.0002735706,0.00036026517,0.0005009875,0.00094218674,0.0012763129,0.00048019973,0.00078259566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002951028,0.0005323687,0.0074067353,0.0014009932,0.0004788233,0.0028061073,0.0017866616,0.13950422,0.1829929,0.030908074,0.04768314,0.5815488],"study_design_scores_gemma":[0.00067264255,0.0013900521,0.002716409,0.00006415704,0.00027667393,0.001255659,0.00033335807,0.8229032,0.08659072,0.0090200845,0.07458384,0.00019316962],"about_ca_topic_score_codex":0.0019522522,"about_ca_topic_score_gemma":0.0025824867,"teacher_disagreement_score":0.0019991163,"about_ca_system_score_codex":0.00039491523,"about_ca_system_score_gemma":0.000685572,"threshold_uncertainty_score":0.0066877007},"labels":[],"label_agreement":null},{"id":"W2090689166","doi":"10.1177/0278364913486362","title":"Scale-free coordinates for multi-robot systems with bearing-only sensors","year":2013,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Robot; Computer science; Generalized coordinates; Algorithm; Computer vision; Artificial intelligence; Mathematics","score_opus":0.06239625674339402,"score_gpt":0.32699012838124875,"score_spread":0.26459387163785475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090689166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031771283,0.00009730393,0.9956073,0.000064976455,0.00004329707,0.000020077732,0.000045732435,0.00019973316,0.0007444872],"genre_scores_gemma":[0.3173315,0.00059139304,0.6775479,0.00010526862,0.0001692706,0.0003053561,0.00030069312,0.00020223462,0.0034463638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932134,0.00017189221,0.000041700947,0.00017986081,0.00023968994,0.00004552367],"domain_scores_gemma":[0.9982186,0.0006193556,0.0002936614,0.0004664017,0.0003120919,0.000089774265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065352244,0.001213359,0.00076390826,0.0010514646,0.00070477295,0.0015524412,0.0017369756,0.000825497,0.0024333666],"category_scores_gemma":[0.0045451256,0.00051251013,0.0008332894,0.0012100847,0.0012739605,0.002184647,0.0017838437,0.0013188851,0.0010818971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071464776,0.000025544909,0.0012939137,0.00013643278,0.0000461959,0.0001847233,0.00019168643,0.68128604,0.005046031,0.26047894,0.0024885526,0.048750535],"study_design_scores_gemma":[0.000023012657,0.000055665703,0.00026426412,0.000013203417,0.000012076177,0.00007095601,0.000030136003,0.9252195,0.0021296225,0.065777615,0.00637923,0.000024688552],"about_ca_topic_score_codex":0.0026539832,"about_ca_topic_score_gemma":0.0022513447,"teacher_disagreement_score":0.0026539832,"about_ca_system_score_codex":0.0010575615,"about_ca_system_score_gemma":0.00068812456,"threshold_uncertainty_score":0.008140385},"labels":[],"label_agreement":null},{"id":"W2091041712","doi":"10.1109/icecs.2012.6463511","title":"A hybrid algorithm for range estimation in RFID systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Range (aeronautics); Computer science; Algorithm; Phase (matter); Software deployment; Phase difference; Real-time computing; Telecommunications; Engineering; Aerospace engineering; Physics","score_opus":0.010946146976974816,"score_gpt":0.22400472925355702,"score_spread":0.2130585822765822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091041712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014598274,0.00018441456,0.9977458,0.000020189826,0.000022975344,0.000009695744,0.0000063041603,0.00021943131,0.00033126218],"genre_scores_gemma":[0.12030101,0.0004627879,0.8744669,0.00007371554,0.00008184108,0.00015547498,0.00011545713,0.00010569551,0.0042370395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943095,0.00014227607,0.000033475906,0.000116043404,0.00024145951,0.000035716705],"domain_scores_gemma":[0.99948907,0.00023020922,0.000046326808,0.00006545837,0.00015545126,0.00001345636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006231964,0.00059945934,0.0006487633,0.00068837486,0.00033618303,0.00086362887,0.0009828799,0.0009579759,0.0018314404],"category_scores_gemma":[0.0020070549,0.00034743256,0.00045809365,0.000955736,0.00037861013,0.001326894,0.0009181993,0.00078896666,0.0012323252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017789088,0.00004266206,0.00049910194,0.00015873389,0.000067787,0.00009463188,0.00011436193,0.36613742,0.023539804,0.01952968,0.002089397,0.5875486],"study_design_scores_gemma":[0.000011825827,0.0000577381,0.00014114591,0.000010435181,0.000008647983,0.00008972354,0.00001124639,0.9897209,0.0036681746,0.0031882466,0.0030762893,0.000015598127],"about_ca_topic_score_codex":0.0012436149,"about_ca_topic_score_gemma":0.0009256974,"teacher_disagreement_score":0.0018314404,"about_ca_system_score_codex":0.00030977448,"about_ca_system_score_gemma":0.00040933426,"threshold_uncertainty_score":0.0061267614},"labels":[],"label_agreement":null},{"id":"W2091582232","doi":"10.1155/asp/2006/20858","title":"A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality","year":2006,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":280,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"City University of Hong Kong","keywords":"Multilateration; RSS; Cramér–Rao bound; Angle of arrival; Estimator; Algorithm; Computer science; Least-squares function approximation; Time of arrival; Non-line-of-sight propagation; Upper and lower bounds; Position (finance); Wireless; Statistics; Mathematics; Estimation theory; Telecommunications; Antenna (radio)","score_opus":0.0077095608325364814,"score_gpt":0.24738996313926417,"score_spread":0.23968040230672769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091582232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007105427,0.00035744318,0.99837613,0.00008127001,0.00001427289,0.000007412139,0.000012991811,0.000037853042,0.00040204576],"genre_scores_gemma":[0.13763028,0.0027518005,0.85461915,0.00021188322,0.0003038012,0.00029190388,0.00022239758,0.00012649593,0.0038423212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99901533,0.0004325129,0.00004271156,0.00018075996,0.0002851254,0.000043518718],"domain_scores_gemma":[0.9981652,0.0013419436,0.00012662784,0.000085784995,0.00025829516,0.00002217606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011799453,0.0010803173,0.0010476192,0.0007469737,0.00033103584,0.0008818716,0.0011224673,0.0014317369,0.0010926999],"category_scores_gemma":[0.00662636,0.00063867995,0.00052029034,0.0017506442,0.0010804165,0.0012489514,0.0012194384,0.0014357911,0.0007546854],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003573173,0.000027682132,0.00033772245,0.00018516519,0.000056946326,0.000055907443,0.00007945959,0.7932378,0.0018591961,0.069585726,0.0020258583,0.1325128],"study_design_scores_gemma":[0.000009980731,0.000029595547,0.00011846452,0.000018957558,0.0000074385134,0.000044699278,0.00001057111,0.966695,0.00056735834,0.029969461,0.0025137556,0.000014718049],"about_ca_topic_score_codex":0.003693627,"about_ca_topic_score_gemma":0.0019483474,"teacher_disagreement_score":0.003693627,"about_ca_system_score_codex":0.0005864358,"about_ca_system_score_gemma":0.0009679016,"threshold_uncertainty_score":0.0073443055},"labels":[],"label_agreement":null},{"id":"W2092323252","doi":"10.1109/tsp.2014.2314064","title":"Efficient Closed-Form Algorithms for AOA Based Self-Localization of Sensor Nodes Using Auxiliary Variables","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":283,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Estimator; Algorithm; Mathematics; Angle of arrival; Initialization; Cramér–Rao bound; Minimum-variance unbiased estimator; Wireless sensor network; Noise (video); Computer science; Mathematical optimization; Estimation theory; Artificial intelligence; Statistics","score_opus":0.017074051812997423,"score_gpt":0.24089011460530252,"score_spread":0.2238160627923051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092323252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005869713,0.0000719273,0.99894816,0.000019976518,0.000010828054,0.000012054871,0.000012130593,0.00012519951,0.00021279632],"genre_scores_gemma":[0.09993053,0.00081674283,0.8942259,0.00011357626,0.00008519832,0.0004537965,0.0004202875,0.00024164411,0.003712275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913317,0.00022655015,0.00005151293,0.00021833082,0.00031385955,0.00005666693],"domain_scores_gemma":[0.9974421,0.001337115,0.00027578152,0.00020421142,0.0006931765,0.00004761609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013802962,0.0013674508,0.0010100921,0.0008469562,0.0005369165,0.0012011536,0.0018196226,0.0010599046,0.0030169107],"category_scores_gemma":[0.005733837,0.0007171145,0.0010382091,0.001261163,0.0009054939,0.0016729063,0.0019586512,0.0023062264,0.0018261202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011763239,0.00007788838,0.0012326388,0.00043320394,0.00008774389,0.00009616491,0.0003335877,0.5661964,0.014416259,0.04927319,0.00435059,0.36338463],"study_design_scores_gemma":[0.000014265718,0.000038030525,0.00020024393,0.000027827387,0.000010910131,0.000055132656,0.000033821823,0.9833974,0.0028704319,0.010562081,0.0027720842,0.000017737591],"about_ca_topic_score_codex":0.0022894582,"about_ca_topic_score_gemma":0.002761613,"teacher_disagreement_score":0.0030169107,"about_ca_system_score_codex":0.0006581079,"about_ca_system_score_gemma":0.0014753683,"threshold_uncertainty_score":0.010092616},"labels":[],"label_agreement":null},{"id":"W2092606621","doi":"10.1109/twc.2013.110813.120379","title":"Mobility-Aided Wireless Sensor Network Localization via Semidefinite Programming","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario; Ontario Tech University","funders":"","keywords":"Semidefinite programming; Wireless sensor network; Computer science; RSS; Cramér–Rao bound; Sensitivity (control systems); Estimator; Ranging; Relaxation (psychology); Convex optimization; Algorithm; Mathematical optimization; Real-time computing; Estimation theory; Regular polygon; Mathematics; Computer network; Electronic engineering; Telecommunications; Engineering","score_opus":0.016927690332562192,"score_gpt":0.2315386613304998,"score_spread":0.2146109709979376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092606621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067279353,0.00013291636,0.99153817,0.000237027,0.000017797294,0.00001857148,0.000036682883,0.0000973697,0.0011936056],"genre_scores_gemma":[0.7305756,0.0007983185,0.26271608,0.00028132167,0.00008035564,0.00041482528,0.0003256777,0.00014736372,0.0046603573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895597,0.00062201713,0.000030281131,0.00013388415,0.00019289802,0.0000649671],"domain_scores_gemma":[0.9963924,0.0026188924,0.0004299387,0.0001326565,0.0003395103,0.00008655402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021185232,0.0012930995,0.0010051635,0.00045785328,0.00032272158,0.001020933,0.0010441213,0.00089004054,0.0014704138],"category_scores_gemma":[0.005383817,0.0007048616,0.00074425375,0.0007327711,0.0012901396,0.0014016558,0.0015132985,0.001475315,0.00036097318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025095585,0.000015994941,0.00016791282,0.00004403459,0.000011855514,0.00005422316,0.00003251099,0.98029983,0.0004640216,0.014018983,0.00038696598,0.0044785636],"study_design_scores_gemma":[0.0000039350703,0.000010717063,0.000014074369,0.0000025768065,0.0000011548913,0.00000738177,0.0000042207203,0.99631304,0.00007571522,0.0034566051,0.000108521606,0.0000020444884],"about_ca_topic_score_codex":0.0028278546,"about_ca_topic_score_gemma":0.0020897505,"teacher_disagreement_score":0.0028278546,"about_ca_system_score_codex":0.00087752094,"about_ca_system_score_gemma":0.0011448076,"threshold_uncertainty_score":0.011204004},"labels":[],"label_agreement":null},{"id":"W2092946865","doi":"10.1007/s11276-007-0034-9","title":"Graphical properties of easily localizable sensor networks","year":2007,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Wireless sensor network; Social connectedness; Euclidean geometry; Graphical model; Euclidean distance; RADIUS; Theoretical computer science; Topology (electrical circuits); Distributed computing; Artificial intelligence; Computer network; Mathematics","score_opus":0.00847854785588914,"score_gpt":0.1892899982370753,"score_spread":0.18081145038118615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092946865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074579105,0.0002649205,0.9100819,0.0004221476,0.00006392572,0.00010599281,0.0006499637,0.0014742032,0.012357868],"genre_scores_gemma":[0.8983812,0.00058907265,0.08594901,0.00028447915,0.00019654715,0.00039482614,0.0013158756,0.00058686227,0.012302258],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981881,0.0004978242,0.0001125942,0.0004669253,0.0005174643,0.00021709516],"domain_scores_gemma":[0.9899951,0.0054219863,0.001990211,0.0010456211,0.0010278461,0.00051915593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010241605,0.0011757419,0.00076725037,0.002081637,0.0006889274,0.0029239794,0.0012638314,0.0010517526,0.0089909285],"category_scores_gemma":[0.009114736,0.0006696143,0.000863942,0.0015885003,0.0013831839,0.0041831653,0.0014912006,0.0012952709,0.0012163856],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033658033,0.00010520514,0.0025221268,0.00030204532,0.00008282015,0.0007881022,0.0009259982,0.04901252,0.016688116,0.87737876,0.0040374757,0.047820225],"study_design_scores_gemma":[0.00010489262,0.00012296495,0.0012943785,0.000054161137,0.000083641164,0.00062244956,0.00018557401,0.14202088,0.0060579176,0.83895946,0.010448328,0.000045273657],"about_ca_topic_score_codex":0.0009579623,"about_ca_topic_score_gemma":0.000907257,"teacher_disagreement_score":0.0089909285,"about_ca_system_score_codex":0.00065304706,"about_ca_system_score_gemma":0.00031663757,"threshold_uncertainty_score":0.030077577},"labels":[],"label_agreement":null},{"id":"W2093590551","doi":"10.1137/040621600","title":"SpaseLoc: An Adaptive Subproblem Algorithm for Scalable Wireless Sensor Network Localization","year":2006,"lang":"en","type":"article","venue":"SIAM Journal on Optimization","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Wireless sensor network; Semidefinite programming; Scalability; Relaxation (psychology); Algorithm; Mathematics; Set (abstract data type); Mathematical optimization; Sequence (biology); Selection (genetic algorithm); Computer science; Artificial intelligence; Computer network","score_opus":0.008219891805363337,"score_gpt":0.20426105875204364,"score_spread":0.1960411669466803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093590551","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029651576,0.00008911308,0.9948749,0.000071945666,0.00003248606,0.000035846275,0.000031244337,0.00074277795,0.0011565753],"genre_scores_gemma":[0.1387384,0.0002176953,0.8566882,0.0002283434,0.000056782927,0.00038910314,0.00034010448,0.00036268018,0.0029786523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958855,0.00010850729,0.00002261211,0.000074491305,0.00016533445,0.000040568913],"domain_scores_gemma":[0.9993955,0.00027590294,0.000054552747,0.0000935629,0.00014024775,0.000040203387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071629824,0.0010041001,0.0009198898,0.0006794824,0.00046764847,0.0006243355,0.0014187294,0.00075030647,0.0030130625],"category_scores_gemma":[0.0018512047,0.00039508374,0.0005026598,0.00083385187,0.0006190691,0.0012601442,0.001697879,0.0013543023,0.0010259433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012834586,0.00010201745,0.0004127356,0.000099998484,0.00004823727,0.00008073163,0.000050252384,0.71943426,0.005155046,0.017367814,0.008360264,0.24876024],"study_design_scores_gemma":[0.000023336981,0.000025431487,0.00003179858,0.0000037173559,0.0000030555514,0.00001924439,0.0000049510063,0.9934325,0.0008087724,0.0041169575,0.0015266303,0.0000036737667],"about_ca_topic_score_codex":0.0029822774,"about_ca_topic_score_gemma":0.0042361673,"teacher_disagreement_score":0.0030130625,"about_ca_system_score_codex":0.00049135124,"about_ca_system_score_gemma":0.001505256,"threshold_uncertainty_score":0.0100797415},"labels":[],"label_agreement":null},{"id":"W2094957730","doi":"10.1109/plans.2014.6851481","title":"Autonomous WLAN heading and position for smartphones","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"","keywords":"Heading (navigation); GNSS applications; Computer science; RSS; Fingerprint (computing); Real-time computing; Wi-Fi; Process (computing); Compass; Global Positioning System; Hybrid positioning system; Wireless; Position (finance); Satellite system; Positioning system; Fingerprint recognition; Wireless network; Artificial intelligence; Telecommunications; Node (physics); Engineering; Geography","score_opus":0.004785414426405841,"score_gpt":0.18800282592118528,"score_spread":0.18321741149477944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094957730","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14562473,0.001972409,0.8074432,0.00037647533,0.0005516509,0.00015720265,0.0009565967,0.026781755,0.016136043],"genre_scores_gemma":[0.8554163,0.0006248628,0.13533145,0.00010288083,0.00015505901,0.00008156337,0.00080074056,0.00020561386,0.0072815535],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978703,0.00003498568,0.000012051126,0.000046149304,0.00009169696,0.000028129909],"domain_scores_gemma":[0.99967873,0.000040485764,0.00005228346,0.00006697905,0.00013480861,0.000026664258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019704556,0.00046255067,0.00034964253,0.0006709705,0.00023053815,0.00046508052,0.0004070142,0.00031714063,0.002029875],"category_scores_gemma":[0.0009996621,0.00018626999,0.00019477562,0.00038333162,0.0001196506,0.00045060416,0.00057377387,0.00026206236,0.0016942024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004937622,0.000071735034,0.019545183,0.00030099877,0.000038428687,0.00041147263,0.0003583743,0.019733775,0.081845105,0.003304474,0.012708194,0.8611885],"study_design_scores_gemma":[0.00020110262,0.0012479253,0.0741265,0.00018497312,0.00020750372,0.002557033,0.0006520095,0.68878716,0.13383839,0.004427842,0.09357818,0.00019137115],"about_ca_topic_score_codex":0.0023415454,"about_ca_topic_score_gemma":0.0026562575,"teacher_disagreement_score":0.0023415454,"about_ca_system_score_codex":0.00020937981,"about_ca_system_score_gemma":0.00022041124,"threshold_uncertainty_score":0.0067905784},"labels":[],"label_agreement":null},{"id":"W2095143852","doi":"10.1109/tmc.2014.2343636","title":"Joint Indoor Localization and Radio Map Construction with Limited Deployment Load","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Mitacs","keywords":"Computer science; RSS; Real-time computing; Bottleneck; Floor plan; Software deployment; Construct (python library); Radio propagation; Set (abstract data type); Data mining; Computer network; Embedded system; Telecommunications","score_opus":0.007109315937893406,"score_gpt":0.18885269386622355,"score_spread":0.18174337792833015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095143852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049001675,0.00006430261,0.9473867,0.000045890454,0.000013053258,0.000041592422,0.00004558929,0.0023657868,0.0010353785],"genre_scores_gemma":[0.7301886,0.00008955444,0.26777628,0.000025717873,0.000022067861,0.000106123014,0.00027098297,0.00011675904,0.0014038804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856085,0.00042425495,0.0000683743,0.0002570744,0.00051123643,0.00017829878],"domain_scores_gemma":[0.9978332,0.0004462349,0.00023168518,0.0011054926,0.0003137628,0.00006961554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008948339,0.00094803894,0.0013873264,0.00084855326,0.00039426223,0.0007797052,0.001188219,0.00056776736,0.0015669634],"category_scores_gemma":[0.0044482276,0.0005092611,0.00050657947,0.0013786041,0.00060615514,0.0020766458,0.002330893,0.00061761664,0.0014374118],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005327788,0.0002491627,0.003856744,0.0001888702,0.00009110696,0.00026749313,0.00023372332,0.49201426,0.045842763,0.0057746405,0.0017729646,0.44917548],"study_design_scores_gemma":[0.00002920523,0.00028110956,0.0024809053,0.000008580321,0.00002729079,0.0002672806,0.000078463905,0.958193,0.03370671,0.002841144,0.002051739,0.000034548964],"about_ca_topic_score_codex":0.0018952732,"about_ca_topic_score_gemma":0.0016261123,"teacher_disagreement_score":0.0018952732,"about_ca_system_score_codex":0.00031155514,"about_ca_system_score_gemma":0.0008084725,"threshold_uncertainty_score":0.0052420497},"labels":[],"label_agreement":null},{"id":"W2095190812","doi":"10.1155/2012/313527","title":"GNSS Spoofing Detection Based on Signal Power Measurements: Statistical Analysis","year":2012,"lang":"en","type":"article","venue":"International Journal of Navigation and Observation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University; University of Calgary","funders":"","keywords":"GNSS applications; Spoofing attack; Computer science; Multipath propagation; Transmitter; Statistical power; Firmware; Real-time computing; SIGNAL (programming language); Global Positioning System; Telecommunications; Computer security; Computer hardware","score_opus":0.026023605228693552,"score_gpt":0.2635738095771282,"score_spread":0.23755020434843468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095190812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10665151,0.00014789453,0.891872,0.00010048377,0.0000129190485,0.000046923367,0.00008742045,0.00023177071,0.00084915076],"genre_scores_gemma":[0.9189282,0.00029505475,0.07980779,0.000033811146,0.000054527565,0.00008141189,0.00023542083,0.000033614382,0.0005301505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985032,0.00035486367,0.00008646525,0.00023563032,0.00073520764,0.00008460376],"domain_scores_gemma":[0.98927456,0.0075624767,0.0012369368,0.0009318258,0.00092550885,0.00006868578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020366062,0.00048106306,0.00042601043,0.0010793426,0.0002373339,0.00065861206,0.00048686712,0.00054298213,0.00052307197],"category_scores_gemma":[0.013790474,0.00024350456,0.00029402805,0.0008933507,0.0009375344,0.0012030745,0.0005237275,0.00066422514,0.00021499288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048544438,0.00020580014,0.057420176,0.00036476064,0.0002260405,0.00044679153,0.00034384048,0.4345781,0.097823896,0.03810083,0.0012653939,0.3687389],"study_design_scores_gemma":[0.0000069990638,0.0001777894,0.01671804,0.000014466055,0.0000278024,0.00033537296,0.0000525041,0.95224833,0.023748688,0.005997432,0.00062910083,0.00004344436],"about_ca_topic_score_codex":0.0006070357,"about_ca_topic_score_gemma":0.0005109668,"teacher_disagreement_score":0.0020366062,"about_ca_system_score_codex":0.0003590468,"about_ca_system_score_gemma":0.0004609331,"threshold_uncertainty_score":0.0107706785},"labels":[],"label_agreement":null},{"id":"W2095632430","doi":"10.1109/ntms.2009.5384706","title":"Robust Cooperative Localization Technique for Wireless Sensor Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Dilution of precision; Outlier; Estimator; Non-line-of-sight propagation; Wireless sensor network; Ranging; Computer science; Algorithm; Coordinate system; Wireless; Artificial intelligence; Mathematics; Global Positioning System; Computer network; Telecommunications","score_opus":0.013293548375005041,"score_gpt":0.21571730246917087,"score_spread":0.20242375409416583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095632430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001685663,0.00023457957,0.99741507,0.00004503262,0.000025046122,0.000008962312,0.0000059395657,0.00018017979,0.00039952298],"genre_scores_gemma":[0.36627543,0.0012760409,0.62736034,0.0001593053,0.00016251131,0.00026825033,0.00014793605,0.00011419158,0.0042360006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999138,0.00022719277,0.00003199594,0.00016988257,0.00038904388,0.000043837576],"domain_scores_gemma":[0.99948174,0.00014846925,0.00009844149,0.00009818338,0.00015844974,0.000014706572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006855524,0.00077079545,0.000721204,0.0006793756,0.00034862355,0.0005838547,0.0012119961,0.00079117523,0.00077649456],"category_scores_gemma":[0.0022680159,0.00030997064,0.000676436,0.0010168024,0.00054369145,0.0012257034,0.0011133272,0.0007984708,0.0005763686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000132299,0.000041804342,0.00047639845,0.00023724917,0.00014059524,0.00024029877,0.00034795934,0.48668286,0.045966085,0.05755004,0.0041289935,0.4040554],"study_design_scores_gemma":[0.000016507409,0.00012749889,0.0001756956,0.000014928501,0.000036367477,0.00017456074,0.000032119417,0.972671,0.008130511,0.009388287,0.009205743,0.00002696649],"about_ca_topic_score_codex":0.0011869667,"about_ca_topic_score_gemma":0.0007720466,"teacher_disagreement_score":0.0012119961,"about_ca_system_score_codex":0.0004530168,"about_ca_system_score_gemma":0.00053183123,"threshold_uncertainty_score":0.0036256313},"labels":[],"label_agreement":null},{"id":"W2096124156","doi":"10.1109/glocom.2010.5683928","title":"Localization of Wireless Sensors via Nuclear Norm for Rank Minimization","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Minification; Computer science; Wireless; Rank (graph theory); Wireless sensor network; Norm (philosophy); Mathematical optimization; Mathematics; Computer network; Telecommunications; Combinatorics; Political science","score_opus":0.004859284037641126,"score_gpt":0.1946291923638244,"score_spread":0.18976990832618326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096124156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017404472,0.000052909716,0.99771035,0.000074048265,0.000009695029,0.0000065582017,0.0000106815905,0.00005589968,0.00033938],"genre_scores_gemma":[0.30340967,0.00090398284,0.6916466,0.00017855946,0.00012162377,0.0002890674,0.0002987448,0.00011885089,0.0030329428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982734,0.0007862037,0.00008199144,0.000261268,0.0005225482,0.000074650925],"domain_scores_gemma":[0.99770784,0.0012164882,0.000325519,0.00026360803,0.0004321574,0.000054283802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018087426,0.000904998,0.00092031003,0.0007096376,0.00035393442,0.0010034154,0.0008031934,0.00093883317,0.0010027632],"category_scores_gemma":[0.007716518,0.0003592277,0.0005972585,0.0011026064,0.0017753273,0.0020765795,0.0013651054,0.00130843,0.0005608297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016495596,0.000041563104,0.000554768,0.00023799915,0.00006376732,0.00015050554,0.00015914756,0.7246171,0.015718011,0.1319639,0.0027608422,0.12356731],"study_design_scores_gemma":[0.000008869115,0.000048876944,0.000095137424,0.000009045215,0.000005363479,0.00004964314,0.00001613519,0.9717553,0.003205719,0.023682624,0.0011102778,0.000012970457],"about_ca_topic_score_codex":0.001068773,"about_ca_topic_score_gemma":0.00073272816,"teacher_disagreement_score":0.0018087426,"about_ca_system_score_codex":0.00063312,"about_ca_system_score_gemma":0.00091751455,"threshold_uncertainty_score":0.009565711},"labels":[],"label_agreement":null},{"id":"W2096731925","doi":"10.1109/cnsr.2008.94","title":"A Testbed for Localizing Wireless LAN Devices Using Received Signal Strength","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Testbed; Computer science; Received signal strength indication; Real-time computing; Wireless; Signal strength; Wireless sensor network; Measure (data warehouse); Wireless network; Wireless lan; Phase (matter); Perspective (graphical); Computer network; Artificial intelligence; Data mining; Telecommunications","score_opus":0.03430511237484437,"score_gpt":0.2369128911636757,"score_spread":0.20260777878883132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096731925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40097457,0.00039376068,0.5777119,0.00034249303,0.00044756755,0.0015837713,0.0018946802,0.0080798995,0.008571342],"genre_scores_gemma":[0.70190495,0.00033897243,0.29053417,0.000095162,0.00004484597,0.0014040079,0.0025123511,0.00016738557,0.002998183],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989973,0.00035937375,0.000109412205,0.00013136654,0.00027480896,0.00012764116],"domain_scores_gemma":[0.9984359,0.00042884666,0.00019570097,0.00041004273,0.00028084192,0.00024857026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012015988,0.0005401395,0.0005064266,0.00076492736,0.0004685366,0.0005099849,0.0015164042,0.0004909919,0.001270026],"category_scores_gemma":[0.0017907966,0.00020864564,0.00028945407,0.00057996955,0.0005038362,0.0009998712,0.0010245718,0.0005875973,0.00050605024],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012622448,0.002905516,0.019758284,0.0012935676,0.00016487685,0.0019810298,0.00094717625,0.10969063,0.66487193,0.029019961,0.01033701,0.15776771],"study_design_scores_gemma":[0.0004736685,0.011173317,0.023693886,0.00023074173,0.00018113076,0.0019275433,0.0007681509,0.32291293,0.55492055,0.0067449207,0.07678702,0.00018617179],"about_ca_topic_score_codex":0.00081046997,"about_ca_topic_score_gemma":0.0009111636,"teacher_disagreement_score":0.0015164042,"about_ca_system_score_codex":0.00039310014,"about_ca_system_score_gemma":0.0003844002,"threshold_uncertainty_score":0.006354749},"labels":[],"label_agreement":null},{"id":"W2097667434","doi":"10.1109/aiccsa.2006.205200","title":"On The Performance of Directional MAC Protocols in Wireless Ad-Hoc Networks","year":2006,"lang":"en","type":"article","venue":"IEEE International Conference on Computer Systems and Applications, 2006.","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless ad hoc network; Computer network; Mobile ad hoc network; Ad hoc wireless distribution service; Multiple Access with Collision Avoidance for Wireless; Wireless; Vehicular ad hoc network; Optimized Link State Routing Protocol; Telecommunications; Network packet","score_opus":0.01864459155828534,"score_gpt":0.24668440390301244,"score_spread":0.2280398123447271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097667434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5623702,0.036668498,0.36026415,0.0048457095,0.0019489801,0.00052750035,0.00082658906,0.001866834,0.030681448],"genre_scores_gemma":[0.97638595,0.0033061919,0.017245002,0.00018094949,0.000541697,0.00012505373,0.00030007586,0.00019540764,0.0017197481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9814465,0.008578204,0.0011396508,0.0009028625,0.006169131,0.0017635591],"domain_scores_gemma":[0.8500356,0.12691168,0.003165618,0.00681137,0.011898542,0.001177142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022164155,0.0027840037,0.0026775685,0.0032962598,0.0021569068,0.0034286163,0.0023376278,0.0023039116,0.0017804836],"category_scores_gemma":[0.09114354,0.001288839,0.0005084683,0.0024287272,0.0033829198,0.0060253004,0.0031150882,0.0027814356,0.0006022907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0051992848,0.0005914964,0.009753095,0.00050278316,0.00030993676,0.0002983391,0.00061801646,0.8105369,0.0096086385,0.034902986,0.0075825346,0.1200959],"study_design_scores_gemma":[0.000113480855,0.00088769617,0.0021231982,0.00007316799,0.00016389297,0.0002956859,0.0002618348,0.975679,0.0046718204,0.014147126,0.0015173525,0.00006582774],"about_ca_topic_score_codex":0.0041147,"about_ca_topic_score_gemma":0.004054571,"teacher_disagreement_score":0.022164155,"about_ca_system_score_codex":0.0033320151,"about_ca_system_score_gemma":0.0022275555,"threshold_uncertainty_score":0.11721665},"labels":[],"label_agreement":null},{"id":"W2097808205","doi":"10.1109/cnsr.2007.71","title":"Wireless Sensor Network: Research vs. Reality Design and Deployment Issues","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wireless sensor network; Software deployment; Computer science; Key distribution in wireless sensor networks; Wireless; Node (physics); Embedded system; Key (lock); Sensor node; Wireless network; Computer network; Telecommunications; Engineering; Computer security; Software engineering","score_opus":0.0686082006418357,"score_gpt":0.33256139840367,"score_spread":0.2639531977618343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097808205","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005989628,0.42617393,0.3841883,0.064198196,0.006330487,0.0002151719,0.00016264021,0.0005024906,0.112239145],"genre_scores_gemma":[0.14799118,0.56601316,0.21808036,0.006139858,0.006749517,0.0006183034,0.00020667934,0.0003418894,0.05385901],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973442,0.0011759476,0.0001610334,0.00030436643,0.00093920954,0.00007532534],"domain_scores_gemma":[0.99714166,0.0015702063,0.00013566103,0.00021046266,0.00082500663,0.00011703682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006366876,0.0008675416,0.0007557696,0.0009963978,0.00056908093,0.0037958943,0.0016098912,0.0027319358,0.0034133578],"category_scores_gemma":[0.006522805,0.0005904619,0.00031560686,0.0017261821,0.0027700367,0.009186751,0.00085437373,0.0018483573,0.001854117],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005035889,0.000042683834,0.00061594497,0.002771442,0.00003849843,0.00018990698,0.00063640875,0.0077574654,0.0026215222,0.5788612,0.026261749,0.3801529],"study_design_scores_gemma":[0.000028501754,0.00024301164,0.0008510453,0.0019343339,0.000054932283,0.0018206183,0.0012021043,0.022366017,0.0029348196,0.19460233,0.7739001,0.00006217643],"about_ca_topic_score_codex":0.00060276216,"about_ca_topic_score_gemma":0.0007646238,"teacher_disagreement_score":0.006366876,"about_ca_system_score_codex":0.0013954698,"about_ca_system_score_gemma":0.00091775577,"threshold_uncertainty_score":0.033671677},"labels":[],"label_agreement":null},{"id":"W2097942803","doi":"10.1115/detc2007-34952","title":"An Indoor Spread Spectrum Acoustic Ranging System","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Ranging; Pseudorandom noise; Multipath propagation; Spread spectrum; Gold code; Computer science; Range (aeronautics); Code (set theory); Multipath interference; Interference (communication); Acoustics; Telecommunications; Code division multiple access; Engineering; Physics","score_opus":0.004807068344669315,"score_gpt":0.20739211464033636,"score_spread":0.20258504629566704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097942803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17617977,0.00081077777,0.77930605,0.00058311375,0.0005294844,0.00030592585,0.00033050848,0.0082104495,0.033743925],"genre_scores_gemma":[0.75426894,0.00041876594,0.21727616,0.00043344076,0.00025205698,0.00023522362,0.0003699525,0.00008150101,0.026664022],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995679,0.00007731386,0.0000158262,0.00008542454,0.0002096736,0.000043806067],"domain_scores_gemma":[0.99976784,0.000030479852,0.000023374705,0.00004885842,0.00009092073,0.000038530183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022940214,0.00038088593,0.0005081316,0.00027764132,0.0004602947,0.0004136898,0.0006352576,0.0005969697,0.004516882],"category_scores_gemma":[0.0003314764,0.00016592958,0.0001880099,0.00025144007,0.00023728525,0.00039936384,0.00055611506,0.00043102814,0.00241257],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006989816,0.0003203786,0.005799631,0.00044047358,0.000085041705,0.0010660143,0.00023495733,0.019213244,0.61881155,0.008941206,0.008313255,0.3360753],"study_design_scores_gemma":[0.00035230973,0.0053629167,0.014401606,0.0001392338,0.0004383148,0.0066935974,0.00020214687,0.21390519,0.5957834,0.003485933,0.1590684,0.000166974],"about_ca_topic_score_codex":0.00049941486,"about_ca_topic_score_gemma":0.00054551446,"teacher_disagreement_score":0.004516882,"about_ca_system_score_codex":0.0002375279,"about_ca_system_score_gemma":0.00040752455,"threshold_uncertainty_score":0.015110433},"labels":[],"label_agreement":null},{"id":"W2098338428","doi":"10.5194/isprsarchives-xl-4-w4-1-2013","title":"Dynamic WIFI-Based Indoor Positioning in 3D Virtual World","year":2013,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Visualization; Context (archaeology); Real-time computing; Indoor positioning system; Wireless; Wireless sensor network; Building model; Embedded system; Simulation; Telecommunications; Computer network; Artificial intelligence; Accelerometer","score_opus":0.00861539306320205,"score_gpt":0.22523463625581946,"score_spread":0.2166192431926174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098338428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08965978,0.00025651112,0.8605727,0.0001345807,0.00016286391,0.00018489728,0.0014561131,0.042400517,0.0051719933],"genre_scores_gemma":[0.760968,0.00023886266,0.23083514,0.0001381776,0.000056529952,0.00027030418,0.0017936189,0.0006896596,0.0050095897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951196,0.00008710268,0.000025150726,0.00013108722,0.0001928065,0.00005197355],"domain_scores_gemma":[0.99954045,0.00008794688,0.00004396788,0.0001407619,0.00011356548,0.0000733755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043467648,0.0008942253,0.00090980076,0.0012914304,0.00042013143,0.0011987637,0.0015332024,0.0009091673,0.005210304],"category_scores_gemma":[0.0010092913,0.00046924234,0.00059112074,0.00080556684,0.0003151162,0.0010438781,0.001997356,0.0004253381,0.0017681171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018757602,0.0005365716,0.021516806,0.0006428435,0.00042051772,0.002596685,0.0012724162,0.0925297,0.14355007,0.0064601074,0.023976373,0.7046221],"study_design_scores_gemma":[0.00025935628,0.0006869718,0.022790587,0.00009567681,0.00026071374,0.0022720727,0.000323606,0.82020265,0.106473826,0.003504851,0.0428133,0.00031642694],"about_ca_topic_score_codex":0.004309977,"about_ca_topic_score_gemma":0.0042874245,"teacher_disagreement_score":0.005210304,"about_ca_system_score_codex":0.00033353845,"about_ca_system_score_gemma":0.00039323562,"threshold_uncertainty_score":0.017430246},"labels":[],"label_agreement":null},{"id":"W2098506373","doi":"10.17722/ijrbt.v3i2.132","title":"Robust Indoor Wi-Fi Positioning System for Android-based Smartphone","year":2013,"lang":"en","type":"article","venue":"International Journal of Research in Business and Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Android (operating system); Computer science; Indoor positioning system; Embedded system; Real-time computing; Computer security; Operating system; Accelerometer","score_opus":0.031320593961384646,"score_gpt":0.2895790585900141,"score_spread":0.25825846462862945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098506373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060523227,0.002925215,0.8734696,0.00056358863,0.00070584525,0.00061686634,0.0025916363,0.03718681,0.021417174],"genre_scores_gemma":[0.75732994,0.0015918126,0.2021559,0.00044247616,0.0003005742,0.00067700044,0.0042239074,0.0004502778,0.03282822],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996277,0.000051036695,0.000028711345,0.00007218087,0.00017362615,0.000046738736],"domain_scores_gemma":[0.9996556,0.00002614847,0.00003840037,0.00007525141,0.00018348885,0.000021120475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018033278,0.0006395363,0.00049490575,0.00060551014,0.00029887914,0.00040681538,0.0006739506,0.00050694606,0.00591013],"category_scores_gemma":[0.00070183055,0.00016923204,0.00032863911,0.00035476102,0.00010335925,0.0004328918,0.00041316403,0.0003623219,0.004197902],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063683104,0.00010264067,0.00905653,0.0008717761,0.0001253756,0.0011373835,0.00031185473,0.0061002607,0.29980335,0.0029328205,0.042881522,0.6360397],"study_design_scores_gemma":[0.00031897883,0.0025906025,0.10814551,0.0003468659,0.0008394568,0.01024243,0.00055365043,0.22360468,0.37018204,0.0024934711,0.2800389,0.00064341724],"about_ca_topic_score_codex":0.0033262167,"about_ca_topic_score_gemma":0.003542717,"teacher_disagreement_score":0.00591013,"about_ca_system_score_codex":0.00018689976,"about_ca_system_score_gemma":0.00028900016,"threshold_uncertainty_score":0.019771338},"labels":[],"label_agreement":null},{"id":"W2099449987","doi":"10.1109/icas.2009.64","title":"A 3-D Localization Algorithm for Robot Swarms under the Presence of Failures","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Scalability; Robot; Position (finance); Terrain; Swarm behaviour; Simultaneous localization and mapping; Real-time computing; Artificial intelligence; Mobile robot; Distributed computing","score_opus":0.010020300182073795,"score_gpt":0.2289564116782373,"score_spread":0.21893611149616352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099449987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026237967,0.0000731311,0.996367,0.000053986987,0.000017765366,0.00001628815,0.0000130200615,0.0005094426,0.0003256096],"genre_scores_gemma":[0.15634017,0.0002289728,0.84100276,0.00008306087,0.00003187538,0.00020330837,0.0001390183,0.00012673075,0.0018441326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975055,0.00003900217,0.000019491164,0.00007199545,0.000094154515,0.00002470375],"domain_scores_gemma":[0.99957687,0.00015181028,0.000060132697,0.00005795819,0.0001277854,0.000025420757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005068719,0.00062140584,0.00070221757,0.0006743396,0.0008216085,0.00073447067,0.0010281154,0.0010621668,0.0012321126],"category_scores_gemma":[0.0019700055,0.00038599366,0.0006169234,0.00075677567,0.0005654166,0.0010361958,0.0013622624,0.00076297123,0.000722989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014873373,0.000033372235,0.0009839745,0.000097852404,0.00005641641,0.00016906913,0.0003232443,0.6155687,0.01236155,0.012038004,0.0036374978,0.35458156],"study_design_scores_gemma":[0.000016042759,0.000026543381,0.000119963894,0.000008047051,0.0000078697085,0.00005475241,0.000021582415,0.9921268,0.0015060517,0.0037944412,0.0023085042,0.000009323487],"about_ca_topic_score_codex":0.0060103233,"about_ca_topic_score_gemma":0.0040974766,"teacher_disagreement_score":0.0060103233,"about_ca_system_score_codex":0.0005427206,"about_ca_system_score_gemma":0.00095228665,"threshold_uncertainty_score":0.011950672},"labels":[],"label_agreement":null},{"id":"W2099544316","doi":"10.1109/glocom.2009.5425343","title":"Networked Ultrasonic Sensors for Target Tracking: An Experimental Study","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ranging; Ultrasonic sensor; Computer science; Tracking (education); Kalman filter; Tracking system; Wireless sensor network; Real-time computing; Filter (signal processing); Node (physics); Position (finance); Computer vision; Artificial intelligence; Engineering; Acoustics; Telecommunications; Computer network","score_opus":0.018267654759664433,"score_gpt":0.26642240490651525,"score_spread":0.24815475014685082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099544316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94701844,0.0007566156,0.047830187,0.00016731408,0.00019192425,0.00017191628,0.00024369746,0.00034569204,0.0032742703],"genre_scores_gemma":[0.9824892,0.00065846706,0.013653571,0.000057673446,0.000041909014,0.00019498641,0.0002574103,0.000022897764,0.0026237152],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993556,0.00013870787,0.000040348586,0.00014812427,0.00022543083,0.00009171842],"domain_scores_gemma":[0.9988601,0.00039385975,0.0001341567,0.00021040418,0.0003278298,0.00007359163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009845734,0.0004406739,0.0005011948,0.00047643046,0.00038286214,0.00036965625,0.00057939655,0.00078822905,0.002739158],"category_scores_gemma":[0.0016271926,0.00017179467,0.0002875611,0.00049444014,0.00050359784,0.0007583524,0.0005565597,0.00044115554,0.00045489118],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002722835,0.0057369526,0.0117847435,0.00083210156,0.00013211365,0.00087683083,0.0007341625,0.022130389,0.8454904,0.0028571228,0.0017369912,0.10496538],"study_design_scores_gemma":[0.0006770693,0.042928763,0.021598812,0.00016491758,0.0003649061,0.0015274254,0.0013324575,0.16451564,0.7550644,0.002047202,0.009671397,0.00010700641],"about_ca_topic_score_codex":0.0007861389,"about_ca_topic_score_gemma":0.0005531354,"teacher_disagreement_score":0.002739158,"about_ca_system_score_codex":0.00021876187,"about_ca_system_score_gemma":0.00026575857,"threshold_uncertainty_score":0.00916338},"labels":[],"label_agreement":null},{"id":"W2099593333","doi":"10.1109/vtcf.2006.579","title":"Using Antenna Array in Multipath Environment for Wireless Sensor Positioning","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Multilateration; Multipath propagation; Computer science; Wireless sensor network; MIMO; Angle of arrival; Node (physics); Cramér–Rao bound; Antenna array; Sensor array; Sensor node; Direction of arrival; Context (archaeology); Real-time computing; Antenna (radio); SIGNAL (programming language); Wireless; Key distribution in wireless sensor networks; Channel (broadcasting); Computer network; Wireless network; Telecommunications; Engineering; Estimation theory; Algorithm; Geography","score_opus":0.016893045550871913,"score_gpt":0.22166092371543997,"score_spread":0.20476787816456804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099593333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009099225,0.0012340854,0.98756325,0.000119732525,0.000084886226,0.000008453518,0.000017831817,0.00035346102,0.001519124],"genre_scores_gemma":[0.37623724,0.004430133,0.6154615,0.0001397703,0.00024102826,0.00005446584,0.00010991432,0.00004326638,0.003282735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963605,0.00014768547,0.000013166049,0.0000602494,0.00012407257,0.000018859044],"domain_scores_gemma":[0.9997907,0.00008580606,0.000028932476,0.000038396985,0.000049942566,0.0000061887918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023654818,0.0004800974,0.00031849046,0.00032218022,0.0001802585,0.000392842,0.00031860342,0.0006555192,0.0006729192],"category_scores_gemma":[0.00070617505,0.0001905439,0.00024166444,0.00068733055,0.0002827804,0.0006462658,0.00033584586,0.0004295746,0.00066673436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019622422,0.00003437147,0.0021806932,0.00037892093,0.0001511579,0.00035129886,0.00015556195,0.25311852,0.22986346,0.032042213,0.0026774316,0.4788501],"study_design_scores_gemma":[0.000024535053,0.00028688528,0.0013650496,0.000035775895,0.000079507365,0.00094757456,0.00005354861,0.8815971,0.07392114,0.013197089,0.028422415,0.000069296395],"about_ca_topic_score_codex":0.00032817601,"about_ca_topic_score_gemma":0.00055263296,"teacher_disagreement_score":0.0006729192,"about_ca_system_score_codex":0.00018058244,"about_ca_system_score_gemma":0.00019107043,"threshold_uncertainty_score":0.0022510886},"labels":[],"label_agreement":null},{"id":"W2099684071","doi":"10.1109/icc.2007.547","title":"A2L: Angle to Landmarks Based Method Positioning for Wireless Sensor Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Wireless sensor network; Computer science; Landmark; Angle of arrival; Node (physics); Key distribution in wireless sensor networks; Wireless; Real-time computing; Wireless network; Computer network; Artificial intelligence; Telecommunications; Engineering","score_opus":0.007956558705085713,"score_gpt":0.2556433683378355,"score_spread":0.2476868096327498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099684071","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027932348,0.0007287557,0.9914596,0.00011728898,0.0003465634,0.00007155312,0.00012650483,0.0020946255,0.0022618815],"genre_scores_gemma":[0.14113283,0.0012053831,0.84723574,0.00025150253,0.00041150645,0.0004939035,0.0006295469,0.00029213284,0.0083474545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880564,0.00036330346,0.00005091401,0.00016495463,0.0005517927,0.000063379775],"domain_scores_gemma":[0.9990181,0.00029307124,0.000111561,0.00019327407,0.00031799902,0.00006605662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078563596,0.0008581002,0.0006769816,0.0017344706,0.00069256115,0.00093240273,0.0018431795,0.0010409743,0.0049424646],"category_scores_gemma":[0.0027376034,0.0002761447,0.00047262263,0.0017900979,0.0005516042,0.0014173047,0.0015157545,0.00079702423,0.0037880263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045315883,0.00010623115,0.0021796543,0.00061802997,0.000100972145,0.0002843682,0.00024137745,0.07748115,0.02454803,0.026604075,0.028316861,0.839066],"study_design_scores_gemma":[0.00025660574,0.00073898264,0.0019902387,0.0001047173,0.00006905152,0.0011471243,0.00016313326,0.8227662,0.024506621,0.020312043,0.12779288,0.00015240406],"about_ca_topic_score_codex":0.002187194,"about_ca_topic_score_gemma":0.0023882396,"teacher_disagreement_score":0.0049424646,"about_ca_system_score_codex":0.00044181294,"about_ca_system_score_gemma":0.0007834481,"threshold_uncertainty_score":0.01653421},"labels":[],"label_agreement":null},{"id":"W2100314550","doi":"10.1109/wisp.2009.5286554","title":"Neural network and fingerprinting-based localization in dynamic channels","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Data mining; Channel (broadcasting); Triangulation; Artificial neural network; State (computer science); Tree (set theory); Channel state information; Real-time computing; Artificial intelligence; Pattern recognition (psychology); Algorithm; Wireless","score_opus":0.004996463581393948,"score_gpt":0.2026184302850949,"score_spread":0.19762196670370097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100314550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26461118,0.00107626,0.72932667,0.00026377602,0.000058983405,0.000036005735,0.00013700576,0.00088215404,0.0036080603],"genre_scores_gemma":[0.9323258,0.00047865557,0.06370603,0.00004598414,0.000028150693,0.000035299585,0.000115988856,0.000024018087,0.0032401006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996762,0.000067655266,0.000020874208,0.000093278744,0.00009490339,0.00004719245],"domain_scores_gemma":[0.9993734,0.0003298056,0.00008329285,0.00004938846,0.00014764933,0.000016624987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006332491,0.0003620704,0.00039100027,0.0006389767,0.00022168593,0.00049437536,0.00045198976,0.00064077275,0.00086942007],"category_scores_gemma":[0.0023519727,0.00021493793,0.00023155303,0.00086468155,0.0003889055,0.00090424856,0.0003076354,0.00032076734,0.00024310671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045474456,0.00011650116,0.0061066966,0.00010222005,0.00006767385,0.00022200154,0.00009077223,0.6844155,0.017446946,0.0026882794,0.00058417034,0.28770447],"study_design_scores_gemma":[0.000006545584,0.00005646842,0.0017075755,0.000005848647,0.000010527494,0.00005877384,0.000014904722,0.9927537,0.0044090413,0.00064159057,0.000326614,0.000008418028],"about_ca_topic_score_codex":0.0063520055,"about_ca_topic_score_gemma":0.0038294175,"teacher_disagreement_score":0.0063520055,"about_ca_system_score_codex":0.0004475835,"about_ca_system_score_gemma":0.0002890236,"threshold_uncertainty_score":0.012630045},"labels":[],"label_agreement":null},{"id":"W2100916320","doi":"10.3390/s150717534","title":"Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National High-tech Research and Development Program; China Scholarship Council; National Natural Science Foundation of China","keywords":"Fingerprint (computing); Computer science; Real-time computing; Fingerprint recognition; Term (time); Global Positioning System; Database; Artificial intelligence; Telecommunications","score_opus":0.021217863686430985,"score_gpt":0.24606836311502098,"score_spread":0.22485049942858998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100916320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28812376,0.0012033975,0.7027344,0.00011280152,0.00021309158,0.00011952328,0.0004983315,0.0037715125,0.003223201],"genre_scores_gemma":[0.85265356,0.00033439894,0.14465825,0.00007088835,0.00006176892,0.00006202197,0.0003340177,0.000049948918,0.0017750844],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923015,0.0000933251,0.000044404795,0.00019413188,0.00035680583,0.000081226746],"domain_scores_gemma":[0.9991935,0.00014334818,0.0001278946,0.00025873462,0.00023441274,0.000042051623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000370981,0.0007027612,0.0010056588,0.001037056,0.0003508195,0.0006130239,0.0009557401,0.00065188366,0.0011168448],"category_scores_gemma":[0.0018010194,0.0003262771,0.00043686878,0.0010732823,0.00013309433,0.0010936304,0.0007585417,0.00027514476,0.000859228],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070844084,0.00023248614,0.019200185,0.00033639607,0.00020742626,0.00051423116,0.00022401725,0.02587284,0.14983279,0.00084497285,0.002360105,0.7996661],"study_design_scores_gemma":[0.00009350909,0.0010745255,0.049209554,0.00010246942,0.00035936656,0.004628956,0.00033385202,0.6809312,0.24803863,0.0017087494,0.0133207645,0.00019833515],"about_ca_topic_score_codex":0.0027223418,"about_ca_topic_score_gemma":0.0046256217,"teacher_disagreement_score":0.0027223418,"about_ca_system_score_codex":0.00022732126,"about_ca_system_score_gemma":0.00031024293,"threshold_uncertainty_score":0.005412936},"labels":[],"label_agreement":null},{"id":"W2101244527","doi":"10.1109/ccece.2002.1015252","title":"The relationship between bandwidth and performance for a spread spectrum acoustic ranging system","year":2003,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Ranging; Bandwidth (computing); Global Positioning System; Computer science; Spread spectrum; Radio spectrum; Electronic engineering; GPS signals; Wireless; Telecommunications; Assisted GPS; Engineering","score_opus":0.015962778539567884,"score_gpt":0.21191749190212905,"score_spread":0.19595471336256118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101244527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32004803,0.0036172126,0.6446209,0.0015241958,0.00020679213,0.00011686664,0.00024133165,0.003120307,0.026504332],"genre_scores_gemma":[0.97006756,0.000893824,0.025645316,0.00016538173,0.00014861356,0.000056594483,0.00018997397,0.00026101104,0.0025717309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974069,0.00062453956,0.00018277533,0.00038180707,0.0008967137,0.00050726114],"domain_scores_gemma":[0.95304567,0.036134146,0.0024577607,0.0022619443,0.005640488,0.00045994326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022474849,0.00068969605,0.0006875025,0.0014831807,0.00073202467,0.0014698551,0.00079866714,0.0021155735,0.002793116],"category_scores_gemma":[0.032810044,0.00035987882,0.00027526545,0.0011161036,0.00094847946,0.0032184296,0.0011481319,0.0010562778,0.0025877785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019222803,0.0003562402,0.029502764,0.0009165812,0.00023584647,0.0013243895,0.0010196738,0.3540251,0.25719976,0.038544998,0.0035807556,0.31137165],"study_design_scores_gemma":[0.0001577532,0.003670169,0.036908966,0.0004331931,0.0004343572,0.0138763925,0.0011196682,0.6330771,0.20371893,0.08988021,0.016332546,0.00039068353],"about_ca_topic_score_codex":0.00079063256,"about_ca_topic_score_gemma":0.00028329232,"teacher_disagreement_score":0.002793116,"about_ca_system_score_codex":0.00050827203,"about_ca_system_score_gemma":0.00031568087,"threshold_uncertainty_score":0.011885941},"labels":[],"label_agreement":null},{"id":"W2101565227","doi":"10.1109/icc.2008.790","title":"Robust Localization in Wireless Sensor Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Ranging; Wireless sensor network; Computer science; Node (physics); Position (finance); Wireless; Upper and lower bounds; Time of arrival; Algorithm; Network topology; Distance measurement; Cramér–Rao bound; Measure (data warehouse); Real-time computing; Topology (electrical circuits); Data mining; Computer network; Artificial intelligence; Estimation theory; Mathematics; Telecommunications; Engineering","score_opus":0.0173533217830948,"score_gpt":0.18400897578931602,"score_spread":0.16665565400622123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101565227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026706615,0.0028192103,0.99210006,0.0002692025,0.00012771974,0.000018584185,0.000038092476,0.00053535757,0.0014210633],"genre_scores_gemma":[0.43947563,0.013868817,0.53802705,0.0004868063,0.00092870696,0.00028851317,0.000460081,0.0003175688,0.006146867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867404,0.00040496083,0.00007264668,0.0002502822,0.0005444232,0.000053710046],"domain_scores_gemma":[0.9992316,0.00033353773,0.00014799093,0.00014387257,0.00012524913,0.000017907714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008312545,0.00069431245,0.0007541109,0.00063502294,0.00029383358,0.0009011388,0.00082775694,0.0011034707,0.0012318328],"category_scores_gemma":[0.0037602566,0.00032214553,0.00039607479,0.0012893964,0.00082986435,0.0014796865,0.001008638,0.00077851024,0.0011725649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009833429,0.00003454664,0.0007011328,0.0006435629,0.00009652876,0.00026716245,0.00013300315,0.48909265,0.017331868,0.08456627,0.0069789398,0.40005594],"study_design_scores_gemma":[0.000020784924,0.00010839221,0.0003942518,0.00006639027,0.000031575295,0.00024205243,0.000045148066,0.91139024,0.0052208262,0.060516026,0.021924978,0.000039446128],"about_ca_topic_score_codex":0.0011683322,"about_ca_topic_score_gemma":0.00051478064,"teacher_disagreement_score":0.0012318328,"about_ca_system_score_codex":0.0003782643,"about_ca_system_score_gemma":0.0004087363,"threshold_uncertainty_score":0.0043962},"labels":[],"label_agreement":null},{"id":"W2101906460","doi":"10.22215/etd/2008-07729","title":"A framework for signal strength based intrusion detection system for link layer attacks in wireless network","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Link layer; Signal strength; Link (geometry); Computer network; Computer science; Layer (electronics); Wireless; Telecommunications; Wireless sensor network","score_opus":0.0138017201188818,"score_gpt":0.24944861214419892,"score_spread":0.2356468920253171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101906460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024165118,0.0002144352,0.99035954,0.00011066647,0.0000532082,0.00015874115,0.000053602205,0.0055653234,0.001067968],"genre_scores_gemma":[0.16039194,0.000605658,0.83124655,0.00018198491,0.00011860226,0.0004932121,0.00045414496,0.00032505204,0.0061828448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896085,0.00022075642,0.00009679562,0.00016722424,0.00046199755,0.00009233273],"domain_scores_gemma":[0.99940336,0.0001607239,0.000053705153,0.00012564899,0.00020184359,0.00005469388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018014137,0.0009340464,0.0010370476,0.0013173097,0.0005920372,0.0021005855,0.0023657007,0.0012123736,0.0031806552],"category_scores_gemma":[0.0019251948,0.0004391521,0.0010048245,0.00042070955,0.00064430706,0.0021049338,0.001647343,0.00136811,0.0014638089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047853962,0.000509277,0.0032156738,0.000448855,0.00045880474,0.0010242788,0.0006376422,0.19604331,0.06268441,0.18305697,0.025582658,0.5258596],"study_design_scores_gemma":[0.000035053945,0.00015061589,0.00035254413,0.000036715624,0.00006211466,0.00030183408,0.000045693854,0.95460427,0.01207032,0.013144279,0.019160625,0.00003593262],"about_ca_topic_score_codex":0.0036999704,"about_ca_topic_score_gemma":0.0032478075,"teacher_disagreement_score":0.0036999704,"about_ca_system_score_codex":0.0007958626,"about_ca_system_score_gemma":0.0012773953,"threshold_uncertainty_score":0.010640323},"labels":[],"label_agreement":null},{"id":"W2102216272","doi":"10.1109/tits.2010.2048562","title":"Intervehicle-Communication-Assisted Localization","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":130,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Global Positioning System; Multipath propagation; Computer science; Robustness (evolution); Hybrid positioning system; Real-time computing; Engineering; Positioning system; Telecommunications","score_opus":0.014820388311247093,"score_gpt":0.23050824733327382,"score_spread":0.21568785902202672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102216272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015404163,0.0011102632,0.97812074,0.00016512026,0.000109408364,0.000024255754,0.000032288415,0.0008747041,0.0041590123],"genre_scores_gemma":[0.85598034,0.0013496074,0.13593645,0.000107787666,0.00012004899,0.00005289719,0.00018593621,0.000045917837,0.0062209894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963653,0.00011099906,0.000010819757,0.000061557505,0.00013874088,0.00004133942],"domain_scores_gemma":[0.9995881,0.000084739484,0.00006691359,0.00007852764,0.00016706693,0.0000146924785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024555274,0.0004814549,0.00040910445,0.0004195789,0.00032006748,0.0004784897,0.001078433,0.00072110153,0.00078973686],"category_scores_gemma":[0.00075630425,0.00014204625,0.000275748,0.00060768717,0.0003598397,0.0008051363,0.00084810663,0.00036857577,0.0006335624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012905266,0.00006918517,0.0047530863,0.000317091,0.00008951519,0.0005139104,0.00018166118,0.33508444,0.040639415,0.018704966,0.005669635,0.59384817],"study_design_scores_gemma":[0.000025350455,0.00023541064,0.0022600077,0.000040150375,0.000051364183,0.00072696176,0.000145485,0.9275343,0.029336229,0.006114342,0.033481445,0.00004901793],"about_ca_topic_score_codex":0.001783853,"about_ca_topic_score_gemma":0.0028255575,"teacher_disagreement_score":0.001783853,"about_ca_system_score_codex":0.00027848568,"about_ca_system_score_gemma":0.00046983518,"threshold_uncertainty_score":0.0035469532},"labels":[],"label_agreement":null},{"id":"W2102839637","doi":"10.1109/vtcf.2006.489","title":"Wireless Indoor Positioning System with Enhanced Nearest Neighbors in Signal Space Algorithm","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Software deployment; k-nearest neighbors algorithm; Wireless; Algorithm; Signal strength; SIGNAL (programming language); Real-time computing; Indoor positioning system; System deployment; Wireless network; Artificial intelligence; Telecommunications","score_opus":0.0035130892501810014,"score_gpt":0.17360490851187324,"score_spread":0.17009181926169223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102839637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013822339,0.0004060779,0.9821138,0.00011138648,0.00011434536,0.0000646373,0.00005752608,0.0011005226,0.0022094005],"genre_scores_gemma":[0.337278,0.00050998316,0.6555153,0.00016331564,0.00017768233,0.0001839441,0.00037065372,0.000054172528,0.0057469653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867564,0.00038853282,0.00006940513,0.0001842375,0.00058627105,0.000095816984],"domain_scores_gemma":[0.9992217,0.00013782865,0.0000646861,0.00016313561,0.0003862604,0.000026327974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000753238,0.0004561474,0.00093286356,0.0006442767,0.0003904434,0.00056664826,0.0013320335,0.00083521806,0.0012770979],"category_scores_gemma":[0.0022507666,0.00019731424,0.00037748477,0.0010450613,0.00022848294,0.0012829902,0.001083262,0.00066643517,0.0011037204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005973576,0.00018859822,0.0027663056,0.00016409453,0.00012658506,0.00027541295,0.0001805496,0.2174756,0.03214355,0.011934097,0.006884109,0.7272637],"study_design_scores_gemma":[0.000066377936,0.00019733755,0.00072315056,0.000010112748,0.000043291097,0.00038701427,0.00002205554,0.9760957,0.010044433,0.0017137864,0.010663548,0.00003326164],"about_ca_topic_score_codex":0.0021549924,"about_ca_topic_score_gemma":0.0023579975,"teacher_disagreement_score":0.0021549924,"about_ca_system_score_codex":0.0003005642,"about_ca_system_score_gemma":0.0004898578,"threshold_uncertainty_score":0.0042849183},"labels":[],"label_agreement":null},{"id":"W2103172627","doi":"10.1109/spawc.2012.6292873","title":"Interference map generation based on Delaunay triangulation in cognitive radio networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Delaunay triangulation; Interpolation (computer graphics); Cognitive radio; Linear interpolation; Computer science; Bowyer–Watson algorithm; Interference (communication); Constrained Delaunay triangulation; Algorithm; Mathematical optimization; Mathematics; Topology (electrical circuits); Computer vision; Artificial intelligence; Wireless; Computer network; Telecommunications; Pattern recognition (psychology); Image (mathematics)","score_opus":0.024456604194840657,"score_gpt":0.2380392084481989,"score_spread":0.21358260425335823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103172627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017429,0.00018482811,0.9807896,0.000027520968,0.000017918213,0.000028723478,0.000011428026,0.00029999792,0.001210958],"genre_scores_gemma":[0.5855249,0.00031901646,0.41306522,0.000026250147,0.000022783046,0.00008705046,0.00006810212,0.00006290529,0.00082371203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918336,0.00025945622,0.000027949427,0.00008289683,0.0003660825,0.00008026901],"domain_scores_gemma":[0.9986518,0.000756013,0.00012672532,0.0001622761,0.0002615585,0.00004169348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010045398,0.00046196306,0.00059825095,0.00086528197,0.0005997673,0.0004485088,0.0009928463,0.00033850328,0.0008102514],"category_scores_gemma":[0.0034607123,0.00024003308,0.00046217645,0.0010407293,0.00045428518,0.0007679873,0.0011504156,0.00040905006,0.0002444257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025175186,0.000048240152,0.0013790121,0.00015692403,0.00005077964,0.00017014086,0.00035001815,0.7306331,0.009399334,0.0066160867,0.0007169554,0.2502277],"study_design_scores_gemma":[0.000012740445,0.000085231615,0.00033259217,0.000010403645,0.000012588627,0.00013939796,0.000061906925,0.9893796,0.006401968,0.0023725978,0.0011731911,0.000017809278],"about_ca_topic_score_codex":0.0029017215,"about_ca_topic_score_gemma":0.002867002,"teacher_disagreement_score":0.0029017215,"about_ca_system_score_codex":0.00052257435,"about_ca_system_score_gemma":0.0005210092,"threshold_uncertainty_score":0.00576967},"labels":[],"label_agreement":null},{"id":"W2103237925","doi":"10.1109/ccece.2003.1226123","title":"Detection and localization in a wireless network of randomly distributed sensors","year":2004,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Wireless sensor network; Computer science; A priori and a posteriori; Noise (video); Novelty; Monte Carlo method; Artificial intelligence; Wireless; Novelty detection; Pattern recognition (psychology); Key distribution in wireless sensor networks; Noise measurement; Data mining; Machine learning; Real-time computing; Wireless network; Computer network; Mathematics","score_opus":0.003989215064116473,"score_gpt":0.17971003716089717,"score_spread":0.1757208220967807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103237925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006592411,0.00022447415,0.99269944,0.000103257764,0.000016154234,0.000014057774,0.000019025374,0.00011851472,0.00021255806],"genre_scores_gemma":[0.47094396,0.0014896406,0.5248764,0.00019128695,0.00020182919,0.00033475432,0.00020035644,0.00005995602,0.0017018641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981035,0.0006101068,0.000086027765,0.00046726145,0.00064544706,0.00008757155],"domain_scores_gemma":[0.9978781,0.0013846654,0.00034559733,0.00016296629,0.00018942083,0.000039402355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018177157,0.00063536316,0.00077570695,0.0009769351,0.00037275208,0.00085836917,0.0015411554,0.0011978699,0.0004812305],"category_scores_gemma":[0.006159959,0.000542537,0.00047788917,0.0012495023,0.0014826724,0.0020248254,0.0009573938,0.00074514444,0.0003521258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016485712,0.00008413873,0.0035771392,0.00029689356,0.000105733074,0.00022083981,0.00014098396,0.75318056,0.022842653,0.058571864,0.0009969226,0.15981746],"study_design_scores_gemma":[0.000015814163,0.000106137726,0.00070872885,0.000015329002,0.000016286838,0.00015076467,0.000027455553,0.97430724,0.004516649,0.018008964,0.0021078887,0.000018694764],"about_ca_topic_score_codex":0.001115934,"about_ca_topic_score_gemma":0.0008898125,"teacher_disagreement_score":0.0018177157,"about_ca_system_score_codex":0.00054717803,"about_ca_system_score_gemma":0.0006776687,"threshold_uncertainty_score":0.009613156},"labels":[],"label_agreement":null},{"id":"W2103544602","doi":"10.1109/crv.2005.81","title":"Topology Inference for a Vision-Based Sensor Network","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Network topology; Inference; Topology (electrical circuits); Wireless sensor network; Logical topology; Artificial intelligence; Computer vision; Computer network; Engineering; Electrical engineering","score_opus":0.009105875000312187,"score_gpt":0.2551123111981438,"score_spread":0.24600643619783163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103544602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068871076,0.00014142535,0.99211067,0.00009123282,0.000013983389,0.000015680216,0.00006998019,0.00030149717,0.00036844824],"genre_scores_gemma":[0.548777,0.00090055895,0.44697466,0.0000856132,0.00010490239,0.0001640728,0.0009067349,0.00015829006,0.0019281562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924517,0.00019722563,0.00003876454,0.00019238553,0.00028012652,0.00004637999],"domain_scores_gemma":[0.99756134,0.0014316452,0.00038608958,0.00028624045,0.00024736632,0.00008731356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009390242,0.0007042808,0.00073574355,0.0019251253,0.0007171715,0.00090589526,0.0015789091,0.00081003836,0.0014898847],"category_scores_gemma":[0.008264673,0.00074961607,0.0009123303,0.0011173327,0.0008514917,0.0028498813,0.0008786316,0.0013064805,0.00041690623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069708934,0.00002343291,0.0011635472,0.0000716697,0.00004390291,0.000079261015,0.00006905883,0.91952837,0.0024122081,0.01971937,0.0006119901,0.056207504],"study_design_scores_gemma":[0.0000039942643,0.000009331804,0.00015714955,0.0000063742395,0.0000044511585,0.000027016355,0.000009585728,0.9831338,0.00054763345,0.015632117,0.00046264782,0.0000059678846],"about_ca_topic_score_codex":0.0059916563,"about_ca_topic_score_gemma":0.00534803,"teacher_disagreement_score":0.0059916563,"about_ca_system_score_codex":0.0013999288,"about_ca_system_score_gemma":0.0009619325,"threshold_uncertainty_score":0.011913598},"labels":[],"label_agreement":null},{"id":"W2103556919","doi":"10.1109/vtcf.2006.586","title":"Performance Analysis and Implementation of a New Position Location System Using DTV TxID Watermark","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Communications Research Centre Canada","funders":"","keywords":"Computer science; Synchronization (alternating current); Transmitter; Global Positioning System; Watermark; Digital television; Position (finance); Positioning system; Digital watermarking; Real-time computing; Digital Video Broadcasting; Electronic engineering; Computer hardware; Telecommunications; Computer vision; Engineering","score_opus":0.007274953324818901,"score_gpt":0.21984158593371728,"score_spread":0.21256663260889838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103556919","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35637772,0.00086811313,0.63468325,0.00022836731,0.00018376045,0.00009967319,0.000058971167,0.0026879134,0.004812238],"genre_scores_gemma":[0.9453898,0.0002590712,0.050919883,0.000051864965,0.000040042683,0.00003531861,0.000084193736,0.00003781167,0.0031820799],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932826,0.0001085552,0.000047908063,0.00010350658,0.0003277302,0.00008414857],"domain_scores_gemma":[0.99847025,0.0005700791,0.00025825363,0.00020932492,0.00044077047,0.000051294395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006374931,0.00046555264,0.00056869077,0.0005463491,0.00029163685,0.00073729677,0.0006332711,0.00072544045,0.0026148169],"category_scores_gemma":[0.0022104534,0.00018748826,0.00022747544,0.00029559276,0.0002934758,0.0013713731,0.00048097505,0.0003201905,0.00082738075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029147854,0.0002684065,0.0072386246,0.00056052924,0.00025685568,0.0006548402,0.00039455233,0.13568896,0.47906104,0.00858112,0.0013992223,0.362981],"study_design_scores_gemma":[0.00012270696,0.0016515848,0.0022971209,0.000019799425,0.00011794786,0.0008014536,0.000043442516,0.7715437,0.21837601,0.00043683662,0.004545949,0.00004346004],"about_ca_topic_score_codex":0.0006428549,"about_ca_topic_score_gemma":0.00053730584,"teacher_disagreement_score":0.0026148169,"about_ca_system_score_codex":0.00041850336,"about_ca_system_score_gemma":0.00023236824,"threshold_uncertainty_score":0.008747458},"labels":[],"label_agreement":null},{"id":"W2103571421","doi":"10.1109/vetecf.2008.252","title":"Vehicular Collaborative Technique for Location Estimate Correction","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Multipath propagation; Vehicular ad hoc network; Global Positioning System; Real-time computing; Computer network; Wireless ad hoc network; Telecommunications; Wireless","score_opus":0.008531506838926412,"score_gpt":0.23054388620598992,"score_spread":0.2220123793670635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103571421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036426443,0.00019754111,0.994304,0.000047407473,0.00009053999,0.000019301284,0.00001893582,0.00044079087,0.0012387766],"genre_scores_gemma":[0.4353744,0.00064336136,0.54998016,0.00015165898,0.00019880215,0.0001328581,0.00023153116,0.00011970949,0.01316759],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886787,0.00019139441,0.000050442086,0.00020482538,0.0005835989,0.000101829275],"domain_scores_gemma":[0.9989548,0.00027634652,0.00010375277,0.00019399545,0.00044053083,0.000030665953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007559364,0.0006751512,0.0009794688,0.0010528568,0.0006648255,0.00075272523,0.0015213882,0.0010854732,0.0027161657],"category_scores_gemma":[0.0028416244,0.00033458756,0.00076529523,0.001469233,0.00039197583,0.0010309039,0.0014159145,0.00094348576,0.0014853368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003990505,0.00009263185,0.0014426466,0.00017633055,0.00014442083,0.0003406473,0.00026344368,0.20576511,0.043010812,0.018818183,0.005796283,0.72375035],"study_design_scores_gemma":[0.000030537587,0.0001399924,0.00048613566,0.000018003282,0.000048714715,0.00043876457,0.000067301495,0.96775997,0.017844107,0.0030128586,0.010119631,0.000034000215],"about_ca_topic_score_codex":0.005292734,"about_ca_topic_score_gemma":0.005613767,"teacher_disagreement_score":0.005292734,"about_ca_system_score_codex":0.00046491937,"about_ca_system_score_gemma":0.00095833285,"threshold_uncertainty_score":0.010523856},"labels":[],"label_agreement":null},{"id":"W2103872878","doi":"","title":"Expected path length for angle and distance-based localized routing","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Link-state routing protocol; Computer network; Destination-Sequenced Distance Vector routing; Geographic routing; Dynamic Source Routing; Static routing; Equal-cost multi-path routing; Wireless Routing Protocol; Routing (electronic design automation); Wireless ad hoc network; Distributed computing; Routing protocol; Wireless; Topology (electrical circuits); Mathematics; Telecommunications","score_opus":0.008248293489481955,"score_gpt":0.2098179486093932,"score_spread":0.20156965511991126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103872878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008732434,0.0012744741,0.98553884,0.0003181752,0.00009917586,0.0000216822,0.000055392422,0.00010936178,0.0038505078],"genre_scores_gemma":[0.78348446,0.0053519,0.20135199,0.0003722377,0.00041758202,0.00027479723,0.00029988887,0.00022681097,0.008220416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988323,0.00050701766,0.000046560664,0.00013833001,0.00033742556,0.00013833595],"domain_scores_gemma":[0.99353516,0.004727602,0.000541479,0.00031056453,0.00072561135,0.00015966441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019828773,0.0008935899,0.0005933741,0.0011476404,0.00049632066,0.0011508552,0.001401806,0.00088969886,0.0034363873],"category_scores_gemma":[0.012039439,0.00041499006,0.0005541346,0.0011529641,0.0013226375,0.0026883266,0.0012230689,0.0011698412,0.0009242151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005799569,0.000023665038,0.00051799166,0.00008778468,0.000024729303,0.00008007734,0.00007094797,0.85479283,0.0012343216,0.12413163,0.001939768,0.017038178],"study_design_scores_gemma":[0.000007061814,0.00004694146,0.00017608245,0.000046066954,0.000011683351,0.0000920729,0.000028627142,0.93373406,0.00045121147,0.06343097,0.0019555911,0.000019595584],"about_ca_topic_score_codex":0.0011133554,"about_ca_topic_score_gemma":0.0009676915,"teacher_disagreement_score":0.0034363873,"about_ca_system_score_codex":0.0019212625,"about_ca_system_score_gemma":0.00072029966,"threshold_uncertainty_score":0.0139398575},"labels":[],"label_agreement":null},{"id":"W2104087894","doi":"10.1145/1097064.1097075","title":"Error analysis of localization systems for sensor networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Wireless sensor network; Component (thermodynamics); Position (finance); Algorithm; Task (project management); Computation; Data mining; Process (computing); Artificial intelligence; Engineering","score_opus":0.011751675060948683,"score_gpt":0.22984327913445476,"score_spread":0.21809160407350608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104087894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013295077,0.0018013405,0.98175657,0.00025947017,0.00009983396,0.000051864896,0.00007726454,0.00025702853,0.0024015591],"genre_scores_gemma":[0.8396546,0.0040737665,0.14763747,0.00018967038,0.0003142264,0.00037128996,0.00057742326,0.00037310467,0.0068084635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962608,0.001077807,0.00021003484,0.00036487967,0.0018837432,0.0002027207],"domain_scores_gemma":[0.9906199,0.0059857927,0.0007709699,0.00069884065,0.0018622568,0.00006219842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033430737,0.0011041915,0.0007853197,0.0019505036,0.0006668599,0.0012315407,0.0010259261,0.0011856395,0.0022182707],"category_scores_gemma":[0.023873968,0.00028797318,0.00071149546,0.0015524507,0.0011348196,0.002016287,0.0014164959,0.0013020836,0.00055350433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011013821,0.000022216294,0.001776496,0.00027398072,0.000076243385,0.000111623485,0.00024919742,0.879245,0.003099331,0.06357377,0.001211937,0.050250143],"study_design_scores_gemma":[0.000005050286,0.00003564226,0.00059551786,0.000034323344,0.000012365964,0.00008645478,0.00003457803,0.9775277,0.0016406327,0.017674044,0.0023359358,0.000017807974],"about_ca_topic_score_codex":0.0041762805,"about_ca_topic_score_gemma":0.0012821698,"teacher_disagreement_score":0.0041762805,"about_ca_system_score_codex":0.0012841437,"about_ca_system_score_gemma":0.00063331734,"threshold_uncertainty_score":0.017680109},"labels":[],"label_agreement":null},{"id":"W2104101254","doi":"10.1109/bwcca.2012.72","title":"ABLE Transit: A Mobile Application for Visually Impaired Users to Navigate Public Transit","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Leverage (statistics); Popularity; Public transport; Global Positioning System; Mobile device; Schedule; Transit (satellite); World Wide Web; Human–computer interaction; Multimedia; Transport engineering; Telecommunications; Engineering; Artificial intelligence","score_opus":0.01283255831653369,"score_gpt":0.24614788791142828,"score_spread":0.2333153295948946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104101254","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28702432,0.0015106407,0.30927315,0.0012709061,0.00028072763,0.003280657,0.010519913,0.33408865,0.052751023],"genre_scores_gemma":[0.7751307,0.0013286453,0.13486981,0.0009029606,0.00014555974,0.0014205639,0.010406883,0.0035911077,0.07220377],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999225,0.00001565092,0.000007628173,0.000011882295,0.00002862575,0.000013766953],"domain_scores_gemma":[0.9997627,0.00009070279,0.000016724256,0.000027285108,0.000055370107,0.00004721796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018484131,0.0005595211,0.00025629118,0.00052194344,0.0002390948,0.00037358972,0.0006182197,0.00061938807,0.016116872],"category_scores_gemma":[0.00097044284,0.00014099182,0.00021698199,0.00025695254,0.00011358607,0.00067235314,0.0010490773,0.00027271983,0.0035445879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023757103,0.0011014603,0.013009089,0.0013826651,0.00014864201,0.004013409,0.0023228957,0.003047729,0.09586451,0.0028070156,0.25669277,0.6172341],"study_design_scores_gemma":[0.0012155753,0.0030300668,0.067527376,0.0006605509,0.00049349654,0.009914828,0.0021394708,0.11936268,0.080938876,0.003998962,0.71021545,0.0005026898],"about_ca_topic_score_codex":0.0015820498,"about_ca_topic_score_gemma":0.0029836658,"teacher_disagreement_score":0.016116872,"about_ca_system_score_codex":0.00011096954,"about_ca_system_score_gemma":0.00019104328,"threshold_uncertainty_score":0.053916335},"labels":[],"label_agreement":null},{"id":"W2104417553","doi":"10.1109/tvt.2007.899962","title":"Concentric Anchor Beacon Localization Algorithm for Wireless Sensor Networks","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Kinexus Bioinformatics Corporation (Canada)","funders":"","keywords":"Beacon; Electric beacon; Wireless sensor network; Algorithm; Triangulation; Node (physics); Computer science; Intersection (aeronautics); Trilateration; Heuristics; Wireless; Range (aeronautics); Key distribution in wireless sensor networks; Position (finance); Wireless network; Real-time computing; Engineering; Computer network; Mathematics; Telecommunications; Geometry","score_opus":0.006334927702289101,"score_gpt":0.21646249736799664,"score_spread":0.21012756966570753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104417553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00188455,0.00053843623,0.99616015,0.00007121896,0.000062828105,0.000025645451,0.000017107684,0.0005018649,0.000738132],"genre_scores_gemma":[0.16488032,0.0019531387,0.82811487,0.0001240879,0.00012949292,0.00029526465,0.00031091447,0.00013278973,0.0040592486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918383,0.0002682572,0.00004498565,0.00013777916,0.0003160658,0.000048997346],"domain_scores_gemma":[0.99924505,0.00021385538,0.00011974471,0.00010667535,0.00028362783,0.00003109081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095748325,0.0006393577,0.00067461026,0.0010752267,0.0005309742,0.00048336436,0.0013336418,0.0006981449,0.001529811],"category_scores_gemma":[0.0030402478,0.00027497194,0.00039491753,0.0014875308,0.0005705995,0.0012940925,0.0009446416,0.0006884883,0.0010122914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003269937,0.00004699842,0.0010273806,0.00038857947,0.00008299136,0.00020261797,0.00042134034,0.20356746,0.01934613,0.06676386,0.012287375,0.6955383],"study_design_scores_gemma":[0.00007857884,0.00014869426,0.00041545858,0.00004546819,0.000040751085,0.0004234136,0.00008813969,0.9169976,0.010661497,0.02705962,0.043994844,0.000045939134],"about_ca_topic_score_codex":0.001947627,"about_ca_topic_score_gemma":0.0012218422,"teacher_disagreement_score":0.001947627,"about_ca_system_score_codex":0.00058221264,"about_ca_system_score_gemma":0.0007590587,"threshold_uncertainty_score":0.0051177144},"labels":[],"label_agreement":null},{"id":"W2104468797","doi":"10.1109/wowmom.2008.4594838","title":"Localization in time and space for wireless sensor networks: A Mobile Beacon approach","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Wireless sensor network; Computer science; Synchronization (alternating current); Global Positioning System; Real-time computing; Network packet; Component (thermodynamics); Wireless; Key distribution in wireless sensor networks; Location awareness; Computer network; Wireless network; Telecommunications","score_opus":0.007608711397172393,"score_gpt":0.19018753543705727,"score_spread":0.18257882403988487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104468797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00082285574,0.004810474,0.99236673,0.0004534092,0.00016625813,0.000018408546,0.000010477996,0.00016909128,0.0011822241],"genre_scores_gemma":[0.15387888,0.025903933,0.8095638,0.00047607181,0.0016414379,0.00030644579,0.00013860843,0.00016919324,0.007921598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991654,0.00031402358,0.000043698405,0.00012755627,0.0002986201,0.000050733834],"domain_scores_gemma":[0.9993531,0.0003176773,0.00007795656,0.00009096097,0.00012682473,0.000033504533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013295809,0.0007649153,0.0009129094,0.0014924848,0.00049909315,0.0014642153,0.0016406735,0.001508703,0.00157068],"category_scores_gemma":[0.0032211565,0.00045694728,0.000727575,0.0024120777,0.001129445,0.0034715137,0.00161496,0.001565903,0.0008764021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013428548,0.000046874196,0.00081659504,0.00065493694,0.00011924842,0.00034246882,0.000574773,0.18922956,0.009315758,0.36889613,0.009175595,0.4206937],"study_design_scores_gemma":[0.000048566963,0.00019495419,0.0003237708,0.00017550572,0.000082159255,0.00066082104,0.00017203203,0.7515138,0.00408108,0.1619888,0.08069024,0.00006829994],"about_ca_topic_score_codex":0.0011436702,"about_ca_topic_score_gemma":0.001197222,"teacher_disagreement_score":0.0016406735,"about_ca_system_score_codex":0.00083076477,"about_ca_system_score_gemma":0.0005911658,"threshold_uncertainty_score":0.0070316195},"labels":[],"label_agreement":null},{"id":"W2104810777","doi":"10.1109/ccece.1999.804941","title":"Ambiguity reduction using spatial variation for two dimensional navigation","year":2003,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Integer (computer science); Ambiguity; Reduction (mathematics); Focus (optics); Position (finance); Range (aeronautics); Computer science; Process (computing); Algorithm; Mathematics; Engineering; Optics; Physics; Geometry; Aerospace engineering","score_opus":0.016477866256948807,"score_gpt":0.25038492988631456,"score_spread":0.23390706362936575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104810777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023276433,0.0003062903,0.99582374,0.000064803426,0.0000488595,0.000010546393,0.000008412311,0.00014640673,0.0012633418],"genre_scores_gemma":[0.116102405,0.0009760449,0.8786669,0.00008780502,0.0001327142,0.00010832178,0.00007321504,0.0001205146,0.003732158],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995011,0.00015736236,0.000018034554,0.0000635173,0.00022489307,0.000035211215],"domain_scores_gemma":[0.9995345,0.00026636713,0.000037323593,0.00008054686,0.00006817681,0.000013111724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051778427,0.0004810124,0.00045582012,0.0005295085,0.00041182945,0.00073091284,0.00044201384,0.000497942,0.0019003872],"category_scores_gemma":[0.00196348,0.00018307168,0.000509198,0.0008026598,0.0011136082,0.00080583215,0.0009250635,0.0011149113,0.0006967075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007547803,0.000040708914,0.00031111046,0.00013727826,0.000035634803,0.0001412115,0.00026445283,0.1408895,0.016541647,0.39351004,0.0026984662,0.44535443],"study_design_scores_gemma":[0.00003103752,0.00009319318,0.00028541914,0.00002896571,0.00002131689,0.0002839107,0.000059862974,0.7140133,0.011403306,0.24389303,0.029830595,0.00005615824],"about_ca_topic_score_codex":0.0007857614,"about_ca_topic_score_gemma":0.0007542033,"teacher_disagreement_score":0.0019003872,"about_ca_system_score_codex":0.00029533333,"about_ca_system_score_gemma":0.00047417264,"threshold_uncertainty_score":0.006357372},"labels":[],"label_agreement":null},{"id":"W2104871033","doi":"10.1109/ccece.2013.6567732","title":"Enhanced GNSS frequency tracking in multipath environments","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Multipath propagation; Multipath mitigation; GNSS applications; Global Positioning System; Computer science; Fading; Robustness (evolution); Rake receiver; Diversity scheme; Delay spread; Electronic engineering; Real-time computing; Telecommunications; Engineering","score_opus":0.0076683457887285035,"score_gpt":0.20037080950400504,"score_spread":0.19270246371527655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104871033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23673303,0.0002831573,0.7580767,0.000090857946,0.00010327957,0.00005245748,0.0000989603,0.0018560537,0.0027054374],"genre_scores_gemma":[0.82227623,0.00012572746,0.17499651,0.00007042287,0.00006292566,0.000037351718,0.0001328623,0.00004851572,0.0022493869],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996271,0.000048368634,0.000014634959,0.000080456135,0.00017592535,0.0000534974],"domain_scores_gemma":[0.9996413,0.00007134193,0.00007410037,0.00009126347,0.00010123982,0.000020631316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022148648,0.0005183428,0.00032381102,0.00044499713,0.00020212635,0.00038628155,0.0005472205,0.0005945114,0.0007391548],"category_scores_gemma":[0.0007928843,0.00019715402,0.00017234332,0.0004248016,0.0002307552,0.0005391103,0.0006329824,0.00035633481,0.00046846003],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005492087,0.00006461328,0.005154222,0.000095704396,0.000059735237,0.00024570263,0.00013740946,0.034621302,0.6859649,0.002260459,0.0006430006,0.2702039],"study_design_scores_gemma":[0.000118720076,0.0015309893,0.013788546,0.000040049366,0.00011007354,0.0025950058,0.000056830013,0.3151123,0.6515949,0.001653064,0.013307532,0.000091984104],"about_ca_topic_score_codex":0.00047715587,"about_ca_topic_score_gemma":0.00095952104,"teacher_disagreement_score":0.0007391548,"about_ca_system_score_codex":0.00021524803,"about_ca_system_score_gemma":0.00026877405,"threshold_uncertainty_score":0.0024726987},"labels":[],"label_agreement":null},{"id":"W2105242528","doi":"10.29173/eureka10295","title":"IR Motion Tracking as a Standard Input Device","year":2011,"lang":"en","type":"article","venue":"Eureka","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Robustness (evolution); Computer science; Interface (matter); Tracking (education); Simulation; Distributed computing; Operating system; Chemistry","score_opus":0.025421521343860037,"score_gpt":0.22428250830331728,"score_spread":0.19886098695945725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105242528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04237134,0.0010106087,0.9298192,0.00028212985,0.0004814515,0.00022035794,0.0002375427,0.0049315067,0.020645825],"genre_scores_gemma":[0.49784526,0.0015556267,0.4736407,0.0007803778,0.00023470711,0.00036774905,0.000783273,0.00053504825,0.024257166],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99718755,0.00071324006,0.00015493807,0.000741641,0.0010543357,0.00014838195],"domain_scores_gemma":[0.99858356,0.0003403984,0.00012102072,0.00040216246,0.00048633033,0.00006657153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020924779,0.0007528092,0.0007464446,0.0007179398,0.0004216991,0.0016567869,0.0015012217,0.0012247377,0.006821583],"category_scores_gemma":[0.0044186446,0.0003709695,0.0005598195,0.0008900804,0.0005084298,0.002204492,0.001504968,0.000831716,0.0031051491],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022569033,0.00029308858,0.0039043464,0.0012621342,0.00015009556,0.00054121326,0.0007619745,0.02053367,0.40806103,0.021337403,0.008970439,0.53192776],"study_design_scores_gemma":[0.0004027826,0.005575293,0.020285621,0.00060838665,0.00061123574,0.0034100036,0.00053988607,0.40693954,0.43838072,0.00963253,0.113204435,0.0004095854],"about_ca_topic_score_codex":0.0010839951,"about_ca_topic_score_gemma":0.0008460057,"teacher_disagreement_score":0.006821583,"about_ca_system_score_codex":0.0004519204,"about_ca_system_score_gemma":0.00040932608,"threshold_uncertainty_score":0.022820413},"labels":[],"label_agreement":null},{"id":"W2105373322","doi":"10.1109/pacrim.2009.5291372","title":"Reducing the error in mobile location estimation using robust window functions","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Estimator; Algorithm; Outlier; Fading; Mean squared error; Non-line-of-sight propagation; Window (computing); Computer science; Robust statistics; Statistics; Multipath propagation; Window function; Mathematics; Telecommunications; Spectral density; Wireless","score_opus":0.019868970363059762,"score_gpt":0.24149245565366592,"score_spread":0.22162348529060616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105373322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061808474,0.00031192933,0.93680435,0.000039096336,0.000048315345,0.000022769811,0.000038933144,0.0006641964,0.000261817],"genre_scores_gemma":[0.40008605,0.00047197862,0.5982052,0.000031811967,0.00007191048,0.00006706528,0.0001623688,0.0001806535,0.0007230034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933463,0.00015907727,0.000059593025,0.00011754292,0.00028159216,0.000047486516],"domain_scores_gemma":[0.99791104,0.0009565287,0.00026310937,0.00038585052,0.00045276,0.000030667354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011878732,0.00089557853,0.0007354218,0.0007935487,0.00017066924,0.00055526185,0.0006468632,0.0006574813,0.0007977266],"category_scores_gemma":[0.0068383967,0.00029093257,0.0005026559,0.0007639389,0.00027827415,0.0015382203,0.0006347864,0.0005143804,0.0004930193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009274178,0.00010239711,0.0028306243,0.00022753004,0.00016704228,0.00018968506,0.00018322942,0.12751964,0.24238905,0.0071511576,0.0011027157,0.6172096],"study_design_scores_gemma":[0.00006124709,0.0005467407,0.005187438,0.000026875383,0.00011746098,0.0003924079,0.00006412607,0.77002984,0.21534827,0.00313054,0.004980645,0.0001143595],"about_ca_topic_score_codex":0.0008463108,"about_ca_topic_score_gemma":0.0007463604,"teacher_disagreement_score":0.0011878732,"about_ca_system_score_codex":0.00023870758,"about_ca_system_score_gemma":0.00034831875,"threshold_uncertainty_score":0.0062821507},"labels":[],"label_agreement":null},{"id":"W2105464056","doi":"10.1109/ccece.2005.1557409","title":"The designing of an indoor acoustic ranging system using the audible spread spectrum LFM (CHIRP) signal","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Ranging; Chirp; Chirp spread spectrum; Acoustics; Bandwidth (computing); Wideband; Center frequency; SIGNAL (programming language); Spread spectrum; Computer science; Physics; Telecommunications; Band-pass filter; Direct-sequence spread spectrum; Optics","score_opus":0.00783315097821385,"score_gpt":0.19925818679864538,"score_spread":0.19142503582043152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105464056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05445717,0.00027145265,0.9379437,0.00019549615,0.0001281557,0.00017840948,0.00004328369,0.0011096247,0.0056727408],"genre_scores_gemma":[0.39569485,0.00033868416,0.5963702,0.0001953184,0.00015584678,0.00018844302,0.00009147023,0.000050144023,0.0069150035],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998323,0.00003087214,0.000009476856,0.000042958873,0.00006693193,0.000017353097],"domain_scores_gemma":[0.9998142,0.000036734655,0.000028978666,0.00001994012,0.000079048135,0.000021155922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019966724,0.0002857418,0.00030834836,0.00026735864,0.00034242336,0.0003129304,0.00041536902,0.0005373524,0.0011280279],"category_scores_gemma":[0.0003975175,0.00020155209,0.0001958989,0.0001927798,0.00019500084,0.0003422391,0.00020292233,0.00031532656,0.0007792038],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001725996,0.00006924342,0.0027859989,0.00037540603,0.000039798586,0.0006886482,0.00026972752,0.012371906,0.7309966,0.0049770772,0.0014760643,0.24577685],"study_design_scores_gemma":[0.000115352355,0.00255404,0.013805613,0.00013011212,0.00024831708,0.005979136,0.0002749836,0.22048447,0.6825355,0.0020791139,0.0716652,0.00012810934],"about_ca_topic_score_codex":0.0005047413,"about_ca_topic_score_gemma":0.0006921395,"teacher_disagreement_score":0.0011280279,"about_ca_system_score_codex":0.00015485975,"about_ca_system_score_gemma":0.00032662225,"threshold_uncertainty_score":0.00377357},"labels":[],"label_agreement":null},{"id":"W2105474416","doi":"10.1109/icc.2006.255702","title":"Concentric Anchor-Beacons (CAB) Localization for Wireless Sensor Networks","year":2006,"lang":"en","type":"article","venue":"2006 IEEE International Conference on Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beacon; Wireless sensor network; Electric beacon; Computer science; Node (physics); Intersection (aeronautics); Centroid; Range (aeronautics); Concentric; Wireless; Position (finance); Key distribution in wireless sensor networks; Real-time computing; Computer network; Wireless network; Engineering; Artificial intelligence; Mathematics; Telecommunications; Geometry","score_opus":0.04343659894178009,"score_gpt":0.2885356928234035,"score_spread":0.24509909388162343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105474416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00214607,0.0022132015,0.99373925,0.00013952171,0.00012442487,0.000030950272,0.000020182293,0.00064556714,0.0009409569],"genre_scores_gemma":[0.21893889,0.005979351,0.7709042,0.00018563104,0.00030947523,0.00023475566,0.00027778692,0.00013079721,0.0030391102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893564,0.00037246716,0.00005392533,0.00017104976,0.00042722654,0.000039644594],"domain_scores_gemma":[0.99895334,0.00034277196,0.00022354617,0.0001928848,0.00024065806,0.00004679749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010265709,0.00076286506,0.0005756807,0.0008135112,0.0006343799,0.0004894671,0.0013866284,0.0008637416,0.0012708718],"category_scores_gemma":[0.003948211,0.00032988185,0.00042343143,0.0017621464,0.0009405114,0.0013985218,0.0010696138,0.00069301703,0.0009550438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028452708,0.000046062887,0.0014271837,0.0009615399,0.00009843482,0.00027611654,0.0004444375,0.13627288,0.031125974,0.112608686,0.015937058,0.700517],"study_design_scores_gemma":[0.00010095967,0.00040223668,0.0009637881,0.00015640282,0.000098830336,0.0015841948,0.00013029516,0.77252877,0.022219134,0.0735787,0.12812407,0.00011261255],"about_ca_topic_score_codex":0.0015529727,"about_ca_topic_score_gemma":0.0012862147,"teacher_disagreement_score":0.0015529727,"about_ca_system_score_codex":0.0005701249,"about_ca_system_score_gemma":0.0006835212,"threshold_uncertainty_score":0.005429089},"labels":[],"label_agreement":null},{"id":"W2105771698","doi":"10.1109/sensorcomm.2009.44","title":"Improving Location Identification in Wireless Ad Hoc/Sensor Networks Using GDOP Theory","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cistel Technology (Canada); Carleton University","funders":"","keywords":"Multilateration; Computer science; Identification (biology); Wireless ad hoc network; Wireless sensor network; Wireless; Real-time computing; Artificial intelligence; Computer network; Telecommunications; Mathematics; Azimuth","score_opus":0.008188572776084854,"score_gpt":0.21436582882088248,"score_spread":0.20617725604479764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105771698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008837559,0.00023125065,0.9898684,0.000058138983,0.000032311727,0.000014227584,0.000009504587,0.00019220726,0.0007563296],"genre_scores_gemma":[0.39988926,0.0013510709,0.59524685,0.00015729186,0.00014876222,0.000109090426,0.00012897167,0.00009740318,0.0028712505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946564,0.00016283113,0.000022858874,0.00006571836,0.00024303891,0.000039862978],"domain_scores_gemma":[0.9991922,0.00043051605,0.000052023563,0.00012376782,0.00018511903,0.000016429512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077908277,0.0006578775,0.00050883996,0.00081512396,0.00040650685,0.00049845537,0.0007134254,0.0004897981,0.00077286665],"category_scores_gemma":[0.003369462,0.00029315279,0.00033826294,0.00067278097,0.0007734056,0.0018880016,0.0014175659,0.00064452295,0.00056287996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011999364,0.000070892056,0.002075602,0.00023587298,0.00006061105,0.0002244033,0.00024747488,0.5585255,0.0216175,0.04906773,0.0022886102,0.36546585],"study_design_scores_gemma":[0.000011446409,0.00006977828,0.00030121845,0.000013025518,0.000016425365,0.0001273265,0.00006501986,0.97436935,0.006016073,0.015917249,0.0030777685,0.000015330403],"about_ca_topic_score_codex":0.0014125939,"about_ca_topic_score_gemma":0.0011478667,"teacher_disagreement_score":0.0014125939,"about_ca_system_score_codex":0.0003410833,"about_ca_system_score_gemma":0.00055358827,"threshold_uncertainty_score":0.0041202307},"labels":[],"label_agreement":null},{"id":"W2108740991","doi":"10.1109/icassp.2008.4518175","title":"Constrained linear least squares approach for TDOA localization: A global optimum solution","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Multilateration; Mathematical optimization; Least-squares function approximation; Common emitter; Computer science; Total least squares; Linear least squares; Noise (video); Algorithm; Non-linear least squares; Generalized least squares; Quadratic growth; Mathematics; Estimation theory; Statistics; Engineering; Electronic engineering; Artificial intelligence","score_opus":0.0395292185094968,"score_gpt":0.26316827218251265,"score_spread":0.22363905367301584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108740991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00053551164,0.00009453353,0.9985998,0.000052537358,0.000012536232,0.0000064191076,0.000010563747,0.00007443367,0.00061361917],"genre_scores_gemma":[0.08177413,0.0006501411,0.91290677,0.00014629276,0.000103835686,0.00015873874,0.0001286851,0.00016085454,0.003970573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995555,0.00012619751,0.000018114433,0.00010970432,0.0001590287,0.00003139289],"domain_scores_gemma":[0.99968064,0.00013702286,0.000030697895,0.00003470727,0.000105434956,0.000011491507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005454471,0.0010560182,0.0008106422,0.00053643406,0.0002919665,0.00070562476,0.00080592505,0.0011368435,0.0029107304],"category_scores_gemma":[0.0020283242,0.00041306912,0.0005732884,0.0010822816,0.00059739116,0.0011838683,0.001024647,0.0011609282,0.0013243392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006179579,0.000044431843,0.00028903436,0.00025423066,0.00007900449,0.00009102037,0.00013052247,0.690521,0.012473651,0.038379744,0.005667141,0.25200847],"study_design_scores_gemma":[0.0000148487225,0.000037594098,0.00009762416,0.000019786925,0.000014324889,0.000053722204,0.000023344688,0.9784863,0.0022205252,0.014454249,0.0045637013,0.000013916744],"about_ca_topic_score_codex":0.0019395115,"about_ca_topic_score_gemma":0.0018658796,"teacher_disagreement_score":0.0029107304,"about_ca_system_score_codex":0.00034525682,"about_ca_system_score_gemma":0.0009898023,"threshold_uncertainty_score":0.009737372},"labels":[],"label_agreement":null},{"id":"W2108825267","doi":"10.1109/pccc.2005.1460516","title":"Performance evaluation of range-free localization methods for wireless sensor networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wireless sensor network; Signal strength; Computer science; Software deployment; Range (aeronautics); Node (physics); Point (geometry); Wireless; Distributed computing; Computer network; Mathematics; Engineering; Telecommunications; Structural engineering","score_opus":0.02431545556425949,"score_gpt":0.3023920911446835,"score_spread":0.278076635580424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108825267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27692342,0.007460183,0.70448285,0.00056922896,0.00025849984,0.00027890241,0.00033627584,0.0037040315,0.0059865504],"genre_scores_gemma":[0.86311936,0.001515802,0.13266516,0.00012255095,0.00012231685,0.00017039748,0.0005887056,0.000213043,0.0014825966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98944515,0.0040680273,0.00045233953,0.0008212325,0.0048227627,0.00039052087],"domain_scores_gemma":[0.9702672,0.021376844,0.0018573264,0.0024290334,0.0037125933,0.00035699303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068460247,0.0015690401,0.0011172524,0.0023551346,0.0009199953,0.0010218784,0.0021224853,0.0015459749,0.001381083],"category_scores_gemma":[0.038086,0.0003739243,0.00040951106,0.002050079,0.0008792949,0.0030305653,0.0018737295,0.0006661056,0.0005518166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021112545,0.00035194575,0.006653635,0.000884481,0.00042141456,0.00018709763,0.0002789217,0.5837019,0.017639844,0.005456761,0.0022988452,0.380014],"study_design_scores_gemma":[0.0000951478,0.00090379204,0.0027492011,0.000038089423,0.00006707937,0.0005177585,0.00010232157,0.9750549,0.017131751,0.0019078751,0.0013644991,0.000067609275],"about_ca_topic_score_codex":0.0021914179,"about_ca_topic_score_gemma":0.0018116771,"teacher_disagreement_score":0.0068460247,"about_ca_system_score_codex":0.0011892833,"about_ca_system_score_gemma":0.0008125621,"threshold_uncertainty_score":0.03620565},"labels":[],"label_agreement":null},{"id":"W2109149546","doi":"10.1109/iembs.2006.260164","title":"Reliable Respiratory Rate Estimation from a Bed Pressure Array","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Élisabeth Bruyère Hospital; University of Ottawa; Carleton University","funders":"","keywords":"Weighting; Metric (unit); Reliability (semiconductor); Respiratory rate; Statistics; Computer science; Estimation; Variance (accounting); Mathematics; Engineering; Medicine","score_opus":0.006198540463381286,"score_gpt":0.19236191268318595,"score_spread":0.18616337221980467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109149546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2954576,0.0007344396,0.7007989,0.00013412206,0.00010193149,0.000052053125,0.00027579357,0.0011317958,0.0013134084],"genre_scores_gemma":[0.87502176,0.0004433546,0.12279676,0.00007099425,0.0001394549,0.00007340309,0.00038659415,0.00007225516,0.0009954874],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992648,0.0001908095,0.000037908132,0.00010894001,0.00035690085,0.000040748808],"domain_scores_gemma":[0.99908495,0.0003103256,0.00014513684,0.000111132445,0.0003193579,0.000029109204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039851767,0.00046480884,0.00056579773,0.000577342,0.000110763904,0.00036521573,0.0004312283,0.00046715233,0.00050680764],"category_scores_gemma":[0.0034628063,0.0002484164,0.00022571915,0.00039707244,0.00012271354,0.00052140735,0.00036434582,0.00033531274,0.0005318667],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008401109,0.00013120667,0.038522568,0.00043508087,0.00020854495,0.0004669417,0.00036857248,0.024321066,0.41060817,0.0006437281,0.001998549,0.52145547],"study_design_scores_gemma":[0.00014249697,0.001640882,0.15161118,0.000089892026,0.00028133954,0.0025576167,0.000233202,0.56755173,0.26852563,0.0015857372,0.005608707,0.00017150576],"about_ca_topic_score_codex":0.0005046249,"about_ca_topic_score_gemma":0.00073945423,"teacher_disagreement_score":0.000577342,"about_ca_system_score_codex":0.000071796014,"about_ca_system_score_gemma":0.00015607222,"threshold_uncertainty_score":0.0021075606},"labels":[],"label_agreement":null},{"id":"W2109541779","doi":"10.1109/vetecf.2002.1040794","title":"A multi-model filter for mobile terminal location tracking","year":2003,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Terminal (telecommunication); Computer science; Kinematics; Trajectory; Filter (signal processing); Process (computing); Real-time computing; Tracking (education); Particle filter; Computer vision; Telecommunications","score_opus":0.024834867123219632,"score_gpt":0.2574140822638854,"score_spread":0.23257921514066576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109541779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016403892,0.000072929586,0.9975103,0.00004421876,0.000039560025,0.000009672954,0.000022350241,0.00030482296,0.00035570812],"genre_scores_gemma":[0.2801702,0.0005962649,0.7079914,0.00024073149,0.0001870487,0.00020292388,0.0004928378,0.00014976245,0.009968845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994753,0.00007744602,0.000024430621,0.00017813391,0.00019513087,0.00004954314],"domain_scores_gemma":[0.9994711,0.00022649758,0.000048959486,0.00006540254,0.00016985464,0.000018063653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084218197,0.00068554166,0.0010038419,0.0005781763,0.0006139296,0.0008587553,0.0009901098,0.0014720578,0.0025324624],"category_scores_gemma":[0.0022017811,0.00043040098,0.0007972505,0.00059387845,0.0003452415,0.0012127466,0.00049908075,0.001215832,0.001399805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003147619,0.00014838934,0.0010484562,0.0001994862,0.00015257135,0.00015697809,0.0001319329,0.44319218,0.03312288,0.020200547,0.004993516,0.49633825],"study_design_scores_gemma":[0.000015667962,0.0000472598,0.00025048375,0.0000065541026,0.000017700684,0.000043564844,0.000004521453,0.9920266,0.0032925422,0.0013304686,0.0029511936,0.000013429049],"about_ca_topic_score_codex":0.009727217,"about_ca_topic_score_gemma":0.010287864,"teacher_disagreement_score":0.009727217,"about_ca_system_score_codex":0.00091415277,"about_ca_system_score_gemma":0.0009641451,"threshold_uncertainty_score":0.01934117},"labels":[],"label_agreement":null},{"id":"W2110478555","doi":"10.1109/infcom.2004.1354536","title":"Non-line-of-sight error mitigation in mobile location","year":2004,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Identification (biology); Wireless; A priori and a posteriori; Base station; Mobile station; Mobile telephony; Real-time computing; Mobile radio; Computer network; Telecommunications","score_opus":0.007643587384279794,"score_gpt":0.22759183357458304,"score_spread":0.21994824619030326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110478555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04760679,0.00092515076,0.9501426,0.00010932036,0.000041623207,0.000020541727,0.0000115055145,0.00017510277,0.000967268],"genre_scores_gemma":[0.833403,0.0016575895,0.162346,0.000060255337,0.00013075031,0.000044193275,0.00004707123,0.000027145174,0.0022839103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999393,0.00021192504,0.000023094382,0.0000875166,0.00022033315,0.000064110696],"domain_scores_gemma":[0.99823976,0.0008419282,0.00034039444,0.00021544914,0.00033384183,0.000028740362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005978258,0.0005548505,0.00047154195,0.00055770675,0.0002821831,0.00037214087,0.00064403476,0.00061533257,0.00043909595],"category_scores_gemma":[0.003112254,0.00024474727,0.00029109904,0.0005158355,0.0005216502,0.0011400079,0.00063071545,0.00034631172,0.00036922478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039782643,0.00012597902,0.004719935,0.00035357932,0.000101417434,0.00041901637,0.00029413772,0.49337584,0.06047775,0.012540004,0.00087756093,0.42631704],"study_design_scores_gemma":[0.000031455824,0.00043373514,0.0035984816,0.000040399595,0.00005615099,0.0008668476,0.00010488285,0.93951154,0.04720322,0.004704378,0.0034106753,0.00003834025],"about_ca_topic_score_codex":0.0010463484,"about_ca_topic_score_gemma":0.00150239,"teacher_disagreement_score":0.0010463484,"about_ca_system_score_codex":0.0001937524,"about_ca_system_score_gemma":0.00043147767,"threshold_uncertainty_score":0.0031616092},"labels":[],"label_agreement":null},{"id":"W2112732068","doi":"10.1109/tsp.2009.2028947","title":"Alleviating Sensor Position Error in Source Localization Using Calibration Emitters at Inaccurate Locations","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Position (finance); Calibration; Cramér–Rao bound; Computer science; Gaussian; Algorithm; Multilateration; Upper and lower bounds; Noise (video); Observational error; Artificial intelligence; Mathematics; Estimation theory; Statistics; Physics; Acoustics; Node (physics)","score_opus":0.018826759125809844,"score_gpt":0.2465502828620269,"score_spread":0.22772352373621707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112732068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036003865,0.00023907304,0.96247315,0.00010318858,0.000024285884,0.000010548094,0.000009992433,0.00017546775,0.0009604736],"genre_scores_gemma":[0.80074036,0.0008489486,0.19640654,0.00007574282,0.00006740106,0.000049101523,0.00008416306,0.00006683388,0.0016609716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879265,0.00041365548,0.000042780583,0.00014396278,0.0005104771,0.000096542055],"domain_scores_gemma":[0.9971909,0.0017745959,0.0002755178,0.00035483047,0.00037389927,0.000030282592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016999374,0.0007954418,0.0007225671,0.0005392295,0.00030328348,0.0006157468,0.0007023277,0.0010374853,0.00059315783],"category_scores_gemma":[0.009942171,0.0004549163,0.00039050574,0.00087762426,0.00071404094,0.001701935,0.0015719697,0.0006766617,0.0003567449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018496583,0.000042536176,0.0013718405,0.00012116561,0.00003973927,0.0001905921,0.0002324582,0.86121935,0.01949885,0.010984378,0.00068794616,0.1054262],"study_design_scores_gemma":[0.000008035112,0.00005112821,0.00034991573,0.000009361819,0.000013452419,0.000081741535,0.000023646393,0.98886347,0.007872959,0.002228714,0.00048663717,0.000010922085],"about_ca_topic_score_codex":0.0014437264,"about_ca_topic_score_gemma":0.0014080321,"teacher_disagreement_score":0.0016999374,"about_ca_system_score_codex":0.00040302236,"about_ca_system_score_gemma":0.0005677165,"threshold_uncertainty_score":0.008990228},"labels":[],"label_agreement":null},{"id":"W2113065846","doi":"10.1109/twc.2002.800542","title":"Hybrid TDOA/AOA mobile user location for wideband CDMA cellular systems","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":595,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multilateration; Angle of arrival; Computer science; Base station; Wideband; Code division multiple access; Macrocell; Real-time computing; Electronic engineering; Antenna (radio); Telecommunications; Engineering; Mathematics; Azimuth","score_opus":0.02240451282096932,"score_gpt":0.22761352298817644,"score_spread":0.2052090101672071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113065846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01295329,0.00038995373,0.98536545,0.000037904894,0.000051410578,0.000015221874,0.000013005748,0.00044507068,0.0007286889],"genre_scores_gemma":[0.5641995,0.00041689366,0.4321548,0.0000775468,0.00010260622,0.000076858465,0.00006231862,0.000037706846,0.0028716626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994912,0.00015317646,0.000025623754,0.00007895081,0.00021309423,0.00003796644],"domain_scores_gemma":[0.9994535,0.00016425512,0.00007741745,0.0001141885,0.00016344881,0.000027189819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000361255,0.00043361477,0.000442828,0.00058228755,0.00039017384,0.00054944545,0.00076656305,0.00045369403,0.0007296066],"category_scores_gemma":[0.0014171172,0.0002638962,0.0003200254,0.00054793316,0.00034211547,0.000808711,0.00072117645,0.00035547014,0.00060151337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005173904,0.0000835138,0.0033095225,0.00021107771,0.00013498259,0.00027785255,0.00024079272,0.221364,0.11652737,0.020326186,0.0021692137,0.6348381],"study_design_scores_gemma":[0.00003884235,0.000189858,0.0006683575,0.000013152991,0.000039301296,0.00037379534,0.00002698121,0.9737153,0.016258042,0.0025420834,0.006096353,0.000037925525],"about_ca_topic_score_codex":0.0014635664,"about_ca_topic_score_gemma":0.0024301682,"teacher_disagreement_score":0.0014635664,"about_ca_system_score_codex":0.00038168722,"about_ca_system_score_gemma":0.00027905765,"threshold_uncertainty_score":0.0029101372},"labels":[],"label_agreement":null},{"id":"W2113086070","doi":"10.1109/icassp.1994.389616","title":"Geolocalization by combined range difference and range rate difference measurements","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Geolocation; Range (aeronautics); Common emitter; Oblate spheroid; Position (finance); Estimator; Latitude; Geodesy; Quadratic equation; Computer science; Physics; Algorithm; Geology; Computational physics; Mathematics; Statistics; Geometry; Aerospace engineering; Engineering; Classical mechanics; Optoelectronics","score_opus":0.02482132804255019,"score_gpt":0.19504197365106957,"score_spread":0.17022064560851938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113086070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012559157,0.00016172309,0.9852806,0.00003956118,0.000019524978,0.0000130264,0.00006739064,0.0005272656,0.0013316979],"genre_scores_gemma":[0.3711496,0.00056844496,0.62259674,0.00007271634,0.00010191277,0.0000925722,0.00064836844,0.00021004534,0.0045596017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991948,0.00020478283,0.00003398201,0.00022393354,0.0003029662,0.000039506456],"domain_scores_gemma":[0.9993224,0.00021376631,0.000119647135,0.000159167,0.00016788187,0.000017153303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054695766,0.00051483023,0.000684489,0.0012595874,0.00016275753,0.0010041202,0.00075188186,0.0004742513,0.0013199112],"category_scores_gemma":[0.0023769916,0.00037168013,0.00058766286,0.0013350174,0.00031304947,0.0017042438,0.0009391207,0.00040554942,0.0012411204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019219903,0.00007997375,0.005463239,0.0002559272,0.00015810042,0.00020709814,0.000175472,0.26313704,0.05799376,0.013817376,0.0027211348,0.6557986],"study_design_scores_gemma":[0.000049794566,0.00019299808,0.0072507556,0.00004497985,0.0001174894,0.0008077351,0.00006842478,0.93161684,0.035951294,0.008047038,0.015769675,0.00008299098],"about_ca_topic_score_codex":0.0011288265,"about_ca_topic_score_gemma":0.00202802,"teacher_disagreement_score":0.0013199112,"about_ca_system_score_codex":0.00023271855,"about_ca_system_score_gemma":0.00030362315,"threshold_uncertainty_score":0.0044155717},"labels":[],"label_agreement":null},{"id":"W2113290405","doi":"10.1109/icves.2009.5400326","title":"Toward increasing the localization accuracy of vehicles in VANET","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Global Positioning System; Weighting; Computer science; Multipath propagation; Vehicular ad hoc network; Scheme (mathematics); Real-time computing; Resilience (materials science); Wireless ad hoc network; Wireless; Computer network; Telecommunications; Mathematics","score_opus":0.013397083612263947,"score_gpt":0.2246349618925799,"score_spread":0.21123787828031595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113290405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066369876,0.0005691108,0.93117154,0.00015103552,0.00003887197,0.000025050738,0.00004329886,0.00043280926,0.0011984176],"genre_scores_gemma":[0.81217694,0.0003780849,0.18584554,0.00008085846,0.000057765894,0.000038010745,0.0001436134,0.000064436,0.0012146811],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987967,0.00042296405,0.00006639461,0.00022267648,0.0003523,0.00013888198],"domain_scores_gemma":[0.99713314,0.0009873932,0.00037836956,0.0005254043,0.0009051416,0.00007049132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015576057,0.0007344962,0.0006581904,0.0010092614,0.00037560397,0.00073421246,0.0013587873,0.00064074737,0.00081390544],"category_scores_gemma":[0.00804912,0.00026499573,0.00024095419,0.0009598827,0.00046678865,0.0014969164,0.0018227674,0.0005866552,0.00040173126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036703778,0.00009294526,0.005341751,0.00019052495,0.00010128716,0.00009224297,0.00019185817,0.5874465,0.036970243,0.011606051,0.0010991184,0.3565005],"study_design_scores_gemma":[0.000016599193,0.00015028946,0.00088602485,0.000019041581,0.000025451503,0.000070986636,0.000055035365,0.9833045,0.010007027,0.0031161748,0.0023321805,0.000016669184],"about_ca_topic_score_codex":0.002811253,"about_ca_topic_score_gemma":0.002554262,"teacher_disagreement_score":0.002811253,"about_ca_system_score_codex":0.00043523207,"about_ca_system_score_gemma":0.000600709,"threshold_uncertainty_score":0.008237481},"labels":[],"label_agreement":null},{"id":"W2114187082","doi":"10.1109/tim.2010.2046366","title":"Initial Position Estimation Using RFID Tags: A Least-Squares Approach","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"PotashCorp (Canada); University of Saskatchewan","funders":"","keywords":"Global Positioning System; Position (finance); Estimator; Computer science; Least-squares function approximation; Simultaneous localization and mapping; Artificial intelligence; GPS signals; Field (mathematics); Identification (biology); Mean squared error; Robotics; Real-time computing; Computer vision; Algorithm; Robot; Assisted GPS; Mobile robot; Mathematics; Statistics; Telecommunications","score_opus":0.02890607633414055,"score_gpt":0.25075943106465964,"score_spread":0.2218533547305191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114187082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005285512,0.0000548304,0.99401206,0.000025950361,0.0000097992115,0.000009941365,0.000010452099,0.00034505318,0.00024637152],"genre_scores_gemma":[0.29951432,0.00024937934,0.6968262,0.000053892785,0.000036854173,0.0000923895,0.00014582391,0.00014536876,0.0029357455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947757,0.00015404791,0.000026698559,0.00012126129,0.00018816922,0.00003234675],"domain_scores_gemma":[0.9992694,0.00030958588,0.00011108111,0.00007811911,0.00021155861,0.00002023412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059892307,0.0008638362,0.0007874401,0.00058267504,0.00034048746,0.000732525,0.0008558819,0.000838469,0.0007683094],"category_scores_gemma":[0.0024666863,0.0005690432,0.0005541518,0.0007647284,0.00039945045,0.00097265973,0.000564714,0.00083831843,0.0010913927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015234837,0.00008184069,0.0016856359,0.00014459365,0.00009328575,0.000118214186,0.00019531643,0.6772473,0.03115394,0.0027501404,0.001121924,0.28525546],"study_design_scores_gemma":[0.000013285732,0.00009116598,0.0004689666,0.0000116958045,0.000018271907,0.00006513301,0.00004087917,0.988149,0.008415136,0.0013046687,0.0013961097,0.00002570807],"about_ca_topic_score_codex":0.003975452,"about_ca_topic_score_gemma":0.0031613247,"teacher_disagreement_score":0.003975452,"about_ca_system_score_codex":0.0003190691,"about_ca_system_score_gemma":0.0006968613,"threshold_uncertainty_score":0.007904649},"labels":[],"label_agreement":null},{"id":"W2114774401","doi":"10.1109/issse.2007.4294528","title":"An Asymmetric Double Sided Two-Way Ranging for Crystal Offset","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":161,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ranging; Offset (computer science); Transceiver; Bandwidth (computing); Computer science; Nanosecond; Synchronization (alternating current); Distance measurement; Electronic engineering; Physics; Telecommunications; Optics; Engineering; Laser; Wireless; Artificial intelligence","score_opus":0.012399077110626381,"score_gpt":0.26209760831748435,"score_spread":0.24969853120685798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114774401","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107896954,0.0013162952,0.8737621,0.00041253067,0.00052895886,0.00017387158,0.00012558638,0.0008634258,0.014920283],"genre_scores_gemma":[0.46982074,0.000553831,0.52127326,0.0002536586,0.00012903444,0.00011350115,0.00013447113,0.000053037045,0.007668499],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992893,0.00012103997,0.00003790133,0.00016965588,0.0003311544,0.00005097093],"domain_scores_gemma":[0.9990921,0.00020178054,0.00016299161,0.00024611235,0.00024980213,0.00004717162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004120236,0.0004385229,0.00047115554,0.00050487154,0.00041852568,0.00044462527,0.0013634118,0.00088619196,0.0013952717],"category_scores_gemma":[0.0010645575,0.00029981407,0.00030567165,0.0004705646,0.0003818024,0.0011040948,0.00089148636,0.0005955958,0.00076898333],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000295906,0.00011348338,0.001023956,0.00031757433,0.00002840187,0.0004560523,0.0001485024,0.0058605545,0.7205283,0.018511515,0.0014769565,0.2512388],"study_design_scores_gemma":[0.00017259657,0.0012179612,0.002544523,0.000074301424,0.0001124166,0.00732679,0.00009641672,0.16800816,0.7735446,0.0060455995,0.040610302,0.0002462625],"about_ca_topic_score_codex":0.00020191041,"about_ca_topic_score_gemma":0.0004520628,"teacher_disagreement_score":0.0013952717,"about_ca_system_score_codex":0.00021926741,"about_ca_system_score_gemma":0.00046547278,"threshold_uncertainty_score":0.0046676993},"labels":[],"label_agreement":null},{"id":"W2114856874","doi":"10.1109/ccece.2011.6030401","title":"Indoor cell-level localization based on RSSI classification","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hidden Markov model; Computer science; Support vector machine; Received signal strength indication; Inference; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Gaussian; Machine learning; Transceiver; k-nearest neighbors algorithm; Data mining; Wireless; Telecommunications","score_opus":0.05478878098963904,"score_gpt":0.208120425285929,"score_spread":0.15333164429628995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114856874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18914397,0.00078632444,0.79224026,0.00017264686,0.00020619559,0.00012809644,0.00092402304,0.0073011727,0.009097278],"genre_scores_gemma":[0.90083164,0.00037739958,0.09530416,0.000060048154,0.000042270192,0.000070980284,0.0007820848,0.00005712746,0.002474294],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993318,0.000116171956,0.000031867352,0.00014440774,0.00026771158,0.00010796276],"domain_scores_gemma":[0.9990097,0.0002160626,0.00014116737,0.00021091849,0.0003711958,0.000050913197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040643555,0.0006764096,0.0011112198,0.0010556777,0.00032615117,0.0007847456,0.0008596437,0.0006554274,0.0013333433],"category_scores_gemma":[0.001819,0.00018218897,0.00033039265,0.0016016512,0.00031757692,0.0007270395,0.0006818417,0.0003549454,0.0021958651],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084465346,0.00022268714,0.039002232,0.00040900122,0.00019388362,0.00046217337,0.00021320388,0.20572679,0.067136034,0.0036599804,0.008128493,0.6740009],"study_design_scores_gemma":[0.000019368628,0.0001813829,0.013311274,0.000024051466,0.00004833352,0.000440079,0.0000657517,0.9498237,0.032377392,0.0011866076,0.0024716184,0.000050534494],"about_ca_topic_score_codex":0.0032649017,"about_ca_topic_score_gemma":0.0059912186,"teacher_disagreement_score":0.0032649017,"about_ca_system_score_codex":0.00037791702,"about_ca_system_score_gemma":0.00041724794,"threshold_uncertainty_score":0.0064917803},"labels":[],"label_agreement":null},{"id":"W2115287871","doi":"10.1109/icc.2007.967","title":"Cooperative Vehicle Position Estimation","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Global Positioning System; Ranging; Computer science; Kalman filter; Position (finance); Reliability (semiconductor); Kinematics; Overhead (engineering); Algorithm; Computational complexity theory; Real-time computing; Artificial intelligence; Telecommunications","score_opus":0.005367275278202332,"score_gpt":0.2176906091814686,"score_spread":0.21232333390326627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115287871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008488108,0.00013120136,0.98946893,0.000040469156,0.00003784759,0.000016584592,0.000020193045,0.00047256667,0.0013241268],"genre_scores_gemma":[0.66538215,0.0002900543,0.32754532,0.000087218425,0.0000872127,0.000102988975,0.00019610976,0.00007402852,0.006234888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988966,0.00014674888,0.000040719613,0.00030958847,0.00046761803,0.00013873793],"domain_scores_gemma":[0.99849606,0.00034546343,0.0001781149,0.0003354059,0.00058754155,0.00005744958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006937503,0.00082576135,0.00092386745,0.00093218207,0.00065791036,0.00081492634,0.0020415715,0.0011612057,0.0013962754],"category_scores_gemma":[0.0033341772,0.00038163102,0.0005064643,0.0010640246,0.00043831038,0.0015321742,0.0018544914,0.0008030759,0.0010271843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020776007,0.000127523,0.003354301,0.00010977643,0.00008521396,0.0002420251,0.00025441256,0.42481875,0.027360348,0.016215533,0.004178291,0.5230461],"study_design_scores_gemma":[0.000022815184,0.000106054176,0.0006937046,0.000008157099,0.00002694158,0.000162048,0.00004388868,0.9767413,0.012026547,0.0042572743,0.005887055,0.000024043025],"about_ca_topic_score_codex":0.004169512,"about_ca_topic_score_gemma":0.0041459585,"teacher_disagreement_score":0.004169512,"about_ca_system_score_codex":0.00050620415,"about_ca_system_score_gemma":0.0008450642,"threshold_uncertainty_score":0.008290529},"labels":[],"label_agreement":null},{"id":"W2115641275","doi":"10.1109/robot.2009.5152214","title":"Decentralized localization for dynamic and sparse robot networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robot; Computer science; Mobile robot; Network topology; Distributed computing; State (computer science); Information exchange; Telecommunications network; Range (aeronautics); Decentralised system; Robot kinematics; Topology (electrical circuits); Computer network; Artificial intelligence; Engineering; Control (management); Algorithm; Telecommunications","score_opus":0.005924278095899322,"score_gpt":0.21432803246115353,"score_spread":0.2084037543652542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115641275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016521808,0.00010494736,0.981955,0.000108240034,0.000011606477,0.00001281152,0.000014665666,0.00023495138,0.0010359068],"genre_scores_gemma":[0.8804176,0.00021628263,0.11708991,0.000048441223,0.000037232814,0.000103976825,0.0000878671,0.000047940648,0.0019507558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995409,0.00013269827,0.00001688824,0.00009730409,0.00016787101,0.000044336462],"domain_scores_gemma":[0.99882346,0.00061478524,0.00019460791,0.00015626109,0.00017318394,0.00003773287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054804207,0.00031237808,0.00044698638,0.00043035255,0.0004859218,0.0004567106,0.00069465826,0.00046218047,0.0007065951],"category_scores_gemma":[0.0032923785,0.00025490482,0.00024226033,0.0003850018,0.0006843924,0.0009365621,0.0011481877,0.00043179345,0.00016032094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074691685,0.00001659653,0.00063202006,0.00007266024,0.000019647574,0.00012409233,0.00010508533,0.90309936,0.007820437,0.03510889,0.0009305585,0.051996008],"study_design_scores_gemma":[0.000008705361,0.000021358312,0.00014332203,0.0000031182544,0.0000030225206,0.00003625017,0.000011205975,0.98793864,0.0008122527,0.010344407,0.00067371066,0.0000039247643],"about_ca_topic_score_codex":0.0014180554,"about_ca_topic_score_gemma":0.0016606617,"teacher_disagreement_score":0.0014180554,"about_ca_system_score_codex":0.0005530717,"about_ca_system_score_gemma":0.0005038233,"threshold_uncertainty_score":0.004012823},"labels":[],"label_agreement":null},{"id":"W2115675575","doi":"10.1002/rob.21415","title":"A landmark‐bounded method for large‐scale underground mine mapping","year":2012,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Universidad Nacional de Colombia","keywords":"Landmark; Global Positioning System; Occupancy grid mapping; Computer science; Scale (ratio); Bounded function; Field (mathematics); Computer vision; Work (physics); Artificial intelligence; Grid; Occupancy; Odometry; Data mining; Engineering; Cartography; Geology; Geography; Geodesy; Civil engineering; Robot; Mathematics; Mobile robot","score_opus":0.01993739491609656,"score_gpt":0.2722569060271618,"score_spread":0.2523195111110652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115675575","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023470698,0.0000349315,0.9970402,0.00001417068,0.000009242545,0.000012277697,0.000018184533,0.00028909306,0.00023475343],"genre_scores_gemma":[0.18267947,0.00009527297,0.81497914,0.00003498575,0.000024370987,0.00017759048,0.0001894666,0.0001569251,0.0016628179],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942565,0.00017918105,0.00002275211,0.00011031804,0.0002122141,0.00004994571],"domain_scores_gemma":[0.9990189,0.00046968213,0.00008144909,0.00019271251,0.00019018218,0.00004699122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069712894,0.00049497583,0.00076031004,0.0009970678,0.00049878354,0.00078104745,0.0015567784,0.00067967316,0.0029152897],"category_scores_gemma":[0.0024158405,0.00035311258,0.0006412266,0.0010699318,0.0006602712,0.0009042357,0.0019497751,0.00070779136,0.00095866126],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031502417,0.00009586826,0.0012151039,0.0002102205,0.000078708596,0.00023134988,0.00024314098,0.5153558,0.024159117,0.037755273,0.0037941395,0.41654623],"study_design_scores_gemma":[0.00001590274,0.000027843982,0.00016216033,0.000007780897,0.0000070677097,0.00005788708,0.000023936856,0.9892775,0.0023410756,0.005849059,0.0022152103,0.0000146173425],"about_ca_topic_score_codex":0.0032107409,"about_ca_topic_score_gemma":0.0029095737,"teacher_disagreement_score":0.0032107409,"about_ca_system_score_codex":0.00040126542,"about_ca_system_score_gemma":0.0009652123,"threshold_uncertainty_score":0.009752631},"labels":[],"label_agreement":null},{"id":"W2115826187","doi":"10.1109/tsp.2011.2166393","title":"Optimal Amplitude Weighting for Near-field Passive Source Localization","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Weighting; Estimator; Monte Carlo method; Energy (signal processing); Detector; Algorithm; Amplitude; Computer science; Bandwidth (computing); Signal-to-noise ratio (imaging); SIGNAL (programming language); Iterative method; Mathematical optimization; Mathematics; Statistics; Telecommunications; Acoustics; Physics","score_opus":0.019350427446197173,"score_gpt":0.227757555743401,"score_spread":0.20840712829720384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115826187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022073837,0.00004564424,0.9972102,0.000023758297,0.0000055496926,0.000008283185,0.0000031625718,0.00004403622,0.000451867],"genre_scores_gemma":[0.31447178,0.00041243306,0.6810195,0.00010246385,0.000083438645,0.0001376324,0.00007898908,0.00012771988,0.00356607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935895,0.00017691274,0.000032065214,0.00009483528,0.00030044233,0.00003685745],"domain_scores_gemma":[0.99947196,0.0002811929,0.000056284072,0.000036442387,0.00014074406,0.000013420745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067471823,0.00065994816,0.0003803306,0.0004943963,0.00024326303,0.00068021106,0.0006230242,0.0007093875,0.0015470057],"category_scores_gemma":[0.0031106095,0.00028374887,0.00029857922,0.0004712761,0.00043781725,0.0015848903,0.0008106927,0.00048339885,0.00081141386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019801508,0.00009726962,0.0005227302,0.00022924872,0.000045791185,0.000101482576,0.00017109734,0.36418808,0.092989385,0.09302321,0.001501509,0.4469322],"study_design_scores_gemma":[0.000016857244,0.000081832346,0.00015184865,0.000014994686,0.00001569737,0.00009115573,0.000017380642,0.9638062,0.012296663,0.02136937,0.0021248907,0.00001306962],"about_ca_topic_score_codex":0.0003248648,"about_ca_topic_score_gemma":0.0004900715,"teacher_disagreement_score":0.0015470057,"about_ca_system_score_codex":0.00038050069,"about_ca_system_score_gemma":0.00042582286,"threshold_uncertainty_score":0.005175233},"labels":[],"label_agreement":null},{"id":"W2115880660","doi":"10.1109/tim.2012.2214952","title":"Probabilistic Sensing Model for Sensor Placement Optimization Based on Line-of-Sight Coverage","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":132,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Centre de Recherches Mathématiques; Defence Research and Development Canada","keywords":"Probabilistic logic; Computer science; Simulated annealing; Optimization problem; Terrain; Wireless sensor network; Covariance matrix; Statistical model; Real-time computing; Algorithm; Artificial intelligence","score_opus":0.046703390710581084,"score_gpt":0.2462753902334124,"score_spread":0.19957199952283133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115880660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030569886,0.00006677624,0.9958823,0.000095425494,0.000010181486,0.000013164042,0.000019131057,0.00006268532,0.00079347816],"genre_scores_gemma":[0.77663594,0.0005527196,0.2167596,0.00023303686,0.000076774435,0.00040719006,0.00020825873,0.00013913303,0.004987354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896014,0.0003487418,0.000035352743,0.00022645283,0.0003366996,0.00009258115],"domain_scores_gemma":[0.99887043,0.0007011823,0.00015160625,0.00008268235,0.00015357685,0.000040542247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011470431,0.0011011384,0.0012744871,0.0005859218,0.00045444045,0.00079424697,0.0022930903,0.001440237,0.0015073043],"category_scores_gemma":[0.0036402505,0.00085439766,0.00092445116,0.0008970107,0.0011656753,0.0019386922,0.0012419611,0.0013867109,0.00032002112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009851518,0.0000065039276,0.000071569106,0.000013847073,0.0000062808995,0.000013181241,0.00001280165,0.9893611,0.00048390072,0.007739898,0.00013777806,0.0021432773],"study_design_scores_gemma":[0.0000019566694,0.0000066844136,0.000025643825,0.0000011133573,0.000001572555,0.0000047527237,0.0000013769135,0.99785787,0.000093018716,0.0019228598,0.00008082855,0.0000022831452],"about_ca_topic_score_codex":0.0047494126,"about_ca_topic_score_gemma":0.00391992,"teacher_disagreement_score":0.0047494126,"about_ca_system_score_codex":0.0013266135,"about_ca_system_score_gemma":0.000959239,"threshold_uncertainty_score":0.009625375},"labels":[],"label_agreement":null},{"id":"W2116094337","doi":"10.1109/icassp.2008.4518047","title":"Sensor selection for mitigation of RSS-based attacks in wireless local area network positioning","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Computer science; Computer network; Context (archaeology); Wireless sensor network; Hybrid positioning system; Resilience (materials science); Wireless; Wi-Fi; Wireless network; Real-time computing; Point (geometry); Positioning system; Telecommunications","score_opus":0.026069905833706605,"score_gpt":0.25149281141926166,"score_spread":0.22542290558555506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116094337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10388673,0.0005408464,0.89429957,0.000104054045,0.0000472134,0.00003287566,0.000015456993,0.00043591324,0.00063720834],"genre_scores_gemma":[0.91246855,0.00020336665,0.08652798,0.000039460872,0.00004483933,0.000021329975,0.000028645358,0.0000119750885,0.00065384945],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947256,0.00017049187,0.000029855226,0.00009072544,0.00019688747,0.000039501614],"domain_scores_gemma":[0.9989023,0.0004680488,0.00017911626,0.00024647685,0.0001640633,0.000039907663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062262826,0.0004207248,0.00063606596,0.00039633142,0.00030623868,0.00023986975,0.0005519455,0.0004531337,0.00044983896],"category_scores_gemma":[0.0019176523,0.00016449933,0.00020004628,0.00032458376,0.00035205577,0.000585524,0.00057935406,0.0003346673,0.00024599867],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091475976,0.00019395803,0.0042295237,0.00013687932,0.000088861874,0.00038757068,0.00017920104,0.1331184,0.32595536,0.004522056,0.00094168144,0.5293318],"study_design_scores_gemma":[0.000064656924,0.0011246843,0.0050518666,0.000021286442,0.000076025455,0.0010896019,0.000067473055,0.8218009,0.16569373,0.002397338,0.0025699355,0.000042498472],"about_ca_topic_score_codex":0.00015983524,"about_ca_topic_score_gemma":0.0003131832,"teacher_disagreement_score":0.00063606596,"about_ca_system_score_codex":0.00016693922,"about_ca_system_score_gemma":0.00015586788,"threshold_uncertainty_score":0.003292799},"labels":[],"label_agreement":null},{"id":"W2116252059","doi":"10.1109/glocom.2009.5425650","title":"Constrained Weighted Least Square Optimization for Vehicle Position Tracking","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Multilateration; Kalman filter; Non-line-of-sight propagation; Computer science; Smoothing; Position (finance); Time of arrival; Ranging; Algorithm; Extended Kalman filter; Range (aeronautics); Mean squared error; Control theory (sociology); Real-time computing; Wireless; Mathematics; Engineering; Artificial intelligence; Computer vision; Statistics; Telecommunications; Azimuth","score_opus":0.00881330077118221,"score_gpt":0.21561919028040757,"score_spread":0.20680588950922538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116252059","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011497765,0.00037699303,0.9974197,0.0000644367,0.000041200725,0.00001266648,0.000029032984,0.00014696184,0.000759243],"genre_scores_gemma":[0.31233194,0.0022813312,0.66885436,0.00026399776,0.00027970626,0.0004811568,0.0007244951,0.00025154196,0.014531556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992908,0.00024157904,0.000030215486,0.00016015349,0.00022908527,0.00004805993],"domain_scores_gemma":[0.9995326,0.00024286703,0.00006164298,0.000037019643,0.00011449109,0.0000114506865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007487037,0.001102836,0.0011869763,0.0004901236,0.00031968567,0.0006619548,0.00072349526,0.000904402,0.0019496342],"category_scores_gemma":[0.0021380484,0.00045726512,0.0005944272,0.0012335975,0.00050954515,0.0008126237,0.0006779301,0.001041437,0.00072711613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035225476,0.000020015632,0.0002049839,0.00010570148,0.00004817076,0.000030507099,0.000030224748,0.9185039,0.0011354436,0.010482296,0.0019474082,0.06745616],"study_design_scores_gemma":[0.0000057629068,0.000015864847,0.000061406085,0.0000046637806,0.0000051363027,0.0000069545217,0.0000041178487,0.99463695,0.00026520283,0.0033296181,0.0016592258,0.0000050126187],"about_ca_topic_score_codex":0.008420165,"about_ca_topic_score_gemma":0.0046697455,"teacher_disagreement_score":0.008420165,"about_ca_system_score_codex":0.0006756239,"about_ca_system_score_gemma":0.0010258533,"threshold_uncertainty_score":0.016742289},"labels":[],"label_agreement":null},{"id":"W2116348465","doi":"10.1109/ccece.2007.271","title":"IEEE 802.11 WLAN Based Real-Time Location Tracking in Indoor and Outdoor Environments","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Path loss; Real-time computing; Wireless lan; Wireless; Received signal strength indication; IEEE 802.11; Tracking (education); Radio propagation; Point (geometry); Wi-Fi; Path (computing); Estimation; Wireless network; Computer network; Telecommunications; Engineering","score_opus":0.00827490966116866,"score_gpt":0.21728493352163522,"score_spread":0.20901002386046655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116348465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05170695,0.00087524334,0.9379977,0.00022223635,0.00019392361,0.000052772375,0.000073357376,0.0033110362,0.005566726],"genre_scores_gemma":[0.66579014,0.0013501917,0.3144833,0.00029159104,0.00016839548,0.00012556971,0.0004390051,0.00009015314,0.01726157],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99947065,0.00011410741,0.000032835927,0.00011535672,0.0002046164,0.00006248898],"domain_scores_gemma":[0.9995747,0.00008362485,0.000075768825,0.000082087216,0.00015256349,0.000031291504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055963785,0.0004187773,0.000486801,0.00053044,0.00043253353,0.000992234,0.0009784433,0.0009077001,0.0010498175],"category_scores_gemma":[0.0012254671,0.00026015492,0.00024137292,0.0006201007,0.00030678516,0.0013850841,0.00052749424,0.0005169548,0.0010689262],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005129379,0.00023086784,0.011613361,0.00026791153,0.000121353245,0.00057324005,0.0004393034,0.06169561,0.097746305,0.009267653,0.008715655,0.8088158],"study_design_scores_gemma":[0.000093692004,0.0007380208,0.012694004,0.00009741071,0.00024541,0.0024547684,0.00025553035,0.82108516,0.11409303,0.0040551987,0.044018988,0.00016885901],"about_ca_topic_score_codex":0.0017231667,"about_ca_topic_score_gemma":0.0020918327,"teacher_disagreement_score":0.0017231667,"about_ca_system_score_codex":0.00032730802,"about_ca_system_score_gemma":0.0003915426,"threshold_uncertainty_score":0.0035119653},"labels":[],"label_agreement":null},{"id":"W2116773410","doi":"10.1109/vetec.1997.600371","title":"Field tests of a cellular telephone positioning system","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Trilateration; Position (finance); Computer science; Identification (biology); Time of arrival; Field (mathematics); SIGNAL (programming language); Process (computing); Real-time computing; Telecommunications; Engineering; Mathematics; Wireless","score_opus":0.0061262825777806265,"score_gpt":0.1656488007979452,"score_spread":0.15952251822016456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116773410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96225166,0.0002115586,0.030630222,0.00040713453,0.0001883668,0.0003351638,0.0006320555,0.0010391658,0.004304547],"genre_scores_gemma":[0.99053395,0.000086095344,0.006002937,0.00016922173,0.000033006978,0.00008956325,0.0006249837,0.000048562153,0.002411726],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99802893,0.0007301487,0.00010981206,0.0002796498,0.00065048435,0.00020095508],"domain_scores_gemma":[0.9935661,0.0026627914,0.00025479592,0.0005297833,0.0025990785,0.00038748578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015309572,0.0006048293,0.0003589446,0.0006537588,0.00073097134,0.00041627183,0.00076651335,0.0011419681,0.0027783264],"category_scores_gemma":[0.004807954,0.0002485875,0.00021501009,0.0005097691,0.0005052521,0.00078081153,0.0006863971,0.00053780794,0.0010689151],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013491148,0.005060884,0.06590632,0.002391501,0.000568222,0.0046790955,0.005431512,0.07895086,0.49376586,0.004337165,0.01527862,0.31013888],"study_design_scores_gemma":[0.0037224735,0.11165238,0.10709146,0.00039564463,0.0006421432,0.0060364082,0.0037874186,0.13711786,0.5738867,0.0027159334,0.05260956,0.00034204157],"about_ca_topic_score_codex":0.003070123,"about_ca_topic_score_gemma":0.0019687703,"teacher_disagreement_score":0.003070123,"about_ca_system_score_codex":0.00040593525,"about_ca_system_score_gemma":0.00042342415,"threshold_uncertainty_score":0.009294391},"labels":[],"label_agreement":null},{"id":"W2116881676","doi":"10.1109/wimob.2009.71","title":"iCCA-MAP: A New Mobile Node Localization Algorithm","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Research Council Canada; National Science Council","keywords":"Computer science; Node (physics); Algorithm; Wireless sensor network; Position (finance); Iterative method; Wireless; Mobile telephony; Real-time computing; Mobile radio; Computer network","score_opus":0.005123234802765321,"score_gpt":0.20617976549199202,"score_spread":0.2010565306892267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116881676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029090084,0.0003900844,0.99370605,0.00014082722,0.00011133075,0.00004569615,0.00007718778,0.0011378952,0.0014819623],"genre_scores_gemma":[0.11253452,0.00074192643,0.8789753,0.00019820745,0.00015979467,0.00040645877,0.00066767615,0.00027503425,0.00604105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995122,0.00008445939,0.000023244833,0.00010182239,0.00022750208,0.000050829523],"domain_scores_gemma":[0.9993,0.0001750791,0.00006915494,0.000089092064,0.00032364193,0.00004294436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005251349,0.00090621895,0.00092103204,0.0015134175,0.00069013686,0.0010350272,0.00211825,0.001091919,0.002052237],"category_scores_gemma":[0.0023976932,0.00041649706,0.00074438314,0.0015002348,0.0005841088,0.0016403134,0.0023230952,0.0012656015,0.0017367565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003022032,0.000073955605,0.0028228704,0.00033283373,0.00015004638,0.00038140692,0.00029788224,0.19593078,0.018624496,0.02759782,0.020565433,0.7329204],"study_design_scores_gemma":[0.0000626219,0.000113530274,0.0006551416,0.000037643134,0.000037886555,0.0005393022,0.00006778841,0.9474257,0.007480069,0.0083193835,0.035207424,0.00005353679],"about_ca_topic_score_codex":0.00309301,"about_ca_topic_score_gemma":0.002921413,"teacher_disagreement_score":0.00309301,"about_ca_system_score_codex":0.00046078398,"about_ca_system_score_gemma":0.0016822658,"threshold_uncertainty_score":0.006865442},"labels":[],"label_agreement":null},{"id":"W2117011519","doi":"10.1109/vetecf.2000.883243","title":"Mobile location estimation in cellular networks using fuzzy logic","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Code division multiple access; Estimator; Fuzzy logic; Computer science; Base station; Cellular network; Division (mathematics); Mobile station; SIGNAL (programming language); Real-time computing; Algorithm; Mobile telephony; Mobile radio; Mathematics; Artificial intelligence; Telecommunications; Statistics","score_opus":0.016297358912805288,"score_gpt":0.21380842796409172,"score_spread":0.19751106905128643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117011519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038068864,0.0003248464,0.9608342,0.000059622795,0.000020340458,0.000013655221,0.000013932901,0.00012249377,0.00054204895],"genre_scores_gemma":[0.9212963,0.00027882808,0.07759987,0.000030565167,0.000029796849,0.000027537457,0.000031167532,0.0000053332806,0.0007006716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974996,0.00006499592,0.000015503554,0.000048759426,0.00009713342,0.000023671251],"domain_scores_gemma":[0.999605,0.00018173497,0.000057309815,0.000018753237,0.00012703925,0.00001015705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004222983,0.0003417499,0.00036304595,0.0003858171,0.00030688138,0.00050686294,0.00042952056,0.0004534439,0.0002871014],"category_scores_gemma":[0.0012875354,0.00017414155,0.00027240277,0.00037910763,0.00031059896,0.00045699585,0.00024677083,0.00030822228,0.00008714476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018539556,0.00003925574,0.0030072515,0.00007504901,0.00006593608,0.00017248494,0.00010402814,0.8076287,0.016059212,0.0058078757,0.00046902345,0.16638587],"study_design_scores_gemma":[0.0000053601107,0.000033589116,0.00027134127,0.000004361801,0.00001035154,0.000024980862,0.0000072601947,0.9972318,0.0012933466,0.00089974434,0.00021164268,0.0000062065747],"about_ca_topic_score_codex":0.0074076303,"about_ca_topic_score_gemma":0.0045097047,"teacher_disagreement_score":0.0074076303,"about_ca_system_score_codex":0.00050718605,"about_ca_system_score_gemma":0.0003298293,"threshold_uncertainty_score":0.014729023},"labels":[],"label_agreement":null},{"id":"W2117041809","doi":"10.1109/icit.2004.1490728","title":"Mobile robot geolocation with received signal strength (RSS) fingerprinting technique and neural networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Non-line-of-sight propagation; Geolocation; RSS; Computer science; Multilateration; Multipath propagation; Real-time computing; Mobile robot; Angle of arrival; Time of arrival; Transmitter; Robot; Artificial intelligence; Wireless; Engineering; Telecommunications","score_opus":0.004161864149917965,"score_gpt":0.1870862179218183,"score_spread":0.18292435377190033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117041809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052483093,0.00050525565,0.94374174,0.000110586865,0.000050590246,0.000023746066,0.000046594098,0.0012454181,0.0017930354],"genre_scores_gemma":[0.65227073,0.0005506062,0.3428776,0.00007651706,0.00006336425,0.00006112865,0.00011382225,0.00003679359,0.0039493567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979824,0.00005908746,0.000013141539,0.000046655452,0.000065161796,0.000017706452],"domain_scores_gemma":[0.9997024,0.00009655979,0.00007276589,0.00004673666,0.00007391588,0.0000075537478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003138758,0.00041918873,0.00028713318,0.00059159327,0.00014155381,0.0003253378,0.00044050443,0.0005836633,0.0007315194],"category_scores_gemma":[0.0011593137,0.00018312388,0.00025571883,0.0006248951,0.0002620044,0.00061840593,0.00026334226,0.00030794766,0.0004856095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030961042,0.000102497645,0.005390708,0.00016450163,0.00010556184,0.00014715907,0.000090208945,0.17629407,0.05180568,0.0027090597,0.0012802344,0.7616007],"study_design_scores_gemma":[0.000016447144,0.00019114143,0.0052145617,0.00002790314,0.00004152115,0.00034473607,0.00003205035,0.9518775,0.03760508,0.001754137,0.0028577528,0.000037182534],"about_ca_topic_score_codex":0.0016623321,"about_ca_topic_score_gemma":0.0021673206,"teacher_disagreement_score":0.0016623321,"about_ca_system_score_codex":0.00020436697,"about_ca_system_score_gemma":0.00017140916,"threshold_uncertainty_score":0.0033052564},"labels":[],"label_agreement":null},{"id":"W2117322335","doi":"10.3390/s110807606","title":"Indoor Pedestrian Navigation Using Foot-Mounted IMU and Portable Ultrasound Range Sensors","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Bentley (Canada); Université de Sherbrooke","funders":"","keywords":"Inertial measurement unit; Computer science; Particle filter; Real-time computing; Tracking (education); Pedestrian; Process (computing); Ranging; Tracking system; Inertial navigation system; Dead reckoning; Artificial intelligence; Position (finance); Computer vision; Simulation; Engineering; Inertial frame of reference; Global Positioning System; Kalman filter; Telecommunications; Transport engineering","score_opus":0.023254401910382392,"score_gpt":0.23020884086404964,"score_spread":0.20695443895366725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117322335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14356433,0.0020661417,0.84178966,0.00010394931,0.00024220068,0.00008449616,0.00025536882,0.0042167897,0.007677021],"genre_scores_gemma":[0.7490821,0.000965446,0.24403468,0.00008520465,0.00009621623,0.00006242945,0.00028622124,0.00004584088,0.0053419955],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997069,0.00007397741,0.000009034643,0.00006973316,0.00010241082,0.000037870836],"domain_scores_gemma":[0.9998652,0.000023501847,0.000032808315,0.000021350528,0.000042535088,0.000014644854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019817457,0.0008656395,0.0005302416,0.0009339574,0.00024658738,0.00040993173,0.0005078977,0.00054184295,0.0010973806],"category_scores_gemma":[0.00040081545,0.0003397956,0.00036876395,0.0008992716,0.0001437137,0.00045847587,0.00045464476,0.00022013675,0.00066906476],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066603516,0.00019555174,0.019977149,0.0005738177,0.00023199148,0.0005846081,0.0003435351,0.025556985,0.1927898,0.0022015672,0.0053456905,0.7515332],"study_design_scores_gemma":[0.00018800331,0.0030127675,0.07575481,0.00032117002,0.0008687376,0.0043425225,0.00045389222,0.5742277,0.28227872,0.0025662808,0.05564647,0.00033899193],"about_ca_topic_score_codex":0.0027966218,"about_ca_topic_score_gemma":0.0047489866,"teacher_disagreement_score":0.0027966218,"about_ca_system_score_codex":0.00016897952,"about_ca_system_score_gemma":0.00022816313,"threshold_uncertainty_score":0.0055607557},"labels":[],"label_agreement":null},{"id":"W2117373029","doi":"10.1145/2480730.2480731","title":"System-level calibration for data fusion in wireless sensor networks","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Office of Integrative Activities; Division of Electrical, Communications and Cyber Systems; Division of Computer and Network Systems; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Wireless sensor network; Real-time computing; Calibration; Overhead (engineering); Testbed; Sensor fusion; Noise (video); False alarm; Artificial intelligence; Computer network","score_opus":0.031991526210966265,"score_gpt":0.23450704401983702,"score_spread":0.20251551780887075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117373029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034825746,0.00016622253,0.99560827,0.000080941594,0.000015776863,0.000019713856,0.0000100561765,0.00027886726,0.00033758936],"genre_scores_gemma":[0.6917621,0.00052711234,0.3062459,0.00020384125,0.000075705546,0.00018437489,0.0001221436,0.00014575908,0.0007331062],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693704,0.0011299258,0.0001385128,0.0006137064,0.0010035038,0.00017733363],"domain_scores_gemma":[0.9967673,0.0015122234,0.00032314664,0.0008069284,0.0005269917,0.000063333486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036959192,0.0010491413,0.0010511611,0.00075111166,0.000818942,0.0011405163,0.0016389318,0.0011574372,0.0009477261],"category_scores_gemma":[0.013720062,0.00065647694,0.0007220075,0.0011979355,0.0012884352,0.003073512,0.0024913598,0.0019063742,0.0003574883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008888331,0.00006985738,0.0011870529,0.00014339997,0.00006909958,0.00006004414,0.00016295498,0.8450916,0.009537848,0.019217081,0.0010799957,0.123292275],"study_design_scores_gemma":[0.0000062234503,0.000035130604,0.00020689741,0.000009578752,0.000008061736,0.000036684247,0.000018562381,0.9845983,0.004795487,0.009329031,0.0009430262,0.000013013135],"about_ca_topic_score_codex":0.0014822432,"about_ca_topic_score_gemma":0.0012859011,"teacher_disagreement_score":0.0036959192,"about_ca_system_score_codex":0.0012396773,"about_ca_system_score_gemma":0.0012466279,"threshold_uncertainty_score":0.019546151},"labels":[],"label_agreement":null},{"id":"W2117374016","doi":"10.1002/navi.113","title":"Weak GPS Signal Acquisition Using Antenna Diversity","year":2015,"lang":"en","type":"article","venue":"NAVIGATION Journal of the Institute of Navigation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multipath propagation; Global Positioning System; Fading; GPS signals; Computer science; Diversity gain; Antenna (radio); Antenna diversity; Novelty; Diversity combining; Electronic engineering; Real-time computing; Telecommunications; Assisted GPS; Engineering; Decoding methods","score_opus":0.03005908008669614,"score_gpt":0.24594164760251258,"score_spread":0.21588256751581644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117374016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37137163,0.00088750693,0.61829036,0.00027870544,0.000045080687,0.00006255093,0.00007647428,0.0007827442,0.00820494],"genre_scores_gemma":[0.9710796,0.00017986435,0.027926043,0.0000453338,0.000023179911,0.000011829953,0.00003939202,0.000014018812,0.0006806419],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992932,0.00021385304,0.000023530285,0.00012538915,0.00024586744,0.00009804865],"domain_scores_gemma":[0.99928766,0.00030933536,0.000112231464,0.00012280549,0.00013043871,0.000037593338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005085364,0.00045893434,0.00046705347,0.0004284363,0.0002964657,0.00090029015,0.00038198553,0.0006330165,0.0006535078],"category_scores_gemma":[0.0017432658,0.00022734402,0.0002628892,0.00063112343,0.0004917976,0.000952301,0.0011285197,0.00047231384,0.00039318125],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011671968,0.00010299379,0.025313515,0.00040497142,0.00025585573,0.0009812327,0.00027653296,0.15972729,0.4375046,0.010112626,0.0014096667,0.36274356],"study_design_scores_gemma":[0.0001038709,0.0016316638,0.02303164,0.000065357104,0.00020998395,0.0042236573,0.00014762892,0.7538466,0.20007741,0.010062618,0.0064931824,0.00010635594],"about_ca_topic_score_codex":0.00051983603,"about_ca_topic_score_gemma":0.0006949698,"teacher_disagreement_score":0.00090029015,"about_ca_system_score_codex":0.000354792,"about_ca_system_score_gemma":0.00041044378,"threshold_uncertainty_score":0.0026894212},"labels":[],"label_agreement":null},{"id":"W2118327872","doi":"10.1007/s11276-015-1071-4","title":"Beacon deployment strategy for guaranteed localization in wireless sensor networks","year":2015,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China; University of Missouri","keywords":"Beacon; Computer science; Wireless sensor network; Topology (electrical circuits); Multidimensional scaling; Network topology; Software deployment; Multilateration; Computer network; Algorithm; Real-time computing; Distributed computing; Mathematics; Azimuth; Machine learning","score_opus":0.023207881394890304,"score_gpt":0.24003954063183425,"score_spread":0.21683165923694395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118327872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012739854,0.0004699347,0.98506784,0.00015588851,0.000084936764,0.000022977707,0.00003409423,0.00027583627,0.0011487019],"genre_scores_gemma":[0.8865834,0.00069626817,0.10930735,0.0001088601,0.00012551379,0.00011590825,0.00014833265,0.00010255261,0.0028117562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993069,0.00023097073,0.0000409373,0.000107776854,0.00017883524,0.00013451195],"domain_scores_gemma":[0.99881244,0.0006185839,0.000097305805,0.00013004326,0.00027337915,0.000068271635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092415314,0.00071085326,0.00093332,0.00075625844,0.0005365226,0.000632857,0.0015780104,0.00076541957,0.0015911622],"category_scores_gemma":[0.0040471894,0.00038945957,0.00035070497,0.0010231535,0.00050310005,0.0012447662,0.0012450715,0.00081419596,0.0004719927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014370588,0.00015342051,0.0014673193,0.00052368705,0.00009567339,0.00043401623,0.0004760344,0.54688025,0.044687394,0.09021232,0.01363851,0.29999426],"study_design_scores_gemma":[0.00004461333,0.00015232802,0.00025192872,0.000015318044,0.000020542191,0.00011897137,0.000048691185,0.9869587,0.0028014313,0.008288668,0.0012849037,0.000013922586],"about_ca_topic_score_codex":0.0014789018,"about_ca_topic_score_gemma":0.0013592474,"teacher_disagreement_score":0.0015911622,"about_ca_system_score_codex":0.0005734193,"about_ca_system_score_gemma":0.00083045725,"threshold_uncertainty_score":0.005322993},"labels":[],"label_agreement":null},{"id":"W2118405758","doi":"10.1145/1066677.1066902","title":"Pocket PC beacons","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wheelchair; Beacon; Computer science; Embedded system; Remote control; Server; Human–computer interaction; Real-time computing; Computer hardware; Computer network","score_opus":0.0040762085295611905,"score_gpt":0.1757805556821647,"score_spread":0.1717043471526035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118405758","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020968927,0.001562869,0.42703712,0.0010045958,0.0018640058,0.0017373126,0.0109486515,0.10466558,0.430211],"genre_scores_gemma":[0.24415693,0.0011082387,0.13750005,0.0010628849,0.00047769127,0.0017964352,0.009204817,0.0044602733,0.60023266],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915195,0.000085398264,0.00004007914,0.00022169603,0.0003699269,0.0001309728],"domain_scores_gemma":[0.99817574,0.0003714849,0.00013219971,0.0004529505,0.0006062748,0.0002614159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005396905,0.0012628302,0.00088998507,0.0016319761,0.0006263792,0.0013293163,0.0020177776,0.0011067241,0.23632073],"category_scores_gemma":[0.0027442232,0.00062082394,0.00029887,0.0013141287,0.00025351386,0.0011745967,0.0016025741,0.0011734655,0.09248056],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016715996,0.0002069604,0.0020026271,0.00065221277,0.000025850679,0.00043018305,0.0008170422,0.0014170144,0.0559343,0.014657082,0.31587905,0.6063061],"study_design_scores_gemma":[0.0002633245,0.0005927164,0.0027040562,0.00011928823,0.000048675072,0.00075645454,0.00014406703,0.009900411,0.025269516,0.0027093787,0.9574045,0.00008759691],"about_ca_topic_score_codex":0.002046345,"about_ca_topic_score_gemma":0.0014165404,"teacher_disagreement_score":0.23632073,"about_ca_system_score_codex":0.00052867463,"about_ca_system_score_gemma":0.0006468287,"threshold_uncertainty_score":0.79057163},"labels":[],"label_agreement":null},{"id":"W2118727061","doi":"10.1109/wcl.2012.020612.110279","title":"Dynamic Propagation Modeling for Mobile Users' Position and Heading Estimation in Wireless Local Area Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Heading (navigation); Computer science; RSS; Real-time computing; Position (finance); Wi-Fi; Parametric statistics; Process (computing); Gaussian process; Wireless network; Wireless; Artificial intelligence; Algorithm; Gaussian; Telecommunications; Statistics; Engineering; Mathematics","score_opus":0.014479900915796163,"score_gpt":0.24633533155932874,"score_spread":0.2318554306435326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118727061","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072238278,0.00013624254,0.99087864,0.00006167543,0.000032619348,0.00000980674,0.000039892642,0.0011711041,0.0004462052],"genre_scores_gemma":[0.7040872,0.001311601,0.28706315,0.00010367071,0.00015832338,0.0001709264,0.00061567023,0.00026713975,0.0062224003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978215,0.000056457895,0.000011312295,0.000046400713,0.0000795108,0.000024147468],"domain_scores_gemma":[0.9997781,0.00008598133,0.00002626878,0.00003576525,0.000064660366,0.000009282743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028573742,0.00051059487,0.00046395423,0.00039787614,0.0003615459,0.00058616116,0.0006675918,0.00051172933,0.00084438646],"category_scores_gemma":[0.0010475308,0.00028231088,0.0004072094,0.0005494218,0.00022907055,0.0007366131,0.00043662707,0.00075199903,0.00068821735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000983425,0.000056309105,0.0019259689,0.00006605981,0.000043882697,0.00012658705,0.00009189068,0.79825896,0.0160437,0.009847676,0.0026188456,0.17082179],"study_design_scores_gemma":[0.000002636982,0.00001522308,0.00018299404,0.0000019631868,0.000005777599,0.000022769078,0.0000043991736,0.9972356,0.0009082365,0.00080964755,0.0008057138,0.000005041773],"about_ca_topic_score_codex":0.006579775,"about_ca_topic_score_gemma":0.0050362283,"teacher_disagreement_score":0.006579775,"about_ca_system_score_codex":0.0003863296,"about_ca_system_score_gemma":0.00047247912,"threshold_uncertainty_score":0.013082981},"labels":[],"label_agreement":null},{"id":"W2118756644","doi":"10.1109/ccece.2002.1015250","title":"Determination of chip rate and center frequency for a spread spectrum acoustic ranging system","year":2003,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Ranging; Center frequency; Bandwidth (computing); Acoustics; Chip; Doppler effect; Spread spectrum; Radio frequency; Global Positioning System; Radio spectrum; Physics; Computer science; Telecommunications; Optics","score_opus":0.006891554257041361,"score_gpt":0.1993329030634515,"score_spread":0.19244134880641015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118756644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23766352,0.0015821767,0.74700934,0.0005605006,0.00027516193,0.00045582978,0.00022092555,0.0016754927,0.010557046],"genre_scores_gemma":[0.6634589,0.0006667628,0.33251652,0.0001710739,0.000056930334,0.00018395045,0.00015198922,0.00017099104,0.0026229382],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986815,0.00029298422,0.00009636611,0.00019435641,0.000585019,0.00014980747],"domain_scores_gemma":[0.9928427,0.0031600816,0.00086505106,0.0003645055,0.002604981,0.00016272393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001705919,0.00056728895,0.0006530387,0.0017577945,0.00051286985,0.0010689609,0.0007539795,0.000952881,0.0018309287],"category_scores_gemma":[0.0109260725,0.00037332217,0.0002457162,0.0005747903,0.00043901964,0.001066886,0.00033912505,0.00059235696,0.0016351218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010813958,0.00028598442,0.029011501,0.00064013904,0.000096375574,0.000900944,0.0006089555,0.031518914,0.6518232,0.010115357,0.0029720382,0.27094513],"study_design_scores_gemma":[0.00014742662,0.0018332996,0.034716934,0.00026946512,0.00024134941,0.0033177084,0.0006194421,0.14352873,0.7845302,0.004049406,0.026437854,0.00030827522],"about_ca_topic_score_codex":0.0010850705,"about_ca_topic_score_gemma":0.0015465778,"teacher_disagreement_score":0.0018309287,"about_ca_system_score_codex":0.00049974676,"about_ca_system_score_gemma":0.0006729523,"threshold_uncertainty_score":0.009021819},"labels":[],"label_agreement":null},{"id":"W2118904946","doi":"10.1109/eit.2008.4554297","title":"Network localization using angle of arrival","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Angle of arrival; Range (aeronautics); Quadratic equation; Computer science; Algorithm; Quadratic programming; Time of arrival; Mathematical optimization; Mathematics; Geometry; Telecommunications; Engineering; Aerospace engineering; Wireless","score_opus":0.02247730094529319,"score_gpt":0.20718525672232146,"score_spread":0.18470795577702828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118904946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010465108,0.00033951114,0.9970233,0.00007671529,0.00005729431,0.000011824206,0.000017916753,0.00017320368,0.0012536702],"genre_scores_gemma":[0.35030153,0.005582815,0.63455725,0.00023084048,0.0005473102,0.00024256723,0.0004941241,0.00017117233,0.007872464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867344,0.00035495666,0.000057109162,0.0002682414,0.00055555895,0.00009057555],"domain_scores_gemma":[0.9989832,0.00035390654,0.000173723,0.0001142147,0.00034127783,0.000033565102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007672906,0.0011730102,0.00093959866,0.0016298222,0.0004360028,0.0015016606,0.0014730681,0.0010263489,0.001983675],"category_scores_gemma":[0.0028072202,0.00039728032,0.0006212436,0.0019947137,0.00057377625,0.0027561123,0.0016722733,0.0011704509,0.0011125067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022436453,0.00008022651,0.0016920539,0.0004764253,0.0001075759,0.00019379264,0.00018760031,0.47264323,0.019730993,0.076331176,0.0049912306,0.4233413],"study_design_scores_gemma":[0.000031288382,0.00010319335,0.00041249554,0.000049421706,0.000031175736,0.00023477727,0.000058806883,0.96363264,0.0052969446,0.013789096,0.01631395,0.000046200592],"about_ca_topic_score_codex":0.0019735112,"about_ca_topic_score_gemma":0.0012840702,"teacher_disagreement_score":0.001983675,"about_ca_system_score_codex":0.0005905685,"about_ca_system_score_gemma":0.0007967371,"threshold_uncertainty_score":0.0066360235},"labels":[],"label_agreement":null},{"id":"W2119668499","doi":"10.1109/lcn.2008.4664251","title":"Hyperbolic location estimation of malicious nodes in mobile WiFi/802.11 networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transmitter; RSS; Path loss; Computer science; Computer network; Bounding overwatch; Node (physics); Range (aeronautics); Real-time computing; Wireless network; Path (computing); Wireless; Telecommunications; Engineering; Artificial intelligence","score_opus":0.0073259803456579,"score_gpt":0.20016744741506362,"score_spread":0.19284146706940572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119668499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3609695,0.00038942345,0.63481677,0.0001472905,0.000031756044,0.000061579725,0.00007810866,0.0009958578,0.0025097346],"genre_scores_gemma":[0.95723486,0.000111812646,0.042039976,0.000019327885,0.000009204271,0.000018208768,0.00006708193,0.000019748531,0.00047977563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988778,0.00037428146,0.00006589132,0.00017656991,0.00039934227,0.00010607413],"domain_scores_gemma":[0.9959662,0.0018737345,0.0009996167,0.0004858825,0.000573058,0.000101535006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014484774,0.00067267526,0.0005872043,0.0013132009,0.0004280772,0.00076118833,0.0009896551,0.0004912901,0.00048604963],"category_scores_gemma":[0.00981141,0.00044885144,0.00024191801,0.00068209646,0.00084906316,0.0015321494,0.0016242386,0.0004664415,0.00033989834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058237865,0.000064012704,0.030516805,0.00010367551,0.000057108424,0.00051177526,0.00042602193,0.76915544,0.018984491,0.006933673,0.0006950237,0.1719696],"study_design_scores_gemma":[0.000011197898,0.00008865641,0.0026527287,0.000013417156,0.000011059357,0.00020534552,0.00008394172,0.98524946,0.009467639,0.0017835608,0.0004085523,0.00002437849],"about_ca_topic_score_codex":0.0028880895,"about_ca_topic_score_gemma":0.0021921948,"teacher_disagreement_score":0.0028880895,"about_ca_system_score_codex":0.00071825116,"about_ca_system_score_gemma":0.00042372014,"threshold_uncertainty_score":0.007660389},"labels":[],"label_agreement":null},{"id":"W2119921278","doi":"10.1109/music.2012.42","title":"Evaluation of Base Station Placement Scenarios for Mobile Node Localization","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Base station; Computer science; Offset (computer science); Global Positioning System; Range (aeronautics); Real-time computing; Wireless; Node (physics); Computer network; Telecommunications; Engineering","score_opus":0.028845408840245224,"score_gpt":0.27895376219446877,"score_spread":0.25010835335422354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119921278","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92131084,0.0010656985,0.06437072,0.00049314654,0.00011421695,0.0004922148,0.00075245,0.00043979334,0.010960939],"genre_scores_gemma":[0.9847633,0.00029635438,0.014020949,0.000021626392,0.000014367427,0.00007685172,0.00022721736,0.000026669019,0.0005525385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99676657,0.0020502808,0.000107667445,0.00016415164,0.00064498436,0.00026631993],"domain_scores_gemma":[0.9822073,0.01506954,0.000846558,0.0005189217,0.0010401676,0.00031746514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037356154,0.0013608536,0.000551315,0.001523119,0.00056154403,0.0009052119,0.0010649561,0.0012500635,0.0018913075],"category_scores_gemma":[0.0156239085,0.00035417578,0.00042408786,0.001490473,0.0006107937,0.0011720285,0.00070887175,0.00043693956,0.00021004445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005308381,0.00016603126,0.0022476993,0.00020607252,0.000048719296,0.00016932677,0.000057980258,0.9801846,0.00250837,0.0014107584,0.0004346339,0.012034917],"study_design_scores_gemma":[0.000108376946,0.0014977594,0.0023708653,0.000035374032,0.000056381043,0.00018501199,0.00016596315,0.98964196,0.003964584,0.0011157786,0.0008385715,0.000019317598],"about_ca_topic_score_codex":0.003622761,"about_ca_topic_score_gemma":0.004269017,"teacher_disagreement_score":0.0037356154,"about_ca_system_score_codex":0.0017777678,"about_ca_system_score_gemma":0.0007297596,"threshold_uncertainty_score":0.019756079},"labels":[],"label_agreement":null},{"id":"W2120447191","doi":"10.1109/glocom.2010.5683802","title":"RELMA: A Range Free Localization Approach Using Mobile Anchor Node for Wireless Sensor Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Wireless sensor network; Computer science; Scalability; Global Positioning System; Efficient energy use; Range (aeronautics); Node (physics); Key distribution in wireless sensor networks; Energy (signal processing); Focus (optics); Sensor node; Real-time computing; Mobile wireless sensor network; Wireless; Computer network; Wireless network; Engineering; Telecommunications; Electrical engineering; Mathematics","score_opus":0.01112133391688663,"score_gpt":0.2192552571873485,"score_spread":0.20813392327046187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120447191","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030289313,0.00066611456,0.9945446,0.00009132042,0.000084588246,0.000026594518,0.000013103139,0.00071736827,0.0008272961],"genre_scores_gemma":[0.2738613,0.0018159552,0.7175681,0.00021593092,0.0001715391,0.00022731976,0.00013808472,0.00012739099,0.005874435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994393,0.0001775075,0.000028561752,0.00012017828,0.00020136652,0.000033075066],"domain_scores_gemma":[0.99961996,0.0001321697,0.00008643508,0.0000630919,0.00008013571,0.00001824089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005256813,0.0005797109,0.0005077343,0.00083721604,0.00038743365,0.00045215443,0.0014341117,0.00066993246,0.0010904475],"category_scores_gemma":[0.0014847444,0.00024375915,0.00054203044,0.0006694851,0.00049554347,0.0011894883,0.0008635646,0.00071298867,0.0007514091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026856788,0.000107493914,0.0011321168,0.00054671057,0.0001860954,0.0005363825,0.00040031917,0.16293828,0.08258427,0.045191,0.0068245237,0.6992843],"study_design_scores_gemma":[0.0000825619,0.00064793037,0.00080672005,0.000065324544,0.00013466354,0.0014892091,0.00015551709,0.8926901,0.0361,0.013816868,0.053898912,0.00011220934],"about_ca_topic_score_codex":0.00081014796,"about_ca_topic_score_gemma":0.00129676,"teacher_disagreement_score":0.0014341117,"about_ca_system_score_codex":0.00030389507,"about_ca_system_score_gemma":0.0004049321,"threshold_uncertainty_score":0.0036478639},"labels":[],"label_agreement":null},{"id":"W2120917188","doi":"10.3390/mi6060793","title":"PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation","year":2015,"lang":"en","type":"article","venue":"Micromachines","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":109,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Trilateration; Dead reckoning; GNSS applications; Inertial navigation system; Computer science; Global Positioning System; Real-time computing; Pedestrian; GPS/INS; Navigation system; Simulation; Embedded system; Engineering; Inertial frame of reference; Telecommunications; Assisted GPS","score_opus":0.023539434348892264,"score_gpt":0.25237765726066774,"score_spread":0.22883822291177547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120917188","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08456368,0.0012094395,0.90389466,0.000101232734,0.00027467517,0.00011774141,0.000118248805,0.0036755444,0.0060448106],"genre_scores_gemma":[0.58248603,0.0005631083,0.41027775,0.00013155058,0.00007515418,0.000090187415,0.00022228895,0.000089684036,0.00606425],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996934,0.000042346066,0.000015576197,0.00006534749,0.00014829943,0.00003507306],"domain_scores_gemma":[0.99981815,0.000022458851,0.000032650023,0.000039708615,0.000075835866,0.000011209679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001960469,0.0007318481,0.00040178196,0.00053918123,0.00029062515,0.00028757454,0.00083826657,0.00040296203,0.0018903967],"category_scores_gemma":[0.0004973332,0.00028473855,0.0003530958,0.0003909119,0.00014684796,0.0005313448,0.0005155875,0.0002995955,0.0007943775],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041279316,0.000105967854,0.0058063213,0.00029355657,0.00010019716,0.0006077804,0.00016655383,0.020261811,0.14858854,0.0026002119,0.0036995537,0.8173567],"study_design_scores_gemma":[0.000167605,0.0012582676,0.019812487,0.000137589,0.00032282533,0.0027842093,0.00020934927,0.564345,0.3548743,0.0021769078,0.053738613,0.00017291661],"about_ca_topic_score_codex":0.0035796682,"about_ca_topic_score_gemma":0.0067931167,"teacher_disagreement_score":0.0035796682,"about_ca_system_score_codex":0.00025492985,"about_ca_system_score_gemma":0.00043366104,"threshold_uncertainty_score":0.0071176887},"labels":[],"label_agreement":null},{"id":"W2121085904","doi":"10.1109/sensorcomm.2009.38","title":"Semidefinite Programming for Wireless Sensor Localization with Lognormal Shadowing","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Semidefinite programming; Wireless sensor network; Mathematical optimization; Estimator; Gaussian; Upper and lower bounds; Mathematics; Wireless; Convex optimization; Fading; Minimax; Computer science; Gaussian noise; Algorithm; Applied mathematics; Regular polygon; Statistics; Telecommunications; Mathematical analysis","score_opus":0.008208475924551832,"score_gpt":0.20456450323525102,"score_spread":0.19635602731069918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121085904","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033335022,0.00028228862,0.9933723,0.00030662716,0.000028634173,0.0000255933,0.00009662891,0.00012085239,0.002433552],"genre_scores_gemma":[0.3973665,0.0019462868,0.5842435,0.00067630946,0.00020369627,0.0010561286,0.00078592787,0.00038091675,0.013340768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985139,0.0009124913,0.000045227003,0.00016883352,0.00027882704,0.00008080413],"domain_scores_gemma":[0.99495125,0.004052507,0.00030931347,0.00015658092,0.00043408383,0.000096229276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029454373,0.0015897289,0.0011428904,0.00041963352,0.0002757167,0.001415254,0.00094063376,0.0010112104,0.0046007154],"category_scores_gemma":[0.0066801133,0.0005606338,0.00059748313,0.0009030737,0.0011862472,0.0013975619,0.0011653317,0.0022112445,0.00090736384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008910017,0.00008051503,0.0002656113,0.00023220337,0.00003334759,0.00014232003,0.000113131166,0.87748104,0.00081233046,0.086494155,0.0037815068,0.030474663],"study_design_scores_gemma":[0.000014248311,0.00003348419,0.00003718506,0.000013268631,0.0000033883202,0.000023601211,0.000021183123,0.9685592,0.000164795,0.030163398,0.00096041925,0.0000057668167],"about_ca_topic_score_codex":0.0017916526,"about_ca_topic_score_gemma":0.0019294966,"teacher_disagreement_score":0.0046007154,"about_ca_system_score_codex":0.0009206113,"about_ca_system_score_gemma":0.0012458806,"threshold_uncertainty_score":0.0155771375},"labels":[],"label_agreement":null},{"id":"W2121419603","doi":"10.1109/glocom.2009.5425419","title":"Spatial Inference Using Networks of RFID Receiver: A Bayesian Approach","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Inference; Statistical inference; Bayesian probability; Computation; Bayesian inference; Statistical model; Position (finance); Data mining; Approximate Bayesian computation; Bayesian network; Artificial intelligence; Algorithm; Mathematics; Statistics","score_opus":0.011868220091264673,"score_gpt":0.2225215821991363,"score_spread":0.2106533621078716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121419603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012866985,0.000053757296,0.99815327,0.00007523828,0.000007815529,0.000006548442,0.00003081845,0.00008287695,0.00030294262],"genre_scores_gemma":[0.25351703,0.0008313393,0.74062914,0.00027050034,0.00029084965,0.00020055078,0.0004383953,0.00016256502,0.0036595643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963988,0.0014184476,0.00017195632,0.0007552964,0.0010407565,0.00021467825],"domain_scores_gemma":[0.9914996,0.0059143966,0.0006737683,0.0009612192,0.0007867645,0.00016440978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047773845,0.0010487845,0.0017010438,0.002613817,0.00095350854,0.0021975688,0.0041624755,0.001764978,0.0025196557],"category_scores_gemma":[0.021106085,0.0013666878,0.0014784983,0.002493636,0.0018923609,0.0054539153,0.00270992,0.0025056242,0.0010422361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001413652,0.000068814734,0.0023688402,0.000116993964,0.00016477174,0.00017321401,0.00020781007,0.7436272,0.0025265543,0.16900036,0.0012628628,0.080341145],"study_design_scores_gemma":[0.000017083366,0.000018112609,0.00022872206,0.00001474723,0.000029482831,0.00005702216,0.000018079165,0.92307884,0.00093190075,0.07445284,0.0011258369,0.00002736339],"about_ca_topic_score_codex":0.006574572,"about_ca_topic_score_gemma":0.006934017,"teacher_disagreement_score":0.006574572,"about_ca_system_score_codex":0.0014841894,"about_ca_system_score_gemma":0.0016400793,"threshold_uncertainty_score":0.025265515},"labels":[],"label_agreement":null},{"id":"W2121457600","doi":"","title":"Reducing multipath effects in vehicle localization by fusing GPS with machine vision","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Global Positioning System; Computer science; Multipath propagation; Computer vision; Kalman filter; Simultaneous localization and mapping; Artificial intelligence; Machine vision; Visibility; Map matching; Intelligent transportation system; Real-time computing; Assisted GPS; Mobile robot; Engineering; Robot; Telecommunications; Geography","score_opus":0.006027331603276832,"score_gpt":0.2282604439937764,"score_spread":0.22223311239049956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121457600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09964946,0.0010767987,0.8971686,0.00013747106,0.00007890844,0.000020161191,0.000031796993,0.0009247895,0.0009120632],"genre_scores_gemma":[0.78095853,0.00081285584,0.21696268,0.0000852678,0.00010162069,0.00002941857,0.00010903858,0.000061816965,0.0008787416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952745,0.00009236687,0.000020479678,0.000086694134,0.00020656579,0.000066347005],"domain_scores_gemma":[0.9991906,0.0003588824,0.0001219033,0.000117394135,0.00018876432,0.000022488677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050564425,0.00082775374,0.00057233893,0.0010172476,0.00027956069,0.0005216937,0.0005675085,0.0009436116,0.0006852183],"category_scores_gemma":[0.002375424,0.0005086266,0.00056954974,0.0010425781,0.00034451947,0.0014814291,0.0011228813,0.00051537226,0.0003422936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035125186,0.00012916511,0.0046968437,0.00016659619,0.00018651572,0.00030558134,0.00017160937,0.3152916,0.089282386,0.0023629977,0.00095129234,0.5861042],"study_design_scores_gemma":[0.000032396558,0.00034720206,0.005201298,0.000024075742,0.00012349906,0.00039529274,0.00005788428,0.95012426,0.036816355,0.004537083,0.0022816225,0.00005908046],"about_ca_topic_score_codex":0.0032220737,"about_ca_topic_score_gemma":0.003117672,"teacher_disagreement_score":0.0032220737,"about_ca_system_score_codex":0.00032337604,"about_ca_system_score_gemma":0.0004035902,"threshold_uncertainty_score":0.006406665},"labels":[],"label_agreement":null},{"id":"W2121553646","doi":"10.1109/isie.2007.4374919","title":"Indoor Fingerprinting Geolocation using Wavelet-Based Features Extracted from the Channel Impulse Response in Conjunction with an Artificial Neural Network","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial neural network; Wavelet; Artificial intelligence; Feature extraction; Signature (topology); Wavelet transform; Mathematics","score_opus":0.015512883976648593,"score_gpt":0.23294052660738276,"score_spread":0.21742764263073416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121553646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06783083,0.00019926629,0.9299616,0.000056137334,0.000045464778,0.000020335669,0.000066959365,0.00065009715,0.0011693924],"genre_scores_gemma":[0.6635602,0.0003095587,0.333789,0.00004822491,0.00006049659,0.000036689013,0.00018475817,0.00004370378,0.0019673926],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985945,0.000023973425,0.000007816923,0.000035405636,0.00005404711,0.000019246443],"domain_scores_gemma":[0.99975497,0.000080829755,0.000048207967,0.000035093286,0.00007096473,0.000010096741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002659923,0.00039766246,0.0003356372,0.000669532,0.00013915505,0.00035906746,0.0003731656,0.00043038023,0.00059216307],"category_scores_gemma":[0.00093189784,0.00019564577,0.00026924853,0.00057925493,0.00016935536,0.00055964023,0.00022990859,0.00032354565,0.00034809715],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002970493,0.00009712003,0.0058120056,0.00013652432,0.00008189502,0.00021774453,0.00008054828,0.09415896,0.078853525,0.0013305419,0.0008797411,0.8180543],"study_design_scores_gemma":[0.000020825622,0.00017844529,0.008509232,0.000024459794,0.00007694462,0.00045268648,0.000041815518,0.9362711,0.050959907,0.001015016,0.0024152696,0.00003424787],"about_ca_topic_score_codex":0.000850913,"about_ca_topic_score_gemma":0.0012191539,"teacher_disagreement_score":0.000850913,"about_ca_system_score_codex":0.0001639067,"about_ca_system_score_gemma":0.00017405655,"threshold_uncertainty_score":0.00198102},"labels":[],"label_agreement":null},{"id":"W2121862004","doi":"10.5081/jgps.3.1.2","title":"GNSS Indoor Location Technologies","year":2004,"lang":"en","type":"article","venue":"Journal of Global Positioning Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Computer science; Remote sensing; Environmental science; Geography; Global Positioning System; Telecommunications","score_opus":0.004626310086811352,"score_gpt":0.2075545377484016,"score_spread":0.20292822766159024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121862004","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032755267,0.007854777,0.7398622,0.0018991104,0.0019294602,0.0000792691,0.0011432994,0.0065067676,0.20796984],"genre_scores_gemma":[0.5314913,0.0114697525,0.1677164,0.0011441103,0.0014285892,0.00015769895,0.0058306376,0.0006165917,0.280145],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995443,0.0000906682,0.000018601415,0.00006668007,0.0002283639,0.000051261304],"domain_scores_gemma":[0.99956185,0.00004099674,0.000033055032,0.00011432478,0.00022491488,0.000024854353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025045217,0.00059848966,0.00047646317,0.000988481,0.0005296761,0.0012300273,0.00057745434,0.0008398036,0.008668959],"category_scores_gemma":[0.00054631656,0.0002492235,0.0002679696,0.001280995,0.00036208818,0.0008984674,0.00092906755,0.00072428305,0.010618367],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018966783,0.000059520546,0.004460486,0.00026763705,0.000059620314,0.00030145142,0.00022502379,0.016697988,0.054277264,0.04454263,0.04095289,0.83796597],"study_design_scores_gemma":[0.000035544344,0.00030275105,0.011204307,0.00025269997,0.00016414392,0.0020430058,0.00033306336,0.04841136,0.06010142,0.020397663,0.8566577,0.00009642881],"about_ca_topic_score_codex":0.002199396,"about_ca_topic_score_gemma":0.0036917496,"teacher_disagreement_score":0.008668959,"about_ca_system_score_codex":0.0004388171,"about_ca_system_score_gemma":0.00054237625,"threshold_uncertainty_score":0.02900058},"labels":[],"label_agreement":null},{"id":"W2122412087","doi":"10.1109/icc.2010.5501946","title":"3D Passive Tag Localization Schemes for Indoor RFID Applications","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Multilateration; Computer science; Scheme (mathematics); Granularity; Power (physics); Real-time computing; Interrogation; Electronic engineering; Embedded system; Engineering","score_opus":0.004836590669267635,"score_gpt":0.21579521904409146,"score_spread":0.21095862837482382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122412087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038350301,0.00018803183,0.9943505,0.000054499516,0.000034593933,0.0000080607115,0.00001866281,0.00073961134,0.0007710246],"genre_scores_gemma":[0.30498907,0.0008961289,0.687856,0.00018443915,0.00008675269,0.0001465461,0.00022014703,0.00013765339,0.005483111],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997476,0.000072121606,0.000013571152,0.000037201433,0.000111936824,0.000017621784],"domain_scores_gemma":[0.9995135,0.000120784476,0.00007894745,0.00014143724,0.00012545245,0.00001982891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035604686,0.0005693164,0.0003723726,0.00052188075,0.00025211365,0.00070706295,0.00096569565,0.0006900839,0.0017315552],"category_scores_gemma":[0.001037824,0.00030786925,0.00051068806,0.000616531,0.00036042885,0.00084136025,0.0011044382,0.00049338886,0.001585525],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033490296,0.00006172254,0.0015230892,0.00040642716,0.000059030623,0.0003278877,0.00044558418,0.13797796,0.21304937,0.029137515,0.006374815,0.61030173],"study_design_scores_gemma":[0.000063460226,0.00027858876,0.0013865088,0.000048107762,0.00007545509,0.0011547625,0.00009922547,0.8163873,0.11984491,0.010522924,0.050028164,0.00011064083],"about_ca_topic_score_codex":0.00039486046,"about_ca_topic_score_gemma":0.00056550984,"teacher_disagreement_score":0.0017315552,"about_ca_system_score_codex":0.00028808744,"about_ca_system_score_gemma":0.00022760626,"threshold_uncertainty_score":0.005792618},"labels":[],"label_agreement":null},{"id":"W2123680668","doi":"10.1145/1865106.1865112","title":"Spatial-geometric approach to physical mobile interaction based on accelerometer and IR sensory data fusion","year":2010,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Mobile interaction; Accelerometer; Mobile phone; Orientation (vector space); Computer vision; Object (grammar); Mobile phone tracking; Mobile device; Sensor fusion; Artificial intelligence; Augmented reality; Human–computer interaction; Interaction technique; Mobile technology; Mobile Web; Telecommunications; Mathematics","score_opus":0.035585034214431224,"score_gpt":0.29045408282147994,"score_spread":0.25486904860704873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123680668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043197707,0.00020749142,0.9928186,0.000083275525,0.000050593717,0.000025860269,0.00003444393,0.0004001085,0.0020598816],"genre_scores_gemma":[0.3885795,0.0010798782,0.60500205,0.00015704174,0.00017822316,0.00017803171,0.0002503921,0.00009304752,0.004481919],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905866,0.00020213876,0.000047236575,0.0001673823,0.0004717974,0.00005275675],"domain_scores_gemma":[0.99972457,0.000054337586,0.00004077078,0.000049670947,0.000118147334,0.000012570357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049832894,0.0007795949,0.0006416796,0.0011924533,0.00035697847,0.0010274408,0.0009640065,0.00059619045,0.0019667484],"category_scores_gemma":[0.0013153446,0.0003820101,0.000990683,0.0010947593,0.0007176952,0.0014814396,0.0012848666,0.0004996522,0.0008452483],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035617923,0.00015193327,0.0032469346,0.00043043183,0.00023758468,0.00057203183,0.00075644575,0.2727381,0.074895315,0.061134785,0.0038404735,0.58163977],"study_design_scores_gemma":[0.000018768875,0.00018400876,0.0025008577,0.000031721647,0.000078228564,0.00052717765,0.00022004094,0.95733654,0.013266668,0.015662927,0.010096941,0.00007616859],"about_ca_topic_score_codex":0.0018489864,"about_ca_topic_score_gemma":0.0024889424,"teacher_disagreement_score":0.0019667484,"about_ca_system_score_codex":0.00048169793,"about_ca_system_score_gemma":0.00047229743,"threshold_uncertainty_score":0.006579399},"labels":[],"label_agreement":null},{"id":"W2124541692","doi":"10.1109/lsp.2010.2047958","title":"OFDM Transmission for Time-Based Range Estimation","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Cramér–Rao bound; Orthogonal frequency-division multiplexing; Upper and lower bounds; Estimator; Maximum likelihood; Transmission (telecommunications); Channel (broadcasting); Algorithm; Estimation theory; Statistics; Range (aeronautics); Signal-to-noise ratio (imaging); Mathematics; Computer science; Telecommunications; Engineering","score_opus":0.006878368925534709,"score_gpt":0.21307243780885174,"score_spread":0.20619406888331704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124541692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006978085,0.0013867886,0.9882718,0.00014507103,0.00009753132,0.00002180826,0.000036586807,0.00019157103,0.0028706717],"genre_scores_gemma":[0.5284916,0.0037199433,0.46128148,0.00039326193,0.00047183063,0.00015163478,0.00017350628,0.000068048175,0.005248776],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999534,0.00013132911,0.000022466134,0.00007397944,0.0001851958,0.000053068943],"domain_scores_gemma":[0.9991738,0.0004144996,0.000119120334,0.0001320374,0.00014227907,0.000018164765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053735194,0.0006375472,0.00052397343,0.00048631473,0.00040423957,0.00065109326,0.00071289425,0.00076276186,0.0013829008],"category_scores_gemma":[0.0026356117,0.00020281225,0.00030675475,0.00059090555,0.0004558933,0.0008880757,0.00078475225,0.0006350321,0.00058100524],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030226327,0.00007664529,0.0029290987,0.0007214895,0.00014532049,0.0013555392,0.0002998176,0.13683651,0.107443325,0.15797433,0.005317866,0.58659774],"study_design_scores_gemma":[0.000025191186,0.0003464142,0.0011365487,0.00010628707,0.00011170406,0.0029987984,0.00007414325,0.91546947,0.0438687,0.012806372,0.022994274,0.000062092855],"about_ca_topic_score_codex":0.00043195792,"about_ca_topic_score_gemma":0.0006273817,"teacher_disagreement_score":0.0013829008,"about_ca_system_score_codex":0.00028442277,"about_ca_system_score_gemma":0.00033707713,"threshold_uncertainty_score":0.0046262145},"labels":[],"label_agreement":null},{"id":"W2124834456","doi":"10.1155/2013/570964","title":"Kalman Filter-Based Hybrid Indoor Position Estimation Technique in Bluetooth Networks","year":2013,"lang":"en","type":"article","venue":"International Journal of Navigation and Observation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Università degli Studi di Pavia; University of Alberta","keywords":"Trilateration; Kalman filter; Position (finance); Computer science; Euclidean distance; Filter (signal processing); Extended Kalman filter; Algorithm; Node (physics); Artificial intelligence; Computer vision; Engineering","score_opus":0.009339468387315284,"score_gpt":0.22833209567583337,"score_spread":0.2189926272885181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124834456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006329444,0.0003428158,0.99156815,0.000028388153,0.00003724392,0.000010551527,0.000027622691,0.0007066336,0.00094912417],"genre_scores_gemma":[0.6865678,0.0014825867,0.30513915,0.000100013676,0.00010015188,0.00012011088,0.00029395375,0.0001020354,0.0060942243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946004,0.00009729761,0.0000392166,0.00014003347,0.00021344819,0.000049925093],"domain_scores_gemma":[0.99962986,0.00011592656,0.00006324934,0.000052262116,0.00012785984,0.000010849744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047388978,0.0005815169,0.000615772,0.0007282894,0.00030980163,0.00052159105,0.0008113424,0.00049165153,0.0008744364],"category_scores_gemma":[0.0011736606,0.00035956246,0.0005449852,0.00073486875,0.00021645508,0.0010903551,0.00047746295,0.00048740534,0.00054221496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028020333,0.000063231055,0.004176519,0.0003464041,0.00018350738,0.00027489036,0.00034574582,0.25434348,0.035603423,0.00820965,0.0026963602,0.6934766],"study_design_scores_gemma":[0.000021111662,0.00013860963,0.0021632677,0.00003098544,0.000062324565,0.00038139088,0.0000465856,0.97901297,0.012043418,0.0015778977,0.0044800104,0.00004134855],"about_ca_topic_score_codex":0.0051235296,"about_ca_topic_score_gemma":0.0043077795,"teacher_disagreement_score":0.0051235296,"about_ca_system_score_codex":0.00030553763,"about_ca_system_score_gemma":0.00039711883,"threshold_uncertainty_score":0.0101873875},"labels":[],"label_agreement":null},{"id":"W2125109510","doi":"10.1007/978-3-642-03841-9_20","title":"On the Impact of Node Placement and Profile Point Selection on Indoor Localization","year":2009,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"RSS; Signal strength; Computer science; k-nearest neighbors algorithm; Node (physics); Algorithm; Set (abstract data type); Point (geometry); Mathematics; Topology (electrical circuits); Artificial intelligence; Wireless sensor network; Geometry; Computer network; Acoustics; Physics","score_opus":0.005619941304085906,"score_gpt":0.2331079335976106,"score_spread":0.22748799229352468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125109510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14228211,0.011957289,0.781646,0.0012012643,0.00061886635,0.00008605094,0.0004931728,0.0013011966,0.060414024],"genre_scores_gemma":[0.9204247,0.0053990777,0.06376661,0.0001203982,0.00022251141,0.00003914823,0.00027665863,0.00029256527,0.009458274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986331,0.00056860707,0.000028133316,0.00016971222,0.00047106599,0.0001294588],"domain_scores_gemma":[0.9881087,0.010557575,0.00020454118,0.0003811031,0.000677039,0.00007112871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015384525,0.000895221,0.0009959503,0.0006072104,0.0005255979,0.00087748794,0.0011670396,0.0006975237,0.0037111014],"category_scores_gemma":[0.012512815,0.0004547611,0.00036064454,0.0018846375,0.0006387993,0.0016845142,0.0010855715,0.00068364694,0.0008908933],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040836347,0.000045068853,0.0021047457,0.00019228029,0.000052515596,0.00013633154,0.00008100017,0.82246494,0.0071653645,0.008415785,0.003985541,0.15494801],"study_design_scores_gemma":[0.000018844832,0.00014444707,0.002321993,0.0000451618,0.00010215088,0.0002149115,0.00009185958,0.9818511,0.0059861946,0.007537103,0.0016570347,0.000029199919],"about_ca_topic_score_codex":0.007389459,"about_ca_topic_score_gemma":0.011973823,"teacher_disagreement_score":0.007389459,"about_ca_system_score_codex":0.0009347479,"about_ca_system_score_gemma":0.0006301227,"threshold_uncertainty_score":0.014692903},"labels":[],"label_agreement":null},{"id":"W2125188374","doi":"10.1109/icra.2011.5979840","title":"Simultaneous localization and environmental mapping with a sensor network","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wireless sensor network; Probability density function; Computer science; Variable (mathematics); Signal strength; Probability distribution; SIGNAL (programming language); Function (biology); Environmental data; Data mining; Artificial intelligence; Algorithm; Statistics; Mathematics; Computer network","score_opus":0.007495243152354302,"score_gpt":0.1510414113209377,"score_spread":0.1435461681685834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125188374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041700737,0.000069450034,0.99483746,0.000042508,0.000018297585,0.000016475014,0.000014124306,0.0003744591,0.00045718675],"genre_scores_gemma":[0.234808,0.00024528196,0.7623604,0.000063775384,0.000048180416,0.00009755647,0.00012233337,0.00012885046,0.0021257105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886936,0.00025193606,0.000043262276,0.00031179213,0.00043762827,0.00008609624],"domain_scores_gemma":[0.99895895,0.00040314437,0.000104264924,0.0003141782,0.00017597468,0.000043355994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010620094,0.0013818555,0.0010740745,0.001291751,0.00082095864,0.0011117555,0.0014796646,0.0010477359,0.0013646439],"category_scores_gemma":[0.003782172,0.000960783,0.0010453082,0.0012954923,0.0009066491,0.0028830376,0.0030044902,0.0012764917,0.00068860705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014046146,0.000048280403,0.0013068939,0.00008236877,0.00009553004,0.00019603211,0.00021710494,0.7704703,0.015733585,0.009981795,0.00071399997,0.20101361],"study_design_scores_gemma":[0.000016439943,0.000059514983,0.00054342067,0.0000125534525,0.000024476947,0.00014078864,0.000054200897,0.9760566,0.0073960167,0.011351563,0.00431654,0.000027953498],"about_ca_topic_score_codex":0.003995093,"about_ca_topic_score_gemma":0.0056297365,"teacher_disagreement_score":0.003995093,"about_ca_system_score_codex":0.00050884986,"about_ca_system_score_gemma":0.0011112266,"threshold_uncertainty_score":0.00794369},"labels":[],"label_agreement":null},{"id":"W2125440076","doi":"10.1109/vetecf.2002.1040350","title":"On geolocation accuracy with prior information in non-line-of-sight environment","year":2003,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geolocation; Non-line-of-sight propagation; Cramér–Rao bound; Computer science; Upper and lower bounds; Line-of-sight; Wireless; Position (finance); Line (geometry); Information retrieval; Algorithm; Telecommunications; Mathematics; World Wide Web; Engineering; Estimation theory","score_opus":0.004472521818200193,"score_gpt":0.1827433287338738,"score_spread":0.17827080691567362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125440076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060886104,0.0025813575,0.9282698,0.0009243751,0.000095070805,0.00002900599,0.00016721111,0.00028647194,0.006760616],"genre_scores_gemma":[0.92664516,0.0025944507,0.06873218,0.00018548362,0.00017352056,0.000057865764,0.0002717998,0.00009079406,0.001248674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965693,0.0012210492,0.0001506226,0.00045072747,0.0011998293,0.00040837398],"domain_scores_gemma":[0.9486679,0.044451334,0.0022352708,0.0023973328,0.0020485488,0.000199756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047789016,0.0010376149,0.0010277824,0.001486265,0.000540209,0.0013424775,0.0014652197,0.0017631482,0.0013171083],"category_scores_gemma":[0.05336497,0.00053087523,0.0004932986,0.0018999315,0.0023972234,0.004454523,0.0022306629,0.001443197,0.0004484327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022226905,0.000026897415,0.0023201115,0.0001893541,0.00003682754,0.0001219075,0.00011977316,0.9309877,0.0028662065,0.029594427,0.0009358014,0.032578655],"study_design_scores_gemma":[0.000016314943,0.000092572474,0.0018735088,0.0000696408,0.0000283186,0.00020782993,0.00006097906,0.97437584,0.004374873,0.018191755,0.000667872,0.0000404631],"about_ca_topic_score_codex":0.0030807615,"about_ca_topic_score_gemma":0.002513464,"teacher_disagreement_score":0.0047789016,"about_ca_system_score_codex":0.0011335928,"about_ca_system_score_gemma":0.0007183364,"threshold_uncertainty_score":0.025273561},"labels":[],"label_agreement":null},{"id":"W2125612666","doi":"10.5539/mas.v5n5p204","title":"Adaptive Line Enhancement for Improved Uplink Time Difference of Arrival Localization","year":2011,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multilateration; Computer science; Multipath propagation; Estimator; Time of arrival; Telecommunications link; Robustness (evolution); Base station; Algorithm; Fading; FDOA; Channel (broadcasting); Telecommunications; Statistics; Mathematics; Acoustics; Physics","score_opus":0.022344415013555916,"score_gpt":0.21078252794526464,"score_spread":0.18843811293170873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125612666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055397272,0.0001634899,0.9429057,0.00004749563,0.000025088324,0.000012762031,0.0000121870025,0.00046063127,0.0009752978],"genre_scores_gemma":[0.6664068,0.0002274309,0.33102295,0.00006334106,0.000043994733,0.00003847472,0.000065709944,0.00004414633,0.002087095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976987,0.00006754291,0.000009295077,0.000043498127,0.00009174306,0.000018085844],"domain_scores_gemma":[0.99924254,0.00033883555,0.00012490933,0.000084268315,0.00019181089,0.000017695025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036833447,0.00026726536,0.00022079259,0.000349946,0.00012791058,0.0003094663,0.00043605434,0.00040595577,0.0006894877],"category_scores_gemma":[0.0016517256,0.00016051551,0.00022016655,0.00045967606,0.00025754192,0.00060778763,0.00041342858,0.00034713114,0.0003675261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049405807,0.0001412295,0.004182192,0.00014503521,0.000058057503,0.00032516115,0.00030991668,0.16604424,0.3219081,0.009868571,0.0011251537,0.49539834],"study_design_scores_gemma":[0.00002992581,0.00027052363,0.0013331542,0.000011210017,0.000028409786,0.0003940612,0.000032960386,0.9269396,0.065932065,0.000979803,0.0040265643,0.000021730004],"about_ca_topic_score_codex":0.0004634152,"about_ca_topic_score_gemma":0.0005620844,"teacher_disagreement_score":0.0006894877,"about_ca_system_score_codex":0.00017841977,"about_ca_system_score_gemma":0.00027303724,"threshold_uncertainty_score":0.002306521},"labels":[],"label_agreement":null},{"id":"W2126785684","doi":"10.1109/eit.2009.5189622","title":"Differential access points for indoor location estimation","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Real-time computing; Estimation; Differential (mechanical device); Path (computing); Calibration; Global Positioning System; Limit (mathematics); Telecommunications; Engineering; Statistics; Computer network","score_opus":0.013561567178618634,"score_gpt":0.2636994856909955,"score_spread":0.2501379185123769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126785684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018831387,0.0004105315,0.9786437,0.00004359626,0.00005891721,0.000019700514,0.00004996973,0.00092060474,0.0010215536],"genre_scores_gemma":[0.68058485,0.0005630981,0.3151549,0.000051994964,0.000082150415,0.00008497392,0.00023567137,0.00003645187,0.0032058107],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922943,0.00023039295,0.00003580103,0.00013014309,0.00030958708,0.00006465053],"domain_scores_gemma":[0.99900764,0.0003260581,0.00011037269,0.0002000605,0.00031696414,0.000038860213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003940702,0.000558465,0.00039882227,0.00094760413,0.00028263865,0.0005377608,0.0009200199,0.000592752,0.0016524603],"category_scores_gemma":[0.0022369365,0.00021111824,0.00021857816,0.0012256602,0.0002541152,0.0010037204,0.0008849638,0.0005206602,0.0009768007],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043061946,0.000063741434,0.0045398017,0.00024256604,0.000056313616,0.00036779616,0.00017710277,0.03175284,0.09670729,0.01231391,0.003369272,0.8499788],"study_design_scores_gemma":[0.0000908432,0.0006609119,0.0090560485,0.00006790753,0.00012877666,0.002126245,0.00016546987,0.7979153,0.1427239,0.01069874,0.036233764,0.00013209517],"about_ca_topic_score_codex":0.0008979869,"about_ca_topic_score_gemma":0.00094109,"teacher_disagreement_score":0.0016524603,"about_ca_system_score_codex":0.00021105807,"about_ca_system_score_gemma":0.00023527462,"threshold_uncertainty_score":0.005527973},"labels":[],"label_agreement":null},{"id":"W2126870850","doi":"10.1109/ccece.2006.277305","title":"Weak Signal GPS Synchronization for Locating in-Building Cellular Telephones","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Global Positioning System; Matched filter; Computer science; Synchronization (alternating current); SIGNAL (programming language); GPS signals; Chip; Filter (signal processing); Code (set theory); Real-time computing; Doppler effect; Electronic engineering; Detection theory; Assisted GPS; Telecommunications; Engineering; Detector; Channel (broadcasting); Physics; Computer vision","score_opus":0.005072955394155275,"score_gpt":0.19304217462501078,"score_spread":0.1879692192308555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126870850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38328317,0.00039550377,0.60964644,0.0002386451,0.00006381956,0.000037811296,0.000057865436,0.0007977853,0.0054789553],"genre_scores_gemma":[0.9737906,0.00014071276,0.024814622,0.000021795775,0.0000192134,0.000011205922,0.00003835301,0.000017170318,0.0011463276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982196,0.00007460014,0.0000042540287,0.000018454177,0.000060434242,0.00002032228],"domain_scores_gemma":[0.999652,0.00016614397,0.000051894353,0.000043109365,0.00007119786,0.000015772022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028975666,0.00023816105,0.00014134731,0.00029107326,0.00022508754,0.0002444271,0.00023613739,0.0002355763,0.0010732234],"category_scores_gemma":[0.0019518065,0.00010823003,0.00008155074,0.0002721956,0.00020812507,0.00041548724,0.00024202136,0.00018287734,0.00029671655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010877193,0.00008494615,0.021366091,0.0003227544,0.000064929285,0.00048966886,0.00027523458,0.48939934,0.21429622,0.019273201,0.0024894655,0.25085038],"study_design_scores_gemma":[0.00005425507,0.00056572945,0.007049038,0.00001897239,0.000067948575,0.0003679543,0.000062588384,0.8915632,0.09190429,0.0025604765,0.005765162,0.000020478763],"about_ca_topic_score_codex":0.0016926185,"about_ca_topic_score_gemma":0.0022531042,"teacher_disagreement_score":0.0016926185,"about_ca_system_score_codex":0.00030128573,"about_ca_system_score_gemma":0.00029105245,"threshold_uncertainty_score":0.0035902262},"labels":[],"label_agreement":null},{"id":"W2127473114","doi":"10.1109/melcon.2006.1653111","title":"UWB Positioning Using Six-port Technology and a Learning Machine","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Multipath propagation; Computer science; Ranging; Robustness (evolution); Positioning system; Signal processing; Rake receiver; Wideband; Impulse response; Real-time computing; Electronic engineering; Channel (broadcasting); Engineering; Telecommunications; Node (physics); Radar","score_opus":0.0038808983197161735,"score_gpt":0.19046164523940892,"score_spread":0.18658074691969276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127473114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014017437,0.000118752134,0.98443735,0.000048170466,0.00003081331,0.000015814405,0.000012260816,0.00066378794,0.0006557956],"genre_scores_gemma":[0.33543566,0.00031779363,0.6611099,0.00006234146,0.00006309052,0.00008292926,0.00010155464,0.00003356821,0.0027931135],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995926,0.00011902511,0.000033164106,0.00007745142,0.00015227144,0.00002553385],"domain_scores_gemma":[0.9994758,0.00020509197,0.000070465554,0.00008256686,0.00014645567,0.000019758452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053182413,0.00044357538,0.0004972559,0.0006452525,0.0002279233,0.0005950678,0.00062537915,0.00076363096,0.0013595355],"category_scores_gemma":[0.0014696599,0.00025406337,0.00038019088,0.0005379779,0.00029236902,0.001001209,0.00045309518,0.00052087486,0.00069363916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028963498,0.00011958626,0.0024727753,0.00015964455,0.00010222094,0.00015197968,0.000097367134,0.0931008,0.052396256,0.00936018,0.0010183102,0.84073126],"study_design_scores_gemma":[0.000023056704,0.00032251622,0.0012666913,0.000015287267,0.00003390486,0.0003318644,0.0000231702,0.96111864,0.029154655,0.0031891419,0.004485386,0.000035659294],"about_ca_topic_score_codex":0.0005671032,"about_ca_topic_score_gemma":0.00043920212,"teacher_disagreement_score":0.0013595355,"about_ca_system_score_codex":0.00021095084,"about_ca_system_score_gemma":0.00023949589,"threshold_uncertainty_score":0.004548073},"labels":[],"label_agreement":null},{"id":"W2127708103","doi":"10.1109/cosera.2015.7330307","title":"Direct estimation of time difference of arrival from compressive sensing measurements","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Royal Military College of Canada","funders":"","keywords":"Multilateration; Estimator; Integrator; Arrival time; Compressed sensing; Computer science; Time of arrival; Time shifting; SIGNAL (programming language); Noise (video); Algorithm; Acoustics; Mathematics; Telecommunications; Artificial intelligence; Statistics; Engineering; Physics; Bandwidth (computing)","score_opus":0.034696620155122725,"score_gpt":0.22751480479388114,"score_spread":0.1928181846387584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127708103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012460683,0.00026873904,0.9849075,0.00009026698,0.00011548528,0.000045685178,0.00008047776,0.00038985317,0.0016413549],"genre_scores_gemma":[0.3032579,0.0010808422,0.6910234,0.00016446717,0.00017870884,0.00012120043,0.0003866992,0.00010738705,0.0036794348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994184,0.000085014195,0.000037683545,0.00012884484,0.00029212085,0.000038042424],"domain_scores_gemma":[0.9992034,0.00035274038,0.00010613937,0.00010883166,0.0002000581,0.000028843155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048244346,0.0009411514,0.00054020825,0.0006345792,0.00022075282,0.00063534774,0.0006123596,0.0006865394,0.0017079512],"category_scores_gemma":[0.0040783724,0.00036730932,0.00032140713,0.0006967245,0.0004121432,0.0013097379,0.0009455312,0.0011178986,0.00079412555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004943667,0.00015348526,0.002955459,0.0007612132,0.000089092784,0.00036397672,0.000323681,0.1643586,0.2586134,0.027826846,0.0036176834,0.54044217],"study_design_scores_gemma":[0.000056491015,0.00021389606,0.0020840196,0.000077820034,0.000035331595,0.0007853813,0.000091498216,0.8926028,0.0839367,0.011341337,0.008698716,0.00007604692],"about_ca_topic_score_codex":0.0011199452,"about_ca_topic_score_gemma":0.001646034,"teacher_disagreement_score":0.0017079512,"about_ca_system_score_codex":0.00025961502,"about_ca_system_score_gemma":0.0007499819,"threshold_uncertainty_score":0.0057136416},"labels":[],"label_agreement":null},{"id":"W2128240131","doi":"10.1109/glocom.2009.5425736","title":"A Lightweight Iterative Positioning Algorithm for Context-Aware Wireless Sensor Networks: Proof of Correctness","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Correctness; Wireless sensor network; Computer science; Algorithm; Flooding (psychology); Context (archaeology); Iterative method; Node (physics); Position (finance); Wireless; Real-time computing; Computer network; Telecommunications; Engineering","score_opus":0.006457399220939316,"score_gpt":0.21518856015579158,"score_spread":0.20873116093485225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128240131","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016167916,0.00011754974,0.9961241,0.00020529916,0.000070337635,0.00007652011,0.000020931768,0.0006257692,0.0011427182],"genre_scores_gemma":[0.17372939,0.00045432415,0.8219169,0.00039100955,0.00017836744,0.0006840875,0.00016063583,0.0002788826,0.0022064522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949274,0.0009735528,0.0003503754,0.00067068235,0.0027044707,0.0003736407],"domain_scores_gemma":[0.9896354,0.005037755,0.00068892824,0.0017641634,0.0026607518,0.00021291462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025969937,0.001175176,0.0009920477,0.00090843515,0.0010546267,0.0016797371,0.0021214655,0.0018567735,0.0033406771],"category_scores_gemma":[0.025838172,0.00075071806,0.001265346,0.00096536253,0.0024549004,0.003495432,0.0041118013,0.003249012,0.0023467902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039434695,0.00024467983,0.0029457682,0.0008839563,0.00019270748,0.0013824773,0.0010501877,0.18246752,0.033849854,0.3552805,0.009963615,0.41134444],"study_design_scores_gemma":[0.00036344116,0.00054747943,0.0011536755,0.00027752662,0.0001396514,0.001944928,0.00019908807,0.77297425,0.034585256,0.1491565,0.038451552,0.00020673966],"about_ca_topic_score_codex":0.0015090278,"about_ca_topic_score_gemma":0.00091395207,"teacher_disagreement_score":0.0033406771,"about_ca_system_score_codex":0.0006665956,"about_ca_system_score_gemma":0.0024575596,"threshold_uncertainty_score":0.013734341},"labels":[],"label_agreement":null},{"id":"W2128307880","doi":"10.1109/newcas.2010.5603716","title":"Device-less capacitive indoors localization and activity tracking system","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Capacitive sensing; Capacitance; Tracking (education); Computer science; Capacitive coupling; Battery (electricity); Tracking system; Real-time computing; Electronic engineering; Electrical engineering; Electrode; Engineering; Artificial intelligence; Power (physics); Voltage; Kalman filter","score_opus":0.010829004485332965,"score_gpt":0.2052085972350912,"score_spread":0.19437959274975822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128307880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060252056,0.0013211721,0.9262376,0.0004867478,0.00043615815,0.00017553277,0.00037888257,0.003664418,0.007047414],"genre_scores_gemma":[0.70564353,0.0011092011,0.27003708,0.0009910655,0.00044723082,0.00030758325,0.00062424643,0.000110486035,0.020729514],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991278,0.00016141972,0.00004995397,0.00026171815,0.00033886224,0.00006030566],"domain_scores_gemma":[0.99959713,0.00007006104,0.000089745336,0.00005961482,0.0001437647,0.000039773393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029616852,0.0005839218,0.00069241726,0.0006836991,0.0003607362,0.000607532,0.0018241067,0.0010549553,0.003416584],"category_scores_gemma":[0.0006454985,0.00033707844,0.00043025616,0.00060584873,0.00026277793,0.0011342971,0.00083260686,0.00043008188,0.001771672],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007682111,0.00034621297,0.0059115477,0.00092696917,0.00016647067,0.00071143685,0.00035889796,0.006826023,0.612599,0.0039931196,0.010451526,0.3569406],"study_design_scores_gemma":[0.00058776344,0.005937888,0.03153588,0.00020851551,0.00070611574,0.013438341,0.0002460948,0.214927,0.56936145,0.0025674389,0.15994109,0.0005424678],"about_ca_topic_score_codex":0.00076380925,"about_ca_topic_score_gemma":0.00090429635,"teacher_disagreement_score":0.003416584,"about_ca_system_score_codex":0.00032067928,"about_ca_system_score_gemma":0.00035899374,"threshold_uncertainty_score":0.0114296675},"labels":[],"label_agreement":null},{"id":"W2128781513","doi":"10.1109/vetecf.2009.5379080","title":"A New Distributed Range-Free Localization Algorithm for Wireless Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"RSS; Computer science; Multilateration; Range (aeronautics); Algorithm; Wireless sensor network; Wireless; Set (abstract data type); Node (physics); Wireless network; Computer network; Position (finance); Telecommunications; Engineering","score_opus":0.005943766821700154,"score_gpt":0.20305594369502328,"score_spread":0.19711217687332314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128781513","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009428198,0.00033901495,0.9964971,0.00010478913,0.00010042321,0.000036785637,0.000023600625,0.00087255065,0.0010829103],"genre_scores_gemma":[0.055186935,0.00071106723,0.9354379,0.00026908086,0.00013684883,0.00027877305,0.00027168935,0.0001833104,0.007524382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991647,0.000119626995,0.00004527643,0.00018336411,0.00044264673,0.000044468587],"domain_scores_gemma":[0.99935275,0.00016543106,0.00006384789,0.000110528395,0.0002745007,0.000032901575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007501731,0.00090012886,0.00089427776,0.0012783392,0.00075639604,0.00095272175,0.0020123224,0.0010759209,0.0032382493],"category_scores_gemma":[0.0024464966,0.00038963617,0.0005089197,0.0014333656,0.00052691135,0.002673029,0.0015890017,0.0011436773,0.0018998971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019322125,0.00006519834,0.0004940627,0.00018071485,0.00007455811,0.00012820715,0.00017552925,0.10617526,0.015991352,0.032903366,0.014207283,0.8294113],"study_design_scores_gemma":[0.000111183595,0.00013254017,0.0002724844,0.00003338362,0.00003925963,0.0003980339,0.000040341638,0.9180433,0.0074244156,0.015893996,0.057550367,0.000060690287],"about_ca_topic_score_codex":0.0016122981,"about_ca_topic_score_gemma":0.0021905578,"teacher_disagreement_score":0.0032382493,"about_ca_system_score_codex":0.0007118427,"about_ca_system_score_gemma":0.00085392565,"threshold_uncertainty_score":0.010833085},"labels":[],"label_agreement":null},{"id":"W2128967361","doi":"10.5194/isprsarchives-xxxix-b4-231-2012","title":"VISION-AIDED CONTEXT-AWARE FRAMEWORK FOR PERSONAL NAVIGATION SERVICES","year":2012,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Turn-by-turn navigation; Context (archaeology); Global Positioning System; Heading (navigation); Navigation system; Computer vision; Mobile device; Mobile robot navigation; Human–computer interaction; Dead reckoning; Location-based service; Orientation (vector space); Mobile phone; Kalman filter; Map matching; Artificial intelligence; Real-time computing; Mobile robot; Engineering; World Wide Web; Telecommunications","score_opus":0.013742740022753438,"score_gpt":0.2581822676305907,"score_spread":0.24443952760783727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128967361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013506517,0.00077443034,0.979251,0.000109559245,0.00006961659,0.00008416525,0.000099724195,0.0032480685,0.0028568304],"genre_scores_gemma":[0.5004932,0.00073810486,0.49406767,0.00012775567,0.000049069848,0.00017647844,0.00040017843,0.0000864373,0.00386118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997912,0.000035699268,0.00001415295,0.000051386705,0.000072635754,0.000034912227],"domain_scores_gemma":[0.9998754,0.000022247277,0.00001092063,0.000017832039,0.000057473335,0.000016028163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031931864,0.00047010102,0.00049771235,0.00053871324,0.00036241868,0.00087936455,0.0008393507,0.0008102406,0.0020990558],"category_scores_gemma":[0.0006156617,0.00023363052,0.00060010696,0.00040724842,0.00023711317,0.0009506226,0.00072311575,0.00074764603,0.00071034057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044756976,0.00039493522,0.0022230444,0.00043992087,0.00019803896,0.00067900424,0.00046935203,0.22354971,0.08596103,0.04013931,0.012364423,0.6331337],"study_design_scores_gemma":[0.000027114933,0.00007080355,0.00060018117,0.00003204618,0.000044649703,0.00015475437,0.000062460145,0.97201467,0.009075193,0.006363894,0.011523319,0.00003082333],"about_ca_topic_score_codex":0.017655442,"about_ca_topic_score_gemma":0.015189648,"teacher_disagreement_score":0.017655442,"about_ca_system_score_codex":0.0005926422,"about_ca_system_score_gemma":0.0010002566,"threshold_uncertainty_score":0.035105348},"labels":[],"label_agreement":null},{"id":"W2129239253","doi":"10.5194/isprsarchives-xxxviii-4-c26-1-2012","title":"INDOOR LOCALIZATION USING WI-FI BASED FINGERPRINTING AND TRILATERATION TECHIQUES FOR LBS APPLICATIONS","year":2012,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"York University","keywords":"Trilateration; RSS; Computer science; Global Positioning System; Fingerprint (computing); Location-based service; Indoor positioning system; Wi-Fi; Matching (statistics); Real-time computing; Mobile device; Position (finance); Signal strength; Wireless network; Wireless; Computer network; Node (physics); Computer vision; Telecommunications; Accelerometer; Engineering; World Wide Web","score_opus":0.018315270913149363,"score_gpt":0.25486381682131354,"score_spread":0.23654854590816418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129239253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.096963115,0.0012531388,0.8916761,0.00021244287,0.00019891019,0.000098082295,0.00019488961,0.002726554,0.0066768094],"genre_scores_gemma":[0.67175204,0.0009669801,0.32190916,0.00010778818,0.000059562128,0.0000943843,0.00027097057,0.00007072914,0.004768316],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994579,0.00015535807,0.000023023704,0.000098294026,0.00020743835,0.00005804925],"domain_scores_gemma":[0.99960583,0.00006247258,0.000065033055,0.00011393188,0.00013722405,0.000015441476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003906065,0.0005517653,0.00033991257,0.0011600715,0.00028734075,0.0007091556,0.0006131633,0.0005598,0.002543278],"category_scores_gemma":[0.00089319795,0.0002008304,0.00034706248,0.0011696868,0.00028467804,0.00097132387,0.0005205252,0.00040581045,0.0016434314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048285106,0.00021449877,0.006198716,0.00030360342,0.00008003196,0.0002842856,0.00022526407,0.0144865895,0.23912895,0.003423641,0.0033577387,0.73181385],"study_design_scores_gemma":[0.00008372414,0.0019212712,0.024680259,0.00019562588,0.0002528858,0.0031904639,0.00043930067,0.49993455,0.42420658,0.0029547587,0.0418829,0.00025759084],"about_ca_topic_score_codex":0.0019160012,"about_ca_topic_score_gemma":0.0024198373,"teacher_disagreement_score":0.002543278,"about_ca_system_score_codex":0.00032048032,"about_ca_system_score_gemma":0.0002238428,"threshold_uncertainty_score":0.008508086},"labels":[],"label_agreement":null},{"id":"W2129612498","doi":"10.1049/ip-map:20030546","title":"GPS signal fading model for urban centres","year":2003,"lang":"en","type":"article","venue":"IEE Proceedings - Microwaves Antennas and Propagation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Society of Interventional Radiology Foundation","keywords":"Fading; Global Positioning System; Computer science; Fade; Downtown; Histogram; Satellite; Geography; Channel (broadcasting); Telecommunications; Engineering","score_opus":0.011990679364994147,"score_gpt":0.20387642757233002,"score_spread":0.19188574820733587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129612498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47619617,0.00038385502,0.5094929,0.00030303656,0.00006179761,0.00020868296,0.0013728724,0.0010149486,0.010965718],"genre_scores_gemma":[0.98948276,0.00034038175,0.005835451,0.000033889937,0.000019307678,0.00007702021,0.00051272346,0.000036761474,0.0036616866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996456,0.00007049305,0.000016696958,0.00009083102,0.00006367854,0.00011276033],"domain_scores_gemma":[0.9994778,0.000108739005,0.00012137054,0.000094759045,0.00016140498,0.000035867964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049315183,0.0005880424,0.00059721863,0.00059919566,0.00032178639,0.0007206615,0.0009924382,0.00061695627,0.0016029518],"category_scores_gemma":[0.001480394,0.00024523571,0.000647688,0.001136707,0.000841214,0.0008064662,0.0005493323,0.0005216833,0.00061543897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006295877,0.000018193718,0.0042716507,0.00003236954,0.000029482133,0.00018483798,0.00016847676,0.97182894,0.0020741185,0.015047386,0.0008268749,0.0054546874],"study_design_scores_gemma":[0.000019647556,0.00004344442,0.0050318087,0.000008516561,0.000029039255,0.00011324215,0.000091946844,0.986699,0.000724766,0.005401827,0.001811972,0.000024735715],"about_ca_topic_score_codex":0.020490885,"about_ca_topic_score_gemma":0.01130136,"teacher_disagreement_score":0.020490885,"about_ca_system_score_codex":0.0008290736,"about_ca_system_score_gemma":0.00053226884,"threshold_uncertainty_score":0.04074323},"labels":[],"label_agreement":null},{"id":"W2130954762","doi":"10.1109/78.823960","title":"Near-optimal range and depth estimation using a vertical array in a correlated multipath environment","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Office of Naval Research","keywords":"Estimator; Range (aeronautics); Covariance matrix; Mathematics; Multipath propagation; Multilateration; Algorithm; Covariance; Weighting; Monte Carlo method; Least-squares function approximation; Statistics; Estimation theory; Acoustics; Azimuth; Geometry","score_opus":0.0128650953385839,"score_gpt":0.21929651304533665,"score_spread":0.20643141770675275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130954762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02963792,0.00011513737,0.9694117,0.000027522507,0.000012359406,0.000008410421,0.000015621876,0.00019393027,0.00057745934],"genre_scores_gemma":[0.37684107,0.00018231194,0.621706,0.000042917814,0.000023494538,0.000037591963,0.000075588425,0.000040190298,0.0010507238],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996623,0.00006809943,0.000013090709,0.00008708811,0.00012452128,0.00004494834],"domain_scores_gemma":[0.99980456,0.000060554743,0.00004367155,0.000027858516,0.000051669464,0.000011741759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002755028,0.0006115218,0.00047513863,0.00038923003,0.00023199832,0.00038201778,0.0004319948,0.00044555275,0.00044454177],"category_scores_gemma":[0.001059257,0.00034354278,0.00029414947,0.00041831346,0.00032598793,0.0007162089,0.00081680546,0.00031786953,0.00025039725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030913125,0.000056350647,0.0026535674,0.00015454242,0.00007462301,0.00022974558,0.00023359689,0.40463564,0.20644173,0.012463983,0.0007617727,0.37198538],"study_design_scores_gemma":[0.000031431548,0.00020851754,0.001355098,0.000017260067,0.000031496966,0.00037653223,0.00007425385,0.9456624,0.04543658,0.004819852,0.0019380213,0.000048549722],"about_ca_topic_score_codex":0.0010398859,"about_ca_topic_score_gemma":0.001380995,"teacher_disagreement_score":0.0010398859,"about_ca_system_score_codex":0.00020988268,"about_ca_system_score_gemma":0.00061284774,"threshold_uncertainty_score":0.0020676255},"labels":[],"label_agreement":null},{"id":"W2131376143","doi":"10.1109/tce.2010.5606338","title":"Robust indoor positioning using differential wi-fi access points","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Path loss; Real-time computing; Differential (mechanical device); Node (physics); Interference (communication); Calibration; Noise (video); Wireless; Telecommunications; Engineering; Statistics","score_opus":0.019854493803506724,"score_gpt":0.2433710278772324,"score_spread":0.22351653407372565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131376143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047246575,0.00019863533,0.95018786,0.000047261943,0.000053401847,0.000019851232,0.000030116185,0.00085537124,0.0013609024],"genre_scores_gemma":[0.75049853,0.00024020161,0.24666436,0.000054865814,0.00006261147,0.00004703294,0.00011981452,0.000026565527,0.0022859601],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994167,0.00011190533,0.000024960309,0.00015042188,0.00024612658,0.000049801718],"domain_scores_gemma":[0.99951994,0.00011652752,0.00008596872,0.00011739778,0.00014204523,0.000018034722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032176706,0.0005442218,0.00035431475,0.000573557,0.00022474462,0.00050342747,0.0010944115,0.00054525194,0.00063659303],"category_scores_gemma":[0.0013770255,0.00025741378,0.00023278092,0.0006764102,0.00027105096,0.000793894,0.00080661074,0.00037897372,0.00061849435],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005216628,0.00007060725,0.0043703876,0.00020484132,0.00009167074,0.00043124263,0.0002144576,0.054983333,0.312438,0.0058727935,0.0013384466,0.61946255],"study_design_scores_gemma":[0.00010367095,0.00082629686,0.0070889355,0.000034386674,0.00012884758,0.0017883213,0.0001077289,0.70293075,0.2726953,0.003222334,0.010960481,0.00011294629],"about_ca_topic_score_codex":0.0006341681,"about_ca_topic_score_gemma":0.0005924671,"teacher_disagreement_score":0.0010944115,"about_ca_system_score_codex":0.00020931732,"about_ca_system_score_gemma":0.00017317949,"threshold_uncertainty_score":0.002129674},"labels":[],"label_agreement":null},{"id":"W2131797872","doi":"10.1109/icassp.2011.5947017","title":"A near-optimal least squares solution to received signal strength difference based geolocation","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Transmitter; Non-linear least squares; RSS; Intersection (aeronautics); Algorithm; Least-squares function approximation; Geolocation; Nonlinear system; Path (computing); Computer science; SIGNAL (programming language); Noise (video); Mathematical optimization; Mathematics; Telecommunications; Estimation theory; Statistics; Engineering","score_opus":0.020033792592560377,"score_gpt":0.19994682898350352,"score_spread":0.17991303639094314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131797872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024582383,0.000029586408,0.9967139,0.000052752628,0.000011269742,0.000009375191,0.00001455202,0.00015075339,0.0005595754],"genre_scores_gemma":[0.108841345,0.00010467381,0.88765746,0.000046757967,0.00003889076,0.00008497982,0.00010101146,0.00006493081,0.0030598429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961686,0.00012971529,0.000018205292,0.000097119664,0.00010851978,0.000029537568],"domain_scores_gemma":[0.99966764,0.00017044872,0.00003312771,0.000036946767,0.00008063569,0.000011195834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048662355,0.0006387353,0.0006866771,0.0003192511,0.00031457978,0.00054867845,0.00059815374,0.0008196372,0.0017959286],"category_scores_gemma":[0.002017677,0.00042151843,0.0004493381,0.0006334746,0.0006194935,0.00058885315,0.00071480346,0.0006681914,0.0009315117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007383185,0.000042184667,0.0003619873,0.000102282145,0.000030907704,0.00007838416,0.00011669185,0.80557525,0.008477818,0.014428236,0.002186113,0.16852637],"study_design_scores_gemma":[0.000015541946,0.000032323533,0.00012738252,0.0000053591843,0.0000043296527,0.00003959033,0.000022290214,0.990248,0.0022215948,0.0057512308,0.0015222875,0.0000101473815],"about_ca_topic_score_codex":0.002581329,"about_ca_topic_score_gemma":0.0027199516,"teacher_disagreement_score":0.002581329,"about_ca_system_score_codex":0.00028952418,"about_ca_system_score_gemma":0.0010312289,"threshold_uncertainty_score":0.0060079694},"labels":[],"label_agreement":null},{"id":"W2131923954","doi":"10.1109/iccw.2009.5207992","title":"A Scheme for Indoor Localization through RF Profiling","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Signal strength; Computer science; Profiling (computer programming); Communication source; Triangulation; Scheme (mathematics); Data mining; SIGNAL (programming language); Received signal strength indication; Artificial intelligence; Pattern recognition (psychology); Computer vision; Real-time computing; Algorithm; Wireless sensor network; Wireless; Computer network; Mathematics; Telecommunications","score_opus":0.015292350380246435,"score_gpt":0.24624692765151882,"score_spread":0.2309545772712724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131923954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003959758,0.00013171401,0.99086666,0.00010700873,0.00007471995,0.00008274184,0.000109295506,0.001833695,0.0028344758],"genre_scores_gemma":[0.22842038,0.00044029523,0.7591144,0.00023572409,0.00012958341,0.00030813503,0.00059702096,0.0001348902,0.010619634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999064,0.00019990854,0.000060963775,0.000203409,0.00037011065,0.000101563135],"domain_scores_gemma":[0.9989219,0.000102451,0.00011002106,0.0006437684,0.00015413723,0.000067841895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065193546,0.0005407378,0.0005900231,0.0011354538,0.0009289149,0.001011335,0.0018244471,0.00094314426,0.0033401486],"category_scores_gemma":[0.0018558998,0.00037626715,0.00050507515,0.0014729608,0.00058263703,0.0015528854,0.0023658527,0.0010527772,0.003896286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045994457,0.00022531084,0.0026634645,0.00021538057,0.00008395502,0.00032500533,0.00058025727,0.025201514,0.108100414,0.086187184,0.01417843,0.76177907],"study_design_scores_gemma":[0.00014949497,0.0012124423,0.0043999255,0.000118847056,0.00018288894,0.003332857,0.0002612317,0.45718974,0.15659471,0.04264117,0.33363613,0.00028052827],"about_ca_topic_score_codex":0.0009060161,"about_ca_topic_score_gemma":0.0011183992,"teacher_disagreement_score":0.0033401486,"about_ca_system_score_codex":0.00033020088,"about_ca_system_score_gemma":0.000519645,"threshold_uncertainty_score":0.011173904},"labels":[],"label_agreement":null},{"id":"W2132107020","doi":"10.1109/wcnc.2005.1424882","title":"Received signal strength based location estimation of a wireless LAN client","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"RSS; Computer science; Wireless lan; Signal strength; Physical layer; SIGNAL (programming language); Implementation; Probabilistic logic; Real-time computing; Wireless; Computer network; IEEE 802.11; Location-based service; Telecommunications; Artificial intelligence","score_opus":0.006814079781973642,"score_gpt":0.20828999053283292,"score_spread":0.20147591075085927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132107020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11902984,0.00027631436,0.8753524,0.0003617046,0.00003696146,0.00004472158,0.000075556854,0.001289759,0.003532745],"genre_scores_gemma":[0.85399556,0.00031217572,0.14054562,0.00006336763,0.000057789675,0.000051660787,0.00014995263,0.000044017175,0.0047798543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995784,0.000103303675,0.000020431171,0.000103501385,0.00014057233,0.000053834206],"domain_scores_gemma":[0.9994361,0.00014289918,0.00009703165,0.00008088693,0.00020977193,0.0000333249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040468608,0.0004666772,0.00068540906,0.00044698233,0.00036067292,0.0008713349,0.0011866275,0.0009985856,0.0013077809],"category_scores_gemma":[0.0023659326,0.00030349696,0.0002490596,0.0005440147,0.00031443918,0.0012468974,0.0008012258,0.0006215711,0.0019250619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007758108,0.0002391105,0.014799029,0.00024505914,0.00007731773,0.0016772128,0.00032479555,0.50779575,0.10562424,0.012575995,0.0033080936,0.35255757],"study_design_scores_gemma":[0.000011381068,0.00008011956,0.0008437134,0.000006086354,0.000012891424,0.00020974365,0.00006119777,0.9799469,0.01689011,0.0011940341,0.00072989037,0.000013900827],"about_ca_topic_score_codex":0.0020017428,"about_ca_topic_score_gemma":0.0015632496,"teacher_disagreement_score":0.0020017428,"about_ca_system_score_codex":0.00048325217,"about_ca_system_score_gemma":0.0006305144,"threshold_uncertainty_score":0.004374981},"labels":[],"label_agreement":null},{"id":"W2132228636","doi":"10.1109/tvt.2010.2082006","title":"On the Feasibility of Wireless Shadowing Correlation Models","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":180,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Autocorrelation; Wireless; Variance (accounting); Correlation; Focus (optics); Shadow mapping; Mathematical optimization; Mathematics; Artificial intelligence; Statistics; Telecommunications","score_opus":0.01581882855189533,"score_gpt":0.22373329199504513,"score_spread":0.2079144634431498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132228636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08224496,0.0007560329,0.8954612,0.001991454,0.00006055168,0.00004324459,0.00019419272,0.0001770296,0.019071303],"genre_scores_gemma":[0.93277156,0.001489503,0.060242947,0.0003035093,0.00015308284,0.0001358959,0.00021649519,0.00011055806,0.0045764423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715364,0.0016454109,0.00010248327,0.00027033853,0.0005294915,0.00029865614],"domain_scores_gemma":[0.96769005,0.027325459,0.001727429,0.0013546649,0.0014884361,0.00041383534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005823407,0.000861363,0.0009096568,0.001106992,0.00069413974,0.001906261,0.0013365683,0.0011583494,0.0034975056],"category_scores_gemma":[0.028230766,0.00071978057,0.0011549531,0.0010325324,0.0028842676,0.0035901025,0.001996533,0.0023807928,0.00043670973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081713784,0.00007046513,0.0012083502,0.000101105536,0.000023082483,0.00022381508,0.00016828158,0.3541742,0.0010435224,0.6341377,0.0012692697,0.007498522],"study_design_scores_gemma":[0.000016689095,0.000055618963,0.00029539573,0.000043314398,0.000010309129,0.00011541225,0.00006250743,0.7928675,0.00038120252,0.20511366,0.0010167868,0.000021654176],"about_ca_topic_score_codex":0.0024964784,"about_ca_topic_score_gemma":0.0016371033,"teacher_disagreement_score":0.005823407,"about_ca_system_score_codex":0.0009834676,"about_ca_system_score_gemma":0.0017003752,"threshold_uncertainty_score":0.030797482},"labels":[],"label_agreement":null},{"id":"W2132281796","doi":"10.3390/s110707243","title":"Direct Sensor Orientation of a Land-Based Mobile Mapping System","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Council","keywords":"Mobile mapping; Orientation (vector space); Calibration; Global Positioning System; Computer science; Inertial measurement unit; Real-time computing; Process (computing); Sensor fusion; GPS/INS; Computer vision; Assisted GPS; Telecommunications","score_opus":0.014285542157262802,"score_gpt":0.1944750906199764,"score_spread":0.1801895484627136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132281796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16505404,0.00031393932,0.8180255,0.00015150958,0.00020040199,0.00018761466,0.00028956248,0.00504718,0.0107302815],"genre_scores_gemma":[0.74028707,0.00018199728,0.2521467,0.00010244027,0.000044498174,0.00013796419,0.0003611211,0.000063689775,0.0066745416],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995925,0.000061556435,0.000018024648,0.000098813696,0.00019328637,0.0000358625],"domain_scores_gemma":[0.99979717,0.00001700815,0.000026541245,0.000041886055,0.00009718332,0.000020258443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021767242,0.00042802785,0.00048285868,0.00040047473,0.00024597286,0.00062298117,0.0006315377,0.00039672473,0.0021873948],"category_scores_gemma":[0.00047841034,0.0002693055,0.00019206043,0.00035178472,0.00016586504,0.00045901988,0.0007221124,0.00033959563,0.0014961098],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004327619,0.00016131805,0.005501069,0.0003189614,0.00006944631,0.0003888753,0.00033849996,0.02213235,0.37571123,0.0036515421,0.0048364047,0.58645767],"study_design_scores_gemma":[0.0001996652,0.0012194987,0.020639027,0.00007163756,0.00016521316,0.0016679788,0.00023190545,0.5382026,0.39648157,0.0015699913,0.03940869,0.00014228838],"about_ca_topic_score_codex":0.0011861506,"about_ca_topic_score_gemma":0.0014836278,"teacher_disagreement_score":0.0021873948,"about_ca_system_score_codex":0.00023188091,"about_ca_system_score_gemma":0.0005441619,"threshold_uncertainty_score":0.0073176026},"labels":[],"label_agreement":null},{"id":"W2132314876","doi":"10.1109/ccece.2007.262","title":"Eye Array Placement in Enclosed Areas","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Retroreflector; Computer science; Reverberation; Optics; Coincidence; Signal processing; Algorithm; Physics; Acoustics; Laser; Telecommunications","score_opus":0.0065347511684852476,"score_gpt":0.22146967496301048,"score_spread":0.21493492379452522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132314876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037924018,0.000093186165,0.95825034,0.000073554176,0.000046625017,0.000031562056,0.000054346452,0.0010158411,0.0025104417],"genre_scores_gemma":[0.3426476,0.00016071815,0.6522947,0.00008658013,0.0000406857,0.0000969375,0.00013962848,0.00028020228,0.004252923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993303,0.00017949488,0.00004890956,0.0001652618,0.00018788598,0.00008817266],"domain_scores_gemma":[0.99862266,0.000498355,0.00017260721,0.00029804322,0.00030955786,0.00009877565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004083953,0.00081379496,0.0007554888,0.00071938353,0.0003943353,0.00090720423,0.0011133839,0.00092636497,0.0041486146],"category_scores_gemma":[0.0029116273,0.0004383773,0.00037220222,0.0005671351,0.0004364257,0.001463963,0.002324112,0.000670811,0.0020293023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015869511,0.00017599139,0.00444855,0.00029181733,0.00011307262,0.001227754,0.0010417444,0.1547106,0.3810961,0.025246473,0.003613868,0.42644712],"study_design_scores_gemma":[0.00017292665,0.000841503,0.0017245195,0.00004957442,0.00007318029,0.0012838697,0.00023850497,0.5715982,0.39422104,0.009847423,0.019862037,0.00008725377],"about_ca_topic_score_codex":0.00054257654,"about_ca_topic_score_gemma":0.00073720305,"teacher_disagreement_score":0.0041486146,"about_ca_system_score_codex":0.00032950216,"about_ca_system_score_gemma":0.0004447344,"threshold_uncertainty_score":0.013878465},"labels":[],"label_agreement":null},{"id":"W2132622290","doi":"10.1109/ccece.2011.6030640","title":"Localization in large-scale underground environments with RFID","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Carleton University","funders":"","keywords":"Global Positioning System; Particle filter; Node (physics); Computer science; Scale (ratio); Satellite; Real-time computing; A priori and a posteriori; Filter (signal processing); Remote sensing; Simultaneous localization and mapping; Computer vision; Artificial intelligence; Engineering; Geography; Telecommunications; Cartography; Mobile robot; Aerospace engineering; Robot","score_opus":0.00924513661438235,"score_gpt":0.17372816913960135,"score_spread":0.164483032525219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132622290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053273212,0.00018174446,0.9451954,0.000050734365,0.000013896047,0.000013236364,0.000022043563,0.00053710316,0.0007125229],"genre_scores_gemma":[0.64920473,0.0003919176,0.34712535,0.000026607451,0.000022187616,0.000033451826,0.00009925098,0.000062983105,0.0030335558],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970716,0.00007606589,0.00001020636,0.00006092767,0.00011341914,0.000032080825],"domain_scores_gemma":[0.99957055,0.0001794918,0.000076292454,0.000088195455,0.00006640735,0.000019106046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041638632,0.00036991178,0.00036451188,0.00040910018,0.00028819757,0.00038985576,0.00049430865,0.0004145706,0.0005995236],"category_scores_gemma":[0.00073658326,0.00027947087,0.00025270157,0.0005706167,0.0005212645,0.00090148195,0.0009083616,0.00026287793,0.00048462683],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044993227,0.00008393593,0.012895049,0.00031633826,0.000117891075,0.0010579313,0.0005636683,0.24880482,0.23241235,0.007774432,0.0016998356,0.49382377],"study_design_scores_gemma":[0.00005945948,0.0006055397,0.010612468,0.00004853941,0.000082989325,0.0015096108,0.0005234933,0.8401109,0.122103564,0.011139046,0.013121318,0.00008310377],"about_ca_topic_score_codex":0.0015807096,"about_ca_topic_score_gemma":0.0027882473,"teacher_disagreement_score":0.0015807096,"about_ca_system_score_codex":0.00019619199,"about_ca_system_score_gemma":0.00023725766,"threshold_uncertainty_score":0.0031430125},"labels":[],"label_agreement":null},{"id":"W2132625148","doi":"10.1109/icassp.1994.389638","title":"An efficient closed-form localization solution from time difference of arrival measurements","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Multilateration; Convergence (economics); Position (finance); Covariance; Iterative method; Algorithm; Computer science; Least-squares function approximation; Computation; Minification; Cramér–Rao bound; Upper and lower bounds; Mathematical optimization; Mathematics; Estimation theory; Statistics; Azimuth","score_opus":0.022091754163115235,"score_gpt":0.20349907267561865,"score_spread":0.1814073185125034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132625148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003308988,0.00003997742,0.99899787,0.00002730933,0.00001852606,0.000011424628,0.00001551864,0.00019782248,0.00036071206],"genre_scores_gemma":[0.020828288,0.00023093552,0.9752695,0.000052767355,0.00004185095,0.00019628779,0.00018796422,0.000079791374,0.0031125718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995521,0.00007764573,0.00002315496,0.00009255617,0.00022595582,0.000028628056],"domain_scores_gemma":[0.99928766,0.00028210387,0.00006491985,0.00006481388,0.00027802415,0.000022457598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006702177,0.001098085,0.000912106,0.00067769265,0.00046317562,0.001310177,0.0013184512,0.0017385681,0.005308641],"category_scores_gemma":[0.0029297431,0.00061050354,0.0007599475,0.0010859272,0.0005484053,0.0014851558,0.0013210481,0.0012938011,0.0034976932],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084753716,0.000081045895,0.00032206837,0.00042596346,0.000052810396,0.00025484656,0.00022990316,0.47044024,0.021438703,0.06261123,0.012056547,0.43200198],"study_design_scores_gemma":[0.00003449882,0.000060459948,0.000103261074,0.000030820138,0.000013501031,0.0002019788,0.000035131136,0.9725195,0.0050168876,0.012411619,0.009547416,0.00002489458],"about_ca_topic_score_codex":0.0016269804,"about_ca_topic_score_gemma":0.0022027357,"teacher_disagreement_score":0.005308641,"about_ca_system_score_codex":0.00048540375,"about_ca_system_score_gemma":0.0016608806,"threshold_uncertainty_score":0.017759144},"labels":[],"label_agreement":null},{"id":"W2133590993","doi":"10.1109/iwcmc.2011.5982708","title":"On using compressive sensing for vehicular traffic detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"National Research Council Canada","keywords":"Compressed sensing; Wireless; Wireless sensor network; Computer science; Energy (signal processing); SIGNAL (programming language); Real-time computing; Key distribution in wireless sensor networks; Work (physics); Electrical engineering; Electronic engineering; Computer network; Wireless network; Telecommunications; Engineering; Artificial intelligence","score_opus":0.0309626518052087,"score_gpt":0.2157922587265128,"score_spread":0.18482960692130412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133590993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03530546,0.0009921474,0.9573477,0.0009726193,0.00009201857,0.000028960758,0.00004067621,0.00014871107,0.005071768],"genre_scores_gemma":[0.7194929,0.0029508064,0.27319816,0.0003565722,0.00035762964,0.000078284385,0.00013902858,0.00004135318,0.0033851853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965894,0.00014725624,0.000013097647,0.000042940286,0.0001131533,0.000024522653],"domain_scores_gemma":[0.9987802,0.00092388835,0.00006653118,0.00007826613,0.00013368034,0.000017384024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000561978,0.00045423344,0.00029241169,0.00028158267,0.0002290994,0.0004945226,0.0003821281,0.00069397304,0.0008954079],"category_scores_gemma":[0.0035922148,0.00016100543,0.00021319446,0.000434715,0.0007749866,0.0011395474,0.0006016249,0.00056791864,0.00021664277],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025201568,0.00008940856,0.0012944425,0.00017969143,0.00004740734,0.00016742856,0.00013839419,0.6417175,0.04393256,0.06526845,0.0028044798,0.24410826],"study_design_scores_gemma":[0.0000075833054,0.0000647964,0.00019345238,0.000011731952,0.000006622058,0.000047876612,0.000021574493,0.98076755,0.004817862,0.012724893,0.0013229556,0.000013054182],"about_ca_topic_score_codex":0.0016386635,"about_ca_topic_score_gemma":0.0016159659,"teacher_disagreement_score":0.0016386635,"about_ca_system_score_codex":0.00026008996,"about_ca_system_score_gemma":0.00027749894,"threshold_uncertainty_score":0.0032582283},"labels":[],"label_agreement":null},{"id":"W2133736098","doi":"10.1109/pacrim.2009.5291272","title":"The application of Wi-Fi radiolocation research to mobile devices","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"RSS; Laptop; Computer science; Global Positioning System; Mobile device; Mobile radio; Telecommunications; Mobile telephony; Embedded system","score_opus":0.01643414178277954,"score_gpt":0.3114802848375256,"score_spread":0.2950461430547461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133736098","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034337457,0.15638016,0.6778576,0.0097717,0.0030146341,0.00017939137,0.00028082306,0.00083266804,0.11734567],"genre_scores_gemma":[0.5721296,0.18442267,0.19759728,0.0028160557,0.0045898263,0.00021300769,0.0004939445,0.00017683199,0.03756075],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992874,0.0002074459,0.000033725442,0.00015295751,0.00025415496,0.00006425449],"domain_scores_gemma":[0.99808335,0.0009015559,0.00018620223,0.0003128464,0.0004546812,0.000061371014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072122936,0.00065686874,0.00037497503,0.0017080456,0.00049292797,0.002244861,0.0010182521,0.0013532417,0.0030519627],"category_scores_gemma":[0.0039236844,0.00037891488,0.00048807086,0.0025024714,0.0015907494,0.0025415656,0.0011437022,0.0012320576,0.0020649033],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000147687,0.00008302063,0.0075659854,0.0012919081,0.00011800174,0.0010772299,0.0007286546,0.015081471,0.019124351,0.21039663,0.010791337,0.7335937],"study_design_scores_gemma":[0.000044465978,0.00054529274,0.010223631,0.0012772039,0.00020894466,0.0067946543,0.0019987281,0.045761127,0.03712218,0.13906714,0.75669134,0.00026537408],"about_ca_topic_score_codex":0.0013869123,"about_ca_topic_score_gemma":0.00074409245,"teacher_disagreement_score":0.0030519627,"about_ca_system_score_codex":0.0007554078,"about_ca_system_score_gemma":0.00039884754,"threshold_uncertainty_score":0.010209858},"labels":[],"label_agreement":null},{"id":"W2133757854","doi":"10.1007/978-3-642-33024-7_19","title":"Impact of Indoor Location Information Reliability on Users’ Trust of an Indoor Positioning System","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Global Positioning System; Positioning system; Reliability (semiconductor); Variance (accounting); Hybrid positioning system; Real-time computing; Simulation; Telecommunications","score_opus":0.007737748100624088,"score_gpt":0.22339996820360422,"score_spread":0.21566222010298014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133757854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886641,0.00032152378,0.007914147,0.00032915885,0.000030417219,0.00001023596,0.00014492324,0.000070835216,0.0025146515],"genre_scores_gemma":[0.9996582,0.000018757097,0.00017555157,0.000004337801,0.000003894729,0.0000010147943,0.000022355687,0.000004017606,0.000111818845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9968682,0.0013535758,0.00017034805,0.0003054238,0.00078107364,0.0005214589],"domain_scores_gemma":[0.8922309,0.08564111,0.0074782236,0.0052605285,0.0077359425,0.0016533117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027579318,0.0003887813,0.0004998532,0.0004132214,0.0004885831,0.0010945742,0.00066981436,0.00086843845,0.0022860593],"category_scores_gemma":[0.049611587,0.00042181287,0.000415871,0.00059170387,0.00069296884,0.002158192,0.0010150699,0.0011722177,0.0003551727],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008979839,0.0005772474,0.34476236,0.00048572427,0.00095675525,0.0018308496,0.0025563526,0.5153864,0.030015973,0.008144,0.003276751,0.083027706],"study_design_scores_gemma":[0.00010819967,0.0022756723,0.2479026,0.00006927639,0.0005982079,0.0012986598,0.0023883402,0.724044,0.014099202,0.005943898,0.001044137,0.00022782128],"about_ca_topic_score_codex":0.007670283,"about_ca_topic_score_gemma":0.004746355,"teacher_disagreement_score":0.007670283,"about_ca_system_score_codex":0.00094732066,"about_ca_system_score_gemma":0.00050720805,"threshold_uncertainty_score":0.015251279},"labels":[],"label_agreement":null},{"id":"W2134038042","doi":"10.1109/glocom.2004.1379096","title":"Self-organizing map for mobile location estimation in DS-CDMA systems","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Code division multiple access; Computer science; Robustness (evolution); Non-line-of-sight propagation; Base station; Scalability; Scheme (mathematics); Real-time computing; Multiuser detection; Mobile telephony; Spread spectrum; Cellular network; Nonlinear system; Electronic engineering; Computer network; Mobile radio; Telecommunications; Wireless; Engineering; Mathematics; Database","score_opus":0.005730830455594302,"score_gpt":0.21187272000973667,"score_spread":0.20614188955414237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134038042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02494152,0.00044279016,0.9731775,0.0000928506,0.000036016732,0.00001552983,0.000029021818,0.00038476917,0.0008799994],"genre_scores_gemma":[0.7704015,0.00046002746,0.22718228,0.00003717205,0.000065009306,0.00009241026,0.000093593764,0.000029439154,0.001638467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998543,0.00005955088,0.000006607652,0.000016498985,0.000049561928,0.000013499819],"domain_scores_gemma":[0.9997073,0.00014797894,0.000022194567,0.00002560296,0.00008498818,0.000011941916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028404148,0.00028137094,0.0002774426,0.0004036314,0.00026409226,0.00031929885,0.0004261985,0.00031633436,0.00044481372],"category_scores_gemma":[0.0009826669,0.00015297781,0.00016887789,0.00041413683,0.00025486495,0.00057083124,0.00032993563,0.00029810876,0.00018663888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012820323,0.000053414467,0.0009898415,0.00009650827,0.00004167253,0.000091730784,0.00011329021,0.7265568,0.009215266,0.014705391,0.0024319326,0.24557593],"study_design_scores_gemma":[0.0000027731323,0.000011526663,0.00012417471,0.0000016933978,0.0000018731519,0.0000123035825,0.000007974661,0.996378,0.00090328767,0.0021249868,0.000427854,0.0000035432727],"about_ca_topic_score_codex":0.0018456463,"about_ca_topic_score_gemma":0.0021473751,"teacher_disagreement_score":0.0018456463,"about_ca_system_score_codex":0.000266532,"about_ca_system_score_gemma":0.00026953567,"threshold_uncertainty_score":0.003669858},"labels":[],"label_agreement":null},{"id":"W2134230709","doi":"10.3390/mi6070926","title":"A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems","year":2015,"lang":"en","type":"article","venue":"Micromachines","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Dead reckoning; Kalman filter; Constraint (computer-aided design); Fuse (electrical); Pedestrian; Inertial navigation system; Navigation system; Computer science; Extended Kalman filter; Artificial intelligence; Real-time computing; Engineering; Computer vision; Simulation; Control engineering; Global Positioning System; Inertial frame of reference; Transport engineering; Telecommunications","score_opus":0.03126898484671116,"score_gpt":0.23473066252963107,"score_spread":0.2034616776829199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134230709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017888248,0.00010579391,0.9973379,0.000023804887,0.000029975708,0.000014136976,0.000015466252,0.00019214729,0.00049194536],"genre_scores_gemma":[0.4328036,0.0008273429,0.5581329,0.00014887884,0.00015773508,0.00031886465,0.0003699822,0.00010823959,0.0071324483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928445,0.00010036546,0.00005113184,0.00023441663,0.0002497509,0.000079884056],"domain_scores_gemma":[0.99959916,0.00010675132,0.00006456727,0.00003176093,0.00017761905,0.000020062225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007718079,0.0010210007,0.00112402,0.00063676626,0.0006171883,0.0007218537,0.0011152108,0.0009416577,0.0020656725],"category_scores_gemma":[0.0016107898,0.0005852724,0.0010317016,0.0008419662,0.00043423194,0.0014063724,0.0010080396,0.0010739953,0.0006450739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002609616,0.000082894,0.0028102677,0.0003833221,0.00021029924,0.00032782822,0.000387022,0.55934846,0.021706577,0.020806616,0.0034652501,0.39021042],"study_design_scores_gemma":[0.000015775886,0.00007158312,0.00039639414,0.000013645134,0.000035763125,0.000055507557,0.000017386672,0.9928028,0.002421925,0.0012901064,0.0028534094,0.00002578551],"about_ca_topic_score_codex":0.020880632,"about_ca_topic_score_gemma":0.015659451,"teacher_disagreement_score":0.020880632,"about_ca_system_score_codex":0.0006886442,"about_ca_system_score_gemma":0.001703835,"threshold_uncertainty_score":0.04151821},"labels":[],"label_agreement":null},{"id":"W2134364522","doi":"10.1109/twc.2008.070112","title":"A New Time of Arrival Estimation Method Using UWB Dual Pulse Signals","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Time of arrival; Computer science; Autocorrelation; Ultra-wideband; Ranging; SIGNAL (programming language); Algorithm; Energy (signal processing); Time-hopping; Pulse (music); Telecommunications; Channel (broadcasting); Detector; Statistics; Mathematics; Pulse-amplitude modulation","score_opus":0.03757832246591187,"score_gpt":0.28518354996080475,"score_spread":0.2476052274948929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134364522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073924195,0.00015144618,0.991524,0.000042787407,0.000070490845,0.000019450275,0.00002219773,0.0003809605,0.00039626262],"genre_scores_gemma":[0.09644922,0.0002488569,0.90104747,0.00005974308,0.00008110791,0.000059071277,0.000091830545,0.00004808524,0.0019145646],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992454,0.0000961445,0.00004480936,0.00021106705,0.0003663506,0.0000362925],"domain_scores_gemma":[0.9989261,0.00027843504,0.00014224272,0.00013463442,0.00045479697,0.000063695064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005174983,0.00063576904,0.00070477463,0.0012889231,0.00030048026,0.00074845436,0.0012373038,0.00081400084,0.0011002739],"category_scores_gemma":[0.0024412633,0.00050340296,0.00041883922,0.0008077926,0.00030450063,0.0014886566,0.00081307365,0.001287078,0.0008496783],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035075776,0.00013227193,0.0012987392,0.00021680816,0.00008938852,0.00014433973,0.00013570329,0.016926456,0.25970235,0.0056102807,0.0012857979,0.71410716],"study_design_scores_gemma":[0.000110381385,0.00043203778,0.0018215005,0.000029041168,0.000092656344,0.0014031332,0.000037345577,0.804005,0.17646779,0.0021073127,0.013361709,0.00013209133],"about_ca_topic_score_codex":0.0007015072,"about_ca_topic_score_gemma":0.0007968757,"teacher_disagreement_score":0.0012889231,"about_ca_system_score_codex":0.0003070807,"about_ca_system_score_gemma":0.0006897364,"threshold_uncertainty_score":0.0036807656},"labels":[],"label_agreement":null},{"id":"W2134745209","doi":"10.1109/glocomw.2010.5700152","title":"Range-based localization in wireless networks using decision trees","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Range (aeronautics); Computer science; Node (physics); Singular value decomposition; Wireless sensor network; Algorithm; Decision tree; Data mining; Artificial intelligence; Computer network; Engineering","score_opus":0.00758266956708872,"score_gpt":0.21848613653042118,"score_spread":0.21090346696333245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134745209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010126924,0.000585421,0.9883222,0.00011990089,0.000033400287,0.000027720725,0.00003071353,0.00024491924,0.00050877244],"genre_scores_gemma":[0.60149777,0.0015439887,0.39502028,0.00012398178,0.000120438024,0.00015674559,0.00024097472,0.00005892907,0.0012368956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989139,0.0004808519,0.000071890616,0.00014221318,0.00031607205,0.00007504878],"domain_scores_gemma":[0.9969662,0.002252533,0.0002556703,0.00010888716,0.00035857246,0.000058153397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015695922,0.0005398299,0.0010245856,0.0010740622,0.00055644015,0.0009187042,0.0009256283,0.000676971,0.00094729546],"category_scores_gemma":[0.00471832,0.00033219802,0.00049851096,0.0015418057,0.00049856986,0.0016677268,0.000829998,0.00072182895,0.00030915343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110965884,0.000047673428,0.0015271517,0.000101567224,0.0000595146,0.000065261855,0.00007310654,0.7847902,0.0014214839,0.009562702,0.0014228084,0.20081744],"study_design_scores_gemma":[0.0000057853517,0.000018463454,0.00010519694,0.0000059990043,0.000006337106,0.00001550044,0.000008111236,0.9947982,0.000352373,0.004338658,0.00034104032,0.0000042684305],"about_ca_topic_score_codex":0.0029869769,"about_ca_topic_score_gemma":0.0020707685,"teacher_disagreement_score":0.0029869769,"about_ca_system_score_codex":0.0005949396,"about_ca_system_score_gemma":0.0005172283,"threshold_uncertainty_score":0.00830096},"labels":[],"label_agreement":null},{"id":"W2134965075","doi":"10.1109/mdm.2009.82","title":"A Hybrid Location Identification Method in Wireless Ad Hoc/Sensor Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cistel Technology (Canada); Carleton University","funders":"","keywords":"Multilateration; Computer science; Identification (biology); Wireless sensor network; Wireless ad hoc network; Activity-based costing; Real-time computing; Wireless; Computer network; Telecommunications; Engineering","score_opus":0.007735449877847037,"score_gpt":0.2381861015172748,"score_spread":0.23045065163942774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134965075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036036526,0.00019040663,0.99516946,0.000035711782,0.000053578813,0.000015893716,0.000008998097,0.0004155428,0.0005067221],"genre_scores_gemma":[0.2312445,0.0004703276,0.7633047,0.00011452474,0.00011667544,0.00013580512,0.00008214029,0.000059320606,0.0044719656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993893,0.00018764548,0.000031364987,0.00012167361,0.0002375339,0.000032430784],"domain_scores_gemma":[0.9995321,0.00013920908,0.000048272617,0.00008580218,0.0001715614,0.000023070164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005077537,0.00043189904,0.0004860077,0.0007528827,0.00042309106,0.00054396404,0.0008209727,0.0006619516,0.0010172558],"category_scores_gemma":[0.0010585886,0.0002791875,0.00032541662,0.000745696,0.00037422133,0.0013299469,0.0008589452,0.00046863777,0.0008804909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015767617,0.000088765366,0.0014041862,0.00023848386,0.0001034316,0.00024687705,0.00024309155,0.102601334,0.06716067,0.020547098,0.0026647088,0.8045436],"study_design_scores_gemma":[0.000029953033,0.00019442901,0.0007055839,0.000024574956,0.000038882616,0.0005817724,0.000084089595,0.9539112,0.022821177,0.007024361,0.014525645,0.000058444355],"about_ca_topic_score_codex":0.0010046341,"about_ca_topic_score_gemma":0.0009066362,"teacher_disagreement_score":0.0010172558,"about_ca_system_score_codex":0.00026280875,"about_ca_system_score_gemma":0.00039566285,"threshold_uncertainty_score":0.003403008},"labels":[],"label_agreement":null},{"id":"W2135073493","doi":"10.1109/vetecf.2004.1404734","title":"Wideband measurements of channel characteristics at 2.4 and 5.8 GHz in underground mining environments","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Delay spread; Wideband; Multipath propagation; Radio channel; Flattening; Radio spectrum; Impulse (physics); Channel (broadcasting); Frequency band; Impulse response; Acoustics; Power delay profile; Radio frequency; Center frequency; Electrical engineering; Physics; Electronic engineering; Telecommunications; Remote sensing; Engineering; Geology; Bandwidth (computing); Mathematics","score_opus":0.02035502217256104,"score_gpt":0.20360927408001758,"score_spread":0.18325425190745653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135073493","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99425805,0.000033274006,0.0053057056,0.000005643517,0.0000021566564,0.0000028991851,0.00005010229,0.000054498294,0.0002876234],"genre_scores_gemma":[0.9987035,0.000020395424,0.0010694294,0.0000028905906,0.0000016795733,0.0000028416268,0.000055457433,0.0000065903855,0.00013725912],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997646,0.00004290234,0.0000066035755,0.000045040397,0.00007564516,0.00006521226],"domain_scores_gemma":[0.9996138,0.00016178327,0.0000910636,0.000026960537,0.00007841248,0.000027970802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014534248,0.00020183767,0.00022002973,0.0004921718,0.000138448,0.00015440931,0.000107327156,0.00022467435,0.0003768428],"category_scores_gemma":[0.00058733695,0.00011815207,0.000075609,0.00037674882,0.00015662561,0.00023615337,0.0001531414,0.00013998763,0.00011486095],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001488623,0.00011844393,0.15675394,0.00013146846,0.000088650704,0.000850086,0.0008403808,0.017233908,0.7567859,0.00029712197,0.00030442284,0.065106936],"study_design_scores_gemma":[0.000065951725,0.0019194421,0.6821902,0.000030480662,0.00023770623,0.0023355251,0.001251612,0.03240925,0.27647093,0.00039534108,0.0026033802,0.00009022272],"about_ca_topic_score_codex":0.0006623934,"about_ca_topic_score_gemma":0.0014220062,"teacher_disagreement_score":0.0006623934,"about_ca_system_score_codex":0.00008496752,"about_ca_system_score_gemma":0.00007064466,"threshold_uncertainty_score":0.0013170838},"labels":[],"label_agreement":null},{"id":"W2135153024","doi":"10.2200/s00115ed1v01y200804mpc004","title":"Location Systems: An Introduction to the Technology Behind Location Awareness","year":2008,"lang":"en","type":"article","venue":"Synthesis lectures on mobile and pervasive computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Business","score_opus":0.011362399298342107,"score_gpt":0.2295009858997261,"score_spread":0.218138586601384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135153024","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019988834,0.16375068,0.7254319,0.013631341,0.008304197,0.000100563724,0.00037238398,0.0014725269,0.08493758],"genre_scores_gemma":[0.090686254,0.28905675,0.38850102,0.009202715,0.02107185,0.00045639096,0.0009708046,0.0009873206,0.19906685],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99940383,0.00015370549,0.000052120355,0.00014588324,0.00019315499,0.000051355382],"domain_scores_gemma":[0.9989441,0.000690179,0.000034421388,0.00008975046,0.00017122747,0.00007027336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093701103,0.0011196502,0.00072511355,0.0013640579,0.0006807282,0.004386235,0.001237391,0.0031125275,0.012051962],"category_scores_gemma":[0.0019390483,0.00075280026,0.00068451115,0.0021478974,0.0027046208,0.0067360667,0.0017358492,0.004043055,0.006632371],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005855816,0.00007352127,0.00048211668,0.000991038,0.00003780788,0.00018911892,0.00091633445,0.004339348,0.0039436053,0.5641504,0.10460712,0.32021114],"study_design_scores_gemma":[0.000012448391,0.000069183094,0.00037258284,0.00046411087,0.00002610772,0.00054314063,0.00024031303,0.0066309418,0.001340432,0.15655039,0.8337042,0.000046207606],"about_ca_topic_score_codex":0.0015278865,"about_ca_topic_score_gemma":0.0019465366,"teacher_disagreement_score":0.012051962,"about_ca_system_score_codex":0.0011983794,"about_ca_system_score_gemma":0.000786119,"threshold_uncertainty_score":0.040317833},"labels":[],"label_agreement":null},{"id":"W2135261830","doi":"10.1109/cse.2009.351","title":"Adaptive and Intelligent Route Learning for Mobile Assets Using Geo-tracking and Context Profiles","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cistel Technology (Canada); University of Ottawa","funders":"","keywords":"Computer science; Context (archaeology); Asset (computer security); Routing (electronic design automation); Tracking (education); Transmission (telecommunications); Track (disk drive); Protocol (science); Real-time computing; Wireless; Computer network; Distributed computing; Artificial intelligence; Computer security; Telecommunications","score_opus":0.02279444551586709,"score_gpt":0.2556979952470267,"score_spread":0.2329035497311596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135261830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13901755,0.000090905996,0.85659146,0.0000806134,0.00001926273,0.00008556083,0.00005770308,0.0022683237,0.001788646],"genre_scores_gemma":[0.8651808,0.00007292927,0.1336123,0.000015844107,0.000008286938,0.00004593003,0.00009255877,0.000049222137,0.00092217524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997217,0.00008653353,0.000022979342,0.00006547892,0.00006446223,0.000038730726],"domain_scores_gemma":[0.99908113,0.00027086135,0.00014456247,0.00028966906,0.00015584163,0.00005792624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005093684,0.00038500965,0.0003422166,0.0005333277,0.00030766122,0.0006067161,0.0008132542,0.0004498149,0.00064657535],"category_scores_gemma":[0.0022522951,0.00021048893,0.0001936189,0.0003927611,0.00034469392,0.0014866259,0.00068363297,0.0004397025,0.00028804239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052096683,0.00035164575,0.009826587,0.00012851828,0.00006947894,0.00033773787,0.00048723037,0.46979544,0.07190157,0.014839147,0.0014676458,0.4302741],"study_design_scores_gemma":[0.000022342783,0.000153563,0.0014412606,0.000008959312,0.00002477457,0.00014989545,0.00007466269,0.9667012,0.025779966,0.0036795943,0.0019378165,0.000025964764],"about_ca_topic_score_codex":0.0021769663,"about_ca_topic_score_gemma":0.0029710396,"teacher_disagreement_score":0.0021769663,"about_ca_system_score_codex":0.0003301795,"about_ca_system_score_gemma":0.0004781263,"threshold_uncertainty_score":0.004328549},"labels":[],"label_agreement":null},{"id":"W2135465567","doi":"10.3390/s120303720","title":"Design and Testing of a Multi-Sensor Pedestrian Location and Navigation Platform","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Computer science; Real-time computing; Accelerometer; Flexibility (engineering); Pedestrian; Embedded system; Simulation; Systems engineering; Engineering; Transport engineering; Telecommunications","score_opus":0.04388043409423579,"score_gpt":0.24318745041287865,"score_spread":0.19930701631864287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135465567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5247951,0.00021122511,0.46277627,0.00029459424,0.00029954177,0.0018539409,0.0006349402,0.0034662115,0.0056682215],"genre_scores_gemma":[0.75607383,0.00012736527,0.23595135,0.00013370761,0.000023062044,0.00080301,0.0003777835,0.000106678606,0.006403165],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906605,0.00014374068,0.00005344173,0.0001972657,0.00042575088,0.00011376367],"domain_scores_gemma":[0.99898213,0.00016393077,0.000120843855,0.00015514971,0.00044651446,0.00013141368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011360608,0.00077200023,0.00056555565,0.000593724,0.00029920528,0.00048417272,0.0015260398,0.00085545675,0.0025316845],"category_scores_gemma":[0.001401262,0.00043200116,0.0002916972,0.0002635447,0.00039675293,0.00095702487,0.00088716653,0.0004231984,0.0007215869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021026107,0.0008937637,0.0178978,0.0013866548,0.00017415077,0.0017128779,0.0011816801,0.07813849,0.666103,0.00512661,0.0046347515,0.2206476],"study_design_scores_gemma":[0.0003688228,0.01361396,0.017893469,0.000129773,0.00018898021,0.0016882154,0.0004851116,0.28981796,0.64167243,0.001239089,0.032739326,0.00016286917],"about_ca_topic_score_codex":0.0013385696,"about_ca_topic_score_gemma":0.0011465887,"teacher_disagreement_score":0.0025316845,"about_ca_system_score_codex":0.000433983,"about_ca_system_score_gemma":0.0013901037,"threshold_uncertainty_score":0.008469284},"labels":[],"label_agreement":null},{"id":"W2136350494","doi":"10.1109/icc.2007.602","title":"ESPRIT-Based Directional MAC Protocol for Mobile Ad Hoc Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Throughput; Computer network; Wireless ad hoc network; Channel (broadcasting); Transmission (telecommunications); Global Positioning System; Omnidirectional antenna; Directional antenna; Real-time computing; Protocol (science); Mobile ad hoc network; Wireless; Antenna (radio); Telecommunications; Network packet","score_opus":0.010839025969792766,"score_gpt":0.2713322389654166,"score_spread":0.26049321299562384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136350494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004187643,0.002514517,0.9760299,0.00048116923,0.0007154067,0.00042594122,0.00024730305,0.0029671823,0.012431035],"genre_scores_gemma":[0.21299034,0.0055612954,0.7416388,0.0018857709,0.00084417965,0.003241404,0.003257276,0.00047601748,0.030104913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984571,0.0004914849,0.00019880844,0.00015077442,0.00059067574,0.000111139845],"domain_scores_gemma":[0.99877614,0.00031101407,0.00016148745,0.0002561325,0.00043545058,0.00005980743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015989435,0.0010936622,0.0008357196,0.0008988257,0.0007164242,0.0011667302,0.0019216066,0.0011655715,0.0050011836],"category_scores_gemma":[0.0030910405,0.00027476964,0.0005297758,0.0010129508,0.0007082816,0.0015541472,0.0015898197,0.0017250084,0.003213862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007650871,0.00028227083,0.00078124856,0.0012464264,0.00019836723,0.0010858681,0.0004841039,0.03302379,0.080563255,0.14356361,0.06171771,0.6762882],"study_design_scores_gemma":[0.0005420386,0.0014816442,0.0011615648,0.0002722297,0.00028403543,0.0043496205,0.00019829605,0.44113123,0.06305274,0.058764867,0.4284694,0.0002924121],"about_ca_topic_score_codex":0.00046551955,"about_ca_topic_score_gemma":0.0006598133,"teacher_disagreement_score":0.0050011836,"about_ca_system_score_codex":0.00051273097,"about_ca_system_score_gemma":0.0007605844,"threshold_uncertainty_score":0.016730666},"labels":[],"label_agreement":null},{"id":"W2136641876","doi":"10.1109/icces.2009.5383296","title":"Enhanced mobile robot outdoor localization using INS/GPS integration","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University; Canadian Armed Forces","funders":"","keywords":"Gyroscope; Inertial measurement unit; Global Positioning System; Odometry; Accelerometer; Inertial navigation system; GPS/INS; Computer science; Kalman filter; Real-time computing; Encoder; Mobile robot; Dead reckoning; Robot; Assisted GPS; Simulation; Inertial frame of reference; Engineering; Artificial intelligence; Aerospace engineering; Telecommunications; Physics","score_opus":0.011779770307382236,"score_gpt":0.2397335312852142,"score_spread":0.227953760977832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136641876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055606186,0.00042344307,0.9327048,0.00007318928,0.00012857177,0.000049207672,0.00010599855,0.006330429,0.0045781946],"genre_scores_gemma":[0.60642576,0.00041566725,0.38439143,0.000099726036,0.00007495538,0.00008534748,0.00047548194,0.00014836536,0.007883347],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980944,0.00002607069,0.000010177252,0.000040866977,0.00009285748,0.000020695448],"domain_scores_gemma":[0.99989307,0.000013314408,0.000017195094,0.00002308563,0.000048402777,0.0000050073177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000109211695,0.0005250053,0.00044215948,0.00036379707,0.0001699782,0.00029233613,0.00035983234,0.00032116793,0.0013938374],"category_scores_gemma":[0.00022381528,0.00023621485,0.00036338615,0.00033939772,0.00011677996,0.0003768724,0.00038239014,0.00029926118,0.0012495939],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004614478,0.00012329598,0.0029822264,0.00032445273,0.000091091955,0.00072543405,0.00022391615,0.06541047,0.43288982,0.0027638064,0.003967618,0.49003643],"study_design_scores_gemma":[0.000074402866,0.00081710616,0.009741721,0.0000560935,0.000169914,0.0010790132,0.000090131594,0.7452232,0.20002176,0.001049666,0.041595276,0.00008174284],"about_ca_topic_score_codex":0.0022944326,"about_ca_topic_score_gemma":0.002203739,"teacher_disagreement_score":0.0022944326,"about_ca_system_score_codex":0.00015417817,"about_ca_system_score_gemma":0.00021383086,"threshold_uncertainty_score":0.0046628118},"labels":[],"label_agreement":null},{"id":"W2136777038","doi":"10.1109/cdc.2006.376721","title":"Using Angle of Arrival (Bearing) Information in Network Localization","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Rigidity (electromagnetism); Uniqueness; Bearing (navigation); Property (philosophy); Computer science; Heading (navigation); Topology (electrical circuits); Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Engineering; Structural engineering; Combinatorics; Mathematical analysis; Aerospace engineering","score_opus":0.009115603924802812,"score_gpt":0.20091995657996922,"score_spread":0.1918043526551664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136777038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045854645,0.0003866363,0.99243516,0.00018777295,0.00006814735,0.000011251677,0.000024660607,0.00008141436,0.0022195352],"genre_scores_gemma":[0.64159256,0.004479785,0.34799415,0.00025712082,0.0004640957,0.000100354904,0.00020580359,0.000090611924,0.004815508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896884,0.00040831038,0.00004363047,0.0001992576,0.00031642753,0.00006364939],"domain_scores_gemma":[0.99853444,0.0007481222,0.00027756856,0.00023159076,0.00016997695,0.000038219274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010694213,0.0008595724,0.0005933054,0.0010379807,0.00045045014,0.0010395839,0.0009728002,0.0011142647,0.0013778945],"category_scores_gemma":[0.0041318308,0.00035810244,0.0006055399,0.0016434267,0.00176327,0.0037206935,0.0016453344,0.0007772064,0.0005490098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011749752,0.000029953822,0.0014542759,0.000309262,0.00006878177,0.00031372497,0.00020077976,0.57865405,0.014736901,0.27798298,0.0010056236,0.12512627],"study_design_scores_gemma":[0.000024334076,0.00022121964,0.0007290028,0.00006710882,0.000058507507,0.00053328247,0.00016578178,0.75392497,0.010508052,0.21821913,0.015469249,0.000079362406],"about_ca_topic_score_codex":0.0012201158,"about_ca_topic_score_gemma":0.0007212225,"teacher_disagreement_score":0.0013778945,"about_ca_system_score_codex":0.00041534725,"about_ca_system_score_gemma":0.00046076838,"threshold_uncertainty_score":0.005655706},"labels":[],"label_agreement":null},{"id":"W2137003714","doi":"10.2514/6.2004-5946","title":"UWB Tracking System Design for Free-Flyers","year":2004,"lang":"en","type":"article","venue":"Space 2004 Conference and Exhibit","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"Johnson Space Center; National Aeronautics and Space Administration","keywords":"Computer science; Tracking (education)","score_opus":0.023765374527124305,"score_gpt":0.21700486568133048,"score_spread":0.1932394911542062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137003714","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019448038,0.00043448003,0.9695579,0.00017110453,0.000103637925,0.00008281985,0.00002859411,0.0008585395,0.009315001],"genre_scores_gemma":[0.612516,0.0006627952,0.3502398,0.0003791893,0.00016080387,0.00026717898,0.00015223952,0.00009896451,0.035522953],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997396,0.000044355296,0.000012173638,0.00006272829,0.0001163389,0.000024851377],"domain_scores_gemma":[0.99978775,0.00003739862,0.00002785252,0.000019672645,0.0001137818,0.0000135162345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002598991,0.0002917826,0.00025633274,0.0002394984,0.00048412746,0.00070861576,0.00056566263,0.000685059,0.002531569],"category_scores_gemma":[0.0004333596,0.00016500376,0.00020533086,0.000154059,0.00015385171,0.0005418613,0.0002866575,0.0002964561,0.001188713],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000348703,0.00007772902,0.0015102751,0.0003819591,0.00007316995,0.00030740548,0.00065343786,0.045065768,0.37177116,0.023215236,0.0054032262,0.5511919],"study_design_scores_gemma":[0.00016146514,0.0015796463,0.0031604406,0.00013148661,0.00012740535,0.0018166364,0.00027328008,0.5335353,0.32190388,0.0057493877,0.13147913,0.00008194143],"about_ca_topic_score_codex":0.0009572707,"about_ca_topic_score_gemma":0.0010895691,"teacher_disagreement_score":0.002531569,"about_ca_system_score_codex":0.00042525292,"about_ca_system_score_gemma":0.00047438286,"threshold_uncertainty_score":0.008468926},"labels":[],"label_agreement":null},{"id":"W2137352707","doi":"10.1504/ijvics.2005.007589","title":"An HSGPS, inertial and map-matching integrated portable vehicular navigation system for uninterrupted real-time vehicular navigation","year":2005,"lang":"en","type":"article","venue":"International Journal of Vehicle Information and Communication Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Map matching; Inertial measurement unit; Inertial navigation system; Computer science; Real-time computing; Navigation system; Dead reckoning; Matching (statistics); Inertial frame of reference; Artificial intelligence; Telecommunications","score_opus":0.006543751754640801,"score_gpt":0.23732659351524413,"score_spread":0.23078284176060332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137352707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21279784,0.00037652592,0.7243764,0.00028093826,0.00043403613,0.00042329202,0.0011385962,0.024567328,0.035605196],"genre_scores_gemma":[0.8016682,0.00020736811,0.15053295,0.00029578572,0.00013533678,0.0002049174,0.0016911332,0.00017927929,0.04508501],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998155,0.000017466187,0.000005606442,0.000038565948,0.00010353749,0.000019257826],"domain_scores_gemma":[0.9998276,0.000010506974,0.000019274263,0.000031396972,0.00008245198,0.000028706849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016646522,0.00031956413,0.00032309038,0.0003990417,0.00027860014,0.0003856338,0.0006620267,0.00040847473,0.004739553],"category_scores_gemma":[0.00026879943,0.00015539033,0.00013145266,0.000408936,0.00016827558,0.0003633314,0.0004613085,0.00035616755,0.0023582142],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006268748,0.00028898375,0.008831671,0.00021193353,0.00007753127,0.00059286115,0.00021934128,0.011588434,0.24381803,0.003972589,0.030518379,0.69925344],"study_design_scores_gemma":[0.0006190145,0.004693242,0.05966781,0.00009999251,0.0003537501,0.006412436,0.00038741666,0.3443796,0.2768717,0.0038551947,0.30242088,0.00023893006],"about_ca_topic_score_codex":0.002402485,"about_ca_topic_score_gemma":0.0041567213,"teacher_disagreement_score":0.004739553,"about_ca_system_score_codex":0.00025199036,"about_ca_system_score_gemma":0.0005095403,"threshold_uncertainty_score":0.015855372},"labels":[],"label_agreement":null},{"id":"W2137983305","doi":"10.1109/iwcmc.2011.5982676","title":"Implementation of the CCA-MAP localization algorithm on a wireless sensor network testbed","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Testbed; Wireless sensor network; Computer science; Algorithm; Real-time computing; Key distribution in wireless sensor networks; Wireless; Distributed computing; Computer network; Wireless network","score_opus":0.0127586033262836,"score_gpt":0.21798004846767388,"score_spread":0.2052214451413903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137983305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2990936,0.000104197745,0.67364246,0.00025168995,0.00018732785,0.0005663229,0.00032679812,0.020350795,0.005476869],"genre_scores_gemma":[0.7161501,0.00007654878,0.2812326,0.000038293903,0.000009619767,0.0003561912,0.00048451935,0.0002713177,0.0013807291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994485,0.00015173273,0.000042359075,0.00008085354,0.00017927265,0.000097213466],"domain_scores_gemma":[0.99895823,0.00028133768,0.00008579259,0.0002328855,0.0003410874,0.0001006983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087698636,0.00056178414,0.0005260903,0.0005187169,0.00036513113,0.00040036847,0.0012525468,0.0004427166,0.0019781506],"category_scores_gemma":[0.0022898535,0.00016202216,0.00026483485,0.0003980159,0.00039769392,0.00070231734,0.00063452264,0.00048237244,0.00055975013],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013936162,0.001211683,0.009822302,0.00071161165,0.00020829585,0.0012552777,0.00055561296,0.53898853,0.17642272,0.0138172405,0.0107553145,0.24485782],"study_design_scores_gemma":[0.0001687619,0.00062683935,0.0019920694,0.000014657289,0.000027591559,0.00019182957,0.00009800153,0.89801013,0.090915866,0.0009763995,0.0069419043,0.000036015357],"about_ca_topic_score_codex":0.0026065253,"about_ca_topic_score_gemma":0.0015328731,"teacher_disagreement_score":0.0026065253,"about_ca_system_score_codex":0.0004501388,"about_ca_system_score_gemma":0.00082782586,"threshold_uncertainty_score":0.0066176057},"labels":[],"label_agreement":null},{"id":"W2138085267","doi":"10.1109/vetecf.1999.798599","title":"An analysis of TOA-based location for IS-95 mobiles","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Ranging; Detector; Computer science; Time of arrival; Multipath propagation; Channel (broadcasting); Algorithm; Mobile radio; Real-time computing; Electronic engineering; Telecommunications; Engineering","score_opus":0.00820289812939413,"score_gpt":0.24486840274243984,"score_spread":0.23666550461304572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138085267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8206786,0.00044032987,0.1683503,0.00025235704,0.000043044027,0.00006103815,0.00022678032,0.00058974617,0.009357884],"genre_scores_gemma":[0.9939797,0.00008730809,0.0047298186,0.000017160435,0.0000083799905,0.000010148486,0.00009954223,0.000016396009,0.0010514612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927396,0.00008746129,0.000015070556,0.00007459075,0.00047443234,0.00007440603],"domain_scores_gemma":[0.9981299,0.0007060484,0.00019980354,0.00018866736,0.0007343288,0.000041175284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046893518,0.000362362,0.00028152176,0.0011052208,0.0003111253,0.0006216671,0.00037764045,0.00044923922,0.0014917552],"category_scores_gemma":[0.0043134796,0.00015756121,0.00023421753,0.0007297008,0.0003365544,0.00071886985,0.00027591683,0.00028260928,0.00054772745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004619799,0.00010643803,0.032005474,0.00016782431,0.00011860959,0.0005003158,0.00016539395,0.79662484,0.05205712,0.01364976,0.0010167906,0.103125386],"study_design_scores_gemma":[0.00001332601,0.000394439,0.021352818,0.000016878717,0.000043451873,0.00060846773,0.00009551499,0.9574775,0.016277377,0.0018990161,0.001793552,0.000027662634],"about_ca_topic_score_codex":0.003748434,"about_ca_topic_score_gemma":0.0027436255,"teacher_disagreement_score":0.003748434,"about_ca_system_score_codex":0.0007497593,"about_ca_system_score_gemma":0.0004299298,"threshold_uncertainty_score":0.007453263},"labels":[],"label_agreement":null},{"id":"W2138736858","doi":"10.1002/j.2161-4296.2003.tb00319.x","title":"A New Positioning Filter: Phase Smoothing in the Position Domain","year":2003,"lang":"en","type":"article","venue":"NAVIGATION Journal of the Institute of Navigation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NovAtel (Canada)","funders":"","keywords":"Kalman filter; Position (finance); Control theory (sociology); Smoothing; Filter (signal processing); Extended Kalman filter; Computer science; Phase (matter); Measure (data warehouse); Mathematics; Physics; Computer vision; Artificial intelligence","score_opus":0.009775922323270493,"score_gpt":0.24736527922583693,"score_spread":0.23758935690256644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138736858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032386458,0.00012642879,0.99536526,0.000076577555,0.00018424111,0.000020150876,0.000049053404,0.0005138848,0.00042579498],"genre_scores_gemma":[0.12788925,0.00052310544,0.86167216,0.00028348845,0.0005289588,0.00013869457,0.0004721566,0.00017138683,0.008320739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992822,0.00009062662,0.000050888826,0.00023897665,0.0002910242,0.00004636712],"domain_scores_gemma":[0.99903464,0.00025138297,0.00010257989,0.00013151749,0.00044887685,0.000031129504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011667663,0.0006181381,0.0008588793,0.000679027,0.00042261503,0.0009790629,0.0012487051,0.0012760194,0.0028379546],"category_scores_gemma":[0.0027261544,0.0004798691,0.0006483096,0.001075026,0.00038733336,0.0015882509,0.00072925066,0.0012133942,0.0014172447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046396512,0.00019716352,0.0024155139,0.0003821444,0.00026645686,0.00020006644,0.00017331027,0.13907136,0.07572546,0.022076301,0.0077263573,0.7513018],"study_design_scores_gemma":[0.0000950595,0.0001805444,0.0014485233,0.000024972374,0.00011663305,0.00018046745,0.000013914351,0.95248634,0.016605636,0.0020848361,0.026711594,0.000051470954],"about_ca_topic_score_codex":0.005408054,"about_ca_topic_score_gemma":0.0071516684,"teacher_disagreement_score":0.005408054,"about_ca_system_score_codex":0.0005395303,"about_ca_system_score_gemma":0.00088178675,"threshold_uncertainty_score":0.010753155},"labels":[],"label_agreement":null},{"id":"W2139209052","doi":"10.1109/vetecf.2008.138","title":"Practical Results of Hybrid AOA/TDOA Geo-Location Estimation in CDMA Wireless Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multilateration; Angle of arrival; Computer science; Estimator; Channel (broadcasting); Time of arrival; Code division multiple access; Telecommunications link; Cramér–Rao bound; Wireless; FDOA; Real-time computing; Electronic engineering; Algorithm; Estimation theory; Telecommunications; Acoustics; Engineering; Mathematics; Statistics","score_opus":0.016668312965580672,"score_gpt":0.24576035676743202,"score_spread":0.22909204380185136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139209052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013965372,0.00089178246,0.9768598,0.00024685563,0.00004226264,0.000020388676,0.000025234973,0.00012557201,0.007822681],"genre_scores_gemma":[0.69993424,0.0021647203,0.2918819,0.00010122379,0.00018229581,0.00011597123,0.000099766294,0.00006118468,0.0054587815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991979,0.00027699512,0.000033348053,0.00010295656,0.00033888296,0.000049964765],"domain_scores_gemma":[0.9975352,0.0018651485,0.00006834578,0.00014006936,0.0003615581,0.000029689229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015125717,0.00054236327,0.00030638473,0.000526991,0.00034693608,0.00090791116,0.00040014467,0.0005226461,0.002668916],"category_scores_gemma":[0.008583383,0.0002532551,0.00020816871,0.00055811275,0.000777586,0.0012579605,0.00090410333,0.0006134754,0.0004942922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016008648,0.000046882295,0.0022087856,0.00025575113,0.000029545994,0.00026009927,0.00019164044,0.7289241,0.010941078,0.10978633,0.0016471837,0.14554854],"study_design_scores_gemma":[0.000007871073,0.00005712977,0.00035341328,0.000019292931,0.000008952056,0.00015832481,0.000053002594,0.9787759,0.0032642493,0.014730864,0.0025606032,0.00001036806],"about_ca_topic_score_codex":0.0021768278,"about_ca_topic_score_gemma":0.0014695479,"teacher_disagreement_score":0.002668916,"about_ca_system_score_codex":0.00058034336,"about_ca_system_score_gemma":0.0004482675,"threshold_uncertainty_score":0.008928418},"labels":[],"label_agreement":null},{"id":"W2139306799","doi":"10.1023/a:1016025802932","title":"Mobile Location Estimation in CDMA Cellular Networks by Using Fuzzy Logic","year":2002,"lang":"en","type":"article","venue":"Wireless Personal Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Government of Canada","keywords":"Computer science; Estimator; Code division multiple access; Fuzzy logic; Base station; Division (mathematics); Path (computing); Path loss; SIGNAL (programming language); Algorithm; Mathematical optimization; Telecommunications; Statistics; Artificial intelligence; Computer network; Mathematics; Wireless","score_opus":0.02781952508323927,"score_gpt":0.24554071685666146,"score_spread":0.2177211917734222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139306799","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09994451,0.00022736823,0.8980724,0.00009088387,0.00002440216,0.000023524084,0.00003468974,0.00017069124,0.0014115071],"genre_scores_gemma":[0.93409663,0.00009420442,0.06505523,0.000027402579,0.000013168192,0.000027133612,0.000026016214,0.0000052156834,0.00065502577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975353,0.00005257458,0.000023607043,0.000059537208,0.00008272934,0.000028039029],"domain_scores_gemma":[0.9993807,0.00033985433,0.00006334542,0.000023336037,0.00017419035,0.000018544846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055057305,0.0002935768,0.00040675956,0.00051503145,0.00050273334,0.0007782295,0.0004811688,0.00043166935,0.0004941365],"category_scores_gemma":[0.0016978784,0.00020936901,0.00031678582,0.00037227428,0.00039121427,0.0007025412,0.00025339666,0.0003126593,0.000083198305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048862613,0.00010312517,0.0055761468,0.00010877262,0.00008377886,0.00020739462,0.00020270437,0.78592724,0.018813659,0.0103198765,0.000645649,0.177523],"study_design_scores_gemma":[0.000007286707,0.000024982344,0.00027891903,0.0000056595977,0.000014796004,0.000016672791,0.000013897186,0.9966444,0.0013596708,0.0015199981,0.0001083247,0.0000055141286],"about_ca_topic_score_codex":0.012312047,"about_ca_topic_score_gemma":0.009098874,"teacher_disagreement_score":0.012312047,"about_ca_system_score_codex":0.00087905827,"about_ca_system_score_gemma":0.00056242006,"threshold_uncertainty_score":0.02448076},"labels":[],"label_agreement":null},{"id":"W2139517656","doi":"10.1109/vetecf.2008.143","title":"Motion Detection of a Real Beacon Using Estimator Correlator","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Multipath propagation; Estimator; Offset (computer science); Real-time computing; Base station; Multipath mitigation; Algorithm; Statistics; Mathematics; Telecommunications","score_opus":0.01609350117346047,"score_gpt":0.2101542028864505,"score_spread":0.19406070171299003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139517656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24586748,0.0013717557,0.74896944,0.000159294,0.00013564462,0.00006009993,0.00006466409,0.0011835496,0.0021882027],"genre_scores_gemma":[0.83395666,0.000391147,0.16341856,0.00008370377,0.000075121716,0.000049958406,0.00009753064,0.000035849313,0.0018913615],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947244,0.00009162283,0.000020219095,0.00011277961,0.0002430424,0.000059834743],"domain_scores_gemma":[0.99895155,0.00035736943,0.00020651195,0.00013217053,0.00031644592,0.000035930745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007050215,0.00027549153,0.0004840878,0.0006351625,0.00013815287,0.00038399544,0.00057923456,0.00053136225,0.0005140486],"category_scores_gemma":[0.0019813974,0.00021443133,0.00015786724,0.00049341423,0.00020884346,0.00047271475,0.0003972328,0.0003945632,0.0002633141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008014908,0.00011135469,0.010825349,0.00022314084,0.000058615304,0.00040739894,0.00021776962,0.01140544,0.6382932,0.004116022,0.0011320221,0.33240816],"study_design_scores_gemma":[0.00014968382,0.0022381768,0.01801008,0.00006185007,0.00012206065,0.004506695,0.000078238176,0.3899141,0.5712658,0.0009806713,0.012554105,0.00011849652],"about_ca_topic_score_codex":0.00047173613,"about_ca_topic_score_gemma":0.0006383069,"teacher_disagreement_score":0.0007050215,"about_ca_system_score_codex":0.0002874331,"about_ca_system_score_gemma":0.0003115061,"threshold_uncertainty_score":0.0037285686},"labels":[],"label_agreement":null},{"id":"W2140701037","doi":"10.1109/vetecs.2011.5956252","title":"Range-Based Localization in Wireless Networks Using the DBSCAN Clustering Algorithm","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"DBSCAN; Computer science; Algorithm; Cluster analysis; Range (aeronautics); Intersection (aeronautics); Metric (unit); Euclidean distance; Singular value decomposition; Artificial intelligence; CURE data clustering algorithm; Correlation clustering","score_opus":0.02156308823529762,"score_gpt":0.20865288168015989,"score_spread":0.18708979344486226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140701037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032803332,0.00028602584,0.99463034,0.000087911176,0.000028972147,0.00004569867,0.00006511594,0.0007562732,0.0008193747],"genre_scores_gemma":[0.13164647,0.0007825199,0.86458945,0.00008462792,0.000057932626,0.00024262255,0.00045531685,0.00014914178,0.0019919518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982804,0.00044344892,0.00011661176,0.0002823368,0.0007839829,0.000093228395],"domain_scores_gemma":[0.99864036,0.00049504876,0.00014514811,0.0001434363,0.0005320042,0.000044027307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012195209,0.0010893184,0.0016733356,0.0032280122,0.0012173483,0.0016399254,0.0022854896,0.0011658836,0.001438162],"category_scores_gemma":[0.0042837514,0.0006035405,0.0010292056,0.005570646,0.00069465325,0.001875964,0.0014112621,0.0012374588,0.00102688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019180073,0.00007141681,0.0015145054,0.00019350923,0.00018060503,0.00015964093,0.00025933355,0.5065279,0.0042521316,0.024137974,0.0061196364,0.45639142],"study_design_scores_gemma":[0.000013730916,0.000027113034,0.00030107473,0.00001364298,0.000014207873,0.00012295018,0.000059405167,0.9846198,0.0024591545,0.009034103,0.0033025746,0.00003229322],"about_ca_topic_score_codex":0.017411295,"about_ca_topic_score_gemma":0.011965748,"teacher_disagreement_score":0.017411295,"about_ca_system_score_codex":0.0012841895,"about_ca_system_score_gemma":0.0015457209,"threshold_uncertainty_score":0.034619868},"labels":[],"label_agreement":null},{"id":"W2140889508","doi":"10.1109/tvt.2003.814222","title":"Location of mobile terminals using time measurements and survey points","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":128,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Non-line-of-sight propagation; Estimator; Microcell; Shadow mapping; Robustness (evolution); Computer science; RSS; Geolocation; Cramér–Rao bound; Kernel density estimation; Upper and lower bounds; Time of arrival; Algorithm; Statistics; Mathematics; Telecommunications; Wireless; Artificial intelligence","score_opus":0.02232426975619553,"score_gpt":0.24056441222988456,"score_spread":0.21824014247368903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140889508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20244956,0.0004200306,0.79400134,0.00009227051,0.000049282975,0.00003194152,0.00026658468,0.00076192315,0.0019271133],"genre_scores_gemma":[0.89880913,0.0003510787,0.09928893,0.000016822456,0.0000370607,0.000042845662,0.00040431885,0.00001785007,0.00103189],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966776,0.00011017726,0.000019778161,0.0000726261,0.00010412042,0.000025524107],"domain_scores_gemma":[0.9991617,0.00021339221,0.00019150367,0.00015283082,0.0002472373,0.000033295742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035594316,0.00046587893,0.00032136973,0.0011810052,0.00020208857,0.00061686867,0.00041026104,0.00066561124,0.00041555476],"category_scores_gemma":[0.0031516685,0.00019297107,0.0003175678,0.001268109,0.00025659974,0.001084184,0.00066150894,0.00032709652,0.0005066659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060677045,0.00008327104,0.028975084,0.000274623,0.00013700259,0.00033894242,0.00030902438,0.41032523,0.08187385,0.00951287,0.0013943845,0.46616885],"study_design_scores_gemma":[0.00003758299,0.00029910257,0.01861964,0.000038910697,0.0000777408,0.00049276726,0.00016437336,0.94207615,0.029368326,0.004274295,0.0044736685,0.00007744371],"about_ca_topic_score_codex":0.0016062312,"about_ca_topic_score_gemma":0.0022466006,"teacher_disagreement_score":0.0016062312,"about_ca_system_score_codex":0.00024817968,"about_ca_system_score_gemma":0.00029453394,"threshold_uncertainty_score":0.003193736},"labels":[],"label_agreement":null},{"id":"W2141802175","doi":"10.1109/tvt.2003.814219","title":"Dynamic model-based filtering for mobile terminal location estimation","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Terminal (telecommunication); Computer science; Trajectory; Real-time computing; Multipath propagation; Kinematics; Telecommunications","score_opus":0.006833798269423345,"score_gpt":0.22983289068822926,"score_spread":0.22299909241880592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141802175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012117805,0.00006422428,0.9982091,0.000028462357,0.000018017392,0.000004235856,0.000011341276,0.00019593193,0.0002569591],"genre_scores_gemma":[0.38972664,0.0010584604,0.6026514,0.0001567093,0.00019495032,0.00016650149,0.0004488322,0.00016897904,0.00542744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996164,0.000080361104,0.000023015587,0.0000969364,0.00014428516,0.000039013827],"domain_scores_gemma":[0.9993843,0.00033617238,0.000059389688,0.000073715055,0.00013574278,0.000010615954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005773797,0.0005263065,0.000829543,0.0005736169,0.00046147255,0.00077611953,0.0007071927,0.0009834723,0.0015802416],"category_scores_gemma":[0.0026410436,0.00046055892,0.00063960644,0.00077155564,0.00034948438,0.00090653595,0.00046508867,0.0010787782,0.0008676048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011396959,0.00006005185,0.00078892155,0.0001231909,0.000081838145,0.000101522026,0.00009515988,0.65705526,0.01630437,0.018596148,0.0027727077,0.30390698],"study_design_scores_gemma":[0.0000068050194,0.000020103393,0.0001980825,0.0000055443365,0.000010266252,0.0000277714,0.000004215468,0.99337876,0.0018944611,0.0025533456,0.0018898237,0.000010758653],"about_ca_topic_score_codex":0.009048152,"about_ca_topic_score_gemma":0.0075412965,"teacher_disagreement_score":0.009048152,"about_ca_system_score_codex":0.00067973597,"about_ca_system_score_gemma":0.0006890167,"threshold_uncertainty_score":0.017991006},"labels":[],"label_agreement":null},{"id":"W2141828101","doi":"10.1109/icccn.2005.1523938","title":"Directed position estimation: a recursive localization approach for wireless sensor networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Wireless sensor network; Position (finance); Wireless; Estimation; Wireless network; Real-time computing; Computer network; Telecommunications; Engineering","score_opus":0.005315829793029735,"score_gpt":0.19385752767688924,"score_spread":0.1885416978838595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141828101","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009647382,0.00011532554,0.99837756,0.000026431871,0.000010454741,0.000010544499,0.000009254386,0.00018109304,0.0003045891],"genre_scores_gemma":[0.15428403,0.00092665426,0.84063345,0.00010818431,0.00009787069,0.00014642501,0.00020191993,0.00013400424,0.0034674818],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933344,0.00021049645,0.00004055787,0.00014988455,0.00021610994,0.000049550566],"domain_scores_gemma":[0.9993635,0.00023267667,0.000082245526,0.0001333853,0.00016655777,0.000021731175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068346364,0.0007040265,0.0006681758,0.0010828448,0.0004019302,0.0006971861,0.001560849,0.0007595218,0.0010723262],"category_scores_gemma":[0.0025809214,0.0003405363,0.00062835176,0.0010977199,0.0005221462,0.0011486247,0.0011301646,0.0007028463,0.00083966594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000909319,0.000051585528,0.0011855295,0.00022982889,0.000087816,0.00025581944,0.0004653598,0.37779796,0.022819417,0.07399729,0.0035636544,0.5194548],"study_design_scores_gemma":[0.000014407993,0.00007784261,0.00033411643,0.000024673831,0.00003977693,0.00022287898,0.000041987736,0.96065265,0.00567111,0.020641638,0.012240944,0.000037921902],"about_ca_topic_score_codex":0.0039868667,"about_ca_topic_score_gemma":0.004943959,"teacher_disagreement_score":0.0039868667,"about_ca_system_score_codex":0.0005247784,"about_ca_system_score_gemma":0.00071910315,"threshold_uncertainty_score":0.007927299},"labels":[],"label_agreement":null},{"id":"W2142098273","doi":"10.1109/rose.2009.5355980","title":"A high precision sensor system for indoor object positioning and monitoring","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Object (grammar); Position (finance); Indoor positioning system; Measure (data warehouse); Real-time computing; Electro-optical sensor; Computer vision; Artificial intelligence; Engineering; Electronic engineering; Accelerometer; Data mining","score_opus":0.0070620572056268646,"score_gpt":0.21383193174148196,"score_spread":0.2067698745358551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142098273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02343097,0.0015111191,0.95429295,0.00020758448,0.00060055905,0.00026319208,0.0004708711,0.0066173393,0.012605366],"genre_scores_gemma":[0.381461,0.0015229598,0.5683098,0.0006253484,0.00042539876,0.0005594609,0.0012917676,0.00034602787,0.04545822],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986915,0.00013573848,0.000050382703,0.00028490523,0.00074642856,0.00009104104],"domain_scores_gemma":[0.9994313,0.000105175895,0.00006211885,0.00015206287,0.00020125508,0.000048110713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062151364,0.00070434087,0.00073157134,0.0010077265,0.000666654,0.0008180018,0.0014643233,0.0013649189,0.006036139],"category_scores_gemma":[0.00076291594,0.0004174419,0.00044414616,0.0011498382,0.0004261642,0.0011795885,0.00087425817,0.0010766387,0.00353285],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048869714,0.0001718929,0.0024844848,0.00058353477,0.00011601708,0.00035483594,0.0002211375,0.0038777785,0.5718838,0.004474421,0.012249448,0.40309387],"study_design_scores_gemma":[0.00020139669,0.0026981218,0.015367827,0.0001415602,0.00036360294,0.0050055957,0.00015625563,0.047022566,0.604079,0.002495388,0.32205862,0.00041003508],"about_ca_topic_score_codex":0.0007608848,"about_ca_topic_score_gemma":0.0009899772,"teacher_disagreement_score":0.006036139,"about_ca_system_score_codex":0.000311214,"about_ca_system_score_gemma":0.0005474681,"threshold_uncertainty_score":0.020192862},"labels":[],"label_agreement":null},{"id":"W2142137929","doi":"10.1109/wcnc.2011.5779231","title":"Support Vector Machines for indoor sensor localization","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Wireless sensor network; Support vector machine; Real-time computing; Node (physics); Position (finance); Indoor positioning system; Embedded system; Computer network; Artificial intelligence; Accelerometer; Engineering; Operating system","score_opus":0.02131941551345031,"score_gpt":0.21640184816815589,"score_spread":0.19508243265470557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142137929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005068079,0.0032763947,0.9875493,0.00038990309,0.00021080294,0.000049005837,0.00028141725,0.001533956,0.0016412553],"genre_scores_gemma":[0.46525338,0.0048876326,0.5154086,0.00021881079,0.00064792583,0.00056218094,0.0021011226,0.0002029317,0.010717405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915683,0.00030315437,0.0000678583,0.00015787898,0.00024216689,0.00007210422],"domain_scores_gemma":[0.9986387,0.00077292544,0.000115654446,0.00011095386,0.00033057135,0.00003112999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083855295,0.0009578422,0.0011287057,0.0007527489,0.00028118325,0.0008175583,0.0008895861,0.0009260001,0.0037201669],"category_scores_gemma":[0.0038545541,0.00025898535,0.00046961088,0.0015071041,0.00030021535,0.0009009002,0.00056125,0.0014518988,0.0017688881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017034986,0.0000903633,0.00105554,0.00035617294,0.000117052725,0.000121458615,0.000060422866,0.30040386,0.003053912,0.015551203,0.010840702,0.668179],"study_design_scores_gemma":[0.000010921067,0.000040793697,0.00051009195,0.000022971284,0.00000935901,0.000035575093,0.000018903522,0.98372805,0.0008950715,0.010350677,0.004362723,0.00001493912],"about_ca_topic_score_codex":0.0027795872,"about_ca_topic_score_gemma":0.0017335668,"teacher_disagreement_score":0.0037201669,"about_ca_system_score_codex":0.0003696323,"about_ca_system_score_gemma":0.0004897486,"threshold_uncertainty_score":0.012445211},"labels":[],"label_agreement":null},{"id":"W2142402644","doi":"10.1109/ccece.2005.1557410","title":"An analysis of multipath for frequency hopping spread spectrum ranging","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"University of Regina","keywords":"Ranging; Frequency-hopping spread spectrum; Multipath propagation; Spread spectrum; Radio spectrum; Radio frequency; Delay spread; Computer science; Electronic engineering; Frequency deviation; Telecommunications; Automatic frequency control; Engineering","score_opus":0.005929723811075521,"score_gpt":0.21806056425426304,"score_spread":0.21213084044318753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142402644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08243951,0.005300151,0.8940451,0.0006316876,0.00020533804,0.000043926993,0.00011270379,0.0003181876,0.016903426],"genre_scores_gemma":[0.91007626,0.005948622,0.075367816,0.00014048723,0.00034151424,0.000048753478,0.000124691,0.00015326495,0.007798609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927384,0.00012503697,0.000015221497,0.00006066024,0.00043125002,0.00009402592],"domain_scores_gemma":[0.99743277,0.0017740211,0.00017141331,0.00015282427,0.00043448186,0.000034511067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006431108,0.00068504957,0.00040841653,0.0008379123,0.00036124582,0.00058745785,0.0004017381,0.00059075066,0.0027032157],"category_scores_gemma":[0.0051607024,0.00025753456,0.0005193853,0.00094371184,0.00041135174,0.0010895749,0.00057135924,0.00058160693,0.00061816553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018777027,0.00003537357,0.0042996192,0.0003561051,0.00013965058,0.0009893855,0.00024304807,0.68579817,0.051353812,0.09764678,0.0017792549,0.15717095],"study_design_scores_gemma":[0.0000100769885,0.00022161154,0.0038043146,0.00009319989,0.00012582938,0.0014665317,0.00011541558,0.94420904,0.012278833,0.024657808,0.01295594,0.00006132786],"about_ca_topic_score_codex":0.001068882,"about_ca_topic_score_gemma":0.0011454856,"teacher_disagreement_score":0.0027032157,"about_ca_system_score_codex":0.00054940325,"about_ca_system_score_gemma":0.0004209043,"threshold_uncertainty_score":0.009043157},"labels":[],"label_agreement":null},{"id":"W2143818815","doi":"10.1109/icc.2011.5962800","title":"MDS-Based Localization Algorithm for RFID Systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multilateration; Computer science; RSS; Multidimensional scaling; Radio-frequency identification; Algorithm; Identification (biology); Computation; Signal strength; Real-time computing; Data mining; Wireless; Node (physics); Telecommunications","score_opus":0.022005908133852194,"score_gpt":0.20336112297289885,"score_spread":0.18135521483904665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143818815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023560892,0.00017296632,0.9960853,0.00008367337,0.000037071524,0.000023430155,0.00005041279,0.00027685,0.0009141515],"genre_scores_gemma":[0.21087039,0.0007305086,0.7805049,0.000107639724,0.00008199574,0.00037275182,0.00059548503,0.00013565927,0.006600782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993625,0.00020414131,0.00004803955,0.00013595643,0.00020612203,0.000043288343],"domain_scores_gemma":[0.9989791,0.00039599347,0.00012323748,0.00011503232,0.0003540597,0.000032550615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008234591,0.00095783995,0.00077897625,0.0010900436,0.00066703535,0.0008319701,0.000986386,0.0008309156,0.0038119059],"category_scores_gemma":[0.0036343958,0.00034878126,0.00061854447,0.0014195518,0.00069438055,0.0013382285,0.0016659915,0.0009267349,0.0017610866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019621936,0.000021115433,0.00081022165,0.00019145053,0.000049914568,0.000119301134,0.00024149405,0.6867635,0.0045569343,0.06895352,0.0048195743,0.2332766],"study_design_scores_gemma":[0.000016321408,0.000034911565,0.00010666255,0.000017724122,0.000008090964,0.00007100712,0.000037978607,0.9757245,0.0019646022,0.015355643,0.0066434066,0.000019140509],"about_ca_topic_score_codex":0.0024754028,"about_ca_topic_score_gemma":0.0017069506,"teacher_disagreement_score":0.0038119059,"about_ca_system_score_codex":0.0008467226,"about_ca_system_score_gemma":0.0011510679,"threshold_uncertainty_score":0.012752056},"labels":[],"label_agreement":null},{"id":"W2144009486","doi":"10.1117/12.450354","title":"Continuous and Discrete Space Particle Filters for Predictions in Acoustic Positioning","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Particle filter; Computer science; Position (finance); Filter (signal processing); SIGNAL (programming language); Acoustics; Monte Carlo localization; Computer vision; Adaptive filter; Algorithm; Artificial intelligence; Physics","score_opus":0.008564799783156427,"score_gpt":0.20593100724415536,"score_spread":0.19736620746099892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144009486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030232146,0.000266534,0.9954978,0.0001885636,0.00008633688,0.000017793716,0.000041662206,0.00015998501,0.0007180979],"genre_scores_gemma":[0.44624245,0.0016122177,0.5421796,0.00030891894,0.0003985677,0.00031794806,0.0004409715,0.00015469728,0.008344639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914324,0.0002552019,0.000055332315,0.00019851238,0.00026979076,0.00007785906],"domain_scores_gemma":[0.9963781,0.0026759093,0.00021304707,0.00020762655,0.0004469188,0.00007834612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002161568,0.000937944,0.0009545338,0.00083307317,0.00052046793,0.0013435156,0.0013933053,0.00179515,0.0021160769],"category_scores_gemma":[0.008714959,0.0006117801,0.00085790723,0.0010361233,0.0012179629,0.0017406116,0.0009855129,0.0023817897,0.00046979217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083116756,0.000031458152,0.000562094,0.00006510198,0.000031334763,0.00004053098,0.000059190676,0.9214721,0.0008419851,0.035726596,0.0010161051,0.040070444],"study_design_scores_gemma":[0.0000038672524,0.000007045541,0.00006864361,0.0000041957014,0.0000023835225,0.0000030615602,0.0000039088177,0.99591786,0.00015626753,0.0034592864,0.00036928148,0.000004274983],"about_ca_topic_score_codex":0.018034257,"about_ca_topic_score_gemma":0.010345811,"teacher_disagreement_score":0.018034257,"about_ca_system_score_codex":0.0014820498,"about_ca_system_score_gemma":0.0018223658,"threshold_uncertainty_score":0.03585857},"labels":[],"label_agreement":null},{"id":"W2144244924","doi":"10.1109/lcn.2010.5735767","title":"Localization scheduling in wireless ad hoc networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Wireless ad hoc network; Scheduling (production processes); Computation; Latency (audio); Location awareness; Real-time computing; Wireless sensor network; Mobile ad hoc network; Wireless; Node (physics); Overhead (engineering); Computer network; Wireless network; Distributed computing; Algorithm; Mathematical optimization; Mathematics; Telecommunications; Engineering","score_opus":0.004687856254382141,"score_gpt":0.19696273156538077,"score_spread":0.19227487531099863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144244924","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044434886,0.002179678,0.94928366,0.00026676888,0.00025574886,0.00014325799,0.000066329725,0.00086833327,0.002501358],"genre_scores_gemma":[0.8149132,0.002005123,0.17942764,0.00012287733,0.0002798416,0.00016832462,0.00016107236,0.00011584989,0.0028060821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926084,0.00034890932,0.000058215803,0.00010126684,0.00016346018,0.00006723476],"domain_scores_gemma":[0.9983753,0.00081389555,0.00021983772,0.0002234588,0.00026792282,0.00009961572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014670213,0.0004921556,0.0006193515,0.00047379968,0.00088980823,0.0008035846,0.00076098996,0.0004510457,0.0008037489],"category_scores_gemma":[0.004111821,0.0003237774,0.00017068324,0.0008500897,0.00047807116,0.001088738,0.00055730506,0.00036891887,0.0003362856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034758216,0.00011791685,0.0019514732,0.0002995648,0.000049744103,0.00017941752,0.00021915043,0.66990906,0.013260748,0.022800349,0.0050713858,0.28579366],"study_design_scores_gemma":[0.00005167551,0.00018240555,0.0005832927,0.000023267887,0.00002630424,0.00011363252,0.00009826932,0.96976376,0.0062400666,0.014396487,0.008497593,0.00002318991],"about_ca_topic_score_codex":0.0024877612,"about_ca_topic_score_gemma":0.002528658,"teacher_disagreement_score":0.0024877612,"about_ca_system_score_codex":0.00078720256,"about_ca_system_score_gemma":0.0010841711,"threshold_uncertainty_score":0.0077584386},"labels":[],"label_agreement":null},{"id":"W2144400442","doi":"10.1109/glocom.2007.205","title":"Position Estimation Error in Edge Detection for Wireless Sensor Networks using Local Convex View","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Convex hull; Wireless sensor network; Computer science; Enhanced Data Rates for GSM Evolution; Position (finance); Node (physics); Range (aeronautics); False positive paradox; Topology (electrical circuits); Set (abstract data type); Regular polygon; Network topology; Algorithm; Mathematics; Artificial intelligence; Computer network; Geometry; Engineering; Combinatorics","score_opus":0.014276237581600168,"score_gpt":0.25349055317854446,"score_spread":0.23921431559694428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144400442","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35623705,0.0004340754,0.6415014,0.0001800302,0.000019170795,0.000047308193,0.00006846313,0.00035651436,0.0011559755],"genre_scores_gemma":[0.95278186,0.000120694116,0.046789214,0.000027872282,0.000010461889,0.000019147663,0.0000639377,0.000023343086,0.00016349251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980202,0.0008776987,0.0000605987,0.00018268483,0.0007047179,0.00015426768],"domain_scores_gemma":[0.9843051,0.01255631,0.0013271526,0.0007640372,0.0008036448,0.00024380021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025745402,0.0007244698,0.0008686301,0.001173412,0.00046256895,0.00081112166,0.0011462693,0.0008009551,0.00042780454],"category_scores_gemma":[0.021715969,0.0003483589,0.00034447157,0.0009915158,0.0011621602,0.0019690392,0.0014798443,0.00066030776,0.000100644786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051013473,0.00004204307,0.007939432,0.00005075959,0.00004200584,0.00015855514,0.000114892624,0.9301886,0.0054705255,0.0023949677,0.00031154242,0.05277666],"study_design_scores_gemma":[0.000004259075,0.0000927001,0.0009469307,0.00000452492,0.00000612828,0.00005248301,0.00002556691,0.9943545,0.003298302,0.0011506069,0.00005542375,0.000008537315],"about_ca_topic_score_codex":0.0036210231,"about_ca_topic_score_gemma":0.0028447001,"teacher_disagreement_score":0.0036210231,"about_ca_system_score_codex":0.0009896165,"about_ca_system_score_gemma":0.0004592251,"threshold_uncertainty_score":0.013615608},"labels":[],"label_agreement":null},{"id":"W2144525880","doi":"10.1109/wcnc.2007.735","title":"Dual and Mixture Monte Carlo Localization Algorithms for Mobile Wireless Sensor Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Wireless sensor network; Algorithm; Dual (grammatical number); Monte Carlo method; Wireless; Monte Carlo localization; Mobile radio; Real-time computing; Computer network; Mathematics; Artificial intelligence; Mobile robot; Telecommunications; Statistics","score_opus":0.006952870234077213,"score_gpt":0.2214383152735281,"score_spread":0.2144854450394509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144525880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045161704,0.0003319398,0.9944009,0.000096492084,0.000023864795,0.000017376537,0.000013229764,0.00021023516,0.0003897473],"genre_scores_gemma":[0.33580682,0.0007614788,0.6602157,0.00019157647,0.00012937677,0.00019559544,0.00017028929,0.00014264804,0.0023864962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99722445,0.0010331246,0.00012671642,0.00038367047,0.0010345958,0.00019749503],"domain_scores_gemma":[0.9935109,0.0037017008,0.0006861568,0.000755117,0.0010810205,0.00026525196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033465168,0.0010588185,0.001448545,0.002153531,0.00067050225,0.0016766686,0.0027595467,0.001936572,0.0017164324],"category_scores_gemma":[0.015385208,0.0008971624,0.0013194731,0.0019078427,0.0015428623,0.003497675,0.0028602541,0.0019374562,0.00076192856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034889593,0.00006365372,0.0019095944,0.00014883556,0.00013833551,0.000095479496,0.00013751263,0.87084866,0.0024794966,0.044352375,0.0011934078,0.07828383],"study_design_scores_gemma":[0.000009152346,0.000020912661,0.00009154769,0.000005602016,0.0000098382125,0.000046874695,0.000006838089,0.99319625,0.00042573715,0.005462785,0.0007124735,0.000011865894],"about_ca_topic_score_codex":0.0027703238,"about_ca_topic_score_gemma":0.0023993135,"teacher_disagreement_score":0.0033465168,"about_ca_system_score_codex":0.0015381748,"about_ca_system_score_gemma":0.0010412913,"threshold_uncertainty_score":0.017698288},"labels":[],"label_agreement":null},{"id":"W2144723957","doi":"10.1109/tmc.2007.1017","title":"Kernel-Based Positioning in Wireless Local Area Networks","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":428,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Computer science; Hybrid positioning system; Wi-Fi; Local area network; Context (archaeology); Signal strength; Kernel (algebra); Location-based service; Wireless; Computer network; Wireless network; Histogram; Point (geometry); Location awareness; Ubiquitous computing; Wireless lan; Real-time computing; Positioning system; Telecommunications; Artificial intelligence; Geography; World Wide Web; Human–computer interaction","score_opus":0.00660713434968295,"score_gpt":0.21523298720016243,"score_spread":0.20862585285047947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144723957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024694266,0.0006429434,0.97261864,0.00010837416,0.00004262782,0.000010595149,0.000017669809,0.0011051054,0.00075978355],"genre_scores_gemma":[0.7454436,0.0008192042,0.25093943,0.00004171163,0.000087948945,0.000028474227,0.0000930846,0.00007035982,0.0024761513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991468,0.00034781575,0.000040763465,0.00013546742,0.0002817525,0.00004729249],"domain_scores_gemma":[0.9987232,0.0004505263,0.00016554505,0.00035567346,0.00027137337,0.0000337553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007516136,0.00023685172,0.0005120556,0.0005683597,0.00027468556,0.00066994206,0.00070615014,0.0004826905,0.0006824323],"category_scores_gemma":[0.0040838225,0.00018646439,0.00017888684,0.0009401199,0.0004963663,0.001224558,0.00073989294,0.0004157018,0.0006521605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002561932,0.000082105216,0.0029554693,0.00012297813,0.000050784245,0.0001550701,0.00022062236,0.39386594,0.015194396,0.035260864,0.002252248,0.5495834],"study_design_scores_gemma":[0.0000062362424,0.000035776313,0.0006154704,0.000004180551,0.0000066838725,0.000071997994,0.000019322304,0.98844725,0.002785598,0.00625792,0.0017372454,0.00001229527],"about_ca_topic_score_codex":0.0026438574,"about_ca_topic_score_gemma":0.001699375,"teacher_disagreement_score":0.0026438574,"about_ca_system_score_codex":0.00043661485,"about_ca_system_score_gemma":0.00030926193,"threshold_uncertainty_score":0.0052570105},"labels":[],"label_agreement":null},{"id":"W2145663347","doi":"10.1109/robot.2008.4543228","title":"Ultrasonic relative positioning for multi-robot systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Beacon; Ultrasonic sensor; Robot; Positioning system; Mobile robot; Computer science; Position (finance); Acoustics; Real-time computing; Simulation; Computer vision; Artificial intelligence; Physics","score_opus":0.03759841882158961,"score_gpt":0.2422516141715518,"score_spread":0.20465319534996218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145663347","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003444908,0.011849887,0.97176445,0.0004722761,0.0005553625,0.000049149727,0.000044242577,0.0013290457,0.010490646],"genre_scores_gemma":[0.38873172,0.01824721,0.5613423,0.0004394584,0.0013910164,0.00035600868,0.00032206133,0.00022546717,0.028944755],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993593,0.00021810427,0.000034868237,0.0000783316,0.00027756402,0.000031770818],"domain_scores_gemma":[0.99955827,0.00015337384,0.000040584713,0.00008836646,0.00014270023,0.000016646882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005161168,0.00049592817,0.00047943043,0.000531996,0.0003000291,0.0007573124,0.0007453233,0.0008638716,0.0057360795],"category_scores_gemma":[0.001588722,0.00021499083,0.00018618387,0.0007463977,0.00040983967,0.0010343918,0.00067651755,0.00071913574,0.0024679971],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016433987,0.00003843253,0.0005126771,0.0009215761,0.00003940367,0.00042417514,0.00039949923,0.05626902,0.04715971,0.16760246,0.014478736,0.71198994],"study_design_scores_gemma":[0.00010059038,0.0008876398,0.0013724709,0.00051819975,0.00011056926,0.0017219059,0.00023222166,0.37609905,0.03784407,0.111519374,0.46944508,0.00014875215],"about_ca_topic_score_codex":0.0006435393,"about_ca_topic_score_gemma":0.00051566487,"teacher_disagreement_score":0.0057360795,"about_ca_system_score_codex":0.00041122292,"about_ca_system_score_gemma":0.00028172394,"threshold_uncertainty_score":0.01918912},"labels":[],"label_agreement":null},{"id":"W2145979466","doi":"10.1109/tsmc.2014.2356437","title":"The Smart-Condo: Optimizing Sensor Placement for Indoor Localization","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Software deployment; Computer science; Cardinality (data modeling); Constraint (computer-aided design); Space (punctuation); Optimization problem; Artificial intelligence; Human–computer interaction; Simple (philosophy); Real-time computing; Computer vision; Distributed computing; Data mining; Algorithm; Mathematics","score_opus":0.01026958772464871,"score_gpt":0.20250357329711965,"score_spread":0.19223398557247096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145979466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023806673,0.00020187028,0.9731611,0.00015714538,0.000028599667,0.00006241948,0.0001340141,0.0005955571,0.0018526991],"genre_scores_gemma":[0.5791899,0.000207727,0.41748396,0.00009371727,0.000033522563,0.00018912714,0.00027434158,0.00019278673,0.002334953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957246,0.00015928152,0.00001395633,0.00008848442,0.00010713214,0.000058758458],"domain_scores_gemma":[0.9993179,0.00036561015,0.00011111257,0.00007660528,0.000081181366,0.00004767894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065222575,0.001219306,0.00075536175,0.0005672162,0.00031810507,0.0004991512,0.00087339594,0.0008336294,0.0017203522],"category_scores_gemma":[0.0027793862,0.00046466614,0.00041927546,0.0007002244,0.0006514088,0.00069589535,0.00081827305,0.00048325697,0.00043325278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042138872,0.000020074413,0.0005047783,0.00003748935,0.0000100391735,0.0000272835,0.000016701355,0.9803388,0.0016209802,0.0018110004,0.0008599942,0.014710704],"study_design_scores_gemma":[0.000007615084,0.000032239455,0.00013524952,0.000003237735,0.0000027534436,0.000018320361,0.000009609419,0.9978109,0.0005022384,0.0011061798,0.00036825708,0.0000033845465],"about_ca_topic_score_codex":0.004897136,"about_ca_topic_score_gemma":0.0072343424,"teacher_disagreement_score":0.004897136,"about_ca_system_score_codex":0.00072918745,"about_ca_system_score_gemma":0.0011681186,"threshold_uncertainty_score":0.009737313},"labels":[],"label_agreement":null},{"id":"W2146084916","doi":"10.1109/glocom.2010.5683801","title":"A Two-Phase Algorithm for Locating Sensors in Irregular Areas","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Network topology; Computer science; Wireless sensor network; Intersection (aeronautics); Position (finance); Range (aeronautics); Convexity; Estimator; Topology (electrical circuits); Regular polygon; Phase (matter); Euclidean distance; Shortest path problem; Mathematics; Artificial intelligence; Theoretical computer science; Engineering","score_opus":0.007577774050827022,"score_gpt":0.253771513080997,"score_spread":0.24619373903016997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146084916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017326619,0.000037929763,0.9977736,0.000045189896,0.000016294774,0.000032863278,0.000007340184,0.0001877687,0.00016625997],"genre_scores_gemma":[0.07126875,0.000112366506,0.9265736,0.00008287331,0.000034236527,0.00031837312,0.00011331424,0.00006594155,0.0014305628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907804,0.00022236608,0.000060923136,0.00020035414,0.00037940487,0.00005879294],"domain_scores_gemma":[0.9989513,0.0004515974,0.00015011328,0.00016448871,0.00022730092,0.000055116205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010998218,0.0009642885,0.0009838571,0.0010067486,0.00072359317,0.0008325775,0.002024916,0.0015715259,0.0017161466],"category_scores_gemma":[0.003607932,0.0005675069,0.00055604224,0.0013253246,0.00084413507,0.0019366153,0.002294343,0.0011070436,0.0009308113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038574406,0.00018045853,0.0009294575,0.00017667557,0.000058105885,0.00018740163,0.00024505614,0.42582878,0.016141584,0.019956268,0.003426963,0.5324835],"study_design_scores_gemma":[0.000081519334,0.00012726446,0.00013314441,0.000006885371,0.000010386462,0.0001431222,0.000021223226,0.9892519,0.0033862572,0.0041488004,0.0026707482,0.000018685792],"about_ca_topic_score_codex":0.0016431662,"about_ca_topic_score_gemma":0.0017681413,"teacher_disagreement_score":0.002024916,"about_ca_system_score_codex":0.000445364,"about_ca_system_score_gemma":0.0013290042,"threshold_uncertainty_score":0.0058165193},"labels":[],"label_agreement":null},{"id":"W2146439054","doi":"10.1109/jsac.2010.100905","title":"Distributed target tracking using signal strength measurements by a wireless sensor network","year":2010,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada); University of British Columbia","funders":"","keywords":"Computer science; Wireless sensor network; Scalability; Tracking (education); Real-time computing; Estimator; Wireless network; Wireless; Computer network; Telecommunications","score_opus":0.03495093487898,"score_gpt":0.27031041445997994,"score_spread":0.23535947958099995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146439054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01793761,0.00013642877,0.98063385,0.00007481264,0.000027137623,0.00001708191,0.000017162984,0.00024372787,0.0009121688],"genre_scores_gemma":[0.73680854,0.00055826723,0.26028025,0.00008190332,0.00008921267,0.000090069254,0.00012956474,0.000030532825,0.0019316307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959344,0.0000931011,0.000017727998,0.00012791762,0.000144597,0.000023251514],"domain_scores_gemma":[0.99942076,0.0002299644,0.00009284263,0.00014888583,0.00008423199,0.000023346898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005388968,0.00048364664,0.00056437554,0.00046734564,0.00030840392,0.0006715984,0.0009560816,0.00070811156,0.0004491401],"category_scores_gemma":[0.0023880273,0.00040372202,0.0004190303,0.00064985675,0.0004654375,0.0017101715,0.00095636357,0.00077234645,0.0002566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019569683,0.00010187282,0.0032436985,0.000098307064,0.00011140265,0.00013090987,0.00011307393,0.70308113,0.03695756,0.012462391,0.00093943754,0.24256454],"study_design_scores_gemma":[0.0000154769,0.0000742314,0.0007645866,0.000006589586,0.000019918916,0.000054964592,0.000011097484,0.9895592,0.0038278108,0.0045974837,0.0010580163,0.0000106866855],"about_ca_topic_score_codex":0.0015621411,"about_ca_topic_score_gemma":0.0017194378,"teacher_disagreement_score":0.0015621411,"about_ca_system_score_codex":0.00039011898,"about_ca_system_score_gemma":0.00063569815,"threshold_uncertainty_score":0.0031060576},"labels":[],"label_agreement":null},{"id":"W2146711757","doi":"10.1145/2628363.2628383","title":"Toffee","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Information and Intelligent Systems; Natural Sciences and Engineering Research Council of Canada","keywords":"Laptop; Computer science; Mobile device; Multilateration; Simple (philosophy); Simulation; Acoustics; Human–computer interaction; Physics","score_opus":0.002264623109640321,"score_gpt":0.14673804468825105,"score_spread":0.14447342157861073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146711757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03336472,0.0042896974,0.67258394,0.0015165868,0.0021598213,0.00082993,0.0063545946,0.044657998,0.23424271],"genre_scores_gemma":[0.31159058,0.0027125645,0.3956291,0.0021452187,0.0003594539,0.0008720619,0.010939708,0.005696531,0.2700548],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913687,0.000067486246,0.000039834777,0.00014427978,0.00051071873,0.00010089936],"domain_scores_gemma":[0.9990287,0.00028929443,0.00007238999,0.00018704793,0.00034966358,0.000072961084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000843356,0.0012018352,0.00062574283,0.0010343973,0.00070624374,0.0013070805,0.0016632646,0.0017316021,0.14175266],"category_scores_gemma":[0.0026340405,0.00045808248,0.0005498346,0.0006729618,0.00046402524,0.0025449272,0.0016855773,0.00091667223,0.040188435],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016287739,0.00018918295,0.0019220357,0.00096291694,0.00007311969,0.0009204486,0.00035768666,0.0035600269,0.08576852,0.019841852,0.15808386,0.72669166],"study_design_scores_gemma":[0.00018550847,0.0006888875,0.0014884609,0.00016036961,0.000039892817,0.002876676,0.00016986483,0.02326952,0.11830281,0.004452781,0.8482182,0.000147147],"about_ca_topic_score_codex":0.00074455596,"about_ca_topic_score_gemma":0.00085310265,"teacher_disagreement_score":0.14175266,"about_ca_system_score_codex":0.00034083982,"about_ca_system_score_gemma":0.0004373605,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2147041146","doi":"10.3390/s140815525","title":"Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Compute Canada","keywords":"Gradient descent; Probabilistic logic; Computer science; Computation; Orientation (vector space); Optimization problem; Set (abstract data type); Position (finance); Descent (aeronautics); Black box; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Engineering; Artificial neural network","score_opus":0.009246588122491541,"score_gpt":0.19922129455878831,"score_spread":0.18997470643629677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147041146","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004767439,0.000054822012,0.9944013,0.000055372202,0.000011380978,0.000013668614,0.000013553505,0.00023795638,0.00044456442],"genre_scores_gemma":[0.36312702,0.00019784416,0.63364655,0.00011244164,0.00006292582,0.00018901732,0.00016090584,0.00025922578,0.0022440178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993538,0.0002371364,0.000022664439,0.000101182624,0.00022202352,0.00006310091],"domain_scores_gemma":[0.9994423,0.00027378122,0.000076382545,0.000074520554,0.00010657528,0.000026369524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078322267,0.001033429,0.0012115366,0.0006899179,0.00036589667,0.0006079224,0.0011264435,0.0010307707,0.00088908366],"category_scores_gemma":[0.002103213,0.00071333494,0.00059572805,0.00087745953,0.0006042536,0.0010001118,0.0009741533,0.0007236473,0.00032421015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029610097,0.000020218424,0.00023704498,0.00002418871,0.000025396008,0.00003509904,0.00002192269,0.966618,0.003182645,0.0024171232,0.0006794526,0.02670929],"study_design_scores_gemma":[0.0000036242668,0.000008412878,0.000053918644,0.0000011318308,0.0000014454898,0.000009066861,0.0000017576341,0.9986594,0.00044432623,0.0006334614,0.00018120825,0.0000022552374],"about_ca_topic_score_codex":0.0056884848,"about_ca_topic_score_gemma":0.005237689,"teacher_disagreement_score":0.0056884848,"about_ca_system_score_codex":0.0005821133,"about_ca_system_score_gemma":0.0009811368,"threshold_uncertainty_score":0.011310756},"labels":[],"label_agreement":null},{"id":"W2147508082","doi":"10.1109/iswpc.2007.342669","title":"Always Best Located, a pervasive positioning system","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Hybrid positioning system; Positioning system; Ubiquitous computing; Indoor positioning system; Real-time computing; Computer security; Embedded system; Engineering; Accelerometer; Operating system","score_opus":0.006480394583309603,"score_gpt":0.19592700265097168,"score_spread":0.1894466080676621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147508082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031440444,0.0020084325,0.8880496,0.0008947111,0.0009210361,0.0002700221,0.00084219873,0.019328078,0.056245595],"genre_scores_gemma":[0.48578382,0.0015668935,0.4542293,0.0009968271,0.00040321713,0.0002543791,0.0013666622,0.0006576801,0.054741252],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910814,0.00011469914,0.000045382974,0.00018428951,0.00046603577,0.00008141287],"domain_scores_gemma":[0.99939454,0.000052148058,0.00006409844,0.00016855173,0.00021904771,0.00010157474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053478155,0.00053081405,0.0006992256,0.0009422187,0.0008874189,0.0019103094,0.001250905,0.0010573777,0.005305729],"category_scores_gemma":[0.0013227636,0.00033236496,0.00022726125,0.00084707007,0.0005647156,0.0018144153,0.0018307563,0.0010644402,0.00561823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010130324,0.00021616922,0.0045112483,0.0006511344,0.00011215129,0.0007783143,0.00044899568,0.022924008,0.11409459,0.07880835,0.06025108,0.71619093],"study_design_scores_gemma":[0.00055049895,0.0020042164,0.0076832254,0.00021327195,0.00033069903,0.0071100583,0.00041481084,0.24700002,0.116247505,0.027863313,0.5901922,0.00039026522],"about_ca_topic_score_codex":0.0021464028,"about_ca_topic_score_gemma":0.0041198484,"teacher_disagreement_score":0.005305729,"about_ca_system_score_codex":0.00046677105,"about_ca_system_score_gemma":0.00095780165,"threshold_uncertainty_score":0.017749488},"labels":[],"label_agreement":null},{"id":"W2147849168","doi":"10.1109/bsc.2008.4563278","title":"Time-of-arrival estimation for IR-UWB systems based on two step energy detection","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Non-line-of-sight propagation; Geolocation; Time of arrival; Computer science; Detector; Energy (signal processing); Ranging; Algorithm; Real-time computing; Transmission (telecommunications); Channel (broadcasting); Ultra-wideband; Wireless; Telecommunications; Statistics; Mathematics","score_opus":0.008965094933934067,"score_gpt":0.20230969599263268,"score_spread":0.1933446010586986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147849168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06358226,0.0002846842,0.93531644,0.00004456133,0.000023933924,0.000023080356,0.0000140707525,0.00023481241,0.0004761872],"genre_scores_gemma":[0.6402148,0.0003887435,0.35811746,0.000058986367,0.000030932875,0.000055347555,0.000076877644,0.000025739346,0.0010311073],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944264,0.00017340109,0.000028191858,0.000072093484,0.00025650737,0.000027114922],"domain_scores_gemma":[0.9988631,0.000634596,0.000149734,0.000119508564,0.00021078097,0.000022337523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007212163,0.00038523896,0.00042939224,0.0004904636,0.00016919927,0.00046712003,0.00055174006,0.00042941901,0.00048815136],"category_scores_gemma":[0.0030442497,0.00022600085,0.00018700353,0.00044942208,0.00022969193,0.0008628355,0.00048315566,0.00043646607,0.0002656094],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010373439,0.00023533156,0.007265157,0.00035624127,0.00018293172,0.00028298277,0.0002412298,0.28514844,0.17777833,0.010835478,0.00092637545,0.5157102],"study_design_scores_gemma":[0.000044366778,0.00046174976,0.0020702558,0.00001745496,0.000038213446,0.0006026526,0.00003463107,0.9495609,0.044485446,0.0016347221,0.0010145459,0.000035112043],"about_ca_topic_score_codex":0.00022674353,"about_ca_topic_score_gemma":0.00040955946,"teacher_disagreement_score":0.0007212163,"about_ca_system_score_codex":0.00018674837,"about_ca_system_score_gemma":0.00037992492,"threshold_uncertainty_score":0.0038142204},"labels":[],"label_agreement":null},{"id":"W2147945245","doi":"10.1109/iros.2010.5652823","title":"Decentralized cooperative simultaneous localization and mapping for dynamic and sparse robot networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Else Kröner-Fresenius-Stiftung","keywords":"Robot; Computer science; Simultaneous localization and mapping; State (computer science); Key (lock); Distributed computing; Landmark; Network topology; Robot kinematics; Telecommunications network; Decentralised system; Mobile robot; Artificial intelligence; Topology (electrical circuits); Computer network; Algorithm; Computer security; Engineering","score_opus":0.005790639430438809,"score_gpt":0.21087450199326638,"score_spread":0.20508386256282757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147945245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04636949,0.000107072156,0.9519828,0.00013768398,0.000009706694,0.000021690425,0.000015518382,0.00015418757,0.001201788],"genre_scores_gemma":[0.9509591,0.00012348998,0.048011992,0.000028755563,0.000026146608,0.00007190355,0.00004203643,0.000017896673,0.00071857043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912935,0.0002956967,0.000025697618,0.00016966446,0.00028573448,0.000093804374],"domain_scores_gemma":[0.9975841,0.001421963,0.00039645022,0.00028633216,0.00024920612,0.00006182914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011470016,0.00039964356,0.0005821347,0.0003996449,0.00068236334,0.0006328247,0.0008204408,0.00066891935,0.00051668484],"category_scores_gemma":[0.004128201,0.0003952301,0.00037692633,0.00048629224,0.0012949507,0.0016762624,0.0016275343,0.0005393719,0.00011591701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007535207,0.000025780042,0.0007606785,0.000073918665,0.000026329028,0.0001740519,0.00015824167,0.9472628,0.008317294,0.017766686,0.00040031513,0.024958499],"study_design_scores_gemma":[0.000009761596,0.00003760107,0.00024238501,0.0000028224804,0.0000049643136,0.000054377786,0.0000369206,0.98823327,0.0009759191,0.010022665,0.00037454284,0.0000048020156],"about_ca_topic_score_codex":0.001847434,"about_ca_topic_score_gemma":0.0022447405,"teacher_disagreement_score":0.001847434,"about_ca_system_score_codex":0.0006670649,"about_ca_system_score_gemma":0.00069665414,"threshold_uncertainty_score":0.0060660243},"labels":[],"label_agreement":null},{"id":"W2148787535","doi":"10.1109/ccece.2011.6030492","title":"Received signal strength localization with an unknown path loss exponent","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Royal Military College of Canada","funders":"","keywords":"Common emitter; RSS; Estimator; Exponent; Cramér–Rao bound; Algorithm; Path (computing); Upper and lower bounds; Position (finance); SIGNAL (programming language); Path loss; Minification; Estimation theory; Computer science; Mathematics; Mathematical optimization; Applied mathematics; Statistics; Mathematical analysis; Electronic engineering; Telecommunications; Engineering; Wireless","score_opus":0.015091991195812213,"score_gpt":0.1880859369180719,"score_spread":0.17299394572225968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148787535","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014960233,0.00035698252,0.983362,0.00007898212,0.000016936003,0.000014339076,0.00004010907,0.000333644,0.00083688606],"genre_scores_gemma":[0.55797654,0.0014148923,0.43425438,0.00011332288,0.000086560554,0.000116999545,0.0003145592,0.00008997742,0.0056328103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991929,0.00024624917,0.00004052719,0.00019871377,0.00026367733,0.000057986723],"domain_scores_gemma":[0.99869245,0.00058095617,0.0002233344,0.00023997306,0.00024467974,0.000018596838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010420945,0.0009601992,0.0007158025,0.0006546866,0.00017550796,0.0008861059,0.00082225277,0.00088157086,0.00077128335],"category_scores_gemma":[0.0039340374,0.0005617702,0.0005252743,0.0010260791,0.0005880054,0.001739314,0.0009923266,0.0006868047,0.0010009831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025922593,0.000051750467,0.004281922,0.00030399908,0.00013585357,0.0006512935,0.0001756482,0.7533586,0.05095799,0.028957713,0.0016153733,0.15925057],"study_design_scores_gemma":[0.000020627116,0.0001030641,0.0015490667,0.00003036321,0.000030060084,0.00046586464,0.000020587575,0.97774863,0.009959884,0.007329149,0.00270556,0.00003710368],"about_ca_topic_score_codex":0.00073843525,"about_ca_topic_score_gemma":0.001019748,"teacher_disagreement_score":0.0010420945,"about_ca_system_score_codex":0.00067809323,"about_ca_system_score_gemma":0.00046042245,"threshold_uncertainty_score":0.0055112243},"labels":[],"label_agreement":null},{"id":"W2149197382","doi":"10.1109/camad.2010.5686963","title":"Patterns in the RSSI traces from an indoor urban environment","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Killam Trusts","keywords":"Transceiver; Computer science; ISM band; Wireless sensor network; Noise (video); Radio spectrum; Interference (communication); Wireless; Bandwidth (computing); Classifier (UML); Real-time computing; Telecommunications; Computer network; Artificial intelligence","score_opus":0.005853123107598008,"score_gpt":0.1948720333484403,"score_spread":0.1890189102408423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149197382","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97478765,0.00006423093,0.02217779,0.00006931938,0.000016090506,0.000028549392,0.00065426755,0.00054586027,0.0016563416],"genre_scores_gemma":[0.9917991,0.00007838326,0.006725574,0.000014357895,0.000007657112,0.000012226186,0.00081444747,0.000032235035,0.0005161033],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997502,0.000027499682,0.00001863995,0.000042280513,0.00012226785,0.000039146216],"domain_scores_gemma":[0.9992811,0.00023241644,0.00013757906,0.00009016109,0.00019276676,0.00006602412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018197439,0.0002731966,0.00028087242,0.0014282593,0.00020335018,0.0003447155,0.00027113754,0.00027498035,0.0006286159],"category_scores_gemma":[0.0012555324,0.00011413469,0.00019357073,0.0018463914,0.00024746894,0.00023817197,0.00023487523,0.00026217406,0.00030886353],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013916147,0.00074301136,0.36859974,0.00048893667,0.00030185372,0.0033797168,0.0016647498,0.10859064,0.14538588,0.0014211658,0.0039811484,0.36405152],"study_design_scores_gemma":[0.00003948424,0.0011722029,0.666948,0.000063070605,0.00018716205,0.0050440757,0.0021115474,0.2480042,0.068926044,0.0016008643,0.0057787057,0.00012464767],"about_ca_topic_score_codex":0.0023489157,"about_ca_topic_score_gemma":0.005028457,"teacher_disagreement_score":0.0023489157,"about_ca_system_score_codex":0.00015185554,"about_ca_system_score_gemma":0.00014828291,"threshold_uncertainty_score":0.004670441},"labels":[],"label_agreement":null},{"id":"W2149433310","doi":"10.1109/plans.1990.66210","title":"Testing a decentralized filter for GPS/INS integration","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Global Positioning System; GPS/INS; Kalman filter; Inertial navigation system; Computer science; Filter (signal processing); Control theory (sociology); Extended Kalman filter; Position (finance); Real-time computing; Algorithm; Assisted GPS; Computer vision; Artificial intelligence; Mathematics; Orientation (vector space); Telecommunications","score_opus":0.04075708873904961,"score_gpt":0.22410311033867258,"score_spread":0.183346021599623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149433310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23653562,0.00013018736,0.75368744,0.00024308659,0.00016426679,0.00017943406,0.00016438229,0.00247324,0.0064223143],"genre_scores_gemma":[0.85963225,0.00004936207,0.13796942,0.000053982752,0.000023181341,0.00010645644,0.00018024704,0.00008761605,0.0018975239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900275,0.0002549323,0.000042780524,0.00019122247,0.00042475082,0.000083597566],"domain_scores_gemma":[0.99788386,0.0008717968,0.0001390718,0.0003390309,0.00069139875,0.00007490772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001573593,0.00043473454,0.00063295104,0.0002817938,0.00049528375,0.00056700595,0.0005938409,0.0008530286,0.0026423465],"category_scores_gemma":[0.005335795,0.00024931855,0.00034606401,0.00024253882,0.00034519724,0.00087030686,0.0005628008,0.00052093196,0.0004662037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010758006,0.00046543582,0.008085386,0.00019903404,0.00016566773,0.00021394002,0.00018223346,0.75103396,0.029416742,0.012626309,0.002525157,0.19401027],"study_design_scores_gemma":[0.0001129484,0.00031851255,0.0015416729,0.000007907922,0.000027900838,0.000036768324,0.000021024554,0.98692256,0.007779009,0.0015098623,0.0017056784,0.000016216934],"about_ca_topic_score_codex":0.010551131,"about_ca_topic_score_gemma":0.0049012923,"teacher_disagreement_score":0.010551131,"about_ca_system_score_codex":0.000656717,"about_ca_system_score_gemma":0.001345005,"threshold_uncertainty_score":0.020979404},"labels":[],"label_agreement":null},{"id":"W2149469067","doi":"10.1109/iciea.2009.5138939","title":"Inter-vehicle range smoothing for NLOS condition in the persistence of GPS outages","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Non-line-of-sight propagation; Global Positioning System; Kalman filter; Computer science; Smoothing; Collision; Ranging; Range (aeronautics); Pseudorange; Real-time computing; Wireless; Telecommunications; Engineering; Computer security; Artificial intelligence; GNSS applications; Aerospace engineering; Computer vision","score_opus":0.019729551724652482,"score_gpt":0.24094374716868786,"score_spread":0.22121419544403537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149469067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4583938,0.0004046061,0.5399438,0.00017197334,0.000025873696,0.000012594604,0.00002434123,0.00032952236,0.00069350214],"genre_scores_gemma":[0.99323034,0.000076334065,0.0065285857,0.000008347162,0.000009272453,0.00000454334,0.000012517913,0.000008844942,0.00012127821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996319,0.00011617514,0.00002408407,0.00006681839,0.00011289985,0.000048165726],"domain_scores_gemma":[0.99622595,0.0023740805,0.00063336076,0.0004149213,0.00029184355,0.000059883634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010850343,0.00022680427,0.00042964812,0.00048773203,0.00027309085,0.0002868333,0.00043818823,0.00033964645,0.00021769342],"category_scores_gemma":[0.0073448736,0.00018616133,0.00020665524,0.00048290542,0.0004388288,0.0007045466,0.00044106538,0.0003808293,0.000068354195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035479263,0.000045171542,0.03275304,0.00010392543,0.00008818505,0.00051644136,0.00052004104,0.8394063,0.01239081,0.0037994802,0.00039024453,0.10963152],"study_design_scores_gemma":[0.000010902559,0.00009298541,0.012505957,0.000010028862,0.00004234418,0.00020172536,0.00011084863,0.9811286,0.0031794335,0.0022580267,0.00044265052,0.000016464084],"about_ca_topic_score_codex":0.0042616674,"about_ca_topic_score_gemma":0.004143154,"teacher_disagreement_score":0.0042616674,"about_ca_system_score_codex":0.0002820123,"about_ca_system_score_gemma":0.00043278636,"threshold_uncertainty_score":0.008473754},"labels":[],"label_agreement":null},{"id":"W2149628778","doi":"10.1109/icc.2005.1495010","title":"Very low cost sensor localization for hostile environments","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Software deployment; Computer science; Battlefield; Key distribution in wireless sensor networks; Real-time computing; Wireless; Computer network; Simple (philosophy); Wireless network; Telecommunications","score_opus":0.007073241654648386,"score_gpt":0.20119262785093334,"score_spread":0.19411938619628494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149628778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009444605,0.0010267118,0.985698,0.00028402923,0.00009632682,0.000023252387,0.000013678241,0.00086475903,0.0025487167],"genre_scores_gemma":[0.54454297,0.0017965512,0.4425695,0.00028558544,0.00015629463,0.00009570897,0.00011861796,0.00016443644,0.010270356],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999509,0.00019382007,0.000013532866,0.000055675624,0.00019395912,0.00003395822],"domain_scores_gemma":[0.9994085,0.00021419102,0.00009472048,0.00013829529,0.00011375285,0.000030551706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039244618,0.00046569845,0.000441078,0.0005167358,0.00041423575,0.00046916763,0.00084879063,0.00061700837,0.0020614883],"category_scores_gemma":[0.0014036696,0.0002198739,0.00026534437,0.00050451554,0.0005357665,0.0012267061,0.0008825067,0.00051296933,0.0011660258],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027391585,0.00006727484,0.0017265882,0.0006532069,0.00007859078,0.00048030107,0.00023815795,0.21073571,0.113320865,0.09184733,0.017276688,0.5633013],"study_design_scores_gemma":[0.000074809504,0.00061306497,0.0017353584,0.000068775655,0.00008454535,0.0019170145,0.00011838982,0.8322413,0.05094738,0.035769347,0.076345235,0.00008474983],"about_ca_topic_score_codex":0.0005818642,"about_ca_topic_score_gemma":0.0008274982,"teacher_disagreement_score":0.0020614883,"about_ca_system_score_codex":0.0003405209,"about_ca_system_score_gemma":0.00025417603,"threshold_uncertainty_score":0.0068963766},"labels":[],"label_agreement":null},{"id":"W2150082340","doi":"10.1109/spawc.2011.5990479","title":"Joint estimation of emitter power and location in cognitive radio networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Cognitive radio; Common emitter; Joint (building); Computer science; Shadow mapping; Power (physics); Joint probability distribution; Iterative method; Algorithm; Grid; Mathematical optimization; Telecommunications; Wireless; Mathematics; Artificial intelligence; Electronic engineering; Statistics; Engineering","score_opus":0.015192716651397441,"score_gpt":0.2051367855410302,"score_spread":0.18994406888963278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150082340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009566897,0.00007104962,0.9899559,0.000045882753,0.0000057831808,0.000008122122,0.0000067443825,0.00007444134,0.00026524693],"genre_scores_gemma":[0.7196885,0.00023515426,0.2790861,0.000040598807,0.000041707175,0.00009569237,0.00005090512,0.00003864946,0.00072278595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910396,0.00042919823,0.000033092587,0.00012987347,0.00023858997,0.000065298314],"domain_scores_gemma":[0.99641967,0.0027059556,0.00033242124,0.00025314948,0.00023276004,0.00005591038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020624825,0.0005204934,0.0007985161,0.0005536685,0.00028212118,0.00085120986,0.0012979803,0.0007973843,0.0004851729],"category_scores_gemma":[0.013562712,0.0006717335,0.0005012815,0.00053987984,0.0011727929,0.0013942383,0.001312747,0.0008608317,0.00018408641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000643358,0.000016303988,0.0007170934,0.000031717558,0.000025154503,0.000047506197,0.000057813246,0.9577026,0.0010339078,0.012792384,0.00017273416,0.027338408],"study_design_scores_gemma":[0.00000709464,0.000009603204,0.00007302065,0.0000027529886,0.0000027033302,0.000012415292,0.0000042461547,0.9947901,0.00038104455,0.0046262513,0.000087259614,0.0000035236455],"about_ca_topic_score_codex":0.0025095541,"about_ca_topic_score_gemma":0.001891872,"teacher_disagreement_score":0.0025095541,"about_ca_system_score_codex":0.00054111885,"about_ca_system_score_gemma":0.0009425352,"threshold_uncertainty_score":0.01090759},"labels":[],"label_agreement":null},{"id":"W2150243972","doi":"10.1109/jsen.2014.2364684","title":"An Ultrasonic and Vision-Based Relative Positioning Sensor for Multirobot Localization","year":2014,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Computer science; Scalability; Node (physics); Global Positioning System; Real-time computing; Wireless sensor network; Ultrasonic sensor; Mean squared error; Range (aeronautics); Simultaneous localization and mapping; Sensor node; Artificial intelligence; Computer vision; Mobile robot; Engineering; Key distribution in wireless sensor networks; Computer network; Telecommunications; Acoustics","score_opus":0.007306061667127501,"score_gpt":0.2537895134151833,"score_spread":0.2464834517480558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150243972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025735825,0.0021409185,0.9652653,0.00025143093,0.0003321938,0.00007701299,0.00012818808,0.0013650425,0.004704109],"genre_scores_gemma":[0.4885629,0.0016460973,0.5008896,0.0004013811,0.00014439058,0.00022706545,0.0004532835,0.0000897455,0.0075856135],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943143,0.00009076229,0.00002913124,0.00009862851,0.0003172057,0.000032820553],"domain_scores_gemma":[0.99963367,0.00008105135,0.000070018585,0.00004817693,0.00013929933,0.00002771993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031591844,0.0004405015,0.00042998593,0.00071661605,0.0002514964,0.00045812072,0.0013038483,0.00082626904,0.0013727726],"category_scores_gemma":[0.0008528652,0.00030579756,0.00030982617,0.00062158145,0.00030031442,0.0011543119,0.0009032587,0.00054230535,0.0007552635],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021890427,0.00011257104,0.002255272,0.00059122563,0.00005647185,0.0003365706,0.00023028355,0.016855842,0.5187648,0.012024951,0.0053003216,0.44325283],"study_design_scores_gemma":[0.00008103167,0.0018126363,0.005997386,0.00015058262,0.00016075777,0.0032407898,0.00023386192,0.40883237,0.4398782,0.0054584583,0.1339372,0.00021672754],"about_ca_topic_score_codex":0.0005569007,"about_ca_topic_score_gemma":0.001054136,"teacher_disagreement_score":0.0013727726,"about_ca_system_score_codex":0.0003985085,"about_ca_system_score_gemma":0.00046546524,"threshold_uncertainty_score":0.0045924187},"labels":[],"label_agreement":null},{"id":"W2150470922","doi":"10.1109/ccece.2007.272","title":"Mobile Location in MIMO Communication Systems by Using Learning Machine","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Multilateration; Trilateration; MIMO; Multipath propagation; Computer science; Base station; Angle of arrival; Real-time computing; Wireless; Mobile station; Redundancy (engineering); Electronic engineering; Antenna (radio); Computer network; Telecommunications; Engineering; Beamforming","score_opus":0.00834866286312608,"score_gpt":0.2324130324113963,"score_spread":0.22406436954827022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150470922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016094422,0.0006300848,0.9807771,0.0001509494,0.00005693444,0.000018113195,0.00002877976,0.0005888215,0.0016549152],"genre_scores_gemma":[0.67082983,0.0009493067,0.32422128,0.00013264162,0.00017537421,0.000072386305,0.0001101818,0.000039605988,0.0034692935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995889,0.00017815075,0.000020495592,0.00008245054,0.00009615426,0.000033872115],"domain_scores_gemma":[0.9993793,0.00036935316,0.00007022136,0.00007248766,0.000095917974,0.000012737052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005286963,0.0005089105,0.0005175923,0.0005341901,0.00029005602,0.0006479779,0.0004878204,0.0006630098,0.00086251245],"category_scores_gemma":[0.0015637223,0.00019540268,0.00035042074,0.0006011649,0.0004247684,0.00093888363,0.0004371575,0.00062513124,0.00058229227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008790417,0.000066282206,0.0021253852,0.00014855087,0.00008521184,0.00018475979,0.00012205682,0.58365583,0.008117586,0.016105428,0.0014239234,0.38787705],"study_design_scores_gemma":[0.0000050366266,0.00003630022,0.00031307116,0.000008772522,0.000008676895,0.00004873356,0.000014108606,0.9911506,0.0019932084,0.0052963137,0.0011134142,0.000011769556],"about_ca_topic_score_codex":0.0019889267,"about_ca_topic_score_gemma":0.0017719066,"teacher_disagreement_score":0.0019889267,"about_ca_system_score_codex":0.00036928235,"about_ca_system_score_gemma":0.0002677464,"threshold_uncertainty_score":0.003954768},"labels":[],"label_agreement":null},{"id":"W2151055632","doi":"10.3390/s130404303","title":"Use of High Sensitivity GNSS Receiver Doppler Measurements for Indoor Pedestrian Dead Reckoning","year":2013,"lang":"en","type":"review","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Multipath propagation; Doppler effect; Computer science; Dead reckoning; Block (permutation group theory); Remote sensing; Sensitivity (control systems); Satellite system; SIGNAL (programming language); Real-time computing; Inertial navigation system; Electronic engineering; Global Positioning System; Telecommunications; Engineering; Geography; Physics; Inertial frame of reference","score_opus":0.1633909857902981,"score_gpt":0.3004327023398516,"score_spread":0.1370417165495535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151055632","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055177948,0.9429745,0.04049203,0.00029795765,0.0005896901,0.0000517596,0.000057604284,0.00013070543,0.009888037],"genre_scores_gemma":[0.038914178,0.9328319,0.020098336,0.00018827694,0.0003315672,0.000052800526,0.0001340737,0.000017143882,0.007431723],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995646,0.00006320427,0.000039510825,0.00008339075,0.00022341264,0.000025791596],"domain_scores_gemma":[0.9995047,0.00013314727,0.00008136305,0.000025754242,0.00024065682,0.000014305101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048299955,0.00089884625,0.00079850317,0.002152699,0.00014865055,0.00065249123,0.0009920798,0.00092737214,0.0011492497],"category_scores_gemma":[0.0006993905,0.00040750825,0.0004921094,0.0018542153,0.00030904557,0.0010526758,0.00042765285,0.00056418893,0.0015974035],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038202208,0.000042234646,0.0005700856,0.004911829,0.00005203121,0.00019931758,0.000049718554,0.0019239631,0.011197152,0.0021384694,0.0034497783,0.97542727],"study_design_scores_gemma":[0.000026141637,0.0008479893,0.0070041567,0.0032328877,0.0003800426,0.006803938,0.00034926727,0.009947626,0.058831178,0.0032275321,0.9091705,0.00017867259],"about_ca_topic_score_codex":0.00081708294,"about_ca_topic_score_gemma":0.0012144453,"teacher_disagreement_score":0.002152699,"about_ca_system_score_codex":0.00029048443,"about_ca_system_score_gemma":0.0004594474,"threshold_uncertainty_score":0.0038445592},"labels":[],"label_agreement":null},{"id":"W2151069923","doi":"10.1109/cisda.2009.5356549","title":"Emitter geolocation using low-accuracy direction-finding sensors","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geolocation; Computer science; Monte Carlo method; Reliability (semiconductor); Common emitter; Accuracy and precision; Software deployment; Algorithm; Real-time computing; Remote sensing; Statistics; Electronic engineering; Mathematics; Engineering; Power (physics); Physics; Geography","score_opus":0.012804069597201777,"score_gpt":0.24025423837883808,"score_spread":0.2274501687816363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151069923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09478955,0.00035186845,0.9011889,0.00010063943,0.00004081531,0.000031843552,0.00005806306,0.0006652314,0.0027731564],"genre_scores_gemma":[0.72028834,0.00023981572,0.27811503,0.000043948778,0.000020432468,0.000032239463,0.000109776476,0.000026956663,0.0011235201],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912125,0.00018451916,0.00003601545,0.00015679693,0.0004398672,0.00006164283],"domain_scores_gemma":[0.9983038,0.0008490915,0.0002691274,0.00026755867,0.00027200233,0.00003843536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087389664,0.00044700873,0.0005213587,0.0008579404,0.00040551636,0.0006637666,0.0011251303,0.00050052546,0.00062180485],"category_scores_gemma":[0.0045698993,0.00034548892,0.00029790448,0.0006934538,0.0006755283,0.0016976828,0.0008002018,0.00052671775,0.00035700793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052060856,0.00009273114,0.02132105,0.0002885052,0.000056293487,0.00019156504,0.00022056572,0.42440277,0.08611684,0.014636753,0.00094320526,0.45120907],"study_design_scores_gemma":[0.0001330109,0.00061655894,0.00997247,0.00006995518,0.000101046055,0.00081194757,0.000109252884,0.8746294,0.09296526,0.007877874,0.012620753,0.000092490154],"about_ca_topic_score_codex":0.004314614,"about_ca_topic_score_gemma":0.0067981696,"teacher_disagreement_score":0.004314614,"about_ca_system_score_codex":0.00093809457,"about_ca_system_score_gemma":0.0006469727,"threshold_uncertainty_score":0.008578956},"labels":[],"label_agreement":null},{"id":"W2151528071","doi":"10.1109/icra.2011.5979783","title":"Distributed and decentralized cooperative simultaneous localization and mapping for dynamic and sparse robot networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robot; Odometry; Computer science; Simultaneous localization and mapping; Markov process; Process (computing); Artificial intelligence; Property (philosophy); Distributed computing; Data association; Distributed algorithm; State (computer science); Mobile robot; Real-time computing; Algorithm; Mathematics","score_opus":0.013512344603351581,"score_gpt":0.20487531774826157,"score_spread":0.19136297314491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151528071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021740794,0.000101016565,0.9763292,0.00009117918,0.000017754686,0.000022680215,0.000015184446,0.00037330305,0.001308864],"genre_scores_gemma":[0.8320124,0.00012840699,0.16491285,0.00004248854,0.00004013113,0.00012390493,0.000072109935,0.00004494572,0.002622731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994229,0.00015596527,0.00001952911,0.00013167769,0.00020337323,0.00006657971],"domain_scores_gemma":[0.99891496,0.0005077854,0.00014063118,0.00020930296,0.00017631464,0.00005101018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072284153,0.00037369155,0.00042078894,0.00036540726,0.0005039246,0.0005285965,0.0008486467,0.00049924257,0.000988434],"category_scores_gemma":[0.0025828492,0.0003329142,0.0002880325,0.0004488377,0.00067001267,0.0011922438,0.0013855274,0.00045085032,0.0002911146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016832935,0.000053816475,0.0009838111,0.000106902706,0.000035239023,0.00019269207,0.00018919045,0.8289489,0.013502483,0.017391693,0.0013565418,0.13707043],"study_design_scores_gemma":[0.000020437428,0.000054954504,0.00026333297,0.0000037994912,0.0000071819613,0.0000543312,0.000039863637,0.9887748,0.0016006764,0.0077750417,0.0013997564,0.00000583886],"about_ca_topic_score_codex":0.0018617192,"about_ca_topic_score_gemma":0.002534862,"teacher_disagreement_score":0.0018617192,"about_ca_system_score_codex":0.00042479287,"about_ca_system_score_gemma":0.00066834077,"threshold_uncertainty_score":0.0038228035},"labels":[],"label_agreement":null},{"id":"W2151774489","doi":"10.1109/wimob.2007.4390827","title":"WLocator: An Indoor Positioning System","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Multitude; Matching (statistics); Term (time); Wireless; Real-time computing; Map matching; Distributed computing; Global Positioning System; Telecommunications","score_opus":0.004593733442186991,"score_gpt":0.2008322513386934,"score_spread":0.1962385178965064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151774489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03213465,0.0011312396,0.7279335,0.00041609057,0.00042036863,0.0007058694,0.004097469,0.17758697,0.055573713],"genre_scores_gemma":[0.4329889,0.0015140158,0.45940438,0.0011764591,0.00040570658,0.0010255066,0.014024489,0.0025283834,0.08693215],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995016,0.00006354004,0.000026322796,0.00011337441,0.00022376723,0.000071386465],"domain_scores_gemma":[0.99968004,0.000042006268,0.000039010058,0.000094491035,0.00007938275,0.00006510924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036360844,0.00082297507,0.00087743235,0.001390221,0.0005532883,0.0012269854,0.0018207845,0.0007863902,0.01713518],"category_scores_gemma":[0.0008839803,0.00035539846,0.00034015116,0.0009180694,0.00040955748,0.0014242342,0.0024015286,0.00090026954,0.013229456],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015948641,0.00027021373,0.0042259046,0.0008093936,0.00011162401,0.0008669158,0.0006262761,0.008109111,0.09194825,0.015320226,0.15546557,0.7206515],"study_design_scores_gemma":[0.00056958053,0.0015025206,0.00717364,0.00017715785,0.00028365114,0.0029789973,0.0002844907,0.14380102,0.12189358,0.004051961,0.7169412,0.00034211727],"about_ca_topic_score_codex":0.0018048183,"about_ca_topic_score_gemma":0.0018284641,"teacher_disagreement_score":0.01713518,"about_ca_system_score_codex":0.00031467338,"about_ca_system_score_gemma":0.0006535692,"threshold_uncertainty_score":0.05732286},"labels":[],"label_agreement":null},{"id":"W2151943957","doi":"10.1109/pacrim.2011.6032920","title":"Ultra-wideband ranging in Non-Line of Sight environments","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Non-line-of-sight propagation; Ranging; Metric (unit); Computer science; Skewness; Joint (building); Line-of-sight; Wideband; Line (geometry); Algorithm; Statistics; Electronic engineering; Wireless; Mathematics; Telecommunications; Engineering; Aerospace engineering","score_opus":0.011942310357456168,"score_gpt":0.18462084991161518,"score_spread":0.17267853955415902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151943957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09545956,0.0010212996,0.90097296,0.00009334299,0.000047738038,0.000014544,0.00002079713,0.0006791498,0.0016905256],"genre_scores_gemma":[0.7622231,0.0007770219,0.23533794,0.00008417004,0.000039900224,0.000025528156,0.00007149968,0.00003610485,0.0014046613],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992906,0.00022261201,0.00005198697,0.00012076525,0.00024582603,0.000068188005],"domain_scores_gemma":[0.9984825,0.0008269365,0.00021983305,0.00018267361,0.00025070002,0.00003738735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068397995,0.0004128056,0.0006098938,0.0005608032,0.00023713057,0.00066766085,0.0005338315,0.00053702295,0.0003180811],"category_scores_gemma":[0.0036711555,0.00026019735,0.00020508685,0.0006351574,0.00028039215,0.0012122899,0.0010152275,0.00035352554,0.0002895344],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049498526,0.00006310863,0.0055420627,0.00036535296,0.00009986174,0.0005779039,0.0002886139,0.17252372,0.27250224,0.008017937,0.001182807,0.53834146],"study_design_scores_gemma":[0.0000696173,0.0005322512,0.009878369,0.00009381773,0.00013089881,0.0030584235,0.00025620582,0.77145153,0.19870335,0.008981449,0.0067129834,0.00013116174],"about_ca_topic_score_codex":0.00046760787,"about_ca_topic_score_gemma":0.0007144119,"teacher_disagreement_score":0.00068397995,"about_ca_system_score_codex":0.00015333605,"about_ca_system_score_gemma":0.00024202945,"threshold_uncertainty_score":0.0036172867},"labels":[],"label_agreement":null},{"id":"W2152034016","doi":"10.1186/2192-1962-3-2","title":"Enhancing Wi-Fi fingerprinting for indoor positioning using human-centric collaborative feedback","year":2013,"lang":"en","type":"article","venue":"Human-centric Computing and Information Sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; University of Regina; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Global Positioning System; Baseline (sea); Context (archaeology); Mobile device; Hybrid positioning system; Bookmarking; Key (lock); Human–computer interaction; Indoor positioning system; Positioning system; Real-time computing; Computer security; World Wide Web; Telecommunications; Node (physics)","score_opus":0.016063934541788654,"score_gpt":0.26624239413164225,"score_spread":0.2501784595898536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152034016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05889112,0.00021534256,0.93807447,0.0001201377,0.00005920774,0.000045780915,0.000039593,0.0015919266,0.00096244214],"genre_scores_gemma":[0.9459055,0.000091808375,0.052789595,0.000048684997,0.0000387247,0.000030534997,0.000037116868,0.000025959067,0.0010321265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998539,0.00035623068,0.00005495104,0.00032728587,0.0005557321,0.00016673299],"domain_scores_gemma":[0.9975618,0.00085731636,0.00031930284,0.0005182366,0.00062456925,0.0001187441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012774166,0.0009473875,0.0011605992,0.0005864302,0.0004943685,0.0007644982,0.0015018242,0.0012511702,0.0008383876],"category_scores_gemma":[0.0044964757,0.00035885582,0.00042745395,0.0005837971,0.0005225923,0.0012785431,0.0010556933,0.0007030074,0.00059486873],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066417805,0.0005214036,0.006101142,0.00018775775,0.00013622481,0.00039380792,0.0002492351,0.55732274,0.109142534,0.003173785,0.002021847,0.3200854],"study_design_scores_gemma":[0.000009539917,0.00014298766,0.00077198795,0.0000050981507,0.000018938423,0.000110893336,0.00001278835,0.9872113,0.010748927,0.00053036126,0.00041990512,0.000017217439],"about_ca_topic_score_codex":0.005081348,"about_ca_topic_score_gemma":0.0051081935,"teacher_disagreement_score":0.005081348,"about_ca_system_score_codex":0.00059704797,"about_ca_system_score_gemma":0.0007423544,"threshold_uncertainty_score":0.010103583},"labels":[],"label_agreement":null},{"id":"W2152128354","doi":"10.1109/icc.2003.1204495","title":"An enhanced two-step least squared approach for TDOA/AOA wireless location","year":2004,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multilateration; Computer science; Wireless; Algorithm; Acoustics; Telecommunications; Physics","score_opus":0.009566481648518036,"score_gpt":0.23213035572007612,"score_spread":0.2225638740715581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152128354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012114934,0.00008255827,0.998005,0.00003588636,0.000045334462,0.000013773063,0.0000134516495,0.00025455846,0.00033804335],"genre_scores_gemma":[0.0785053,0.00025617803,0.9161618,0.00010426676,0.000111513174,0.00010992052,0.0001206205,0.00010113555,0.004529278],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932694,0.0001688563,0.000041138424,0.00013011124,0.0003011685,0.00003184687],"domain_scores_gemma":[0.99930847,0.00017643053,0.000058938444,0.000090469075,0.0003381361,0.00002763475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059137936,0.0009373918,0.0006809741,0.00068616914,0.00033866774,0.0006104803,0.001284135,0.0008076775,0.0025856688],"category_scores_gemma":[0.0016610277,0.00044424826,0.00071904226,0.00079122535,0.00030154752,0.0011969665,0.001013086,0.0009538343,0.0018225142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027836233,0.00011175573,0.0011774532,0.00034276626,0.00013251099,0.00020059,0.00015313616,0.116129205,0.09923324,0.010239408,0.005217458,0.7667841],"study_design_scores_gemma":[0.000046038585,0.00016877872,0.00069864956,0.000016115508,0.000042658397,0.00039471328,0.000024817868,0.96108,0.02135673,0.0024428756,0.013673398,0.000055109544],"about_ca_topic_score_codex":0.0015561967,"about_ca_topic_score_gemma":0.0021104799,"teacher_disagreement_score":0.0025856688,"about_ca_system_score_codex":0.00027291256,"about_ca_system_score_gemma":0.00079776277,"threshold_uncertainty_score":0.008649945},"labels":[],"label_agreement":null},{"id":"W2152210784","doi":"10.1109/tvt.2010.2049040","title":"Enhanced Detection Performance of Indoor GNSS Signals Based on Synthetic Aperture","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multipath propagation; GNSS applications; Computer science; Fading; Decorrelation; Antenna (radio); Rayleigh fading; Electronic engineering; Remote sensing; Telecommunications; Global Positioning System; Engineering; Computer vision; Channel (broadcasting); Geography","score_opus":0.0039107193352133736,"score_gpt":0.18814437062114656,"score_spread":0.1842336512859332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152210784","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7763685,0.00051370467,0.2178066,0.000096760385,0.000041368457,0.00001197267,0.000070725684,0.0008918224,0.0041986727],"genre_scores_gemma":[0.9633142,0.00014113837,0.035657667,0.00003467107,0.000017543718,0.000005606759,0.00010652038,0.000023875547,0.00069878594],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935514,0.00015789345,0.000017358767,0.00011554707,0.0002622787,0.00009174532],"domain_scores_gemma":[0.9992436,0.00032252987,0.00013790165,0.00007477024,0.00018546167,0.000035703404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006355779,0.00045952073,0.000500354,0.00038965905,0.00014093147,0.00044407844,0.0003201325,0.00041849358,0.00047196646],"category_scores_gemma":[0.0014496862,0.00016446043,0.0002350762,0.00038343505,0.00044796997,0.00040620775,0.00050663494,0.00030173073,0.00021709687],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026837012,0.00016180755,0.020769479,0.00025076233,0.00025647454,0.0004963286,0.00036074975,0.2700982,0.47877964,0.007175939,0.0011276215,0.2178393],"study_design_scores_gemma":[0.000035165147,0.00043445072,0.008821959,0.000013891746,0.00006285255,0.00038830558,0.0000582081,0.7945847,0.19391386,0.00062675227,0.0010084298,0.00005134139],"about_ca_topic_score_codex":0.00095319847,"about_ca_topic_score_gemma":0.0009442776,"teacher_disagreement_score":0.00095319847,"about_ca_system_score_codex":0.00024824927,"about_ca_system_score_gemma":0.00034760454,"threshold_uncertainty_score":0.0033612847},"labels":[],"label_agreement":null},{"id":"W2152233657","doi":"10.1109/glocom.2010.5683630","title":"Received Signal Compensation-Based Position Estimation of Outdoor RFID Nodes","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Compensation (psychology); Position (finance); Computer science; SIGNAL (programming language); Estimation; Engineering; Business","score_opus":0.005867800412140932,"score_gpt":0.20777514497665045,"score_spread":0.2019073445645095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152233657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03513361,0.0003120495,0.9617007,0.000057877587,0.000060798244,0.000018411018,0.000035710298,0.0009862235,0.0016947403],"genre_scores_gemma":[0.6547727,0.00028955872,0.34067416,0.00006687944,0.00007980624,0.00006480379,0.0001831299,0.000056044933,0.0038129298],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966276,0.00006093519,0.000014083331,0.0000836504,0.00014596558,0.000032666998],"domain_scores_gemma":[0.99963033,0.0000756146,0.000091622445,0.000051642375,0.0001341831,0.000016592574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028580774,0.00052702037,0.00056309404,0.0007047162,0.0002787768,0.0004079873,0.0009625151,0.00040449575,0.00063014374],"category_scores_gemma":[0.0010253311,0.00025847694,0.0002606607,0.0005501523,0.00027432412,0.0006273038,0.00036291187,0.0002737047,0.0008313884],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033397012,0.00009889618,0.008050373,0.00020304327,0.000078720834,0.0003194407,0.00016254176,0.27209768,0.1507878,0.0048021874,0.0024555214,0.5606098],"study_design_scores_gemma":[0.000036763722,0.00019879227,0.003213339,0.00001767076,0.00004014089,0.00039208503,0.000034631474,0.9250628,0.06482036,0.0008752914,0.0052603856,0.000047863723],"about_ca_topic_score_codex":0.0017188854,"about_ca_topic_score_gemma":0.001981188,"teacher_disagreement_score":0.0017188854,"about_ca_system_score_codex":0.00034128787,"about_ca_system_score_gemma":0.00045476525,"threshold_uncertainty_score":0.0034177303},"labels":[],"label_agreement":null},{"id":"W2152506275","doi":"10.1109/icassp.2006.1661382","title":"Location Tracking in Wireless Local Area Networks with Adaptive Radio MAPS","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Estimator; Kalman filter; Computer science; Wireless; Kernel (algebra); Algorithm; Real-time computing; Computer vision; Artificial intelligence; Mathematics; Telecommunications; Statistics","score_opus":0.006686362601449917,"score_gpt":0.17774402627708466,"score_spread":0.17105766367563474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152506275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03714542,0.00039289356,0.96115303,0.000052739557,0.000021888487,0.0000092907885,0.000016135402,0.0005349338,0.00067370734],"genre_scores_gemma":[0.8582951,0.00048952113,0.13874291,0.000053922842,0.00006696383,0.000035809844,0.0000778678,0.000038527694,0.0021993816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995622,0.00014132062,0.000018603558,0.000082833576,0.0001566676,0.00003840487],"domain_scores_gemma":[0.99928147,0.00034010524,0.00009458587,0.00010034191,0.000165597,0.000017928262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000615366,0.00028846835,0.00036553267,0.0004970604,0.0002193908,0.00039922583,0.0005937144,0.00037859136,0.00027136592],"category_scores_gemma":[0.0029316493,0.0002697447,0.00019603605,0.00051094586,0.0002947283,0.0012104893,0.0005270073,0.00024111253,0.00024800678],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025971956,0.0000674857,0.007284026,0.000095141775,0.000102598075,0.0002087861,0.0001713725,0.5130993,0.022315025,0.007475617,0.0011467297,0.44777423],"study_design_scores_gemma":[0.000009940083,0.00008843815,0.0017391652,0.000006650216,0.00003085298,0.00013403484,0.000028055654,0.98571694,0.00775476,0.002569381,0.001908254,0.000013535037],"about_ca_topic_score_codex":0.001918463,"about_ca_topic_score_gemma":0.0018943298,"teacher_disagreement_score":0.001918463,"about_ca_system_score_codex":0.00020041628,"about_ca_system_score_gemma":0.00021642147,"threshold_uncertainty_score":0.0038145185},"labels":[],"label_agreement":null},{"id":"W2152632381","doi":"10.1109/robot.2006.1642174","title":"A practical algorithm for network topology inference","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Robustness (evolution); Computer science; Network topology; Wireless sensor network; Inference; Probabilistic logic; Algorithm; Maximization; Monte Carlo method; Robot; Topology (electrical circuits); Graph; Distributed computing; Theoretical computer science; Artificial intelligence; Mathematical optimization; Mathematics; Computer network","score_opus":0.014646495122756108,"score_gpt":0.2737281642240607,"score_spread":0.2590816691013046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152632381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040325578,0.00002948638,0.9986004,0.0000748465,0.000016832504,0.000032648502,0.000025044556,0.00039818953,0.00041922368],"genre_scores_gemma":[0.035956077,0.00010757824,0.96146303,0.00009966341,0.00005170869,0.0003408849,0.00025312963,0.00013330372,0.0015946853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983656,0.0004751357,0.00010618791,0.00042150007,0.00052135944,0.00011021301],"domain_scores_gemma":[0.99712926,0.0015171292,0.000185059,0.0005661893,0.00051960413,0.00008276745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021775332,0.0012998367,0.001028717,0.0015484802,0.0012762394,0.0016588642,0.002146462,0.0019276654,0.008900635],"category_scores_gemma":[0.01185154,0.000773148,0.00088736817,0.0018381516,0.0012364426,0.00287688,0.0023595754,0.0022577033,0.0034267057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015390714,0.00009601237,0.0007013829,0.00018976569,0.00007693856,0.00013515557,0.0001496347,0.37287125,0.0033514472,0.08701237,0.011328726,0.5239335],"study_design_scores_gemma":[0.000052426527,0.000034251312,0.000111397974,0.000020583082,0.000011292105,0.0001532574,0.000026914384,0.9223371,0.001200951,0.069857076,0.0061792163,0.000015516565],"about_ca_topic_score_codex":0.0025835668,"about_ca_topic_score_gemma":0.0027197136,"teacher_disagreement_score":0.008900635,"about_ca_system_score_codex":0.0011417717,"about_ca_system_score_gemma":0.0020698565,"threshold_uncertainty_score":0.02977562},"labels":[],"label_agreement":null},{"id":"W2152643891","doi":"10.1109/icassp.2011.5946847","title":"A Radio Frequency Identification System for accurate indoor localization","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"FP7 Information and Communication Technologies; Office of Naval Research; Ministerio de Ciencia e Innovación; Stony Brook University; National Science Foundation","keywords":"Ultra high frequency; Radio-frequency identification; Computer science; Backscatter (email); Component (thermodynamics); Identification (biology); Set (abstract data type); Radio frequency; Binary number; Frequency modulation; Electronic engineering; Telecommunications; Wireless; Engineering; Computer security; Physics","score_opus":0.023769973695222157,"score_gpt":0.21457842628968238,"score_spread":0.19080845259446022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152643891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012453016,0.0011898592,0.969442,0.0002987253,0.00063524186,0.00011765627,0.0001557364,0.004002567,0.011705093],"genre_scores_gemma":[0.38494906,0.0013296212,0.5801187,0.00088820216,0.00043248397,0.00036006045,0.00073390047,0.00021155097,0.030976396],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993685,0.00010851351,0.00004236849,0.00015415557,0.00028206402,0.000044353506],"domain_scores_gemma":[0.9994475,0.000089477,0.000081475795,0.00013704617,0.00020337572,0.00004104525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004455797,0.00057940016,0.0005153664,0.0006447644,0.0008360369,0.0010125422,0.00093629706,0.0012374736,0.005470185],"category_scores_gemma":[0.0007544186,0.0002629907,0.00027430843,0.0006026999,0.0003797062,0.0013080749,0.00090124196,0.0008726086,0.0063297767],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039533435,0.00016207501,0.002100969,0.00073094224,0.00006236606,0.0006121082,0.00034387573,0.00587799,0.3835609,0.020633195,0.017625216,0.56789505],"study_design_scores_gemma":[0.00013799107,0.0012749583,0.0058528567,0.00020573025,0.00023229013,0.0053359796,0.00016785451,0.09361777,0.36666575,0.0050746584,0.5212174,0.00021686261],"about_ca_topic_score_codex":0.00045286832,"about_ca_topic_score_gemma":0.00058540795,"teacher_disagreement_score":0.005470185,"about_ca_system_score_codex":0.00037563426,"about_ca_system_score_gemma":0.00053238636,"threshold_uncertainty_score":0.01829958},"labels":[],"label_agreement":null},{"id":"W2153191316","doi":"10.1109/sensorcomm.2009.9","title":"Sensor Nodes Localization Algorithm in Noisy Environments","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Robustness (evolution); Computer science; Wireless sensor network; Computation; Algorithm; Metric (unit); Position (finance); Iterative method; Real-time computing; Distributed computing; Computer network; Engineering","score_opus":0.004863082993116543,"score_gpt":0.19109147629897885,"score_spread":0.1862283933058623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153191316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068376614,0.0001283045,0.99156076,0.00007419005,0.000027654907,0.000029084209,0.00002101448,0.00037431315,0.0009469368],"genre_scores_gemma":[0.23627576,0.0005339632,0.7565003,0.000071841736,0.000056314417,0.00024296912,0.00014214861,0.000114578004,0.006062139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961406,0.00008006549,0.000027153734,0.00008979146,0.0001608093,0.00002822279],"domain_scores_gemma":[0.9996592,0.00008428568,0.00006054767,0.00004626114,0.00013185687,0.000017940887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058397243,0.0004137229,0.00057227403,0.0005080853,0.0005140128,0.00069069123,0.0012482734,0.0005908405,0.0012127045],"category_scores_gemma":[0.0019409696,0.00021342229,0.00024006674,0.00068747596,0.00047855717,0.00076108467,0.000973496,0.00038924653,0.00089007826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000206401,0.000032205146,0.0016953797,0.00019277183,0.00005012283,0.0003127647,0.00048230018,0.62193674,0.019749684,0.031205265,0.0042366246,0.3198997],"study_design_scores_gemma":[0.000035808687,0.00005770833,0.0003341476,0.000017606946,0.000016588565,0.0001705825,0.00007730739,0.9750694,0.007204913,0.009277135,0.0077238297,0.000014892942],"about_ca_topic_score_codex":0.0022390885,"about_ca_topic_score_gemma":0.0015620094,"teacher_disagreement_score":0.0022390885,"about_ca_system_score_codex":0.0004244233,"about_ca_system_score_gemma":0.00084654836,"threshold_uncertainty_score":0.004452169},"labels":[],"label_agreement":null},{"id":"W2153350326","doi":"10.1109/crv.2011.10","title":"Range-based Navigation System for a Mobile Robot","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beacon; Mobile robot; Computer science; Motion planning; Ranging; Range (aeronautics); Electric beacon; Entropy (arrow of time); Mobile robot navigation; Robot; Artificial intelligence; Navigation system; Real-time computing; Computer vision; Robot control; Engineering; Telecommunications","score_opus":0.017940232498634138,"score_gpt":0.20479389731876768,"score_spread":0.18685366482013355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153350326","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00853758,0.0005177998,0.9531571,0.0002682946,0.00031087772,0.00020465975,0.00034860263,0.01644456,0.020210546],"genre_scores_gemma":[0.20611675,0.00061411416,0.7561861,0.0005950624,0.0002277692,0.00057518185,0.0013399397,0.0006556757,0.033689383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966455,0.000037285256,0.000018234152,0.00010372168,0.00015235228,0.000023896993],"domain_scores_gemma":[0.99968326,0.000047458798,0.000029560382,0.00008549878,0.00012185074,0.00003241694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029936852,0.0006128731,0.00043580774,0.000703332,0.0005635118,0.00064423395,0.0013754508,0.00078331673,0.017358486],"category_scores_gemma":[0.00075438444,0.0002157934,0.00026602577,0.0004274505,0.000271951,0.0008789134,0.0010995782,0.0006581998,0.010828572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003959967,0.00017265565,0.0013651851,0.00053479825,0.000087311746,0.0004271384,0.0004085818,0.011449866,0.1837397,0.02750745,0.03937617,0.7345351],"study_design_scores_gemma":[0.0002898379,0.0014239447,0.004793039,0.00027609957,0.00030494214,0.0030819823,0.00019850914,0.19537155,0.21389504,0.018023124,0.5620828,0.00025908332],"about_ca_topic_score_codex":0.0011527274,"about_ca_topic_score_gemma":0.0013251897,"teacher_disagreement_score":0.017358486,"about_ca_system_score_codex":0.0003053204,"about_ca_system_score_gemma":0.00048516694,"threshold_uncertainty_score":0.058069885},"labels":[],"label_agreement":null},{"id":"W2153532012","doi":"10.1109/ccece.2008.4564615","title":"Alternate amplitude weighting approach for passive source localization using the energy-based grid search algorithm","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Weighting; Estimator; Algorithm; Monte Carlo method; Energy (signal processing); White noise; Computer science; Gaussian; Amplitude; Additive white Gaussian noise; Grid; Cramér–Rao bound; Mathematical optimization; Mathematics; Estimation theory; Statistics; Telecommunications; Physics","score_opus":0.021076592980715927,"score_gpt":0.199108521171722,"score_spread":0.17803192819100608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153532012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027265782,0.000038626466,0.99658465,0.000020887792,0.000009933368,0.000007952783,0.000004335042,0.00008604624,0.00052099477],"genre_scores_gemma":[0.20024401,0.00016929537,0.7974018,0.000056124598,0.000029674246,0.00009353319,0.000054635944,0.000064700194,0.0018861594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996884,0.00009156112,0.000017201904,0.000038739818,0.00014808316,0.000015990618],"domain_scores_gemma":[0.9996532,0.0001550487,0.000027843675,0.000053249274,0.00009862736,0.00001195816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043758107,0.00033291706,0.00044301426,0.00047557522,0.00019199871,0.00050549343,0.00070762215,0.000427682,0.0020219272],"category_scores_gemma":[0.0017811958,0.00017921874,0.0002332975,0.0005774111,0.00030248504,0.00085581833,0.0006261583,0.00033679238,0.0006608422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023899223,0.00009492435,0.0016895054,0.00014444724,0.000049460825,0.000115597475,0.00013254631,0.27905416,0.035354096,0.046798084,0.0017197583,0.6346084],"study_design_scores_gemma":[0.000027995668,0.000071992516,0.00030216036,0.000008771428,0.000011451003,0.00011576481,0.000015365002,0.9811865,0.0064583113,0.009058119,0.002732318,0.000011184503],"about_ca_topic_score_codex":0.0007684441,"about_ca_topic_score_gemma":0.001051695,"teacher_disagreement_score":0.0020219272,"about_ca_system_score_codex":0.00021447404,"about_ca_system_score_gemma":0.0004532762,"threshold_uncertainty_score":0.0067640543},"labels":[],"label_agreement":null},{"id":"W2153673387","doi":"10.1109/issse.2007.4294529","title":"Fingerprinting Localization Using Ultra-Wideband and Neural Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Computer science; RSS; Angle of arrival; Multipath propagation; Artificial neural network; Interpolation (computer graphics); Pillar; Channel (broadcasting); Signal strength; Real-time computing; Artificial intelligence; Data mining; Telecommunications; Wireless; Engineering","score_opus":0.009704830040494935,"score_gpt":0.21729425101727629,"score_spread":0.20758942097678135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153673387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046673577,0.0013268112,0.94822663,0.00018597321,0.00005859111,0.000022797272,0.00004003637,0.00071182975,0.002753777],"genre_scores_gemma":[0.7204025,0.0010624877,0.2728457,0.00010959312,0.000071224305,0.000046230565,0.00009084316,0.000030505584,0.0053409366],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974245,0.000078717414,0.000015736396,0.000060133654,0.00007026395,0.000032698215],"domain_scores_gemma":[0.9994696,0.00027844575,0.000069891415,0.000047425263,0.00012025484,0.0000144034175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053528615,0.00034370364,0.0003332184,0.0007243497,0.00020615202,0.0005804877,0.00038735426,0.0006500237,0.0010172146],"category_scores_gemma":[0.0016557928,0.0001867101,0.00022803961,0.0007218097,0.0002876006,0.0010906907,0.0003574539,0.00033986152,0.00030511495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023380599,0.0000820305,0.0035445224,0.00013899378,0.000113671485,0.00012627318,0.0000755864,0.29215997,0.018862402,0.005829017,0.00087107223,0.6779627],"study_design_scores_gemma":[0.0000064203095,0.0000515562,0.0011966383,0.00001922077,0.000019759185,0.00008577345,0.000023257002,0.9879171,0.0074442527,0.0020941717,0.0011263702,0.000015433441],"about_ca_topic_score_codex":0.0027481397,"about_ca_topic_score_gemma":0.0032230495,"teacher_disagreement_score":0.0027481397,"about_ca_system_score_codex":0.00042494014,"about_ca_system_score_gemma":0.00018671116,"threshold_uncertainty_score":0.005464256},"labels":[],"label_agreement":null},{"id":"W2154172324","doi":"10.1109/ccst.2002.1049246","title":"Field testing of outdoor intrusion detection sensors","year":2003,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Senstar (Canada)","funders":"","keywords":"Firmware; Computer science; Real-time computing; ALARM; Field (mathematics); Remote sensing; Environmental science; Embedded system; Engineering; Electrical engineering; Computer hardware","score_opus":0.010923868251749918,"score_gpt":0.19968086871997454,"score_spread":0.1887570004682246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154172324","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97055006,0.0001775376,0.023205495,0.00007044294,0.000067686065,0.00028217808,0.000495216,0.0010287791,0.0041225716],"genre_scores_gemma":[0.9697369,0.00020909928,0.024821108,0.0001278642,0.000016831276,0.00016797849,0.0008000824,0.00008740695,0.004032757],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990144,0.0001738257,0.00004814185,0.00019514361,0.0004477083,0.00012087357],"domain_scores_gemma":[0.9978194,0.00046340327,0.00016227497,0.00023851353,0.0011964636,0.000119858494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007612863,0.00062612555,0.00037447223,0.00040884895,0.00031781982,0.00037069127,0.001063325,0.00053135416,0.0017247193],"category_scores_gemma":[0.0017156287,0.00020500533,0.00025914639,0.00038106824,0.0003294877,0.000528217,0.00042374578,0.0002833677,0.00053904834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012598318,0.0009402481,0.0376939,0.0004511328,0.00010497463,0.00060444226,0.0005941401,0.011724975,0.8288259,0.00055866747,0.0034572913,0.113784514],"study_design_scores_gemma":[0.0001710742,0.018069634,0.057255577,0.000072754745,0.00013066924,0.0009619847,0.0008706428,0.023840988,0.883677,0.0003148238,0.014565888,0.000068939786],"about_ca_topic_score_codex":0.0020994951,"about_ca_topic_score_gemma":0.0033528751,"teacher_disagreement_score":0.0020994951,"about_ca_system_score_codex":0.0004556661,"about_ca_system_score_gemma":0.00033188463,"threshold_uncertainty_score":0.005769789},"labels":[],"label_agreement":null},{"id":"W2154394329","doi":"10.1109/tap.2007.901862","title":"Mobile Terminal Location for MIMO Communication Systems","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Base station; Multipath propagation; Computer science; Terminal (telecommunication); Position (finance); Context (archaeology); Cramér–Rao bound; Angle of arrival; Delay spread; MIMO; Mobile station; Mobile telephony; Direction of arrival; Square root; SIGNAL (programming language); Root mean square; Mean squared error; Set (abstract data type); Algorithm; Telecommunications; Estimation theory; Mobile radio; Mathematics; Statistics; Electrical engineering; Engineering; Geometry; Antenna (radio)","score_opus":0.012492386276716587,"score_gpt":0.2387459541325298,"score_spread":0.22625356785581321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154394329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045130644,0.0011610095,0.99039334,0.00017223398,0.0001948929,0.000018841205,0.000059274815,0.00034158642,0.0031458032],"genre_scores_gemma":[0.5385849,0.004058798,0.44351473,0.0003876653,0.0007748228,0.00018357526,0.00037685307,0.00007473068,0.012043947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996276,0.0001228086,0.000013581344,0.00007496851,0.00012953226,0.000031552194],"domain_scores_gemma":[0.99962854,0.00015363804,0.00005236177,0.00006113118,0.00009197861,0.000012331708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000281313,0.0006774928,0.00049694174,0.00033490633,0.0003848631,0.0006456532,0.0004786167,0.0008440631,0.0025623108],"category_scores_gemma":[0.0013994831,0.00018823861,0.00029342403,0.00053483737,0.00031174437,0.0006632996,0.0005215811,0.00074825325,0.0019185991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002473565,0.00005124503,0.0016867309,0.0004801039,0.00007420625,0.0008395605,0.00021373612,0.47601378,0.053132966,0.120426945,0.008996787,0.33783665],"study_design_scores_gemma":[0.00003150573,0.00018259358,0.0006353068,0.00005614811,0.000033951877,0.0006609473,0.00005370397,0.9439573,0.009550529,0.026595134,0.018195542,0.000047313202],"about_ca_topic_score_codex":0.00081690465,"about_ca_topic_score_gemma":0.0013619415,"teacher_disagreement_score":0.0025623108,"about_ca_system_score_codex":0.0003231243,"about_ca_system_score_gemma":0.00035318566,"threshold_uncertainty_score":0.008571804},"labels":[],"label_agreement":null},{"id":"W2154997940","doi":"10.1109/vtcf.2006.557","title":"Vehicle Localization in Vehicular Networks","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Ranging; Computer science; Position (finance); Euclidean distance; Noise (video); Distance measurement; Algorithm; Artificial intelligence; Computer vision; Real-time computing; Telecommunications","score_opus":0.00609301947978528,"score_gpt":0.19155264920612233,"score_spread":0.18545962972633706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154997940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008859269,0.0016091779,0.98695976,0.0001571513,0.000094688134,0.00003123439,0.000037818638,0.0005373408,0.0017136759],"genre_scores_gemma":[0.7076652,0.004201771,0.28044012,0.00013657015,0.00020351628,0.00015798194,0.00035168655,0.00007476543,0.006768483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991904,0.00026489556,0.000034005236,0.00018416958,0.00022739565,0.00009916829],"domain_scores_gemma":[0.9996309,0.00013117086,0.00005892104,0.00005165147,0.00010714587,0.000020248972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059258565,0.00067266816,0.0008069655,0.00083824975,0.0006696373,0.0009959262,0.0014060885,0.0010290704,0.0007158769],"category_scores_gemma":[0.0017502643,0.00034272854,0.0003408936,0.0012852717,0.0005885075,0.0012298204,0.0015462235,0.0005387684,0.0006371056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010298791,0.000025026207,0.0014989835,0.00017896199,0.000054885106,0.00020648807,0.00012385876,0.7842519,0.006191652,0.034145825,0.0035084148,0.16971101],"study_design_scores_gemma":[0.000027832311,0.000072940995,0.00037691678,0.00002744951,0.0000229241,0.00018107954,0.000081472666,0.9626901,0.0040581794,0.017596468,0.0148393065,0.00002533849],"about_ca_topic_score_codex":0.004491744,"about_ca_topic_score_gemma":0.0036474648,"teacher_disagreement_score":0.004491744,"about_ca_system_score_codex":0.00067997474,"about_ca_system_score_gemma":0.00060210447,"threshold_uncertainty_score":0.00893122},"labels":[],"label_agreement":null},{"id":"W2156499774","doi":"10.1109/jsen.2010.2044238","title":"Synergism of INS and PDR in Self-Contained Pedestrian Tracking With a Miniature Sensor Module","year":2010,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gyroscope; Pedestrian; Dead reckoning; Accelerometer; Tracking (education); Computer science; Inertial measurement unit; Inertial navigation system; Tracking system; Computer vision; Position (finance); Artificial intelligence; Real-time computing; Inertial frame of reference; Engineering; Global Positioning System; Kalman filter; Telecommunications","score_opus":0.004748462968871655,"score_gpt":0.1939587599914745,"score_spread":0.18921029702260284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156499774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15611614,0.00041748123,0.8350538,0.00017918737,0.00013593417,0.00005885284,0.000039939845,0.0016325228,0.0063661444],"genre_scores_gemma":[0.8321102,0.00019366853,0.16411017,0.000079950165,0.00004773159,0.000040600902,0.000034936835,0.000037932692,0.0033448471],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977535,0.00007624247,0.000011116974,0.000047745456,0.00007309741,0.00001654345],"domain_scores_gemma":[0.9997801,0.00008966132,0.000029081717,0.000042211355,0.000047005633,0.000011957693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047418664,0.00031020804,0.00035839513,0.0002753912,0.00015886403,0.00034639097,0.00044899475,0.00044606952,0.0008366426],"category_scores_gemma":[0.0005737861,0.0004150403,0.00025789157,0.00023573091,0.00020490214,0.00065248,0.00040213508,0.00019309604,0.00038964918],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012670273,0.0002834178,0.0076507297,0.0003015928,0.00016626438,0.0005725857,0.00043977302,0.08198138,0.40485206,0.008292902,0.0021436203,0.49204862],"study_design_scores_gemma":[0.000064986416,0.0007484772,0.0074770777,0.000037488175,0.00012793174,0.0007848736,0.000066374414,0.85628104,0.1247783,0.0016741082,0.007900015,0.000059238333],"about_ca_topic_score_codex":0.0002935032,"about_ca_topic_score_gemma":0.000678279,"teacher_disagreement_score":0.0008366426,"about_ca_system_score_codex":0.00012876636,"about_ca_system_score_gemma":0.00018187292,"threshold_uncertainty_score":0.0027988553},"labels":[],"label_agreement":null},{"id":"W2156878441","doi":"10.1145/1409635.1409650","title":"CILoS","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; SIGNAL (programming language); Signal strength; Code division multiple access; Transmission (telecommunications); Cellular radio; Real-time computing; Electronic engineering; Computer network; Telecommunications; Base station; Wireless; Engineering","score_opus":0.011552008211966612,"score_gpt":0.1614798139527269,"score_spread":0.14992780574076028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156878441","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.089872286,0.0026789526,0.20537265,0.0020820054,0.0019264771,0.0013943387,0.017990809,0.11166409,0.56701833],"genre_scores_gemma":[0.52110034,0.0020092062,0.1350116,0.0015379416,0.00047317866,0.0010208951,0.037294492,0.0038468402,0.29770553],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991373,0.000070167735,0.000038387265,0.00020522342,0.00040990254,0.00013912025],"domain_scores_gemma":[0.9988838,0.00012055065,0.00009344457,0.00021451082,0.0004618826,0.00022581917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006070018,0.00084095675,0.00070587266,0.001874008,0.0010444875,0.0020200585,0.0013067785,0.0006289797,0.056911536],"category_scores_gemma":[0.0019013019,0.00032303462,0.0002892389,0.0011461366,0.00052980456,0.0014513825,0.0022223282,0.00085538457,0.02399966],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026123864,0.0003673968,0.013536011,0.0007289371,0.000070030364,0.0006467075,0.0006939965,0.003636516,0.040185165,0.026921721,0.2577117,0.6528894],"study_design_scores_gemma":[0.00040057735,0.00060451374,0.005749585,0.00016769621,0.00008603201,0.0010609445,0.00039305468,0.025125897,0.026943076,0.004514277,0.9348436,0.00011074248],"about_ca_topic_score_codex":0.0045007593,"about_ca_topic_score_gemma":0.005677386,"teacher_disagreement_score":0.056911536,"about_ca_system_score_codex":0.0010124674,"about_ca_system_score_gemma":0.0015017629,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2157583789","doi":"","title":"Integration of GPS and Cellular Networks to Improve Wireless Location Performance","year":2003,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Kalman filter; Computer science; Fading; Real-time computing; GPS signals; Wireless; Epoch (astronomy); Precision Lightweight GPS Receiver; Assisted GPS; Telecommunications; Artificial intelligence; Channel (broadcasting); Gps receiver","score_opus":0.004912536982823048,"score_gpt":0.1789628434385569,"score_spread":0.17405030645573386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157583789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074854344,0.0018511914,0.91672015,0.00029929914,0.00012450838,0.00003877803,0.000029613779,0.0016048839,0.00447721],"genre_scores_gemma":[0.8776007,0.0010076562,0.11869741,0.00010847385,0.00011661608,0.00003031229,0.00007834731,0.000032856773,0.002327649],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963903,0.00009652255,0.000018429313,0.00004432952,0.00015335434,0.000048224305],"domain_scores_gemma":[0.999491,0.00014902995,0.000054585176,0.00006850754,0.00022231208,0.000014580797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041919848,0.0005906651,0.00034618512,0.00042118205,0.00020666746,0.0004458791,0.000388501,0.0004918613,0.00088296004],"category_scores_gemma":[0.0015617976,0.00021713701,0.00018108248,0.00078079,0.00021856878,0.0008504546,0.0005799579,0.000399539,0.00038574263],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029048356,0.00011898069,0.011272668,0.0002072244,0.00016307275,0.00035568903,0.00016919825,0.28838417,0.094406605,0.012069445,0.0023979412,0.59016454],"study_design_scores_gemma":[0.00004129457,0.00052788586,0.0053596343,0.000036372818,0.00015506176,0.00050546863,0.00006878658,0.9230588,0.049983248,0.0032073644,0.017021617,0.00003444724],"about_ca_topic_score_codex":0.002060949,"about_ca_topic_score_gemma":0.0028903247,"teacher_disagreement_score":0.002060949,"about_ca_system_score_codex":0.0002634678,"about_ca_system_score_gemma":0.00032822829,"threshold_uncertainty_score":0.004097879},"labels":[],"label_agreement":null},{"id":"W2157764920","doi":"10.1109/crisis.2010.5764921","title":"Solution to the wireless evil-twin transmitter attack","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transmitter; Computer science; Wireless network; Computer network; Identity (music); Wireless; Algorithm; Telecommunications; Physics; Channel (broadcasting)","score_opus":0.009961450754120646,"score_gpt":0.2196703472294655,"score_spread":0.20970889647534485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157764920","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012836034,0.0003888743,0.98049086,0.00069056364,0.00012609335,0.00006423442,0.0000435831,0.0006349778,0.0047248723],"genre_scores_gemma":[0.52018064,0.0009397598,0.46318457,0.00066735613,0.00019879645,0.00018779571,0.00027239503,0.00012892703,0.0142398095],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99813545,0.00059547066,0.000098730474,0.00030154586,0.00063682516,0.00023187717],"domain_scores_gemma":[0.99765766,0.0007351741,0.0002765969,0.00071984605,0.0004928613,0.000117871146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012023281,0.0008102865,0.0010100324,0.0007932059,0.00092543074,0.0013246041,0.0021605808,0.0019873835,0.0031369524],"category_scores_gemma":[0.0058533424,0.00046930363,0.00059307757,0.00068905245,0.0011766573,0.0022924568,0.003923992,0.0018386112,0.0016470326],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010050817,0.00025077834,0.0039150985,0.00058850326,0.00028153436,0.001438545,0.0010948718,0.19977029,0.028854415,0.19817282,0.021091145,0.54353696],"study_design_scores_gemma":[0.00017208264,0.00036533998,0.0005968574,0.00010114992,0.000100723584,0.002164255,0.00040261986,0.88482636,0.018526055,0.06050619,0.032174837,0.00006352411],"about_ca_topic_score_codex":0.00074235117,"about_ca_topic_score_gemma":0.00046586784,"teacher_disagreement_score":0.0031369524,"about_ca_system_score_codex":0.00048711084,"about_ca_system_score_gemma":0.0009067256,"threshold_uncertainty_score":0.010494173},"labels":[],"label_agreement":null},{"id":"W2157837881","doi":"10.1109/issse.2007.4294530","title":"On the TOA Estimation for UWB Ranging in Complex Confined Area","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Ranging; Time of arrival; Computer science; Multipath propagation; Real-time computing; SIGNAL (programming language); Channel (broadcasting); Algorithm; Direction of arrival; Wireless; Sensitivity (control systems); Electronic engineering; Telecommunications; Engineering","score_opus":0.0260085003608562,"score_gpt":0.24374563742768146,"score_spread":0.21773713706682526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157837881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007787974,0.001175418,0.9898746,0.00012705174,0.0000749195,0.000019764286,0.000017250723,0.00011029701,0.0008128008],"genre_scores_gemma":[0.33999106,0.007285946,0.64664084,0.00030990274,0.0006593308,0.00019006224,0.0001869813,0.00013722951,0.004598555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993162,0.00027598237,0.000037679547,0.00013244417,0.00021260633,0.000025124706],"domain_scores_gemma":[0.99767166,0.001778127,0.00014151508,0.00016242231,0.00022292555,0.000023312508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009563559,0.00071393885,0.00071454333,0.0006656731,0.0004276577,0.0006262088,0.0006058236,0.0010047776,0.000995562],"category_scores_gemma":[0.0066111013,0.0003189283,0.0004643575,0.00096359645,0.0008545288,0.0009818939,0.0006761324,0.00083651906,0.00062427536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000322736,0.00006491947,0.0014123741,0.00042003512,0.00014208574,0.0003056288,0.00026853647,0.47884053,0.03984448,0.03571057,0.0019184193,0.44074968],"study_design_scores_gemma":[0.000012765138,0.00008901917,0.0005590404,0.000044372715,0.000034332028,0.00030657405,0.000044336502,0.9779018,0.00843106,0.008330238,0.0042046774,0.000041800824],"about_ca_topic_score_codex":0.0013396632,"about_ca_topic_score_gemma":0.00071317184,"teacher_disagreement_score":0.0013396632,"about_ca_system_score_codex":0.00028719503,"about_ca_system_score_gemma":0.00031368106,"threshold_uncertainty_score":0.005057752},"labels":[],"label_agreement":null},{"id":"W2157851304","doi":"10.1016/j.isatra.2013.09.016","title":"Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach","year":2013,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"RSS; Beacon; Mobile robot; Robot; Controller (irrigation); Trajectory; Computer science; Tracking (education); Real-time computing; Linearization; Simulation; Control theory (sociology); Artificial intelligence; Control (management)","score_opus":0.050078594315775375,"score_gpt":0.24708947438609355,"score_spread":0.19701088007031817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157851304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012408577,0.00043631476,0.9856236,0.000068480476,0.0000650616,0.0000066896837,0.000033145738,0.0002960325,0.0010621181],"genre_scores_gemma":[0.6932897,0.0014487113,0.29864895,0.000120092816,0.00026917364,0.000039950068,0.00020377088,0.00011656079,0.005863035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939156,0.0001420133,0.000031778603,0.00018623676,0.00019212328,0.000056210672],"domain_scores_gemma":[0.99925596,0.00023734415,0.00013963139,0.00016926348,0.00017608407,0.000021715743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004515034,0.00092284335,0.0010594128,0.0009163817,0.00036108334,0.0010209015,0.0010403354,0.0012014615,0.000685121],"category_scores_gemma":[0.001967634,0.0007039208,0.0007183974,0.0015216301,0.0005822961,0.0016212156,0.00086107006,0.00070166605,0.00087849516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005350405,0.00015604385,0.005717542,0.00036575765,0.00032452113,0.0006404852,0.00029530138,0.465549,0.07794043,0.012816113,0.0018729193,0.43378687],"study_design_scores_gemma":[0.00001627983,0.00012629609,0.0019225008,0.000023349063,0.00011074623,0.00041874385,0.000059603,0.976857,0.013897421,0.0044075614,0.0021207856,0.00003967524],"about_ca_topic_score_codex":0.0014813358,"about_ca_topic_score_gemma":0.0017776528,"teacher_disagreement_score":0.0014813358,"about_ca_system_score_codex":0.0003154345,"about_ca_system_score_gemma":0.00034060612,"threshold_uncertainty_score":0.0029454827},"labels":[],"label_agreement":null},{"id":"W2158146974","doi":"10.1504/ijsnet.2009.024681","title":"Localised convex hulls to identify boundary nodes in sensor networks","year":2009,"lang":"en","type":"article","venue":"International Journal of Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; A priori and a posteriori; Wireless sensor network; Convex hull; Probabilistic logic; Node (physics); Set (abstract data type); Boundary (topology); Beacon; Regular polygon; Algorithm; Computer network; Artificial intelligence; Mathematics; Geometry","score_opus":0.008475980750562667,"score_gpt":0.2658292742023203,"score_spread":0.2573532934517576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158146974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045813184,0.00027602614,0.95240295,0.00010213386,0.000016126176,0.00004268798,0.000043183554,0.0002920735,0.001011679],"genre_scores_gemma":[0.7603651,0.00032506022,0.23774676,0.000048767797,0.00003113415,0.000088258486,0.00020131159,0.00014320877,0.0010503584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835217,0.00070455874,0.000059245805,0.00019540818,0.00054983434,0.00013872964],"domain_scores_gemma":[0.9929732,0.0044273566,0.00078348443,0.0009487113,0.0006014515,0.0002657924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021367965,0.00077637506,0.0012222072,0.001633446,0.0007292239,0.0013450935,0.0015767787,0.0009474173,0.0008828011],"category_scores_gemma":[0.013459897,0.0006580687,0.00068811834,0.0012051006,0.0021682954,0.0026428895,0.002884893,0.0011177011,0.00034650572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018299726,0.000032524542,0.0022662894,0.000056807672,0.000022412858,0.00012936513,0.00029070483,0.907637,0.0039359583,0.020135181,0.0006400573,0.06467059],"study_design_scores_gemma":[0.000005616776,0.000041906085,0.00036402475,0.000009142386,0.0000041016083,0.00004513256,0.000057329707,0.98617435,0.0018876344,0.011136225,0.00025974298,0.0000148053205],"about_ca_topic_score_codex":0.003934936,"about_ca_topic_score_gemma":0.0027876068,"teacher_disagreement_score":0.003934936,"about_ca_system_score_codex":0.0009154079,"about_ca_system_score_gemma":0.00057387503,"threshold_uncertainty_score":0.011300623},"labels":[],"label_agreement":null},{"id":"W2158195655","doi":"10.1109/ccece.2007.381","title":"Synchronization of Weak Indoor GPS Signals with Doppler Using a Segmented Matched Filter and Accumulation","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Global Positioning System; GPS signals; Computer science; Doppler effect; Multipath propagation; Assisted GPS; Wireless; Real-time computing; Matched filter; Offset (computer science); Bandwidth (computing); Filter (signal processing); Telecommunications; Physics; Computer vision","score_opus":0.023801160337111955,"score_gpt":0.2533911459846119,"score_spread":0.2295899856474999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158195655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18166928,0.0001932283,0.81461364,0.00006710433,0.000045001987,0.000032077172,0.000023515342,0.00066126726,0.0026948291],"genre_scores_gemma":[0.8387632,0.00010891054,0.15878141,0.00003808309,0.000034175217,0.000021817514,0.00004463567,0.00002715143,0.0021806636],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981433,0.0000296939,0.000008518503,0.000034945613,0.00008328297,0.00002924313],"domain_scores_gemma":[0.99976605,0.00009100888,0.0000625835,0.000027634433,0.000039421826,0.00001330566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023366862,0.000328866,0.00022920886,0.0002834844,0.00023837392,0.00030525666,0.0004217948,0.00036091625,0.00077588216],"category_scores_gemma":[0.0009045855,0.00017177878,0.00022826933,0.00037084558,0.00022607195,0.0005105929,0.00029193034,0.00018020626,0.00027901828],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015350953,0.00015492989,0.007498107,0.00020286014,0.00014241211,0.0004762079,0.00038427883,0.17222479,0.33063906,0.013220205,0.0009024273,0.47261953],"study_design_scores_gemma":[0.00008073418,0.0007391405,0.0039785346,0.000023620034,0.00010020661,0.0005287469,0.00006351807,0.833116,0.1528381,0.0020239542,0.0064723995,0.000035196164],"about_ca_topic_score_codex":0.0017194337,"about_ca_topic_score_gemma":0.002408561,"teacher_disagreement_score":0.0017194337,"about_ca_system_score_codex":0.00035272483,"about_ca_system_score_gemma":0.0003559067,"threshold_uncertainty_score":0.0034188032},"labels":[],"label_agreement":null},{"id":"W2158836019","doi":"10.1109/ccece.2002.1015249","title":"Pulse shaping and signal modulation techniques to improve the multipath and noise performance of narrowband precision RF ranging","year":2003,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Multipath propagation; Ranging; Narrowband; Computer science; Electronic engineering; Bandwidth (computing); Multipath interference; Radio frequency; Carrier recovery; Telecommunications; Demodulation; Engineering","score_opus":0.009001661803096604,"score_gpt":0.21118805526666062,"score_spread":0.202186393463564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158836019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09122498,0.0017746858,0.90289664,0.00016638925,0.000061124265,0.000051183382,0.00001279449,0.0005934255,0.0032186967],"genre_scores_gemma":[0.5923985,0.0017197888,0.40227857,0.0001188014,0.00009458459,0.00006277026,0.000036142,0.000087718625,0.0032031322],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996282,0.0000933564,0.000020578424,0.000038478127,0.00019867506,0.00002067493],"domain_scores_gemma":[0.9986455,0.00081113173,0.00019419729,0.00013628375,0.0001974832,0.000015480387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062261464,0.00053582294,0.000263699,0.0005309815,0.00023680295,0.00038040042,0.00038219188,0.0004785376,0.0011280199],"category_scores_gemma":[0.003275946,0.00018391394,0.0002628436,0.0006162455,0.00039162528,0.0006729757,0.00029004607,0.00045375133,0.00035327722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037051245,0.00013041137,0.0015173737,0.00046431657,0.00013147594,0.00031327127,0.0003548852,0.09049623,0.4532894,0.013741048,0.00040179453,0.43878925],"study_design_scores_gemma":[0.0001129189,0.0022186798,0.0032962547,0.00013589104,0.000277982,0.0033663888,0.00009870325,0.30175677,0.65575147,0.008618366,0.024265682,0.00010092383],"about_ca_topic_score_codex":0.00027386314,"about_ca_topic_score_gemma":0.00040723445,"teacher_disagreement_score":0.0011280199,"about_ca_system_score_codex":0.00023897082,"about_ca_system_score_gemma":0.00022464586,"threshold_uncertainty_score":0.00377357},"labels":[],"label_agreement":null},{"id":"W2159473362","doi":"10.1109/icma.2005.1626630","title":"Localization of multiple robots with simple sensors","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trilateration; Robot; Particle filter; Monte Carlo localization; Mobile robot; Range (aeronautics); Centroid; Computer science; Position (finance); Artificial intelligence; Computer vision; Compass; Filter (signal processing); Engineering; Mathematics; Geography; Triangulation","score_opus":0.0046454115406733745,"score_gpt":0.17543451149349396,"score_spread":0.17078909995282057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159473362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004704933,0.000084922605,0.9944305,0.000033310036,0.000025308576,0.000021169088,0.0000075046605,0.00021067915,0.00048171298],"genre_scores_gemma":[0.20793302,0.00034221364,0.787072,0.00010277272,0.00007440165,0.00020936644,0.00007708613,0.000052179086,0.0041368976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990933,0.00016457688,0.000045199253,0.00025791227,0.00038500238,0.000053954525],"domain_scores_gemma":[0.99934775,0.00023880572,0.00011227301,0.00014931659,0.000113003465,0.000038787974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088661886,0.0008602303,0.0012591897,0.00067163876,0.00045319434,0.00075410336,0.0014005863,0.0012022698,0.0010587575],"category_scores_gemma":[0.0019365924,0.00059498416,0.0008190898,0.00060105143,0.00091773574,0.0015372093,0.0013943227,0.0008725043,0.00067766133],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027920204,0.00016437548,0.0018769084,0.00035635146,0.00017209047,0.00030618932,0.0003320055,0.59715223,0.056024425,0.020406637,0.0018256976,0.32110396],"study_design_scores_gemma":[0.000048252456,0.000153084,0.0005340446,0.000018906298,0.000024397224,0.00011956752,0.00002640511,0.9775687,0.010470799,0.0054012183,0.0056058527,0.000028732375],"about_ca_topic_score_codex":0.0030271038,"about_ca_topic_score_gemma":0.0027867854,"teacher_disagreement_score":0.0030271038,"about_ca_system_score_codex":0.0006605948,"about_ca_system_score_gemma":0.0007561766,"threshold_uncertainty_score":0.0060189962},"labels":[],"label_agreement":null},{"id":"W2159796823","doi":"10.1109/cca.2000.897437","title":"Heading-aided odometry and range-data integration for positioning of autonomous mining vehicles","year":2002,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Odometry; Inclinometer; Heading (navigation); Global Positioning System; Kalman filter; Computer vision; Computer science; Visual odometry; Artificial intelligence; Gyroscope; Engineering; Mobile robot; Robot; Geodesy; Aerospace engineering; Geology","score_opus":0.03836756312199486,"score_gpt":0.23984579665756448,"score_spread":0.20147823353556962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159796823","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046681765,0.00049779017,0.95044225,0.00007929329,0.00006309802,0.000022601947,0.000058167992,0.0011544398,0.0010006505],"genre_scores_gemma":[0.6633136,0.0003557428,0.33344775,0.000044136214,0.000058688132,0.000065543376,0.0002853231,0.000039239425,0.0023900177],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984944,0.000032134354,0.000007995014,0.000023436705,0.0000693977,0.000017565138],"domain_scores_gemma":[0.9998425,0.000025157604,0.000023767594,0.000025792593,0.00007600613,0.0000066810894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018114655,0.00024437212,0.00025042135,0.00032972934,0.0001391374,0.00021997205,0.0003207108,0.0002737008,0.00054200157],"category_scores_gemma":[0.00046873424,0.00017703424,0.00014500403,0.00037870722,0.00013072068,0.00035512957,0.00037012028,0.0001946529,0.00031497952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031645515,0.00006430752,0.004385754,0.00016695108,0.000054976343,0.00016351849,0.00014692228,0.10054881,0.12577808,0.004760234,0.003024116,0.76058984],"study_design_scores_gemma":[0.00006476521,0.00036147947,0.0075377068,0.000020444451,0.000053521293,0.00030069816,0.000057177178,0.9123019,0.058444515,0.0028718542,0.017944977,0.00004102086],"about_ca_topic_score_codex":0.0015902596,"about_ca_topic_score_gemma":0.002370325,"teacher_disagreement_score":0.0015902596,"about_ca_system_score_codex":0.00012542288,"about_ca_system_score_gemma":0.00032464255,"threshold_uncertainty_score":0.0031619668},"labels":[],"label_agreement":null},{"id":"W2159893568","doi":"10.1109/milcom.2009.5379870","title":"Wormhole attack detection based on distance verification and the Use of hypothesis testing for wireless ad hoc networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Node (physics); Wireless ad hoc network; Network packet; Computer network; Mobile ad hoc network; Optimized Link State Routing Protocol; Communication source; Wormhole; Vehicular ad hoc network; Wireless; Routing protocol; Distributed computing","score_opus":0.03965881970826345,"score_gpt":0.21428754305344924,"score_spread":0.17462872334518578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159893568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009573163,0.00048571534,0.98872656,0.00019924821,0.000058914797,0.00006084527,0.000013644273,0.0001765548,0.0007053411],"genre_scores_gemma":[0.5526211,0.0010777813,0.4448708,0.00020234317,0.00023623837,0.00024401856,0.00006446306,0.000034581542,0.0006485568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9913123,0.0049945926,0.00044454966,0.0008441503,0.0022342792,0.00017002047],"domain_scores_gemma":[0.9646197,0.029254138,0.002396276,0.0020596052,0.0014846738,0.00018558893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066024354,0.00076289085,0.00090174185,0.0018674255,0.00044470414,0.0011449973,0.0014140616,0.0016006839,0.0007425316],"category_scores_gemma":[0.027221093,0.00037721405,0.0008311713,0.00097010954,0.0029314584,0.0032422445,0.0011605826,0.0012526816,0.00023043551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006467901,0.00028113005,0.009427218,0.00095979474,0.0005911037,0.0010899953,0.0005809793,0.17802331,0.03334067,0.19024378,0.0019713428,0.58284384],"study_design_scores_gemma":[0.00011770967,0.0009696935,0.0025233321,0.000114392955,0.00013432102,0.0022379663,0.00014360686,0.85773015,0.03109005,0.09993788,0.0048573697,0.00014347873],"about_ca_topic_score_codex":0.0002715929,"about_ca_topic_score_gemma":0.00019751018,"teacher_disagreement_score":0.0066024354,"about_ca_system_score_codex":0.00057498785,"about_ca_system_score_gemma":0.0008222484,"threshold_uncertainty_score":0.034917414},"labels":[],"label_agreement":null},{"id":"W2160687055","doi":"10.1109/glocom.2010.5683692","title":"Probabilistic Estimation of Location Error in Wireless Ad Hoc Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Robustness (evolution); Probabilistic logic; Computer science; Cramér–Rao bound; Wireless ad hoc network; Upper and lower bounds; Algorithm; Network topology; Scaling; Variance (accounting); Probability density function; Function (biology); Wireless sensor network; Mathematics; Estimation theory; Wireless; Statistics; Artificial intelligence","score_opus":0.006371265075601369,"score_gpt":0.21478393847815097,"score_spread":0.2084126734025496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160687055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027525822,0.0005395554,0.9709901,0.000076333316,0.000024314344,0.000021086104,0.000020863748,0.00021450633,0.000587487],"genre_scores_gemma":[0.8733343,0.0009839467,0.12477095,0.000035116827,0.00008759323,0.00008138397,0.00008795452,0.000052897292,0.0005659583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806494,0.0007351446,0.0000958995,0.00018436874,0.0008552142,0.00006447285],"domain_scores_gemma":[0.9923907,0.0054165144,0.00068149925,0.0006404798,0.0008263177,0.00004448907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021600504,0.000619052,0.00057305733,0.0012613366,0.00045930393,0.0008536153,0.0010036788,0.00063690165,0.0002889823],"category_scores_gemma":[0.017685497,0.00057483494,0.00029540824,0.0013109759,0.0009550142,0.0017913815,0.0010390497,0.0006978578,0.00013456326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035593504,0.000013369604,0.0013685918,0.000052772266,0.000028656536,0.000036320835,0.000042988206,0.96107066,0.0021271566,0.006551188,0.0001730841,0.028499663],"study_design_scores_gemma":[0.0000048491142,0.000024059073,0.0005426775,0.000008077623,0.0000084602725,0.000035581088,0.000009466061,0.9922151,0.0012254058,0.00552392,0.00038739477,0.000014943563],"about_ca_topic_score_codex":0.0017565916,"about_ca_topic_score_gemma":0.0015149356,"teacher_disagreement_score":0.0021600504,"about_ca_system_score_codex":0.000529118,"about_ca_system_score_gemma":0.0004528204,"threshold_uncertainty_score":0.011423588},"labels":[],"label_agreement":null},{"id":"W2160726205","doi":"10.1109/icc.2011.5962929","title":"Analysis of the Impact of the Physical Environment on a Wireless Sensor Network","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Wireless sensor network; Computer science; Signal strength; Variance (accounting); Statistical analysis; Set (abstract data type); Wireless; Wireless network; Real-time computing; Data mining; Computer network; Telecommunications; Statistics; Mathematics","score_opus":0.0110810838531269,"score_gpt":0.20294575525036918,"score_spread":0.19186467139724228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160726205","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8832501,0.00077929033,0.11085095,0.000107569795,0.000071180504,0.00015707148,0.0004606767,0.0004174376,0.0039056705],"genre_scores_gemma":[0.9829784,0.00039347337,0.015623605,0.000021629028,0.000013817859,0.00007423405,0.00026964996,0.000035872123,0.00058935175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99848986,0.0004508145,0.00007008107,0.00016297417,0.0006943985,0.00013175979],"domain_scores_gemma":[0.9957004,0.0029399656,0.00040291532,0.00033894525,0.00052008074,0.00009764276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007683332,0.00065581326,0.00040081143,0.0006965681,0.00031428362,0.0004973476,0.00040353558,0.00030540774,0.0015527672],"category_scores_gemma":[0.0038067035,0.00017649758,0.0003838798,0.00056803745,0.00037769644,0.0008527641,0.0005078621,0.00023840982,0.00020960871],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016604945,0.0006831614,0.050024133,0.0015221622,0.0004989107,0.0013151801,0.00029549914,0.4056681,0.38408843,0.0036672638,0.0009728548,0.14960384],"study_design_scores_gemma":[0.00006265645,0.0057817777,0.1471557,0.00011536347,0.00057738536,0.0014753595,0.00093382725,0.546514,0.2875661,0.0033555722,0.006333164,0.00012906856],"about_ca_topic_score_codex":0.0005456671,"about_ca_topic_score_gemma":0.0007461946,"teacher_disagreement_score":0.0015527672,"about_ca_system_score_codex":0.00032191648,"about_ca_system_score_gemma":0.00026919926,"threshold_uncertainty_score":0.005194485},"labels":[],"label_agreement":null},{"id":"W2160883679","doi":"10.1109/sarnof.2011.5876449","title":"Advanced diagnostic system with ventilation on demand for underground mines","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Ventilation (architecture); Wireless sensor network; Communications system; Wireless; Computer science; Mining engineering; Engineering; Real-time computing; Telecommunications; Computer network","score_opus":0.013845742359109205,"score_gpt":0.19286036522241723,"score_spread":0.17901462286330802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160883679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38876808,0.0030334042,0.55897397,0.0014925165,0.001048283,0.00067539513,0.0011663539,0.022541681,0.022300372],"genre_scores_gemma":[0.9532209,0.00025705952,0.038591623,0.00040320528,0.00012476371,0.00010371397,0.0002707036,0.00007373122,0.0069542797],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994809,0.000060143615,0.0000317785,0.00012138018,0.0002531822,0.00005260819],"domain_scores_gemma":[0.99953675,0.000099708115,0.00006321263,0.000051297495,0.00020420415,0.00004477081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025373197,0.0005744195,0.0005497707,0.0007722592,0.00035938545,0.00046924368,0.0011051091,0.00085871527,0.0035777893],"category_scores_gemma":[0.00063034013,0.00019351627,0.00027156365,0.00027852232,0.00020275736,0.0007314922,0.00075028714,0.00033292407,0.0009753183],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012146732,0.0003223886,0.014828966,0.00093920244,0.000075414406,0.0027876657,0.00067393156,0.005603316,0.5458818,0.0019944038,0.01489014,0.4107881],"study_design_scores_gemma":[0.00037521,0.0023297195,0.02261381,0.0002500317,0.00029381667,0.012823746,0.00052828423,0.33072245,0.53150165,0.0026835268,0.09564383,0.00023388902],"about_ca_topic_score_codex":0.00052774017,"about_ca_topic_score_gemma":0.0006306244,"teacher_disagreement_score":0.0035777893,"about_ca_system_score_codex":0.000335067,"about_ca_system_score_gemma":0.0002516919,"threshold_uncertainty_score":0.011968911},"labels":[],"label_agreement":null},{"id":"W2162814504","doi":"","title":"Sequential Monte Carlo for simultaneous passive device-free tracking and sensor localization using received signal strength measurements","year":2011,"lang":"en","type":"article","venue":"Information Processing in Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"RSS; Wireless sensor network; Testbed; Computer science; Particle filter; Monte Carlo method; Real-time computing; Tracking (education); Tracking system; Calibration; SIGNAL (programming language); Artificial intelligence; Kalman filter; Computer network","score_opus":0.043208244354570026,"score_gpt":0.2417035649233902,"score_spread":0.1984953205688202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162814504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006629046,0.00007367064,0.99231565,0.000039555012,0.00001392851,0.000030513553,0.0000158285,0.0002810792,0.00060074776],"genre_scores_gemma":[0.5127707,0.00022571872,0.48374173,0.00009522323,0.00006043695,0.00031288908,0.00018754581,0.00015806592,0.0024477083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854785,0.0005078191,0.00006031206,0.00022354475,0.0005706614,0.00008971844],"domain_scores_gemma":[0.9953566,0.0032337168,0.00036056124,0.00042340427,0.0005080885,0.00011753548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025906726,0.0008190516,0.0009847329,0.00090636837,0.00054352253,0.00095714116,0.0017664798,0.0010340051,0.0015047673],"category_scores_gemma":[0.00834376,0.00081312633,0.0008361035,0.0008639145,0.0009938007,0.0014575343,0.0011895897,0.0009184185,0.00043774588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001201368,0.000043993503,0.0009983707,0.000047991627,0.000044842975,0.00005420637,0.000054275635,0.9535325,0.0016875946,0.011198495,0.0003294809,0.03188804],"study_design_scores_gemma":[0.0000097632,0.000015828462,0.00009032391,0.0000032485007,0.00000544173,0.000017087807,0.0000022708575,0.99706584,0.00049460697,0.002013956,0.00027610452,0.0000055581895],"about_ca_topic_score_codex":0.008045721,"about_ca_topic_score_gemma":0.0077612596,"teacher_disagreement_score":0.008045721,"about_ca_system_score_codex":0.0012860561,"about_ca_system_score_gemma":0.0014550484,"threshold_uncertainty_score":0.015997827},"labels":[],"label_agreement":null},{"id":"W2163049891","doi":"10.3390/s120708507","title":"Step Length Estimation Using Handheld Inertial Sensors","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":227,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Accelerometer; Mobile device; Inertial measurement unit; Short-time Fourier transform; Computer science; Two step; Process (computing); Set (abstract data type); Acoustics; SIGNAL (programming language); Artificial intelligence; Simulation; Fourier transform; Mathematics; Fourier analysis","score_opus":0.016409096296125292,"score_gpt":0.23524747484331776,"score_spread":0.21883837854719246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163049891","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17571473,0.0005473227,0.8205048,0.000057692505,0.000061003513,0.00007546756,0.00031430562,0.0011116237,0.0016130945],"genre_scores_gemma":[0.8887819,0.0005337274,0.107070275,0.000040014635,0.00002436277,0.00009903009,0.00047565254,0.000040597395,0.0029343928],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999892,0.000017408222,0.0000057239918,0.00003484184,0.000041510164,0.000008488111],"domain_scores_gemma":[0.99991214,0.000030170591,0.000015801754,0.000015208519,0.000021897551,0.0000046516743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009816229,0.0005034104,0.0003662481,0.00039879832,0.00011769733,0.0002895504,0.0004923578,0.00040817188,0.00096719235],"category_scores_gemma":[0.00042338236,0.00017848339,0.0003851395,0.00031649033,0.000104268,0.0003516613,0.00022723027,0.00022871027,0.0005303508],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005640246,0.00016476725,0.017062781,0.00044930802,0.0002664969,0.0005124151,0.00017897566,0.32123095,0.17300777,0.0012020625,0.0017168216,0.48364356],"study_design_scores_gemma":[0.00003052875,0.00035768052,0.025489537,0.000038055474,0.00008731936,0.00048579994,0.000062737425,0.93613034,0.03362954,0.000746426,0.0028995078,0.000042566695],"about_ca_topic_score_codex":0.0025217852,"about_ca_topic_score_gemma":0.003069097,"teacher_disagreement_score":0.0025217852,"about_ca_system_score_codex":0.00014244707,"about_ca_system_score_gemma":0.0002031653,"threshold_uncertainty_score":0.0050142407},"labels":[],"label_agreement":null},{"id":"W2163640822","doi":"10.1109/robot.2009.5152800","title":"Inferring a probability distribution function for the pose of a sensor network using a mobile robot","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of British Columbia","funders":"","keywords":"Odometry; Mobile robot; Computer science; Convergence (economics); Robot; Artificial intelligence; Probability distribution; Wireless sensor network; Scheme (mathematics); Probability density function; Computer vision; Real-time computing; Algorithm; Mathematics","score_opus":0.01807076347533734,"score_gpt":0.23239600180764577,"score_spread":0.21432523833230843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163640822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004909288,0.000016175602,0.99480164,0.00002397732,0.0000043250748,0.000007989031,0.0000069782773,0.00014927032,0.000080277096],"genre_scores_gemma":[0.40243077,0.00017030015,0.59601724,0.000045435914,0.00005253466,0.00011063869,0.000114216404,0.00009837387,0.0009604704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992791,0.00020672925,0.000030632582,0.00023064204,0.00020176596,0.00005112903],"domain_scores_gemma":[0.9981602,0.0010862419,0.0002514853,0.00025253725,0.00017455748,0.00007510638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012605325,0.00095570687,0.00081816973,0.0009187165,0.0004919567,0.00088192115,0.0013705943,0.0009130051,0.0011115755],"category_scores_gemma":[0.006520389,0.0006566053,0.00074290636,0.00059045793,0.0011478661,0.0018163854,0.001115772,0.0012814788,0.0004902572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012993319,0.000055648707,0.0025591461,0.000089209585,0.000052917567,0.00015371022,0.00011571727,0.8659595,0.010444328,0.015725756,0.00045853658,0.104255475],"study_design_scores_gemma":[0.000009254149,0.0000398152,0.00041327532,0.000006960221,0.0000080413,0.00006914548,0.00002019236,0.9878099,0.0033507566,0.007747716,0.00051271357,0.000012254189],"about_ca_topic_score_codex":0.0027965195,"about_ca_topic_score_gemma":0.0029775754,"teacher_disagreement_score":0.0027965195,"about_ca_system_score_codex":0.0007476759,"about_ca_system_score_gemma":0.0012154956,"threshold_uncertainty_score":0.006666422},"labels":[],"label_agreement":null},{"id":"W2163764873","doi":"10.1109/acssc.2008.5074551","title":"A regularized least squares approach for ultra-wideband time-of-arrival estimation with wavelet denoising","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Ranging; Computer science; Time of arrival; Estimator; Algorithm; Robustness (evolution); Ultra-wideband; Wavelet; Signal-to-noise ratio (imaging); Noise reduction; Wireless; Artificial intelligence; Statistics; Telecommunications; Mathematics","score_opus":0.009781081848839506,"score_gpt":0.18874535031839432,"score_spread":0.17896426846955482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163764873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014446786,0.000025935979,0.9983134,0.000028045244,0.000011392307,0.0000052198684,0.0000052219198,0.00006868888,0.000097344324],"genre_scores_gemma":[0.072691835,0.00015159951,0.92542005,0.000069251146,0.00005302029,0.00007294908,0.00010854692,0.00007668994,0.0013561161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994173,0.0001881566,0.000034960263,0.00012889632,0.00020183274,0.00002896495],"domain_scores_gemma":[0.9991874,0.0003602522,0.000094174655,0.00012276109,0.00020936163,0.000025993935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010186222,0.00067069655,0.00073251664,0.0004861664,0.00026213558,0.00054154295,0.0009160776,0.0010693235,0.00070068275],"category_scores_gemma":[0.0034269372,0.00046256938,0.00079460663,0.00067826576,0.00062611356,0.0007729093,0.00065833033,0.0013531684,0.00065244426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121659534,0.00009074614,0.0006399734,0.000118358446,0.0001098963,0.00011509588,0.00013096452,0.7270758,0.032846298,0.015018422,0.002013154,0.22171967],"study_design_scores_gemma":[0.0000055258606,0.000022708917,0.00007139006,0.0000029497403,0.0000062788,0.000029918336,0.000004754222,0.9952335,0.002577343,0.0013006874,0.0007360486,0.000008960252],"about_ca_topic_score_codex":0.0016341668,"about_ca_topic_score_gemma":0.0013738967,"teacher_disagreement_score":0.0016341668,"about_ca_system_score_codex":0.00031327398,"about_ca_system_score_gemma":0.0006835034,"threshold_uncertainty_score":0.005387068},"labels":[],"label_agreement":null},{"id":"W2164320264","doi":"","title":"Probabilistic self-localization for sensor networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Probabilistic logic; Maxima and minima; Markov chain Monte Carlo; Computer science; Wireless sensor network; Range (aeronautics); Markov chain; Representation (politics); Probability distribution; Probability density function; Monte Carlo method; Algorithm; Artificial intelligence; Bayesian probability; Mathematics; Machine learning; Statistics; Engineering","score_opus":0.004762330251755504,"score_gpt":0.1850267777378111,"score_spread":0.18026444748605558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164320264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062366703,0.00073969155,0.9973551,0.000115898605,0.000038806993,0.000011962387,0.00002378973,0.00022033663,0.00087081187],"genre_scores_gemma":[0.2563957,0.006340792,0.72712666,0.00022869807,0.00048827883,0.00052010256,0.000458783,0.00032760145,0.008113315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849415,0.00048738343,0.00006140499,0.00019041884,0.0007146406,0.00005202704],"domain_scores_gemma":[0.99838376,0.00095699186,0.00013136854,0.00026137984,0.00022906357,0.000037316084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012159848,0.00069478736,0.0006893793,0.0010663174,0.00043761884,0.0010210882,0.0012256468,0.00089734787,0.002132698],"category_scores_gemma":[0.005140133,0.0005135581,0.00070132373,0.0014675781,0.0011586689,0.001876917,0.0015109113,0.0013468644,0.0009756613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030717172,0.000020427518,0.000526785,0.0002868046,0.000070722876,0.0001283214,0.00014398231,0.48129898,0.0025494765,0.36708453,0.0045516603,0.14330757],"study_design_scores_gemma":[0.000008746098,0.000015950287,0.00012281677,0.000023072233,0.000010639438,0.00009763722,0.00001188587,0.8327647,0.0008701853,0.15223995,0.013820995,0.000013429361],"about_ca_topic_score_codex":0.0014861397,"about_ca_topic_score_gemma":0.0011971787,"teacher_disagreement_score":0.002132698,"about_ca_system_score_codex":0.0007100682,"about_ca_system_score_gemma":0.0006388857,"threshold_uncertainty_score":0.007134557},"labels":[],"label_agreement":null},{"id":"W2164497124","doi":"10.1186/1687-6180-2012-188","title":"Direct path detection using multipath interference cancelation for communication-based positioning system","year":2012,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Electronics and Telecommunications Research Institute","keywords":"Multipath propagation; Computer science; Multipath interference; Preamble; Delay spread; Interference (communication); Estimator; Algorithm; SIGNAL (programming language); Real-time computing; Electronic engineering; Channel (broadcasting); Telecommunications; Mathematics; Statistics; Engineering","score_opus":0.017499838336846298,"score_gpt":0.27455283485963233,"score_spread":0.257052996522786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164497124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0266967,0.0007775938,0.96977824,0.00009740993,0.00007483101,0.000038917195,0.000040370498,0.0006765834,0.0018193409],"genre_scores_gemma":[0.6043084,0.00073682127,0.3914434,0.00010342278,0.000091512935,0.00008292164,0.00013803894,0.000028070455,0.0030674373],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930155,0.00015120042,0.000026318103,0.00010263705,0.00037242172,0.000045919904],"domain_scores_gemma":[0.9995499,0.00012559073,0.000063994,0.00005089382,0.00019673433,0.000012803194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032994003,0.0006323342,0.00049939053,0.00047062672,0.00025751613,0.00045140076,0.00063311425,0.00050071016,0.0010166091],"category_scores_gemma":[0.0011532612,0.00018685647,0.00030062124,0.0005736806,0.00024941415,0.00041458695,0.0006400799,0.00038676994,0.0004686871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004357582,0.00010239205,0.0034519276,0.00039160627,0.00018120688,0.00044070563,0.00017976812,0.13205418,0.21221049,0.009175022,0.0029050813,0.6384719],"study_design_scores_gemma":[0.000044312885,0.0004378266,0.0018554407,0.000034256456,0.00009370344,0.00071903015,0.000028426153,0.9071802,0.08237231,0.0016102825,0.0055631856,0.00006116561],"about_ca_topic_score_codex":0.0011981786,"about_ca_topic_score_gemma":0.0018155791,"teacher_disagreement_score":0.0011981786,"about_ca_system_score_codex":0.00037619687,"about_ca_system_score_gemma":0.00066704396,"threshold_uncertainty_score":0.0034009218},"labels":[],"label_agreement":null},{"id":"W2164824354","doi":"10.1109/infcom.2004.1354686","title":"Rigidity, computation, and randomization in network localization","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":521,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Rigidity (electromagnetism); Construct (python library); Graph theory; Computer science; Computation; Theoretical computer science; Belief propagation; Algorithm; Mathematics; Discrete mathematics; Combinatorics; Computer network; Engineering","score_opus":0.004680104294377368,"score_gpt":0.19741955542083364,"score_spread":0.19273945112645627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164824354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06573758,0.0003678117,0.9222274,0.0020026595,0.000066663706,0.00006792092,0.000228388,0.0004259318,0.008875683],"genre_scores_gemma":[0.8562681,0.00067195145,0.13708262,0.0003242921,0.00013075316,0.0002666739,0.00041086084,0.00016438878,0.0046803416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99672085,0.0014926754,0.000144117,0.0007305717,0.0005416144,0.00037017412],"domain_scores_gemma":[0.9830036,0.01125175,0.001535169,0.0031221646,0.00060276245,0.00048440573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002288796,0.00070459663,0.0011166635,0.0013469119,0.0017633664,0.0024597556,0.0017916792,0.002107331,0.0049829455],"category_scores_gemma":[0.017180573,0.0006941616,0.0014567232,0.0017614706,0.0056868508,0.0077171684,0.0035570883,0.0026857352,0.0005977927],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000814279,0.000024137371,0.00043721075,0.00006645514,0.00001870077,0.00007520938,0.00014006252,0.16081864,0.0010485392,0.82789534,0.0008788228,0.0085154055],"study_design_scores_gemma":[0.000023956734,0.00003283804,0.000248794,0.00001625139,0.000011185971,0.00005867277,0.000059260532,0.23366734,0.00081280235,0.76327014,0.0017754404,0.000023262019],"about_ca_topic_score_codex":0.0024529567,"about_ca_topic_score_gemma":0.0021365983,"teacher_disagreement_score":0.0049829455,"about_ca_system_score_codex":0.0025542534,"about_ca_system_score_gemma":0.0015720556,"threshold_uncertainty_score":0.018532515},"labels":[],"label_agreement":null},{"id":"W2165303182","doi":"10.1109/cnsr.2009.23","title":"Secure Localization of Nodes in Wireless Sensor Networks with Limited Number of Truth Tellers","year":2009,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Wireless sensor network; Computer science; Key distribution in wireless sensor networks; Computer network; Process (computing); Position (finance); Wireless; Wireless network; Telecommunications","score_opus":0.006991262579917087,"score_gpt":0.21020442913691784,"score_spread":0.20321316655700075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165303182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036084242,0.0011301282,0.9603919,0.000572632,0.00006426144,0.0000560059,0.000052300613,0.00059216365,0.0010564798],"genre_scores_gemma":[0.7773275,0.0015226647,0.21777245,0.00013478179,0.00014309704,0.00013666376,0.00016626228,0.00008835464,0.0027082777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971539,0.0008637554,0.00022697484,0.00056717166,0.0009266275,0.00026166905],"domain_scores_gemma":[0.9905158,0.004690217,0.0016273172,0.0020652302,0.0008093112,0.00029206922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029443954,0.0011393804,0.0013777845,0.0013473076,0.0012887089,0.0022650429,0.0019360201,0.001898608,0.0018102567],"category_scores_gemma":[0.019107308,0.00059745123,0.0007270323,0.0012687885,0.003220679,0.005841129,0.0042826147,0.0013990373,0.0013277169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001949689,0.0001636576,0.005263227,0.0008462879,0.0001578454,0.0010961172,0.0014402872,0.49231574,0.031423718,0.14863008,0.003573341,0.31314],"study_design_scores_gemma":[0.0002171807,0.000670455,0.00066316733,0.00013721667,0.00011338294,0.0009726353,0.00047971107,0.829814,0.05010844,0.10845112,0.008284977,0.00008776693],"about_ca_topic_score_codex":0.00048208472,"about_ca_topic_score_gemma":0.00045307018,"teacher_disagreement_score":0.0029443954,"about_ca_system_score_codex":0.0008964997,"about_ca_system_score_gemma":0.0010284524,"threshold_uncertainty_score":0.015571654},"labels":[],"label_agreement":null},{"id":"W2166563477","doi":"10.1109/isspit.2010.5711760","title":"Localization of wireless sensor network using Bees Optimization Algorithm","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wireless sensor network; Bees algorithm; Computer science; Node (physics); RSS; Estimator; Algorithm; Network topology; Population; Estimation of distribution algorithm; Wireless; Real-time computing; Mathematics; Metaheuristic; Statistics; Engineering; Computer network; Telecommunications","score_opus":0.006843756982315746,"score_gpt":0.20600120631358,"score_spread":0.19915744933126425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166563477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024012892,0.00035528064,0.97335035,0.00012081603,0.000022056436,0.000045422432,0.000016672873,0.0002211633,0.0018552989],"genre_scores_gemma":[0.59612143,0.0005468103,0.39857733,0.00008940856,0.00003398385,0.00031405853,0.00010348613,0.00006081269,0.004152709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998437,0.00005601814,0.000008273762,0.000029251212,0.00004702737,0.00001565515],"domain_scores_gemma":[0.99985003,0.00006440204,0.000023349623,0.000012478002,0.000043412634,0.000006293552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034564364,0.0004570096,0.0005561506,0.0003757361,0.0003162065,0.00039137914,0.0005538725,0.00063226884,0.0007331347],"category_scores_gemma":[0.0007744436,0.00020434298,0.0004037786,0.00036531198,0.00030133373,0.00044453543,0.0004354587,0.00027951723,0.00019112727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006580137,0.000026225296,0.0010646827,0.000057022582,0.000048797447,0.000047002486,0.00006067209,0.9157747,0.0064148027,0.0055095707,0.00083205954,0.07009867],"study_design_scores_gemma":[0.0000072081702,0.00001947491,0.00012746861,0.0000039453544,0.000004847781,0.000011503826,0.0000062800073,0.99801123,0.00064072444,0.0007228175,0.00044179682,0.00000267329],"about_ca_topic_score_codex":0.0020855365,"about_ca_topic_score_gemma":0.0013378711,"teacher_disagreement_score":0.0020855365,"about_ca_system_score_codex":0.00029074016,"about_ca_system_score_gemma":0.00039661129,"threshold_uncertainty_score":0.0041467547},"labels":[],"label_agreement":null},{"id":"W2166811577","doi":"10.1109/vetecf.2008.86","title":"TOA Estimation Enhancement based on Blind Calibration of Synthetic Arrays","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multipath propagation; Beamforming; Computer science; Antenna (radio); Calibration; Multipath mitigation; Antenna array; Electronic engineering; SIGNAL (programming language); Non-line-of-sight propagation; Monte Carlo method; Noise (video); Telecommunications; Wireless; Artificial intelligence; Engineering; Physics; Channel (broadcasting); Mathematics","score_opus":0.014354404290191409,"score_gpt":0.21346038388418223,"score_spread":0.19910597959399082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166811577","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04442559,0.000073635805,0.95350283,0.000074539115,0.00004890941,0.000015952053,0.000019186398,0.0005134902,0.0013259167],"genre_scores_gemma":[0.6849632,0.00016718906,0.31304753,0.000101858706,0.000059869482,0.00005007905,0.000084942505,0.00006319926,0.0014621593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993455,0.00024166785,0.00003171595,0.00009408828,0.000245779,0.000041329044],"domain_scores_gemma":[0.99878436,0.0003892546,0.00024825466,0.00025057403,0.00029299513,0.000034515575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061660237,0.00050890807,0.0005052165,0.0004535163,0.00020936324,0.0006276886,0.0004056225,0.00065686245,0.00063684274],"category_scores_gemma":[0.003595218,0.00023864841,0.00037801918,0.00037832526,0.0005532586,0.0008365828,0.0007306656,0.0004595816,0.00038223507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007844046,0.00011867351,0.002300523,0.00016006058,0.00009718419,0.00015293258,0.00024067039,0.40419632,0.339278,0.017974548,0.001302715,0.23339406],"study_design_scores_gemma":[0.00002319166,0.00018392898,0.0008408688,0.000011279303,0.000027565344,0.00035542203,0.000028227245,0.89061594,0.102368504,0.0024424212,0.0030502034,0.00005243375],"about_ca_topic_score_codex":0.0003592784,"about_ca_topic_score_gemma":0.00042482387,"teacher_disagreement_score":0.00065686245,"about_ca_system_score_codex":0.00024063322,"about_ca_system_score_gemma":0.00053283764,"threshold_uncertainty_score":0.0032609105},"labels":[],"label_agreement":null},{"id":"W2167154013","doi":"10.1109/ccece.2011.6030635","title":"Received signal strength difference emitter geolocation least squares algorithm comparison","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of National Defence","funders":"","keywords":"Geolocation; Computer science; Algorithm; SIGNAL (programming language); Least-squares function approximation; Signal strength; Common emitter; Statistics; Electronic engineering; Telecommunications; Mathematics; Wireless; Engineering","score_opus":0.023929959722592,"score_gpt":0.2116353262943598,"score_spread":0.18770536657176778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167154013","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16171092,0.0009853115,0.8247395,0.000241996,0.0001554756,0.00020919232,0.00032042494,0.0024000127,0.009237203],"genre_scores_gemma":[0.53698266,0.0007374633,0.45364636,0.00011205539,0.000040091993,0.00024708454,0.0009702383,0.00028385417,0.006980288],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832827,0.00051764084,0.0001884705,0.00022068061,0.0006497233,0.00009523297],"domain_scores_gemma":[0.99600166,0.001672628,0.00020063679,0.00043905346,0.0016217511,0.00006419871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021329974,0.00065430894,0.0010087005,0.0013969084,0.00023769215,0.0011101427,0.0011103197,0.0010638685,0.002899148],"category_scores_gemma":[0.0061610453,0.00019835887,0.0005371542,0.0013066498,0.00021604745,0.00094007415,0.00074613915,0.00042071936,0.0013636183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017053812,0.0003385414,0.007891769,0.00054756505,0.0004841505,0.00009351167,0.00014939305,0.35203773,0.03525915,0.004397877,0.0024070013,0.59468794],"study_design_scores_gemma":[0.0003674793,0.0012884911,0.012427885,0.00006150174,0.00019048384,0.0005493979,0.000210387,0.8837557,0.086133935,0.0025073683,0.012392072,0.00011533238],"about_ca_topic_score_codex":0.000846023,"about_ca_topic_score_gemma":0.0008861514,"teacher_disagreement_score":0.002899148,"about_ca_system_score_codex":0.00032927006,"about_ca_system_score_gemma":0.00050728465,"threshold_uncertainty_score":0.011280477},"labels":[],"label_agreement":null},{"id":"W2167642049","doi":"10.1109/vtcf.2006.518","title":"Ordinal MDS-Based Localization for Wireless Sensor Networks","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Multidimensional scaling; Computer science; Network topology; Euclidean distance; Monotonic function; Euclidean geometry; Constraint (computer-aided design); Wireless; Algorithm; Position (finance); Topology (electrical circuits); Mathematics; Theoretical computer science; Artificial intelligence; Computer network; Machine learning; Combinatorics","score_opus":0.008207718698963331,"score_gpt":0.20508513454492677,"score_spread":0.19687741584596344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167642049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028917973,0.00037338556,0.99542636,0.00013900094,0.00004596634,0.000018267398,0.00007743801,0.00018996203,0.0008377262],"genre_scores_gemma":[0.41801247,0.0025174185,0.57356244,0.00017368777,0.00022103256,0.0003402395,0.00077214494,0.000120667726,0.004279791],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881124,0.00047254658,0.00009335592,0.0001664951,0.00040481638,0.000051496776],"domain_scores_gemma":[0.9979456,0.00085445173,0.00038346546,0.00026221445,0.00048072764,0.000073587806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013715384,0.000993177,0.0007260903,0.0017768126,0.0006870656,0.0010357399,0.0011639589,0.0006299762,0.0019384541],"category_scores_gemma":[0.0060388637,0.0003593072,0.0006712401,0.00211813,0.0012940919,0.0018421393,0.0015516402,0.0008322769,0.0006694863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014420759,0.000015866806,0.0010752771,0.00030925745,0.00005840827,0.00015770912,0.00020238495,0.69162804,0.003384914,0.21265876,0.0035901482,0.08677502],"study_design_scores_gemma":[0.000008614501,0.00004239244,0.00018579919,0.000022334301,0.000010969185,0.00007390109,0.000037445785,0.9317462,0.00087696884,0.061276864,0.005695834,0.000022604405],"about_ca_topic_score_codex":0.0023103182,"about_ca_topic_score_gemma":0.0016571702,"teacher_disagreement_score":0.0023103182,"about_ca_system_score_codex":0.0013836694,"about_ca_system_score_gemma":0.0009737492,"threshold_uncertainty_score":0.01003921},"labels":[],"label_agreement":null},{"id":"W2167817458","doi":"10.1109/pacrim.2011.6032944","title":"An improved Taylor series based location algorithm for IEEE 802.15.4a channels","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Taylor series; Multilateration; Algorithm; Computer science; Series (stratigraphy); Non-line-of-sight propagation; Channel (broadcasting); Position (finance); Time of arrival; Telecommunications; Wireless; Mathematics; Finance; Azimuth","score_opus":0.017839248053522095,"score_gpt":0.2166517978385266,"score_spread":0.19881254978500448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167817458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016571752,0.000070918184,0.99714124,0.000035225836,0.00004577342,0.0000142082345,0.000009963325,0.00042604,0.0005993599],"genre_scores_gemma":[0.102870286,0.00037107797,0.8901555,0.00011151651,0.00012722268,0.00014558733,0.00015269736,0.00021531644,0.0058507845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912184,0.0002147931,0.00004896002,0.00015228306,0.00041460415,0.0000475832],"domain_scores_gemma":[0.99861395,0.0004751986,0.00010838186,0.00014414777,0.0006278367,0.000030491017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010795961,0.00090927555,0.0009461611,0.00094464474,0.0005412496,0.00071061146,0.0013236728,0.0009378068,0.0034945388],"category_scores_gemma":[0.0034170062,0.0003553236,0.0005936572,0.0010859009,0.00048912084,0.0016911164,0.0006253145,0.0011293757,0.0028877861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019752211,0.00006405819,0.00062908855,0.00013333773,0.000048681897,0.00014807102,0.00014769279,0.31912148,0.021767026,0.016656486,0.005228836,0.6358577],"study_design_scores_gemma":[0.000014332344,0.000059576803,0.00012463189,0.000008173375,0.000009923503,0.00012037037,0.000014150569,0.98866886,0.005215103,0.0021062251,0.0036396072,0.000019085886],"about_ca_topic_score_codex":0.0025260346,"about_ca_topic_score_gemma":0.0022136117,"teacher_disagreement_score":0.0034945388,"about_ca_system_score_codex":0.00061832316,"about_ca_system_score_gemma":0.00094461633,"threshold_uncertainty_score":0.011690378},"labels":[],"label_agreement":null},{"id":"W2167968888","doi":"10.5555/2693848.2694257","title":"Streamlining an indoor positioning architecture based on field testing in pipe spool fabrication shop","year":2014,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"RSS; Profiling (computer programming); Computer science; Real-time computing; Fabrication; Tracking (education); Operating system","score_opus":0.021255093555640703,"score_gpt":0.25357377882601057,"score_spread":0.23231868527036986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167968888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2354453,0.000057657424,0.7555243,0.00010417338,0.0000388476,0.00023579945,0.00008650462,0.0048610223,0.0036464606],"genre_scores_gemma":[0.8648092,0.000044337292,0.13321747,0.0000232533,0.0000059952463,0.0001237261,0.00006494637,0.000062345476,0.0016486355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937123,0.00018700576,0.000030919826,0.000118952456,0.00020358685,0.00008822379],"domain_scores_gemma":[0.99920374,0.00024390875,0.00011648232,0.00018818102,0.00018803445,0.000059645256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000716932,0.0006165525,0.00045736716,0.0005585534,0.00039332727,0.00058060366,0.0012217648,0.00065733877,0.001848345],"category_scores_gemma":[0.0012604407,0.00035513073,0.0002486688,0.00031707864,0.00060411287,0.00063573814,0.0006504497,0.00042126517,0.000636647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056809315,0.00030437022,0.009656257,0.0002626101,0.000031359366,0.00063836505,0.00076597376,0.6406079,0.12953846,0.0039130524,0.0010525753,0.21266097],"study_design_scores_gemma":[0.000085323,0.00086280535,0.0052473294,0.000039119972,0.00004000763,0.00029514538,0.00014982381,0.89799494,0.08692756,0.0011335366,0.0071667,0.00005769088],"about_ca_topic_score_codex":0.002521375,"about_ca_topic_score_gemma":0.0023788535,"teacher_disagreement_score":0.002521375,"about_ca_system_score_codex":0.00060395745,"about_ca_system_score_gemma":0.00080070447,"threshold_uncertainty_score":0.0061833262},"labels":[],"label_agreement":null},{"id":"W2168974696","doi":"10.3390/s110706771","title":"Data Fusion Algorithms for Multiple Inertial Measurement Units","year":2011,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Filter (signal processing); Global Positioning System; Sensor fusion; Computer science; Frame (networking); Inertial navigation system; Context (archaeology); Computer vision; Kalman filter; Extended Kalman filter; Artificial intelligence; Units of measurement; Real-time computing; Inertial frame of reference; Telecommunications; Geography","score_opus":0.19389198161626547,"score_gpt":0.25519908338327557,"score_spread":0.0613071017670101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168974696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015235035,0.0005855436,0.99696845,0.00007506853,0.0000673861,0.000022809678,0.000024157023,0.00026412157,0.00046896227],"genre_scores_gemma":[0.13382527,0.0020638846,0.85961264,0.00013932091,0.00020275249,0.00027695225,0.00031939088,0.00009137917,0.0034683987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985071,0.00026176317,0.00015526652,0.0003296646,0.00066313194,0.000083139894],"domain_scores_gemma":[0.9987099,0.00036525953,0.000160742,0.00016336553,0.0005766135,0.000024100646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023509022,0.0010199924,0.0011763155,0.0015825892,0.0007435286,0.0014274156,0.0013720315,0.00115193,0.0018754443],"category_scores_gemma":[0.0049535176,0.00056288583,0.0011863343,0.0022433428,0.00047918942,0.0026222565,0.0014806018,0.0012466373,0.001080798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014825327,0.000048259484,0.001241995,0.00028022268,0.00020312051,0.0000866736,0.00022972822,0.19409862,0.009999226,0.038548492,0.0034716937,0.75164366],"study_design_scores_gemma":[0.000027635537,0.0001091055,0.0010425361,0.00007315991,0.00008656477,0.0001361712,0.00006134422,0.9439055,0.012203347,0.023506785,0.018793236,0.000054599568],"about_ca_topic_score_codex":0.003513568,"about_ca_topic_score_gemma":0.0026474344,"teacher_disagreement_score":0.003513568,"about_ca_system_score_codex":0.00089275383,"about_ca_system_score_gemma":0.0010097197,"threshold_uncertainty_score":0.012432873},"labels":[],"label_agreement":null},{"id":"W2169810737","doi":"10.4017/gt.2012.11.02.558.00","title":"Tunnel boring machine positioning automation in tunnel construction","year":2012,"lang":"en","type":"article","venue":"Gerontechnology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Automation; Engineering; Tunnel construction; Computer science; Construction engineering; Forensic engineering; Automotive engineering; Structural engineering; Mechanical engineering","score_opus":0.007405841121464645,"score_gpt":0.20261376182043697,"score_spread":0.19520792069897233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169810737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13227697,0.0013920596,0.85190034,0.00018942745,0.00012819833,0.00021101328,0.00019642226,0.0036154275,0.010090155],"genre_scores_gemma":[0.69159603,0.00053533976,0.30294016,0.000049536597,0.00004649518,0.000083266124,0.00021480504,0.000094047704,0.004440297],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985607,0.0002950013,0.000061157436,0.00025720286,0.0007070635,0.00011886755],"domain_scores_gemma":[0.99897015,0.00016537184,0.0001608885,0.00028908195,0.00035604017,0.000058487865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006907323,0.00059728697,0.0005028778,0.00075821835,0.00040060232,0.000679608,0.0009816001,0.00067282584,0.0023721533],"category_scores_gemma":[0.0010764448,0.00036333402,0.00030802956,0.0008437652,0.000576373,0.0006860184,0.00078973954,0.00044296926,0.0010363187],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003472234,0.00011712561,0.012417049,0.00083964015,0.00003382353,0.00041702547,0.0005427648,0.042423286,0.2601716,0.004007801,0.0030407915,0.67564195],"study_design_scores_gemma":[0.00019860553,0.0037119137,0.10018606,0.0005562492,0.0001804138,0.0055495817,0.0009469203,0.39117676,0.3278348,0.008394114,0.160927,0.00033758138],"about_ca_topic_score_codex":0.0021057683,"about_ca_topic_score_gemma":0.0023928406,"teacher_disagreement_score":0.0023721533,"about_ca_system_score_codex":0.0003250932,"about_ca_system_score_gemma":0.00080265856,"threshold_uncertainty_score":0.007935643},"labels":[],"label_agreement":null},{"id":"W2170396887","doi":"10.1109/sensorcomm.2008.119","title":"Decentralized Node Selection for Localization in Wireless Unattended Ground Sensor Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Node (physics); Wireless sensor network; Computer science; Computer network; Set (abstract data type); Transmission (telecommunications); Key distribution in wireless sensor networks; Selection (genetic algorithm); Sensor node; Wireless; Wireless network; Telecommunications; Engineering; Artificial intelligence","score_opus":0.012908724726477815,"score_gpt":0.2153055184977422,"score_spread":0.2023967937712644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170396887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018707275,0.0002692312,0.9795425,0.00015940807,0.000039708728,0.000047077003,0.00001914982,0.00023582521,0.0009797418],"genre_scores_gemma":[0.7934362,0.00034327258,0.20262744,0.00010001087,0.00009686567,0.00021587356,0.00012727677,0.00006350964,0.0029895343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989465,0.00038786136,0.000036607235,0.00018321117,0.00035459723,0.00009125414],"domain_scores_gemma":[0.99882644,0.0005883621,0.00017788843,0.00014945903,0.00019343998,0.000064371765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001179861,0.00044183334,0.00085250044,0.0005442003,0.00081507117,0.00062194414,0.0014459154,0.0005263288,0.0009991342],"category_scores_gemma":[0.0030421764,0.00026891913,0.00026229507,0.0008211726,0.0008955085,0.001218827,0.0010650565,0.00044778915,0.0003034371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003737044,0.00010959366,0.0020235972,0.00016701892,0.00007145154,0.00030252335,0.00028420443,0.7670064,0.015332506,0.036083765,0.0033775566,0.17486763],"study_design_scores_gemma":[0.000058837493,0.00010509595,0.00032209544,0.000008073412,0.000012579795,0.000082994666,0.000035581634,0.9823507,0.002344518,0.012704259,0.0019637484,0.000011523959],"about_ca_topic_score_codex":0.0010599096,"about_ca_topic_score_gemma":0.0023422341,"teacher_disagreement_score":0.0014459154,"about_ca_system_score_codex":0.00062981265,"about_ca_system_score_gemma":0.0007849752,"threshold_uncertainty_score":0.006239772},"labels":[],"label_agreement":null},{"id":"W2171445605","doi":"10.1109/icc.2011.5963278","title":"Wi-Fi-Based Indoor Positioning Using Human-Centric Collaborative Feedback","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Bookmarking; Baseline (sea); Human–computer interaction; Real-time computing; World Wide Web","score_opus":0.02862552517852368,"score_gpt":0.23078225228371527,"score_spread":0.20215672710519159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171445605","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029162409,0.000244997,0.96649164,0.00013438972,0.000065345586,0.00004413179,0.00007433265,0.0025737023,0.0012091552],"genre_scores_gemma":[0.918341,0.0002035917,0.07829202,0.0000778677,0.00007927381,0.000061022372,0.00010786863,0.000047230766,0.002790027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99884677,0.00025909668,0.000045797828,0.0003147595,0.0004155545,0.00011793085],"domain_scores_gemma":[0.9983553,0.0005217373,0.0002587191,0.00036839055,0.00040730732,0.00008849265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000880399,0.0012525092,0.0012448842,0.0005705413,0.0005644092,0.00076721085,0.0021503381,0.0011038376,0.0009651998],"category_scores_gemma":[0.0033207182,0.00051173934,0.0003915258,0.00073619804,0.0006944871,0.0014532808,0.0009102655,0.0006533907,0.00084686105],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088016567,0.00031087638,0.0058069695,0.00026488732,0.00019367355,0.00050676,0.00037500745,0.60132813,0.05917282,0.0065680924,0.002754367,0.32183826],"study_design_scores_gemma":[0.000024003059,0.00019168243,0.0011563546,0.000008956389,0.000040087918,0.00024109153,0.000024573073,0.9844738,0.011021456,0.0016688268,0.001112894,0.000036221943],"about_ca_topic_score_codex":0.00775032,"about_ca_topic_score_gemma":0.009120848,"teacher_disagreement_score":0.00775032,"about_ca_system_score_codex":0.00069173396,"about_ca_system_score_gemma":0.0008530579,"threshold_uncertainty_score":0.015410423},"labels":[],"label_agreement":null},{"id":"W2171589063","doi":"10.1109/vetecf.2004.1404722","title":"Joint TOA/DOA wireless position location using matrix pencil","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Matrix pencil; Computer science; Fading; Pencil (optics); Position (finance); Base station; SIGNAL (programming language); Algorithm; Direction of arrival; Angle of arrival; Wireless; Time of arrival; Joint (building); Transmission (telecommunications); Telecommunications; Engineering; Decoding methods; Physics; Optics; Antenna (radio)","score_opus":0.01668334366261042,"score_gpt":0.23411873900748176,"score_spread":0.21743539534487133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171589063","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043861377,0.00019857421,0.99185914,0.000112995156,0.00016432484,0.00004810081,0.0001022619,0.0007994055,0.0023290357],"genre_scores_gemma":[0.22627497,0.0006958087,0.75854856,0.00021332123,0.00026528537,0.00027315936,0.00043372012,0.00014240998,0.013152751],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920315,0.0002363757,0.000045437355,0.00018762055,0.00026572886,0.00006175798],"domain_scores_gemma":[0.9993406,0.00018384944,0.00011108723,0.00016552374,0.00015966459,0.000039348193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068913755,0.0013859584,0.0008247925,0.0011394802,0.00052150554,0.0018973869,0.0007412791,0.0007701515,0.0055514616],"category_scores_gemma":[0.0031851698,0.0003027346,0.00044042803,0.0014005364,0.00049759797,0.0014555029,0.0009526268,0.00068946305,0.0033631427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008020534,0.000108909764,0.00083194533,0.00024190034,0.00011452724,0.00033997503,0.00022309847,0.10438251,0.044432484,0.046098456,0.0061168894,0.79630727],"study_design_scores_gemma":[0.00016098528,0.0006690777,0.0012081873,0.00008167617,0.00008350826,0.0008138758,0.000109181026,0.8612585,0.07122935,0.022043867,0.042189,0.00015267734],"about_ca_topic_score_codex":0.0010151351,"about_ca_topic_score_gemma":0.0015591736,"teacher_disagreement_score":0.0055514616,"about_ca_system_score_codex":0.00034111337,"about_ca_system_score_gemma":0.00067636155,"threshold_uncertainty_score":0.018571496},"labels":[],"label_agreement":null},{"id":"W2172279476","doi":"10.1145/2659766.2661217","title":"Investigating inertial measurement units for spatial awareness in multi-surface environments","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Units of measurement; Warrant; Inertial frame of reference; Computer science; Sensor fusion; Inertial reference unit; Work (physics); Real-time computing; Inertial navigation system; Computer vision; Artificial intelligence; Engineering; Business","score_opus":0.057498193274207784,"score_gpt":0.24056489041524923,"score_spread":0.18306669714104146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172279476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7583806,0.00064629025,0.23443282,0.00033277372,0.000060894592,0.00025814225,0.00012906859,0.0010284856,0.0047308616],"genre_scores_gemma":[0.9407147,0.00021773162,0.058091924,0.000060566457,0.000013492042,0.000074973985,0.00006031952,0.00003538161,0.000730986],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990671,0.00047296053,0.000046347104,0.00010065379,0.00021359861,0.000099451725],"domain_scores_gemma":[0.9967354,0.0019659328,0.0002529258,0.00031737622,0.0006175392,0.00011077914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010241338,0.00067578134,0.00040452468,0.0005094133,0.00033238778,0.0008889511,0.0006475676,0.0006439498,0.0019724465],"category_scores_gemma":[0.0066582477,0.00024030195,0.0003135779,0.00043180562,0.00036805155,0.0019572498,0.0008953669,0.0003759144,0.0003956887],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020557682,0.0007423492,0.06419695,0.0020007829,0.00025873331,0.0011903786,0.009048193,0.025636572,0.25371465,0.0054982896,0.0020072483,0.6336502],"study_design_scores_gemma":[0.00029908217,0.014827967,0.14868239,0.0006314137,0.0008151075,0.004439029,0.015826005,0.36003333,0.40456814,0.0074856207,0.042031642,0.0003602882],"about_ca_topic_score_codex":0.0010278005,"about_ca_topic_score_gemma":0.0019675386,"teacher_disagreement_score":0.0019724465,"about_ca_system_score_codex":0.00017351402,"about_ca_system_score_gemma":0.00029775457,"threshold_uncertainty_score":0.0065984726},"labels":[],"label_agreement":null},{"id":"W2176250548","doi":"10.1109/pacrim.2015.7334887","title":"NLOS channel identification based on energy detection in 60 GHz communication systems","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Non-line-of-sight propagation; Channel (broadcasting); Identification (biology); Computer science; Detector; Energy (signal processing); Time of arrival; Skewness; Electronic engineering; Algorithm; Real-time computing; Telecommunications; Wireless; Engineering; Statistics; Mathematics","score_opus":0.019180497863341844,"score_gpt":0.21188892905239853,"score_spread":0.1927084311890567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2176250548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28849056,0.0012676425,0.70472866,0.00018714067,0.00005727911,0.000029624587,0.00004538455,0.00071166846,0.0044819885],"genre_scores_gemma":[0.96936405,0.0005377713,0.028989604,0.000028851828,0.000024355759,0.000018517067,0.000035354813,0.000020348562,0.0009810397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953973,0.00016879743,0.000015684263,0.000052286363,0.00017062093,0.000052864372],"domain_scores_gemma":[0.99963844,0.00018147969,0.000083988656,0.000033211232,0.000051999516,0.000010896312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032734705,0.00026274836,0.00036035894,0.00045615176,0.0002514415,0.00045268942,0.00023399254,0.00028963736,0.0004240537],"category_scores_gemma":[0.001215186,0.00017629204,0.000110623834,0.000568227,0.0004902577,0.00068838167,0.00046261703,0.00023866203,0.00019298983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006969952,0.00011377918,0.010837959,0.00015521336,0.00007177988,0.00052191823,0.00027512698,0.6440841,0.09030914,0.018916408,0.0015724252,0.23244523],"study_design_scores_gemma":[0.000012698108,0.00017906341,0.0032592942,0.000023151928,0.000017843295,0.00041439253,0.00010070329,0.95873183,0.031834353,0.003747981,0.0016443619,0.000034283552],"about_ca_topic_score_codex":0.001317469,"about_ca_topic_score_gemma":0.0015755947,"teacher_disagreement_score":0.001317469,"about_ca_system_score_codex":0.00026876893,"about_ca_system_score_gemma":0.00031835993,"threshold_uncertainty_score":0.0026195645},"labels":[],"label_agreement":null},{"id":"W2181675000","doi":"10.1109/ipin.2015.7346754","title":"Wi-Fi based indoor location positioning employing random forest classifier","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Random forest; Computer science; Indoor positioning system; Classifier (UML); Signal strength; Artificial intelligence; Received signal strength indication; Hybrid positioning system; Real-time computing; Wireless; Positioning system; Telecommunications; Engineering","score_opus":0.02341158784310755,"score_gpt":0.22107799647919482,"score_spread":0.19766640863608728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2181675000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06546494,0.0006589941,0.92863655,0.00011414734,0.000118623386,0.00009522916,0.00030752606,0.0026150562,0.0019888713],"genre_scores_gemma":[0.69536984,0.00067826104,0.29917914,0.000082424165,0.00010561835,0.00012828494,0.0011024162,0.00007123645,0.00328284],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949014,0.000079258825,0.000032001724,0.00013268343,0.00016691124,0.00009890097],"domain_scores_gemma":[0.99936515,0.00022380796,0.000065926106,0.000058892227,0.00026246376,0.000023855688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069635874,0.00077932037,0.0012413929,0.0014815673,0.000506557,0.00064038596,0.0011245034,0.00090877403,0.0010708398],"category_scores_gemma":[0.0014296861,0.00025833945,0.00068737176,0.0014646351,0.00020741446,0.00091915723,0.0003343372,0.0004891987,0.0012836325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035755945,0.00019920891,0.0074210833,0.00015565046,0.00012286537,0.00030085156,0.00005472256,0.23645873,0.016611092,0.0010561865,0.0037895683,0.73347247],"study_design_scores_gemma":[0.0000125946535,0.00009234605,0.0025656961,0.000015214292,0.00003547439,0.00017323554,0.000025131321,0.99002403,0.0052655945,0.0006726453,0.0010967185,0.000021346787],"about_ca_topic_score_codex":0.010053717,"about_ca_topic_score_gemma":0.008007156,"teacher_disagreement_score":0.010053717,"about_ca_system_score_codex":0.00036837097,"about_ca_system_score_gemma":0.0005640868,"threshold_uncertainty_score":0.019990385},"labels":[],"label_agreement":null},{"id":"W2182021612","doi":"10.22215/etd/2010-08622","title":"Strategy for detection and localization of evil-twin transmitters in wireless networks","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless; Computer network; Telecommunications","score_opus":0.006609894891068862,"score_gpt":0.22080940948098252,"score_spread":0.21419951458991365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182021612","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033968415,0.0002565033,0.9612997,0.0001597562,0.000094446455,0.0000723529,0.00002096065,0.00023484901,0.003893059],"genre_scores_gemma":[0.8189214,0.00037368134,0.16771771,0.00017415058,0.00006412834,0.00012285095,0.00008606768,0.000037813792,0.012502322],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996443,0.00007800605,0.000020633583,0.00009303349,0.00010491587,0.000059199843],"domain_scores_gemma":[0.99955446,0.00014415394,0.00004409296,0.000051689505,0.00016608056,0.00003945357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045612545,0.0005817841,0.00060971593,0.0008347883,0.00040199095,0.0008462742,0.0010137053,0.00070949696,0.0015770445],"category_scores_gemma":[0.0014913774,0.00024402307,0.00029200412,0.0003230987,0.0005191873,0.00080745126,0.0011799678,0.00044388312,0.00063511805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079527416,0.0002304516,0.0047365464,0.00033484495,0.00017973676,0.00092211505,0.0007636259,0.14578381,0.24747427,0.06880741,0.0056280345,0.52434397],"study_design_scores_gemma":[0.00006194359,0.00039791033,0.0013664108,0.00002893875,0.00007066507,0.00075627223,0.00028138462,0.92665064,0.052354246,0.013957947,0.0040371865,0.00003644394],"about_ca_topic_score_codex":0.00081119704,"about_ca_topic_score_gemma":0.001400571,"teacher_disagreement_score":0.0015770445,"about_ca_system_score_codex":0.00044789742,"about_ca_system_score_gemma":0.0005451995,"threshold_uncertainty_score":0.005275786},"labels":[],"label_agreement":null},{"id":"W2182261117","doi":"10.1109/ipin.2015.7346761","title":"GIPSy: Geomagnetic indoor positioning system for smartphones","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Viterbi algorithm; Computer science; Orientation (vector space); Real-time computing; Tracking (education); Set (abstract data type); Gesture; State (computer science); Computer vision; Algorithm; Artificial intelligence; Hidden Markov model","score_opus":0.016463794254184948,"score_gpt":0.20245380285241624,"score_spread":0.18599000859823128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182261117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03820966,0.0012929675,0.8841293,0.000446327,0.000478128,0.0003024158,0.0038295588,0.060431246,0.010880363],"genre_scores_gemma":[0.62759507,0.00069995015,0.34057444,0.0004377917,0.00018250481,0.00046733618,0.007099524,0.00038906044,0.02255431],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980146,0.000031939755,0.000012326244,0.000050221606,0.00006798852,0.000036178095],"domain_scores_gemma":[0.9998423,0.000014807595,0.000022416383,0.00004478488,0.00005588056,0.000019826963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018933394,0.0007643467,0.0006280509,0.0005379456,0.00024603712,0.00045847695,0.00105833,0.0007191899,0.0055693886],"category_scores_gemma":[0.00063321803,0.00022782099,0.0002563039,0.0005225913,0.00014076196,0.00036802873,0.00083235383,0.0006345021,0.005676361],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010914031,0.00012238474,0.009341705,0.00081890053,0.0002185834,0.0009476113,0.00028422105,0.022931837,0.1153457,0.008883727,0.09506831,0.7449456],"study_design_scores_gemma":[0.0006088033,0.00144327,0.030159488,0.00022657771,0.00024380723,0.003924429,0.00021884708,0.60833293,0.11079716,0.00701976,0.2367774,0.00024751818],"about_ca_topic_score_codex":0.0036422196,"about_ca_topic_score_gemma":0.0061036595,"teacher_disagreement_score":0.0055693886,"about_ca_system_score_codex":0.00028849972,"about_ca_system_score_gemma":0.0005122328,"threshold_uncertainty_score":0.018631518},"labels":[],"label_agreement":null},{"id":"W2182446056","doi":"10.1109/ipin.2015.7346947","title":"Automated detection of burned-out luminaries using indoor positioning","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Leverage (statistics); Computer science; Real-time computing; Dynamic time warping; Mobile device; Embedded system; Internet of Things; Participatory sensing; Artificial intelligence; Data science; Operating system","score_opus":0.025176030452724527,"score_gpt":0.24754155845575967,"score_spread":0.22236552800303516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182446056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38439074,0.00046659764,0.6076871,0.00013620782,0.00016932092,0.00008074134,0.00022252595,0.0019894359,0.004857309],"genre_scores_gemma":[0.8965749,0.00015849234,0.101213805,0.000057803918,0.00004310663,0.000051755138,0.00019574068,0.00007777136,0.0016266542],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99939454,0.0001314935,0.000019577279,0.00015747454,0.00021406655,0.000082888895],"domain_scores_gemma":[0.9990914,0.0002680232,0.00017981447,0.00018467577,0.00021225667,0.00006384776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038449912,0.000742683,0.00082890416,0.00081991486,0.00037180417,0.0005924332,0.0008671634,0.0005586391,0.0007176959],"category_scores_gemma":[0.0016165352,0.0002688317,0.00034600793,0.00074343325,0.0003090326,0.00056923635,0.001289168,0.00052962033,0.0006002631],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074497925,0.00024202664,0.043851584,0.00053115113,0.00018810124,0.00095835293,0.0011362373,0.069850095,0.27686,0.0021743525,0.0032482464,0.60021496],"study_design_scores_gemma":[0.000059532966,0.0006316552,0.046622425,0.00006622263,0.00012383383,0.0011960834,0.00075812434,0.79189295,0.14652601,0.003340279,0.008664284,0.000118691634],"about_ca_topic_score_codex":0.00069878553,"about_ca_topic_score_gemma":0.0017687803,"teacher_disagreement_score":0.0008671634,"about_ca_system_score_codex":0.00018487674,"about_ca_system_score_gemma":0.00030000106,"threshold_uncertainty_score":0.0024009347},"labels":[],"label_agreement":null},{"id":"W2184627358","doi":"","title":"Localization in Medical Sensory Systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Sensory system; Computer science; Psychology; Neuroscience","score_opus":0.011094157566064725,"score_gpt":0.2241503986002078,"score_spread":0.21305624103414308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184627358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009383682,0.0113031315,0.9633293,0.0013893672,0.0005667342,0.000041044925,0.00014807879,0.00054740877,0.01329128],"genre_scores_gemma":[0.7755948,0.018154599,0.16920716,0.000770838,0.0012037008,0.0001281733,0.0004884251,0.00014304549,0.03430937],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991239,0.00026080868,0.00006173247,0.00018994258,0.00028531492,0.00007843109],"domain_scores_gemma":[0.9993185,0.00031994167,0.000059155103,0.00009802626,0.00017579239,0.000028535324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000735431,0.00046534967,0.0007079253,0.0008039082,0.00047086706,0.0018562637,0.00063543615,0.0011051302,0.004008328],"category_scores_gemma":[0.0027262853,0.00035822982,0.00041420554,0.0012187383,0.0011875315,0.0018083594,0.0012965328,0.0007560473,0.0011230208],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003000028,0.00007492364,0.0022705551,0.0010670999,0.000112831236,0.00061289937,0.00045636576,0.14912343,0.020295698,0.36049068,0.013285483,0.45191014],"study_design_scores_gemma":[0.00003913987,0.00022519361,0.0031698162,0.0002669274,0.0000912353,0.0013868405,0.00042067212,0.68789786,0.014078666,0.2382269,0.05410272,0.00009407596],"about_ca_topic_score_codex":0.0020163485,"about_ca_topic_score_gemma":0.0015541007,"teacher_disagreement_score":0.004008328,"about_ca_system_score_codex":0.0006906271,"about_ca_system_score_gemma":0.00050943735,"threshold_uncertainty_score":0.013409197},"labels":[],"label_agreement":null},{"id":"W2184629929","doi":"","title":"Modelling Framework for Radio Frequency Spatial Measurement","year":2006,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Antenna (radio); Radio frequency; Radio propagation; Computer science; Electronic engineering; Frequency domain; Finite element method; Set (abstract data type); Engineering; Simulation; Algorithm; Telecommunications; Computer vision","score_opus":0.015041167084037202,"score_gpt":0.18747995434961623,"score_spread":0.17243878726557904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184629929","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037125885,0.00026139052,0.996905,0.0001362723,0.00006125012,0.000017853807,0.0001317401,0.00021889414,0.0018963097],"genre_scores_gemma":[0.28360865,0.0038127063,0.6666044,0.00046827836,0.0005441491,0.0008004704,0.0026213655,0.00071221014,0.040827777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984509,0.00061008957,0.00010159164,0.00027688543,0.00044377806,0.000116749405],"domain_scores_gemma":[0.9985499,0.00068179716,0.00013897562,0.00019095177,0.00037916668,0.00005921065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018399111,0.0012436315,0.0013646426,0.0010002241,0.0004994948,0.0026575273,0.0030689817,0.0021127306,0.0075274627],"category_scores_gemma":[0.0045010243,0.00085190363,0.0019992106,0.0014878879,0.00089155737,0.002221289,0.0023121578,0.0022692594,0.0043245303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006223951,0.000040593637,0.00048224872,0.00022591445,0.00012987647,0.00021067365,0.0001709677,0.6661857,0.0016723158,0.2755637,0.005090993,0.050164785],"study_design_scores_gemma":[0.000008683227,0.000022345877,0.000095697724,0.000032491887,0.000024887036,0.000070289185,0.000026302898,0.9257722,0.00031201186,0.061393894,0.012225443,0.000015769425],"about_ca_topic_score_codex":0.012421995,"about_ca_topic_score_gemma":0.008328005,"teacher_disagreement_score":0.012421995,"about_ca_system_score_codex":0.001063099,"about_ca_system_score_gemma":0.0015586517,"threshold_uncertainty_score":0.02518189},"labels":[],"label_agreement":null},{"id":"W2184757907","doi":"10.1109/ipin.2015.7346964","title":"Performance analysis of different SIC-based methods for multipath cancelation","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Federation for the Humanities and Social Sciences","keywords":"Multipath propagation; Thresholding; Interference (communication); Multipath interference; Multipath mitigation; Computer science; Algorithm; Electronic engineering; Acoustics; Artificial intelligence; Physics; Telecommunications; Engineering; Channel (broadcasting)","score_opus":0.035342773091759046,"score_gpt":0.31189838545929405,"score_spread":0.276555612367535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184757907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12629564,0.0016447407,0.8657077,0.000101082784,0.00013327337,0.00009428729,0.00012739575,0.001971958,0.0039239437],"genre_scores_gemma":[0.5330939,0.00066241325,0.46336955,0.000106143714,0.00009260456,0.00012080285,0.0005234671,0.00013934256,0.0018917038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982578,0.00046674974,0.00009422667,0.00017501495,0.00085763476,0.00014856274],"domain_scores_gemma":[0.99651164,0.0014524779,0.00024287583,0.0003482131,0.0013414484,0.00010336882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022739095,0.0012102402,0.0006121997,0.0016626852,0.0003961543,0.0007183915,0.0009918517,0.0008861456,0.0014318656],"category_scores_gemma":[0.00618423,0.00028909056,0.0005669859,0.001173099,0.00047884215,0.0006371897,0.0007901321,0.0006680469,0.00072815624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017019963,0.00020286236,0.006126004,0.00054622244,0.00045147043,0.00009602694,0.00015368595,0.097312436,0.07217025,0.002809063,0.0010819223,0.81734806],"study_design_scores_gemma":[0.00008444218,0.0009533958,0.00868714,0.000046484965,0.00019475777,0.00057572074,0.000075526696,0.88310117,0.10264622,0.00063334237,0.0029055765,0.000096315496],"about_ca_topic_score_codex":0.0015070009,"about_ca_topic_score_gemma":0.0018637141,"teacher_disagreement_score":0.0022739095,"about_ca_system_score_codex":0.00032521563,"about_ca_system_score_gemma":0.00067433575,"threshold_uncertainty_score":0.012025714},"labels":[],"label_agreement":null},{"id":"W2185033097","doi":"","title":"Integrated GPS/INS System for Pedestrian Navigation in a Signal Degraded Environment","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":149,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dead reckoning; Inertial measurement unit; Inertial navigation system; Global Positioning System; GPS/INS; Computer science; Step detection; Navigation system; GPS signals; Acceleration; Accelerometer; Heading (navigation); Units of measurement; Computer vision; Simulation; Algorithm; Artificial intelligence; Engineering; Real-time computing; Assisted GPS; Inertial frame of reference; Aerospace engineering; Telecommunications","score_opus":0.008704283476981498,"score_gpt":0.1834507262328623,"score_spread":0.1747464427558808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185033097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25728312,0.0016474484,0.64860487,0.0006814633,0.0011765094,0.00038800144,0.0034601325,0.041729037,0.04502944],"genre_scores_gemma":[0.8088009,0.0005454409,0.15340753,0.00032909954,0.0001559755,0.0001966416,0.0034960855,0.00033970206,0.032728586],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997403,0.000047378657,0.000009836947,0.000057311503,0.0001090196,0.000036079546],"domain_scores_gemma":[0.9996847,0.000016090004,0.000018409444,0.000038161626,0.00020192082,0.000040827596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031618058,0.00046411395,0.0006752605,0.0006123331,0.000612707,0.00053363584,0.0005595085,0.0004716741,0.008389334],"category_scores_gemma":[0.00033561463,0.00025140386,0.00019689304,0.00047404587,0.00017146017,0.00044648044,0.00062305894,0.0005430531,0.004553883],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035341561,0.00063086173,0.021939864,0.00038704695,0.00020653431,0.0008359625,0.00053700135,0.016313922,0.2225887,0.0045146,0.074912585,0.65359867],"study_design_scores_gemma":[0.000722773,0.004141543,0.05868217,0.00016518717,0.000833603,0.0029787575,0.00043837418,0.55388117,0.18342021,0.0031802924,0.19127089,0.00028497993],"about_ca_topic_score_codex":0.004407499,"about_ca_topic_score_gemma":0.008473825,"teacher_disagreement_score":0.008389334,"about_ca_system_score_codex":0.00035756003,"about_ca_system_score_gemma":0.0007791466,"threshold_uncertainty_score":0.028065085},"labels":[],"label_agreement":null},{"id":"W2185338102","doi":"10.1109/trustcom.2015.461","title":"Secure Location Validation with Wi-Fi Geo-fencing and NFC","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Trustcom/BigDataSE/ISPA","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Thompson Rivers University","keywords":"Computer science; Access control; Simple (philosophy); Wireless; Workstation; Computer network; Scheme (mathematics); Mobile device; Computer security; Telecommunications; Operating system","score_opus":0.0164946365640071,"score_gpt":0.2192573700287794,"score_spread":0.2027627334647723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185338102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048952866,0.0004036752,0.93787384,0.00045336218,0.00021745908,0.00024558854,0.00011788322,0.002614749,0.009120676],"genre_scores_gemma":[0.89187074,0.00025368456,0.09811066,0.0002443568,0.00010415633,0.00023113732,0.00020682457,0.0000700092,0.008908386],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9939585,0.0014120294,0.0004634182,0.00074365095,0.0027586094,0.0006637352],"domain_scores_gemma":[0.9919979,0.0013753761,0.0012090746,0.0041641453,0.0010970045,0.00015649355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025180914,0.0007377793,0.0011244863,0.0019579718,0.0022250907,0.0017516363,0.001992539,0.0022305972,0.0021489605],"category_scores_gemma":[0.008533451,0.00043356765,0.0007517504,0.0012520988,0.0023462276,0.004795816,0.00466167,0.0015278502,0.0015053693],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022641004,0.00049664534,0.00843134,0.0004131928,0.0002566166,0.0023493294,0.0017845792,0.06574496,0.14099002,0.20310289,0.011139058,0.5630272],"study_design_scores_gemma":[0.00026146663,0.0009563751,0.004468353,0.00037290415,0.0001938988,0.005386081,0.0008555951,0.6184019,0.2386003,0.07845723,0.05158907,0.00045683552],"about_ca_topic_score_codex":0.002587919,"about_ca_topic_score_gemma":0.0012803858,"teacher_disagreement_score":0.002587919,"about_ca_system_score_codex":0.0016223707,"about_ca_system_score_gemma":0.0015501841,"threshold_uncertainty_score":0.013317108},"labels":[],"label_agreement":null},{"id":"W2186226354","doi":"10.22215/etd/2009-06479","title":"Location tracking mitigation for honest nodes and location estimation of uncooperative devices in wireless mobile networks","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Tracking (education); Wireless; Wireless network; Telecommunications; Computer network; Psychology","score_opus":0.005927409712060075,"score_gpt":0.24415377971556457,"score_spread":0.2382263700035045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186226354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15819313,0.00086684333,0.83753073,0.0004358421,0.00017522753,0.000033753655,0.000037119287,0.00025795124,0.0024693916],"genre_scores_gemma":[0.94122666,0.0007234298,0.05090678,0.00007426765,0.00014294265,0.00003280224,0.000083343526,0.000026793454,0.0067829923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993693,0.00016882755,0.000030705432,0.00014215417,0.0001987263,0.00009027471],"domain_scores_gemma":[0.9972518,0.0015081915,0.00033966004,0.00036206382,0.0004598395,0.000078432546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083693565,0.00042192367,0.0006302098,0.0004510212,0.0004716325,0.0005885022,0.0008113787,0.00064852095,0.00063484965],"category_scores_gemma":[0.006797155,0.0002982961,0.00025896856,0.00046361453,0.0005120089,0.0014016284,0.0009504188,0.0005845652,0.00026407465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060653954,0.00015002758,0.00918216,0.00021186142,0.00015748174,0.0005380077,0.00072244636,0.57888806,0.037960492,0.019774234,0.00432402,0.34748465],"study_design_scores_gemma":[0.000011344598,0.00012461998,0.0019354053,0.00001584654,0.00003598556,0.00015882448,0.000093658564,0.98584795,0.0065319682,0.0043114875,0.00091593515,0.000016978269],"about_ca_topic_score_codex":0.0023227956,"about_ca_topic_score_gemma":0.002679642,"teacher_disagreement_score":0.0023227956,"about_ca_system_score_codex":0.0004920158,"about_ca_system_score_gemma":0.00069234136,"threshold_uncertainty_score":0.0046185255},"labels":[],"label_agreement":null},{"id":"W2186672336","doi":"10.1109/ipin.2015.7346770","title":"Real-time attitude tracking of mobile devices","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Heading (navigation); Accelerometer; Kalman filter; Computer science; Magnetometer; Gyroscope; Attitude and heading reference system; Tracking (education); Real-time computing; Computer vision; Mobile phone; Mobile device; Artificial intelligence; Engineering; Telecommunications; Psychology","score_opus":0.017699322483843656,"score_gpt":0.24398717686682025,"score_spread":0.2262878543829766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186672336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058678176,0.0007360547,0.93401974,0.00008036707,0.00018801577,0.000031317795,0.000150531,0.002316188,0.0037995626],"genre_scores_gemma":[0.8463894,0.0006076494,0.14682026,0.000067391804,0.000072250594,0.000051433915,0.00033027338,0.00008766911,0.005573661],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995889,0.00006529936,0.00002289082,0.00008754811,0.00020151252,0.000033902943],"domain_scores_gemma":[0.9996556,0.00006243752,0.00006458817,0.000054568765,0.00014984953,0.000012965496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021038498,0.00037927952,0.00025945887,0.00041998626,0.00017099567,0.0005270937,0.0003940629,0.00031446165,0.00081055565],"category_scores_gemma":[0.001153777,0.00016540498,0.00017805879,0.00041963052,0.000121978905,0.0004960861,0.00032669856,0.000266859,0.00057439075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034572254,0.000054822704,0.007508594,0.00032537628,0.00007844119,0.00022637118,0.00020441884,0.08751988,0.097150825,0.0039809863,0.0046885214,0.79791605],"study_design_scores_gemma":[0.000050936982,0.00037922402,0.017309798,0.00007821879,0.00006597873,0.00060308963,0.000111986104,0.87518245,0.077545546,0.0019043147,0.026703876,0.00006462908],"about_ca_topic_score_codex":0.0015138598,"about_ca_topic_score_gemma":0.0018303703,"teacher_disagreement_score":0.0015138598,"about_ca_system_score_codex":0.0001698196,"about_ca_system_score_gemma":0.00015501976,"threshold_uncertainty_score":0.0030100346},"labels":[],"label_agreement":null},{"id":"W2186831608","doi":"10.1109/ipin.2015.7346775","title":"An efficient method for evaluating the performance of integrated multiple pedestrian navigation systems","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Dead reckoning; Kalman filter; Sensor fusion; Computer science; Inertial navigation system; Pedestrian; Inertial measurement unit; Real-time computing; Navigation system; State (computer science); Artificial intelligence; Computer vision; Engineering; Global Positioning System; Inertial frame of reference; Telecommunications; Transport engineering","score_opus":0.050871425957052206,"score_gpt":0.3283309329080439,"score_spread":0.2774595069509917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186831608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08086473,0.00039178916,0.91387177,0.000024625553,0.00007886003,0.00016339851,0.0003784491,0.0014489538,0.0027774593],"genre_scores_gemma":[0.648537,0.00027869997,0.34825376,0.000023884153,0.000027550828,0.00037956817,0.0008004761,0.00012855673,0.0015706174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985008,0.00022503962,0.00011618389,0.00021392936,0.0008535926,0.0000904904],"domain_scores_gemma":[0.9982224,0.0005458478,0.00017993327,0.00021978667,0.0007924429,0.0000396045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014301696,0.001144269,0.00094131596,0.0019602655,0.00037740238,0.00085052114,0.0006831211,0.00066279,0.0019121943],"category_scores_gemma":[0.0049752113,0.00024910615,0.0004504506,0.0013948977,0.0002449032,0.0010200036,0.0008120288,0.00041016092,0.0005826625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008078314,0.00027940946,0.01989848,0.00061010395,0.00049029494,0.0002551183,0.00021102504,0.3435937,0.08121698,0.0043302765,0.001947468,0.5463594],"study_design_scores_gemma":[0.000028325274,0.0006789002,0.015352439,0.000037408194,0.00009890962,0.0002936697,0.000102849146,0.93543833,0.043636948,0.0011099831,0.0031453774,0.00007692576],"about_ca_topic_score_codex":0.0023594452,"about_ca_topic_score_gemma":0.0021383995,"teacher_disagreement_score":0.0023594452,"about_ca_system_score_codex":0.0005146516,"about_ca_system_score_gemma":0.0005598123,"threshold_uncertainty_score":0.0075635314},"labels":[],"label_agreement":null},{"id":"W2187865965","doi":"10.1109/ipin.2015.7346785","title":"Multiple sensors integration for pedestrian indoor navigation","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Kalman filter; Accelerometer; Gyroscope; Sensor fusion; Computer science; Indoor positioning system; Magnetometer; Focus (optics); Real-time computing; Dead reckoning; Pressure sensor; Wireless; Computer vision; Global Positioning System; Artificial intelligence; Engineering; Telecommunications; Aerospace engineering","score_opus":0.03149164849147465,"score_gpt":0.24535813222062042,"score_spread":0.21386648372914577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187865965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063380934,0.002028338,0.9303815,0.00010397689,0.00016634581,0.00003283362,0.000055588993,0.0013561655,0.0024943438],"genre_scores_gemma":[0.8965393,0.0006969055,0.100023754,0.000053346022,0.000060250397,0.000031021198,0.00008234358,0.000028342885,0.0024847414],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996146,0.00010314498,0.000013669044,0.00008861296,0.00013904793,0.000040835752],"domain_scores_gemma":[0.9998061,0.000039177987,0.000024988642,0.000028733784,0.000087175176,0.00001385043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029507498,0.00066594,0.0005156113,0.00038567072,0.00023226193,0.0003293721,0.00044112,0.0004467862,0.0009119718],"category_scores_gemma":[0.00049017335,0.00028157572,0.00040136778,0.00038666563,0.0001642354,0.0005717234,0.00049660983,0.00030087386,0.00040139953],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009080538,0.00015888088,0.0064550634,0.00044863354,0.00024634603,0.00081039255,0.00026872137,0.13512789,0.18738928,0.007757516,0.0030792712,0.65734994],"study_design_scores_gemma":[0.000047220776,0.0010658333,0.008411506,0.00007742526,0.00028812414,0.0009518317,0.00012853552,0.8437916,0.12571476,0.004285096,0.015146205,0.00009198749],"about_ca_topic_score_codex":0.0013548596,"about_ca_topic_score_gemma":0.0022206127,"teacher_disagreement_score":0.0013548596,"about_ca_system_score_codex":0.00023812926,"about_ca_system_score_gemma":0.00031193584,"threshold_uncertainty_score":0.0030508637},"labels":[],"label_agreement":null},{"id":"W2187935563","doi":"10.1109/ipin.2015.7346764","title":"Using Wi-Fi/magnetometers for indoor location and personal navigation","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Magnetometer; Computer science; Orientation (vector space); Accelerometer; Compass; Android (operating system); Global Positioning System; Fingerprint recognition; Real-time computing; Gaussian; Signal strength; Computer vision; Artificial intelligence; Fingerprint (computing); Magnetic field; Geography; Telecommunications; Physics; Mathematics; Wireless","score_opus":0.05767151592929473,"score_gpt":0.27375863212093504,"score_spread":0.2160871161916403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187935563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04603881,0.0011549643,0.94756275,0.00013337439,0.00021776435,0.000069263304,0.00020962974,0.0019915677,0.0026218116],"genre_scores_gemma":[0.53276,0.0013729973,0.45993537,0.00013421598,0.00014662536,0.00011363832,0.00056535867,0.000058517362,0.004913166],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996308,0.00007446896,0.000025714635,0.000091736154,0.00012893615,0.000048268867],"domain_scores_gemma":[0.9996599,0.000055822064,0.0000577736,0.00006996209,0.00014084332,0.000015686179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003338346,0.00091324345,0.0005376106,0.0008557518,0.00032718855,0.0008677329,0.0009885309,0.00078285404,0.0011460733],"category_scores_gemma":[0.00077632105,0.0002868869,0.00045224532,0.0010488464,0.00023301653,0.001004737,0.0005482761,0.00043084144,0.0013351917],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007029625,0.0002097475,0.013980672,0.00053328753,0.0002534145,0.00037777057,0.00008924807,0.03711492,0.1262638,0.0044214465,0.0035946246,0.8124581],"study_design_scores_gemma":[0.000109103596,0.0009628841,0.020271374,0.000115160234,0.00043846728,0.0029722713,0.00016388453,0.7069679,0.23203298,0.00340764,0.032341056,0.00021725504],"about_ca_topic_score_codex":0.0016804673,"about_ca_topic_score_gemma":0.0033040754,"teacher_disagreement_score":0.0016804673,"about_ca_system_score_codex":0.00022744853,"about_ca_system_score_gemma":0.000404952,"threshold_uncertainty_score":0.0038340092},"labels":[],"label_agreement":null},{"id":"W2188172207","doi":"10.1109/ipin.2015.7346784","title":"A modularized real-time indoor navigation algorithm on smartphones","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Real-time computing; Embedded system","score_opus":0.012312367945843443,"score_gpt":0.21523278205518104,"score_spread":0.2029204141093376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188172207","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020744612,0.00015760191,0.9709815,0.000033069955,0.0000768338,0.0000613694,0.0001207331,0.006198787,0.0016254837],"genre_scores_gemma":[0.2717652,0.000143871,0.7219086,0.00006127844,0.00003178814,0.00013603154,0.00051440735,0.00012256746,0.005316234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997696,0.000018198994,0.000015969072,0.00007132285,0.0000963797,0.000028416953],"domain_scores_gemma":[0.9997867,0.000019159375,0.000019398092,0.000038645907,0.00012146896,0.000014619006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014732496,0.0005811179,0.00047038746,0.00044601507,0.00025182057,0.00038298513,0.00070527045,0.00036570994,0.0022107163],"category_scores_gemma":[0.000565537,0.00020060784,0.00037309216,0.0003462686,0.00009818044,0.00040305272,0.00050499255,0.00029758515,0.0014635908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030687623,0.000072969895,0.0035680633,0.00014833493,0.000066394554,0.00021225559,0.000120199315,0.036798656,0.076577745,0.0018312441,0.005530725,0.87476665],"study_design_scores_gemma":[0.00010073169,0.00034305014,0.0059140124,0.000037486712,0.00007953363,0.00092365046,0.00010452007,0.90131855,0.068025224,0.00108414,0.022002853,0.00006627822],"about_ca_topic_score_codex":0.0058306446,"about_ca_topic_score_gemma":0.007599775,"teacher_disagreement_score":0.0058306446,"about_ca_system_score_codex":0.00021289485,"about_ca_system_score_gemma":0.00053639244,"threshold_uncertainty_score":0.011593401},"labels":[],"label_agreement":null},{"id":"W2189107196","doi":"","title":"Mitigation of NLOS Error in AOA Wireless Location","year":2003,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Non-line-of-sight propagation; Multipath propagation; Computer science; Wireless; Real-time computing; Angle of arrival; Time of arrival; Channel (broadcasting); Telecommunications; Antenna (radio)","score_opus":0.007740349233112752,"score_gpt":0.20915167381624242,"score_spread":0.20141132458312966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189107196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037174616,0.0005585746,0.95898753,0.00019178842,0.00012465827,0.000023866574,0.000040617153,0.00063416065,0.0022641968],"genre_scores_gemma":[0.6115273,0.0009532809,0.38269207,0.00015050375,0.00029145644,0.0001012549,0.00019700074,0.00012881252,0.0039583887],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992151,0.00019543055,0.00004339479,0.00012744007,0.00034334406,0.00007535647],"domain_scores_gemma":[0.9982698,0.00057070784,0.00036153325,0.00029448056,0.00045496135,0.000048479014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007328001,0.00068895234,0.0005942559,0.00082558667,0.00054425636,0.0005212983,0.00060436764,0.0006645796,0.00068961154],"category_scores_gemma":[0.004891351,0.00035717606,0.0003175348,0.0008012376,0.0005556059,0.001355443,0.0013791629,0.0006138734,0.000890777],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006044306,0.000089080604,0.0054438803,0.0003042487,0.00013200274,0.00034616457,0.00042569594,0.17293349,0.14828151,0.010165877,0.0017232837,0.65955037],"study_design_scores_gemma":[0.00014695067,0.0007971938,0.010762978,0.00013890206,0.00022157314,0.0022159726,0.00033017204,0.7681817,0.17573713,0.013390975,0.027930945,0.00014545818],"about_ca_topic_score_codex":0.0009178914,"about_ca_topic_score_gemma":0.0012742286,"teacher_disagreement_score":0.0009178914,"about_ca_system_score_codex":0.00018709681,"about_ca_system_score_gemma":0.00057404954,"threshold_uncertainty_score":0.0038754344},"labels":[],"label_agreement":null},{"id":"W2192655756","doi":"10.1002/wcm.2653","title":"Three dimensional compressed sensing for wireless networks‐based multiple node localization in multi‐floor buildings","year":2015,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Node (physics); Compressed sensing; Wireless; Position (finance); Transmission (telecommunications); Wireless network; Fading; Shadow mapping; Real-time computing; Radio propagation; Noise (video); Path loss; Algorithm; Telecommunications; Artificial intelligence; Acoustics","score_opus":0.03622053266496306,"score_gpt":0.2649202017488933,"score_spread":0.22869966908393025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2192655756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108055264,0.0003770474,0.8895287,0.00027105928,0.00006784229,0.000027818478,0.000043343887,0.00030403383,0.0013248799],"genre_scores_gemma":[0.9041288,0.00025770624,0.09485383,0.000058320842,0.00002855685,0.0000371823,0.000058555244,0.000015985595,0.0005611408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997508,0.000086073655,0.000009992582,0.000033030967,0.00010339917,0.000016611531],"domain_scores_gemma":[0.9994405,0.00032764635,0.00007316295,0.000045942492,0.00009229394,0.000020366298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003567378,0.00032032028,0.00029971206,0.00041686866,0.00017772683,0.0003628783,0.00034790605,0.00034123973,0.0005280383],"category_scores_gemma":[0.0012319661,0.00014127414,0.00023745839,0.00042020355,0.0004247291,0.0005249575,0.00058957445,0.0003250402,0.00009392164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021914976,0.00005463029,0.0012884508,0.0001559682,0.00004003242,0.00023841672,0.0001751973,0.80733967,0.041705657,0.008500873,0.0010444949,0.13923745],"study_design_scores_gemma":[0.0000036576873,0.000022318422,0.00022529825,0.0000037190027,0.0000027479643,0.000021356242,0.000012281602,0.9963819,0.0022617788,0.00084405916,0.00021636265,0.000004435859],"about_ca_topic_score_codex":0.0022138404,"about_ca_topic_score_gemma":0.0015177109,"teacher_disagreement_score":0.0022138404,"about_ca_system_score_codex":0.0002980977,"about_ca_system_score_gemma":0.0003383339,"threshold_uncertainty_score":0.004401922},"labels":[],"label_agreement":null},{"id":"W2194333542","doi":"10.1139/l2012-094","title":"A framework for indoor construction resources tracking by applying wireless sensor networks<sup>1</sup>This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Trilateration; Wireless sensor network; Global Positioning System; Reliability (semiconductor); Tracking (education); Computer science; Real-time computing; Wireless; Signal strength; Engineering; Computer network; Telecommunications","score_opus":0.005779558936286598,"score_gpt":0.18194370361315423,"score_spread":0.17616414467686764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2194333542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006912204,0.00049335824,0.99332774,0.0001809599,0.000096002004,0.000058829813,0.000049303773,0.00038866428,0.0047139362],"genre_scores_gemma":[0.044451706,0.0022421835,0.9440522,0.00008239745,0.00013924137,0.0003144596,0.00027756786,0.00014478342,0.008295514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991678,0.00025458517,0.000054027547,0.00017772825,0.00027756058,0.000068252164],"domain_scores_gemma":[0.99971396,0.00008009052,0.000037476042,0.00005087568,0.000074083066,0.00004350905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010954886,0.0012128368,0.0008310125,0.001244343,0.0011502753,0.0023774935,0.0028268031,0.0015661974,0.0033406042],"category_scores_gemma":[0.0010005144,0.0006282431,0.0016743197,0.0016429205,0.0017130882,0.0022284035,0.0030631488,0.001627129,0.0014273382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036958478,0.00006866817,0.0008365574,0.0003792428,0.00007223845,0.0008300933,0.0007112166,0.116251945,0.008565482,0.7698756,0.008098491,0.094273545],"study_design_scores_gemma":[0.000017815675,0.000111937814,0.00061329117,0.00021219491,0.00006954661,0.00090991653,0.00037771018,0.585131,0.00450598,0.1388063,0.26916537,0.00007900461],"about_ca_topic_score_codex":0.006765469,"about_ca_topic_score_gemma":0.008255146,"teacher_disagreement_score":0.006765469,"about_ca_system_score_codex":0.0010776616,"about_ca_system_score_gemma":0.0015472008,"threshold_uncertainty_score":0.013452172},"labels":[],"label_agreement":null},{"id":"W2195769232","doi":"10.3390/s151229852","title":"Adaptive Environmental Source Localization and Tracking with Unknown Permittivity and Path Loss Coefficients","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"RSS; SIGNAL (programming language); Wireless sensor network; Computer science; Radio propagation; Tracking (education); Path loss; Received signal strength indication; Wireless; Control theory (sociology); Algorithm; Real-time computing; Artificial intelligence; Telecommunications; Control (management)","score_opus":0.009773832084039032,"score_gpt":0.1820926492150203,"score_spread":0.17231881713098127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2195769232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028468829,0.00006998761,0.9702688,0.00004126155,0.000008156167,0.0000101485775,0.0000056679933,0.00016007478,0.00096704875],"genre_scores_gemma":[0.8146974,0.00018773192,0.18290462,0.000036894053,0.000015906755,0.000046526326,0.00003136881,0.00002960091,0.0020498936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997547,0.00004255116,0.00000956696,0.000073262425,0.00010298614,0.000016967284],"domain_scores_gemma":[0.9996118,0.00016001178,0.00008550359,0.0000586939,0.00007539458,0.000008501617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003497814,0.00044323853,0.0002573598,0.00030896722,0.00017481216,0.00034747165,0.00067660783,0.00042863216,0.00029115402],"category_scores_gemma":[0.0013832287,0.000213214,0.00031982744,0.00036104152,0.00046618414,0.00085126946,0.0006550365,0.0003386687,0.00015829488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008941455,0.000047300226,0.0016490635,0.00007844826,0.000030359137,0.00021824647,0.00017881417,0.7749204,0.0727144,0.009847624,0.00037580723,0.13985008],"study_design_scores_gemma":[0.000008159041,0.00004008192,0.0003958831,0.000003563612,0.000006912928,0.00008202563,0.000014375827,0.9887504,0.008859171,0.0013553353,0.0004756647,0.000008291875],"about_ca_topic_score_codex":0.0011044261,"about_ca_topic_score_gemma":0.0010718476,"teacher_disagreement_score":0.0011044261,"about_ca_system_score_codex":0.00027108457,"about_ca_system_score_gemma":0.0002984859,"threshold_uncertainty_score":0.0021959543},"labels":[],"label_agreement":null},{"id":"W2201574122","doi":"10.1093/forestscience/55.4.293","title":"An Accurate Approximation for Distance Distributions Arising in Airtanker Positioning and Related Problems","year":2009,"lang":"en","type":"article","venue":"Forest Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mathematics; Statistics; Econometrics; Computer science; Statistical physics; Environmental science; Applied mathematics; Physics","score_opus":0.006956739230741092,"score_gpt":0.23847258294178303,"score_spread":0.23151584371104195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2201574122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059494837,0.00092943764,0.9898145,0.0002990985,0.00012486022,0.00001491604,0.00008210264,0.00008080369,0.0027047223],"genre_scores_gemma":[0.48227015,0.0049211862,0.48914662,0.0005300021,0.0006841966,0.00022427898,0.0008602524,0.00031662165,0.021046706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906343,0.00031828985,0.00003598175,0.00013836152,0.00033466646,0.00010933767],"domain_scores_gemma":[0.9952095,0.0036022188,0.00021937609,0.00030089024,0.00054997404,0.00011806112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020021906,0.0012615271,0.0013311754,0.0018541031,0.00076385954,0.0017831435,0.002228868,0.0024819544,0.002774614],"category_scores_gemma":[0.013791018,0.000722002,0.0010887105,0.0024149376,0.0012877587,0.0033952326,0.0018504991,0.0028351115,0.00086411193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004802988,0.000034213408,0.0006613005,0.00014827735,0.000024524035,0.000105412146,0.000086027925,0.85685354,0.00092353305,0.111355565,0.0029753998,0.026784245],"study_design_scores_gemma":[0.0000027906224,0.0000048688585,0.00008034992,0.000009461203,0.000004412087,0.000031041207,0.0000137836505,0.9745505,0.00011424275,0.024399562,0.0007833319,0.0000057159305],"about_ca_topic_score_codex":0.006936615,"about_ca_topic_score_gemma":0.006269659,"teacher_disagreement_score":0.006936615,"about_ca_system_score_codex":0.0015948906,"about_ca_system_score_gemma":0.0009976916,"threshold_uncertainty_score":0.013792455},"labels":[],"label_agreement":null},{"id":"W2204949160","doi":"10.1109/lwc.2015.2483509","title":"Wireless Access Point Localization Using Nonlinear Least Squares and Multi-Level Quality Control","year":2015,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"RSS; Computer science; Wireless; A priori and a posteriori; Nonlinear system; Path loss; Path (computing); Non-linear least squares; Algorithm; Point (geometry); Radio propagation; Wireless network; Real-time computing; Mathematical optimization; Estimation theory; Computer network; Mathematics; Telecommunications","score_opus":0.13378755560969374,"score_gpt":0.33436905778277043,"score_spread":0.2005815021730767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204949160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006172882,0.00004662634,0.9932233,0.000033606375,0.000011368678,0.0000123886,0.000008232024,0.00021793293,0.00027358567],"genre_scores_gemma":[0.5634945,0.00021241591,0.4340295,0.00007887306,0.000056784516,0.00011725821,0.000096188596,0.00009302864,0.0018214654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984763,0.00037253066,0.000078824174,0.0003758994,0.0006165238,0.000079911995],"domain_scores_gemma":[0.998531,0.0004786909,0.0003084748,0.00021425108,0.00042573252,0.000041876276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001008726,0.00072961074,0.00055376714,0.0005099421,0.00035307067,0.00087172026,0.0011378021,0.0006028796,0.00071841164],"category_scores_gemma":[0.0045366287,0.00035536036,0.0005439148,0.00082338066,0.0008249355,0.0015722227,0.0013132979,0.0008894865,0.0003656606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025179578,0.00014298713,0.0042771045,0.00024149283,0.00011330637,0.00012250953,0.00030631138,0.4614474,0.055569593,0.01397646,0.0010304454,0.4625207],"study_design_scores_gemma":[0.000012919851,0.00005490677,0.0006332699,0.0000043510304,0.000009017101,0.000031558528,0.00001110314,0.9922414,0.0052317716,0.0012029248,0.00055051246,0.000016188966],"about_ca_topic_score_codex":0.0058382587,"about_ca_topic_score_gemma":0.0044205543,"teacher_disagreement_score":0.0058382587,"about_ca_system_score_codex":0.0007465809,"about_ca_system_score_gemma":0.0008392244,"threshold_uncertainty_score":0.011608541},"labels":[],"label_agreement":null},{"id":"W2213047121","doi":"10.1016/j.comcom.2015.09.002","title":"Smartphone positioning in sparse Wi-Fi environments","year":2015,"lang":"en","type":"article","venue":"Computer Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Accelerometer; Global Positioning System; Gyroscope; Real-time computing; Artificial intelligence; Mobile device; Computer vision; Position (finance); Telecommunications","score_opus":0.03911038953729748,"score_gpt":0.2319059165397991,"score_spread":0.19279552700250163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2213047121","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5970887,0.0008539146,0.39183936,0.000323945,0.00014251657,0.0000356304,0.0005898557,0.000528134,0.008597932],"genre_scores_gemma":[0.98350406,0.0003201979,0.0143057965,0.000027055177,0.000048541206,0.000012279743,0.00025732792,0.000010013109,0.0015147453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968326,0.00007125108,0.000016236574,0.00006724227,0.000091220136,0.0000709486],"domain_scores_gemma":[0.99954695,0.0001858897,0.00006709219,0.000051569812,0.00011689943,0.00003155723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018743874,0.0004259418,0.00043771206,0.0005452387,0.00035695054,0.00063132256,0.000388021,0.0005035957,0.0008319427],"category_scores_gemma":[0.0019256127,0.00026253753,0.00018127839,0.0009990637,0.0003474263,0.0007855158,0.00080833235,0.00025812746,0.00046741913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014283645,0.00013813053,0.08170774,0.0006230522,0.00017730596,0.0019961356,0.0010154992,0.45027244,0.08941619,0.011523595,0.005326895,0.3563747],"study_design_scores_gemma":[0.000033834334,0.0003326583,0.045835517,0.000047999947,0.00007718455,0.0012776722,0.00072291924,0.9311177,0.01246805,0.004851627,0.0031917566,0.00004300968],"about_ca_topic_score_codex":0.006025906,"about_ca_topic_score_gemma":0.011973496,"teacher_disagreement_score":0.006025906,"about_ca_system_score_codex":0.00020341591,"about_ca_system_score_gemma":0.0003602904,"threshold_uncertainty_score":0.011981666},"labels":[],"label_agreement":null},{"id":"W2219134023","doi":"10.1109/jsen.2015.2477444","title":"Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation","year":2015,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Dead reckoning; Gyroscope; Computer science; Kalman filter; Inertial navigation system; Real-time computing; Mobile device; Vibrating structure gyroscope; Heading (navigation); Navigation system; Noise (video); Engineering; Artificial intelligence; Global Positioning System; Telecommunications; Inertial frame of reference; Aerospace engineering; Physics","score_opus":0.03323863665533345,"score_gpt":0.2632200366535584,"score_spread":0.22998139999822498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219134023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16349368,0.0011095457,0.82818747,0.00012520135,0.00021549317,0.000075986565,0.00010573399,0.0020013885,0.0046854876],"genre_scores_gemma":[0.90225065,0.00036205433,0.0935953,0.00013604658,0.00005052664,0.000059935468,0.00012520415,0.000034477376,0.0033856393],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999617,0.00004880262,0.000018294246,0.00009010732,0.0001765695,0.000049149607],"domain_scores_gemma":[0.999813,0.000022455395,0.000034516514,0.000032840973,0.00008469552,0.000012348412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019141159,0.0006205193,0.00037340957,0.00042203165,0.00021568642,0.00030177244,0.0006332331,0.000443557,0.0009300364],"category_scores_gemma":[0.0005334589,0.0002529573,0.0004156033,0.00039015024,0.00014569063,0.0006325259,0.0007501764,0.00031924984,0.0004257705],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043745906,0.00012755734,0.018948646,0.00037391603,0.00020957751,0.00092879945,0.00039367954,0.035183165,0.32209107,0.0033952694,0.0030968927,0.6148139],"study_design_scores_gemma":[0.000099342324,0.0017149966,0.04306847,0.00013701699,0.0004601417,0.001989173,0.00039991495,0.6081868,0.3064402,0.0032556558,0.03404885,0.00019943023],"about_ca_topic_score_codex":0.0030263814,"about_ca_topic_score_gemma":0.004898453,"teacher_disagreement_score":0.0030263814,"about_ca_system_score_codex":0.00022030326,"about_ca_system_score_gemma":0.00036657348,"threshold_uncertainty_score":0.0060175657},"labels":[],"label_agreement":null},{"id":"W2219838081","doi":"10.1109/iot.2015.7356554","title":"Sensing WiFi network for personal IoT analytics","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Default gateway; Cloud computing; Analytics; Key (lock); Computer network; Distributed computing; Real-time computing; Database; Operating system","score_opus":0.03393636469150647,"score_gpt":0.2347027240472289,"score_spread":0.20076635935572243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219838081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12035496,0.00042656454,0.85498184,0.0005288681,0.00016842003,0.0003423877,0.0003401985,0.0068942024,0.015962461],"genre_scores_gemma":[0.87492377,0.00025927182,0.118948594,0.00016803491,0.000059420545,0.00015159104,0.0003560269,0.000117554984,0.0050157662],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995165,0.00005244445,0.000027545297,0.00010353615,0.00022805372,0.000071975366],"domain_scores_gemma":[0.99962544,0.00009413798,0.000044148986,0.000109551715,0.0000908174,0.000035964586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003922315,0.0005392933,0.00035644646,0.00048738671,0.000418103,0.0010455472,0.0010035855,0.00046548495,0.0030911758],"category_scores_gemma":[0.0013158658,0.00021357494,0.00019430502,0.0004590122,0.00030907858,0.0018071351,0.0012136722,0.00041946993,0.0007087583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085306424,0.0004105346,0.03192478,0.0005362981,0.00016515292,0.0014893996,0.000986462,0.075508654,0.17081825,0.05670475,0.022295,0.63830763],"study_design_scores_gemma":[0.00006464091,0.00062569213,0.010340548,0.000101123245,0.00012651151,0.0015654532,0.0004013368,0.73506117,0.14700745,0.02340243,0.08118526,0.000118296106],"about_ca_topic_score_codex":0.002622945,"about_ca_topic_score_gemma":0.003563682,"teacher_disagreement_score":0.0030911758,"about_ca_system_score_codex":0.00055429235,"about_ca_system_score_gemma":0.0004667495,"threshold_uncertainty_score":0.010341048},"labels":[],"label_agreement":null},{"id":"W2227098268","doi":"10.1109/twc.2016.2586844","title":"RSSI-Based Distributed Self-Localization for Wireless Sensor Networks Used in Precision Agriculture","year":2016,"lang":"en","type":"preprint","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Node (physics); Wireless sensor network; Scalability; Real-time computing; Path (computing); Distributed computing; Computer network; Algorithm; Engineering","score_opus":0.018242436940290218,"score_gpt":0.24880741254951586,"score_spread":0.23056497560922565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2227098268","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020221348,0.00015245122,0.99682933,0.00003900543,0.000021490654,0.000012451769,0.000009448356,0.00048099956,0.00043255853],"genre_scores_gemma":[0.4476615,0.00093024416,0.5452098,0.00014044263,0.00013013557,0.0001799435,0.00023742746,0.00023765549,0.0052728103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999116,0.0002058993,0.000039920422,0.00018614726,0.00040315703,0.00004885931],"domain_scores_gemma":[0.9992723,0.00021711078,0.000117288575,0.00014185964,0.00022756118,0.000023931512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009032661,0.00073687534,0.000981551,0.00082972675,0.0003751116,0.00076281786,0.0018873304,0.0007158144,0.00097234943],"category_scores_gemma":[0.0021803954,0.00037126843,0.00062405056,0.000981956,0.0006143686,0.001422101,0.00083281123,0.0006509499,0.0008837474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012585688,0.00010479345,0.0019160536,0.000272239,0.0001200463,0.000116580006,0.00021838561,0.41226983,0.022394134,0.018939264,0.0025973006,0.54092556],"study_design_scores_gemma":[0.000013292699,0.00007034205,0.00047280308,0.000010529761,0.000025797019,0.00008725188,0.000021073192,0.9835212,0.0071317377,0.0047887275,0.0038361386,0.000021164973],"about_ca_topic_score_codex":0.001377354,"about_ca_topic_score_gemma":0.0019928813,"teacher_disagreement_score":0.0018873304,"about_ca_system_score_codex":0.0005978061,"about_ca_system_score_gemma":0.0005505059,"threshold_uncertainty_score":0.0047769547},"labels":[],"label_agreement":null},{"id":"W2232190770","doi":"10.1007/0-387-32015-6_4","title":"Enabling Mobile Commerce Through Location Based Services","year":2006,"lang":"en","type":"book-chapter","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Commercialization; Mobile commerce; Business; Business model; Mobile business development; Location-based service; Services computing; Computer science; Telecommunications; Process management; Mobile technology; Mobile computing; Marketing; World Wide Web; Web service; Mobile Web","score_opus":0.010246688354455193,"score_gpt":0.2064535209915357,"score_spread":0.1962068326370805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2232190770","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025668042,0.020175034,0.045661326,0.0026645397,0.0006572513,0.00004654877,0.000062027364,0.00040767068,0.92775875],"genre_scores_gemma":[0.059422873,0.072266795,0.067669354,0.0017150865,0.0009013572,0.00012810911,0.0002717407,0.00028121006,0.79734343],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998129,0.00004031788,0.0000071175327,0.000019175597,0.00009280441,0.000027600383],"domain_scores_gemma":[0.9999069,0.00003911466,0.000005433182,0.00001271896,0.00002375571,0.000012065474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002082769,0.0006328672,0.00023571854,0.00072022073,0.00059740053,0.0037397435,0.0006053137,0.0013978566,0.015309424],"category_scores_gemma":[0.00042927495,0.00026055906,0.00026910123,0.0012610628,0.00082354306,0.0039590104,0.0013335891,0.001347529,0.010016776],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000105304125,0.000029239905,0.00012501125,0.00025095753,0.0000046575374,0.0002789603,0.00091089506,0.0013962309,0.0027979934,0.6774304,0.06725493,0.2495103],"study_design_scores_gemma":[0.0000022469148,0.000009914382,0.000079874306,0.00014700399,0.0000029088442,0.00035002557,0.00019030605,0.0011106686,0.00065858924,0.037436098,0.96000683,0.0000054609536],"about_ca_topic_score_codex":0.0011062478,"about_ca_topic_score_gemma":0.0017882133,"teacher_disagreement_score":0.015309424,"about_ca_system_score_codex":0.00082694367,"about_ca_system_score_gemma":0.0007150345,"threshold_uncertainty_score":0.051215112},"labels":[],"label_agreement":null},{"id":"W2233247915","doi":"","title":"Le Wi-Fi pour le positionnement et la navigation en intérieur","year":2007,"lang":"fr","type":"book-chapter","venue":"XYZ eBooks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Physics; Geography; Art","score_opus":0.015324023118091067,"score_gpt":0.24469093616279142,"score_spread":0.22936691304470036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2233247915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016254738,0.1098758,0.5280589,0.0025491617,0.0028668046,0.00017942213,0.0003928193,0.0021928074,0.3376295],"genre_scores_gemma":[0.055637766,0.06263664,0.0925561,0.0006785786,0.0007508903,0.00010982631,0.00035345842,0.00040615845,0.78687054],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995585,0.00004311693,0.000015919357,0.000090845184,0.00024207462,0.000049449125],"domain_scores_gemma":[0.9996208,0.00014352263,0.00002284172,0.000051906147,0.00014163993,0.000019185192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004634689,0.0012902122,0.00048090905,0.0012942805,0.0007665835,0.002446598,0.001070514,0.0016070871,0.018784009],"category_scores_gemma":[0.0011052164,0.0004490673,0.0003794502,0.0019488427,0.00091375184,0.002826763,0.00066701975,0.001198754,0.011323333],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010114874,0.000031001924,0.00078976573,0.00059660827,0.000025549542,0.00041170468,0.00088945846,0.004211466,0.02901075,0.07320123,0.032617066,0.85811424],"study_design_scores_gemma":[0.00000829605,0.0000956506,0.0013632355,0.00032248508,0.00003607763,0.0010557817,0.00029393198,0.0039759036,0.013701813,0.008721994,0.97037446,0.00005033285],"about_ca_topic_score_codex":0.010275481,"about_ca_topic_score_gemma":0.01453575,"teacher_disagreement_score":0.018784009,"about_ca_system_score_codex":0.0014238309,"about_ca_system_score_gemma":0.0012954329,"threshold_uncertainty_score":0.06283879},"labels":[],"label_agreement":null},{"id":"W2235733071","doi":"10.1142/s0218126615501492","title":"Improving Ultra-Wideband Positioning Security Using a Pseudo-Random Turnaround Delay Protocol","year":2015,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Ranging; Computer science; Preamble; Computer network; Frame (networking); Ultra-wideband; Authentication (law); Physical layer; Wireless; Computer security; Telecommunications; Channel (broadcasting)","score_opus":0.016702436043478045,"score_gpt":0.23422062168357027,"score_spread":0.21751818564009223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2235733071","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032587886,0.00108402,0.9609525,0.00025250303,0.00021719448,0.0001786128,0.000026019194,0.0011520563,0.003549194],"genre_scores_gemma":[0.83042574,0.0012134372,0.16318457,0.00025238338,0.00012790276,0.00021812922,0.00011270987,0.00007537122,0.0043896725],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876237,0.00032183542,0.0001023985,0.00017372503,0.0004560544,0.00018366525],"domain_scores_gemma":[0.9978703,0.00075733184,0.00033295917,0.0004539681,0.0005124811,0.000072933784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011701465,0.00079286355,0.00062401424,0.00082160387,0.0007059739,0.0012199758,0.001281853,0.0008226877,0.0012839674],"category_scores_gemma":[0.0027725142,0.0002633194,0.00048809923,0.00054525613,0.0009522502,0.0021514723,0.0017601212,0.0011343597,0.00060113193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012407873,0.0003627274,0.0022082552,0.0007363308,0.00022011822,0.0014274423,0.00070505025,0.09347953,0.3942534,0.20164445,0.006324622,0.29739732],"study_design_scores_gemma":[0.00024196523,0.0019586189,0.00090571406,0.000116094314,0.0003272622,0.0030547467,0.00023620801,0.6617556,0.2718255,0.02079861,0.03855148,0.00022825261],"about_ca_topic_score_codex":0.00046909702,"about_ca_topic_score_gemma":0.00036382294,"teacher_disagreement_score":0.0012839674,"about_ca_system_score_codex":0.0006136786,"about_ca_system_score_gemma":0.00072668836,"threshold_uncertainty_score":0.0061883926},"labels":[],"label_agreement":null},{"id":"W2239705293","doi":"10.1504/ijsnet.2015.071632","title":"Localisation algorithms for wireless sensor networks: a review","year":2015,"lang":"en","type":"review","venue":"International Journal of Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Wireless sensor network; Computer science; Open research; Field (mathematics); Key distribution in wireless sensor networks; Data science; Wireless network; Wireless; Telecommunications; Computer network; World Wide Web","score_opus":0.04883604041074129,"score_gpt":0.3289144180394472,"score_spread":0.2800783776287059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2239705293","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00065452064,0.9825881,0.011307296,0.0004768194,0.00065319805,0.00003526585,0.000065367756,0.00010279919,0.0041166507],"genre_scores_gemma":[0.004396092,0.98341215,0.009294146,0.00023352169,0.0005870376,0.00003856764,0.00017514803,0.00002758277,0.0018357877],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933416,0.000098910146,0.00009441881,0.00013125628,0.0002968438,0.000044265445],"domain_scores_gemma":[0.9985077,0.0007665623,0.00012522978,0.00006802862,0.0004909666,0.00004149913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076742005,0.0013130646,0.001321734,0.0025332347,0.00042220793,0.0013722335,0.0018893677,0.0013512978,0.0041640173],"category_scores_gemma":[0.0025347495,0.0005577573,0.0007727642,0.005129257,0.0006719237,0.003218512,0.0009223713,0.0014381963,0.0038449885],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035691315,0.00006198693,0.00032357732,0.010071322,0.000045541565,0.00011653449,0.00009198386,0.0025993385,0.001477495,0.0061501437,0.022114731,0.9569116],"study_design_scores_gemma":[0.000012702272,0.0001786983,0.0011258711,0.005295418,0.00012972836,0.0018656048,0.0001969207,0.0032208683,0.0015869591,0.009324982,0.9769857,0.00007653352],"about_ca_topic_score_codex":0.0013764715,"about_ca_topic_score_gemma":0.0009932654,"teacher_disagreement_score":0.0041640173,"about_ca_system_score_codex":0.0005493079,"about_ca_system_score_gemma":0.0012063594,"threshold_uncertainty_score":0.013930023},"labels":[],"label_agreement":null},{"id":"W2240666255","doi":"10.1109/iisa.2015.7388079","title":"A practical comparison between filtering algorithms for enhanced RFID localization in smart environments","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Radio-frequency identification; Kalman filter; Wireless sensor network; Identification (biology); Wireless; Field (mathematics); Real-time computing; Particle filter; Tracking (education); Smart environment; Filter (signal processing); Algorithm; Embedded system; Artificial intelligence; Telecommunications; Computer network; Computer vision; Computer security; Internet of Things","score_opus":0.06928239686686329,"score_gpt":0.3172191426578895,"score_spread":0.2479367457910262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2240666255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028468814,0.0017949246,0.9647835,0.00016766244,0.00022166084,0.00006656804,0.000051200223,0.00076085585,0.0036847994],"genre_scores_gemma":[0.290132,0.0025783526,0.7030232,0.0001306727,0.00011310021,0.00012601654,0.0003151503,0.00018537478,0.0033961919],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980148,0.00055710244,0.0001729992,0.00028965034,0.00083038426,0.00013504206],"domain_scores_gemma":[0.99337655,0.0034639325,0.0002768082,0.0007421114,0.0020634169,0.00007712386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003018968,0.0007877504,0.0007560628,0.0014851297,0.000466685,0.0013669716,0.00081776635,0.0014801134,0.0020610765],"category_scores_gemma":[0.011373306,0.00025708706,0.00071562245,0.0017129569,0.00040197445,0.0019681202,0.00054978486,0.000533905,0.0010853718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009813844,0.00017217732,0.0039172783,0.0005880335,0.0001783187,0.00010414111,0.00021621882,0.11637442,0.01737992,0.011095003,0.0023422905,0.8466509],"study_design_scores_gemma":[0.00012601692,0.0010042079,0.00879982,0.00016574281,0.00021848152,0.00091505743,0.00031737637,0.92539155,0.0384783,0.0057314676,0.01871892,0.00013310622],"about_ca_topic_score_codex":0.0023314164,"about_ca_topic_score_gemma":0.0017584983,"teacher_disagreement_score":0.003018968,"about_ca_system_score_codex":0.00061662914,"about_ca_system_score_gemma":0.00069420825,"threshold_uncertainty_score":0.015966058},"labels":[],"label_agreement":null},{"id":"W2244101296","doi":"10.1109/lcomm.2015.2496940","title":"A Hybrid WiFi/Magnetic Matching/PDR Approach for Indoor Navigation With Smartphone Sensors","year":2015,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Dead reckoning; Real-time computing; Pedestrian; Matching (statistics); Mobile device; Computer vision; Dependency (UML); Embedded system; Artificial intelligence; Simulation; Global Positioning System; Telecommunications; Engineering; Mathematics","score_opus":0.026539597774921493,"score_gpt":0.23264777604058065,"score_spread":0.20610817826565916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2244101296","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030735098,0.00034728934,0.96603286,0.000037279697,0.00006325037,0.000050473736,0.00005056021,0.0014619253,0.0012213613],"genre_scores_gemma":[0.46745148,0.0002482588,0.5295368,0.000084080915,0.00005276684,0.00007162247,0.00017241527,0.0000628937,0.0023196442],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999514,0.00007633703,0.000030368426,0.00011500472,0.00020720185,0.00005713096],"domain_scores_gemma":[0.99971586,0.000044711924,0.000047425234,0.000057780304,0.00011771533,0.000016483427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038237026,0.0007636022,0.0006613107,0.00096963695,0.00026536814,0.00042001408,0.0009891842,0.00050939823,0.00073436496],"category_scores_gemma":[0.0009681678,0.00031871948,0.00046936402,0.00067944423,0.00015543212,0.0006394857,0.00068912376,0.0002763175,0.0006174316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032588976,0.000088488705,0.0050805877,0.00022956908,0.00010282486,0.00021506438,0.00012937705,0.021993931,0.089789465,0.0015842186,0.0012463927,0.8792143],"study_design_scores_gemma":[0.000107059524,0.0010142077,0.01268896,0.000060326925,0.00022304546,0.0027751522,0.0001628799,0.8545957,0.11009758,0.0021204017,0.016028311,0.00012636899],"about_ca_topic_score_codex":0.001930192,"about_ca_topic_score_gemma":0.002930721,"teacher_disagreement_score":0.001930192,"about_ca_system_score_codex":0.00020217897,"about_ca_system_score_gemma":0.00030671086,"threshold_uncertainty_score":0.0038378835},"labels":[],"label_agreement":null},{"id":"W2247616362","doi":"10.1007/978-3-642-12707-6_8","title":"Optimal Local Map Registration for Wireless Sensor Network Localization Problems","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Pairwise comparison; Affine transformation; Wireless sensor network; Global Map; Map projection; Set (abstract data type); Rotation (mathematics); Algorithm; Artificial intelligence; Mathematics; Computer network","score_opus":0.0060831588251815845,"score_gpt":0.1885450401238826,"score_spread":0.182461881298701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2247616362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028167558,0.00023606651,0.99578094,0.000072137314,0.000027323345,0.000013734574,0.00002323602,0.00016752908,0.0008622997],"genre_scores_gemma":[0.35726997,0.0015092989,0.62805176,0.00009677174,0.00028045793,0.00039274758,0.00050523033,0.000535157,0.011358623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991345,0.0003123139,0.000040737938,0.00020822554,0.00023430995,0.00007001894],"domain_scores_gemma":[0.9990709,0.0005434312,0.00008409978,0.00016256033,0.000107326814,0.000031657437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009530962,0.00086848985,0.0016360935,0.00094437297,0.00046229304,0.0010857111,0.0016794524,0.0010997568,0.002644095],"category_scores_gemma":[0.0043630823,0.000701303,0.0008614493,0.0022125356,0.0010778229,0.002262134,0.0026582186,0.0015709776,0.00095825666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023590683,0.00007298002,0.00027835375,0.00021502288,0.000057097783,0.000067627574,0.00010687341,0.7395179,0.0057127937,0.043692857,0.0058827843,0.20415981],"study_design_scores_gemma":[0.0000136811195,0.000039206618,0.00008725803,0.000009448524,0.000011005498,0.000033062548,0.000019805193,0.9649637,0.0012758259,0.031786703,0.0017506542,0.000009693923],"about_ca_topic_score_codex":0.0017833071,"about_ca_topic_score_gemma":0.0014141863,"teacher_disagreement_score":0.002644095,"about_ca_system_score_codex":0.0006781898,"about_ca_system_score_gemma":0.0007608221,"threshold_uncertainty_score":0.008845389},"labels":[],"label_agreement":null},{"id":"W2249105759","doi":"10.1109/iisa.2015.7388030","title":"Sensor placement for indoor multi-occupant tracking","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Health Services","keywords":"Computer science; Ambiguity; Trajectory; Tracking (education); Heuristic; Real-time computing; Motion sensors; Motion (physics); Space (punctuation); Location tracking; Computer vision; Artificial intelligence","score_opus":0.07280193923149833,"score_gpt":0.28168770742182897,"score_spread":0.20888576819033064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2249105759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024228409,0.00026487626,0.9733778,0.00010593449,0.000059827307,0.000053396237,0.0000718166,0.00042088647,0.0014170228],"genre_scores_gemma":[0.7289919,0.00042485294,0.26613623,0.00007816146,0.00006466509,0.00014509087,0.0001938805,0.000096691096,0.0038684483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991456,0.00035272233,0.000029180877,0.00021016625,0.00015578527,0.000106437416],"domain_scores_gemma":[0.9986833,0.0006858283,0.00019531665,0.00017498138,0.00014843795,0.00011203359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009990856,0.0014433512,0.0012174757,0.00087248016,0.0008597948,0.00092175015,0.0015709829,0.0016374096,0.0022138362],"category_scores_gemma":[0.0035541572,0.0007801474,0.00092740747,0.0012444592,0.000914754,0.0013209437,0.0016503778,0.0007089826,0.00058961945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066347675,0.00003308018,0.0007495494,0.00005208032,0.000020209847,0.00014108002,0.00008737347,0.9758992,0.002047322,0.0034179287,0.00043552226,0.017050248],"study_design_scores_gemma":[0.00000754409,0.00005186429,0.0002086542,0.0000049644464,0.0000071067384,0.000053358675,0.000041101106,0.99497956,0.0008409871,0.0032312637,0.0005639772,0.000009570388],"about_ca_topic_score_codex":0.0055195587,"about_ca_topic_score_gemma":0.005110914,"teacher_disagreement_score":0.0055195587,"about_ca_system_score_codex":0.0009354803,"about_ca_system_score_gemma":0.0008031949,"threshold_uncertainty_score":0.010974884},"labels":[],"label_agreement":null},{"id":"W2266091754","doi":"10.1049/el.2015.1724","title":"Smartphone‐based WiFi access point localisation and propagation parameter estimation using crowdsourcing","year":2015,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BP (Canada); University of Calgary","funders":"","keywords":"Upload; Crowdsourcing; Computer science; Real-time computing; Variance (accounting); Point (geometry); Estimation; Noise (video); Artificial intelligence; Engineering; Mathematics","score_opus":0.02544001231891788,"score_gpt":0.24636661440716276,"score_spread":0.22092660208824488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266091754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12455697,0.0003217585,0.85612935,0.00021500028,0.00016585247,0.00047524893,0.00053762726,0.009993875,0.007604295],"genre_scores_gemma":[0.8611323,0.00019003291,0.1319141,0.00007999307,0.00008829197,0.00024800948,0.0006539042,0.0001547186,0.0055385074],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994081,0.00008278969,0.000026233827,0.00014230036,0.00027004094,0.00007051779],"domain_scores_gemma":[0.99939144,0.0001281605,0.00006549032,0.00015497337,0.00019740975,0.00006251168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039184693,0.0008823214,0.0010424966,0.001178012,0.0005009756,0.0005625576,0.0015257617,0.00075015274,0.0025863403],"category_scores_gemma":[0.0014418351,0.00038744195,0.00051878317,0.00078402576,0.00034490137,0.0006191526,0.0016974735,0.00040382266,0.0017623102],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001147929,0.0003620023,0.009679423,0.0006648812,0.00022791767,0.0017197166,0.00095950614,0.10482286,0.1824797,0.0029346973,0.012030308,0.6829711],"study_design_scores_gemma":[0.00016149618,0.0003347651,0.009653679,0.00004888135,0.00007403479,0.0009988323,0.00026461674,0.9254598,0.04667107,0.003305726,0.012861853,0.00016527643],"about_ca_topic_score_codex":0.009223974,"about_ca_topic_score_gemma":0.0067034503,"teacher_disagreement_score":0.009223974,"about_ca_system_score_codex":0.00043213772,"about_ca_system_score_gemma":0.0005408925,"threshold_uncertainty_score":0.018340528},"labels":[],"label_agreement":null},{"id":"W2266339649","doi":"10.1145/2856636.2856654","title":"Experiences with using iBeacons for Indoor Positioning","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Beacon; Bluetooth Low Energy; Bluetooth; Computer science; Broadcasting (networking); Protocol (science); Embedded system; Real-time computing; Computer network; Wireless; Telecommunications","score_opus":0.011871567555472523,"score_gpt":0.2157088925860145,"score_spread":0.20383732503054197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266339649","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5743974,0.0068350197,0.21290973,0.0063509326,0.0013643464,0.000786608,0.0011014563,0.003785058,0.19246946],"genre_scores_gemma":[0.8073412,0.0052210735,0.10310065,0.0016676591,0.0003264444,0.00025529275,0.0014633111,0.0008915718,0.079732716],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9973279,0.0013703655,0.00008924195,0.0002491607,0.00062643766,0.00033681424],"domain_scores_gemma":[0.9967968,0.001148291,0.00007868941,0.00039789756,0.0009603159,0.0006179991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038273206,0.0010929764,0.00043848052,0.00071263814,0.0014998099,0.0020818443,0.0017768305,0.0016520693,0.01067805],"category_scores_gemma":[0.008858074,0.00042501322,0.0006062733,0.0010664973,0.0010478831,0.0026213164,0.002160443,0.001332277,0.0053452104],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013359978,0.003143763,0.022211913,0.0017030195,0.00018713564,0.0068668914,0.10691193,0.005685742,0.035978127,0.007755666,0.050426938,0.75779295],"study_design_scores_gemma":[0.00020596263,0.005613856,0.021506308,0.0009169749,0.00031690014,0.0108613325,0.049679928,0.010894493,0.03221131,0.0038966842,0.8634363,0.0004600206],"about_ca_topic_score_codex":0.0060957125,"about_ca_topic_score_gemma":0.011012003,"teacher_disagreement_score":0.01067805,"about_ca_system_score_codex":0.00060961605,"about_ca_system_score_gemma":0.0005922308,"threshold_uncertainty_score":0.03572166},"labels":[],"label_agreement":null},{"id":"W2281659009","doi":"","title":"Development and Evaluation of Models and Algorithms for Locating RWIS Stations","year":2015,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Computer science; Algorithm","score_opus":0.04003592159953784,"score_gpt":0.24990264643424437,"score_spread":0.20986672483470653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2281659009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024179488,0.000746813,0.96643007,0.0004131497,0.00010177439,0.0001502647,0.00030727754,0.0007168495,0.0069543947],"genre_scores_gemma":[0.5447633,0.0015918356,0.4435795,0.00020613041,0.00014635958,0.0007804932,0.0010550601,0.00022154249,0.0076557677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993499,0.00025378933,0.000038536007,0.00011709617,0.00015195525,0.00008870718],"domain_scores_gemma":[0.996014,0.0027462833,0.0002635654,0.00013317337,0.0007679057,0.00007513375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022045497,0.0011186874,0.0013293384,0.001154814,0.0006810419,0.0019281307,0.0024608348,0.00181495,0.0034870347],"category_scores_gemma":[0.006516112,0.00066903926,0.0012758492,0.0009683805,0.0005623059,0.0015171609,0.000975049,0.0016218825,0.00078968087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017598704,0.000022811932,0.00032395544,0.000031380772,0.0000117843765,0.0000094516545,0.000010002922,0.98656416,0.00012381165,0.0025486317,0.00033388098,0.010002545],"study_design_scores_gemma":[0.0000022297938,0.000008195568,0.000036281373,0.0000046471473,0.0000026478751,0.000002161454,0.0000055599135,0.9990544,0.00007549813,0.00061173085,0.00019496743,0.0000017288978],"about_ca_topic_score_codex":0.030140415,"about_ca_topic_score_gemma":0.016959477,"teacher_disagreement_score":0.030140415,"about_ca_system_score_codex":0.0023829092,"about_ca_system_score_gemma":0.0033842304,"threshold_uncertainty_score":0.059929907},"labels":[],"label_agreement":null},{"id":"W2288520491","doi":"10.1109/msn.2015.14","title":"RSSI-Based Bluetooth Indoor Localization","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Bluetooth; Bluetooth Low Energy; Computer science; Global Positioning System; Real-time computing; Indoor positioning system; Non-line-of-sight propagation; Hybrid positioning system; Object (grammar); Embedded system; Wireless; Telecommunications; Positioning system; Artificial intelligence; Engineering","score_opus":0.020199193022075408,"score_gpt":0.21113831994965637,"score_spread":0.19093912692758097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288520491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014723554,0.0017740894,0.9527108,0.00015919335,0.00024096397,0.00015580609,0.0008409546,0.009370564,0.02002407],"genre_scores_gemma":[0.7124893,0.0037670212,0.24681704,0.00037570452,0.00027563362,0.00052507233,0.0043409364,0.00047374165,0.030935563],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908745,0.00018254231,0.000050434748,0.00017977037,0.00041077507,0.00008905893],"domain_scores_gemma":[0.99936455,0.00010101595,0.000093652496,0.00013331613,0.0002812045,0.000026319889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032054418,0.0009335921,0.00074408564,0.0014975129,0.00037378617,0.0007713344,0.0012424896,0.00054362946,0.0033634077],"category_scores_gemma":[0.0012865835,0.00029360648,0.0004540343,0.0016611732,0.00030804865,0.00079965603,0.00096567447,0.00046582965,0.0043300954],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007362924,0.000173419,0.007371594,0.0014330632,0.00019163213,0.00075852795,0.00032761562,0.055827573,0.078351766,0.011014482,0.030658694,0.81315535],"study_design_scores_gemma":[0.00024165997,0.0010764218,0.01729882,0.0003890582,0.00036376005,0.0059312047,0.0003621037,0.5722188,0.17609587,0.007907462,0.21768288,0.00043193434],"about_ca_topic_score_codex":0.0012270403,"about_ca_topic_score_gemma":0.0014379224,"teacher_disagreement_score":0.0033634077,"about_ca_system_score_codex":0.0002571096,"about_ca_system_score_gemma":0.00031853936,"threshold_uncertainty_score":0.011251688},"labels":[],"label_agreement":null},{"id":"W2291315001","doi":"10.1109/glocom.2015.7417730","title":"Automatic Device-Transparent RSS-Based Indoor Localization","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Computer science; Set (abstract data type); k-nearest neighbors algorithm; Signal strength; Point (geometry); Transformation (genetics); Data mining; Mobile device; Artificial intelligence; Algorithm; Wireless; Mathematics","score_opus":0.10578393134962144,"score_gpt":0.32774544238991604,"score_spread":0.2219615110402946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291315001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033455502,0.0002794106,0.95389265,0.00007884228,0.00006803575,0.000058486377,0.00016120693,0.009173499,0.0028324218],"genre_scores_gemma":[0.7831094,0.00021760442,0.21069135,0.00013796064,0.00009411697,0.000099046425,0.00055986585,0.00025723534,0.0048333537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890614,0.00020384988,0.000050125167,0.0002176163,0.0005469579,0.00007534732],"domain_scores_gemma":[0.9988869,0.00017445814,0.00017938427,0.00044672957,0.00028423383,0.00002837075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005083222,0.0007152556,0.00078146585,0.001026723,0.000374702,0.0008336993,0.0016108389,0.00063611206,0.0016812835],"category_scores_gemma":[0.0016917201,0.00036017862,0.00036551978,0.001147053,0.0003493603,0.0010820337,0.0013230054,0.00057683163,0.0028870213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000628185,0.0001579711,0.0069393963,0.000363748,0.0001284688,0.0004609122,0.00039056034,0.033781733,0.17559698,0.003874953,0.007007212,0.77066994],"study_design_scores_gemma":[0.00010686614,0.0005956029,0.016393637,0.00007059299,0.00014228321,0.0027433604,0.00018042375,0.70251703,0.2456773,0.004959993,0.026420848,0.0001920612],"about_ca_topic_score_codex":0.0007218269,"about_ca_topic_score_gemma":0.0010496766,"teacher_disagreement_score":0.0016812835,"about_ca_system_score_codex":0.00022279458,"about_ca_system_score_gemma":0.00029836412,"threshold_uncertainty_score":0.005624473},"labels":[],"label_agreement":null},{"id":"W2291398142","doi":"10.1109/globalsip.2015.7418216","title":"RSS difference-aware graph-based semi-supervised learning (RG-SSL) RSS smoothing method for crowdsourcing indoor localization","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Crowdsourcing; Computer science; Signal strength; Smoothing; Graph; Exploit; Workload; Artificial intelligence; Data mining; Machine learning; Computer vision; Antenna (radio); Theoretical computer science","score_opus":0.025407534244045887,"score_gpt":0.25758638761705965,"score_spread":0.23217885337301378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291398142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017222006,0.00012113911,0.979931,0.00010756945,0.000043937453,0.0000506173,0.00006383747,0.0016744267,0.00078559446],"genre_scores_gemma":[0.7560628,0.00017020239,0.23889384,0.00026014342,0.00011138388,0.0002115742,0.0005018113,0.0002850465,0.0035031522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892575,0.00029436508,0.000053525568,0.00030888908,0.00033329733,0.000084215084],"domain_scores_gemma":[0.99786574,0.000801658,0.00027951368,0.00037467215,0.0005709741,0.00010745282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010024379,0.00096828764,0.0013401335,0.0010343702,0.00060286553,0.00063773134,0.002766722,0.0009812579,0.0012751105],"category_scores_gemma":[0.003689591,0.00042094584,0.0009142407,0.0009885408,0.0009310751,0.0011137982,0.0015150107,0.0010151167,0.0007071893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041527307,0.0002692358,0.0024978623,0.00031235968,0.00014066532,0.0002294861,0.0003623541,0.5316515,0.01689093,0.0047295797,0.0052068885,0.4372938],"study_design_scores_gemma":[0.0000113536935,0.000036395733,0.0003329653,0.0000050356007,0.000009642979,0.000036545673,0.000019202409,0.9946337,0.002375699,0.0019330955,0.0005928374,0.000013553957],"about_ca_topic_score_codex":0.005451118,"about_ca_topic_score_gemma":0.0063007926,"teacher_disagreement_score":0.005451118,"about_ca_system_score_codex":0.00069382065,"about_ca_system_score_gemma":0.0011467115,"threshold_uncertainty_score":0.010838747},"labels":[],"label_agreement":null},{"id":"W2291400366","doi":"10.1109/pccc.2015.7410310","title":"Optimum reference node deployment for indoor localization based on the average Mean Square Error minimization","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Node (physics); Global Positioning System; Computer science; Minification; Mean squared error; Square (algebra); Scheme (mathematics); Process (computing); Wireless; Software deployment; Real-time computing; Topology (electrical circuits); Algorithm; Computer network; Mathematics; Telecommunications; Statistics; Engineering","score_opus":0.04580962400892296,"score_gpt":0.25147065186407874,"score_spread":0.20566102785515578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291400366","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007611358,0.00025065316,0.99108374,0.00004176477,0.000016941216,0.00001239596,0.000011573615,0.00014113165,0.0008303786],"genre_scores_gemma":[0.57890964,0.00068376045,0.41765052,0.00004742985,0.000042503318,0.00009520041,0.00009854685,0.00006178843,0.002410627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995809,0.00014863416,0.000017139604,0.00009334869,0.00012275287,0.000037224436],"domain_scores_gemma":[0.99968386,0.00011687419,0.0000537683,0.000040181327,0.000092067065,0.000013282431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004718949,0.0006227749,0.0007395814,0.00047030876,0.0003260784,0.00039577266,0.0008397932,0.0005760734,0.0007287011],"category_scores_gemma":[0.0014007424,0.00022720253,0.00032216855,0.00072776875,0.0003727281,0.0006429486,0.0006425722,0.00032478463,0.00033873666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019057041,0.000040905063,0.0011155332,0.00019751981,0.000043649983,0.00021647035,0.0001906444,0.7802691,0.03915352,0.01847307,0.0028703832,0.15723872],"study_design_scores_gemma":[0.000010225382,0.000110505134,0.00035491207,0.0000092570435,0.000013844101,0.00012945049,0.00002873468,0.99028885,0.0055688736,0.0018718354,0.0015997218,0.000013712465],"about_ca_topic_score_codex":0.0014461983,"about_ca_topic_score_gemma":0.002038434,"teacher_disagreement_score":0.0014461983,"about_ca_system_score_codex":0.00039685503,"about_ca_system_score_gemma":0.00048568033,"threshold_uncertainty_score":0.0028793216},"labels":[],"label_agreement":null},{"id":"W2291859485","doi":"10.1016/j.neucom.2016.02.055","title":"Deep Neural Networks for wireless localization in indoor and outdoor environments","year":2016,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":269,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless; Autoencoder; Artificial intelligence; Hidden Markov model; Artificial neural network; Deep learning; Wireless network; Coherence (philosophical gambling strategy); Deep neural networks; Pattern recognition (psychology); Real-time computing; Telecommunications; Mathematics","score_opus":0.0070570809485196164,"score_gpt":0.1964330567160516,"score_spread":0.18937597576753198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291859485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03684388,0.002732481,0.9547918,0.00065516785,0.00017641441,0.000015536501,0.00033239342,0.00093978946,0.003512586],"genre_scores_gemma":[0.87281543,0.0019941262,0.11158857,0.00027618703,0.00016464018,0.000049055376,0.00069406454,0.000086857544,0.012331145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998648,0.000028102815,0.0000073619826,0.000038194834,0.000028583643,0.00003285292],"domain_scores_gemma":[0.9997373,0.00010316434,0.00003322133,0.000030810592,0.000082575396,0.000012869625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028052132,0.0006799456,0.00046210334,0.00038354617,0.00025692314,0.00062123,0.00069585023,0.00088013726,0.0015376667],"category_scores_gemma":[0.0012053043,0.00027906962,0.00032330392,0.00074899127,0.00030402493,0.0010213894,0.0007428635,0.0009908623,0.00042419785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115465366,0.00007928813,0.0016027944,0.00012583818,0.00007492659,0.0000749817,0.00005433711,0.61021596,0.005557738,0.0119347945,0.005986313,0.36417753],"study_design_scores_gemma":[0.0000020037273,0.0000087460485,0.00030257806,0.000007890642,0.000008194632,0.000009050791,0.0000106112775,0.99385744,0.0008996379,0.0042508673,0.0006389617,0.0000040195137],"about_ca_topic_score_codex":0.013703359,"about_ca_topic_score_gemma":0.017462265,"teacher_disagreement_score":0.013703359,"about_ca_system_score_codex":0.00073761307,"about_ca_system_score_gemma":0.00051193597,"threshold_uncertainty_score":0.02724719},"labels":[],"label_agreement":null},{"id":"W2294136539","doi":"","title":"Towards improved performance and compliance in healthcare using wearables and bluetooth technologies","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Wearable computer; Bluetooth; Smartwatch; Computer science; Wearable technology; Health care; Domain (mathematical analysis); Corporate governance; Compliance (psychology); Human–computer interaction; Data science; Embedded system; Risk analysis (engineering); Wireless; Business; Telecommunications","score_opus":0.02597620681379306,"score_gpt":0.23070539263030063,"score_spread":0.20472918581650756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294136539","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25602925,0.0037865597,0.7027784,0.009771255,0.00035338165,0.00024361891,0.00035122997,0.0029125502,0.023773804],"genre_scores_gemma":[0.8811857,0.0014786453,0.11416719,0.0006524313,0.00022179552,0.00011565901,0.00021355758,0.00014335854,0.0018217021],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9935114,0.003150256,0.0004901005,0.0009302268,0.0015667039,0.00035143524],"domain_scores_gemma":[0.9894952,0.003856283,0.0023290887,0.0019287143,0.0020308183,0.00035982905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005759698,0.0009175871,0.0007970751,0.0012206614,0.00074396946,0.0038800086,0.0010281139,0.0014169951,0.0012304286],"category_scores_gemma":[0.014223028,0.00035435663,0.00037305284,0.0018307458,0.0011424773,0.0040911715,0.003140275,0.0014469809,0.00089168217],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003986929,0.000811235,0.08337205,0.0008988519,0.00018443559,0.00028214327,0.0034623735,0.03614788,0.042557795,0.03061883,0.007775207,0.79349065],"study_design_scores_gemma":[0.0001876513,0.005818821,0.23220268,0.0024435362,0.00053545536,0.0020159578,0.012813987,0.3524706,0.14280705,0.13635734,0.111671016,0.0006759662],"about_ca_topic_score_codex":0.00093499286,"about_ca_topic_score_gemma":0.0012255417,"teacher_disagreement_score":0.005759698,"about_ca_system_score_codex":0.0006636695,"about_ca_system_score_gemma":0.001038586,"threshold_uncertainty_score":0.030460596},"labels":[],"label_agreement":null},{"id":"W2294498689","doi":"10.1109/embc.2015.7319058","title":"A magnetometer-free indoor human localization based on loosely coupled IMU/UWB fusion","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Gyroscope; Magnetometer; Inertial measurement unit; Accelerometer; Kalman filter; Computer science; Compass; Extended Kalman filter; Computer vision; Artificial intelligence; Engineering; Physics; Magnetic field; Aerospace engineering","score_opus":0.01952616695693611,"score_gpt":0.2300406335951685,"score_spread":0.21051446663823237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294498689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025502428,0.0002300778,0.97180474,0.00006244898,0.00007720183,0.000022172466,0.000036015386,0.0011722698,0.0010925005],"genre_scores_gemma":[0.7357073,0.0002650758,0.25974646,0.00017109847,0.000082850405,0.00009415224,0.00023507673,0.00006100797,0.0036370826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995827,0.000079459394,0.000023468829,0.00014327948,0.00012312712,0.00004798154],"domain_scores_gemma":[0.9997936,0.000027203714,0.000039082708,0.000046445515,0.0000742193,0.000019418809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030699171,0.0006865858,0.0008288145,0.00047926232,0.00037958333,0.0005455995,0.000936529,0.0006508091,0.0007948887],"category_scores_gemma":[0.0006954335,0.00031305634,0.00043045572,0.0005745309,0.0002661534,0.00089854485,0.0011530718,0.00047318247,0.0007819825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062587275,0.00017036528,0.0064872145,0.00030826346,0.00021691834,0.0004311448,0.00043414693,0.08735481,0.16381547,0.004724358,0.004047383,0.731384],"study_design_scores_gemma":[0.00006791282,0.0004549364,0.0055405796,0.00003922584,0.00012263437,0.0008125757,0.00011230051,0.90817124,0.07555586,0.002369864,0.006663568,0.00008928812],"about_ca_topic_score_codex":0.0016366778,"about_ca_topic_score_gemma":0.0017642227,"teacher_disagreement_score":0.0016366778,"about_ca_system_score_codex":0.00022726471,"about_ca_system_score_gemma":0.00044507397,"threshold_uncertainty_score":0.0032542944},"labels":[],"label_agreement":null},{"id":"W2299515439","doi":"10.1016/j.comcom.2016.03.001","title":"Improved particle filter based on WLAN RSSI fingerprinting and smart sensors for indoor localization","year":2016,"lang":"en","type":"article","venue":"Computer Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"CMC Microsystems","keywords":"Computer science; Initialization; Particle filter; Fingerprint (computing); Fingerprint recognition; Wireless sensor network; Weighting; Convergence (economics); Filter (signal processing); Algorithm; Process (computing); Real-time computing; Artificial intelligence; Kalman filter; Computer vision; Computer network; Acoustics","score_opus":0.020798011534609067,"score_gpt":0.23233789166138985,"score_spread":0.2115398801267808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2299515439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068602026,0.00040786288,0.99093026,0.0000662286,0.00015883475,0.000014237768,0.00003099298,0.0004679664,0.0010635067],"genre_scores_gemma":[0.4541835,0.0015792424,0.5356465,0.00020781926,0.000254047,0.000117127995,0.0003635791,0.00009031179,0.0075579328],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995437,0.00007705628,0.00002136248,0.00010522204,0.00021521705,0.000037436548],"domain_scores_gemma":[0.9996722,0.000081569066,0.00003156847,0.00006282596,0.00013991688,0.000011971554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036004646,0.0006705626,0.00090443814,0.0005231313,0.00028598652,0.0005539276,0.0007135542,0.0007871284,0.0012568209],"category_scores_gemma":[0.00087348704,0.0003304103,0.0006345862,0.0009301149,0.00021620387,0.0010021377,0.00042819433,0.000690733,0.0008589579],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004763286,0.00018899236,0.004879326,0.0003509592,0.00024533516,0.00023486256,0.00017314128,0.16581433,0.09073051,0.010399901,0.0066124015,0.719894],"study_design_scores_gemma":[0.000031244566,0.00011240565,0.00261263,0.000014148752,0.00008060481,0.000240642,0.00002242663,0.9753102,0.015231628,0.0012182816,0.0050923317,0.000033346885],"about_ca_topic_score_codex":0.0036531342,"about_ca_topic_score_gemma":0.003824689,"teacher_disagreement_score":0.0036531342,"about_ca_system_score_codex":0.00032782674,"about_ca_system_score_gemma":0.0005436598,"threshold_uncertainty_score":0.0072637796},"labels":[],"label_agreement":null},{"id":"W2299764504","doi":"10.22060/eej.2014.441","title":"Indoor Positioning and Pre-processing of RSS Measurements","year":2014,"lang":"en","type":"article","venue":"AUT Journal of Electrical Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Computer science; Reliability (semiconductor); Scheme (mathematics); Artificial intelligence; Data mining; Algorithm; Mathematics","score_opus":0.005969988414160487,"score_gpt":0.19316895207914936,"score_spread":0.18719896366498887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2299764504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027488505,0.00014109896,0.9638692,0.00007002258,0.00011319102,0.000095354306,0.00027555815,0.0047126487,0.0032344216],"genre_scores_gemma":[0.51753,0.00045553298,0.4695618,0.00011751835,0.0001417508,0.00026559757,0.0018538401,0.00031177577,0.009762256],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993357,0.000075530115,0.000035122823,0.00015181708,0.00033644482,0.00006540577],"domain_scores_gemma":[0.9995603,0.00007760254,0.000052226438,0.00014211885,0.0001523653,0.000015392032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023247371,0.0008727699,0.00079809775,0.0007849258,0.00033378118,0.0005236581,0.00096013426,0.00057494367,0.00390671],"category_scores_gemma":[0.001190985,0.00041001238,0.00054439355,0.0011116872,0.0003204956,0.0006421972,0.0007567111,0.00072710874,0.0047016917],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035008948,0.00015437939,0.003508604,0.00043674448,0.000050633556,0.0004072923,0.00022722814,0.055268284,0.17966007,0.002588956,0.004826149,0.7525216],"study_design_scores_gemma":[0.000046874447,0.0007209012,0.026903411,0.000060599847,0.000075714575,0.001532623,0.00026022008,0.63104635,0.30178115,0.003657758,0.033815008,0.00009933243],"about_ca_topic_score_codex":0.0024582283,"about_ca_topic_score_gemma":0.0033920924,"teacher_disagreement_score":0.00390671,"about_ca_system_score_codex":0.00023149519,"about_ca_system_score_gemma":0.0006596162,"threshold_uncertainty_score":0.013069212},"labels":[],"label_agreement":null},{"id":"W2304599448","doi":"10.1109/icnp.2015.18","title":"EMoD: Efficient Motion Detection of Device-Free Objects Using Passive RFID Tags","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Computer vision; Motion (physics); Artificial intelligence","score_opus":0.021653765016072465,"score_gpt":0.22281355233394512,"score_spread":0.20115978731787265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2304599448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04951466,0.00043790892,0.94346225,0.0000996523,0.00010220791,0.000046291607,0.00010685444,0.003910346,0.0023198707],"genre_scores_gemma":[0.6887671,0.00042342462,0.30375606,0.00047151974,0.00005264938,0.0000992551,0.0003574924,0.00013554175,0.0059369737],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997002,0.000040122628,0.00002003731,0.000085363674,0.00011947374,0.000034823526],"domain_scores_gemma":[0.99967,0.000095246076,0.00007740011,0.00007464772,0.00006585082,0.000016833012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026841502,0.00051371305,0.00041279732,0.00062616466,0.00017810716,0.0004996001,0.0009892139,0.0005937362,0.0006808421],"category_scores_gemma":[0.0008929546,0.00028392125,0.00027125573,0.0003403896,0.00028275023,0.00090083026,0.0008990146,0.00033679348,0.0006489494],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006001515,0.00018632118,0.012075917,0.00041911035,0.00009830392,0.0007635508,0.00036278003,0.024959605,0.38615087,0.0052755144,0.007047156,0.56206083],"study_design_scores_gemma":[0.000093883704,0.00057356065,0.010043317,0.00006227061,0.000095406635,0.002017231,0.000106930485,0.62067556,0.33289158,0.002745464,0.030572621,0.00012219592],"about_ca_topic_score_codex":0.00038173195,"about_ca_topic_score_gemma":0.0005157059,"teacher_disagreement_score":0.0009892139,"about_ca_system_score_codex":0.00025002842,"about_ca_system_score_gemma":0.0001593641,"threshold_uncertainty_score":0.0022776127},"labels":[],"label_agreement":null},{"id":"W2316018670","doi":"10.2495/sdp-v11-n1-65-78","title":"Rail detection using lidar sensors","year":2016,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lidar; Computer science; Matching (statistics); Position (finance); Remote sensing; Track (disk drive); Satellite; Global Positioning System; Real-time computing; Computer vision; Artificial intelligence; Engineering; Telecommunications; Geography; Aerospace engineering","score_opus":0.011455622931648249,"score_gpt":0.2280897340375462,"score_spread":0.21663411110589795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316018670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11827543,0.0045224503,0.84735817,0.0004750424,0.00031323754,0.00012169485,0.00053837243,0.0026586151,0.025736941],"genre_scores_gemma":[0.7999538,0.0027726546,0.18819149,0.0003613779,0.00011005103,0.00008269541,0.00055349513,0.00007977245,0.007894654],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993525,0.00010630424,0.000024854811,0.00014542237,0.00030954048,0.00006138421],"domain_scores_gemma":[0.99957603,0.00009342911,0.00006364291,0.000040220508,0.00021089238,0.000015766027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003547051,0.00043794265,0.00039901162,0.0010649352,0.00027785872,0.0009006307,0.0009657338,0.0008418787,0.0016329227],"category_scores_gemma":[0.0010675214,0.00032982335,0.00037102625,0.00088868005,0.00020079843,0.0015960322,0.00094064046,0.00037612853,0.0012945666],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033164682,0.000113924376,0.014560721,0.001207595,0.00011943374,0.00049063185,0.0004689368,0.019157501,0.33998784,0.008781232,0.0051970594,0.60958344],"study_design_scores_gemma":[0.00007872429,0.001121805,0.02026726,0.000721986,0.00031086986,0.0029958126,0.0015485317,0.38535327,0.4721515,0.008956246,0.10620971,0.00028430173],"about_ca_topic_score_codex":0.0012122552,"about_ca_topic_score_gemma":0.0018457191,"teacher_disagreement_score":0.0016329227,"about_ca_system_score_codex":0.00034870263,"about_ca_system_score_gemma":0.00030402027,"threshold_uncertainty_score":0.005462706},"labels":[],"label_agreement":null},{"id":"W2317179323","doi":"10.1109/tvt.2016.2537403","title":"Mobile Sensors Deployment Subject to Location Estimation Error","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Hydro-Québec; Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Voronoi diagram; Computer science; Software deployment; Wireless sensor network; Global Positioning System; Node (physics); Real-time computing; Exploit; Set (abstract data type); Algorithm; Computer network; Engineering; Mathematics; Telecommunications","score_opus":0.008143694696633444,"score_gpt":0.22851980674419517,"score_spread":0.22037611204756172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317179323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07680413,0.0008937976,0.9206731,0.0001501932,0.00004300736,0.0000331699,0.00006379079,0.00014299712,0.0011958936],"genre_scores_gemma":[0.94484013,0.00048201843,0.053695217,0.00002401864,0.00002547364,0.000047453384,0.000077663215,0.000024326459,0.00078370934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982298,0.00072077144,0.000106828775,0.00022826158,0.0005120134,0.00020222334],"domain_scores_gemma":[0.99015325,0.0072673056,0.0008920016,0.0006631547,0.00086708425,0.000157256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018037544,0.0005852523,0.0009354192,0.0007942674,0.00048812694,0.00097609556,0.0009922633,0.00083445036,0.00045732313],"category_scores_gemma":[0.01931495,0.0004380506,0.00044674228,0.0010231863,0.00080994994,0.0013298912,0.001553653,0.00041996283,0.00013709869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001378611,0.000013755871,0.0024056225,0.00009134615,0.00003183346,0.0002038075,0.00010630226,0.95254225,0.0025309345,0.021170586,0.00036433828,0.0204014],"study_design_scores_gemma":[0.00001041794,0.000028953878,0.00029738594,0.000007661244,0.0000062874706,0.00012559086,0.000020695483,0.9920135,0.0013607572,0.0055089206,0.0006135472,0.0000062591603],"about_ca_topic_score_codex":0.002973101,"about_ca_topic_score_gemma":0.0015834061,"teacher_disagreement_score":0.002973101,"about_ca_system_score_codex":0.00087955344,"about_ca_system_score_gemma":0.0005850532,"threshold_uncertainty_score":0.009539306},"labels":[],"label_agreement":null},{"id":"W2320527016","doi":"10.1109/jproc.2016.2529600","title":"Overview of Spatial Processing Approaches for GNSS Structural Interference Detection and Mitigation","year":2016,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Spoofing attack; GNSS applications; Computer science; Interference (communication); Real-time computing; Signal processing; Authentication (law); Synchronization (alternating current); Global Positioning System; Computer security; Telecommunications; Channel (broadcasting)","score_opus":0.03246899053827516,"score_gpt":0.2290784168754301,"score_spread":0.19660942633715495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320527016","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022321222,0.02018597,0.9670951,0.0002032977,0.0001698697,0.00009270414,0.00014487993,0.00061657943,0.009259534],"genre_scores_gemma":[0.057583254,0.07616657,0.85029006,0.00036217502,0.00072979415,0.00026282476,0.0009433458,0.00018633422,0.013475601],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924564,0.00011528808,0.00007405917,0.00012454516,0.00039694036,0.00004360362],"domain_scores_gemma":[0.9991999,0.00025360807,0.00008329413,0.00011021869,0.0003364952,0.000016457334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076897704,0.00117164,0.0005595029,0.0024118642,0.00041421305,0.0014698859,0.0010607344,0.0011665841,0.004389964],"category_scores_gemma":[0.001070544,0.0005497597,0.00083454535,0.0025446052,0.0004723839,0.00144891,0.0007029092,0.00089282444,0.0030927528],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011810498,0.00007764979,0.0010990155,0.0015712723,0.00012581541,0.0001815121,0.00012163018,0.020678757,0.04057851,0.024192508,0.0055143405,0.9057411],"study_design_scores_gemma":[0.000051543975,0.0009114779,0.0060016005,0.0011521628,0.00044222354,0.0035653622,0.00034177146,0.31958172,0.12138745,0.037731297,0.5085591,0.00027428527],"about_ca_topic_score_codex":0.0012822266,"about_ca_topic_score_gemma":0.00118286,"teacher_disagreement_score":0.004389964,"about_ca_system_score_codex":0.00044309383,"about_ca_system_score_gemma":0.00058287446,"threshold_uncertainty_score":0.014685929},"labels":[],"label_agreement":null},{"id":"W2322388554","doi":"10.1515/jag-2012-0037","title":"An efficient and robust maneuvering mode to calibrate low cost magnetometer for improved heading estimation for pedestrian navigation","year":2013,"lang":"en","type":"article","venue":"Journal of Applied Geodesy","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"","keywords":"Heading (navigation); Global Positioning System; Dead reckoning; Inertial navigation system; Computer science; Inertial measurement unit; Magnetometer; Mode (computer interface); Wind triangle; Calibration; Pedestrian; Computer vision; Process (computing); Real-time computing; Artificial intelligence; Engineering; Orientation (vector space); Aerospace engineering; Magnetic field; Mathematics","score_opus":0.009047124023460817,"score_gpt":0.23722856750594923,"score_spread":0.22818144348248842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322388554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1494569,0.0002611554,0.8457689,0.000103661034,0.00012215397,0.000072074494,0.00011087287,0.0016912522,0.0024129192],"genre_scores_gemma":[0.80264544,0.00012430688,0.19527604,0.0000541753,0.00002966722,0.00005808399,0.00019380578,0.00006482315,0.0015537273],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997193,0.00007406701,0.000013509124,0.000060430975,0.000108868124,0.000023779243],"domain_scores_gemma":[0.9995024,0.00006524772,0.00007491982,0.00012424671,0.00021375649,0.000019350398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034061164,0.00065703125,0.00037193834,0.00051303924,0.00021201592,0.00026346353,0.0004631734,0.0004213951,0.0009916357],"category_scores_gemma":[0.0009992889,0.00016962396,0.00023865479,0.0004962608,0.00013691736,0.00030812592,0.0003547663,0.00028931448,0.00062361534],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090945174,0.00012159287,0.018185357,0.00031822093,0.00008920629,0.0003338537,0.000490535,0.02268638,0.41530746,0.0019395939,0.0045132944,0.53510493],"study_design_scores_gemma":[0.00015038425,0.0022375982,0.051449075,0.00009617242,0.00023616948,0.002591532,0.0003679546,0.4301151,0.4765124,0.0010977085,0.034956228,0.00018969794],"about_ca_topic_score_codex":0.0008729816,"about_ca_topic_score_gemma":0.001235432,"teacher_disagreement_score":0.0009916357,"about_ca_system_score_codex":0.00014728465,"about_ca_system_score_gemma":0.00020926025,"threshold_uncertainty_score":0.003317356},"labels":[],"label_agreement":null},{"id":"W2331182310","doi":"10.1007/s10291-016-0531-3","title":"Using multiple portable/wearable devices for enhanced misalignment estimation in portable navigation","year":2016,"lang":"en","type":"article","venue":"GPS Solutions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Wearable computer; Computer science; Wearable technology; Embedded system; Real-time computing; Simulation; Engineering","score_opus":0.03322612386732999,"score_gpt":0.266265674273739,"score_spread":0.233039550406409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331182310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13535285,0.0012362964,0.85409635,0.00017494925,0.0003880856,0.000068326844,0.00022609952,0.001756468,0.006700548],"genre_scores_gemma":[0.70711625,0.0010842737,0.28378692,0.00017332699,0.00015103439,0.00006190269,0.00029505236,0.00012198089,0.007209132],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996555,0.00006773934,0.000017806808,0.000096888434,0.00012322348,0.000038903552],"domain_scores_gemma":[0.9997303,0.00006347596,0.000045669036,0.000050368297,0.000093653354,0.000016525377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021758466,0.0011448931,0.0005568834,0.0006421874,0.00026948994,0.00064945366,0.0004886674,0.00073973474,0.00273359],"category_scores_gemma":[0.0008239402,0.0003104983,0.00037488405,0.0010400722,0.0001486018,0.00091191614,0.0006378629,0.0003623741,0.0011956004],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059303455,0.00012346434,0.0074022505,0.00047135982,0.00013736819,0.00056023215,0.00020016405,0.010730397,0.26833937,0.0012266061,0.0022283527,0.7079875],"study_design_scores_gemma":[0.00014280545,0.0017069185,0.05065135,0.00042649425,0.0006619322,0.0053107743,0.00054137764,0.39964887,0.5008872,0.0030618296,0.03674348,0.00021685765],"about_ca_topic_score_codex":0.0011591739,"about_ca_topic_score_gemma":0.0027303162,"teacher_disagreement_score":0.00273359,"about_ca_system_score_codex":0.00015534027,"about_ca_system_score_gemma":0.0002513346,"threshold_uncertainty_score":0.009144783},"labels":[],"label_agreement":null},{"id":"W2333140461","doi":"10.1109/jsen.2016.2535386","title":"Touchscreen Surface Based on Interaction of Ultrasonic Guided Waves With a Contact Impedance","year":2016,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Canadian Arthritis Network; Natural Sciences and Engineering Research Council of Canada","keywords":"Ultrasonic sensor; Acoustics; Touchscreen; Electrical impedance; Materials science; Surface wave; Computer science; Optics; Electrical engineering; Engineering; Physics; Human–computer interaction","score_opus":0.013287226960164591,"score_gpt":0.23374303630873797,"score_spread":0.22045580934857337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333140461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26981413,0.002806869,0.7148622,0.0003174059,0.00038774507,0.00016006846,0.00015376817,0.0028956472,0.008602147],"genre_scores_gemma":[0.88366425,0.00083273946,0.10832399,0.00023771354,0.00007098023,0.00009644008,0.00010038276,0.00010752667,0.006565942],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960166,0.00005345197,0.000016760963,0.00009226587,0.00020244198,0.000033415785],"domain_scores_gemma":[0.9994924,0.000223525,0.00006372613,0.0000865571,0.00009291793,0.0000407918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016079369,0.0005255781,0.000656594,0.00037278247,0.00017691357,0.0007982205,0.0009823893,0.00092902454,0.0021201477],"category_scores_gemma":[0.0006913715,0.00025852057,0.0003714568,0.00027255347,0.0004743487,0.0009380646,0.0008376114,0.00033136294,0.0008537776],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021533751,0.00005020678,0.0007368579,0.00034607179,0.000028322362,0.00070334977,0.00020305319,0.00088364276,0.9213699,0.001898111,0.00079941354,0.072765574],"study_design_scores_gemma":[0.000057580393,0.001836663,0.0065050772,0.00008792446,0.00014144008,0.004143889,0.000207713,0.04778781,0.91365635,0.0015964556,0.023841064,0.00013797086],"about_ca_topic_score_codex":0.000117983865,"about_ca_topic_score_gemma":0.00012003604,"teacher_disagreement_score":0.0021201477,"about_ca_system_score_codex":0.00016287519,"about_ca_system_score_gemma":0.00009981432,"threshold_uncertainty_score":0.007092595},"labels":[],"label_agreement":null},{"id":"W2336236622","doi":"10.1109/iccve.2015.4","title":"Analyzing accuracy of GPS data for vehicular parameters","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Global Positioning System; Heading (navigation); Computer science; Acceleration; Real-time computing; Collision; Telecommunications; Engineering; Computer security; Aerospace engineering","score_opus":0.07052644526068806,"score_gpt":0.2866941189709986,"score_spread":0.21616767371031054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336236622","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9136018,0.00043447342,0.08173866,0.00008352978,0.0000700623,0.0000407393,0.0010347917,0.00091488985,0.0020810608],"genre_scores_gemma":[0.98862576,0.00011448172,0.010104712,0.000010066848,0.000009745108,0.000014706847,0.00070943264,0.000035801204,0.00037539096],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985097,0.0002932634,0.00011941662,0.00025101425,0.00066638534,0.00016023385],"domain_scores_gemma":[0.9961696,0.0011911046,0.00034958965,0.0007064657,0.0015220068,0.000061191866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085332536,0.0004914726,0.00039451747,0.0015486903,0.00025069894,0.00053697685,0.0004737281,0.00046108165,0.000692355],"category_scores_gemma":[0.0066387984,0.0001739945,0.0002359947,0.0016590095,0.0002037768,0.0005700214,0.00033217584,0.0003433699,0.00042369062],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018003611,0.00023223058,0.47407156,0.00054133486,0.0004378513,0.0005315605,0.0008098605,0.13869028,0.1238634,0.0019209905,0.001789046,0.25531155],"study_design_scores_gemma":[0.000061422885,0.0010865716,0.35895357,0.00008821797,0.0003402856,0.0008688701,0.00076346326,0.41480154,0.21612284,0.0009769351,0.005824638,0.00011160424],"about_ca_topic_score_codex":0.0040234257,"about_ca_topic_score_gemma":0.0041512772,"teacher_disagreement_score":0.0040234257,"about_ca_system_score_codex":0.00031237927,"about_ca_system_score_gemma":0.00034901695,"threshold_uncertainty_score":0.008000016},"labels":[],"label_agreement":null},{"id":"W2338247040","doi":"10.1109/twc.2017.2669963","title":"Joint Device Positioning and Clock Synchronization in 5G Ultra-Dense Networks","year":2017,"lang":"en","type":"preprint","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Tekes","keywords":"Computer science; Extended Kalman filter; Synchronization (alternating current); Clock synchronization; Key (lock); Real-time computing; Telecommunications link; Time of arrival; Kalman filter; Computer network; Telecommunications; Wireless","score_opus":0.025598188577994778,"score_gpt":0.25570682624532104,"score_spread":0.23010863766732625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338247040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023360325,0.00072488654,0.9739009,0.00010824355,0.00010146609,0.000021229296,0.00004034618,0.00027453486,0.0014678854],"genre_scores_gemma":[0.8791377,0.0010364326,0.11732428,0.00008410088,0.00012390189,0.000051771214,0.00015996586,0.000023710865,0.002058177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950826,0.00011905162,0.00002799723,0.000113970535,0.0001563742,0.000074431715],"domain_scores_gemma":[0.99960726,0.00014041734,0.00007731366,0.00007597469,0.00008000739,0.000019110794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054368365,0.00067455263,0.00060132245,0.00036438403,0.0003826815,0.00069347915,0.0006340049,0.0007299195,0.00063370285],"category_scores_gemma":[0.0018206093,0.00023183889,0.00027454182,0.0005854225,0.00038503535,0.0011915852,0.0011194006,0.00058098085,0.00025856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025518835,0.00004107372,0.002704993,0.00019803859,0.00006474726,0.00030292146,0.00022207844,0.6607104,0.012199928,0.026464095,0.001726178,0.2951103],"study_design_scores_gemma":[0.000013995979,0.00010037378,0.0010548219,0.000020629674,0.000025806405,0.00013267319,0.000052542142,0.9863537,0.004013791,0.0050077387,0.003204187,0.000019737627],"about_ca_topic_score_codex":0.00406865,"about_ca_topic_score_gemma":0.0034776146,"teacher_disagreement_score":0.00406865,"about_ca_system_score_codex":0.00038779466,"about_ca_system_score_gemma":0.0007322772,"threshold_uncertainty_score":0.0080899},"labels":[],"label_agreement":null},{"id":"W2343107556","doi":"10.3390/s16050596","title":"Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":446,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Beacon; Bluetooth Low Energy; Bluetooth; Molecular beacon; Embedded system; Computer science; Energy (signal processing); Real-time computing; Telecommunications; Wireless; Chemistry; Physics","score_opus":0.0038498416426480998,"score_gpt":0.16521117629611484,"score_spread":0.16136133465346675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343107556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088844486,0.0011155998,0.9028146,0.00012373972,0.00011963155,0.000055166314,0.00012408337,0.004209061,0.002593612],"genre_scores_gemma":[0.88665223,0.00058914826,0.10991229,0.00008692149,0.0000572411,0.000065836495,0.0002892011,0.000040776133,0.0023062984],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954706,0.000085082735,0.000027730539,0.00008425445,0.00020867679,0.00004716741],"domain_scores_gemma":[0.99951184,0.00008993791,0.00009224835,0.00010301829,0.00018039402,0.000022573317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034785148,0.0005483692,0.0005443728,0.00068902003,0.00016211737,0.0003304629,0.0006984946,0.0004693374,0.0005908033],"category_scores_gemma":[0.0010376885,0.00021419919,0.0004062821,0.0006190785,0.00015877347,0.0005870101,0.0005840548,0.00033029597,0.00055949064],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069473917,0.00011956641,0.02101139,0.00049079244,0.00015228102,0.00073738117,0.00031856017,0.08636799,0.11810586,0.002443076,0.005129048,0.7644294],"study_design_scores_gemma":[0.000094818875,0.00068607356,0.013977185,0.00006553209,0.00012208482,0.001668093,0.0001610022,0.91712683,0.0524507,0.0009992903,0.0125598805,0.00008848651],"about_ca_topic_score_codex":0.0020952837,"about_ca_topic_score_gemma":0.002583536,"teacher_disagreement_score":0.0020952837,"about_ca_system_score_codex":0.00019406581,"about_ca_system_score_gemma":0.00024723358,"threshold_uncertainty_score":0.004166186},"labels":[],"label_agreement":null},{"id":"W2343495200","doi":"","title":"HeadScan: A Wearable System for Radio-Based Sensing of Head and Mouth-Related Activities","year":2016,"lang":"en","type":"article","venue":"UCL Discovery (University College London)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Wearable computer; Computer science; Accelerometer; Context (archaeology); Pipeline (software); Wireless; Gyroscope; Wearable technology; Embedded system; Engineering; Telecommunications","score_opus":0.005730131440986956,"score_gpt":0.16777892601493127,"score_spread":0.1620487945739443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343495200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4164064,0.005554131,0.45795318,0.00064959365,0.0012734339,0.0035554932,0.03093073,0.05355683,0.03012022],"genre_scores_gemma":[0.71759975,0.0026268961,0.21874861,0.0015324706,0.00041541125,0.0024387778,0.017053477,0.0009254527,0.038659267],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946016,0.000060149363,0.000028458919,0.00013664008,0.0002711642,0.000043407665],"domain_scores_gemma":[0.999424,0.000092359856,0.00006832997,0.000063963234,0.00028959938,0.000061648396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005041084,0.0011283982,0.00096951827,0.0014472937,0.0004359176,0.00064266863,0.0012469485,0.00070245186,0.008976946],"category_scores_gemma":[0.0009459308,0.00037712685,0.00033957636,0.0011753886,0.00042761432,0.00077904825,0.0008196262,0.0004740269,0.0027806303],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002241483,0.000550541,0.022548351,0.0018933034,0.00061670423,0.0011310489,0.0009962047,0.0021878039,0.37000862,0.0015565499,0.09626491,0.50000453],"study_design_scores_gemma":[0.0005860253,0.0043298188,0.17300595,0.00029755713,0.0013562101,0.00608027,0.00093713583,0.061412234,0.5152823,0.0019415702,0.23397356,0.0007974368],"about_ca_topic_score_codex":0.0040657255,"about_ca_topic_score_gemma":0.011717883,"teacher_disagreement_score":0.008976946,"about_ca_system_score_codex":0.00038167232,"about_ca_system_score_gemma":0.0006105824,"threshold_uncertainty_score":0.030030847},"labels":[],"label_agreement":null},{"id":"W2343689302","doi":"10.1109/tbme.2015.2502138","title":"Simultaneous Electromagnetic Tracking and Calibration for Dynamic Field Distortion Compensation","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Distortion (music); Tracking (education); Computer science; Computer vision; Compensation (psychology); Field (mathematics); Field of view; Calibration; Tracking system; Artificial intelligence; Orientation (vector space); BitTorrent tracker; Workspace; Sensor fusion; Observational error; Physics; Eye tracking; Mathematics; Filter (signal processing); Robot; Telecommunications","score_opus":0.00809518700994196,"score_gpt":0.21416715465877176,"score_spread":0.2060719676488298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343689302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010649138,0.00009074203,0.9882202,0.000036481902,0.000039171824,0.000019441435,0.000013641058,0.00046186047,0.0004692769],"genre_scores_gemma":[0.35901058,0.00024316086,0.6387135,0.000107950815,0.000057548183,0.00007526715,0.00011823783,0.00012831231,0.0015454668],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985368,0.00027963208,0.00008196898,0.0002917897,0.00072026404,0.000089477006],"domain_scores_gemma":[0.99861467,0.0003080905,0.00023035707,0.00041448005,0.00039148267,0.00004090582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009862492,0.0008856618,0.00070297124,0.00073265936,0.00036668146,0.0005981443,0.00075540127,0.0008615379,0.0011896119],"category_scores_gemma":[0.0039193034,0.00048725458,0.0005299882,0.0010431554,0.00041944973,0.0011207936,0.0015197109,0.00085325266,0.00086931157],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032034836,0.000112625436,0.0028140922,0.00017877729,0.00007706485,0.0001406087,0.00028345856,0.06267203,0.27824354,0.003924217,0.0018536327,0.6493796],"study_design_scores_gemma":[0.000056232468,0.0004483682,0.0069200206,0.000044375283,0.00009627763,0.0014713607,0.000085749096,0.6822031,0.28819713,0.0034742616,0.016879153,0.00012405861],"about_ca_topic_score_codex":0.0009933803,"about_ca_topic_score_gemma":0.001312957,"teacher_disagreement_score":0.0011896119,"about_ca_system_score_codex":0.00037098577,"about_ca_system_score_gemma":0.0007661307,"threshold_uncertainty_score":0.0052158237},"labels":[],"label_agreement":null},{"id":"W2345032261","doi":"10.1109/tsmc.2016.2521823","title":"A Novel Biomechanical Model-Aided IMU/UWB Fusion for Magnetometer-Free Lower Body Motion Capture","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Magnetometer; Inertial measurement unit; Motion capture; Computer vision; Tracking (education); Computer science; Kalman filter; Accelerometer; Euler angles; Artificial intelligence; Match moving; Sensor fusion; Orientation (vector space); Motion (physics); Physics; Magnetic field; Mathematics","score_opus":0.014248364992691229,"score_gpt":0.20756496057624046,"score_spread":0.19331659558354924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345032261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012220433,0.00021002126,0.9856549,0.00004166231,0.000066110646,0.000024272553,0.00004642433,0.0008844465,0.00085177616],"genre_scores_gemma":[0.5451774,0.00036932805,0.44868562,0.00028873148,0.000105404746,0.00021275964,0.0005034669,0.000094599345,0.0045627025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997073,0.000036911497,0.000017504683,0.00008780315,0.00011230055,0.000038221966],"domain_scores_gemma":[0.99981517,0.000032240012,0.00003204408,0.000032971842,0.000071179056,0.000016380223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003109189,0.00067768845,0.0006797846,0.00055567507,0.00032808344,0.00055293646,0.0008258137,0.0007131545,0.0009394765],"category_scores_gemma":[0.0007816707,0.00036230276,0.0004766213,0.0005085417,0.00020959793,0.0007108428,0.0012103751,0.00056228344,0.00063739566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036950308,0.00016026094,0.003696823,0.000264672,0.00017027733,0.00026916413,0.00023907838,0.088503234,0.10291003,0.0036089243,0.004116627,0.79569143],"study_design_scores_gemma":[0.000042346554,0.00028685742,0.0045060366,0.000042465166,0.00007273679,0.00042008897,0.00006122517,0.9499976,0.035615057,0.0017069004,0.0071904054,0.00005816396],"about_ca_topic_score_codex":0.0016090255,"about_ca_topic_score_gemma":0.0018383478,"teacher_disagreement_score":0.0016090255,"about_ca_system_score_codex":0.00020784682,"about_ca_system_score_gemma":0.00051893026,"threshold_uncertainty_score":0.003199339},"labels":[],"label_agreement":null},{"id":"W2347637477","doi":"10.2316/journal.206.2016.3.206-4334","title":"FUSION OF DRSSI AND AOA FOR AERIAL LOCALIZATION OF AN RF SOURCE WITH UNKNOWN TRANSMITTED POWER","year":2016,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Non-line-of-sight propagation; Angle of arrival; Fusion; Power (physics); Sight; RF power amplifier; Physics; Computer science; Optics; Telecommunications; Antenna (radio); Wireless","score_opus":0.004643567769642982,"score_gpt":0.2119053647985483,"score_spread":0.20726179702890532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2347637477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0147889685,0.00045531662,0.9829773,0.000074913856,0.00008779785,0.00001659527,0.000044302276,0.00048723837,0.0010675668],"genre_scores_gemma":[0.52771014,0.0011485411,0.4676772,0.000127627,0.00020897627,0.00007968085,0.0004675517,0.00007902767,0.0025013105],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995067,0.00008623601,0.000039777573,0.00012652381,0.00020165705,0.000039085735],"domain_scores_gemma":[0.9995927,0.00008070942,0.00006315081,0.00006825345,0.0001753095,0.00001983438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005562137,0.0007595265,0.00057620276,0.00089798763,0.00021277838,0.00041477138,0.0005964601,0.00043581854,0.00047269603],"category_scores_gemma":[0.0012705154,0.000245041,0.0006109102,0.0007369698,0.00032611695,0.00079986744,0.00076653715,0.00058154325,0.0006720652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035649672,0.000059493344,0.0038668285,0.00037783562,0.00012902432,0.00028575223,0.0003639349,0.09988041,0.19407359,0.01137756,0.0017342537,0.68749493],"study_design_scores_gemma":[0.00003284693,0.00038500098,0.0044721374,0.0000498248,0.00014174421,0.00058666576,0.00013850443,0.92113256,0.058153197,0.0044844737,0.010344843,0.00007819346],"about_ca_topic_score_codex":0.0009898805,"about_ca_topic_score_gemma":0.0015121987,"teacher_disagreement_score":0.0009898805,"about_ca_system_score_codex":0.00017828966,"about_ca_system_score_gemma":0.0004254642,"threshold_uncertainty_score":0.0029416084},"labels":[],"label_agreement":null},{"id":"W2358862806","doi":"","title":"DV-Hop Localization Algorithm Based on Algebraic Reconstruction Technique","year":2010,"lang":"en","type":"article","venue":"Jisuanji gongcheng","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lambton College","funders":"","keywords":"Computer science; Wireless sensor network; Node (physics); Algorithm; Hop (telecommunications); Algebraic number; Mathematics; Computer network","score_opus":0.0047698580356088024,"score_gpt":0.20330615547047362,"score_spread":0.1985362974348648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2358862806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038142307,0.00017269589,0.99406505,0.000074285206,0.000036361085,0.000022121816,0.000018377657,0.00018752307,0.0016093816],"genre_scores_gemma":[0.33786994,0.0010766843,0.6514954,0.00009498855,0.000102200305,0.00019022648,0.0002467046,0.000059765014,0.008864101],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971074,0.000060200946,0.000016302405,0.00006292517,0.00012805553,0.000021679802],"domain_scores_gemma":[0.9998253,0.000041396863,0.000019795225,0.000018634753,0.00008441538,0.000010553667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002690907,0.00031804867,0.00043220792,0.00055817363,0.00038433328,0.00059557875,0.00066774205,0.00036239272,0.0013670296],"category_scores_gemma":[0.00080869097,0.00016224665,0.00030351983,0.000755865,0.00032257795,0.0008938207,0.0006560079,0.00054852443,0.00050954975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001976793,0.000049085535,0.0014107313,0.0002818636,0.00006395535,0.00016510818,0.00033886402,0.20895655,0.04094912,0.114562824,0.005073461,0.6279508],"study_design_scores_gemma":[0.000059764163,0.00015659325,0.0003980892,0.000019507022,0.000025350912,0.0004594839,0.000075802236,0.95324,0.014402493,0.016801352,0.014322911,0.00003868171],"about_ca_topic_score_codex":0.0015534386,"about_ca_topic_score_gemma":0.00088030554,"teacher_disagreement_score":0.0015534386,"about_ca_system_score_codex":0.00032256194,"about_ca_system_score_gemma":0.00059174164,"threshold_uncertainty_score":0.0045731664},"labels":[],"label_agreement":null},{"id":"W2376914131","doi":"","title":"Investigation on Localization and Tracking of Moving Target in WSN","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wireless sensor network; Computer science; Tracking (education); Wireless; Nonlinear system; Real-time computing; Variable (mathematics); Match moving; Key distribution in wireless sensor networks; Extended Kalman filter; Kalman filter; Artificial intelligence; Motion (physics); Wireless network; Telecommunications; Computer network; Mathematics","score_opus":0.010920664181481377,"score_gpt":0.2038108434519355,"score_spread":0.19289017927045413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2376914131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08745121,0.00785602,0.8885278,0.000736051,0.00013473142,0.00004269448,0.000038457307,0.00018141855,0.015031624],"genre_scores_gemma":[0.91663367,0.012766456,0.062134065,0.00017873777,0.00013589782,0.000055321812,0.00009774799,0.000038906168,0.007959141],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960274,0.00008122728,0.000021294198,0.000089756366,0.00017005141,0.000034981826],"domain_scores_gemma":[0.9995672,0.00018932899,0.000049707385,0.000024943818,0.00015639653,0.0000124087155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000634692,0.00026631253,0.00029107407,0.0004327618,0.00026688876,0.00049573695,0.0003346625,0.00050195114,0.00080119935],"category_scores_gemma":[0.0020370705,0.00018728724,0.00034159582,0.00078182784,0.0004006741,0.0016917025,0.00029093868,0.00030597838,0.0001635832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016084636,0.00006469383,0.012580329,0.0012292928,0.00012541107,0.0012431489,0.0007687335,0.51513785,0.05993647,0.12447675,0.0025850092,0.28169146],"study_design_scores_gemma":[0.000009982531,0.00018151336,0.004141294,0.000085171094,0.000045844987,0.0008496823,0.0002859533,0.9532001,0.011990329,0.016628144,0.012555403,0.000026680416],"about_ca_topic_score_codex":0.002243915,"about_ca_topic_score_gemma":0.0009994064,"teacher_disagreement_score":0.002243915,"about_ca_system_score_codex":0.00041887735,"about_ca_system_score_gemma":0.00030386957,"threshold_uncertainty_score":0.0044617653},"labels":[],"label_agreement":null},{"id":"W2393615181","doi":"10.1109/lmwc.2016.2549264","title":"Localization of Printed Chipless RFID in 3-D Space","year":2016,"lang":"en","type":"article","venue":"IEEE Microwave and Wireless Components Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Chipless RFID; Ultra-wideband; Acoustics; Computer science; Physics; Electronic engineering; Engineering; Optics; Telecommunications; Resonator","score_opus":0.009205269186270102,"score_gpt":0.18888347983141626,"score_spread":0.17967821064514616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2393615181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039781135,0.00041617732,0.95693326,0.00010849161,0.00007321878,0.000013257165,0.000025090207,0.0011986712,0.0014507264],"genre_scores_gemma":[0.6159598,0.0005433879,0.38040924,0.0001885901,0.000041846943,0.000030770785,0.000077718,0.00007199602,0.0026766814],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963415,0.00008623894,0.000018945284,0.00008205812,0.00015188198,0.000026626505],"domain_scores_gemma":[0.9995702,0.00009724079,0.00011198037,0.00010007002,0.000106964784,0.000013492431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023238042,0.00037084238,0.00032376105,0.00040362793,0.00019851213,0.00057422626,0.00072929513,0.0006554655,0.0005496418],"category_scores_gemma":[0.00074299803,0.00028890074,0.00039998046,0.00039762058,0.00045934995,0.00080818986,0.0006573974,0.00027693913,0.00045521045],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029143473,0.00003545237,0.0012737901,0.00037387572,0.00006242995,0.00073198834,0.00037413323,0.052131094,0.76504356,0.008469523,0.0013748244,0.16983783],"study_design_scores_gemma":[0.0000578334,0.00039935845,0.0020243763,0.000043292912,0.00006785678,0.001911497,0.0001264936,0.43848336,0.5413159,0.0037449915,0.011688691,0.00013640225],"about_ca_topic_score_codex":0.00041672503,"about_ca_topic_score_gemma":0.0003737199,"teacher_disagreement_score":0.00072929513,"about_ca_system_score_codex":0.0003150528,"about_ca_system_score_gemma":0.00025041084,"threshold_uncertainty_score":0.0022858977},"labels":[],"label_agreement":null},{"id":"W2394087192","doi":"","title":"A TOA-based Location Algorithm for Mitigating the Effect of NLOS","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Algorithm; Kalman filter; Time of arrival; Filter (signal processing); Position (finance); Real-time computing; Wireless; Range (aeronautics); Telecommunications; Artificial intelligence; Computer vision","score_opus":0.003629971831414446,"score_gpt":0.22076463066511043,"score_spread":0.21713465883369598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2394087192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044045,0.00015570168,0.9936033,0.00008216076,0.00013439167,0.00003788664,0.00002678427,0.00084884546,0.00070641155],"genre_scores_gemma":[0.15060404,0.00037915746,0.84262574,0.00019249889,0.00017129404,0.00027067072,0.00024689126,0.00013502693,0.005374661],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995871,0.00007145272,0.000029103288,0.000115847666,0.00016636768,0.000030203297],"domain_scores_gemma":[0.99935955,0.00013143473,0.00008734625,0.00007534343,0.00032108152,0.000025243458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005104569,0.0008035007,0.0008994258,0.0009367763,0.0008449125,0.00075919006,0.0012630497,0.0010742387,0.0019855222],"category_scores_gemma":[0.0019819057,0.00031597025,0.0005514643,0.0010453585,0.00048223312,0.0012448783,0.0007225933,0.0008694855,0.0013714959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026831208,0.00012776433,0.0013759941,0.00019585944,0.00012009,0.00017487945,0.0002230065,0.121422715,0.04685754,0.012245309,0.004971001,0.81201744],"study_design_scores_gemma":[0.00008708827,0.0002449736,0.0010402739,0.000022931368,0.00008841846,0.0005277073,0.00005689648,0.95585495,0.021349177,0.0045374357,0.01612509,0.00006501525],"about_ca_topic_score_codex":0.0027468435,"about_ca_topic_score_gemma":0.0032357418,"teacher_disagreement_score":0.0027468435,"about_ca_system_score_codex":0.00045009155,"about_ca_system_score_gemma":0.0010031789,"threshold_uncertainty_score":0.0066422224},"labels":[],"label_agreement":null},{"id":"W2396747260","doi":"10.5281/zenodo.43974","title":"Iterative Grid Search For Rss-Based Emitter Localization","year":2014,"lang":"en","type":"article","venue":"INFM-OAR (INFN Catania)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Initialization; RSS; Computer science; Grid; Hyperparameter optimization; Computational complexity theory; Iterative and incremental development; Iterative method; Algorithm; Search algorithm; Process (computing); Artificial intelligence; Mathematics","score_opus":0.011267719276818232,"score_gpt":0.23218833917105933,"score_spread":0.22092061989424108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396747260","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047888393,0.00028435737,0.99288505,0.000044782155,0.00005827308,0.000028925751,0.000062669205,0.0005673791,0.0012797479],"genre_scores_gemma":[0.29837123,0.00040478725,0.6949822,0.000071782786,0.000076006036,0.0002513181,0.0007087539,0.00022945905,0.0049044583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999121,0.00033145538,0.000046647685,0.00011642758,0.00030068375,0.00008362708],"domain_scores_gemma":[0.99847764,0.0009001797,0.00006414446,0.00017185489,0.00034519608,0.00004098251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010239541,0.0008444106,0.0013476978,0.0013111376,0.00045315112,0.0010216287,0.0016083776,0.0009337505,0.0030329085],"category_scores_gemma":[0.0053755194,0.0006561471,0.00070441904,0.001980608,0.00054260425,0.000928307,0.0017164481,0.0008469554,0.0016022017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048144616,0.000095968644,0.0011699828,0.00018820899,0.0001106086,0.00009650554,0.00015545682,0.66411674,0.0045290333,0.0096922815,0.0059804083,0.31338334],"study_design_scores_gemma":[0.00001876262,0.000024642755,0.00013909042,0.0000073146593,0.000006340648,0.000022749044,0.000013976384,0.9954672,0.00073796365,0.002630514,0.00092614937,0.0000052958244],"about_ca_topic_score_codex":0.008508804,"about_ca_topic_score_gemma":0.008342643,"teacher_disagreement_score":0.008508804,"about_ca_system_score_codex":0.00047062003,"about_ca_system_score_gemma":0.0012015497,"threshold_uncertainty_score":0.01691854},"labels":[],"label_agreement":null},{"id":"W2414461007","doi":"10.1139/tcsme-2009-0051","title":"DESIGN AND IMPLEMENTATION OF AN INDOOR LOCALIZATION SYSTEM FOR THE OMNIBOT OMNI-DIRECTIONAL PLATFORM","year":2009,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"University of Ontario Institute of Technology","keywords":"Trilateration; Beacon; Global Positioning System; Ceiling (cloud); Positioning system; Computer science; Real-time computing; Position (finance); Indoor positioning system; Tracking system; Navigation system; Simulation; Embedded system; Engineering; Artificial intelligence; Telecommunications; Kalman filter; Accelerometer","score_opus":0.012085421776077795,"score_gpt":0.21998230611719852,"score_spread":0.20789688434112072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2414461007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025231866,0.00019569078,0.96595556,0.000094064104,0.00013827927,0.0002259172,0.000070289156,0.0032524848,0.004835847],"genre_scores_gemma":[0.38990572,0.00037578374,0.59300834,0.00021479905,0.00007687003,0.00057739485,0.00036809887,0.00017265005,0.01530031],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995627,0.000044066666,0.000023208884,0.00008608077,0.00021511769,0.00006888991],"domain_scores_gemma":[0.9996561,0.000026735383,0.000045320216,0.000031612668,0.00018600134,0.000054287666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040237975,0.0005175849,0.00047633628,0.0004490314,0.00033340335,0.00060538755,0.0013780344,0.00047542495,0.002307886],"category_scores_gemma":[0.0005024766,0.00027602998,0.00022507508,0.00020877538,0.0002005451,0.0004177086,0.0005867643,0.00049897894,0.0016039801],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006375186,0.00016759797,0.0042462787,0.0006547655,0.00008171641,0.0011134198,0.0006747194,0.018554706,0.50833625,0.011034706,0.0076280027,0.4468704],"study_design_scores_gemma":[0.0003808271,0.004102824,0.013134508,0.00021966174,0.0002913344,0.0041547236,0.0003327489,0.28268027,0.4696746,0.002001981,0.22273521,0.00029126467],"about_ca_topic_score_codex":0.001304011,"about_ca_topic_score_gemma":0.0011141739,"teacher_disagreement_score":0.002307886,"about_ca_system_score_codex":0.0003128967,"about_ca_system_score_gemma":0.00094088784,"threshold_uncertainty_score":0.0077206492},"labels":[],"label_agreement":null},{"id":"W2414603255","doi":"10.1007/978-3-319-24560-7_29","title":"Stance Phase Detection for Walking and Running Using an IMU Periodicity-based Approach","year":2015,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Inertial measurement unit; Computer science; Gait; Gait cycle; Phase (matter); Inertial frame of reference; Artificial intelligence; SIGNAL (programming language); Units of measurement; Tracking (education); Computer vision; Physical medicine and rehabilitation; Kinematics; Psychology; Physics; Medicine","score_opus":0.04438847749363616,"score_gpt":0.29681800418198406,"score_spread":0.2524295266883479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2414603255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12051534,0.002899217,0.8618243,0.00012808766,0.000465985,0.00014175539,0.0016339528,0.0024622895,0.0099290935],"genre_scores_gemma":[0.5930118,0.0020817444,0.39302832,0.00015202294,0.0003291512,0.00021218121,0.0024195982,0.00024342499,0.008521764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988866,0.00000929455,0.000007070921,0.00003616745,0.000036484085,0.000022361777],"domain_scores_gemma":[0.99989724,0.000022344448,0.000014036695,0.0000127767535,0.00004335746,0.000010187896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015944146,0.00073867914,0.0006896592,0.00125011,0.0002490171,0.000505306,0.0004484496,0.0005242842,0.0021956656],"category_scores_gemma":[0.0003585877,0.00024072961,0.00049807347,0.0012420239,0.000111226225,0.00037312115,0.00041331846,0.00032147087,0.0020648155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042645846,0.000111760135,0.0061303815,0.00029575996,0.000100912395,0.00020688903,0.00007501146,0.009519142,0.11917067,0.00081373873,0.0044235564,0.8587258],"study_design_scores_gemma":[0.00006780851,0.00053874595,0.061323106,0.00015696355,0.00028937246,0.0015533362,0.00031557263,0.84733665,0.07125259,0.0036018374,0.013458752,0.00010524084],"about_ca_topic_score_codex":0.0011151194,"about_ca_topic_score_gemma":0.0025279545,"teacher_disagreement_score":0.0021956656,"about_ca_system_score_codex":0.000086479435,"about_ca_system_score_gemma":0.00024319298,"threshold_uncertainty_score":0.0073451996},"labels":[],"label_agreement":null},{"id":"W2435125193","doi":"10.3390/s16101570","title":"Assessment of Receiver Signal Strength Sensing for Location Estimation Based on Fisher Information","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"RSS; Computer science; Wireless; Context (archaeology); Data mining; Real-time computing; Set (abstract data type); Algorithm; Telecommunications","score_opus":0.0072288445128980425,"score_gpt":0.22608829423047042,"score_spread":0.21885944971757237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2435125193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08833195,0.0003163081,0.9084672,0.00020834837,0.000017689423,0.000040393334,0.000084413485,0.00019009235,0.002343642],"genre_scores_gemma":[0.8553702,0.00029521468,0.14336583,0.00005069848,0.000024557414,0.000052550975,0.00019130032,0.000029216237,0.00062047597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998982,0.00033856896,0.00004547628,0.00012667828,0.00043970253,0.00006750005],"domain_scores_gemma":[0.99710435,0.001987959,0.00024144666,0.00023639084,0.00037417538,0.00005573168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002426636,0.000688506,0.00054210075,0.00073953666,0.00028511262,0.00068933424,0.00067443826,0.00066353515,0.0009988329],"category_scores_gemma":[0.012138773,0.00034060545,0.00038196548,0.0005795045,0.0007927283,0.0018067722,0.0011226892,0.0005793988,0.00031012078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004575627,0.00011714013,0.012342906,0.00028411555,0.00013890293,0.0003131396,0.00029777837,0.6885198,0.04719802,0.05240486,0.0010676115,0.19685818],"study_design_scores_gemma":[0.000008415866,0.00011309917,0.0030676639,0.00002391164,0.000017553457,0.00010271276,0.000028401111,0.9807262,0.007413375,0.00798979,0.0004782351,0.000030713112],"about_ca_topic_score_codex":0.0023928327,"about_ca_topic_score_gemma":0.002306943,"teacher_disagreement_score":0.002426636,"about_ca_system_score_codex":0.0006343796,"about_ca_system_score_gemma":0.00087067724,"threshold_uncertainty_score":0.012833476},"labels":[],"label_agreement":null},{"id":"W2463130876","doi":"10.1109/tcomm.2016.2590436","title":"Accurate Range-Free Localization in Multi-Hop Wireless Sensor Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; Université du Québec à Montréal; Institut National de la Recherche Scientifique; University of Toronto","funders":"","keywords":"Wireless sensor network; Computer science; Hop (telecommunications); Range (aeronautics); Wireless; Computer network; Real-time computing; Algorithm; Telecommunications; Engineering","score_opus":0.031939828820753864,"score_gpt":0.2588538454936258,"score_spread":0.22691401667287192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2463130876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014563656,0.0007702553,0.98363584,0.000070919596,0.000026280648,0.000009200914,0.000010052889,0.0003005224,0.0006132752],"genre_scores_gemma":[0.7615433,0.0012325344,0.23555739,0.00009118538,0.00008156261,0.00005325328,0.00007683889,0.000066270484,0.0012976999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878556,0.00039381607,0.00005776437,0.00017425655,0.0005250693,0.000063529544],"domain_scores_gemma":[0.9982845,0.0008726914,0.00025352242,0.0003316105,0.00022962937,0.000028015616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009777802,0.0005427418,0.0005242557,0.0007047629,0.00037640907,0.0005096338,0.0008815755,0.000735062,0.00026652843],"category_scores_gemma":[0.004949444,0.00034553077,0.0003084298,0.0008110162,0.00060275843,0.0018891268,0.0012271514,0.00052936893,0.00022379948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014940096,0.000045217417,0.0017665558,0.00027624742,0.00007151069,0.00021525745,0.00026299673,0.732448,0.029816462,0.024825597,0.0012337306,0.20888896],"study_design_scores_gemma":[0.000011641543,0.000083332285,0.00057637156,0.000019278268,0.0000169071,0.00024270618,0.000035474528,0.97855717,0.00975747,0.008874466,0.0018018185,0.000023318164],"about_ca_topic_score_codex":0.0006775416,"about_ca_topic_score_gemma":0.0005931233,"teacher_disagreement_score":0.0009777802,"about_ca_system_score_codex":0.000286669,"about_ca_system_score_gemma":0.0002997029,"threshold_uncertainty_score":0.0051710606},"labels":[],"label_agreement":null},{"id":"W2465084039","doi":"10.14257/ijsh.2016.10.6.22","title":"TOA Analysis Based on Energy for 60 GHz Signals","year":2016,"lang":"en","type":"article","venue":"International Journal of Smart Home","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Energy (signal processing); Environmental science; Computer science; Statistics; Mathematics","score_opus":0.008953959544762366,"score_gpt":0.23076718174315689,"score_spread":0.22181322219839453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2465084039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06643637,0.000639218,0.9270354,0.00017185009,0.000078838464,0.00002927756,0.00006939293,0.00043932116,0.005100253],"genre_scores_gemma":[0.90389454,0.0010419006,0.091268726,0.00012616097,0.0000684937,0.000045830297,0.00019292661,0.0001069864,0.0032544201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999635,0.00005165296,0.000016254968,0.000048936607,0.00021282834,0.000035355108],"domain_scores_gemma":[0.99961996,0.0001518135,0.000059102225,0.000055108932,0.00010204287,0.000012023709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000275985,0.000382542,0.0003490662,0.00097176264,0.00021927492,0.0006317523,0.00027669818,0.00039797,0.0014563827],"category_scores_gemma":[0.0015797631,0.00011642471,0.0005195982,0.0007250279,0.0003280709,0.00073421875,0.00044378225,0.00041088223,0.0004937574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003055776,0.000074967764,0.005154385,0.00018439807,0.00010483375,0.00051344594,0.00025637491,0.5689731,0.12684348,0.052953698,0.0019463683,0.24268937],"study_design_scores_gemma":[0.000003922991,0.000039967403,0.0013897134,0.000014131463,0.000014496869,0.0002603514,0.00003630806,0.9781531,0.012529889,0.0052990946,0.0022392184,0.000019798834],"about_ca_topic_score_codex":0.0010070964,"about_ca_topic_score_gemma":0.0006612133,"teacher_disagreement_score":0.0014563827,"about_ca_system_score_codex":0.0003617114,"about_ca_system_score_gemma":0.00034609123,"threshold_uncertainty_score":0.0048720837},"labels":[],"label_agreement":null},{"id":"W2491241558","doi":"10.4018/978-1-60566-396-8.ch015","title":"Accuracy Bounds for Wireless Localization Methods","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Victoria","funders":"","keywords":"Estimator; Cramér–Rao bound; Wireless sensor network; Upper and lower bounds; Context (archaeology); Computer science; Node (physics); Wireless; Process (computing); Algorithm; Line (geometry); Wireless network; Mathematics; Estimation theory; Computer network; Statistics; Telecommunications; Engineering; Geography","score_opus":0.019459258965162374,"score_gpt":0.2888063236390287,"score_spread":0.2693470646738663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2491241558","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014399061,0.025798012,0.9299762,0.0012305614,0.00072348426,0.00005093856,0.00012476562,0.000386064,0.04027004],"genre_scores_gemma":[0.2082463,0.07695674,0.650654,0.0016958922,0.00440279,0.00096857274,0.0007737586,0.0013469581,0.054954898],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9919309,0.001791111,0.00038473564,0.001062511,0.004394875,0.00043579735],"domain_scores_gemma":[0.9852681,0.010304213,0.0006195004,0.0014326031,0.0022108157,0.00016481386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005440655,0.002416453,0.0015082324,0.003133793,0.0009439366,0.004109418,0.0029692955,0.002171155,0.008888612],"category_scores_gemma":[0.030658124,0.0009524916,0.0014199439,0.0032022365,0.002866511,0.0060058497,0.0037604375,0.0059796497,0.005966256],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007448105,0.000033102377,0.00037828984,0.00086267234,0.00006669191,0.000104146195,0.0003045539,0.07656883,0.0020362379,0.6938653,0.014074058,0.21163172],"study_design_scores_gemma":[0.000024103749,0.0000920819,0.00052916905,0.0008837506,0.00006790322,0.00044268408,0.0001260489,0.2611904,0.003682979,0.62571436,0.107176185,0.000070294336],"about_ca_topic_score_codex":0.0014239561,"about_ca_topic_score_gemma":0.00062818296,"teacher_disagreement_score":0.008888612,"about_ca_system_score_codex":0.0028133113,"about_ca_system_score_gemma":0.0010255484,"threshold_uncertainty_score":0.029735327},"labels":[],"label_agreement":null},{"id":"W2500606337","doi":"10.1007/978-981-10-0934-1_29","title":"Indoor Map Aiding/Map Matching Smartphone Navigation Using Auxiliary Particle Filter","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Map matching; Particle filter; Computer science; Computer vision; Inertial navigation system; Artificial intelligence; Matching (statistics); Dead reckoning; Position (finance); Pedestrian; Filter (signal processing); Engineering; Global Positioning System; Inertial frame of reference; Mathematics","score_opus":0.009111098190118554,"score_gpt":0.20440936375913898,"score_spread":0.19529826556902044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500606337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021975843,0.00028889856,0.9677039,0.00005804946,0.00026445012,0.000042874643,0.0001949567,0.0035209802,0.0059500295],"genre_scores_gemma":[0.48644927,0.00046308502,0.49737886,0.00012663107,0.000119911325,0.00009047688,0.0008668667,0.00028379844,0.014221047],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997453,0.000022766986,0.000007943583,0.000075075724,0.000110813766,0.000037987826],"domain_scores_gemma":[0.99983823,0.000022059796,0.000008966478,0.0000432985,0.00007778901,0.000009606159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018110855,0.0008595031,0.00083351816,0.0006188034,0.0003146956,0.0005639424,0.0007049815,0.00071636104,0.0031180824],"category_scores_gemma":[0.0005448371,0.00035438637,0.00064030505,0.00090550655,0.00014568924,0.0005967945,0.0008012635,0.00057206216,0.0026326042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035854205,0.00013024134,0.0024339182,0.00018100043,0.00008558943,0.00023608257,0.0001375199,0.030529978,0.0731283,0.0023652303,0.0101198,0.8802938],"study_design_scores_gemma":[0.00004918171,0.0001939114,0.00589758,0.000035988898,0.000117194126,0.00072250835,0.000087781715,0.9132967,0.0587549,0.0020838156,0.01870949,0.000051037932],"about_ca_topic_score_codex":0.0055428706,"about_ca_topic_score_gemma":0.006288022,"teacher_disagreement_score":0.0055428706,"about_ca_system_score_codex":0.00019001178,"about_ca_system_score_gemma":0.0005516288,"threshold_uncertainty_score":0.011021197},"labels":[],"label_agreement":null},{"id":"W2506132628","doi":"","title":"Development of application software for a wireless position tracking system","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kwantlen Polytechnic University","funders":"","keywords":"Software; Computer science; Wireless; Tracking (education); Work (physics); Tracking system; Product (mathematics); Software engineering; Engineering; Telecommunications; Operating system; Artificial intelligence; Kalman filter","score_opus":0.009671480186577174,"score_gpt":0.22279878118894755,"score_spread":0.21312730100237037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2506132628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026993955,0.00021849321,0.84202284,0.0001949755,0.0001967845,0.0015716718,0.0007789194,0.119868115,0.008154154],"genre_scores_gemma":[0.19650124,0.00057522615,0.7506741,0.0005497768,0.00015229509,0.0023038306,0.0061306804,0.014051764,0.029061014],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909055,0.00011327613,0.000117504016,0.00019424122,0.00039329706,0.000091092355],"domain_scores_gemma":[0.9980896,0.00069100404,0.00010254875,0.0002365423,0.0007410931,0.00013912567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011727705,0.0011025608,0.0008396771,0.0011666091,0.00040050436,0.00078016816,0.0016276815,0.00087028614,0.010321886],"category_scores_gemma":[0.003323944,0.0007915991,0.00066901755,0.0005508901,0.0002877119,0.0009936903,0.00077589246,0.0014868921,0.006312364],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001108372,0.0012748527,0.00756492,0.0011113387,0.00026868176,0.0025786057,0.0010263629,0.016650615,0.21067907,0.005911446,0.04200781,0.709818],"study_design_scores_gemma":[0.00091893383,0.0016656145,0.017256858,0.00043017595,0.00039571355,0.004616816,0.00022960184,0.35633218,0.3455904,0.0042133355,0.2680009,0.00034955255],"about_ca_topic_score_codex":0.0012854096,"about_ca_topic_score_gemma":0.00048749617,"teacher_disagreement_score":0.010321886,"about_ca_system_score_codex":0.00024825186,"about_ca_system_score_gemma":0.00076389516,"threshold_uncertainty_score":0.034530163},"labels":[],"label_agreement":null},{"id":"W2507874657","doi":"10.1109/phosst.2016.7548770","title":"Indoor localization using low-complexity luminaires and ambient light sensors","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Dead reckoning; Convergence (economics); Tracking (education); Real-time computing; Wireless sensor network; Computational complexity theory; Occupancy; Algorithm; Telecommunications; Computer network; Engineering; Architectural engineering; Global Positioning System","score_opus":0.015257426388912046,"score_gpt":0.21509592619702056,"score_spread":0.19983849980810853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507874657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027137907,0.0002288353,0.9660738,0.000067162,0.00006986461,0.00003417167,0.000044917557,0.0018492482,0.0044942093],"genre_scores_gemma":[0.6054562,0.0006572727,0.38581765,0.00011919789,0.000067189765,0.00007076764,0.00023205418,0.00015839041,0.007421281],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993542,0.00015741623,0.000028013024,0.00015844176,0.0002227995,0.000079149555],"domain_scores_gemma":[0.99936825,0.00018569967,0.00010974383,0.00018082434,0.00012188846,0.000033616998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002922021,0.0010678098,0.000635013,0.00036071555,0.00040465235,0.0009642829,0.0010018016,0.0005350209,0.002287198],"category_scores_gemma":[0.001378541,0.00037458434,0.00055276917,0.00049432775,0.00035778593,0.0011518811,0.0009158908,0.00065717683,0.0015079173],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008175041,0.00041381194,0.007244899,0.00075031264,0.00025824367,0.0006079539,0.00025805263,0.20703451,0.34479246,0.013762222,0.005095943,0.4189641],"study_design_scores_gemma":[0.00006152597,0.00073085463,0.0046880473,0.00007004235,0.00014373098,0.0008578998,0.000077794364,0.6881455,0.28579313,0.0021337052,0.017202098,0.00009565731],"about_ca_topic_score_codex":0.0024387084,"about_ca_topic_score_gemma":0.0041593793,"teacher_disagreement_score":0.0024387084,"about_ca_system_score_codex":0.0005091546,"about_ca_system_score_gemma":0.00049231,"threshold_uncertainty_score":0.0076514482},"labels":[],"label_agreement":null},{"id":"W2509806318","doi":"10.1109/tase.2016.2599864","title":"Localization of Indoor Mobile Robot Using Minimum Variance Unbiased FIR Filter","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimum-variance unbiased estimator; Finite impulse response; Computer science; A priori and a posteriori; Kalman filter; Algorithm; Particle filter; Filter (signal processing); Extended Kalman filter; Inertial navigation system; Control theory (sociology); Real-time computing; Artificial intelligence; Mathematics; Computer vision; Mean squared error; Statistics","score_opus":0.013265400884600802,"score_gpt":0.22500153929955347,"score_spread":0.21173613841495267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509806318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010871318,0.00017149573,0.98709744,0.00004775571,0.000030495557,0.00000923664,0.000014464743,0.0005899724,0.0011678954],"genre_scores_gemma":[0.7071511,0.00049295067,0.28729331,0.00012749151,0.00006036481,0.00008667897,0.00012585186,0.00004727157,0.0046149716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996613,0.000067452165,0.000014855064,0.00009671526,0.00012365375,0.00003591926],"domain_scores_gemma":[0.9998318,0.000052941476,0.000033216867,0.00002275261,0.000052426036,0.000006745173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029702872,0.00039314115,0.00050572306,0.0003529798,0.0002814705,0.00032464284,0.00045859718,0.00070686213,0.00089496427],"category_scores_gemma":[0.0008781136,0.00020319299,0.0003830655,0.00036209973,0.00023548986,0.00045736588,0.00033815365,0.00035513227,0.00039304935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034019185,0.0000913322,0.0027290422,0.00024588342,0.000093644965,0.00028638818,0.00018606873,0.4616774,0.09626144,0.010880519,0.0024194578,0.42478862],"study_design_scores_gemma":[0.000018095501,0.00009678875,0.00074149494,0.00001249493,0.000022503984,0.00009859774,0.000014316876,0.9870859,0.009045457,0.0012561415,0.0015913832,0.000016830001],"about_ca_topic_score_codex":0.0024394034,"about_ca_topic_score_gemma":0.0017680664,"teacher_disagreement_score":0.0024394034,"about_ca_system_score_codex":0.00028023022,"about_ca_system_score_gemma":0.00043481824,"threshold_uncertainty_score":0.0048503876},"labels":[],"label_agreement":null},{"id":"W2511410182","doi":"10.1109/glocomw.2016.7848938","title":"Joint 3D Positioning and Network Synchronization in 5G Ultra-Dense Networks Using UKF and EKF","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Tekes","keywords":"Computer science; Extended Kalman filter; Real-time computing; Synchronization (alternating current); Kalman filter; Offset (computer science); Hybrid positioning system; Wireless network; Positioning system; Node (physics); Channel (broadcasting); Wireless; Computer network; Telecommunications; Engineering; Artificial intelligence","score_opus":0.010125375622584526,"score_gpt":0.20720656126222778,"score_spread":0.19708118563964325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511410182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031300798,0.0002674208,0.96626276,0.00008298994,0.000057413406,0.00002198163,0.00004566191,0.00023020906,0.0017309469],"genre_scores_gemma":[0.8508383,0.00045865562,0.14676751,0.00004994493,0.000034944478,0.000062063824,0.00015679545,0.000025823489,0.0016058685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996929,0.0000910175,0.000016128515,0.00005959256,0.00010272484,0.000037711874],"domain_scores_gemma":[0.9997061,0.000117687916,0.000060028335,0.000033065826,0.00006730449,0.00001577587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045259923,0.000579319,0.0005754283,0.0003279646,0.0003050879,0.00068405236,0.00046563763,0.00068385515,0.00057460513],"category_scores_gemma":[0.0013374564,0.00023546204,0.0005003385,0.0005297849,0.00041720897,0.000778669,0.00077597017,0.0005016051,0.00020016971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052038875,0.00000965573,0.0012628569,0.00003621912,0.000019747173,0.00008535706,0.00006245975,0.9554271,0.0017810641,0.0031788666,0.000304176,0.03778038],"study_design_scores_gemma":[0.000004729183,0.000012014486,0.000287545,0.0000032760033,0.0000034551729,0.000016319224,0.000012691594,0.9985266,0.00036495327,0.00048379682,0.00027952547,0.0000051848606],"about_ca_topic_score_codex":0.016726075,"about_ca_topic_score_gemma":0.00980651,"teacher_disagreement_score":0.016726075,"about_ca_system_score_codex":0.0005411356,"about_ca_system_score_gemma":0.0007897135,"threshold_uncertainty_score":0.033257425},"labels":[],"label_agreement":null},{"id":"W2514604457","doi":"10.1109/access.2016.2607232","title":"A Scheme on Indoor Tracking of Ship Dynamic Positioning Based on Distributed Multi-Sensor Data Fusion","year":2016,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Guangdong University of Petrochemical Technology","keywords":"Computer science; Sensor fusion; Global Positioning System; Kalman filter; Dynamic positioning; Real-time computing; Position (finance); Positioning system; Transformation (genetics); Tracking (education); Tracking system; Computer vision; Artificial intelligence; Engineering; Telecommunications","score_opus":0.05718792146476624,"score_gpt":0.31165696450642616,"score_spread":0.2544690430416599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514604457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009341519,0.00015394572,0.9892825,0.000043156517,0.000053334094,0.000023749702,0.000014703471,0.0003188859,0.0007681293],"genre_scores_gemma":[0.60915524,0.00037816286,0.3876861,0.000088492954,0.000077008386,0.00011400025,0.00014540418,0.000033323475,0.0023222563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999366,0.00011215445,0.00004455286,0.00020490133,0.00022066048,0.00005174683],"domain_scores_gemma":[0.9997178,0.000046267687,0.000032089516,0.00008680231,0.000098167235,0.00001890588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006098403,0.00048596025,0.00062883523,0.00057595456,0.000709378,0.00048320793,0.0009127394,0.0006814445,0.0006789895],"category_scores_gemma":[0.0009279365,0.00024679836,0.0006031975,0.0007950599,0.00040627125,0.0013929516,0.0012806345,0.00058660185,0.00027137058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003932593,0.00013320848,0.0024331752,0.00023877181,0.00012401248,0.00026178214,0.0005309095,0.19564627,0.09174532,0.024539132,0.0027580508,0.68119615],"study_design_scores_gemma":[0.00004408358,0.00019138446,0.00082488084,0.000014316349,0.000053622065,0.0002213359,0.000042370357,0.96254,0.028444994,0.003057453,0.004518367,0.000047233927],"about_ca_topic_score_codex":0.0021908174,"about_ca_topic_score_gemma":0.0016396412,"teacher_disagreement_score":0.0021908174,"about_ca_system_score_codex":0.0004963112,"about_ca_system_score_gemma":0.0007495279,"threshold_uncertainty_score":0.004356146},"labels":[],"label_agreement":null},{"id":"W2515643737","doi":"10.1109/cits.2016.7546410","title":"Accuracy analysis of an impulse radio 60 GHz positioning system","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Non-line-of-sight propagation; Additive white Gaussian noise; Ranging; Computer science; Impulse radio; Impulse (physics); Channel (broadcasting); Cramér–Rao bound; Time of arrival; Signal-to-noise ratio (imaging); Electronic engineering; Telecommunications; Wireless; Algorithm; Engineering; Physics; Estimation theory; Ultra-wideband","score_opus":0.005703505907996473,"score_gpt":0.21489319848957908,"score_spread":0.2091896925815826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515643737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4562416,0.001900609,0.52467847,0.00038138832,0.00008299943,0.000029115681,0.00022448091,0.0015488592,0.014912461],"genre_scores_gemma":[0.9840366,0.00030089723,0.014194578,0.000041585266,0.000018935743,0.0000070723095,0.0001055568,0.000025900044,0.0012688567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988361,0.00017877111,0.000054061453,0.00015484943,0.0006285875,0.00014764372],"domain_scores_gemma":[0.99852294,0.0006244631,0.00023923363,0.00015775082,0.0004277142,0.000027923708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073098665,0.0003199824,0.00049112394,0.00073579507,0.00025315807,0.0006646028,0.00041731537,0.0005669476,0.0012828332],"category_scores_gemma":[0.0035976325,0.00014445905,0.00027667655,0.0006473656,0.00032484843,0.00045638942,0.00044360268,0.00028993684,0.00045225787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009197536,0.000042621712,0.020223271,0.00039202793,0.00016000033,0.00064164744,0.00027631054,0.67875624,0.11009994,0.009991419,0.0012857964,0.1772109],"study_design_scores_gemma":[0.000026392749,0.0006827493,0.018833354,0.000057209207,0.00016047395,0.0009942995,0.00016226944,0.9017576,0.070477255,0.0018036715,0.004969161,0.00007570016],"about_ca_topic_score_codex":0.0029373933,"about_ca_topic_score_gemma":0.0012011016,"teacher_disagreement_score":0.0029373933,"about_ca_system_score_codex":0.00060519023,"about_ca_system_score_gemma":0.00045329396,"threshold_uncertainty_score":0.0058405995},"labels":[],"label_agreement":null},{"id":"W2516715272","doi":"10.1109/access.2016.2606486","title":"A Look at the Recent Wireless Positioning Techniques With a Focus on Algorithms for Moving Receivers","year":2016,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":119,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Focus (optics); Wireless; Base station; Context (archaeology); Algorithm; Real-time computing; Sensor fusion; Telecommunications; Artificial intelligence","score_opus":0.01826038367459223,"score_gpt":0.2594567652690632,"score_spread":0.24119638159447096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516715272","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030423712,0.5958746,0.36848697,0.0037491557,0.0047395932,0.00008842524,0.00017245258,0.0003014146,0.023544958],"genre_scores_gemma":[0.026175762,0.7766787,0.17113774,0.0025710335,0.008677202,0.000109422945,0.0004601736,0.00010319812,0.014086894],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993981,0.00012049201,0.00007227862,0.00015815794,0.00021264382,0.00003822414],"domain_scores_gemma":[0.9989766,0.0004797966,0.00009301757,0.000081919265,0.00034866104,0.000020005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008354934,0.0010027168,0.0006447002,0.001972882,0.0003526512,0.0013091835,0.0011012219,0.001542686,0.0034341377],"category_scores_gemma":[0.0022561366,0.0005518797,0.0007075833,0.004829057,0.0008732932,0.00290224,0.00069921085,0.0030197217,0.0033342643],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009807523,0.00010547063,0.0014000987,0.0047251936,0.00009216083,0.00039345736,0.0003087942,0.010816793,0.0077852537,0.11241577,0.039361596,0.82249737],"study_design_scores_gemma":[0.000012204103,0.00043835977,0.0025301324,0.0011216173,0.00012256116,0.0034266955,0.0003297453,0.026181364,0.0054260404,0.032253288,0.92800874,0.00014914831],"about_ca_topic_score_codex":0.0011991023,"about_ca_topic_score_gemma":0.0010652285,"teacher_disagreement_score":0.0034341377,"about_ca_system_score_codex":0.00054947496,"about_ca_system_score_gemma":0.00053651514,"threshold_uncertainty_score":0.011488378},"labels":[],"label_agreement":null},{"id":"W2516986061","doi":"10.1177/1550147716660904","title":"Correlation-sum-deviation ranging method for vehicular node based on IEEE 802.11p short preamble","year":2016,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Ranging; Real-time computing; Multipath propagation; Satellite navigation; BeiDou Navigation Satellite System; Global Positioning System; Satellite system; Channel (broadcasting); Telecommunications; GNSS applications","score_opus":0.00968330902095127,"score_gpt":0.25308675746533765,"score_spread":0.24340344844438638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516986061","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01328007,0.0005431971,0.9838868,0.00008787599,0.00013412669,0.000039135593,0.00002614629,0.00072302844,0.001279542],"genre_scores_gemma":[0.5610507,0.0009455178,0.4333485,0.00012327202,0.00012898904,0.00017825917,0.000203368,0.000055160217,0.003966229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943906,0.00013250872,0.000034057488,0.000100225836,0.00025384346,0.00004020223],"domain_scores_gemma":[0.999508,0.00011379088,0.00005414577,0.00006243711,0.00024203931,0.000019669169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004130717,0.0005683616,0.00044733295,0.00069629203,0.0004085059,0.00035711582,0.0009731255,0.00042073603,0.00075076526],"category_scores_gemma":[0.0015252583,0.00021345972,0.0003859554,0.0006163938,0.00025759963,0.00069103163,0.00061511266,0.0005001621,0.00042861523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036125138,0.00007987373,0.0026289672,0.00029966148,0.00008521794,0.00038004114,0.0003411856,0.108739786,0.120811075,0.012764717,0.0050484207,0.74845976],"study_design_scores_gemma":[0.000064966596,0.0004272431,0.001986951,0.0000377238,0.00006472676,0.0010562182,0.00009730362,0.93388957,0.051389582,0.0024230492,0.008458584,0.00010416411],"about_ca_topic_score_codex":0.0016652659,"about_ca_topic_score_gemma":0.001919116,"teacher_disagreement_score":0.0016652659,"about_ca_system_score_codex":0.0002871923,"about_ca_system_score_gemma":0.00094680814,"threshold_uncertainty_score":0.0033112168},"labels":[],"label_agreement":null},{"id":"W2518317881","doi":"","title":"Fast particle flow particle filters via clustering","year":2016,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Particle filter; Auxiliary particle filter; Particle (ecology); Algorithm; Cluster analysis; Computer science; Flow (mathematics); Overhead (engineering); Tracking (education); Filter (signal processing); Mathematical optimization; Mathematics; Artificial intelligence; Ensemble Kalman filter; Computer vision; Kalman filter; Geometry","score_opus":0.016493667350158225,"score_gpt":0.2272876004070345,"score_spread":0.21079393305687627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518317881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008072998,0.00009191117,0.9980615,0.00005083859,0.00003610129,0.00002833882,0.000023908418,0.0003789169,0.000521185],"genre_scores_gemma":[0.1038636,0.0004914681,0.888858,0.00018579989,0.0001403733,0.0003610509,0.00046196196,0.0002842314,0.005353446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887985,0.0002610946,0.00005487353,0.00023594004,0.00046744835,0.00010071384],"domain_scores_gemma":[0.9979576,0.0010086464,0.00013652668,0.00022656584,0.00061006617,0.00006057318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020816717,0.0015020866,0.0015251313,0.001726196,0.0010990074,0.0015626313,0.0016730767,0.0023365007,0.0033565802],"category_scores_gemma":[0.006713965,0.000923335,0.0012261962,0.0018947978,0.00083569053,0.001992031,0.0015893233,0.0020072039,0.0016734344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118223776,0.000049439193,0.0005635442,0.00011922683,0.00007440851,0.000054564945,0.00010443823,0.7375629,0.0024354374,0.02792492,0.0064007547,0.22459224],"study_design_scores_gemma":[0.000010594667,0.000008660255,0.000080271246,0.000006745713,0.0000042458264,0.000010485916,0.00000593856,0.9925137,0.0005940178,0.005175498,0.0015814292,0.000008320371],"about_ca_topic_score_codex":0.021280115,"about_ca_topic_score_gemma":0.014345234,"teacher_disagreement_score":0.021280115,"about_ca_system_score_codex":0.0014822842,"about_ca_system_score_gemma":0.0027626243,"threshold_uncertainty_score":0.042312503},"labels":[],"label_agreement":null},{"id":"W2519081221","doi":"10.1109/hpcsim.2016.7568382","title":"A novel approach to provide safe indoor industrial environment","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hidden Markov model; Computer science; Motion (physics); Routing (electronic design automation); Wireless sensor network; Real-time computing; Artificial intelligence; Wireless; Motion detection; Computer vision; Pattern recognition (psychology); Embedded system; Computer network; Telecommunications","score_opus":0.02526575935507133,"score_gpt":0.18762327605816487,"score_spread":0.16235751670309354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519081221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008502601,0.00039466922,0.98340976,0.00016146405,0.00015521125,0.000085088825,0.000060614766,0.0016909803,0.00553959],"genre_scores_gemma":[0.21033487,0.000964924,0.76878244,0.00024191188,0.00011514404,0.00014958953,0.0003018541,0.00014552045,0.0189637],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938524,0.00006560537,0.000029609515,0.000169323,0.00029559646,0.000054521654],"domain_scores_gemma":[0.99971837,0.000036185436,0.000037440015,0.000076497105,0.00010461341,0.000026853959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028891835,0.00071026344,0.00048611753,0.0009039763,0.0005913354,0.0008836531,0.0013843997,0.00088162295,0.0028325042],"category_scores_gemma":[0.00049287063,0.00031605942,0.00064206927,0.00060413155,0.00036031945,0.0011043851,0.0012899739,0.0006091421,0.0022489438],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014752531,0.00018834112,0.0021371143,0.00055345235,0.00006625596,0.0006765847,0.0005055005,0.017255526,0.16701908,0.01228098,0.0067214807,0.79244804],"study_design_scores_gemma":[0.00010253082,0.0011851161,0.006556709,0.00021059718,0.00025617873,0.0059775687,0.0010013594,0.42165184,0.27215397,0.013142617,0.27750638,0.00025517974],"about_ca_topic_score_codex":0.0011263293,"about_ca_topic_score_gemma":0.0018723115,"teacher_disagreement_score":0.0028325042,"about_ca_system_score_codex":0.0002831268,"about_ca_system_score_gemma":0.00077601447,"threshold_uncertainty_score":0.009475648},"labels":[],"label_agreement":null},{"id":"W2519433470","doi":"10.1109/cscwd.2016.7566062","title":"Implicit occupancy detection for energy conservation in commercial buildings: A review","year":2016,"lang":"en","type":"review","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Occupancy; Computer science; Key (lock); Data collection; Sensor fusion; Ground truth; Focus (optics); Data science; Architectural engineering; Artificial intelligence; Engineering; Computer security","score_opus":0.028417278712369924,"score_gpt":0.3045676977582514,"score_spread":0.27615041904588145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519433470","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000694942,0.99043274,0.0046445704,0.00026686265,0.00033653015,0.000020100624,0.00010308007,0.000049537477,0.0034516684],"genre_scores_gemma":[0.0065041957,0.98783386,0.003783427,0.00013446868,0.0002599958,0.000020924577,0.00016326869,0.000016860304,0.0012829591],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948967,0.0000758636,0.0000563341,0.00012820853,0.00021607161,0.000033817243],"domain_scores_gemma":[0.9989065,0.00063300173,0.00009351204,0.0000363331,0.000304815,0.000025832273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008977725,0.0011361955,0.0013182183,0.0024181183,0.0002442051,0.0013445498,0.0015102222,0.0009593689,0.004196874],"category_scores_gemma":[0.0018154106,0.00051296956,0.00089543685,0.0033485214,0.00040113082,0.001964438,0.00076078693,0.000732546,0.0022563203],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004327147,0.000054693952,0.00054601807,0.017004183,0.00008633681,0.000084876614,0.00006866443,0.0016380794,0.0011738369,0.0036943604,0.012577253,0.9630284],"study_design_scores_gemma":[0.000012260429,0.0002500475,0.002906503,0.0090557495,0.00040423204,0.0011399033,0.00031865874,0.00361756,0.0035102942,0.004288382,0.9743815,0.000114959395],"about_ca_topic_score_codex":0.002472054,"about_ca_topic_score_gemma":0.002848398,"teacher_disagreement_score":0.004196874,"about_ca_system_score_codex":0.000462932,"about_ca_system_score_gemma":0.001016458,"threshold_uncertainty_score":0.014039993},"labels":[],"label_agreement":null},{"id":"W2520929227","doi":"","title":"A hybrid autonomous positioning system for public transportation","year":2005,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; York University","funders":"","keywords":"Geography; Global Positioning System; Cartography; Public transport; Transport engineering; Computer science; Engineering; Telecommunications","score_opus":0.008464049782588313,"score_gpt":0.1927902580696132,"score_spread":0.1843262082870249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520929227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07226151,0.0014925555,0.8698552,0.0006092054,0.0006872981,0.00017876613,0.0011090388,0.017120313,0.036686026],"genre_scores_gemma":[0.76676935,0.00067181577,0.17389525,0.0003616965,0.00021656626,0.00025098058,0.0020250594,0.00014853737,0.055660732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998305,0.000023751403,0.000005230653,0.000044434186,0.00007585669,0.000020278718],"domain_scores_gemma":[0.99989676,0.00000957312,0.000008181923,0.000024126986,0.00004981756,0.000011510225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013353367,0.00031672817,0.00031098624,0.00031135822,0.0003764966,0.00043591636,0.0006144919,0.00057145103,0.00635623],"category_scores_gemma":[0.00016957139,0.00014170723,0.00017431033,0.00038163102,0.00013818173,0.0005772738,0.00054182694,0.0003640535,0.003583531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005473216,0.0001192345,0.0022272975,0.00018168039,0.00006083262,0.0003165783,0.00015707375,0.019994875,0.16502136,0.01434779,0.029447975,0.767578],"study_design_scores_gemma":[0.00021317528,0.001944723,0.011402798,0.00004723566,0.00019202588,0.0012978044,0.00013643764,0.56700915,0.075391814,0.007173476,0.33501714,0.00017433373],"about_ca_topic_score_codex":0.0027922555,"about_ca_topic_score_gemma":0.0026246358,"teacher_disagreement_score":0.00635623,"about_ca_system_score_codex":0.00028052533,"about_ca_system_score_gemma":0.00043741177,"threshold_uncertainty_score":0.021263719},"labels":[],"label_agreement":null},{"id":"W2529641732","doi":"10.1016/j.adhoc.2016.09.020","title":"Localization in wireless sensor networks: A Dempster-Shafer evidence theoretical approach","year":2016,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Georgia State University; National Aeronautics and Space Administration; Cummings Foundation; National Science Foundation","keywords":"Dempster–Shafer theory; Wireless sensor network; Sensor fusion; Computer science; Node (physics); Set (abstract data type); Signal strength; Sampling (signal processing); Algorithm; Data mining; Artificial intelligence; Engineering; Telecommunications; Computer network","score_opus":0.010451779308757509,"score_gpt":0.21266713020794625,"score_spread":0.20221535089918874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529641732","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004785251,0.0074025774,0.9777794,0.0031398246,0.00012979511,0.000070993716,0.00020654114,0.00007054395,0.006415118],"genre_scores_gemma":[0.625978,0.040731322,0.31593385,0.0013405067,0.0020824678,0.0007842575,0.00058810366,0.00009205342,0.012469411],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9944805,0.003219513,0.00033646493,0.000608668,0.0011467676,0.00020809895],"domain_scores_gemma":[0.94145954,0.052280165,0.0018941897,0.0014163735,0.0025750883,0.00037459508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011554702,0.0018746555,0.0045502847,0.011016085,0.001513746,0.0042241337,0.004093589,0.005023149,0.005519975],"category_scores_gemma":[0.06160589,0.0015566042,0.0019117757,0.011064117,0.008340837,0.011854847,0.004124108,0.0043739397,0.0008237455],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045897486,0.000053843472,0.0011311322,0.0005670868,0.00017854833,0.0002263242,0.00015996744,0.18149403,0.00016258933,0.7717645,0.004052485,0.04016364],"study_design_scores_gemma":[0.000029992472,0.0000484389,0.00029590755,0.00017377005,0.00006877101,0.00020477205,0.00009505572,0.22776462,0.00014252105,0.76890296,0.0022404504,0.000032805077],"about_ca_topic_score_codex":0.0037105726,"about_ca_topic_score_gemma":0.0022386208,"teacher_disagreement_score":0.011554702,"about_ca_system_score_codex":0.0037938752,"about_ca_system_score_gemma":0.0021104198,"threshold_uncertainty_score":0.061107814},"labels":[],"label_agreement":null},{"id":"W2531669634","doi":"10.1016/j.jnca.2016.10.004","title":"Compressive sensing based data quality improvement for crowd-sensing applications","year":2016,"lang":"en","type":"article","venue":"Journal of Network and Computer Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Missing data; Compressed sensing; Data quality; Data mining; Variety (cybernetics); Quality (philosophy); Artificial intelligence; Machine learning","score_opus":0.026660297955409095,"score_gpt":0.27331323960299425,"score_spread":0.24665294164758517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531669634","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051024392,0.00050043786,0.94490033,0.00044685777,0.00015860298,0.000070931645,0.0001433903,0.00043755412,0.002317452],"genre_scores_gemma":[0.75196785,0.00055119954,0.24334823,0.00021213673,0.00024981468,0.00008579031,0.0004092518,0.000058312737,0.0031173455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992111,0.00017486913,0.00003361973,0.00012188998,0.00037131083,0.000087242865],"domain_scores_gemma":[0.9980166,0.0008667267,0.00016357674,0.00024776638,0.0006112758,0.00009404008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009324179,0.0007386654,0.00059110386,0.00069220184,0.00049359933,0.00046877915,0.00065719034,0.000786951,0.0018946537],"category_scores_gemma":[0.0044176923,0.00023006627,0.00027493,0.00079206005,0.0005483321,0.0011396892,0.0013676365,0.0008153469,0.0004308301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019855862,0.00040160483,0.0031997424,0.0003466983,0.00011749963,0.0003856403,0.00032672202,0.2586761,0.18119384,0.011707124,0.0068140454,0.5348455],"study_design_scores_gemma":[0.00003536459,0.00022571074,0.0011096938,0.000020140684,0.00002795577,0.00019181543,0.00009799348,0.96044254,0.03185779,0.003696823,0.002269851,0.000024287283],"about_ca_topic_score_codex":0.0016462235,"about_ca_topic_score_gemma":0.002593746,"teacher_disagreement_score":0.0018946537,"about_ca_system_score_codex":0.0003561859,"about_ca_system_score_gemma":0.00064628985,"threshold_uncertainty_score":0.0063382387},"labels":[],"label_agreement":null},{"id":"W2533106845","doi":"10.1109/embc.2016.7591808","title":"An RF-based wearable sensor system for indoor tracking to facilitate efficient healthcare management","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wearable computer; Computer science; Tracking system; Reliability (semiconductor); Health care; Global Positioning System; Microcontroller; Real-time computing; Analytics; Tracking (education); Transceiver; Embedded system; Kalman filter; Telecommunications; Database; Wireless; Artificial intelligence","score_opus":0.02876514049378404,"score_gpt":0.2419394703861666,"score_spread":0.21317432989238255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2533106845","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068622746,0.0020479257,0.90975726,0.0003719695,0.000688058,0.00029767666,0.0004714078,0.005890174,0.011852736],"genre_scores_gemma":[0.6706307,0.0016593811,0.30866238,0.00073680567,0.00029471915,0.00041450752,0.0005872677,0.00013562363,0.016878659],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996947,0.00006353384,0.000026470881,0.00008897184,0.00010306327,0.000023230376],"domain_scores_gemma":[0.99980444,0.00003147506,0.000040390114,0.00003535119,0.00006901936,0.000019374309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030193044,0.00049499236,0.00040739946,0.00043153687,0.00025353752,0.00038210626,0.0006304702,0.0005662056,0.002938803],"category_scores_gemma":[0.00046326572,0.0001745723,0.0002708526,0.0004757346,0.0001337873,0.0005055869,0.00042180103,0.00029751734,0.0016027549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037785218,0.00021689029,0.004878052,0.0006998707,0.00009710661,0.00063269667,0.00029432026,0.0064762346,0.5262605,0.0036921506,0.012221912,0.44415247],"study_design_scores_gemma":[0.00021657726,0.0044555385,0.031639338,0.00034739898,0.00047849253,0.0071564284,0.00024056282,0.23031634,0.530048,0.0028559822,0.19195701,0.0002883276],"about_ca_topic_score_codex":0.00030453547,"about_ca_topic_score_gemma":0.00045021408,"teacher_disagreement_score":0.002938803,"about_ca_system_score_codex":0.00016981605,"about_ca_system_score_gemma":0.00030036597,"threshold_uncertainty_score":0.009831309},"labels":[],"label_agreement":null},{"id":"W2535108461","doi":"10.1109/icscs.2009.5412692","title":"Distributed processing techniques for beamforming in wireless sensor networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Institut National de la Recherche Scientifique","funders":"","keywords":"Beamforming; Computer science; Node (physics); Wireless sensor network; Null (SQL); Interference (communication); Computer network; Wireless network; Wireless; Power (physics); Point (geometry); Distributed computing; Channel (broadcasting); Telecommunications; Engineering; Mathematics","score_opus":0.007342334104090934,"score_gpt":0.22620885624493617,"score_spread":0.21886652214084523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2535108461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028320707,0.00180421,0.9960998,0.00020623156,0.000121558434,0.000021176756,0.000016351227,0.00011559895,0.0013317158],"genre_scores_gemma":[0.06826933,0.015425279,0.90684617,0.0005063912,0.0009930102,0.00063468824,0.00020784237,0.00013545898,0.006981801],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999041,0.0003655183,0.000065968736,0.00012936011,0.0003582568,0.000039889837],"domain_scores_gemma":[0.99912053,0.00053148577,0.00006784334,0.000113402006,0.00014944687,0.000017198858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011688726,0.0012906399,0.0008868095,0.000746088,0.00045709015,0.0010940842,0.00093082944,0.0012860359,0.0033414457],"category_scores_gemma":[0.0030406252,0.0004647709,0.00066534744,0.0022249012,0.0012209197,0.0013258518,0.00096551294,0.002521471,0.0021495577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010165684,0.000060206254,0.000288265,0.00076638436,0.00010353755,0.00020449824,0.00026343646,0.25242436,0.012392207,0.33313844,0.010289633,0.38996735],"study_design_scores_gemma":[0.00005812052,0.000119972174,0.00019215667,0.0001528523,0.00003581166,0.00020617545,0.00005827376,0.6677344,0.0032546464,0.27779314,0.050345168,0.000049244274],"about_ca_topic_score_codex":0.0009806999,"about_ca_topic_score_gemma":0.0011011746,"teacher_disagreement_score":0.0033414457,"about_ca_system_score_codex":0.00067769847,"about_ca_system_score_gemma":0.00062705297,"threshold_uncertainty_score":0.0111781955},"labels":[],"label_agreement":null},{"id":"W2535272587","doi":"10.1109/icscs.2009.5412288","title":"A stochastic approach of mobile robot navigation using customized RFID systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Trilateration; Mobile robot; Extended Kalman filter; Computer science; Robot; Kalman filter; Mobile robot navigation; Real-time computing; Radio navigation; Computer vision; Artificial intelligence; Engineering; Robot control; Global Positioning System; Telecommunications","score_opus":0.012007600892802612,"score_gpt":0.2259603085313738,"score_spread":0.2139527076385712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2535272587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007506799,0.00012295748,0.9911963,0.000046458106,0.000018433677,0.0000116384645,0.000019124132,0.000055561304,0.0010226503],"genre_scores_gemma":[0.8050031,0.0011100532,0.18575715,0.0000902376,0.00013957279,0.00016738738,0.00015861588,0.000062160376,0.007511733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952364,0.00012624296,0.000023548911,0.00012996174,0.00015283923,0.0000436552],"domain_scores_gemma":[0.9994617,0.0002236077,0.00013190476,0.000042497413,0.000117256335,0.00002310011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005364497,0.0005617258,0.00058008725,0.00056028576,0.00031236617,0.00060225563,0.00083787524,0.000552761,0.00095726567],"category_scores_gemma":[0.0013865214,0.00041065316,0.00078733277,0.0005524047,0.000739506,0.0006977325,0.0006705419,0.00050439505,0.00022716355],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016216809,0.00001116188,0.00039262383,0.000045018085,0.000020241665,0.0000893364,0.000038961316,0.9594257,0.0016573411,0.02989124,0.00018625075,0.00822599],"study_design_scores_gemma":[0.0000034637915,0.000025682046,0.00014628313,0.0000033171973,0.000005965599,0.000023040478,0.0000056694867,0.99536264,0.00022527116,0.0038014476,0.00038816236,0.000008984409],"about_ca_topic_score_codex":0.008186772,"about_ca_topic_score_gemma":0.0068919878,"teacher_disagreement_score":0.008186772,"about_ca_system_score_codex":0.0008272676,"about_ca_system_score_gemma":0.0007269543,"threshold_uncertainty_score":0.016278207},"labels":[],"label_agreement":null},{"id":"W2537105705","doi":"10.3390/s16101752","title":"Design and Implementation of Foot-Mounted Inertial Sensor Based Wearable Electronic Device for Game Play Application","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council","keywords":"Wearable computer; Inertial measurement unit; Foot (prosody); Computer science; Wearable technology; Inertial frame of reference; Embedded system; Human–computer interaction; Simulation; Engineering; Artificial intelligence; Physics","score_opus":0.008072346701900332,"score_gpt":0.2502608991694376,"score_spread":0.2421885524675373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2537105705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06498293,0.00069705944,0.918379,0.00030498547,0.0004520468,0.00076054526,0.0003771872,0.0032261612,0.010820115],"genre_scores_gemma":[0.60906875,0.00091642636,0.3694649,0.00040603318,0.00012613805,0.00076914375,0.0005159091,0.000090714704,0.018642003],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999723,0.00003628703,0.0000298095,0.00007319138,0.0001054165,0.000032215958],"domain_scores_gemma":[0.9998367,0.000016460945,0.000020615365,0.000016570646,0.00009289216,0.000016759312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021861754,0.0005531818,0.00038969095,0.0005815782,0.000203582,0.00040887212,0.0011499571,0.0005313192,0.004197085],"category_scores_gemma":[0.00035926557,0.0002314076,0.00025356462,0.00026173776,0.000136233,0.0004435773,0.0004148971,0.00024390285,0.0015228862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039806758,0.00021717,0.00552826,0.000806321,0.000084738975,0.0009934938,0.0003430348,0.003103273,0.6749361,0.0035226515,0.00771307,0.30235383],"study_design_scores_gemma":[0.00026298454,0.005102388,0.02942957,0.0002752099,0.0003305389,0.0052896617,0.0003493848,0.1337974,0.6851994,0.0011344589,0.13862169,0.00020736048],"about_ca_topic_score_codex":0.00048057473,"about_ca_topic_score_gemma":0.0006436166,"teacher_disagreement_score":0.004197085,"about_ca_system_score_codex":0.00014805487,"about_ca_system_score_gemma":0.00033806273,"threshold_uncertainty_score":0.014040649},"labels":[],"label_agreement":null},{"id":"W2540120841","doi":"10.1109/pimrc.2014.7136396","title":"Cooperative node positioning in vehicular networks using inter-node distance measurements","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fuse (electrical); Computer science; Node (physics); Kalman filter; Wireless ad hoc network; Position (finance); Multilateration; Vehicular ad hoc network; Computer network; Real-time computing; Artificial intelligence; Wireless; Telecommunications; Engineering; Electrical engineering","score_opus":0.016843205716097015,"score_gpt":0.226589932387259,"score_spread":0.20974672667116198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2540120841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024382574,0.00056665344,0.9738927,0.000039431532,0.000045012042,0.000015529406,0.000012454297,0.00018048313,0.00086523074],"genre_scores_gemma":[0.874145,0.0006314976,0.123703204,0.00003903356,0.000057706075,0.00004941891,0.000052212108,0.000017322938,0.0013047003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989623,0.00028416558,0.000040147308,0.00021912048,0.00043181982,0.000062515595],"domain_scores_gemma":[0.9990833,0.0003816232,0.00012308589,0.00015458613,0.00023187038,0.000025554302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073368364,0.00059815234,0.00048290612,0.00075184315,0.00042102992,0.0005145302,0.0011267231,0.0006694988,0.00020914037],"category_scores_gemma":[0.0025778764,0.00029888513,0.00031537947,0.0010297509,0.00047954152,0.0012160091,0.0009982598,0.00045617105,0.00017520048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019046593,0.00007098791,0.0042037554,0.0002389052,0.00013061208,0.0002973436,0.00045205466,0.50867367,0.0525104,0.014950131,0.0009075865,0.41737407],"study_design_scores_gemma":[0.000019139095,0.00023061565,0.0013066832,0.000015685677,0.000046795165,0.00022611352,0.000074525014,0.9762053,0.014772471,0.003733438,0.0033356058,0.00003364729],"about_ca_topic_score_codex":0.0026692017,"about_ca_topic_score_gemma":0.0029927776,"teacher_disagreement_score":0.0026692017,"about_ca_system_score_codex":0.0003282671,"about_ca_system_score_gemma":0.0004738979,"threshold_uncertainty_score":0.0053073764},"labels":[],"label_agreement":null},{"id":"W2542043134","doi":"10.1007/s00221-016-4803-5","title":"The effects of obstacle proximity on aperture crossing behaviours","year":2016,"lang":"en","type":"article","venue":"Experimental Brain Research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obstacle; Neuroscience; Physics; Psychology; Anatomy; Biology; Geography","score_opus":0.023712864431357523,"score_gpt":0.3322586513356751,"score_spread":0.3085457869043176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542043134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.998064,0.00008090881,0.0007023388,0.00002320923,0.00001291474,0.000009662173,0.00005962684,0.000013379478,0.0010339431],"genre_scores_gemma":[0.9980478,0.00009374511,0.0006284531,0.000026406113,0.0000068754707,0.00003867454,0.00008321451,0.00003915555,0.0010355338],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994499,0.00015558397,0.000042765732,0.00013716104,0.00012702137,0.00008754479],"domain_scores_gemma":[0.99596715,0.0025045737,0.0004916611,0.0004306175,0.00015192477,0.0004540812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034334502,0.00045051822,0.0004612918,0.00038985148,0.00022973811,0.00074799196,0.00045070273,0.0004877462,0.005951007],"category_scores_gemma":[0.0072830482,0.0003362268,0.00024069335,0.00023011936,0.00091094483,0.000762477,0.0010117997,0.0007357418,0.00030945125],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.027982954,0.0036341527,0.017544946,0.0006200072,0.00022380112,0.0005713586,0.002136665,0.0059033628,0.8969655,0.001571001,0.00058241904,0.04226388],"study_design_scores_gemma":[0.0009840361,0.031215215,0.68408394,0.00024851447,0.00070089474,0.001767448,0.0029963478,0.022672078,0.24490926,0.0066574863,0.0035954372,0.00016931916],"about_ca_topic_score_codex":0.0014051596,"about_ca_topic_score_gemma":0.0014693373,"teacher_disagreement_score":0.005951007,"about_ca_system_score_codex":0.00027241916,"about_ca_system_score_gemma":0.00037878036,"threshold_uncertainty_score":0.01990813},"labels":[],"label_agreement":null},{"id":"W2546985209","doi":"10.1109/ccece.2016.7726813","title":"Spatial direction corrections to improve indoor localization using inertial navigation with sensors on a smartphone","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Robustness (evolution); Computer science; Inertial navigation system; Heading (navigation); Computer vision; Artificial intelligence; Variance (accounting); Inertial measurement unit; Algorithm; Dilution of precision; Inertial frame of reference; Global Positioning System; Geodesy; Telecommunications; Physics; Geography","score_opus":0.0070665453724984015,"score_gpt":0.20740668606429113,"score_spread":0.20034014069179273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546985209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20503837,0.0008369856,0.7846981,0.00018000738,0.00028765167,0.000080665675,0.00026125784,0.005305349,0.0033115689],"genre_scores_gemma":[0.7622917,0.00035155617,0.23472774,0.00006554928,0.000054908945,0.000046537814,0.00032845495,0.000094428586,0.0020391918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972945,0.000047444155,0.000018408984,0.000049083184,0.00012686015,0.000028851353],"domain_scores_gemma":[0.9995802,0.000078987665,0.000053760014,0.00010162404,0.00017459723,0.000010929886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020864344,0.00063738227,0.0003140314,0.00041859053,0.00013124626,0.0002491667,0.00039852146,0.00027633805,0.0014138233],"category_scores_gemma":[0.0011082053,0.00013084638,0.00031527245,0.00043904386,0.00010819205,0.00030656238,0.0003435143,0.00022007275,0.0006812659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004446687,0.00011339651,0.010462425,0.0004626174,0.00008943223,0.00064552104,0.00025755065,0.075874224,0.22589794,0.0015106277,0.0045112623,0.67973036],"study_design_scores_gemma":[0.00012457155,0.001147827,0.03057517,0.00010213942,0.00023225139,0.0020806852,0.00020992274,0.70100325,0.23705971,0.0009560223,0.026407694,0.000100780155],"about_ca_topic_score_codex":0.004131495,"about_ca_topic_score_gemma":0.0068855444,"teacher_disagreement_score":0.004131495,"about_ca_system_score_codex":0.00011583813,"about_ca_system_score_gemma":0.0003549126,"threshold_uncertainty_score":0.008214891},"labels":[],"label_agreement":null},{"id":"W2547531497","doi":"10.1002/9780470487068.ch25","title":"Nonparametric Techniques for Pedestrian Tracking in Wireless Local Area Networks","year":2010,"lang":"en","type":"other","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pedestrian; Tracking (education); Nonparametric statistics; Computer science; Wireless; Artificial intelligence; Computer vision; Geography; Telecommunications; Mathematics; Psychology; Statistics","score_opus":0.010929049083616714,"score_gpt":0.23631422065766886,"score_spread":0.22538517157405213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547531497","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002296668,0.0004989125,0.9963427,0.000045903907,0.00003107851,0.0000051026186,0.00002678215,0.00008720531,0.0006657205],"genre_scores_gemma":[0.3936547,0.005410417,0.5781763,0.00015914433,0.00053926295,0.00020127275,0.0004886893,0.0001607493,0.021209395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954826,0.0001771122,0.000019427453,0.000085751875,0.00012913899,0.000040294024],"domain_scores_gemma":[0.9986265,0.0008518904,0.0001087739,0.00017922671,0.00021159355,0.000022020708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010629765,0.00051112706,0.0007231609,0.0008750928,0.0004514322,0.00091056735,0.00086435856,0.000636529,0.0016707645],"category_scores_gemma":[0.004834115,0.0004235719,0.00057863526,0.0017219966,0.00072097685,0.0013096088,0.0009893893,0.0012626059,0.000588847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009821633,0.000058355028,0.0016195169,0.00017895497,0.00008863177,0.00010353411,0.0001253187,0.5027176,0.0035407736,0.09590983,0.0038140544,0.39174515],"study_design_scores_gemma":[0.0000053173662,0.000019407002,0.0007660883,0.000015287509,0.000016007762,0.000063535605,0.00002396838,0.95328426,0.00072111504,0.0421695,0.002902154,0.000013275711],"about_ca_topic_score_codex":0.0037888826,"about_ca_topic_score_gemma":0.0042607747,"teacher_disagreement_score":0.0037888826,"about_ca_system_score_codex":0.00053924957,"about_ca_system_score_gemma":0.00060819567,"threshold_uncertainty_score":0.0075336695},"labels":[],"label_agreement":null},{"id":"W2547612877","doi":"10.1109/ccece.2016.7726781","title":"User-induced antenna variation and its impact on the performance of RSS-based indoor positioning","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"RSS; Computer science; Antenna (radio); Antenna height considerations; Wireless; Radiation pattern; Telecommunications","score_opus":0.009025374823616884,"score_gpt":0.22304242633554563,"score_spread":0.21401705151192876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547612877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9306991,0.00044463642,0.06543862,0.00006495628,0.000036183486,0.000019129227,0.00014965533,0.0007422787,0.0024054947],"genre_scores_gemma":[0.9977901,0.000059569335,0.0018702693,0.00001083057,0.0000039887045,0.0000042198567,0.000042942575,0.000021913309,0.00019604234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894935,0.00032654725,0.000043494478,0.0001731972,0.00032456953,0.00018287147],"domain_scores_gemma":[0.9973496,0.0015757299,0.00030332868,0.00039168049,0.00032242027,0.000057280715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000493665,0.00066116196,0.00047245593,0.00041084425,0.00022599653,0.00047724432,0.00053262664,0.0007733341,0.0009426887],"category_scores_gemma":[0.0034020562,0.0002301321,0.00025370825,0.00060075533,0.0003542992,0.00045553318,0.00039684708,0.0003287544,0.00040547654],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018156066,0.00019924522,0.05365695,0.0005708602,0.00042620642,0.002351511,0.00045505198,0.44128066,0.40821722,0.0012480849,0.0005148773,0.0892636],"study_design_scores_gemma":[0.000041206124,0.0029096247,0.09611708,0.00003939802,0.00032748454,0.004782178,0.00030475977,0.44374716,0.4487072,0.0006709567,0.002190111,0.0001628233],"about_ca_topic_score_codex":0.0008138589,"about_ca_topic_score_gemma":0.0007863381,"teacher_disagreement_score":0.0009426887,"about_ca_system_score_codex":0.00028378432,"about_ca_system_score_gemma":0.00018325174,"threshold_uncertainty_score":0.0031535625},"labels":[],"label_agreement":null},{"id":"W2550686015","doi":"10.1109/tmc.2016.2632715","title":"DV-maxHop: A Fast and Accurate Range-Free Localization Algorithm for Anisotropic Wireless Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Wireless sensor network; Algorithm; Network topology; Node (physics); Isotropy; Wireless; Range (aeronautics); Distributed computing; Computer network; Telecommunications","score_opus":0.007684205611487831,"score_gpt":0.21500571793343587,"score_spread":0.20732151232194804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550686015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038833274,0.00019506614,0.99424756,0.00008291711,0.000033909215,0.0000223916,0.00002354609,0.00042618837,0.0010851078],"genre_scores_gemma":[0.20864445,0.000608266,0.78644013,0.000101189784,0.000049365888,0.0001140072,0.00022201997,0.00018994324,0.0036306316],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997372,0.000061153216,0.0000143917705,0.000035824614,0.00013002161,0.000021424441],"domain_scores_gemma":[0.99968195,0.00012251768,0.000046915306,0.000046682955,0.000083214465,0.000018673993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054872053,0.00045189846,0.00046212977,0.00068440614,0.00039029063,0.0005953591,0.001065729,0.00051021,0.0009782112],"category_scores_gemma":[0.0014738686,0.00025286135,0.00032096374,0.0007148873,0.0003738106,0.0009904484,0.0009583758,0.000666446,0.00033404093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018074737,0.00005348612,0.0010822226,0.00018487782,0.000060266964,0.00018455693,0.00025725714,0.41888982,0.017901402,0.036847666,0.0103695365,0.51398814],"study_design_scores_gemma":[0.000028505045,0.000054789245,0.0001725452,0.0000107474725,0.000009041678,0.00014046286,0.000030578707,0.97956926,0.004682366,0.0056732562,0.009607177,0.00002135813],"about_ca_topic_score_codex":0.001853908,"about_ca_topic_score_gemma":0.0025653874,"teacher_disagreement_score":0.001853908,"about_ca_system_score_codex":0.0004732686,"about_ca_system_score_gemma":0.00061965646,"threshold_uncertainty_score":0.0036861897},"labels":[],"label_agreement":null},{"id":"W2551441692","doi":"10.1145/2910674.2910718","title":"Real-Time Indoor Localization in Smart Homes Using Ultrasound Technology","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Economic shortage; Computer science; Home automation; Position (finance); Population ageing; Population; Simulation; Real-time computing; Human–computer interaction; Telecommunications; Business; Medicine; Environmental health","score_opus":0.007099485488363648,"score_gpt":0.21295403370353935,"score_spread":0.2058545482151757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2551441692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20940283,0.0012434587,0.7824054,0.00022144934,0.00012229758,0.00007271304,0.00007542206,0.0033591369,0.0030972317],"genre_scores_gemma":[0.8306899,0.00063278864,0.16583166,0.00011991286,0.000084818996,0.000063728374,0.00007109062,0.00005352667,0.0024525432],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996414,0.0001378637,0.000014863357,0.000057849717,0.00011441875,0.00003360974],"domain_scores_gemma":[0.9996333,0.00017104406,0.00006289053,0.00004271227,0.000070436814,0.000019627201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034307767,0.00033779212,0.00031805472,0.00040348675,0.00014999093,0.00038665143,0.00038533265,0.00057705486,0.00095545425],"category_scores_gemma":[0.00093269954,0.00021397852,0.00018038592,0.00031395152,0.0003337764,0.0007187671,0.00047236454,0.00018109422,0.00041683923],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073074247,0.0001585353,0.007800699,0.000464946,0.00007594466,0.0010705783,0.0012427729,0.030591227,0.46437663,0.0044178567,0.0033076985,0.4857623],"study_design_scores_gemma":[0.00022812755,0.0032921482,0.02500584,0.00025885366,0.00033084818,0.004950416,0.0010666117,0.52802354,0.39863652,0.0045879437,0.03334957,0.0002695575],"about_ca_topic_score_codex":0.000886787,"about_ca_topic_score_gemma":0.0009569816,"teacher_disagreement_score":0.00095545425,"about_ca_system_score_codex":0.00016330877,"about_ca_system_score_gemma":0.00013134991,"threshold_uncertainty_score":0.0031963587},"labels":[],"label_agreement":null},{"id":"W2551532594","doi":"10.1002/9781118104750.ch24","title":"Polynomial‐Based Methods for Localization in Multiagent Systems","year":2011,"lang":"en","type":"other","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Semidefinite programming; Polynomial; Node (physics); Computer science; Relaxation (psychology); Wireless sensor network; Set (abstract data type); Noise (video); Range (aeronautics); Field (mathematics); Frame (networking); Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Engineering","score_opus":0.02612847281619988,"score_gpt":0.29375537103823673,"score_spread":0.26762689822203684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2551532594","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056668336,0.0018343487,0.99319226,0.000317398,0.00008559116,0.000019342506,0.00003498362,0.0000769293,0.0038725797],"genre_scores_gemma":[0.32822523,0.01904329,0.624721,0.0005629278,0.0011567129,0.00055217114,0.00042006923,0.00030453026,0.025014075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992964,0.00029702883,0.000033586384,0.00010802269,0.00021836089,0.00004656191],"domain_scores_gemma":[0.9990513,0.00060043647,0.0000999113,0.00006848615,0.0001482053,0.000031684845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010529647,0.0014653215,0.00090659724,0.0006803121,0.0004903341,0.0011281117,0.001062648,0.000887824,0.0046513136],"category_scores_gemma":[0.002445782,0.00041815362,0.000918565,0.0011459144,0.0010995393,0.0012839454,0.0014556512,0.0023498556,0.0012598496],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033151853,0.00003647031,0.00025754838,0.00049542467,0.00006621698,0.00009756806,0.00015072919,0.48131776,0.0017909089,0.4331525,0.006169753,0.076431915],"study_design_scores_gemma":[0.000011784386,0.000029326313,0.00008869252,0.000042993204,0.000011220454,0.000042485466,0.00003598467,0.8535475,0.0004058462,0.13158129,0.014188956,0.00001388753],"about_ca_topic_score_codex":0.0026202244,"about_ca_topic_score_gemma":0.002211438,"teacher_disagreement_score":0.0046513136,"about_ca_system_score_codex":0.0010952553,"about_ca_system_score_gemma":0.0009998078,"threshold_uncertainty_score":0.01556015},"labels":[],"label_agreement":null},{"id":"W2554598674","doi":"10.1109/ipin.2016.7743629","title":"An indoor location positioning algorithm for portable devices and autonomous machines","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Hybrid positioning system; Drone; Indoor positioning system; Real-time computing; Algorithm; Positioning system; Distributed computing; Engineering","score_opus":0.00569689957474498,"score_gpt":0.22107281501143075,"score_spread":0.21537591543668577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554598674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001223802,0.00029413798,0.9953271,0.000076557095,0.00012456035,0.00003065713,0.00004989977,0.000931491,0.0019417449],"genre_scores_gemma":[0.052701935,0.00060025335,0.9366853,0.00010090747,0.00013846028,0.00013538319,0.0003391218,0.00011851237,0.009180124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999686,0.000043669876,0.000019205045,0.00007573117,0.00015108762,0.000024330273],"domain_scores_gemma":[0.99984527,0.000026753809,0.000022822416,0.00003267016,0.00006480461,0.000007730918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018555472,0.000665768,0.00043300487,0.0006848834,0.0005276916,0.0006835509,0.0008776949,0.00087801646,0.0032032249],"category_scores_gemma":[0.00078569737,0.00026883607,0.0004800512,0.0010177928,0.0002883523,0.0010979516,0.0007703508,0.0007036558,0.0030365558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100750905,0.00003527894,0.00066275406,0.00025658254,0.000048567334,0.00031443938,0.00012966564,0.056303907,0.031444076,0.04493175,0.017870687,0.8479015],"study_design_scores_gemma":[0.00006398099,0.00025956056,0.0015624273,0.00012477413,0.00007728135,0.0017528795,0.00010214106,0.72869927,0.032194678,0.022521349,0.21254306,0.00009853723],"about_ca_topic_score_codex":0.0014584124,"about_ca_topic_score_gemma":0.0016301722,"teacher_disagreement_score":0.0032032249,"about_ca_system_score_codex":0.0003363501,"about_ca_system_score_gemma":0.00048995356,"threshold_uncertainty_score":0.010715842},"labels":[],"label_agreement":null},{"id":"W2557200782","doi":"10.4018/ijdst.2017010101","title":"A Comparative Study of Range-Free and Range-Based Localization Protocols for Wireless Sensor Network","year":2016,"lang":"en","type":"article","venue":"International Journal of Distributed Systems and Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"King Fahd University of Petroleum and Minerals; Acadia University","keywords":"RSS; Computer science; Wireless sensor network; Range (aeronautics); Centroid; Computer network; Node (physics); Distance-vector routing protocol; Signal strength; Cellular network; Routing protocol; Real-time computing; Topology (electrical circuits); Routing (electronic design automation); Artificial intelligence; Optimized Link State Routing Protocol","score_opus":0.02152895052205508,"score_gpt":0.2735545859626593,"score_spread":0.25202563544060425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557200782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2736724,0.087767825,0.5832446,0.0013978524,0.0013700183,0.0007356463,0.00040640778,0.001908791,0.04949641],"genre_scores_gemma":[0.8677573,0.02513983,0.099380344,0.00026416947,0.00033438322,0.0002634826,0.00053251145,0.00015190664,0.006176125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99660873,0.0009487399,0.0002701939,0.00025477167,0.0017602111,0.00015741869],"domain_scores_gemma":[0.9899922,0.005868186,0.00056283403,0.00077008543,0.0026663884,0.00014036943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002485976,0.00064685923,0.00067785446,0.0019393269,0.00062410557,0.0011624387,0.0012749528,0.00067864597,0.0012286777],"category_scores_gemma":[0.009329692,0.0002344906,0.0005231256,0.0023300038,0.00046006922,0.003781351,0.0006758561,0.00056287,0.00028693094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001678913,0.00053258415,0.008324949,0.0022566319,0.0005471366,0.00052258844,0.0006301246,0.12777072,0.03535996,0.059920624,0.0069213244,0.7555344],"study_design_scores_gemma":[0.00020441861,0.009090117,0.0144657,0.00059675105,0.0012108372,0.0065598907,0.0017302957,0.79374164,0.060742795,0.025791597,0.08551999,0.00034595453],"about_ca_topic_score_codex":0.0006696022,"about_ca_topic_score_gemma":0.0007393179,"teacher_disagreement_score":0.002485976,"about_ca_system_score_codex":0.0007904239,"about_ca_system_score_gemma":0.000589638,"threshold_uncertainty_score":0.013147235},"labels":[],"label_agreement":null},{"id":"W2557926121","doi":"10.1109/iemcon.2016.7746335","title":"Joint localization algorithm combining information from accelerometer and available reference nodes","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Accelerometer; Computer science; Joint (building); Wireless; Wireless sensor network; Real-time computing; Algorithm; Telecommunications; Engineering; Computer network","score_opus":0.019363420249368248,"score_gpt":0.18918495042088915,"score_spread":0.1698215301715209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557926121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004925082,0.0002765013,0.99361503,0.000063403495,0.000049018454,0.000027213859,0.000020841684,0.00053438195,0.0004885094],"genre_scores_gemma":[0.28051376,0.0007136029,0.7114737,0.00012991349,0.00013986284,0.00028114283,0.00034002637,0.00008331306,0.0063245986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992805,0.000100465426,0.00005701639,0.0002602515,0.0002451184,0.000056541994],"domain_scores_gemma":[0.99957186,0.00007603934,0.00007271803,0.00005733918,0.00019872676,0.000023348648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059878745,0.00093402545,0.0011342964,0.0010288346,0.0005227046,0.00086678006,0.0014517881,0.0009059121,0.0012088052],"category_scores_gemma":[0.0012640547,0.00040358154,0.00045541552,0.0013666345,0.00044447277,0.0016728108,0.0012981342,0.0005860271,0.00092061877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028923422,0.00009310378,0.0021524483,0.00018306621,0.00011464877,0.00015482286,0.000195213,0.12134816,0.025649842,0.0065298835,0.0034428039,0.8398467],"study_design_scores_gemma":[0.00011569185,0.00029712392,0.001524437,0.000030090958,0.000101134916,0.00052545394,0.00007387033,0.96718585,0.018282792,0.004299015,0.007502443,0.0000620303],"about_ca_topic_score_codex":0.002081764,"about_ca_topic_score_gemma":0.0024668702,"teacher_disagreement_score":0.002081764,"about_ca_system_score_codex":0.00032653165,"about_ca_system_score_gemma":0.0011839377,"threshold_uncertainty_score":0.0041392446},"labels":[],"label_agreement":null},{"id":"W2558112192","doi":"10.1109/cec.2016.7744146","title":"Evolutionary algorithmic deployment of radio beacons for indoor positioning","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Beacon; Software deployment; Mobile computing; Mobile device; Radio spectrum; Mobile telephony; Computer network; Mobile radio; Telecommunications; Distributed computing","score_opus":0.007219566202075557,"score_gpt":0.2030989430223106,"score_spread":0.19587937682023504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558112192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03018795,0.0002886443,0.964262,0.00016727818,0.000041162322,0.00005380208,0.000018020339,0.00011388467,0.0048671816],"genre_scores_gemma":[0.57809085,0.00044890004,0.41588038,0.0001483538,0.000046670157,0.00037188956,0.000098895754,0.00008073353,0.004833318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996511,0.00014930851,0.000014949821,0.000057290268,0.00007878592,0.0000486928],"domain_scores_gemma":[0.99935657,0.00038995742,0.000072250885,0.00005006386,0.00009644633,0.000034684508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000761213,0.000692677,0.000650046,0.00079552445,0.00048254582,0.0005871811,0.0012010112,0.0010357532,0.0016792845],"category_scores_gemma":[0.0034074944,0.00043509394,0.00054044335,0.0006774192,0.00074726634,0.0006858841,0.0010524277,0.00073803175,0.00027348692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026158135,0.000039902752,0.0007406165,0.0000346352,0.000028436067,0.00006465834,0.00007094885,0.94886124,0.0015436797,0.017906014,0.0005970206,0.030086735],"study_design_scores_gemma":[0.0000103712,0.000029971194,0.00010046051,0.0000065288127,0.000007979703,0.000024215207,0.0000135375,0.99529797,0.00020152272,0.0035222005,0.0007812707,0.000003936963],"about_ca_topic_score_codex":0.0019338755,"about_ca_topic_score_gemma":0.0019238447,"teacher_disagreement_score":0.0019338755,"about_ca_system_score_codex":0.0005711771,"about_ca_system_score_gemma":0.0006149078,"threshold_uncertainty_score":0.005617738},"labels":[],"label_agreement":null},{"id":"W2559011985","doi":"10.1115/1.4035295","title":"Observability Analysis of Relative Localization Filters Subjected to Platform Velocity Constraints","year":2016,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Observability; Uniqueness; Relative velocity; Computer science; Position (finance); Control theory (sociology); Real-time computing; Mathematics; Artificial intelligence; Physics; Control (management); Applied mathematics","score_opus":0.015689779497899944,"score_gpt":0.20709734839491523,"score_spread":0.19140756889701527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559011985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027084693,0.00009471687,0.9716067,0.000078679936,0.000010040918,0.000015410771,0.000022062115,0.00006109284,0.0010266335],"genre_scores_gemma":[0.96707046,0.0003787934,0.030124253,0.00005388915,0.000038883143,0.0000881645,0.000105412684,0.00005545817,0.0020846368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811184,0.00049419864,0.00008974301,0.00041209368,0.00064640236,0.00024574826],"domain_scores_gemma":[0.98995566,0.00654155,0.0016778577,0.0004084961,0.0012622678,0.00015409764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031618627,0.0010348881,0.001044766,0.0007515144,0.0005250683,0.0013778408,0.0008141769,0.0010676185,0.0018194407],"category_scores_gemma":[0.015369751,0.0004887925,0.00089519966,0.0004894938,0.0016823554,0.0022120692,0.0016304255,0.0012299481,0.00019879096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016415447,0.000044158405,0.0020467099,0.00021738785,0.000078667894,0.00024764115,0.00028060697,0.89894193,0.010783521,0.059514113,0.00033952604,0.027341513],"study_design_scores_gemma":[0.0000073546503,0.00008553928,0.00064297725,0.0000138996365,0.000016066546,0.000053905693,0.00004155043,0.99006224,0.002114452,0.0067490395,0.00019818178,0.000014838281],"about_ca_topic_score_codex":0.0037423794,"about_ca_topic_score_gemma":0.0013365275,"teacher_disagreement_score":0.0037423794,"about_ca_system_score_codex":0.0011276992,"about_ca_system_score_gemma":0.0012879273,"threshold_uncertainty_score":0.016721725},"labels":[],"label_agreement":null},{"id":"W2559200081","doi":"10.1155/2016/4502867","title":"An Indoor Ultrasonic Positioning System Based on TOA for Internet of Things","year":2016,"lang":"en","type":"article","venue":"Mobile Information Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Qinglan Project of Jiangsu Province of China; Fundamental Research Funds for the Central Universities; Government of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Hybrid positioning system; Ultrasonic sensor; Real-time computing; Positioning system; Ranging; Correctness; Global Positioning System; Indoor positioning system; Node (physics); Telecommunications; Algorithm; Acoustics","score_opus":0.004623836150614741,"score_gpt":0.1976379983018394,"score_spread":0.19301416215122466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559200081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02786938,0.001871339,0.91093665,0.00073217205,0.001991162,0.00039208325,0.0006365426,0.01085962,0.044710938],"genre_scores_gemma":[0.5170636,0.0019826526,0.41625753,0.0012765113,0.00055588054,0.00088239944,0.0017121624,0.00026051002,0.06000876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994098,0.00010331757,0.00003744635,0.00011847023,0.0002775076,0.000053577507],"domain_scores_gemma":[0.9997032,0.000030515956,0.000037759804,0.000055738088,0.00014709137,0.000025689047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031025623,0.00068106886,0.0006523193,0.0012372748,0.0007818357,0.0006720524,0.0009099099,0.00086211105,0.0050712577],"category_scores_gemma":[0.0005448226,0.00023222738,0.0005110647,0.0012142973,0.00026867594,0.0007591665,0.00070376147,0.000603443,0.0027864592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040132614,0.00017525785,0.0048723123,0.0010044489,0.00014583317,0.0010145657,0.0005148193,0.0064995554,0.20412837,0.02769965,0.03575121,0.7177926],"study_design_scores_gemma":[0.0002740347,0.0027949475,0.0153429825,0.00029907387,0.00076653465,0.009845264,0.000690407,0.15526177,0.20809713,0.013487357,0.592626,0.0005145011],"about_ca_topic_score_codex":0.0010251591,"about_ca_topic_score_gemma":0.0018001504,"teacher_disagreement_score":0.0050712577,"about_ca_system_score_codex":0.00039031295,"about_ca_system_score_gemma":0.0005781893,"threshold_uncertainty_score":0.016965032},"labels":[],"label_agreement":null},{"id":"W2561875741","doi":"10.1002/wcm.2763","title":"Characterizing multi‐hop localization for Internet of things","year":2016,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; United Arab Emirates University","keywords":"Computer science; Hop (telecommunications); Implementation; Internet of Things; Distributed computing; Overhead (engineering); Gaussian; The Internet; Computer network; Reliability (semiconductor); Computer security; World Wide Web","score_opus":0.018826842725960902,"score_gpt":0.25235106371969335,"score_spread":0.23352422099373243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2561875741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20939401,0.0005664827,0.78492904,0.00071197323,0.00007856515,0.00008250717,0.00013143681,0.00022593635,0.0038800754],"genre_scores_gemma":[0.98098123,0.0002605244,0.018010892,0.000053279397,0.00002129385,0.000046146954,0.00008449934,0.000019558607,0.00052263134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991265,0.00031220153,0.000044122615,0.00013467067,0.00026869294,0.000113832735],"domain_scores_gemma":[0.9957761,0.0026483643,0.0006725268,0.00042439037,0.0003943773,0.00008429484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011103881,0.00044570316,0.00039525345,0.00071363006,0.0004218724,0.00086092076,0.00055670034,0.0011127287,0.000663586],"category_scores_gemma":[0.008227218,0.00028268178,0.00042668107,0.0007996046,0.00063102896,0.0014285777,0.0009246195,0.00045994844,0.00013554121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044652053,0.000030455805,0.004493477,0.00006622018,0.000034528264,0.00014880144,0.00006846312,0.95965576,0.004399032,0.021259451,0.00052652555,0.009272737],"study_design_scores_gemma":[0.0000030307997,0.00003020912,0.0009991926,0.000008165947,0.000006593966,0.00013044052,0.000036874844,0.9871516,0.0011759272,0.009921115,0.00052800454,0.000008751596],"about_ca_topic_score_codex":0.0018643297,"about_ca_topic_score_gemma":0.001405547,"teacher_disagreement_score":0.0018643297,"about_ca_system_score_codex":0.0008529803,"about_ca_system_score_gemma":0.00046868404,"threshold_uncertainty_score":0.0061888695},"labels":[],"label_agreement":null},{"id":"W2562747653","doi":"10.6125/13-0826-761","title":"Comparative Analysis of Different Approaches for Multi-camera System Calibration","year":2013,"lang":"en","type":"article","venue":"Journal of aeronautics astronautics and aviation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Orientation (vector space); Calibration; Computer vision; Computer science; Artificial intelligence; Photogrammetry; Camera resectioning; Position (finance); Set (abstract data type); Remote sensing; Geography; Mathematics","score_opus":0.03989764161656162,"score_gpt":0.2446682568863112,"score_spread":0.20477061526974957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562747653","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093964346,0.006570763,0.8922486,0.00019795018,0.00008224953,0.00015390698,0.00016896347,0.0017242725,0.0048889224],"genre_scores_gemma":[0.62902576,0.0038984152,0.36344725,0.00009179621,0.000041489824,0.00012773296,0.0006068224,0.0003954506,0.0023652571],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971064,0.0010217954,0.00013579098,0.00037891226,0.0011833269,0.00017388116],"domain_scores_gemma":[0.9917385,0.004826714,0.00045509762,0.0010275979,0.0018545771,0.0000975234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003994987,0.0009573838,0.00091426214,0.0030167631,0.0005646373,0.0012731246,0.0013969383,0.0011575299,0.0022224244],"category_scores_gemma":[0.012782922,0.0005967066,0.0013582485,0.0025265035,0.00034706478,0.0022456355,0.0010710373,0.00070233137,0.00066263083],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005147125,0.0001433042,0.0057543973,0.00086592045,0.0005423628,0.00012364531,0.00027620554,0.3749706,0.010070065,0.005002588,0.0014015695,0.6003347],"study_design_scores_gemma":[0.00004672773,0.00030945925,0.009261291,0.00014756837,0.000299728,0.00041067615,0.00020815161,0.95725244,0.024442982,0.0019859779,0.005534274,0.00010070029],"about_ca_topic_score_codex":0.007102936,"about_ca_topic_score_gemma":0.007500212,"teacher_disagreement_score":0.007102936,"about_ca_system_score_codex":0.0011465389,"about_ca_system_score_gemma":0.000990413,"threshold_uncertainty_score":0.02112776},"labels":[],"label_agreement":null},{"id":"W2563767042","doi":"10.1007/978-3-319-51204-4_22","title":"An Accurate Passive RFID Indoor Localization System Based on Sense-a-Tag and Zoning Algorithm","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Landmark; Ultra high frequency; Radio-frequency identification; Interrogation; Context (archaeology); Tag system; Set (abstract data type); Identification (biology); Computer vision; Artificial intelligence; Algorithm; Telecommunications; Geography; Computer security","score_opus":0.011847566149576676,"score_gpt":0.21745063292297137,"score_spread":0.20560306677339468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563767042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01580779,0.0007877368,0.97702575,0.00013233736,0.00027241115,0.000049243416,0.000078670346,0.0029200728,0.002926062],"genre_scores_gemma":[0.37252095,0.0007153732,0.61309946,0.00033753202,0.00019456254,0.0001434089,0.00045474814,0.00014043837,0.012393599],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994912,0.00011305386,0.000028216442,0.00015954406,0.00016719595,0.000040711147],"domain_scores_gemma":[0.9997054,0.000047647598,0.00003328839,0.00006406716,0.00013259285,0.000017032118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037239637,0.00079915323,0.0012397959,0.00069186854,0.00055220147,0.0007037236,0.0015180367,0.00085765636,0.0019417632],"category_scores_gemma":[0.0005024646,0.00047173342,0.000478497,0.001014284,0.0003359447,0.0016245818,0.00091876107,0.0006020465,0.0017745788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076561543,0.00014234653,0.002583761,0.00040489,0.00012670153,0.00027476528,0.00025063445,0.029362684,0.2879914,0.011718445,0.00999493,0.65638375],"study_design_scores_gemma":[0.000254169,0.0010661981,0.006378797,0.000082327075,0.0003767629,0.0028321494,0.00017970624,0.7447762,0.17569837,0.005535944,0.062552765,0.00026658308],"about_ca_topic_score_codex":0.0012559718,"about_ca_topic_score_gemma":0.0015058002,"teacher_disagreement_score":0.0019417632,"about_ca_system_score_codex":0.00034938814,"about_ca_system_score_gemma":0.0005728771,"threshold_uncertainty_score":0.006495893},"labels":[],"label_agreement":null},{"id":"W2563796420","doi":"10.1007/978-3-319-51204-4_2","title":"Relative Localization for Small Wireless Sensor Networks","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Communications Research Centre Canada","funders":"","keywords":"Ranging; Multidimensional scaling; Wireless; Computer science; Maximum likelihood; Wireless sensor network; Scaling; Wireless network; Algorithm; Mathematics; Statistics; Telecommunications; Machine learning; Computer network","score_opus":0.018906229751777127,"score_gpt":0.21498464833737782,"score_spread":0.19607841858560068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563796420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002248723,0.014466348,0.9673941,0.00020516872,0.00042280916,0.000023131084,0.000058141668,0.00062907964,0.014552541],"genre_scores_gemma":[0.27764943,0.06559185,0.5420941,0.00040368363,0.0015027043,0.0004253628,0.0008187475,0.0009232509,0.11059079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996107,0.000106873165,0.000017921584,0.00008750528,0.0001576828,0.00001942815],"domain_scores_gemma":[0.99972695,0.00014926914,0.00002052021,0.000055665994,0.000038254228,0.000009355016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003596797,0.001126934,0.0007819769,0.00054654054,0.00024254089,0.0007868437,0.0012828106,0.00064086344,0.00850346],"category_scores_gemma":[0.001356739,0.00039790513,0.00042778373,0.0011155679,0.00067118375,0.0020198885,0.001228078,0.0009814225,0.002946003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009093573,0.000037735244,0.00017552476,0.0015540901,0.00005386593,0.00019473591,0.00019809518,0.1182423,0.020226337,0.3041274,0.02272636,0.53237265],"study_design_scores_gemma":[0.000029653334,0.0002758295,0.00048064295,0.00025377647,0.00007892653,0.0010140627,0.00012255361,0.44732228,0.009478829,0.30296424,0.23791054,0.000068673995],"about_ca_topic_score_codex":0.0005100926,"about_ca_topic_score_gemma":0.0006407324,"teacher_disagreement_score":0.00850346,"about_ca_system_score_codex":0.00048186394,"about_ca_system_score_gemma":0.00024678,"threshold_uncertainty_score":0.028446913},"labels":[],"label_agreement":null},{"id":"W2565231174","doi":"","title":"Qualitative RFID Tracking for ADL Recognition","year":2016,"lang":"en","type":"article","venue":"Constellation (Université du Québec à Chicoutimi)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Université de Sherbrooke","funders":"","keywords":"Computer science; Tracking (education); Embedded system; Artificial intelligence; Psychology","score_opus":0.024398699896370384,"score_gpt":0.22439405629577888,"score_spread":0.1999953563994085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565231174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018492315,0.0004377931,0.97461444,0.00014897007,0.0001191115,0.00007420494,0.00011298027,0.00076278375,0.005237434],"genre_scores_gemma":[0.51682276,0.0006662781,0.4709171,0.00027257937,0.00006705624,0.00018242907,0.00027717566,0.0000999439,0.010694686],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9993826,0.00014096616,0.000026766485,0.00014781962,0.0002477903,0.000053991207],"domain_scores_gemma":[0.9995377,0.00012581854,0.000051551386,0.00007841152,0.0001820681,0.00002437936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048793596,0.00050958764,0.00042525984,0.00093612896,0.00045396917,0.0010486142,0.0009721312,0.00071472855,0.0026489897],"category_scores_gemma":[0.0010269441,0.00019373524,0.00035561214,0.0006829714,0.0005465069,0.0009204478,0.0009896717,0.00038281616,0.0023775753],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023457334,0.00013629015,0.008285806,0.0006341265,0.000042079428,0.00041823118,0.0007487129,0.010637516,0.33076793,0.01299428,0.0031661075,0.6319342],"study_design_scores_gemma":[0.000054668464,0.00075787713,0.021594765,0.00028104437,0.000120061995,0.0032504362,0.0012234224,0.46772152,0.41837662,0.014143125,0.072208,0.00026839113],"about_ca_topic_score_codex":0.002034328,"about_ca_topic_score_gemma":0.0031817486,"teacher_disagreement_score":0.0026489897,"about_ca_system_score_codex":0.00046083907,"about_ca_system_score_gemma":0.00040555332,"threshold_uncertainty_score":0.008861721},"labels":[],"label_agreement":null},{"id":"W2566989580","doi":"10.1109/camad.2016.7790338","title":"Impact of orientation and wire placement on received signal strengths","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"MacEwan University","keywords":"Omnidirectional antenna; Orientation (vector space); Node (physics); Transceiver; Antenna (radio); Radio frequency; Wireless; SIGNAL (programming language); Computer science; Position (finance); Received signal strength indication; Acoustics; Telecommunications; Electrical engineering; Engineering; Physics","score_opus":0.006915077773182078,"score_gpt":0.24304818659225522,"score_spread":0.23613310881907315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566989580","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9720569,0.0001884638,0.020957736,0.00007510398,0.000083768566,0.0000267637,0.00022093242,0.0002968435,0.0060934676],"genre_scores_gemma":[0.9958324,0.00017525634,0.0024056225,0.000023645915,0.0000062642653,0.000009840028,0.00023281464,0.000047641046,0.0012665121],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989048,0.00038073337,0.000050932715,0.00016011207,0.00031939105,0.00018400898],"domain_scores_gemma":[0.9945767,0.0030926722,0.0007755664,0.0005193506,0.0008310422,0.00020474894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008711838,0.0007125934,0.00035178772,0.00037240027,0.0001955389,0.0007428313,0.0003240612,0.0003347662,0.0026681444],"category_scores_gemma":[0.0071171005,0.00028331744,0.00022312769,0.00058158534,0.00037911558,0.000605301,0.00050449796,0.00034168406,0.0012481742],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040518246,0.00037493522,0.36083007,0.00046030027,0.00019747036,0.0023760302,0.0005591166,0.27577105,0.21703525,0.0029576041,0.002106214,0.13328011],"study_design_scores_gemma":[0.00017318314,0.007224238,0.4946294,0.000118541735,0.00073791295,0.003682171,0.0039851954,0.22189863,0.2520722,0.002324624,0.01287425,0.0002797972],"about_ca_topic_score_codex":0.001618384,"about_ca_topic_score_gemma":0.0019293535,"teacher_disagreement_score":0.0026681444,"about_ca_system_score_codex":0.00030894924,"about_ca_system_score_gemma":0.00033267183,"threshold_uncertainty_score":0.008925796},"labels":[],"label_agreement":null},{"id":"W2567042612","doi":"10.1109/milcom.2016.7795330","title":"Path loss exponent estimation and RSS localization using the linearizing variable constraint","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Estimator; Constraint (computer-aided design); Position (finance); Algorithm; Estimation theory; Mathematics; Statistics","score_opus":0.011552940431800027,"score_gpt":0.21047204593100224,"score_spread":0.19891910549920222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567042612","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034038818,0.00032605714,0.9940959,0.00020947093,0.000025269124,0.000014111194,0.00009791878,0.00015730536,0.0016700517],"genre_scores_gemma":[0.4267936,0.0042501744,0.54720396,0.00045832724,0.00045736227,0.00040651922,0.0016310827,0.00045845314,0.018340558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988979,0.00036989735,0.00004264984,0.00030490107,0.0002846482,0.00010005795],"domain_scores_gemma":[0.99740005,0.0017430432,0.0002646288,0.00020959649,0.00034376592,0.000038877795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014625653,0.0013643177,0.0008148107,0.0010431728,0.00038872482,0.0013796298,0.0013535513,0.0009560457,0.0038348052],"category_scores_gemma":[0.008074215,0.0007353794,0.0008195001,0.001844984,0.0011820354,0.0025871412,0.001233735,0.0018831466,0.0014693722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012077684,0.00003059748,0.0018319775,0.00021765492,0.0000751047,0.00034473286,0.00011635495,0.8123467,0.0050238525,0.072046764,0.0052222237,0.102623194],"study_design_scores_gemma":[0.0000143345915,0.000044880588,0.00080832304,0.000036582187,0.000018821382,0.00013659557,0.000025504894,0.9590593,0.0018159574,0.033228148,0.0047767833,0.00003474091],"about_ca_topic_score_codex":0.007401161,"about_ca_topic_score_gemma":0.00457264,"teacher_disagreement_score":0.007401161,"about_ca_system_score_codex":0.0010816277,"about_ca_system_score_gemma":0.0011840448,"threshold_uncertainty_score":0.014716148},"labels":[],"label_agreement":null},{"id":"W2567070911","doi":"10.1109/jsen.2016.2639530","title":"Robust Biomechanical Model-Based 3-D Indoor Localization and Tracking Method Using UWB and IMU","year":2016,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Kalman filter; Computer science; Computer vision; Outlier; Artificial intelligence; Filter (signal processing); Position (finance); Indoor positioning system; Sensor fusion; Accelerometer","score_opus":0.04078312875824564,"score_gpt":0.26349487056067983,"score_spread":0.2227117418024342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567070911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005613659,0.00009562921,0.9933687,0.000023574574,0.000026222628,0.000008295374,0.000015830436,0.0005010548,0.0003470455],"genre_scores_gemma":[0.41170222,0.00038198024,0.5844474,0.000099074445,0.000075689015,0.00012687607,0.00022003234,0.000107104075,0.0028397203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995919,0.000054078733,0.000022693457,0.00014070312,0.00015963682,0.00003101778],"domain_scores_gemma":[0.9998054,0.000028062232,0.000045756173,0.00004054008,0.00006562975,0.000014599773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003395398,0.0007597423,0.00088422257,0.00087141176,0.00035616415,0.0005671881,0.0010290664,0.00068462826,0.0007425662],"category_scores_gemma":[0.00084474625,0.00046619325,0.00082224025,0.00086091994,0.0002651044,0.00080128416,0.0008547776,0.0006439992,0.0006346394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018052061,0.00009028919,0.0025995448,0.00018971498,0.00018117097,0.00025668435,0.00030181243,0.3260184,0.061883237,0.005090738,0.0022285508,0.6009793],"study_design_scores_gemma":[0.00001405907,0.00009346306,0.00126976,0.000012475642,0.000055102257,0.00022312798,0.00003484621,0.98258334,0.011843668,0.0012131043,0.0026214365,0.000035665696],"about_ca_topic_score_codex":0.00228717,"about_ca_topic_score_gemma":0.0019176324,"teacher_disagreement_score":0.00228717,"about_ca_system_score_codex":0.0003160308,"about_ca_system_score_gemma":0.0005790797,"threshold_uncertainty_score":0.0045476556},"labels":[],"label_agreement":null},{"id":"W2567803688","doi":"10.1109/upinlbs.2016.7809958","title":"Enhancing Wi-Fi based indoor Pedestrian Dead Reckoning with security cameras","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Dead reckoning; Heading (navigation); Computer science; Pedestrian; Android (operating system); Computer vision; Artificial intelligence; Real-time computing; Global Positioning System; Telecommunications; Geography; Engineering; Transport engineering","score_opus":0.005610126268188047,"score_gpt":0.18868654070550955,"score_spread":0.1830764144373215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567803688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0878863,0.00103158,0.904943,0.00007762896,0.00016797819,0.000082024824,0.00014041956,0.0022684527,0.003402681],"genre_scores_gemma":[0.57324195,0.0010598737,0.42100066,0.00010829878,0.00007954402,0.000068866604,0.00032866548,0.00008997837,0.004022073],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996413,0.00004714141,0.000017521832,0.00007568547,0.00016358434,0.000054769702],"domain_scores_gemma":[0.9995921,0.00007567778,0.000056152614,0.00007114987,0.00018044618,0.000024540996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026077477,0.00096972706,0.0005444849,0.0009916768,0.00026443845,0.00049975317,0.00077832775,0.00039524178,0.0010904175],"category_scores_gemma":[0.00093748426,0.0003161803,0.0004248202,0.00071581785,0.00022261344,0.0009887114,0.000691881,0.0005197159,0.000839116],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007346027,0.00014449908,0.0044624093,0.0003685373,0.0001124285,0.00032750046,0.0002494509,0.03182642,0.19272193,0.001084474,0.001985029,0.7659827],"study_design_scores_gemma":[0.0000938967,0.0009165176,0.020977948,0.00009931559,0.00030409635,0.002274634,0.00025040357,0.625608,0.32753438,0.0010634664,0.020681174,0.00019613116],"about_ca_topic_score_codex":0.0031271533,"about_ca_topic_score_gemma":0.0053457404,"teacher_disagreement_score":0.0031271533,"about_ca_system_score_codex":0.00025582954,"about_ca_system_score_gemma":0.0003948153,"threshold_uncertainty_score":0.006217897},"labels":[],"label_agreement":null},{"id":"W2568111820","doi":"10.1177/1541931215591385","title":"Experimental Evaluation of Indoor Navigation Devices","year":2015,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mobile device; Computer science; Workload; Wearable technology; Wearable computer; Usability; Context (archaeology); Augmented reality; Human–computer interaction; Turn-by-turn navigation; Task (project management); Embedded system; Artificial intelligence; Engineering; Mobile robot; World Wide Web","score_opus":0.031234819815128458,"score_gpt":0.258965215952272,"score_spread":0.22773039613714355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568111820","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98134583,0.0002937599,0.013844769,0.000045495493,0.00012989699,0.00092188455,0.00054530904,0.0002906741,0.0025824374],"genre_scores_gemma":[0.9480061,0.0006280136,0.04061663,0.000120630815,0.000094760995,0.0026105118,0.0013718941,0.00012583687,0.0064255563],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99808854,0.0007212168,0.00025287387,0.00033374067,0.0004463893,0.00015717236],"domain_scores_gemma":[0.9941969,0.0024838487,0.00038765543,0.000548957,0.0020665315,0.0003160792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015095631,0.00089971384,0.0005671097,0.00051460747,0.00044938122,0.00060541637,0.00095318747,0.0006714707,0.0061925147],"category_scores_gemma":[0.007904913,0.00032260007,0.00041551734,0.0004421618,0.00038556507,0.00070269156,0.0009945101,0.00033077525,0.0011159222],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015817247,0.017979406,0.02917474,0.0071096607,0.00040040535,0.0010134937,0.0095672,0.0067429654,0.6095645,0.0013003326,0.005073678,0.29625645],"study_design_scores_gemma":[0.0031151718,0.20350532,0.2792773,0.001016427,0.0017100846,0.0022070617,0.0069805826,0.024885641,0.4282102,0.00094479613,0.04764511,0.00050232105],"about_ca_topic_score_codex":0.0008518216,"about_ca_topic_score_gemma":0.0012405189,"teacher_disagreement_score":0.0061925147,"about_ca_system_score_codex":0.00022513088,"about_ca_system_score_gemma":0.00042348588,"threshold_uncertainty_score":0.020716012},"labels":[],"label_agreement":null},{"id":"W2580813787","doi":"10.1111/2041-210x.12745","title":"Novel, continuous monitoring of fine‐scale movement using fixed‐position radiotelemetry arrays and random forest location fingerprinting","year":2017,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Scale (ratio); Position (finance); Random forest; Global Positioning System; Remote sensing; Signal strength; Location data; SIGNAL (programming language); Wildlife; Telemetry; Movement (music); Path loss; Real-time computing; Environmental science; Data mining; Antenna (radio); Geography; Cartography; Artificial intelligence; Ecology; Telecommunications; Wireless; Acoustics","score_opus":0.01770016027106613,"score_gpt":0.3016742070317432,"score_spread":0.28397404676067706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580813787","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46805838,0.00058058824,0.5253859,0.000087217384,0.00006955506,0.000074159776,0.0010800041,0.0014716743,0.0031925547],"genre_scores_gemma":[0.7733446,0.00015110477,0.22448494,0.000049481252,0.000041598505,0.00008595728,0.00065055094,0.000049300845,0.0011425507],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966216,0.00006492442,0.000013532489,0.00013724252,0.0000933415,0.000028793722],"domain_scores_gemma":[0.99937785,0.00018180771,0.00017287304,0.00007611323,0.0001594052,0.00003194678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003596377,0.0002728592,0.00028645588,0.0008144439,0.00013363379,0.00040125605,0.0004591328,0.0002786028,0.0005280292],"category_scores_gemma":[0.00086339266,0.00013461204,0.0001704258,0.00062987115,0.00018547582,0.00047423167,0.00032136514,0.00023315432,0.00026851177],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033621182,0.00027613793,0.14319794,0.00026155886,0.00016667269,0.00018823934,0.00015070394,0.036932237,0.21465218,0.0013036651,0.0031263863,0.59940815],"study_design_scores_gemma":[0.00005215117,0.00043794088,0.24112049,0.000047746373,0.0001233449,0.0011067786,0.00012569805,0.66612107,0.081790656,0.0019878773,0.0069928714,0.000093336945],"about_ca_topic_score_codex":0.0022481177,"about_ca_topic_score_gemma":0.004786582,"teacher_disagreement_score":0.0022481177,"about_ca_system_score_codex":0.00016218972,"about_ca_system_score_gemma":0.0001758485,"threshold_uncertainty_score":0.0044700503},"labels":[],"label_agreement":null},{"id":"W2583012081","doi":"","title":"Adaptive Wireless Biomedical Capsule Localization and Tracking","year":2015,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Wireless; Tracking (education); Computer science; Capsule; Telecommunications; Biology; Psychology","score_opus":0.010198181261796588,"score_gpt":0.19371303905335566,"score_spread":0.18351485779155907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583012081","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00802346,0.0009186415,0.98707914,0.00010931846,0.00006258133,0.000026071437,0.000026814794,0.0005451618,0.0032087597],"genre_scores_gemma":[0.5365504,0.0065518334,0.43153685,0.00042795576,0.000296119,0.0002353924,0.00041085633,0.00014421932,0.023846334],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996216,0.000058060585,0.000021150678,0.00011964495,0.00015442683,0.000025054678],"domain_scores_gemma":[0.9996271,0.0001047589,0.00008105369,0.00005880377,0.00011719323,0.000011156541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033637395,0.00045655062,0.00034915685,0.0005199698,0.00018116106,0.0006273414,0.0008819544,0.0006532092,0.0013120502],"category_scores_gemma":[0.0011091179,0.00020899047,0.0004051848,0.0005956577,0.000308177,0.00081978674,0.0007375948,0.00039621486,0.00092453347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014374095,0.00006271456,0.0014306864,0.00041371683,0.000059732774,0.00022465436,0.00014343135,0.125396,0.15336277,0.01448116,0.0035867651,0.7006946],"study_design_scores_gemma":[0.000022876055,0.00020219116,0.002059446,0.00004411633,0.000041323558,0.0004428513,0.000039578823,0.9123325,0.057618298,0.0029354577,0.024209643,0.00005166095],"about_ca_topic_score_codex":0.0009827061,"about_ca_topic_score_gemma":0.0007117141,"teacher_disagreement_score":0.0013120502,"about_ca_system_score_codex":0.00034595333,"about_ca_system_score_gemma":0.0003569584,"threshold_uncertainty_score":0.0043892264},"labels":[],"label_agreement":null},{"id":"W2583922629","doi":"10.1007/s12652-017-0451-2","title":"Perfomance comparison of three localization protocols in WSN using Cooja","year":2017,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Wireless sensor network; Centroid; Protocol (science); Power consumption; Fingerprint (computing); Node (physics); Real-time computing; Artificial intelligence; Power (physics); Computer network","score_opus":0.09300757897464045,"score_gpt":0.35286869502085577,"score_spread":0.2598611160462153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583922629","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89407057,0.0029706424,0.088508904,0.0005146728,0.0006004537,0.00023341544,0.00033694867,0.0025178548,0.01024657],"genre_scores_gemma":[0.9836072,0.00048264637,0.013143101,0.000058973634,0.000031674506,0.000059069884,0.0003026327,0.00008158526,0.0022330808],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982364,0.0005508257,0.00013622454,0.00018385219,0.0005673252,0.00032536892],"domain_scores_gemma":[0.9939856,0.0031394598,0.00027830128,0.00051589665,0.0018658398,0.00021482824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023983049,0.00075462495,0.00069654785,0.0012309268,0.0008444364,0.001302023,0.001033917,0.0006499432,0.002046612],"category_scores_gemma":[0.006744166,0.00022030997,0.00033304596,0.0008704263,0.0004993965,0.0015791316,0.000790439,0.00049711467,0.0002743291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017812345,0.0016389228,0.029209005,0.0026271313,0.00088433025,0.0008632708,0.0010373683,0.22918291,0.13085924,0.010894521,0.010050075,0.5649409],"study_design_scores_gemma":[0.0004613522,0.009120121,0.021985486,0.00014663007,0.0010017433,0.00085281587,0.0024097837,0.826846,0.12372233,0.0025860332,0.010696558,0.00017112444],"about_ca_topic_score_codex":0.003662438,"about_ca_topic_score_gemma":0.0045314743,"teacher_disagreement_score":0.003662438,"about_ca_system_score_codex":0.0009143144,"about_ca_system_score_gemma":0.0011089176,"threshold_uncertainty_score":0.01268363},"labels":[],"label_agreement":null},{"id":"W2585637862","doi":"10.1109/glocom.2016.7842144","title":"Physical Topology Discovery Scheme for Wireless Sensor Networks Using Random Walk Process","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Wireless sensor network; Computer science; Topology (electrical circuits); Computer network; Network topology; Multilateration; Random walk; Key distribution in wireless sensor networks; Wireless; Distributed computing; Wireless network; Node (physics); Engineering; Mathematics; Telecommunications","score_opus":0.010908983980888085,"score_gpt":0.24915350731260574,"score_spread":0.23824452333171767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585637862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016745498,0.00013017112,0.98189306,0.00011054057,0.0000295175,0.00007690578,0.000020592803,0.0003044666,0.00068924023],"genre_scores_gemma":[0.7600713,0.0002895101,0.23740666,0.00008562898,0.000040068102,0.0003513863,0.00017937849,0.000040178304,0.0015358464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987117,0.00041124018,0.000098263736,0.00024311835,0.00044935438,0.00008635556],"domain_scores_gemma":[0.99809676,0.0008092427,0.00030601697,0.00033510796,0.00036883872,0.00008408215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014140833,0.00049290305,0.0007754058,0.0010158322,0.0007347248,0.0007528009,0.0016113712,0.0007809972,0.00064077514],"category_scores_gemma":[0.0050722994,0.00026214457,0.0005891863,0.00097451766,0.0007188274,0.0017241546,0.0012911869,0.00062633626,0.00025426425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035563647,0.00016448565,0.0020350812,0.0002463408,0.00013560784,0.0005323844,0.00057808246,0.6818734,0.034563553,0.08140191,0.0022945364,0.19581898],"study_design_scores_gemma":[0.00003011873,0.00008151313,0.00017769364,0.0000049536984,0.000015917474,0.00014465886,0.000021755237,0.99110854,0.0021109423,0.005287469,0.0009992295,0.000017130264],"about_ca_topic_score_codex":0.000874137,"about_ca_topic_score_gemma":0.0009425832,"teacher_disagreement_score":0.0016113712,"about_ca_system_score_codex":0.00060070155,"about_ca_system_score_gemma":0.00067738787,"threshold_uncertainty_score":0.007478535},"labels":[],"label_agreement":null},{"id":"W2586746317","doi":"10.1007/s12652-017-0458-8","title":"Two-stage weighted centroid localization for large-scale wireless sensor networks in ambient intelligence environment","year":2017,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computational intelligence; Wireless sensor network; Computer science; Centroid; Ambient intelligence; Scale (ratio); Wireless; Stage (stratigraphy); Artificial intelligence; Data mining; Computer network; Telecommunications; Geology; Cartography","score_opus":0.018510009048015478,"score_gpt":0.2555383384152732,"score_spread":0.2370283293672577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586746317","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005305261,0.00019126697,0.9936562,0.000044958677,0.000043730393,0.000030294019,0.000022145176,0.0003191359,0.00038693412],"genre_scores_gemma":[0.3298358,0.00044360824,0.6620182,0.00008391221,0.00013227633,0.00022154124,0.00031549035,0.00016282001,0.006786357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986021,0.00028120144,0.00009199858,0.00035162203,0.000513261,0.00015970493],"domain_scores_gemma":[0.9990128,0.00026095734,0.000097355216,0.00019042222,0.0003773439,0.00006104419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090962177,0.0012163097,0.001777656,0.0010374399,0.0010894736,0.0011200422,0.0031337407,0.0013850438,0.0021174175],"category_scores_gemma":[0.0027167366,0.0005861447,0.00091613433,0.0022093337,0.00058744976,0.0025428848,0.0025380852,0.000869109,0.0009957639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007422248,0.00021621551,0.0021493162,0.00036880883,0.00023925572,0.00023959846,0.00037555554,0.34643373,0.042734135,0.010252851,0.0061932816,0.5900551],"study_design_scores_gemma":[0.00002149828,0.000101489546,0.00049207726,0.00000545559,0.00003079454,0.0000868091,0.00004968572,0.99088657,0.004800578,0.001996164,0.001504149,0.000024806463],"about_ca_topic_score_codex":0.008732526,"about_ca_topic_score_gemma":0.013612971,"teacher_disagreement_score":0.008732526,"about_ca_system_score_codex":0.00081785023,"about_ca_system_score_gemma":0.0014751443,"threshold_uncertainty_score":0.01736337},"labels":[],"label_agreement":null},{"id":"W2587627422","doi":"10.1109/wf-iot.2016.7845406","title":"Location-based services on a smart campus: A system and a study","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Services computing; Location-based service; Web service; Android (operating system); Scope (computer science); World Wide Web; Service delivery framework; Service (business); Database; Computer network; Business","score_opus":0.004465165142618355,"score_gpt":0.1879194434336071,"score_spread":0.18345427829098873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587627422","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96013135,0.0013273176,0.015651202,0.0023733738,0.00005365326,0.00028412743,0.00044173593,0.0002392812,0.019497875],"genre_scores_gemma":[0.9856655,0.0012742819,0.0067571993,0.00018718935,0.000043201944,0.00006768773,0.00029585516,0.00003660499,0.0056722853],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998667,0.00057183544,0.00008233067,0.00023255199,0.00032490943,0.000121383964],"domain_scores_gemma":[0.99854964,0.00048675764,0.00010604576,0.00020745116,0.0004526196,0.00019745088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011708752,0.00021790563,0.00036945843,0.00096563075,0.0014229307,0.002662738,0.00062578934,0.0010493711,0.0027893092],"category_scores_gemma":[0.003422333,0.00017716899,0.0003756432,0.0022854784,0.0013637512,0.0049261884,0.0014139378,0.00074822083,0.0009774123],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012715885,0.003297075,0.4632084,0.0012642174,0.00020319334,0.0077182027,0.047287118,0.031268366,0.028140405,0.107996464,0.022648435,0.2856966],"study_design_scores_gemma":[0.00023595178,0.003140191,0.2569546,0.00048286095,0.0003007254,0.008435162,0.09247167,0.43218687,0.020562919,0.011800817,0.17298046,0.0004477698],"about_ca_topic_score_codex":0.03158657,"about_ca_topic_score_gemma":0.01769027,"teacher_disagreement_score":0.03158657,"about_ca_system_score_codex":0.0024457625,"about_ca_system_score_gemma":0.0009995598,"threshold_uncertainty_score":0.062805414},"labels":[],"label_agreement":null},{"id":"W2588347432","doi":"10.1155/2017/4939261","title":"Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models","year":2017,"lang":"en","type":"article","venue":"International Journal of Computer Games Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Hidden Markov model; Position (finance); Supervised learning; Statistical model; Artificial neural network; Track (disk drive); Markov model","score_opus":0.018236764325318216,"score_gpt":0.2739173827366038,"score_spread":0.2556806184112856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588347432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032257666,0.000057390967,0.9959965,0.00006467623,0.000010539142,0.000008753426,0.0000050689077,0.00020521376,0.00042605627],"genre_scores_gemma":[0.4619726,0.00026611227,0.5353638,0.0001736659,0.00006820451,0.00015719225,0.00008657566,0.000070184964,0.0018416252],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991679,0.00032805718,0.00004368922,0.00015750072,0.00025278813,0.000050052833],"domain_scores_gemma":[0.9980883,0.0012071744,0.0001816282,0.00024991462,0.00022334173,0.00004960649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011414646,0.00061345287,0.0008081575,0.0006652641,0.00034883697,0.00081829063,0.001063294,0.0007417982,0.0012186244],"category_scores_gemma":[0.004912589,0.00046069198,0.00063787267,0.0005882919,0.00086549553,0.0014796167,0.0014904985,0.0011741807,0.0004728461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010992652,0.00014750531,0.0016654873,0.000120297715,0.000104937346,0.00013292157,0.00015697563,0.71934384,0.006835577,0.04638554,0.0011707834,0.2238262],"study_design_scores_gemma":[0.0000032859862,0.000016566139,0.00008417667,0.000002828254,0.0000039926485,0.0000097077955,0.0000026639927,0.9934918,0.0005648285,0.0055296877,0.0002861137,0.0000042383817],"about_ca_topic_score_codex":0.0031277079,"about_ca_topic_score_gemma":0.0029018524,"teacher_disagreement_score":0.0031277079,"about_ca_system_score_codex":0.000619642,"about_ca_system_score_gemma":0.0009520489,"threshold_uncertainty_score":0.00621897},"labels":[],"label_agreement":null},{"id":"W2593139211","doi":"10.1109/ibcast.2017.7868140","title":"Contribution to develop a generic hybrid technique of satellite system for RFI geolocation","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Geolocation; Computer science; Communications satellite; Satellite; Broadcasting (networking); Wireless; Transmission (telecommunications); Telecommunications; Radio frequency; Remote sensing; Interference (communication); Electromagnetic interference; Radio spectrum; Real-time computing; Computer network; Engineering; Geography","score_opus":0.0122720338767077,"score_gpt":0.23156346004027978,"score_spread":0.21929142616357208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593139211","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020099767,0.0005976396,0.97375125,0.00013625916,0.00010997968,0.00006601421,0.000059066053,0.0006768674,0.004503159],"genre_scores_gemma":[0.44405934,0.001037234,0.5427972,0.0002490662,0.00020855835,0.00013588389,0.00035089976,0.00008171602,0.011080074],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995803,0.00007046036,0.00001807676,0.000088746594,0.00020416087,0.00003819879],"domain_scores_gemma":[0.99968755,0.000046721256,0.000030452302,0.00006770967,0.00015111594,0.000016449858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002887818,0.00058619183,0.00035659835,0.0008859044,0.00029605278,0.0005936301,0.0007823701,0.0007061579,0.0024521274],"category_scores_gemma":[0.00054551475,0.00016887739,0.00047071197,0.00078961236,0.00032223706,0.0010180387,0.0006416781,0.00042183016,0.0018058203],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018261062,0.00009079825,0.007875064,0.000339572,0.000117247015,0.00047395687,0.00038005755,0.024106363,0.1997598,0.011135794,0.0036248313,0.75191396],"study_design_scores_gemma":[0.0000667861,0.0011957199,0.011572483,0.00011670852,0.00029005273,0.0061429734,0.0005345352,0.68582726,0.19817786,0.007864187,0.08806897,0.00014251533],"about_ca_topic_score_codex":0.0009902597,"about_ca_topic_score_gemma":0.0013432794,"teacher_disagreement_score":0.0024521274,"about_ca_system_score_codex":0.00023023963,"about_ca_system_score_gemma":0.00032123007,"threshold_uncertainty_score":0.008203208},"labels":[],"label_agreement":null},{"id":"W2594681962","doi":"10.1109/tim.2017.2666278","title":"High-Accuracy Localization Platform Using Asynchronous Time Difference of Arrival Technology","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Multilateration; Computer science; Asynchronous communication; Real-time computing; Time of arrival; Ranging; Non-line-of-sight propagation; Transmitter; Channel (broadcasting); Reliability (semiconductor); Baseband; Synchronization (alternating current); Electronic engineering; Computer hardware; Embedded system; Wireless; Telecommunications; Engineering; Bandwidth (computing)","score_opus":0.030033937376731507,"score_gpt":0.24314804946480542,"score_spread":0.2131141120880739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594681962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031166218,0.00022050236,0.9593843,0.00012918853,0.00018401783,0.00009903557,0.00012633328,0.004852466,0.0038379575],"genre_scores_gemma":[0.5669266,0.00032803224,0.42491657,0.00013976048,0.00007504807,0.00026094043,0.00040529473,0.00012262756,0.0068251314],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995308,0.00006449504,0.000021604083,0.00009032737,0.00024198853,0.00005082417],"domain_scores_gemma":[0.9995623,0.000062404186,0.00006946774,0.00010040266,0.00017193842,0.000033470187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040548987,0.00055397255,0.00049245264,0.00057760184,0.0002964903,0.000606501,0.0015177297,0.0006803374,0.0026897374],"category_scores_gemma":[0.0008183312,0.00022494551,0.00034824104,0.00043453876,0.00025656723,0.00090143684,0.00096035615,0.0006279528,0.0014655141],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060557167,0.0002876328,0.005292888,0.00081812235,0.00012852809,0.0009903418,0.000495088,0.054385934,0.55973077,0.019673614,0.010372245,0.34721923],"study_design_scores_gemma":[0.00030873044,0.001882212,0.0043985103,0.00009295112,0.00015159628,0.0019192803,0.00017106718,0.5974657,0.32167765,0.004812616,0.06693504,0.00018465839],"about_ca_topic_score_codex":0.0008697551,"about_ca_topic_score_gemma":0.00076989137,"teacher_disagreement_score":0.0026897374,"about_ca_system_score_codex":0.00029479014,"about_ca_system_score_gemma":0.0006032877,"threshold_uncertainty_score":0.008998096},"labels":[],"label_agreement":null},{"id":"W2597417397","doi":"10.1109/vtcfall.2016.7881071","title":"Localization for Mobile Sensor Networks in Mines","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Distributive property; Asynchronous communication; Computer science; Real-time computing; Mobile telephony; Mobile computing; Wireless sensor network; Tracking (education); Distributed computing; Mobile radio; Computer network","score_opus":0.00634354538073161,"score_gpt":0.20611881233892676,"score_spread":0.19977526695819514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597417397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020122554,0.0038644671,0.9699172,0.00082764233,0.00019949595,0.000040518964,0.00006425609,0.0006605046,0.004303369],"genre_scores_gemma":[0.842458,0.004194593,0.14624587,0.00021303486,0.00018565872,0.00012792401,0.00014827533,0.00006394392,0.00636267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99958545,0.00018281193,0.00002252715,0.00006949268,0.00010847063,0.000031249077],"domain_scores_gemma":[0.99968517,0.00013441869,0.00004388878,0.000037425423,0.0000853427,0.00001385392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048248627,0.0002982987,0.00037250255,0.0004233479,0.00048827258,0.0005524099,0.0006289815,0.00067901314,0.00092489435],"category_scores_gemma":[0.0016575735,0.00021911632,0.00029665374,0.0007374475,0.0004422796,0.001000759,0.00076766685,0.00044734744,0.00034542358],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015614294,0.00003317061,0.0021674663,0.00041523372,0.00007255486,0.0005060276,0.00021561909,0.76802516,0.016804859,0.035104115,0.00561158,0.17088807],"study_design_scores_gemma":[0.000017238131,0.00007603812,0.00053453934,0.00004275444,0.000016283875,0.00028424553,0.00008531766,0.9679993,0.0030664105,0.015654594,0.012205595,0.000017741404],"about_ca_topic_score_codex":0.0024995082,"about_ca_topic_score_gemma":0.0035843807,"teacher_disagreement_score":0.0024995082,"about_ca_system_score_codex":0.0004994986,"about_ca_system_score_gemma":0.00045133152,"threshold_uncertainty_score":0.0049699545},"labels":[],"label_agreement":null},{"id":"W2597896410","doi":"","title":"Indoor Navigation with iPhone/iPad: Floor Plan Based Monocular Vision Navigation","year":2012,"lang":"en","type":"article","venue":"Proceedings of the 25th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2012)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Floor plan; Computer science; Dead reckoning; Computer vision; Global Positioning System; Artificial intelligence; Landmark; Compass; Inertial measurement unit; Real-time computing; Pedestrian; Engineering; Geography; Telecommunications","score_opus":0.011021865155981237,"score_gpt":0.23517278983359707,"score_spread":0.22415092467761583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597896410","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051440094,0.00093848084,0.9005268,0.00013887437,0.00032030404,0.00015640623,0.0012738282,0.023479123,0.02172614],"genre_scores_gemma":[0.5387504,0.00050808996,0.43728986,0.00030519575,0.00008101024,0.00017587702,0.0023586638,0.00039164483,0.020139238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997389,0.00002327775,0.000005350682,0.000060212402,0.00013448541,0.000037777285],"domain_scores_gemma":[0.9999006,0.0000068014424,0.000008611886,0.000019996367,0.00005111552,0.000012859481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011904946,0.0005094211,0.00040595364,0.0005806321,0.00017700267,0.0003753962,0.0004322176,0.00035255,0.0063607283],"category_scores_gemma":[0.00019470969,0.00018345576,0.00029547134,0.00040844106,0.00013182533,0.00032702106,0.00059054693,0.00025439519,0.0024951713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029465999,0.0001391951,0.0019934329,0.00019911033,0.000047986094,0.00019468556,0.00012728559,0.0099147195,0.10169009,0.0010953738,0.027129851,0.8571736],"study_design_scores_gemma":[0.0002070282,0.0010736407,0.035180096,0.00009752144,0.00013563031,0.0022359686,0.00019231178,0.6846915,0.1615737,0.0018322758,0.11254893,0.0002313508],"about_ca_topic_score_codex":0.0073685525,"about_ca_topic_score_gemma":0.010711797,"teacher_disagreement_score":0.0073685525,"about_ca_system_score_codex":0.00022988777,"about_ca_system_score_gemma":0.00043645504,"threshold_uncertainty_score":0.021278799},"labels":[],"label_agreement":null},{"id":"W2600954401","doi":"10.1109/vtcfall.2016.7881110","title":"Non-Cooperative Wi-Fi Localization via Monitoring Probe Request Frames","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Science and Technology Major Project; National Science Foundation","keywords":"Computer science; Computer network","score_opus":0.007964101211235019,"score_gpt":0.21704191806757495,"score_spread":0.20907781685633992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600954401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06413257,0.00020230126,0.9325489,0.00009629533,0.000029041907,0.000051247956,0.000036444053,0.0012095574,0.0016937589],"genre_scores_gemma":[0.912146,0.00016045984,0.08513255,0.00007240911,0.000025721629,0.00008041862,0.00009137146,0.000033003987,0.002258024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937636,0.00014275566,0.000022816237,0.00014548437,0.00023066823,0.00008191766],"domain_scores_gemma":[0.9993038,0.0002223262,0.0001480886,0.00013564798,0.00016416649,0.000025940573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043274387,0.00059216196,0.00065182,0.00044193043,0.00036155662,0.00050468603,0.0009215473,0.00066470855,0.0006266885],"category_scores_gemma":[0.0023280536,0.00027778454,0.00022414693,0.00059410866,0.00035944217,0.0011214275,0.0007275583,0.00036076232,0.0005724955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001178602,0.0002616573,0.011408058,0.00027238962,0.0001115331,0.00095073314,0.0008700422,0.114647485,0.20231381,0.007242629,0.0033479661,0.65739506],"study_design_scores_gemma":[0.00003432882,0.00031699124,0.006149438,0.000017713244,0.0000365079,0.001102951,0.00014778059,0.94287807,0.0433994,0.0026332948,0.003238143,0.000045300563],"about_ca_topic_score_codex":0.002423437,"about_ca_topic_score_gemma":0.0030729335,"teacher_disagreement_score":0.002423437,"about_ca_system_score_codex":0.00033283583,"about_ca_system_score_gemma":0.00047887457,"threshold_uncertainty_score":0.0048187375},"labels":[],"label_agreement":null},{"id":"W2601425944","doi":"10.1109/csci.2016.0247","title":"Hierarchical Temporal Mobility Analysis with Semantic Labeling","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Salient; Hierarchy; Construct (python library); Mobile phone; Inference; Identification (biology); Phone; Data mining; Artificial intelligence; Real-time computing; Computer network; Telecommunications","score_opus":0.006872939258875237,"score_gpt":0.2032375880186248,"score_spread":0.19636464875974954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601425944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016597068,0.00015078353,0.98114866,0.0001221335,0.000022006463,0.000031293304,0.00037968263,0.0006828291,0.0008655422],"genre_scores_gemma":[0.5671989,0.00030078343,0.42711377,0.00010932426,0.00008979049,0.00015161332,0.0024712386,0.00019395523,0.0023705924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993967,0.000109712,0.000034797158,0.00022659759,0.00013879997,0.000093429],"domain_scores_gemma":[0.999233,0.0002738612,0.00011445285,0.00017577686,0.00015667934,0.000046191082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063406746,0.0006190756,0.00061961805,0.001876574,0.00062477065,0.00096384715,0.0013553649,0.0007248106,0.001744051],"category_scores_gemma":[0.0025251207,0.00043646977,0.0009728396,0.0016713917,0.0005985059,0.0017897256,0.0012152779,0.0008860201,0.00048246232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038664252,0.00017858601,0.0065695173,0.00020619204,0.00013438454,0.00030292536,0.00051114976,0.5147841,0.015809622,0.07512923,0.008397262,0.3775903],"study_design_scores_gemma":[0.000005248214,0.00001187902,0.00062491983,0.0000074014642,0.000011229414,0.000029342313,0.000038650825,0.9736511,0.001074473,0.023166314,0.0013716955,0.000007766513],"about_ca_topic_score_codex":0.010770765,"about_ca_topic_score_gemma":0.012843666,"teacher_disagreement_score":0.010770765,"about_ca_system_score_codex":0.0010296097,"about_ca_system_score_gemma":0.0009755721,"threshold_uncertainty_score":0.021416128},"labels":[],"label_agreement":null},{"id":"W2603855048","doi":"10.1007/s11276-017-1493-2","title":"New path planning model for mobile anchor-assisted localization in wireless sensor networks","year":2017,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; Dalhousie University","funders":"","keywords":"Computer science; Wireless sensor network; Path (computing); Collinearity; Energy consumption; Distributed computing; Set (abstract data type); Computer network; Energy (signal processing); Event (particle physics); Real-time computing","score_opus":0.018339211808512657,"score_gpt":0.2525537816487973,"score_spread":0.23421456984028466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603855048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001998563,0.00023331013,0.9960996,0.00012032843,0.00004489733,0.00002467944,0.000093244475,0.00011540176,0.0012699789],"genre_scores_gemma":[0.5310975,0.002914316,0.4453172,0.00026512457,0.00023304658,0.0006711213,0.0010031696,0.00025446402,0.018244013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935335,0.000170191,0.000032950516,0.00016342403,0.00020359176,0.00007644307],"domain_scores_gemma":[0.9993432,0.00033932098,0.00006797168,0.000043598688,0.00017047128,0.000035345736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008749807,0.0011110886,0.0013240725,0.0009450661,0.00060996093,0.0012061582,0.002769634,0.0013563534,0.0032402957],"category_scores_gemma":[0.0021964973,0.0007400404,0.00094366824,0.002430788,0.000764,0.002057165,0.0012898681,0.0015459278,0.00071573735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014313749,0.000010058684,0.00009999362,0.0000346422,0.00001058278,0.0000317416,0.000024146128,0.978438,0.00031384148,0.011466441,0.0007583265,0.00879787],"study_design_scores_gemma":[0.0000019459646,0.0000049171235,0.000014136669,0.0000024208987,0.0000031874536,0.0000059199656,0.0000026424061,0.99716216,0.00004950042,0.0024819656,0.00026868097,0.0000024181184],"about_ca_topic_score_codex":0.016824907,"about_ca_topic_score_gemma":0.011766887,"teacher_disagreement_score":0.016824907,"about_ca_system_score_codex":0.0016773159,"about_ca_system_score_gemma":0.0018284243,"threshold_uncertainty_score":0.03345394},"labels":[],"label_agreement":null},{"id":"W2604490975","doi":"10.1609/aaai.v31i2.19093","title":"Real-Time Indoor Localization in Smart Homes Using Semi-Supervised Learning","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Train; Real-time computing; Scope (computer science); Supervised learning; Task (project management); Home automation; Deep learning; Artificial intelligence; Human–computer interaction; Telecommunications; Artificial neural network; Engineering","score_opus":0.048261808242944806,"score_gpt":0.2768741957613913,"score_spread":0.22861238751844648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604490975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052430987,0.0001007069,0.94454306,0.00009315438,0.00001939253,0.000059844668,0.0000668885,0.0021118687,0.00057402765],"genre_scores_gemma":[0.79738027,0.00008035183,0.20044796,0.00009965208,0.000050933668,0.00014895658,0.00038043578,0.00012049304,0.0012909591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989034,0.00036507953,0.00006848614,0.0003341527,0.000225204,0.00010369214],"domain_scores_gemma":[0.99631363,0.0018682347,0.0004508418,0.0004950065,0.00072378834,0.000148482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002003647,0.0006902819,0.0010157818,0.00060430804,0.0003927383,0.00075316546,0.0020395187,0.0008168649,0.0008750424],"category_scores_gemma":[0.0044378545,0.0005157364,0.0007048451,0.00050565513,0.0008752838,0.0013963239,0.0012903031,0.0010392764,0.00062999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038330886,0.00039479096,0.005031257,0.0001635055,0.000121912984,0.00015736427,0.00031873633,0.71073264,0.008635631,0.0014655099,0.0021062978,0.2704891],"study_design_scores_gemma":[0.000006020173,0.00002355629,0.00030751145,0.0000028146455,0.0000027113394,0.00001084754,0.000011428956,0.99773645,0.0011633586,0.0006261196,0.00010490976,0.000004379259],"about_ca_topic_score_codex":0.0046612914,"about_ca_topic_score_gemma":0.0059396303,"teacher_disagreement_score":0.0046612914,"about_ca_system_score_codex":0.0006644191,"about_ca_system_score_gemma":0.00093846425,"threshold_uncertainty_score":0.010596395},"labels":[],"label_agreement":null},{"id":"W2604599742","doi":"10.1017/s0373463317000121","title":"Context-Aware Adaptive Multipath Compensation Based on Channel Pattern Recognition for GNSS Receivers","year":2017,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multipath propagation; Computer science; GNSS applications; Artificial intelligence; Pattern recognition (psychology); Multipath mitigation; Support vector machine; Context (archaeology); Real-time computing; Computer vision; Channel (broadcasting); Global Positioning System; Telecommunications; Geography","score_opus":0.043196730252755756,"score_gpt":0.2609022523661571,"score_spread":0.21770552211340136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604599742","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14142984,0.00057017483,0.85491586,0.000080992446,0.00009782718,0.000043588807,0.000049281498,0.0013801799,0.0014321889],"genre_scores_gemma":[0.8667676,0.00028362995,0.1317421,0.00004843326,0.000039829476,0.000036849244,0.00008386109,0.000024313626,0.00097339216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998142,0.000030332505,0.000008887218,0.000045781806,0.000074270116,0.000026494707],"domain_scores_gemma":[0.9998043,0.00004210069,0.000038288355,0.000033430526,0.00007245628,0.0000094991565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001796153,0.00036544612,0.00034490973,0.00039542472,0.00014863213,0.00026328073,0.0004167918,0.00040641867,0.00032162722],"category_scores_gemma":[0.0006804509,0.000143502,0.00022330263,0.00032953167,0.00013999364,0.0004166885,0.0002758111,0.0003411529,0.00027219276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038838474,0.00011496973,0.0052939747,0.00011136067,0.00006655061,0.00023449809,0.00008422096,0.14391495,0.18416534,0.0016771645,0.0012081483,0.66274035],"study_design_scores_gemma":[0.000012598854,0.00018155701,0.004990488,0.000010521045,0.00003288625,0.00026482678,0.000019036766,0.9470812,0.045504026,0.0006005323,0.0012800418,0.000022177797],"about_ca_topic_score_codex":0.0011555793,"about_ca_topic_score_gemma":0.0020859852,"teacher_disagreement_score":0.0011555793,"about_ca_system_score_codex":0.00018395603,"about_ca_system_score_gemma":0.00026032116,"threshold_uncertainty_score":0.0022976398},"labels":[],"label_agreement":null},{"id":"W2605833825","doi":"10.1109/ipin43642.2018","title":"2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)","year":2018,"lang":"en","type":"paratext","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.01734182707001288,"score_gpt":0.2548288553524827,"score_spread":0.2374870282824698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605833825","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032094754,0.013935949,0.39407465,0.0076516434,0.04364022,0.00044289112,0.0040753577,0.015895186,0.48818934],"genre_scores_gemma":[0.070979975,0.011071631,0.041576512,0.00084075367,0.004024871,0.00035576738,0.009042265,0.0011789248,0.86092937],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99942446,0.000085236185,0.00002410961,0.00011872362,0.00023292287,0.000114626746],"domain_scores_gemma":[0.99902594,0.000099271,0.000027166578,0.00016389639,0.0005101738,0.00017358056],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00079450017,0.0010345939,0.0011669324,0.0010685174,0.0008493933,0.0020620753,0.0010313198,0.0013848954,0.09760256],"category_scores_gemma":[0.0011696712,0.00014950031,0.0004060802,0.0012272923,0.00052899396,0.0016796828,0.0017485389,0.0011074397,0.06859691],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022490499,0.00017365128,0.0012156058,0.0002882656,0.000015207148,0.0002466172,0.00015079275,0.0018478782,0.0065782247,0.010895965,0.41578493,0.56257796],"study_design_scores_gemma":[0.000013817496,0.00019281078,0.0023246289,0.000116178744,0.00003174845,0.0004007018,0.00025058805,0.01631861,0.005206944,0.0051963977,0.9699289,0.000018744488],"about_ca_topic_score_codex":0.002357844,"about_ca_topic_score_gemma":0.0022007686,"teacher_disagreement_score":0.90239745,"about_ca_system_score_codex":0.00045339874,"about_ca_system_score_gemma":0.0013623798,"threshold_uncertainty_score":0.3265131},"labels":[],"label_agreement":null},{"id":"W2606276298","doi":"10.1186/s13638-017-0851-1","title":"Localization algorithms for asynchronous time difference of arrival positioning systems","year":2017,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cramér–Rao bound; Asynchronous communication; Algorithm; Convergence (economics); Multilateration; Semidefinite programming; Upper and lower bounds; Synchronization (alternating current); Time of arrival; Variance (accounting); Channel (broadcasting); Mathematical optimization; Estimation theory; Telecommunications; Node (physics); Mathematics","score_opus":0.03066008911659349,"score_gpt":0.2691121223503867,"score_spread":0.2384520332337932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606276298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006999524,0.00011448738,0.9980818,0.000055850356,0.000021456182,0.000009712419,0.000014658409,0.00008272154,0.00091930537],"genre_scores_gemma":[0.24053381,0.0017085904,0.7434822,0.00030358808,0.00025778016,0.00038639747,0.00035475328,0.00020434713,0.012768549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990676,0.00025316078,0.000052386516,0.00021871954,0.00034372386,0.00006441691],"domain_scores_gemma":[0.9989716,0.00046191947,0.00013475154,0.000076644355,0.0003287449,0.00002632367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009533337,0.00088884984,0.00062774663,0.0007169651,0.00052013516,0.0010180916,0.0012555532,0.0009775725,0.0035733043],"category_scores_gemma":[0.0031913195,0.0004041425,0.0005093985,0.0011894776,0.0005280225,0.0013726321,0.0012424581,0.0014232979,0.0015215143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006270205,0.000033727167,0.00047450638,0.00021215978,0.0000334416,0.000091208414,0.00017817017,0.6599997,0.0062083323,0.11610479,0.0060568,0.21054435],"study_design_scores_gemma":[0.000012519623,0.000025884769,0.000090496076,0.000012864423,0.000005870247,0.00005165791,0.000017356597,0.9775047,0.00078464247,0.016844772,0.0046388684,0.000010447377],"about_ca_topic_score_codex":0.0023301912,"about_ca_topic_score_gemma":0.0019712294,"teacher_disagreement_score":0.0035733043,"about_ca_system_score_codex":0.0007145392,"about_ca_system_score_gemma":0.00075082574,"threshold_uncertainty_score":0.01195389},"labels":[],"label_agreement":null},{"id":"W2606944528","doi":"10.1007/s10470-017-0969-4","title":"ZigBee-based indoor localization system with the personal dynamic positioning method and modified particle filter estimation","year":2017,"lang":"en","type":"article","venue":"Analog Integrated Circuits and Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Gaziantep Üniversitesi; Ryerson University","keywords":"Trilateration; Particle filter; Resampling; Computer science; Position (finance); Real-time computing; Wireless sensor network; Algorithm; Computation; Filter (signal processing); Engineering; Computer vision","score_opus":0.010972892932725603,"score_gpt":0.23300975548437702,"score_spread":0.22203686255165142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606944528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03318197,0.00073921395,0.9564586,0.00015572541,0.00018263148,0.00006641997,0.00015468958,0.003170837,0.005889887],"genre_scores_gemma":[0.7135276,0.0007951272,0.27272886,0.00018922299,0.00013571697,0.00022837475,0.00067839917,0.00011449601,0.011602113],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995117,0.00008906676,0.000028046794,0.00012905143,0.00020772235,0.00003444154],"domain_scores_gemma":[0.99975187,0.000038547758,0.000031323685,0.00004192396,0.00012162261,0.000014739602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031391886,0.0006322902,0.00069291436,0.00058282644,0.00036431124,0.00055143854,0.0006790176,0.0005898615,0.0012162692],"category_scores_gemma":[0.0005069212,0.00034087652,0.0004048688,0.0008975219,0.00018991488,0.0009050856,0.00048427403,0.0005621264,0.0006634896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058924395,0.00020993657,0.007925322,0.00048285892,0.00029249024,0.00015983931,0.00030974607,0.060448803,0.08325209,0.0053566787,0.007786648,0.8331864],"study_design_scores_gemma":[0.00024295542,0.00064859824,0.016605077,0.00007965286,0.0003688971,0.0008413873,0.00011285468,0.8748524,0.07267023,0.0033998657,0.03000499,0.00017308518],"about_ca_topic_score_codex":0.002411963,"about_ca_topic_score_gemma":0.0026541334,"teacher_disagreement_score":0.002411963,"about_ca_system_score_codex":0.00029850408,"about_ca_system_score_gemma":0.000698724,"threshold_uncertainty_score":0.0047958493},"labels":[],"label_agreement":null},{"id":"W2610105425","doi":"10.4236/jcc.2017.56006","title":"Efficient and Innovative Techniques for Collective Acquisition of Weak GNSS Signals","year":2017,"lang":"en","type":"article","venue":"Journal of Computer and Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Computer science; Sensitivity (control systems); Real-time computing; Process (computing); Position (finance); Satellite system; Satellite; Satellite navigation; Electronic engineering; Global Positioning System; Engineering; Telecommunications","score_opus":0.020919177289355915,"score_gpt":0.28230859030241495,"score_spread":0.261389413013059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610105425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063265036,0.00064020156,0.991759,0.00005848431,0.000027239603,0.00001471313,0.0000094146335,0.00030480372,0.00085968385],"genre_scores_gemma":[0.17785704,0.0012639866,0.8171991,0.00006791682,0.00007294455,0.00006606654,0.00008369525,0.000055256125,0.003333981],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969757,0.000050538463,0.0000106681255,0.00005910597,0.00016052548,0.000021551914],"domain_scores_gemma":[0.99978715,0.0000507979,0.00004137417,0.000055954533,0.000054234442,0.000010574139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002906645,0.00047729505,0.00043922427,0.0007910373,0.00032172137,0.00049523916,0.0007841535,0.0004084568,0.00090471184],"category_scores_gemma":[0.00061320764,0.0002397056,0.00035868224,0.00090547843,0.00045196677,0.0009126129,0.0012031874,0.0006181464,0.0005509263],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007433153,0.000047771075,0.0012257366,0.0002655497,0.00006864127,0.00012877445,0.00025830884,0.034209058,0.15750898,0.023553604,0.0018982606,0.780761],"study_design_scores_gemma":[0.000063731626,0.0004425033,0.0032063304,0.0001106195,0.00012643858,0.0011917474,0.00022390914,0.7852145,0.12659213,0.022397706,0.06033837,0.000092099],"about_ca_topic_score_codex":0.00052710425,"about_ca_topic_score_gemma":0.00094596366,"teacher_disagreement_score":0.00090471184,"about_ca_system_score_codex":0.0002407668,"about_ca_system_score_gemma":0.00036295358,"threshold_uncertainty_score":0.003026545},"labels":[],"label_agreement":null},{"id":"W2610539179","doi":"10.3390/app7050467","title":"Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data","year":2017,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"RSS; Crowdsourcing; Exploit; Computer science; Signal strength; Graph; Data mining; Software deployment; Artificial intelligence; Pattern recognition (psychology); Machine learning; Theoretical computer science; Computer network; Wireless sensor network","score_opus":0.06942950754993024,"score_gpt":0.2943753198973772,"score_spread":0.22494581234744696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610539179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01815006,0.0003371176,0.9785922,0.0003080579,0.00007016911,0.00007826011,0.00020757216,0.0013464461,0.0009101777],"genre_scores_gemma":[0.73167837,0.0004522411,0.25964478,0.0005921077,0.00027128166,0.00047561675,0.002110747,0.00032147838,0.004453396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984829,0.0004959854,0.00008380705,0.00052647537,0.00025818328,0.00015272843],"domain_scores_gemma":[0.9951292,0.0028883228,0.00048561962,0.0004981228,0.00081009924,0.00018858495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017934198,0.001649008,0.002163976,0.0013674716,0.0008358382,0.0009992007,0.0033335087,0.0017832502,0.0018221855],"category_scores_gemma":[0.006334868,0.000793026,0.0013631437,0.0014677445,0.00132681,0.0015575369,0.0020801844,0.002140353,0.00084655965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024450544,0.00018769242,0.0016625606,0.00026490595,0.00013175921,0.00016929678,0.00025781462,0.82092226,0.002197722,0.0053541083,0.004595392,0.16401201],"study_design_scores_gemma":[0.000007840171,0.0000131662155,0.00009269674,0.000004887037,0.0000044679523,0.000007920214,0.000012104684,0.99663585,0.00022376279,0.0027905726,0.00020135473,0.0000053474696],"about_ca_topic_score_codex":0.013503147,"about_ca_topic_score_gemma":0.012374835,"teacher_disagreement_score":0.013503147,"about_ca_system_score_codex":0.0011766343,"about_ca_system_score_gemma":0.0016850061,"threshold_uncertainty_score":0.026849091},"labels":[],"label_agreement":null},{"id":"W2611526661","doi":"10.1109/percom.2017.7917859","title":"LocMe: Human locomotion and map exploitation based indoor localization","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Constraint (computer-aided design); Computer science; Convergence (economics); Inertial measurement unit; Artificial intelligence; Real-time computing; Computer vision; Simulation; Engineering","score_opus":0.01568639020063589,"score_gpt":0.24324837657594273,"score_spread":0.22756198637530684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611526661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051538948,0.000476763,0.91935533,0.00022993975,0.0001957199,0.00012966171,0.0005293496,0.022027835,0.0055164574],"genre_scores_gemma":[0.7149771,0.00028351767,0.27155775,0.00029964745,0.00012484103,0.00027409356,0.0016867816,0.0003287414,0.01046746],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996197,0.00007120458,0.000012866859,0.00007159346,0.00016267585,0.000061970786],"domain_scores_gemma":[0.99955815,0.00008014917,0.000056558136,0.00012188512,0.0001253019,0.00005787503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027130978,0.00079345115,0.0007069854,0.0010710505,0.0003704286,0.0004706467,0.0010724039,0.0007544843,0.0029116077],"category_scores_gemma":[0.0010330142,0.00020884346,0.0003305442,0.00059633906,0.00038014085,0.0008492885,0.0019740362,0.0004249573,0.0016023619],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007492802,0.0003216729,0.008285858,0.00057693827,0.00013178964,0.0010271582,0.00051397743,0.03221454,0.10070984,0.007201285,0.03552702,0.8127406],"study_design_scores_gemma":[0.00015131732,0.0007266979,0.011579656,0.00006977727,0.00007796015,0.0023839406,0.00034967647,0.85826707,0.064840384,0.00533388,0.056060906,0.0001587111],"about_ca_topic_score_codex":0.0013223428,"about_ca_topic_score_gemma":0.0027778402,"teacher_disagreement_score":0.0029116077,"about_ca_system_score_codex":0.00021282585,"about_ca_system_score_gemma":0.00043353747,"threshold_uncertainty_score":0.009740293},"labels":[],"label_agreement":null},{"id":"W2616772019","doi":"10.1109/tsmc.2017.2695080","title":"Automatic Visual Fingerprinting for Indoor Image-Based Localization Applications","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Software deployment; Focus (optics); Image (mathematics); Pattern recognition (psychology)","score_opus":0.011609760843495161,"score_gpt":0.24329000671924025,"score_spread":0.2316802458757451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616772019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03116884,0.0008258665,0.9509652,0.00013478736,0.00010651745,0.000114467424,0.0004166153,0.013468151,0.0027995605],"genre_scores_gemma":[0.58454,0.00083762663,0.40881935,0.00023837482,0.000100560115,0.00016505459,0.00090845197,0.00028430377,0.0041062324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996909,0.000055756325,0.000015504189,0.0000771895,0.00011671506,0.00004400638],"domain_scores_gemma":[0.99948275,0.00010643272,0.00006296256,0.00014595015,0.00017031257,0.000031613217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003094412,0.0005995553,0.0006132325,0.0012347132,0.00022411681,0.0005295375,0.0011403001,0.0007202822,0.004665426],"category_scores_gemma":[0.0012342586,0.00023102819,0.00030391768,0.00083695713,0.00017777039,0.0006199743,0.00065058423,0.00032356172,0.002427706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005229055,0.00014303991,0.0022152713,0.00032368532,0.000040068877,0.0002850367,0.00007829432,0.0067922547,0.16976635,0.0010678871,0.0074161794,0.8113489],"study_design_scores_gemma":[0.00017281863,0.0005642005,0.012419072,0.00014327688,0.0001387067,0.0025354691,0.00016904762,0.50296366,0.44153127,0.0035395331,0.035693746,0.00012922194],"about_ca_topic_score_codex":0.0010943938,"about_ca_topic_score_gemma":0.0015003056,"teacher_disagreement_score":0.004665426,"about_ca_system_score_codex":0.0002319459,"about_ca_system_score_gemma":0.000245971,"threshold_uncertainty_score":0.015607417},"labels":[],"label_agreement":null},{"id":"W2616923412","doi":"10.48550/arxiv.1705.07511","title":"ARABIS: an Asynchronous Acoustic Indoor Positioning System for Mobile Devices","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Testbed; Computer science; Multilateration; Ranging; Asynchronous communication; Real-time computing; Outlier; Robustness (evolution); Acoustics; Computer network; Telecommunications; Artificial intelligence","score_opus":0.036641581114758154,"score_gpt":0.19196870569520066,"score_spread":0.1553271245804425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616923412","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046926476,0.0008124302,0.881469,0.00029876962,0.0005336418,0.0004268326,0.0016562499,0.04440024,0.023476336],"genre_scores_gemma":[0.47962517,0.00062967883,0.48278353,0.00035747534,0.00022987947,0.000928934,0.0033998538,0.000518075,0.031527463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995266,0.000103324135,0.000028063932,0.00010467038,0.00019941622,0.000037761125],"domain_scores_gemma":[0.9996935,0.000033374643,0.000051975803,0.00006387137,0.00011024095,0.000047110076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046329288,0.0009581357,0.000580995,0.0010007767,0.0003963212,0.0007166864,0.0010867278,0.0005792031,0.004889116],"category_scores_gemma":[0.0008015797,0.0002009896,0.0002220432,0.0006071725,0.0002741262,0.00074848864,0.0011196523,0.00071245077,0.0034755326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010016931,0.00021689231,0.0056698225,0.0006068525,0.00012896705,0.000479175,0.00051253726,0.020209001,0.17275725,0.016882274,0.053843033,0.7276924],"study_design_scores_gemma":[0.0007400843,0.002048744,0.010587845,0.00012817276,0.00020899686,0.0018148432,0.00034380946,0.43054155,0.15065476,0.0051579047,0.39756098,0.00021235348],"about_ca_topic_score_codex":0.001171197,"about_ca_topic_score_gemma":0.0017653805,"teacher_disagreement_score":0.004889116,"about_ca_system_score_codex":0.00036579432,"about_ca_system_score_gemma":0.000601788,"threshold_uncertainty_score":0.016355753},"labels":[],"label_agreement":null},{"id":"W2619603874","doi":"10.1109/jiot.2017.2708719","title":"Beacon Deployment for Unambiguous Positioning","year":2017,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Beacon; Computer science; Software deployment; Bluetooth Low Energy; Bluetooth; Integer (computer science); Integer programming; Real-time computing; Mobile device; Cloud computing; Computer network; Algorithm; Wireless; Telecommunications","score_opus":0.015550895597170931,"score_gpt":0.25404856697250616,"score_spread":0.23849767137533523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619603874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008398612,0.0004484151,0.98778534,0.00024812034,0.00008512284,0.000040753053,0.00004364083,0.0005123372,0.0024376654],"genre_scores_gemma":[0.694168,0.0012672383,0.29971114,0.00025341913,0.00013548361,0.00019186304,0.0002921711,0.00016599073,0.0038147331],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982992,0.0006216597,0.000088666835,0.0002808294,0.0004347556,0.00027499144],"domain_scores_gemma":[0.9977946,0.0010175077,0.00026562845,0.00050919264,0.0003111737,0.00010191376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009561928,0.0008447,0.0008501168,0.0005684198,0.00078699936,0.0008658065,0.0015413769,0.0010941463,0.0034176656],"category_scores_gemma":[0.0053855022,0.0005202583,0.00048271354,0.00090034504,0.0007372023,0.0019288044,0.002360899,0.0014019723,0.0013795306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072259206,0.00015238581,0.003699202,0.0008876916,0.00006457014,0.0008533803,0.0006844696,0.39388728,0.058288436,0.11755509,0.014274722,0.40893018],"study_design_scores_gemma":[0.000102726146,0.00040143382,0.0010106078,0.000108057226,0.000043158245,0.0009166226,0.00038786154,0.91023535,0.018472724,0.044643424,0.023628242,0.000049832313],"about_ca_topic_score_codex":0.0014674084,"about_ca_topic_score_gemma":0.0016580777,"teacher_disagreement_score":0.0034176656,"about_ca_system_score_codex":0.00069738977,"about_ca_system_score_gemma":0.0008358589,"threshold_uncertainty_score":0.011433244},"labels":[],"label_agreement":null},{"id":"W2620249651","doi":"10.48550/arxiv.1705.07483","title":"TuRF: Fast Data Collection for Fingerprint-based Indoor Localization","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Fingerprint (computing); Computer science; Process (computing); Fingerprint recognition; Data collection; Gaussian process; Gaussian; Path (computing); Kriging; Data mining; Artificial intelligence; Real-time computing; Machine learning; Computer network; Statistics; Mathematics","score_opus":0.09568740241224952,"score_gpt":0.2063026617908221,"score_spread":0.11061525937857258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620249651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0140707,0.00027462398,0.9528716,0.000101733145,0.00009456328,0.00014110986,0.0012736797,0.030227784,0.0009441676],"genre_scores_gemma":[0.38949677,0.00041388578,0.5996647,0.00022948776,0.00010719476,0.0005132907,0.0055336943,0.0011336631,0.0029073174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987658,0.00022639733,0.000056219855,0.0003044473,0.000486808,0.00016034358],"domain_scores_gemma":[0.9975368,0.00053354277,0.00021817394,0.0011446103,0.00044197126,0.00012483641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011817906,0.0010762617,0.0010512081,0.0019092598,0.0005408747,0.0008725983,0.0024992668,0.0009972673,0.003937002],"category_scores_gemma":[0.005975757,0.0004982216,0.00046129533,0.0018115771,0.00045536668,0.0017687542,0.0025131807,0.0010710403,0.003288691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084114267,0.00026496884,0.010045146,0.00032724466,0.00017973046,0.0005466427,0.0003569727,0.03816435,0.043401618,0.005586587,0.043710195,0.85657537],"study_design_scores_gemma":[0.0001825052,0.00052963366,0.011726938,0.00007306368,0.00006234931,0.0017679315,0.00022810389,0.8548988,0.06795642,0.010307711,0.052074153,0.00019245366],"about_ca_topic_score_codex":0.0030746842,"about_ca_topic_score_gemma":0.0040088766,"teacher_disagreement_score":0.003937002,"about_ca_system_score_codex":0.00045309975,"about_ca_system_score_gemma":0.00075547723,"threshold_uncertainty_score":0.0131706},"labels":[],"label_agreement":null},{"id":"W2620640655","doi":"10.1109/isiss.2017.7935662","title":"An inertial navigation system with acoustic obstacle detection for pedestrian applications","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association; University of Windsor","keywords":"Inertial measurement unit; Inertial navigation system; Obstacle; Computer science; Computer vision; Dead reckoning; Position (finance); Artificial intelligence; Real-time computing; Pedestrian; Track (disk drive); Navigation system; Inertial frame of reference; Global Positioning System; Engineering; Telecommunications","score_opus":0.009223416876754572,"score_gpt":0.23480192502922806,"score_spread":0.2255785081524735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620640655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043786854,0.00085720443,0.9272605,0.00019183109,0.0004678669,0.0002121324,0.00031298885,0.010259085,0.016651433],"genre_scores_gemma":[0.4529551,0.0010204572,0.50733876,0.00036047114,0.00027959343,0.0003970077,0.0009906894,0.00025011445,0.03640787],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999801,0.000028496655,0.00001211541,0.000037619106,0.00009920534,0.000021663262],"domain_scores_gemma":[0.9998461,0.000016101934,0.000016740712,0.000023658993,0.00007989481,0.000017535789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020961692,0.00047498688,0.00041596443,0.0004432026,0.00034857864,0.00036452943,0.00071476976,0.00048129127,0.005058383],"category_scores_gemma":[0.00037300377,0.00021986448,0.00022905417,0.00047181596,0.00010780688,0.00046485377,0.0004516188,0.00029999105,0.002532854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045185655,0.00016219821,0.0057249353,0.00034043804,0.00006696179,0.00051375845,0.0003037075,0.004343438,0.20751198,0.004591465,0.017864253,0.75812495],"study_design_scores_gemma":[0.00025064754,0.003920634,0.029742956,0.00018402393,0.0004984389,0.00532082,0.0002467675,0.20741028,0.25196427,0.0024739215,0.49772668,0.00026056333],"about_ca_topic_score_codex":0.0015414292,"about_ca_topic_score_gemma":0.0026154972,"teacher_disagreement_score":0.005058383,"about_ca_system_score_codex":0.00016976004,"about_ca_system_score_gemma":0.0003984652,"threshold_uncertainty_score":0.016921937},"labels":[],"label_agreement":null},{"id":"W2621386686","doi":"10.3390/s17061272","title":"A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Calgary","keywords":"Inertial measurement unit; Particle filter; Extended Kalman filter; Computer science; Inertial navigation system; Real-time computing; Map matching; Kalman filter; Indoor positioning system; Global Positioning System; Computer vision; Artificial intelligence; Orientation (vector space); Accelerometer; Mathematics; Telecommunications","score_opus":0.012837131267788056,"score_gpt":0.2310715795134802,"score_spread":0.21823444824569216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621386686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015443263,0.0004058109,0.9726589,0.00016480568,0.0002491291,0.000106374275,0.00022597788,0.005146299,0.0055994987],"genre_scores_gemma":[0.50453705,0.00075791014,0.47873822,0.00033672978,0.00022221317,0.0002533689,0.0011871385,0.00014380694,0.0138236005],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996915,0.000033687767,0.000017541204,0.0000672799,0.00015435253,0.000035773748],"domain_scores_gemma":[0.9998067,0.00001278169,0.00001735244,0.000037640493,0.00010712628,0.000018372662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002525202,0.00063901965,0.00048347493,0.00066097226,0.00040557998,0.00067060837,0.0011959124,0.000765087,0.0032107397],"category_scores_gemma":[0.0003598771,0.00022629947,0.0003380389,0.00065697334,0.00018004929,0.0010679059,0.00079863897,0.00057141535,0.0026679274],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040663936,0.00024371235,0.00525506,0.0004168729,0.00012105492,0.0004860106,0.00020684523,0.024321798,0.11512063,0.010722909,0.016619526,0.8260791],"study_design_scores_gemma":[0.000100868725,0.00096814625,0.009180905,0.00008840206,0.00029987653,0.0014159946,0.00018777217,0.7297357,0.10796717,0.004169304,0.14574488,0.00014101532],"about_ca_topic_score_codex":0.002491213,"about_ca_topic_score_gemma":0.002674641,"teacher_disagreement_score":0.0032107397,"about_ca_system_score_codex":0.00030611083,"about_ca_system_score_gemma":0.0007870649,"threshold_uncertainty_score":0.010741055},"labels":[],"label_agreement":null},{"id":"W262433993","doi":"10.1007/978-3-662-43984-5_25","title":"Integrated Indoor Positioning with Mobile Devices for Location-Based Service Applications","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Location-based service; Service (business); Hybrid positioning system; Real-time computing; Embedded system; Telecommunications; Positioning system; Engineering","score_opus":0.0072961960245556765,"score_gpt":0.21185718038560972,"score_spread":0.20456098436105405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W262433993","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012269163,0.014368639,0.9035605,0.00033380173,0.0009977965,0.00007923051,0.00044520374,0.0043314206,0.063614205],"genre_scores_gemma":[0.36436322,0.016868211,0.44610274,0.0005369136,0.0009941829,0.00016829203,0.001897842,0.0006455983,0.16842303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996723,0.000046317098,0.000014756671,0.00006799472,0.00015215903,0.00004645038],"domain_scores_gemma":[0.9998398,0.000031965814,0.000009559156,0.000040455463,0.000066718676,0.0000115089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002064144,0.0010831553,0.0006530969,0.0006351767,0.00028729555,0.001027945,0.0013493064,0.0010632632,0.01226422],"category_scores_gemma":[0.00039271283,0.00042705543,0.00049875764,0.0016663658,0.00021272543,0.0012343421,0.0012007774,0.0008399384,0.0081567615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016735245,0.00006448561,0.0010680584,0.00066786795,0.00007096239,0.00032422016,0.00021310076,0.008342652,0.08641556,0.015871927,0.019329494,0.8674643],"study_design_scores_gemma":[0.00005035364,0.0009820578,0.005362173,0.00048623706,0.00043930853,0.0036557375,0.00034318573,0.11753997,0.1547433,0.013353336,0.70289576,0.0001485009],"about_ca_topic_score_codex":0.0010066034,"about_ca_topic_score_gemma":0.0018382357,"teacher_disagreement_score":0.01226422,"about_ca_system_score_codex":0.00032522928,"about_ca_system_score_gemma":0.0002760784,"threshold_uncertainty_score":0.041027904},"labels":[],"label_agreement":null},{"id":"W2624786158","doi":"10.1016/j.neucom.2016.10.099","title":"An UWB ranging method based on wavelet packet decomposition","year":2017,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Ranging; Computer science; Ultra-wideband; Energy (signal processing); Wavelet packet decomposition; Network packet; Wavelet; SIGNAL (programming language); Transmission (telecommunications); Wireless sensor network; Wireless; Time domain; Real-time computing; Algorithm; Electronic engineering; Artificial intelligence; Telecommunications; Wavelet transform; Mathematics; Engineering; Computer vision; Computer network; Statistics","score_opus":0.011589533754811853,"score_gpt":0.305007323326829,"score_spread":0.29341778957201714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624786158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009273696,0.00021565381,0.98952407,0.000052371197,0.00008967576,0.000015192593,0.000016613076,0.00013503677,0.000677692],"genre_scores_gemma":[0.19729854,0.0007566933,0.79732627,0.000109466746,0.00012717162,0.00007545376,0.00012073632,0.000055207467,0.0041305567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982363,0.00003025288,0.000010527313,0.000041916814,0.00007911532,0.000014584426],"domain_scores_gemma":[0.9998436,0.000040026112,0.000013580859,0.000018238256,0.000073182906,0.0000113458145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024010704,0.0004403241,0.000496188,0.00042163194,0.00024924037,0.00039476223,0.00042114212,0.0004662963,0.0008970646],"category_scores_gemma":[0.000558572,0.00022629344,0.00038961275,0.0006763026,0.00017113873,0.00068684504,0.00044518913,0.0005829978,0.0004069979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016938783,0.000103334285,0.00059058674,0.00017977411,0.00007412218,0.00010628798,0.000070296235,0.0345127,0.15812615,0.0071761906,0.0021396296,0.7967515],"study_design_scores_gemma":[0.000024154124,0.00012481016,0.0010788247,0.00001648625,0.00006883581,0.00040132084,0.000034503068,0.9586757,0.032925975,0.0030105468,0.0036004938,0.000038216553],"about_ca_topic_score_codex":0.00055029447,"about_ca_topic_score_gemma":0.00056034507,"teacher_disagreement_score":0.0008970646,"about_ca_system_score_codex":0.0001173261,"about_ca_system_score_gemma":0.00042669591,"threshold_uncertainty_score":0.003000915},"labels":[],"label_agreement":null},{"id":"W2626063980","doi":"10.3390/s17061391","title":"TrackCC: A Practical Wireless Indoor Localization System Based on Less-Expensive Chips","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Non-line-of-sight propagation; Computer science; Wireless; Position (finance); Line (geometry); Power (physics); Indoor positioning system; Real-time computing; Chip; Wireless network; State (computer science); Exploit; Matching (statistics); Construct (python library); Algorithm; Embedded system; Telecommunications; Computer network; Mathematics","score_opus":0.024319218418731664,"score_gpt":0.25742968275298106,"score_spread":0.2331104643342494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626063980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12611249,0.00086759386,0.85083675,0.00036953896,0.00034011723,0.00024599987,0.00047586285,0.008418714,0.012332823],"genre_scores_gemma":[0.7755403,0.00035712714,0.21321462,0.0005072865,0.00009803933,0.0002084695,0.000666429,0.00009826464,0.0093095135],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971634,0.000038485483,0.000016977572,0.00007929839,0.00010604943,0.0000428353],"domain_scores_gemma":[0.9996556,0.000047724803,0.00006339488,0.00006079208,0.00013798718,0.000034450768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002319313,0.00043397347,0.00038541254,0.00048733773,0.0003231816,0.00044637237,0.0010737261,0.0005172505,0.002951639],"category_scores_gemma":[0.00046255547,0.00014759113,0.0002307737,0.00052122877,0.00023109007,0.0009075692,0.00051693706,0.00031191026,0.00085639546],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090884155,0.00027565958,0.011626148,0.000816333,0.00011187746,0.00079614075,0.00028254685,0.03062652,0.29309052,0.013249748,0.03259923,0.61561644],"study_design_scores_gemma":[0.00038657003,0.0047330256,0.013921416,0.00011890282,0.00033178422,0.004860721,0.00024629696,0.5425939,0.25350022,0.0020341894,0.17695236,0.00032060954],"about_ca_topic_score_codex":0.0015012294,"about_ca_topic_score_gemma":0.002743156,"teacher_disagreement_score":0.002951639,"about_ca_system_score_codex":0.00034165508,"about_ca_system_score_gemma":0.00065347325,"threshold_uncertainty_score":0.009874284},"labels":[],"label_agreement":null},{"id":"W2637786890","doi":"10.1109/ccece.2017.7946749","title":"EKF and UKF localization of a moving RF ground target using a flying vehicle","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Extended Kalman filter; Kalman filter; Control theory (sociology); Computer science; Process (computing); Filter (signal processing); Position (finance); SIGNAL (programming language); Ground truth; Computer vision; Artificial intelligence","score_opus":0.01910197344419966,"score_gpt":0.2405573156778625,"score_spread":0.22145534223366284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2637786890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061477028,0.00027988813,0.93570733,0.00007600457,0.00006410637,0.000016734088,0.00003420408,0.00023075682,0.002113931],"genre_scores_gemma":[0.86193144,0.00024475442,0.13226506,0.00002880862,0.000023494442,0.00004777277,0.00008571212,0.000018068078,0.005354963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998716,0.000026554764,0.000010082417,0.000031537904,0.00004311028,0.000016955353],"domain_scores_gemma":[0.99979895,0.00007581915,0.000025362277,0.000017858789,0.00007409007,0.000007874434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020842948,0.00029919445,0.00033613786,0.0002154361,0.00021756352,0.0003727045,0.00030215934,0.00056214916,0.00069290743],"category_scores_gemma":[0.00081894983,0.00011201235,0.000252891,0.00021569755,0.00026610694,0.00041837012,0.0002330034,0.0003132047,0.00023290081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009254535,0.00001776479,0.00221971,0.00009540459,0.000028212975,0.00022555093,0.00012827138,0.86086637,0.014771432,0.0062907883,0.00067928946,0.11458461],"study_design_scores_gemma":[0.000004639207,0.00003108576,0.0005430465,0.000003443807,0.000005533543,0.000054787568,0.000017258295,0.99623704,0.0019023861,0.000436936,0.0007565423,0.0000072583493],"about_ca_topic_score_codex":0.012133782,"about_ca_topic_score_gemma":0.005377162,"teacher_disagreement_score":0.012133782,"about_ca_system_score_codex":0.0002527929,"about_ca_system_score_gemma":0.0004050989,"threshold_uncertainty_score":0.024126291},"labels":[],"label_agreement":null},{"id":"W2723826663","doi":"10.1109/ccece.2017.7946751","title":"Accurate UWB and IMU based indoor localization for autonomous robots","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Inertial measurement unit; Computer science; Robustness (evolution); Kalman filter; Mobile robot; Scalability; Robot; Sensor fusion; Real-time computing; Ultra-wideband; Odometry; Extended Kalman filter; Artificial intelligence; Acceleration; Computer vision; Simultaneous localization and mapping; Telecommunications","score_opus":0.01783498547053294,"score_gpt":0.2506168765200475,"score_spread":0.23278189104951455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2723826663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024991645,0.0016634272,0.9681903,0.000116477255,0.00018001097,0.000014106516,0.000061564686,0.0019266838,0.0028558616],"genre_scores_gemma":[0.7240897,0.0010633956,0.26896742,0.00015128125,0.00015864408,0.000060127622,0.00021304493,0.000089381574,0.0052070622],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994875,0.00010837316,0.000020464257,0.00008862131,0.00025233102,0.000042576965],"domain_scores_gemma":[0.9997476,0.000037212965,0.00005594597,0.000062201354,0.00008590609,0.000011173337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026827303,0.0005028781,0.00048683767,0.0008123984,0.00024357239,0.00047196352,0.00053083553,0.00061488524,0.0008127649],"category_scores_gemma":[0.0008477903,0.00024981968,0.0002785563,0.0007756304,0.00030945303,0.0008045979,0.0006464029,0.00038818765,0.0009298305],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030772603,0.000049528528,0.0024327503,0.00034486316,0.000101265934,0.00040040037,0.00023320607,0.08412118,0.1543866,0.006449295,0.00443569,0.74673754],"study_design_scores_gemma":[0.0000417467,0.00040618854,0.0073213615,0.00008186698,0.00012328615,0.0014280652,0.00020432295,0.79509103,0.15564145,0.00640092,0.03314986,0.000109855086],"about_ca_topic_score_codex":0.0010366441,"about_ca_topic_score_gemma":0.0009801156,"teacher_disagreement_score":0.0010366441,"about_ca_system_score_codex":0.0002143191,"about_ca_system_score_gemma":0.00022515235,"threshold_uncertainty_score":0.002718985},"labels":[],"label_agreement":null},{"id":"W2725906624","doi":"","title":"LC-KDE: A novel scheme for Wi-Fi localization","year":2016,"lang":"en","type":"article","venue":"Wireless Personal Multimedia Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Kernel density estimation; Computer science; Kernel (algebra); Fingerprint (computing); Artificial intelligence; Probability density function; Pattern recognition (psychology); Process (computing); Linear discriminant analysis; Selection (genetic algorithm); Kernel Fisher discriminant analysis; Scheme (mathematics); Multivariate statistics; Feature selection; Feature extraction; Discriminant; Machine learning; Mathematics; Statistics","score_opus":0.03187044238233092,"score_gpt":0.26160109008421667,"score_spread":0.22973064770188575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2725906624","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010127005,0.00028125447,0.98732823,0.00010973162,0.000061225954,0.000046165245,0.00008851845,0.00093095744,0.0010269261],"genre_scores_gemma":[0.39088878,0.00050644577,0.6000939,0.00029941005,0.000065498745,0.00016841703,0.0005729306,0.00009597221,0.0073085586],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993399,0.00011392373,0.00004716555,0.00016234271,0.0002588572,0.000077839635],"domain_scores_gemma":[0.9992331,0.00014169562,0.000077298144,0.00027056664,0.00023803349,0.00003935342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053733325,0.00059460435,0.0008906276,0.0010527581,0.00061491516,0.0005712988,0.0013456419,0.0008802942,0.0019216293],"category_scores_gemma":[0.0025178995,0.0002866895,0.0003777535,0.0011807751,0.00057830504,0.0019867225,0.002223354,0.0007064241,0.0014193747],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036459893,0.00012858931,0.001802735,0.00019924736,0.000057465655,0.0002332154,0.00015691754,0.03013466,0.07982558,0.011535391,0.005001248,0.87056035],"study_design_scores_gemma":[0.000081703416,0.00023087225,0.0025891198,0.00003375728,0.00004164375,0.0018140166,0.00010338489,0.90440387,0.061851725,0.0079615,0.020745551,0.00014279569],"about_ca_topic_score_codex":0.0013134073,"about_ca_topic_score_gemma":0.0020031722,"teacher_disagreement_score":0.0019216293,"about_ca_system_score_codex":0.00038966534,"about_ca_system_score_gemma":0.0006192951,"threshold_uncertainty_score":0.00642848},"labels":[],"label_agreement":null},{"id":"W2730709293","doi":"10.1007/s12652-017-0531-3","title":"An experimental comparative study of RSSI-based positioning algorithms for passive RFID localization in smart environments","year":2017,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Radio-frequency identification; Kalman filter; Identification (biology); Wireless; Wireless sensor network; Computational intelligence; Field (mathematics); Particle filter; Real-time computing; Ambient intelligence; Received signal strength indication; Tracking (education); Smart environment; Filter (signal processing); Algorithm; Embedded system; Telecommunications; Artificial intelligence; Computer vision; Computer network; Internet of Things; Computer security","score_opus":0.049408559941191206,"score_gpt":0.32653489841837713,"score_spread":0.27712633847718593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2730709293","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9186087,0.00041495592,0.075787164,0.00009689023,0.0001789344,0.00016147918,0.0004198177,0.00059223606,0.0037397752],"genre_scores_gemma":[0.9724248,0.0002549183,0.024667634,0.000032525953,0.000023823632,0.00008549334,0.0004150367,0.0000713293,0.0020244266],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982083,0.00071773544,0.00015429308,0.00028365175,0.00048828404,0.0001477713],"domain_scores_gemma":[0.99386346,0.0032821975,0.00034625543,0.0006841874,0.0016973423,0.00012660201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017036225,0.00077405653,0.0006472132,0.0011382755,0.00039183587,0.00078281865,0.0011090852,0.0008972893,0.0037382175],"category_scores_gemma":[0.0074212705,0.00033982575,0.00035140544,0.0009952106,0.0006127815,0.0017589872,0.0008958593,0.00036446203,0.000795822],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017184693,0.0044355574,0.025197515,0.0029870926,0.00061403576,0.00062297244,0.0023200067,0.06856954,0.36588785,0.004474225,0.0027321193,0.50497437],"study_design_scores_gemma":[0.001234367,0.037773114,0.09244701,0.00022613631,0.0013044483,0.0026680043,0.0045923064,0.45329568,0.39233503,0.0024565062,0.011285339,0.0003820854],"about_ca_topic_score_codex":0.0015249047,"about_ca_topic_score_gemma":0.0015003426,"teacher_disagreement_score":0.0037382175,"about_ca_system_score_codex":0.00039493066,"about_ca_system_score_gemma":0.00033511172,"threshold_uncertainty_score":0.012505591},"labels":[],"label_agreement":null},{"id":"W2732107624","doi":"10.1145/3090094","title":"Gain Without Pain","year":2017,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"Cisco Systems","keywords":"RSS; Fingerprint (computing); Computer science; Matching (statistics); Construct (python library); Ambiguity; Percentile; Data mining; Real-time computing; Artificial intelligence; Computer network; Statistics; Mathematics; World Wide Web","score_opus":0.009640496580766779,"score_gpt":0.24104209202164753,"score_spread":0.23140159544088076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732107624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031868298,0.005924278,0.66721565,0.0066225785,0.0030144255,0.00041158442,0.0011472149,0.024858933,0.2589371],"genre_scores_gemma":[0.5710361,0.0046523027,0.22739021,0.00612473,0.0018735625,0.00047855868,0.0023847858,0.0015326091,0.18452717],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99838686,0.00014438735,0.0000648343,0.00031812713,0.0007951725,0.00029062785],"domain_scores_gemma":[0.99844044,0.00032812142,0.00008588726,0.00047724997,0.0005328075,0.00013546592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007378489,0.0019773103,0.0014526794,0.0013254074,0.0010862335,0.0031316513,0.0022662901,0.0021697443,0.08636271],"category_scores_gemma":[0.004591453,0.0004717984,0.00080362515,0.0012722453,0.0006766649,0.003887659,0.004377263,0.0019217181,0.054003533],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065150205,0.00018827787,0.001198627,0.0004078489,0.000056998775,0.0004685494,0.00014975674,0.006302289,0.037690967,0.032700323,0.058624677,0.8615602],"study_design_scores_gemma":[0.00043573396,0.0021485204,0.0055352207,0.00046016488,0.0004091527,0.009840702,0.0008615117,0.16951813,0.06266617,0.088837564,0.65895694,0.00033024093],"about_ca_topic_score_codex":0.0014016251,"about_ca_topic_score_gemma":0.0019108205,"teacher_disagreement_score":0.08636271,"about_ca_system_score_codex":0.0007754561,"about_ca_system_score_gemma":0.0010040083,"threshold_uncertainty_score":0.28891206},"labels":[],"label_agreement":null},{"id":"W2735043621","doi":"10.1109/cse-euc-dcabes.2016.158","title":"Towards Zero-Configuration Indoor Localization Using Asynchronous Acoustic Beacons","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Beacon; Asynchronous communication; Computer science; Zero (linguistics); Computer network","score_opus":0.012876260760080787,"score_gpt":0.2244780691176192,"score_spread":0.21160180835753842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735043621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014741008,0.00015969694,0.981913,0.000075594224,0.000042040825,0.000014197368,0.000013016736,0.0013310547,0.0017104117],"genre_scores_gemma":[0.60032207,0.00038771014,0.3938388,0.00012961625,0.00007328251,0.00007967093,0.00016162908,0.00020030512,0.0048069838],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989801,0.00033064306,0.000035393732,0.00020387546,0.00034436304,0.00010560421],"domain_scores_gemma":[0.99868315,0.0003927207,0.00013686302,0.00036926806,0.00034034892,0.00007757616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008968278,0.0007769461,0.0006019028,0.0008045389,0.00038695565,0.0009366896,0.0021282893,0.00078048796,0.0017081707],"category_scores_gemma":[0.0023094194,0.0004887594,0.00032004676,0.0006228449,0.0008459371,0.0016549905,0.002428189,0.00085146783,0.0016517533],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006699432,0.00021805908,0.0077943946,0.00052007573,0.000096671814,0.00058526685,0.0010863183,0.15148547,0.23110378,0.04989991,0.005826214,0.55071396],"study_design_scores_gemma":[0.00011118575,0.0008571698,0.0033159622,0.00009966598,0.000073427924,0.0009792603,0.00027956683,0.8730135,0.07776267,0.018774722,0.024613619,0.00011925624],"about_ca_topic_score_codex":0.0010615754,"about_ca_topic_score_gemma":0.0010982241,"teacher_disagreement_score":0.0021282893,"about_ca_system_score_codex":0.00043101743,"about_ca_system_score_gemma":0.00045713285,"threshold_uncertainty_score":0.0057144165},"labels":[],"label_agreement":null},{"id":"W2736291874","doi":"10.1109/icdcs.2017.142","title":"Robust Indoor Wireless Localization Using Sparse Recovery","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Angle of arrival; Subcarrier; Wireless; Real-time computing; Algorithm; Electronic engineering; Orthogonal frequency-division multiplexing; Antenna (radio); Telecommunications; Engineering","score_opus":0.04342986970057186,"score_gpt":0.23609678504767667,"score_spread":0.1926669153471048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736291874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009416451,0.00011916577,0.98812175,0.000101019876,0.000023533641,0.000011732817,0.000047868547,0.0007648475,0.0013936701],"genre_scores_gemma":[0.52435035,0.00051607896,0.47084075,0.0002085569,0.00011366065,0.00010266612,0.00045790744,0.00012098237,0.0032890462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995435,0.00012335158,0.000018108962,0.00010192944,0.00016342627,0.000049576534],"domain_scores_gemma":[0.99958915,0.00015230155,0.00008059515,0.000081062666,0.00008069512,0.000016314278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032606345,0.0005486659,0.0005460774,0.0004628645,0.00023503564,0.0005258893,0.00060802634,0.00053974526,0.0011819189],"category_scores_gemma":[0.0014944451,0.0002691405,0.00039639682,0.00074749923,0.0003563756,0.0010496485,0.0012814025,0.0006069584,0.00075881503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002659523,0.0000758693,0.001539009,0.00020695502,0.000104233266,0.00022819436,0.00017679096,0.4662535,0.08913087,0.015054302,0.0047211535,0.42224318],"study_design_scores_gemma":[0.000025089907,0.00007882082,0.0004857089,0.000015210602,0.00001807958,0.00013465484,0.00003279908,0.978509,0.0132221775,0.0044825147,0.0029737744,0.000022070466],"about_ca_topic_score_codex":0.0015309439,"about_ca_topic_score_gemma":0.0015335266,"teacher_disagreement_score":0.0015309439,"about_ca_system_score_codex":0.00028243204,"about_ca_system_score_gemma":0.00044780885,"threshold_uncertainty_score":0.0039539337},"labels":[],"label_agreement":null},{"id":"W2736765318","doi":"10.3390/mi8070225","title":"Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter","year":2017,"lang":"en","type":"article","venue":"Micromachines","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Particle filter; Inertial navigation system; Kalman filter; Computer science; Map matching; Extended Kalman filter; Filter (signal processing); Navigation system; Cascade; Computer vision; Inertial measurement unit; Real-time computing; Control theory (sociology); Artificial intelligence; Engineering; Inertial frame of reference; Global Positioning System","score_opus":0.029492777223185017,"score_gpt":0.27386129125552616,"score_spread":0.24436851403234114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736765318","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072786543,0.00009117417,0.9912375,0.000025293177,0.000057372683,0.000013341793,0.00002907983,0.0003943509,0.0008731936],"genre_scores_gemma":[0.6496126,0.0004245854,0.34465718,0.00007909882,0.00009626266,0.0001216084,0.00026538508,0.00006754527,0.0046757334],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972695,0.000049459395,0.000011638397,0.00007884452,0.00009747314,0.00003552811],"domain_scores_gemma":[0.99973124,0.00006916177,0.000027466529,0.000037347316,0.00011814869,0.000016694696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035590262,0.00071515405,0.00076908915,0.0006052396,0.00038722483,0.00051791285,0.00084163126,0.00055522716,0.001132999],"category_scores_gemma":[0.0008433942,0.00029233532,0.00065343524,0.0006480854,0.0002593847,0.0006912855,0.00065355765,0.0005357231,0.0005370979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004794986,0.0001502361,0.0049260147,0.00026273003,0.00017630887,0.00025852336,0.0002792706,0.42002985,0.026994217,0.0119674755,0.004366478,0.5301094],"study_design_scores_gemma":[0.000015257734,0.000051860647,0.00071822584,0.0000057238617,0.000025091518,0.00005528956,0.000014669934,0.9927089,0.0035939713,0.00093491265,0.001863112,0.00001287002],"about_ca_topic_score_codex":0.0066359933,"about_ca_topic_score_gemma":0.0054607503,"teacher_disagreement_score":0.0066359933,"about_ca_system_score_codex":0.00034240933,"about_ca_system_score_gemma":0.00089364615,"threshold_uncertainty_score":0.01319474},"labels":[],"label_agreement":null},{"id":"W2737707020","doi":"10.1109/icra.2017.7989373","title":"Robust sensor fusion for finding HRI partners in a crowd","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Occupancy grid mapping; Computer science; Mobile robot; Probabilistic logic; Robot; Sensor fusion; Grid; Modalities; Artificial intelligence; Human–computer interaction; Occupancy; Human–robot interaction; Probability density function; Computer vision; Engineering; Mathematics","score_opus":0.0581108496830197,"score_gpt":0.29041438668756187,"score_spread":0.23230353700454215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737707020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038504507,0.00027057825,0.9581858,0.0001375613,0.000039877195,0.00004633889,0.0000613206,0.0008345859,0.0019194852],"genre_scores_gemma":[0.8673392,0.00016291853,0.13101338,0.00007542158,0.00003972577,0.00007589186,0.000090574445,0.000052988536,0.0011498225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926835,0.00017957398,0.000020864734,0.00016812222,0.00026270177,0.000100437],"domain_scores_gemma":[0.999503,0.00018456538,0.00008457354,0.00010175807,0.00008342824,0.000042677064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010009082,0.0007783585,0.0008832962,0.0006504414,0.0005448958,0.0006562396,0.0011274988,0.0008797005,0.0014210782],"category_scores_gemma":[0.002783076,0.00040951907,0.0004847622,0.0004626362,0.0009046411,0.0016013408,0.0025906628,0.00068414264,0.0005027904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005252591,0.00014615132,0.0026015593,0.00020937846,0.00009902362,0.000598861,0.00075607956,0.66358316,0.0978711,0.020743964,0.0024526317,0.21041287],"study_design_scores_gemma":[0.00001419254,0.000102268576,0.0008190436,0.000010147575,0.00001395675,0.00016626746,0.000092591654,0.97809917,0.010196599,0.009381073,0.001074578,0.000030049934],"about_ca_topic_score_codex":0.0019585262,"about_ca_topic_score_gemma":0.0015500763,"teacher_disagreement_score":0.0019585262,"about_ca_system_score_codex":0.00052361324,"about_ca_system_score_gemma":0.0004951325,"threshold_uncertainty_score":0.0052933693},"labels":[],"label_agreement":null},{"id":"W2740760200","doi":"10.11575/prism/24620","title":"3D Building Model-Assisted Snapshot GNSS Positioning Method","year":2017,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"GNSS applications; Snapshot (computer storage); Computer science; Global Positioning System; Geodesy; Geography; Database; Telecommunications","score_opus":0.011985226026988888,"score_gpt":0.23556245131492576,"score_spread":0.22357722528793686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740760200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007110906,0.00011169892,0.9873863,0.00005493682,0.000028900458,0.000052154795,0.00049285335,0.0014744505,0.0032878534],"genre_scores_gemma":[0.29592794,0.0005582051,0.6938018,0.00009006203,0.000032423322,0.00033138576,0.0030763259,0.0004073684,0.0057745446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997309,0.000036743048,0.00001155002,0.000055869805,0.00013927913,0.000025631267],"domain_scores_gemma":[0.9998178,0.000036373665,0.000018307454,0.00003418215,0.0000805823,0.00001275407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017933258,0.0007892674,0.0006726768,0.0008705914,0.00026773234,0.0010692042,0.0012928414,0.00080189673,0.004351556],"category_scores_gemma":[0.00055644836,0.0005260286,0.0010122702,0.0010608864,0.0002500928,0.00051096897,0.0011490312,0.0007351983,0.0023366506],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006627027,0.000042377193,0.0021962284,0.00022152833,0.00006730193,0.00022416955,0.00015881684,0.8008671,0.015576131,0.008597625,0.0040840358,0.16789846],"study_design_scores_gemma":[0.000006031833,0.000013571495,0.00041247255,0.00001389303,0.000012923671,0.00007692898,0.000038281607,0.9915774,0.0018907688,0.0014563256,0.0044889874,0.000012307099],"about_ca_topic_score_codex":0.0056034965,"about_ca_topic_score_gemma":0.0062356303,"teacher_disagreement_score":0.0056034965,"about_ca_system_score_codex":0.00036079634,"about_ca_system_score_gemma":0.0009082487,"threshold_uncertainty_score":0.014557421},"labels":[],"label_agreement":null},{"id":"W2741698662","doi":"10.1109/icc.2017.7996459","title":"A measurement-based boundary estimation approach for localization in industrial WSNs","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer science; Beamforming; Noise measurement; Noise (video); Impulse (physics); Path loss; Interference (communication); Real-time computing; Semidefinite programming; Wireless; Algorithm; Noise reduction; Electronic engineering; Channel (broadcasting); Artificial intelligence; Mathematical optimization; Mathematics; Engineering; Telecommunications; Computer network","score_opus":0.05801224817487717,"score_gpt":0.25356862322306084,"score_spread":0.19555637504818368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741698662","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017359463,0.00003296164,0.99790657,0.000021588732,0.0000058276983,0.0000047279914,0.000005783036,0.000057717607,0.00022886961],"genre_scores_gemma":[0.52567047,0.00034763367,0.4714586,0.00013263596,0.00005974062,0.00020261713,0.00021861738,0.00011625506,0.0017933891],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992549,0.00025148777,0.00004012959,0.00019494392,0.0002046413,0.000053830176],"domain_scores_gemma":[0.99882776,0.0005924218,0.00017737385,0.00012897541,0.00023105051,0.000042420423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001005789,0.0007958512,0.00090362196,0.00056405773,0.00036410138,0.0007312668,0.00135934,0.00077452214,0.00096515706],"category_scores_gemma":[0.0033931662,0.00049677334,0.0007104994,0.000724205,0.0008733993,0.0016941242,0.0017167307,0.0012727961,0.00028658265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007512901,0.000034122222,0.00045290147,0.00010974692,0.000022323246,0.0001008261,0.00013120452,0.9119468,0.006421179,0.018126775,0.0007314344,0.06184754],"study_design_scores_gemma":[0.0000028432573,0.000018652327,0.00003445364,0.000004309809,0.0000023010473,0.000015759155,0.0000070967953,0.99626213,0.0006839902,0.0027522775,0.00021171453,0.0000044290573],"about_ca_topic_score_codex":0.001661307,"about_ca_topic_score_gemma":0.0010120494,"teacher_disagreement_score":0.001661307,"about_ca_system_score_codex":0.00045206863,"about_ca_system_score_gemma":0.0006564107,"threshold_uncertainty_score":0.005319178},"labels":[],"label_agreement":null},{"id":"W2744480499","doi":"10.1109/ict.2017.7998234","title":"RSS based localization by using lognormal mixture shadowing model","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"RSS; Log-normal distribution; Estimator; Computer science; Shadow mapping; Maximum likelihood; Mixture model; Algorithm; Artificial intelligence; Mathematics; Statistics","score_opus":0.01681994809053453,"score_gpt":0.23549614881841632,"score_spread":0.2186762007278818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744480499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009349533,0.00014935184,0.988993,0.000044978253,0.000013369279,0.0000096471595,0.000024794263,0.00036519402,0.0010501086],"genre_scores_gemma":[0.7849278,0.0010966444,0.20777419,0.000074996526,0.00005642127,0.00008117793,0.00021092774,0.00011331883,0.005664575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994869,0.0001540996,0.00001841421,0.00008384285,0.00022143115,0.0000352739],"domain_scores_gemma":[0.9997304,0.00010083575,0.000033630975,0.000050737548,0.00007460776,0.000009847297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034506907,0.00053380197,0.0005009799,0.00056786643,0.00020598168,0.0005682002,0.0008318214,0.00052024523,0.0009386802],"category_scores_gemma":[0.0010495561,0.00026028568,0.0005329311,0.00079988,0.00039183142,0.0011456099,0.000738921,0.00042459473,0.00061434286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009380798,0.00003324096,0.0016683649,0.00009773445,0.000064132204,0.00023617534,0.00015058533,0.8583615,0.021753002,0.019868108,0.0009270917,0.09674628],"study_design_scores_gemma":[0.0000027790857,0.000020024594,0.00029065995,0.000003566539,0.000010039122,0.00012333272,0.00000990165,0.9946831,0.001744656,0.0022468166,0.00085481955,0.000010403356],"about_ca_topic_score_codex":0.0023085547,"about_ca_topic_score_gemma":0.0024160256,"teacher_disagreement_score":0.0023085547,"about_ca_system_score_codex":0.00038268443,"about_ca_system_score_gemma":0.00036965605,"threshold_uncertainty_score":0.0045902133},"labels":[],"label_agreement":null},{"id":"W2748640347","doi":"10.3390/s17081892","title":"Secure Localization in the Presence of Colluders in WSNs","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nokia (Canada); Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversary; Node (physics); Adversarial system; Wireless sensor network; Computer science; Position (finance); Wireless; Range (aeronautics); Computer network; Computer security; Artificial intelligence; Engineering; Telecommunications","score_opus":0.01144697833478982,"score_gpt":0.2347161456488858,"score_spread":0.22326916731409596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748640347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10653765,0.00040962143,0.8912819,0.00019676318,0.000035630244,0.000026329659,0.000017940461,0.00024374084,0.001250375],"genre_scores_gemma":[0.953968,0.0003086455,0.044948105,0.000031635,0.00003325443,0.000029537778,0.000028372984,0.000026551808,0.0006260694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984505,0.0005465812,0.00007205971,0.0002860173,0.0004861663,0.00015869617],"domain_scores_gemma":[0.99678767,0.0017719475,0.00058764854,0.00051625434,0.00024771443,0.00008882147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014411206,0.00072993967,0.0009720507,0.0006202798,0.00071738,0.0008789318,0.0009021582,0.0009595526,0.00042079258],"category_scores_gemma":[0.006732753,0.00036318545,0.00044477024,0.00060200086,0.0018975312,0.0015020531,0.0030070394,0.000796804,0.00025233848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024152748,0.000030075655,0.0034059878,0.00008743817,0.00007087959,0.0010214992,0.00028679965,0.929782,0.01963906,0.016936423,0.00032817942,0.028170183],"study_design_scores_gemma":[0.0000146241,0.00011135051,0.00044864588,0.000010114585,0.000017518789,0.00026428563,0.000075568336,0.9809965,0.0068147685,0.010540113,0.0006915949,0.00001487908],"about_ca_topic_score_codex":0.00095476076,"about_ca_topic_score_gemma":0.00058478507,"teacher_disagreement_score":0.0014411206,"about_ca_system_score_codex":0.0005528553,"about_ca_system_score_gemma":0.00045608415,"threshold_uncertainty_score":0.007621467},"labels":[],"label_agreement":null},{"id":"W2749301731","doi":"10.3390/s17081904","title":"Dynamic Fuzzy-Logic Based Path Planning for Mobility-Assisted Localization in Wireless Sensor Networks","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; Dalhousie University","funders":"","keywords":"Fuzzy logic; Node (physics); Motion planning; Wireless sensor network; Path (computing); Mobility model; Computer science; Context (archaeology); Software deployment; Real-time computing; Computer network; Distributed computing; Engineering; Artificial intelligence","score_opus":0.015723495390129766,"score_gpt":0.2571879706592691,"score_spread":0.24146447526913936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749301731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009139062,0.00027675406,0.9879479,0.000085029664,0.000030284424,0.00003540486,0.00002685652,0.00016874114,0.0022900337],"genre_scores_gemma":[0.855147,0.0006205185,0.14136532,0.00007417949,0.000032055224,0.00014029964,0.00008455928,0.000021025939,0.002515102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976295,0.000045366225,0.000015563055,0.000056325913,0.00009547057,0.000024338618],"domain_scores_gemma":[0.9998417,0.00006293929,0.000031241707,0.000012731115,0.00004312289,0.000008203083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032518525,0.0004391912,0.0002723041,0.0004301784,0.00039213093,0.00049749366,0.00090739445,0.0005685461,0.0010394214],"category_scores_gemma":[0.0006913142,0.00018154616,0.00044907353,0.00044104643,0.0004520167,0.00067034096,0.00036078828,0.0005029931,0.00017242912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102636346,0.000059034217,0.000742761,0.00014274236,0.000033220404,0.00018796747,0.00014574619,0.85182637,0.013326158,0.02289803,0.0008882791,0.10964708],"study_design_scores_gemma":[0.000007958397,0.000052893683,0.00014987537,0.000011134302,0.000013063356,0.000044389642,0.000014295432,0.9934645,0.001637653,0.0036701683,0.0009236654,0.000010354199],"about_ca_topic_score_codex":0.0064207576,"about_ca_topic_score_gemma":0.0056716744,"teacher_disagreement_score":0.0064207576,"about_ca_system_score_codex":0.0008085161,"about_ca_system_score_gemma":0.0008677453,"threshold_uncertainty_score":0.0127667785},"labels":[],"label_agreement":null},{"id":"W2751532433","doi":"10.1109/iscc.2017.8024601","title":"JLPR: Joint range-based localization using trilateration and packet routing in Wireless Sensor Networks with mobile sinks","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Trilateration; Computer science; Wireless sensor network; Network packet; Global Positioning System; Real-time computing; Computer network; Geographic routing; Beacon; Triangular routing; Routing protocol; DSRFLOW; Source routing; Overhead (engineering); Node (physics); Dynamic Source Routing; Engineering; Telecommunications","score_opus":0.014706684485479673,"score_gpt":0.21936001692519844,"score_spread":0.20465333243971875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751532433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009821629,0.0006771501,0.98674417,0.00010142406,0.00006179304,0.000050906703,0.000025537856,0.0016589236,0.0008584893],"genre_scores_gemma":[0.45167902,0.0013322355,0.5431777,0.00018308213,0.00011854962,0.0002352549,0.00032004324,0.00018533248,0.0027687268],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987828,0.00029642522,0.00009238955,0.0002341415,0.00049064937,0.000103596],"domain_scores_gemma":[0.9989448,0.00030887497,0.00025238143,0.00019444768,0.0002614891,0.00003797881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014999798,0.00083215674,0.0011692719,0.0015460014,0.000590431,0.0010157896,0.0019071319,0.0009132557,0.00066582445],"category_scores_gemma":[0.0025848965,0.00036999185,0.0007176867,0.0015497443,0.00064541085,0.0021538425,0.001817412,0.0006487964,0.00046849303],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005903963,0.00022551407,0.002283121,0.00061365153,0.00019166466,0.00045086988,0.00044601463,0.35046142,0.049235716,0.018232057,0.0042970907,0.57297254],"study_design_scores_gemma":[0.00004424582,0.000407353,0.0006063959,0.00002510748,0.00004756255,0.00044168017,0.00006409714,0.97615117,0.014790216,0.003582024,0.003794412,0.000045605862],"about_ca_topic_score_codex":0.0017787438,"about_ca_topic_score_gemma":0.0012887102,"teacher_disagreement_score":0.0019071319,"about_ca_system_score_codex":0.0004614156,"about_ca_system_score_gemma":0.0007337169,"threshold_uncertainty_score":0.007932723},"labels":[],"label_agreement":null},{"id":"W2754052029","doi":"10.1145/3035968","title":"i <sup>2</sup> tag","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Multipath propagation; Multipath interference; Radio-frequency identification; Identification (biology); Dynamic time warping; Fingerprint (computing); Real-time computing; Interference (communication); Artificial intelligence; Telecommunications; Computer security","score_opus":0.017192009452037325,"score_gpt":0.23989495372512115,"score_spread":0.22270294427308382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754052029","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038771346,0.003557879,0.63458735,0.0033851287,0.007823565,0.0006437508,0.006938964,0.04107046,0.26322156],"genre_scores_gemma":[0.40472603,0.0016036301,0.1608068,0.0075467327,0.0013099212,0.00040096376,0.011561616,0.003137352,0.40890703],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993424,0.00006770327,0.00005796007,0.00012473397,0.00032565018,0.000081409984],"domain_scores_gemma":[0.9987471,0.00015429688,0.00013366164,0.00034472972,0.00056134554,0.00005879208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005057202,0.00084322464,0.00053114706,0.00060673367,0.00047488572,0.0018002437,0.0014931131,0.0013959536,0.046469547],"category_scores_gemma":[0.0014677576,0.00030109767,0.00033784477,0.0008379723,0.00047327153,0.0016729819,0.00097107404,0.00060166675,0.040990178],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020050404,0.00011324892,0.007821329,0.00074667414,0.000117197706,0.0017449431,0.00025103358,0.0038455403,0.1646905,0.016181191,0.30561084,0.49687245],"study_design_scores_gemma":[0.00008238592,0.00058770884,0.0033656377,0.00009633452,0.00013746848,0.0035859137,0.00021751014,0.041875873,0.256062,0.003972799,0.68990344,0.00011295799],"about_ca_topic_score_codex":0.00044812547,"about_ca_topic_score_gemma":0.00088196114,"teacher_disagreement_score":0.046469547,"about_ca_system_score_codex":0.0004639339,"about_ca_system_score_gemma":0.00030065648,"threshold_uncertainty_score":0.15545613},"labels":[],"label_agreement":null},{"id":"W2754432013","doi":"10.1145/3130906","title":"Rapid","year":2017,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Identification (biology); Noise (video); Fuse (electrical); Key (lock); Channel (broadcasting); Real-time computing; Gait; Artificial intelligence; Telecommunications; Engineering; Computer security; Electrical engineering","score_opus":0.011328564907588604,"score_gpt":0.2364010572457525,"score_spread":0.2250724923381639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754432013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027433788,0.0024446393,0.39409584,0.0012700141,0.0020494438,0.0014889204,0.019932752,0.2786612,0.27262342],"genre_scores_gemma":[0.27113938,0.002165378,0.289524,0.002778956,0.0007233349,0.0018283532,0.05115543,0.012691673,0.3679936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999092,0.00011620124,0.00005692349,0.000262567,0.00035810086,0.00011413991],"domain_scores_gemma":[0.99865216,0.00018221326,0.0000858958,0.00045478763,0.0005089234,0.000115942246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087041885,0.0011905726,0.0007114751,0.0011432802,0.0005858157,0.0015574723,0.0019705335,0.001067253,0.10985904],"category_scores_gemma":[0.0025688733,0.00046033505,0.000543913,0.0007201882,0.00031726915,0.0023929647,0.0023944757,0.0007795952,0.09251197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012604985,0.00022381406,0.0027754107,0.0010352276,0.00009915705,0.00057017856,0.0003416508,0.00331891,0.037409905,0.015786257,0.39053333,0.5466456],"study_design_scores_gemma":[0.00020114734,0.00048453445,0.00248749,0.00014400488,0.000072702,0.0014155067,0.00017671414,0.03544238,0.035270937,0.0068220333,0.9173391,0.00014354815],"about_ca_topic_score_codex":0.0012164147,"about_ca_topic_score_gemma":0.0017326906,"teacher_disagreement_score":0.10985904,"about_ca_system_score_codex":0.0004438738,"about_ca_system_score_gemma":0.0009015666,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2755432422","doi":"10.5194/isprs-annals-iv-2-w4-425-2017","title":"INDOOR MAP AIDED INS/WI-FI INTEGRATED LBS ON SMARTPHONE PLATFORMS","year":2017,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Particle filter; Computer science; Indoor positioning system; Real-time computing; Kalman filter; Hybrid positioning system; Global Positioning System; Position (finance); Positioning system; Inertial navigation system; Embedded system; Orientation (vector space); Computer vision; Accelerometer; Artificial intelligence; Engineering; Telecommunications; Node (physics)","score_opus":0.04242743731915041,"score_gpt":0.28308589121901623,"score_spread":0.24065845389986582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755432422","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2505407,0.00051953364,0.72244155,0.00034834878,0.00029542472,0.0002279588,0.0008172176,0.012545504,0.012263719],"genre_scores_gemma":[0.87703633,0.00017342759,0.11610114,0.00010538997,0.000031711963,0.00007127099,0.0004622544,0.00005480203,0.005963638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997483,0.00003564929,0.000012737916,0.00004928369,0.00011518097,0.000038866772],"domain_scores_gemma":[0.99978536,0.00001797381,0.000022360278,0.000055248354,0.00010440403,0.000014612855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014244205,0.00065339607,0.0004114237,0.00045752307,0.00024487145,0.0004589884,0.0006268507,0.00043184162,0.0029180064],"category_scores_gemma":[0.00039748603,0.00018084297,0.0002864507,0.00036874448,0.00014614951,0.000544988,0.0005904017,0.00024197383,0.0018702308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091728923,0.00025285993,0.019483862,0.0004910254,0.00014480077,0.0016740212,0.00041133427,0.041180253,0.22754876,0.0027060695,0.013985427,0.6912043],"study_design_scores_gemma":[0.00011398917,0.0008341185,0.024780393,0.00008617016,0.0001625548,0.0014551801,0.00046761456,0.74914587,0.1790078,0.0014546284,0.042390097,0.00010150634],"about_ca_topic_score_codex":0.005683045,"about_ca_topic_score_gemma":0.0070115435,"teacher_disagreement_score":0.005683045,"about_ca_system_score_codex":0.00022604049,"about_ca_system_score_gemma":0.00044305815,"threshold_uncertainty_score":0.011299908},"labels":[],"label_agreement":null},{"id":"W2755591834","doi":"10.1007/s10291-017-0661-2","title":"3D building model-assisted snapshot positioning algorithm","year":2017,"lang":"en","type":"article","venue":"GPS Solutions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates - Technology Futures","keywords":"GNSS applications; Snapshot (computer storage); Pseudorange; Algorithm; Geodesy; Position (finance); Computer science; Position error; Global Positioning System; Hotspot (geology); Mathematics; Geography; Geology; Statistics; Telecommunications; Calibration","score_opus":0.030935612797850707,"score_gpt":0.2586985781010405,"score_spread":0.22776296530318982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755591834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066542346,0.00015718048,0.98818564,0.00004232303,0.00007033758,0.000031894317,0.00032538685,0.0024426754,0.0020903472],"genre_scores_gemma":[0.23533314,0.00046406587,0.75740075,0.00006824466,0.000040109022,0.00017026828,0.0024412973,0.0002849371,0.0037971274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996263,0.00003691495,0.000013675359,0.00008745926,0.00019246957,0.000043187843],"domain_scores_gemma":[0.99973196,0.00002720689,0.00002190927,0.000064322325,0.00013513397,0.000019404246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018913402,0.0010065914,0.00106642,0.0011820755,0.0003980941,0.00096699654,0.0014457041,0.0006951066,0.003176635],"category_scores_gemma":[0.00076054235,0.0005847984,0.0008330386,0.0018954589,0.00023192423,0.0007471467,0.0015550664,0.00082246825,0.0034065635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024633133,0.00008239499,0.0031129017,0.0002208243,0.00013004069,0.00024721082,0.00017153031,0.34843636,0.035790313,0.008010795,0.012292896,0.59125847],"study_design_scores_gemma":[0.00001660135,0.000031676882,0.0009080847,0.000015682763,0.000028907776,0.00017498666,0.00004078649,0.983255,0.008111737,0.0018613116,0.005531487,0.000023756265],"about_ca_topic_score_codex":0.0064756884,"about_ca_topic_score_gemma":0.0098186955,"teacher_disagreement_score":0.0064756884,"about_ca_system_score_codex":0.0003873797,"about_ca_system_score_gemma":0.001150797,"threshold_uncertainty_score":0.012875974},"labels":[],"label_agreement":null},{"id":"W2757145559","doi":"","title":"RSS-based WLAN Indoor Positioning and Tracking System Using Compressive Sensing and Its Implementation on Mobile Devices","year":2010,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Strong","keywords":"RSS; Computer science; Indoor positioning system; Real-time computing; Mobile device; Hybrid positioning system; Tracking (education); Compressed sensing; Location awareness; Tracking system; Embedded system; Global Positioning System; Positioning system; Computer vision; Engineering; Artificial intelligence; Computer network; Telecommunications","score_opus":0.01442507212550492,"score_gpt":0.3212413538031006,"score_spread":0.30681628167759567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757145559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117469884,0.00030338395,0.8753949,0.00023365863,0.00008487142,0.000073205745,0.00007668239,0.0018945464,0.004468845],"genre_scores_gemma":[0.71466637,0.0003780101,0.28010833,0.00008663079,0.000066557324,0.00010178498,0.00013509855,0.000032553846,0.0044246935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971193,0.000072153496,0.000016829063,0.000052328196,0.00012451809,0.000022265798],"domain_scores_gemma":[0.9997496,0.00005853897,0.000027528711,0.000062254,0.00009023856,0.0000119046135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029465105,0.00025561472,0.0003663066,0.00027994582,0.0001680894,0.00038119577,0.00057047827,0.00042448807,0.0013048733],"category_scores_gemma":[0.0006353877,0.00014215018,0.00018860979,0.0003683499,0.00016727574,0.0005758722,0.000370088,0.00024893117,0.000522178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007277382,0.00018151985,0.0028201602,0.00018956541,0.00007063493,0.0002736973,0.00019609947,0.05885767,0.27498162,0.0075962176,0.004075133,0.65002996],"study_design_scores_gemma":[0.00012793145,0.0009967859,0.003885386,0.00003535649,0.00008539156,0.0008746964,0.000085457876,0.83097,0.1513716,0.0013424673,0.010170382,0.000054496955],"about_ca_topic_score_codex":0.0005696211,"about_ca_topic_score_gemma":0.00053897745,"teacher_disagreement_score":0.0013048733,"about_ca_system_score_codex":0.00017628432,"about_ca_system_score_gemma":0.00018986237,"threshold_uncertainty_score":0.0043652058},"labels":[],"label_agreement":null},{"id":"W2759966690","doi":"","title":"Computation Reduction for Angle of Arrival Estimation Based on Interferometer Principle","year":2017,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo; Cisco Systems","keywords":"Computation; Reduction (mathematics); Interferometry; Estimation; Computer science; Algorithm; Geodesy; Optics; Physics; Mathematics; Engineering; Geography; Geometry; Systems engineering","score_opus":0.01219866587598058,"score_gpt":0.22596873041490975,"score_spread":0.21377006453892916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759966690","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007962662,0.00019698174,0.9892424,0.00010501465,0.00006564977,0.000024311275,0.00003799427,0.00033197217,0.0020330488],"genre_scores_gemma":[0.15647958,0.00076256087,0.8355107,0.000095732204,0.00015899159,0.00015224087,0.00041045493,0.00017027387,0.0062595946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964535,0.00006197498,0.000013803267,0.000050767692,0.00019644939,0.000031567262],"domain_scores_gemma":[0.9996902,0.00012609913,0.000025835532,0.000047920657,0.000097750024,0.000012297809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026496593,0.0005434992,0.0004425727,0.0005446736,0.00037741405,0.0006327342,0.00059266586,0.00039359642,0.0039086384],"category_scores_gemma":[0.0010889854,0.00021397036,0.00076157757,0.00066514045,0.00030575093,0.0008034885,0.00066101673,0.0008216043,0.0016230072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024849555,0.00010375456,0.0018046104,0.0003377796,0.00010734486,0.00019226197,0.00026519454,0.14448881,0.09932254,0.040409006,0.007083778,0.7056364],"study_design_scores_gemma":[0.00002074049,0.00011445878,0.0012300205,0.000020321262,0.000027320795,0.00021210994,0.000077395074,0.9595247,0.017014379,0.009308411,0.012425699,0.000024567398],"about_ca_topic_score_codex":0.0024396298,"about_ca_topic_score_gemma":0.0023754633,"teacher_disagreement_score":0.0039086384,"about_ca_system_score_codex":0.0003118047,"about_ca_system_score_gemma":0.0007630919,"threshold_uncertainty_score":0.013075709},"labels":[],"label_agreement":null},{"id":"W2762090892","doi":"10.23977/isspj.2017.21001","title":"High Performance, Low Cost Loran-C Cycle Identification and ECD Estimation","year":2017,"lang":"en","type":"article","venue":"Information Systems and Signal Processing Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multilateration; Identification (biology); Time of arrival; SIGNAL (programming language); Computer science; Algorithm; Radio navigation; Point (geometry); Real-time computing; Control theory (sociology); Engineering; Global Positioning System; Mathematics; Telecommunications; Artificial intelligence","score_opus":0.008209621113817601,"score_gpt":0.2187082589482631,"score_spread":0.2104986378344455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762090892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050421298,0.0005304273,0.93762714,0.00017777088,0.00008160084,0.00012055705,0.0001690446,0.00358247,0.007289666],"genre_scores_gemma":[0.60161495,0.00033393243,0.38926357,0.00013163403,0.00007115855,0.00013191768,0.0006349906,0.0000830805,0.0077348207],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943,0.00007456762,0.000023026112,0.000095650255,0.00031684877,0.00005991288],"domain_scores_gemma":[0.99952865,0.0000623319,0.000051065086,0.00008820749,0.00024252284,0.000027203716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034742264,0.00053356594,0.0005925244,0.0010937977,0.00046383927,0.0006740896,0.0010038634,0.0005458505,0.0020162843],"category_scores_gemma":[0.0011239937,0.00020678002,0.00020348984,0.00093099225,0.00021009053,0.0008254093,0.00079801324,0.00044346528,0.0013537323],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057601015,0.00013760601,0.006558486,0.00024572975,0.000056461973,0.0002543394,0.00016447715,0.042452503,0.06935894,0.0048270635,0.0070277415,0.8683406],"study_design_scores_gemma":[0.00012029566,0.0006058005,0.0073917154,0.000037861733,0.00006496411,0.0016843919,0.00012980915,0.8464795,0.10445657,0.002539375,0.03636356,0.0001262384],"about_ca_topic_score_codex":0.0030004806,"about_ca_topic_score_gemma":0.0026820758,"teacher_disagreement_score":0.0030004806,"about_ca_system_score_codex":0.00047365404,"about_ca_system_score_gemma":0.000738062,"threshold_uncertainty_score":0.0067451},"labels":[],"label_agreement":null},{"id":"W2762669156","doi":"10.5539/mas.v11n10p166","title":"Wireless LAN Service Quality Optimization in Academic Environments","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mean squared error; Computer science; Adaptive neuro fuzzy inference system; Metric (unit); Terrain; Data mining; Field (mathematics); Test data; Fuzzy logic; Inference; Principal component analysis; Wireless; Artificial intelligence; Statistics; Fuzzy control system; Mathematics; Telecommunications; Engineering","score_opus":0.03147347882710709,"score_gpt":0.2807152525500755,"score_spread":0.2492417737229684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762669156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38235918,0.00090197456,0.60834605,0.0004445017,0.000054181764,0.000085414504,0.00011751396,0.0006915162,0.006999716],"genre_scores_gemma":[0.98673046,0.00020792961,0.012015133,0.000023367624,0.000021563405,0.00002093398,0.000050492796,0.000013104149,0.00091707945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99916077,0.00031568107,0.000036603353,0.00014543801,0.00018504738,0.00015647568],"domain_scores_gemma":[0.99936706,0.00026807492,0.0001474289,0.000033078988,0.00014456274,0.000039820672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011631021,0.00065462705,0.00060476875,0.00052525854,0.00032618566,0.0010017868,0.00064313284,0.0005042126,0.0005277573],"category_scores_gemma":[0.0019570068,0.00020601408,0.0003009559,0.0007489827,0.00042363955,0.0006564835,0.00068002986,0.00045317705,0.00014731233],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010268493,0.00008975278,0.0022767617,0.00006372387,0.000027942526,0.000069297195,0.00004682251,0.95445275,0.0057021813,0.0014803992,0.0003247383,0.035362903],"study_design_scores_gemma":[0.00000659691,0.000105929736,0.0011678498,0.0000032894598,0.000008882347,0.000015020406,0.00005102617,0.99603987,0.0016112179,0.0006556085,0.00032975353,0.000005048557],"about_ca_topic_score_codex":0.0059075733,"about_ca_topic_score_gemma":0.00419802,"teacher_disagreement_score":0.0059075733,"about_ca_system_score_codex":0.0011121893,"about_ca_system_score_gemma":0.0007510862,"threshold_uncertainty_score":0.011746347},"labels":[],"label_agreement":null},{"id":"W2765800664","doi":"10.1109/apusncursinrsm.2017.8072744","title":"Indoor localization with UWB and 2.4 GHz bands","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Non-line-of-sight propagation; Ultra-wideband; Computer science; Electronic engineering; Time of arrival; Radio propagation; Telecommunications; Real-time computing; Wireless; Engineering","score_opus":0.005532531479910089,"score_gpt":0.18947252993996455,"score_spread":0.18393999846005446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765800664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11313467,0.0026139605,0.87525046,0.00025179636,0.00031675695,0.00003460505,0.00010545036,0.0020176095,0.0062746927],"genre_scores_gemma":[0.8159249,0.0010911518,0.17736481,0.00019052079,0.00014782521,0.00007844509,0.00015349798,0.0000727016,0.0049760793],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908996,0.0002883316,0.000034577246,0.00020640371,0.00022773977,0.00015311102],"domain_scores_gemma":[0.99949694,0.00014239893,0.00013187357,0.00010231542,0.00010547139,0.00002101286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041542624,0.0007399262,0.0007819903,0.0011355403,0.00037415267,0.00085090497,0.00082715525,0.0009773332,0.0010284758],"category_scores_gemma":[0.00093765894,0.00030811797,0.0006499763,0.00077677955,0.00041395097,0.00095333054,0.0009764285,0.0004427105,0.0007987543],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012869406,0.00023912395,0.010353786,0.0010227395,0.00040921598,0.0013552102,0.00052705745,0.07669969,0.39418167,0.009252383,0.0026803634,0.50199175],"study_design_scores_gemma":[0.00017380538,0.002595242,0.02313061,0.0003265241,0.0008107192,0.0072241635,0.0010959845,0.32374474,0.57496977,0.008549108,0.057014644,0.0003647148],"about_ca_topic_score_codex":0.0007350193,"about_ca_topic_score_gemma":0.0008391852,"teacher_disagreement_score":0.0011355403,"about_ca_system_score_codex":0.00031355934,"about_ca_system_score_gemma":0.00032430535,"threshold_uncertainty_score":0.0034406185},"labels":[],"label_agreement":null},{"id":"W2765930120","doi":"10.1109/lcomm.2017.2765642","title":"Distributed TOA-Based Positioning in Wireless Sensor Networks: A Potential Game Approach","year":2017,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless sensor network; Nash equilibrium; Potential game; Convergence (economics); Cognitive radio; Wireless; Game theory; Node (physics); Best response; Algorithm; Position (finance); Mathematical optimization; Computer network; Mathematics; Telecommunications","score_opus":0.01618347142301384,"score_gpt":0.2347993585775521,"score_spread":0.21861588715453825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765930120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008304581,0.00028116844,0.98770523,0.00036915197,0.000056249835,0.000050795006,0.000016166212,0.000020854104,0.003195838],"genre_scores_gemma":[0.8709666,0.0009298167,0.121630825,0.00025052144,0.00010669217,0.00038310097,0.000035535697,0.000026661395,0.005670147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890006,0.0006322713,0.000041705356,0.00014559503,0.00018872975,0.00009162287],"domain_scores_gemma":[0.9990601,0.0006331637,0.000081362,0.00004075565,0.000113381626,0.000071323775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001423578,0.0009428032,0.001184202,0.00055797317,0.00076305046,0.0014119113,0.0015811017,0.0016336786,0.0013285012],"category_scores_gemma":[0.0029232816,0.00039537347,0.00083219726,0.0007637751,0.0017236465,0.0020834,0.0019904787,0.0012282424,0.00018048845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049897062,0.00004246595,0.00034649318,0.00010143233,0.000056056924,0.00019793477,0.00019010494,0.76488894,0.0018035038,0.21900027,0.00064693,0.01267596],"study_design_scores_gemma":[0.000013371066,0.00003832861,0.00003975739,0.0000074183035,0.00000987997,0.000029415549,0.000028895087,0.9665772,0.000120506,0.03257679,0.0005496521,0.000008865035],"about_ca_topic_score_codex":0.00350928,"about_ca_topic_score_gemma":0.002255131,"teacher_disagreement_score":0.00350928,"about_ca_system_score_codex":0.0011526967,"about_ca_system_score_gemma":0.0013230167,"threshold_uncertainty_score":0.008363426},"labels":[],"label_agreement":null},{"id":"W2766010555","doi":"10.3390/s17102377","title":"Sensor-Data Fusion for Multi-Person Indoor Location Estimation","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; AGE-WELL","keywords":"Sensor fusion; Computer science; Fusion; Estimation; Real-time computing; Artificial intelligence; Engineering; Systems engineering","score_opus":0.0827458121506196,"score_gpt":0.3074397182280245,"score_spread":0.22469390607740491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766010555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015518795,0.00035623022,0.9831646,0.00010734465,0.000046558645,0.000018664075,0.00009403545,0.0001912401,0.0005026502],"genre_scores_gemma":[0.752287,0.00064762647,0.24529053,0.0001063932,0.00010462033,0.000079653146,0.00031467952,0.000025412317,0.0011441287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991148,0.00032487506,0.000052883635,0.00021314323,0.00020757371,0.0000866714],"domain_scores_gemma":[0.99917716,0.00038455636,0.00012358715,0.0001561354,0.00012576403,0.000032681597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011800419,0.000677828,0.0009873332,0.00065682654,0.00051066733,0.00067540305,0.000784343,0.00096188573,0.00085571804],"category_scores_gemma":[0.0036105455,0.0002893492,0.00063865585,0.0013390356,0.00042736286,0.0012804638,0.0012802553,0.00078049797,0.0003955139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000383026,0.00016171852,0.004935035,0.00033020243,0.0002045952,0.00034772494,0.00034123275,0.7217329,0.018607454,0.013526208,0.0018830353,0.23754685],"study_design_scores_gemma":[0.0000100632415,0.0000806876,0.0016561708,0.000015808077,0.000025321822,0.00010884203,0.00007533467,0.9838463,0.005375735,0.0072980323,0.0014854486,0.000022171385],"about_ca_topic_score_codex":0.0025620088,"about_ca_topic_score_gemma":0.0037117377,"teacher_disagreement_score":0.0025620088,"about_ca_system_score_codex":0.00040144258,"about_ca_system_score_gemma":0.00050928927,"threshold_uncertainty_score":0.0062407255},"labels":[],"label_agreement":null},{"id":"W2766478756","doi":"10.23919/eusipco.2017.8081703","title":"An empirical study on gamma shadow fading based localization","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"RSS; Fading; Gamma distribution; Log-normal distribution; Computer science; Shadow mapping; Estimator; Testbed; Generalized gamma distribution; Shadow (psychology); Algorithm; Statistics; Artificial intelligence; Mathematics; Computer network","score_opus":0.03223616509928424,"score_gpt":0.3125560797582806,"score_spread":0.28031991465899636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766478756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35203424,0.00769866,0.62618506,0.0016780031,0.00014209987,0.00010176321,0.0005353687,0.0005910293,0.011033833],"genre_scores_gemma":[0.97846186,0.0025968992,0.017287387,0.0001527207,0.000089394634,0.00003827343,0.00051216385,0.00008134787,0.000779963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968354,0.0013998478,0.00014549319,0.0006433646,0.0007717766,0.0002041678],"domain_scores_gemma":[0.9542531,0.034039583,0.0037478937,0.0042453487,0.0034076436,0.00030639593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006429525,0.0008754628,0.0005409518,0.0018066526,0.0005770926,0.0011774342,0.0015820708,0.00092458504,0.002058292],"category_scores_gemma":[0.050646618,0.0004067588,0.00042253706,0.0030577818,0.0018744486,0.004323522,0.001105545,0.001088947,0.00039588622],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032351472,0.00011515575,0.19986522,0.0008363742,0.0003449001,0.0011391216,0.0014927696,0.53901446,0.0042699506,0.07659697,0.0065789907,0.1694226],"study_design_scores_gemma":[0.00004183765,0.00037985385,0.06289543,0.0004930915,0.00022021146,0.004435653,0.0021295568,0.8513876,0.007985702,0.048934665,0.020911574,0.00018489556],"about_ca_topic_score_codex":0.0029147991,"about_ca_topic_score_gemma":0.0019007265,"teacher_disagreement_score":0.006429525,"about_ca_system_score_codex":0.0012591436,"about_ca_system_score_gemma":0.0006793141,"threshold_uncertainty_score":0.03400296},"labels":[],"label_agreement":null},{"id":"W2768026417","doi":"10.3390/s17112573","title":"A Smartphone Step Counter Using IMU and Magnetometer for Navigation and Health Monitoring Applications","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Computer science; Dead reckoning; Step detection; Mobile device; Real-time computing; Artificial intelligence; Android (operating system); Task (project management); Classifier (UML); Computer vision; Match moving; Engineering; Motion (physics); Global Positioning System; Telecommunications","score_opus":0.037433047433177116,"score_gpt":0.311351734730768,"score_spread":0.2739186872975909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768026417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30027398,0.0051171365,0.6261593,0.00085218373,0.0015870999,0.001297768,0.005031086,0.025427753,0.034253743],"genre_scores_gemma":[0.75160486,0.0014808234,0.21951841,0.00077021046,0.00022786044,0.00049876113,0.0021991015,0.00013690461,0.023563102],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982315,0.000017593024,0.000012835612,0.00004299123,0.00008754517,0.000015827976],"domain_scores_gemma":[0.9997446,0.00003590581,0.00002794481,0.000035952544,0.00013167599,0.000023931296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012539071,0.0007839212,0.000570272,0.0008066772,0.00022611463,0.00033269022,0.00054210075,0.00057479384,0.0052322084],"category_scores_gemma":[0.0005119796,0.00017835581,0.00029659126,0.0004956559,0.00008958019,0.0003270276,0.00033055712,0.00025411206,0.0026193908],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009038292,0.00022267872,0.015542227,0.0011504632,0.00014095764,0.0010649903,0.00015276749,0.0020145895,0.29695258,0.000976867,0.030095479,0.6507826],"study_design_scores_gemma":[0.00033362032,0.0040829834,0.13430215,0.0004186742,0.0008223877,0.013211637,0.00041618553,0.23800498,0.45285934,0.0012138523,0.1539212,0.00041295108],"about_ca_topic_score_codex":0.001291222,"about_ca_topic_score_gemma":0.0033974533,"teacher_disagreement_score":0.0052322084,"about_ca_system_score_codex":0.000107900574,"about_ca_system_score_gemma":0.0002940527,"threshold_uncertainty_score":0.0175035},"labels":[],"label_agreement":null},{"id":"W2768934089","doi":"10.1109/lcn.2017.68","title":"Wireless Positioning Sensor Network Integrated with Cloud for Industrial Automation","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wireless sensor network; Computer science; Automation; Scalability; Real-time computing; Cloud computing; Reliability (semiconductor); Wireless; Redundancy (engineering); Embedded system; Engineering; Computer network; Telecommunications","score_opus":0.018567916211760165,"score_gpt":0.22461334182552467,"score_spread":0.20604542561376452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768934089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13234435,0.0064615,0.83210105,0.0012409736,0.00082881923,0.00018684349,0.00027970635,0.003835277,0.022721516],"genre_scores_gemma":[0.908521,0.0017337209,0.08061765,0.0003044451,0.00012352594,0.000076483964,0.0003323166,0.000050641793,0.008240075],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997569,0.00003992093,0.0000115795365,0.00005335021,0.000094701376,0.00004357528],"domain_scores_gemma":[0.99982244,0.00002451198,0.000023734501,0.000038520528,0.000072132505,0.00001875177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019131726,0.0002629288,0.00024842258,0.00026024523,0.00030406695,0.00037057712,0.0007957246,0.0003146692,0.0019292708],"category_scores_gemma":[0.00034378507,0.00010116986,0.00017008309,0.0005098332,0.00012628188,0.00086253753,0.0006119854,0.0004020567,0.00052224647],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077644107,0.00033754815,0.007402867,0.00056584773,0.00011063478,0.0010062201,0.00016184327,0.116802596,0.1401646,0.037706293,0.026642246,0.66832286],"study_design_scores_gemma":[0.000059540922,0.00042553377,0.0040530185,0.000056659967,0.00007509326,0.0006575464,0.00013305694,0.88433254,0.04818414,0.009416201,0.05256861,0.000038005797],"about_ca_topic_score_codex":0.0017854722,"about_ca_topic_score_gemma":0.0024125332,"teacher_disagreement_score":0.0019292708,"about_ca_system_score_codex":0.00037396493,"about_ca_system_score_gemma":0.00053130323,"threshold_uncertainty_score":0.00645411},"labels":[],"label_agreement":null},{"id":"W2769139641","doi":"10.1109/ipin.2017.8115926","title":"Step-size estimation using fusion of multiple wearable inertial sensors","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Kalman filter; Sensor fusion; Computer science; Wearable computer; STRIDE; Step detection; Fuse (electrical); Computer vision; Wearable technology; Artificial intelligence; Filter (signal processing); Real-time computing; Engineering; Embedded system","score_opus":0.017088395673694065,"score_gpt":0.24387381297181687,"score_spread":0.2267854172981228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769139641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07341204,0.00032499514,0.9235927,0.000036994978,0.00008775978,0.00003486411,0.00010120884,0.001351965,0.0010574872],"genre_scores_gemma":[0.6950568,0.00035388456,0.30244508,0.000050784744,0.00006541469,0.00006785142,0.000318743,0.00006054049,0.0015809183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997123,0.00002618871,0.000018428238,0.00009050753,0.0001305626,0.000021845708],"domain_scores_gemma":[0.99970955,0.000054356882,0.00005330095,0.000036641384,0.00013017049,0.000016000948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025322736,0.0006782962,0.0008166163,0.0008291087,0.00025160986,0.00035905192,0.0004769512,0.00032486703,0.00072907313],"category_scores_gemma":[0.0008524972,0.00028818246,0.00045488856,0.0006742496,0.00011302121,0.0006785283,0.0005702616,0.00035835325,0.00045846344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039176707,0.00014080727,0.0109082395,0.0002125449,0.00015730628,0.00025040354,0.00012852893,0.07190075,0.097419545,0.000915264,0.0019574442,0.81561744],"study_design_scores_gemma":[0.000037773767,0.00032454557,0.022733316,0.000035870016,0.00009754566,0.00046537185,0.00006609698,0.92212254,0.049650066,0.0012560415,0.003150361,0.000060389542],"about_ca_topic_score_codex":0.0016006487,"about_ca_topic_score_gemma":0.0020949298,"teacher_disagreement_score":0.0016006487,"about_ca_system_score_codex":0.00016455974,"about_ca_system_score_gemma":0.0002821707,"threshold_uncertainty_score":0.00318259},"labels":[],"label_agreement":null},{"id":"W2770628360","doi":"10.1109/cbms.2017.126","title":"Indoor Localization: A Cost-Effectiveness vs. Accuracy Study","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Beacon; Global Positioning System; Computer science; Assisted living; Variety (cybernetics); Work (physics); Human–computer interaction; Real-time computing; Telecommunications; Artificial intelligence; Engineering","score_opus":0.03255810615329328,"score_gpt":0.3039252561249273,"score_spread":0.271367149971634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770628360","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7495711,0.04225497,0.17520864,0.0031557078,0.0006868588,0.0005761826,0.0023076271,0.0012714028,0.02496753],"genre_scores_gemma":[0.9730166,0.0032607017,0.02135158,0.0001383918,0.0002065356,0.000064318585,0.00047356158,0.00013523921,0.0013529528],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9907747,0.0048350226,0.00045656017,0.0008020977,0.0024216692,0.00070981897],"domain_scores_gemma":[0.92850375,0.06109818,0.0024634355,0.0032783486,0.0042000553,0.00045616893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00556072,0.0014404675,0.0014565567,0.0033570314,0.00044952388,0.0019102386,0.0015969923,0.00158967,0.0038118877],"category_scores_gemma":[0.036495425,0.00034835076,0.0009276497,0.0050572837,0.0008208996,0.0032905,0.0012194773,0.00073801447,0.0006594656],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005853653,0.0011681583,0.052067295,0.0017400893,0.0009883902,0.0006470567,0.00022027953,0.45554128,0.0083551975,0.006045839,0.005781672,0.46159106],"study_design_scores_gemma":[0.00032118623,0.0061085154,0.054247122,0.00028552298,0.0016907443,0.0028460056,0.00088895916,0.90929824,0.014612664,0.004047303,0.005440168,0.00021349125],"about_ca_topic_score_codex":0.0072354414,"about_ca_topic_score_gemma":0.005999408,"teacher_disagreement_score":0.0072354414,"about_ca_system_score_codex":0.0022743281,"about_ca_system_score_gemma":0.0008457345,"threshold_uncertainty_score":0.029408276},"labels":[],"label_agreement":null},{"id":"W2770715484","doi":"10.1109/globalsip.2017.8309075","title":"Performance evaluation of beacons for indoor localization in smart buildings","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Beacon; Computer science; Kalman filter; Real-time computing; Bluetooth; Simple (philosophy); SIGNAL (programming language); Signal strength; Embedded system; Telecommunications; Wireless; Artificial intelligence","score_opus":0.029520725266757412,"score_gpt":0.2770758086940763,"score_spread":0.24755508342731886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770715484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7554676,0.00436122,0.23005712,0.0002780698,0.00028294942,0.00010741679,0.0002277458,0.0023481897,0.0068697133],"genre_scores_gemma":[0.99170285,0.00032092672,0.007287629,0.000014624936,0.000014296808,0.000016900467,0.000111686684,0.00003568482,0.00049548974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982034,0.0007012017,0.00008576988,0.00018972598,0.00059278344,0.00022705925],"domain_scores_gemma":[0.9914683,0.0055433293,0.0005622114,0.00045565685,0.0016799072,0.0002906047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027382765,0.0007013885,0.0008798187,0.0012222932,0.00041777032,0.0007047293,0.00079408655,0.0007159707,0.0013623182],"category_scores_gemma":[0.010420118,0.00020375366,0.00027056452,0.0010775529,0.00036098302,0.0008461359,0.0008006602,0.00032289486,0.00038560329],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007921202,0.0003921541,0.026525117,0.0009798161,0.00024520408,0.000310561,0.0005232667,0.6347523,0.04208464,0.0050801826,0.0022064792,0.27897912],"study_design_scores_gemma":[0.00014825865,0.0031925559,0.008280517,0.000055199172,0.00012158925,0.00020970304,0.00022277219,0.96308756,0.022228345,0.00071761984,0.0016880198,0.00004792244],"about_ca_topic_score_codex":0.0027417436,"about_ca_topic_score_gemma":0.0014575864,"teacher_disagreement_score":0.0027417436,"about_ca_system_score_codex":0.0007521556,"about_ca_system_score_gemma":0.00042032593,"threshold_uncertainty_score":0.014481604},"labels":[],"label_agreement":null},{"id":"W2772271650","doi":"10.4018/978-1-5225-3528-7.ch010","title":"Multi-Sensor Integrated Navigation in Urban and Indoor Environments","year":2017,"lang":"en","type":"book-chapter","venue":"Advances in wireless technologies and telecommunication book series","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"GNSS applications; Inertial measurement unit; Computer science; Sensor fusion; Real-time computing; Lidar; Ranging; Global Positioning System; Inertial navigation system; Remote sensing; Artificial intelligence; Geography; Telecommunications; Inertial frame of reference","score_opus":0.00929382780494454,"score_gpt":0.2271232245930133,"score_spread":0.21782939678806876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772271650","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010233864,0.031510126,0.8296921,0.000684382,0.0012613958,0.00006799017,0.00023643277,0.0030838456,0.12322995],"genre_scores_gemma":[0.18377513,0.046980828,0.43129927,0.0008598773,0.00047356437,0.00015752476,0.0008963284,0.00055378646,0.33500376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998604,0.000012309326,0.0000047083863,0.000032069373,0.00008025909,0.0000101934975],"domain_scores_gemma":[0.9999465,0.000010545428,0.0000043591504,0.000008759124,0.000025132855,0.0000046172886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011856628,0.00061793753,0.00035835258,0.00033403904,0.00020405503,0.0008686822,0.00062531064,0.0006558713,0.0051224483],"category_scores_gemma":[0.00018773762,0.00025849984,0.0002519248,0.0006956215,0.00024040871,0.0013212998,0.0007329607,0.00058120384,0.0031426388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059691283,0.000041839594,0.0004594107,0.00055390014,0.000039380942,0.00023932639,0.0002462262,0.038682185,0.04014762,0.051684227,0.03077433,0.83707184],"study_design_scores_gemma":[0.000012321409,0.0002445758,0.0016732828,0.0003978817,0.0000612094,0.0009712085,0.00024145014,0.149378,0.035940636,0.028573811,0.782437,0.00006864948],"about_ca_topic_score_codex":0.001180823,"about_ca_topic_score_gemma":0.0022100126,"teacher_disagreement_score":0.0051224483,"about_ca_system_score_codex":0.00032279422,"about_ca_system_score_gemma":0.00032099403,"threshold_uncertainty_score":0.017136335},"labels":[],"label_agreement":null},{"id":"W2772625173","doi":"10.1109/iemcon.2017.8117170","title":"Sensor fusion for floor detection","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"RSS; Computer science; Mobile device; Sensor fusion; Kalman filter; Identification (biology); Wireless; Real-time computing; Signal strength; Pressure sensor; Accelerometer; Embedded system; Artificial intelligence; Telecommunications; Engineering","score_opus":0.013434019552448731,"score_gpt":0.2322986372451808,"score_spread":0.21886461769273208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772625173","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029943181,0.0006104408,0.9650576,0.00007897524,0.00017033001,0.000039767678,0.00018427642,0.0014620716,0.0024533698],"genre_scores_gemma":[0.72990113,0.0006862755,0.2651136,0.00012350998,0.00008021992,0.000066295615,0.00055465143,0.000077159544,0.0033971951],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995388,0.00006694987,0.000021258806,0.00010432962,0.00020599841,0.00006275925],"domain_scores_gemma":[0.99976844,0.000048381422,0.000030844156,0.00004497593,0.000094435425,0.000012904229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003157143,0.00074510253,0.00077690976,0.0006936812,0.00028603288,0.00054489553,0.00049177813,0.0005816458,0.0021176129],"category_scores_gemma":[0.0008267829,0.00023436452,0.0005249822,0.0007444389,0.00019549324,0.0007942806,0.00082085025,0.00062275317,0.0010592182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034862905,0.000118721495,0.0031594809,0.00034485667,0.00009721505,0.00028294188,0.00015835444,0.10433101,0.15825161,0.004212719,0.0045102015,0.7241842],"study_design_scores_gemma":[0.000011918156,0.00016363064,0.005474374,0.000033024222,0.000035986042,0.00019372521,0.0000691996,0.9296262,0.054949313,0.0028562292,0.006549725,0.00003663077],"about_ca_topic_score_codex":0.0012479394,"about_ca_topic_score_gemma":0.0017625407,"teacher_disagreement_score":0.0021176129,"about_ca_system_score_codex":0.00026885606,"about_ca_system_score_gemma":0.00038564077,"threshold_uncertainty_score":0.0070840716},"labels":[],"label_agreement":null},{"id":"W2776148705","doi":"","title":"Rapid: a multimodal and device-free approach using noise estimation for robust person identification","year":2017,"lang":"en","type":"article","venue":"RMIT Research Repository (RMIT University Library)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Noise (video); Identification (biology); Fuse (electrical); Key (lock); Real-time computing; Channel (broadcasting); Gait; Noise measurement; Artificial intelligence; Computer vision; Noise reduction; Telecommunications; Engineering; Computer security","score_opus":0.07119363818632841,"score_gpt":0.26321890732681275,"score_spread":0.19202526914048434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2776148705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027731359,0.00031653052,0.9649966,0.00010042512,0.00010500121,0.00008076915,0.00026275436,0.0046226513,0.0017839322],"genre_scores_gemma":[0.39765006,0.0004538325,0.5914223,0.00037874436,0.00018530841,0.00026480592,0.0012123368,0.00039743655,0.008035146],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990533,0.00021312706,0.000041174484,0.00026854232,0.00032022057,0.000103615355],"domain_scores_gemma":[0.9994754,0.00013476022,0.0000604545,0.00011294614,0.00017667167,0.00003978649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083622383,0.0012608604,0.0012337951,0.0013379661,0.00034417052,0.000507384,0.0014035171,0.0009054552,0.0026366424],"category_scores_gemma":[0.002179122,0.0003305234,0.0006009358,0.00071431353,0.00027382295,0.0011303083,0.0017554491,0.0005484876,0.0025535477],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093280524,0.00032854962,0.0056371735,0.00037342843,0.0002256302,0.0006607317,0.000324323,0.02967535,0.099080816,0.0027892278,0.009134595,0.85083735],"study_design_scores_gemma":[0.000069980364,0.00061518594,0.008114641,0.000054859534,0.00016068392,0.0021117553,0.0002512593,0.9194327,0.052476596,0.0050657415,0.011501361,0.00014515115],"about_ca_topic_score_codex":0.0012960278,"about_ca_topic_score_gemma":0.0025147947,"teacher_disagreement_score":0.0026366424,"about_ca_system_score_codex":0.00022356809,"about_ca_system_score_gemma":0.00042301425,"threshold_uncertainty_score":0.008820415},"labels":[],"label_agreement":null},{"id":"W2781745903","doi":"","title":"Cooperative Localization and Mapping in Sparsely-Communicating Robot Networks","year":2012,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; University of Alberta","keywords":"Computer science; Robot; Artificial intelligence; Human–computer interaction; Computer vision","score_opus":0.018758712796371133,"score_gpt":0.21923709463307198,"score_spread":0.20047838183670086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781745903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0768288,0.0013642256,0.9154557,0.0004224228,0.00004174156,0.00002718715,0.00001989946,0.00015633655,0.005683711],"genre_scores_gemma":[0.89170843,0.0024153143,0.100045495,0.00005124167,0.000090216694,0.00010838906,0.00006025067,0.000020722628,0.005499909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996618,0.00009819066,0.000009244049,0.00006557129,0.00013559492,0.000029505447],"domain_scores_gemma":[0.9993228,0.00045782025,0.00006983718,0.000046169727,0.00008348203,0.000019913601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044760527,0.00029276428,0.00032110483,0.00035611918,0.0003139611,0.0005656284,0.00041249915,0.00042031307,0.0003733923],"category_scores_gemma":[0.0019756295,0.00023348288,0.00017641514,0.00056886266,0.00065138337,0.0007221234,0.0007081881,0.00035885445,0.000099206634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000422355,0.000027578526,0.0006314555,0.000105738545,0.00002401568,0.00020610272,0.0003698192,0.8545521,0.0067289406,0.059901487,0.0010070306,0.07640352],"study_design_scores_gemma":[0.000010777335,0.000028463008,0.000363295,0.000013173184,0.000007462089,0.000048953607,0.000077933444,0.9558765,0.0013429391,0.039768986,0.0024557752,0.0000057508887],"about_ca_topic_score_codex":0.0025159637,"about_ca_topic_score_gemma":0.0020915263,"teacher_disagreement_score":0.0025159637,"about_ca_system_score_codex":0.00047642007,"about_ca_system_score_gemma":0.00045974262,"threshold_uncertainty_score":0.0050026774},"labels":[],"label_agreement":null},{"id":"W2781816717","doi":"10.1155/2018/9317291","title":"Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Context (archaeology); Traffic congestion; Traffic flow (computer networking); Intelligent transportation system; Standardization; Cluster analysis; Smart city; Advanced Traffic Management System; Field (mathematics); Real-time computing; Transport engineering; Computer network; Engineering; Computer security; Artificial intelligence","score_opus":0.008560469821874158,"score_gpt":0.22738835025204282,"score_spread":0.21882788043016865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781816717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05187217,0.00010939574,0.9449049,0.00014668856,0.000023924289,0.00003665445,0.00006556296,0.00093873864,0.0019019053],"genre_scores_gemma":[0.7504256,0.00021822396,0.24751434,0.000062837295,0.00004331378,0.00008375226,0.00025050205,0.000101038095,0.0013004256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993937,0.00015782712,0.000023611805,0.00014041612,0.00021750627,0.00006690225],"domain_scores_gemma":[0.9990206,0.0003259358,0.000112472735,0.00018878798,0.00031417035,0.000038068687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007583516,0.0004497847,0.00056167255,0.00095566316,0.00034698506,0.00072463206,0.0012056223,0.0006176458,0.0007534666],"category_scores_gemma":[0.0025388882,0.00035271826,0.00040863192,0.0010870607,0.0005333163,0.0012056626,0.0013320467,0.00044977464,0.0005095369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017523397,0.00015315841,0.0057970607,0.000110254965,0.000048759706,0.00010545332,0.00030643248,0.56152606,0.034039147,0.027579887,0.0019493796,0.36820912],"study_design_scores_gemma":[0.000007931358,0.000041095205,0.001408254,0.000005816922,0.000009261049,0.000046875928,0.00004617414,0.9843977,0.006544947,0.0062044067,0.0012691038,0.00001844942],"about_ca_topic_score_codex":0.0034081677,"about_ca_topic_score_gemma":0.004103858,"teacher_disagreement_score":0.0034081677,"about_ca_system_score_codex":0.0006557082,"about_ca_system_score_gemma":0.00063287927,"threshold_uncertainty_score":0.006776631},"labels":[],"label_agreement":null},{"id":"W2782795431","doi":"10.3390/s18010205","title":"Plils: A Practical Indoor Localization System through Less Expensive Wireless Chips via Subregion Clustering","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Wireless; Computer science; Exploit; Cluster analysis; Power (physics); Support vector machine; Artificial neural network; Phase (matter); Measure (data warehouse); Signal strength; Wireless network; Real-time computing; SIGNAL (programming language); Pattern recognition (psychology); Artificial intelligence; Electronic engineering; Data mining; Engineering; Telecommunications","score_opus":0.02603130270641757,"score_gpt":0.2536941574232174,"score_spread":0.2276628547167998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782795431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026285443,0.00016747719,0.9616179,0.00010990389,0.000060141785,0.000077704586,0.00020133788,0.008031943,0.0034481746],"genre_scores_gemma":[0.40879023,0.00019144257,0.5791068,0.00021535721,0.000044039898,0.00020594799,0.0006097901,0.0002263138,0.010610013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966705,0.000051700157,0.00001575491,0.0000895098,0.00013548083,0.00004050761],"domain_scores_gemma":[0.999579,0.000048320246,0.00006479285,0.00012233402,0.00015278342,0.00003280668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025122103,0.00059926964,0.00057543186,0.00074150413,0.00033314218,0.00046221833,0.0016135145,0.00046128235,0.0030504616],"category_scores_gemma":[0.0005602343,0.00022180624,0.00027947352,0.0007980273,0.00029224806,0.0010772044,0.0011160026,0.00039644117,0.0028992265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057032687,0.00013828847,0.003968159,0.00035069344,0.00007666577,0.00031439227,0.00031001732,0.06271182,0.11509171,0.005881451,0.0135329515,0.7970535],"study_design_scores_gemma":[0.00014669364,0.0008050473,0.006679664,0.00004706651,0.000105405845,0.0011973465,0.00032774676,0.75894934,0.15735455,0.004896881,0.0693272,0.00016313375],"about_ca_topic_score_codex":0.0019138503,"about_ca_topic_score_gemma":0.003170771,"teacher_disagreement_score":0.0030504616,"about_ca_system_score_codex":0.00040339903,"about_ca_system_score_gemma":0.00059382844,"threshold_uncertainty_score":0.010204852},"labels":[],"label_agreement":null},{"id":"W2782823649","doi":"10.1109/glocom.2017.8253976","title":"A Novel Passive Road Side Unit Detection Scheme in Vehicular Networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Correctness; Computer science; Estimator; Scheme (mathematics); Routing (electronic design automation); Computer network; Simple (philosophy); Vehicular ad hoc network; Real-time computing; The Internet; SIGNAL (programming language); Task (project management); Transient (computer programming); Topology (electrical circuits); Wireless; Telecommunications; Algorithm; Wireless ad hoc network; Engineering; Electrical engineering","score_opus":0.0134598498679114,"score_gpt":0.22352955854543613,"score_spread":0.21006970867752472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782823649","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019505534,0.0004383433,0.97782874,0.000085275366,0.00008840021,0.00005343051,0.00003596146,0.0005488868,0.0014153613],"genre_scores_gemma":[0.79362917,0.00058365235,0.20054406,0.00013891532,0.000080134545,0.00011464586,0.00017207659,0.000038673003,0.004698748],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915266,0.00023382087,0.00003912312,0.00021556165,0.00026945953,0.000089472436],"domain_scores_gemma":[0.9993198,0.00016929286,0.00009844554,0.00012741999,0.0002467638,0.00003841281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005328686,0.00073902373,0.00079860585,0.00079660275,0.00051142386,0.00062295207,0.0019247752,0.00073300157,0.000603255],"category_scores_gemma":[0.0017800591,0.00034003702,0.00037244044,0.0007428136,0.0005716646,0.0015899288,0.0011779509,0.0005349401,0.00051894906],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077788223,0.00015548665,0.0029174595,0.0003804939,0.00011326779,0.00073158956,0.0004625345,0.18001205,0.15997739,0.035498004,0.004630334,0.6143436],"study_design_scores_gemma":[0.00003654089,0.0003881044,0.0005880241,0.000015171358,0.000041054103,0.0005730623,0.00006350185,0.9603659,0.028663548,0.0034144835,0.0057916525,0.000059058286],"about_ca_topic_score_codex":0.0018777791,"about_ca_topic_score_gemma":0.0017691406,"teacher_disagreement_score":0.0019247752,"about_ca_system_score_codex":0.00048864784,"about_ca_system_score_gemma":0.0006761134,"threshold_uncertainty_score":0.0037336946},"labels":[],"label_agreement":null},{"id":"W2782840052","doi":"10.1109/glocom.2017.8254137","title":"Linear Regression Algorithm against Device Diversity for Indoor WLAN Localization System","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"RSS; Crowdsourcing; Signal strength; Computer science; Reliability (semiconductor); Algorithm; Linear regression; SIGNAL (programming language); Data mining; Machine learning; Telecommunications; Wireless","score_opus":0.02228197410437346,"score_gpt":0.24993220324933296,"score_spread":0.2276502291449595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782840052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010526589,0.00029591113,0.9868237,0.00013433707,0.00003820957,0.00003634117,0.000027146058,0.0012597878,0.0008579945],"genre_scores_gemma":[0.60480416,0.0007516693,0.38462943,0.00027235306,0.0001533471,0.00035379876,0.0004264379,0.00030985713,0.008298935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749434,0.00076509966,0.00013560435,0.000697847,0.0006512601,0.00025590506],"domain_scores_gemma":[0.997647,0.0010133439,0.00026047256,0.00019352746,0.0008251571,0.00006050422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017780514,0.001272064,0.0013750945,0.0011915421,0.0007890351,0.0010194553,0.0017612735,0.0010989226,0.0020466568],"category_scores_gemma":[0.0057060374,0.00046413892,0.0009174507,0.0016389895,0.00056973,0.001358788,0.0012755479,0.0017005776,0.0020545216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003401392,0.00013938437,0.003570783,0.00015891303,0.000160374,0.00027707274,0.000255073,0.48597562,0.016182374,0.0044404943,0.0031029612,0.48539683],"study_design_scores_gemma":[0.000010924928,0.00005941721,0.00042459962,0.0000058931296,0.000017662274,0.00008765824,0.00002594909,0.99500185,0.0027802724,0.0008007883,0.0007708853,0.000014188938],"about_ca_topic_score_codex":0.005695885,"about_ca_topic_score_gemma":0.0029164571,"teacher_disagreement_score":0.005695885,"about_ca_system_score_codex":0.00066857744,"about_ca_system_score_gemma":0.0010868483,"threshold_uncertainty_score":0.011325479},"labels":[],"label_agreement":null},{"id":"W2782915604","doi":"10.23919/acc.2018.8431528","title":"Localizability-Constrained Deployment of Mobile Robotic Networks with Noisy Range Measurements","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Computer science; Range (aeronautics); Estimator; Mobile robot; Robot; Position (finance); Upper and lower bounds; Motion planning; Function (biology); Rigidity (electromagnetism); Topology (electrical circuits); Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Engineering; Mathematical analysis","score_opus":0.01967443203061465,"score_gpt":0.22631527018463427,"score_spread":0.20664083815401962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782915604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056359217,0.00007622616,0.9419681,0.00014823631,0.000009451547,0.000026090634,0.000025404855,0.00019407681,0.0011932469],"genre_scores_gemma":[0.9271556,0.00015746691,0.070927374,0.0000444441,0.000021330412,0.00009015793,0.000074095726,0.00005675008,0.0014726984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989549,0.0004449045,0.000042040698,0.00023493858,0.00022408846,0.00009905401],"domain_scores_gemma":[0.99686337,0.0016031564,0.0006725487,0.0004795313,0.00025125127,0.00013032158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012883451,0.0008729779,0.00064776425,0.0005450104,0.000492157,0.0006486167,0.0012841431,0.00060986966,0.0006067883],"category_scores_gemma":[0.008053701,0.0005664177,0.0003476569,0.0006008086,0.0014043929,0.0018872486,0.0021328146,0.0007863853,0.00020173166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000676497,0.000015901409,0.0005942745,0.00002898358,0.000013391848,0.000099340796,0.00010527078,0.9709791,0.0045153745,0.012059317,0.00016168905,0.011359767],"study_design_scores_gemma":[0.000008878828,0.00007433102,0.00037712176,0.000006195177,0.0000072032176,0.00003953388,0.000045330176,0.9870609,0.0019266795,0.01008034,0.00036535444,0.000008171053],"about_ca_topic_score_codex":0.0023093247,"about_ca_topic_score_gemma":0.0026951353,"teacher_disagreement_score":0.0023093247,"about_ca_system_score_codex":0.00081598706,"about_ca_system_score_gemma":0.00047582528,"threshold_uncertainty_score":0.0068134665},"labels":[],"label_agreement":null},{"id":"W2783296116","doi":"10.1109/glocom.2017.8253939","title":"Radio Map Noise Reduction Method Using Hankel Matrix for WLAN Indoor Positioning System","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Multipath propagation; Noise (video); Reduction (mathematics); Noise reduction; Interference (communication); Hankel matrix; Multipath interference; SIGNAL (programming language); Real-time computing; Electronic engineering; Channel (broadcasting); Algorithm; Telecommunications; Engineering; Artificial intelligence; Mathematics","score_opus":0.02028208347283396,"score_gpt":0.3000520662195777,"score_spread":0.27976998274674375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783296116","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008178206,0.00021316421,0.9897396,0.000052924264,0.000045850335,0.000018751502,0.000018040211,0.00042865527,0.0013048461],"genre_scores_gemma":[0.39059183,0.0009874828,0.59884137,0.00016554828,0.0001585042,0.000117633375,0.00027401908,0.00017538574,0.008688308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993161,0.000116971896,0.00003166811,0.00011017832,0.0003760389,0.00004902636],"domain_scores_gemma":[0.9995858,0.00011126018,0.00003562705,0.000046343976,0.00020233724,0.000018597502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033800228,0.000721646,0.00048627416,0.0006091848,0.0003893538,0.00055709394,0.0004808075,0.0004811007,0.0019751517],"category_scores_gemma":[0.0010720734,0.00024830914,0.00047180473,0.00061610574,0.00028478226,0.0007149578,0.00054474524,0.0005971552,0.00090812496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028600183,0.000077646066,0.0011702274,0.00031519588,0.00008274403,0.00026486148,0.00028316773,0.121190846,0.15631089,0.007930174,0.003295272,0.708793],"study_design_scores_gemma":[0.000027994305,0.00017482345,0.0016710409,0.000023944296,0.000057012978,0.00045959285,0.00009589142,0.9105951,0.075203784,0.0028312665,0.0087980395,0.00006153806],"about_ca_topic_score_codex":0.0018302101,"about_ca_topic_score_gemma":0.002028438,"teacher_disagreement_score":0.0019751517,"about_ca_system_score_codex":0.00025059714,"about_ca_system_score_gemma":0.00047459183,"threshold_uncertainty_score":0.0066075325},"labels":[],"label_agreement":null},{"id":"W2784127507","doi":"10.1139/cjce-2017-0406","title":"Energy loss in cement-based material for efficient sensor deployment at a site","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"HFSS; SIGNAL (programming language); Software deployment; Benchmark (surveying); Attenuation; Computer science; Energy (signal processing); Realization (probability); Simulation; Electronic engineering; Real-time computing; Engineering; Telecommunications; Physics; Geology","score_opus":0.006063769054769225,"score_gpt":0.17886871850394315,"score_spread":0.17280494944917393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784127507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84326935,0.00082611036,0.14844169,0.00040884042,0.000049386843,0.000036487883,0.00014833889,0.00024362809,0.0065762745],"genre_scores_gemma":[0.9928415,0.00018345735,0.0062188576,0.000024210587,0.0000024143756,0.000010342851,0.00003146701,0.00001624094,0.000671553],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998223,0.000031235562,0.000007068463,0.000025179033,0.000090551504,0.000023637504],"domain_scores_gemma":[0.9994553,0.00029859509,0.00011704599,0.000034843197,0.0000819478,0.000012268635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033561655,0.00029419042,0.00014517794,0.0002791811,0.00020330965,0.00044507053,0.0003173977,0.0003069136,0.00084514875],"category_scores_gemma":[0.0014205518,0.00014940184,0.0001548958,0.00027844348,0.0003269397,0.00076118077,0.000250145,0.00023973668,0.00016700926],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026405256,0.00013964124,0.010684105,0.0002525341,0.000022531904,0.00045495713,0.0001959652,0.41232997,0.5278278,0.007125685,0.00075373455,0.039948955],"study_design_scores_gemma":[0.000023775825,0.00032476088,0.00880338,0.00003293208,0.000040187017,0.00023913092,0.000160724,0.75398403,0.2316464,0.001980629,0.0027367256,0.000027303007],"about_ca_topic_score_codex":0.0011802152,"about_ca_topic_score_gemma":0.0021517363,"teacher_disagreement_score":0.0011802152,"about_ca_system_score_codex":0.0005669394,"about_ca_system_score_gemma":0.00033351398,"threshold_uncertainty_score":0.0041134953},"labels":[],"label_agreement":null},{"id":"W2785447041","doi":"10.1109/apwimob.2017.8284011","title":"Network-managed localization protocol for WiMAX","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Cramér–Rao bound; Computer science; WiMAX; Upper and lower bounds; Estimator; Protocol (science); Interoperability; Range (aeronautics); Covariance matrix; Computer network; Dilution of precision; Consistency (knowledge bases); Real-time computing; Algorithm; Telecommunications; Mathematics; Statistics; Wireless; Estimation theory; Global Positioning System; Engineering; Artificial intelligence","score_opus":0.02808742884363515,"score_gpt":0.28490365405384444,"score_spread":0.2568162252102093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785447041","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00686083,0.0016472847,0.98331565,0.00045350665,0.00028302567,0.00014967297,0.00006565965,0.00084422604,0.0063800956],"genre_scores_gemma":[0.70101476,0.0027738812,0.27400896,0.0007777313,0.00038214703,0.0011656311,0.0005788387,0.00010131306,0.019196723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944884,0.00014740526,0.000041386957,0.000093556824,0.00021702719,0.000051814575],"domain_scores_gemma":[0.999589,0.000086723696,0.000067029265,0.00007283414,0.00016637515,0.00001795599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006579423,0.00037171273,0.0002874035,0.00055553473,0.00062589615,0.0008561877,0.000839998,0.0005284474,0.0015942135],"category_scores_gemma":[0.0015982105,0.00012280508,0.00015616912,0.0005613907,0.00041183888,0.0011532635,0.0011689361,0.0007716274,0.0007338204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032714495,0.000106815554,0.0012419716,0.0006357873,0.0001069655,0.0006362242,0.00038899845,0.04864638,0.06850462,0.3014269,0.016894124,0.56108415],"study_design_scores_gemma":[0.00015293597,0.00073186937,0.001768771,0.00019745383,0.00022114898,0.0019161452,0.00021048338,0.6007789,0.071963735,0.06906568,0.25281873,0.00017424775],"about_ca_topic_score_codex":0.001022061,"about_ca_topic_score_gemma":0.0010199253,"teacher_disagreement_score":0.0015942135,"about_ca_system_score_codex":0.00066806027,"about_ca_system_score_gemma":0.00087524956,"threshold_uncertainty_score":0.005333185},"labels":[],"label_agreement":null},{"id":"W2786575360","doi":"10.1109/pimrc.2017.8292429","title":"RSSI quantization for indoor localization services","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Quantization (signal processing); Computer science; Signal strength; Algorithm; Profiling (computer programming); Reduction (mathematics); Data mining; Artificial intelligence; Real-time computing; Wireless sensor network; Mathematics; Computer network","score_opus":0.012072264646808665,"score_gpt":0.24672924760645046,"score_spread":0.2346569829596418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786575360","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006860771,0.0011417804,0.9796769,0.00044054585,0.00016427368,0.00009883967,0.00035305024,0.0049492707,0.006314537],"genre_scores_gemma":[0.51613456,0.00153427,0.47317716,0.00034199766,0.00018321502,0.00022827041,0.0015323239,0.00042342258,0.0064447513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984434,0.00045100355,0.00010091399,0.00018194251,0.0007282151,0.00009455559],"domain_scores_gemma":[0.99873406,0.00030565247,0.00013493294,0.0003955705,0.00038377428,0.00004605245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013245165,0.00088473066,0.0008057726,0.0008046834,0.00042379549,0.001232344,0.0014674558,0.00070258137,0.005500933],"category_scores_gemma":[0.0044558994,0.000292979,0.00042239492,0.0018793644,0.000500153,0.0011596533,0.00095332967,0.0010076329,0.0038035582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000479574,0.00017585428,0.0019040558,0.00049983314,0.000069889786,0.00020220428,0.00022339492,0.17172351,0.04251628,0.05185822,0.023338336,0.70700896],"study_design_scores_gemma":[0.00008065546,0.0002914637,0.0020290678,0.00012334995,0.00005134308,0.00043584633,0.00018777115,0.8721868,0.03846978,0.037217785,0.048841845,0.00008425835],"about_ca_topic_score_codex":0.002187682,"about_ca_topic_score_gemma":0.002116636,"teacher_disagreement_score":0.005500933,"about_ca_system_score_codex":0.00080228195,"about_ca_system_score_gemma":0.0007476841,"threshold_uncertainty_score":0.018402457},"labels":[],"label_agreement":null},{"id":"W2786827427","doi":"10.5539/ijsp.v7n2p39","title":"WSN Node Positioning and Mathematics Modeling Based on Genetic Method","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chongqing Municipal Education Commission; China Scholarship Council; Ministry of Education of the People's Republic of China; National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Wireless sensor network; Node (physics); Computer science; Genetic algorithm; Trajectory; Sampling (signal processing); Algorithm; Sample (material); Real-time computing; Computer network; Machine learning; Computer vision; Engineering","score_opus":0.014011171644971612,"score_gpt":0.27156010564576416,"score_spread":0.25754893400079254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786827427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011138135,0.0006152497,0.9836029,0.00019529847,0.00006126842,0.000028973847,0.000053575557,0.00018046935,0.0041240943],"genre_scores_gemma":[0.7493826,0.004787863,0.23180631,0.00012920622,0.00016969882,0.000291688,0.00030272492,0.00010473103,0.013025185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997292,0.000059174898,0.00001384506,0.0000667038,0.00011094585,0.00002009655],"domain_scores_gemma":[0.9998461,0.00006230819,0.000024402283,0.000012201112,0.00004803457,0.0000069131356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029001982,0.00054065225,0.0005171938,0.00080209086,0.0003839764,0.0006927348,0.00097283773,0.0006656097,0.0010090845],"category_scores_gemma":[0.0007212263,0.00023327483,0.0007763159,0.0011227785,0.00052699295,0.0010541952,0.0004581899,0.00056026684,0.0003589518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016179632,0.000014311303,0.00095465034,0.000069544774,0.00002140348,0.00010743893,0.00008622519,0.94469756,0.0036391383,0.025139479,0.00051708263,0.024736986],"study_design_scores_gemma":[0.0000027702504,0.00001379562,0.0002522101,0.000005296232,0.0000058475753,0.00004229983,0.000009950477,0.9927375,0.00039431555,0.0054461206,0.0010810462,0.000008739024],"about_ca_topic_score_codex":0.0095165195,"about_ca_topic_score_gemma":0.003370751,"teacher_disagreement_score":0.0095165195,"about_ca_system_score_codex":0.00066929276,"about_ca_system_score_gemma":0.00067494204,"threshold_uncertainty_score":0.01892221},"labels":[],"label_agreement":null},{"id":"W2789151399","doi":"10.1109/lsp.2018.2799699","title":"AUV-Aided Joint Localization and Time Synchronization for Underwater Acoustic Sensor Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Research and Development Corporation of Newfoundland and Labrador","keywords":"Synchronization (alternating current); Computer science; Underwater; Scalability; Wireless sensor network; Real-time computing; Underwater acoustic communication; Nonlinear system; Network packet; Measure (data warehouse); Range (aeronautics); Underwater acoustics; Control theory (sociology); Telecommunications; Artificial intelligence; Engineering; Computer network; Geology","score_opus":0.010317258986930072,"score_gpt":0.2051855735355893,"score_spread":0.19486831454865924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789151399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021908129,0.00031800114,0.97553617,0.00010492577,0.00005714113,0.000010090106,0.00001406884,0.00021074772,0.0018406619],"genre_scores_gemma":[0.8815398,0.00037119375,0.11245335,0.000050335642,0.000052869156,0.000052795745,0.00006715514,0.00003120265,0.0053812694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996729,0.00012283116,0.00001231486,0.000051363622,0.00011255981,0.000028003336],"domain_scores_gemma":[0.9997763,0.000096681746,0.00002899476,0.00003143536,0.00005843896,0.000008268283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036887967,0.00030464554,0.00030638764,0.00023596207,0.00027323148,0.00029312848,0.00034344403,0.00033934225,0.00081788143],"category_scores_gemma":[0.0012395958,0.00014299715,0.0001396012,0.00039214027,0.00034394654,0.00063378044,0.0007771249,0.0003693996,0.00020804927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011469225,0.000028665734,0.00080441165,0.000080444275,0.0000286223,0.00011891977,0.00020458763,0.74024135,0.022563178,0.04406327,0.0022497782,0.18950215],"study_design_scores_gemma":[0.000004006223,0.000024500283,0.00007597876,0.0000019137779,0.0000027847643,0.000015564196,0.0000132958485,0.9944501,0.002086311,0.0020546396,0.0012670648,0.000003840392],"about_ca_topic_score_codex":0.0030399254,"about_ca_topic_score_gemma":0.0037159577,"teacher_disagreement_score":0.0030399254,"about_ca_system_score_codex":0.00033644275,"about_ca_system_score_gemma":0.00071494794,"threshold_uncertainty_score":0.0060444474},"labels":[],"label_agreement":null},{"id":"W2789696295","doi":"10.1049/iet-com.2017.1177","title":"Detailed analysis of energy detection‐based millimetre‐wave time‐of‐arrival measurement system","year":2018,"lang":"en","type":"article","venue":"IET Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Millimetre wave; Computer science; Real-time computing; Energy (signal processing); Telecommunications; Physics; Statistics; Mathematics; Optics","score_opus":0.028313683490626167,"score_gpt":0.22204058058239695,"score_spread":0.1937268970917708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789696295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21664496,0.0007153901,0.7737275,0.00018336697,0.00006370331,0.000055025514,0.00019657455,0.00093615253,0.0074773487],"genre_scores_gemma":[0.9740666,0.00022210587,0.02240677,0.000036876314,0.000014532875,0.000026071557,0.00015597872,0.000042619686,0.0030284883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965894,0.000041496016,0.000015653393,0.00005890696,0.00019114799,0.000033928514],"domain_scores_gemma":[0.9997181,0.00008991693,0.00004448575,0.000037496287,0.000101101235,0.000008891705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023120646,0.0002766702,0.000288863,0.00041393455,0.00020044454,0.00046383706,0.00042699702,0.00038461055,0.0017274265],"category_scores_gemma":[0.00063033326,0.00015216225,0.00027332816,0.00034110848,0.00014704301,0.00067727844,0.00027740476,0.00025861274,0.00045502864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005889191,0.00007472842,0.020473417,0.0004547595,0.00015441346,0.0008449194,0.00035096414,0.41406628,0.25069132,0.015661398,0.002114062,0.29452485],"study_design_scores_gemma":[0.000006018548,0.00012882391,0.008468953,0.000012648914,0.000028574445,0.00031697223,0.000045474662,0.9470209,0.03924879,0.0014866175,0.003209785,0.000026429685],"about_ca_topic_score_codex":0.0008721805,"about_ca_topic_score_gemma":0.0006357178,"teacher_disagreement_score":0.0017274265,"about_ca_system_score_codex":0.00038425892,"about_ca_system_score_gemma":0.00032177963,"threshold_uncertainty_score":0.005778849},"labels":[],"label_agreement":null},{"id":"W2789707442","doi":"","title":"Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Computer science; Tracking (education); Context (archaeology); Bridge (graph theory); Data science; World Wide Web; Telecommunications; Geography","score_opus":0.013658543171737016,"score_gpt":0.21270415726094555,"score_spread":0.19904561408920854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789707442","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0177633,0.06529081,0.6624893,0.028901218,0.06570987,0.00092419033,0.003116336,0.00758833,0.14821665],"genre_scores_gemma":[0.094535,0.05876114,0.26691377,0.005182833,0.017688474,0.00078393344,0.0137522565,0.002280889,0.54010177],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986778,0.00039206693,0.00011103101,0.0002284113,0.00042636684,0.00016429632],"domain_scores_gemma":[0.99753654,0.000626058,0.00009454126,0.00038446498,0.00090340665,0.00045502838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021535524,0.0014393074,0.0016058581,0.0012022714,0.00082837825,0.0036573817,0.0022892032,0.0019144015,0.03704357],"category_scores_gemma":[0.004474935,0.00057367544,0.00089914014,0.0014508463,0.0007462668,0.0052206186,0.0022707926,0.003078216,0.022728166],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003705025,0.00018308488,0.0015001008,0.00039658733,0.00011008558,0.00062845263,0.0004978672,0.0019454809,0.004205785,0.0077088755,0.56461096,0.4178422],"study_design_scores_gemma":[0.000034827015,0.00011529775,0.0010463302,0.00018179476,0.00006881749,0.0006387414,0.00029376094,0.007934489,0.001568714,0.0047992445,0.9832772,0.000040789702],"about_ca_topic_score_codex":0.0037924105,"about_ca_topic_score_gemma":0.006388209,"teacher_disagreement_score":0.03704357,"about_ca_system_score_codex":0.00063746155,"about_ca_system_score_gemma":0.0012399424,"threshold_uncertainty_score":0.12392306},"labels":[],"label_agreement":null},{"id":"W2792091676","doi":"10.1155/2018/5989678","title":"An Improved Particle Filter Algorithm for Geomagnetic Indoor Positioning","year":2018,"lang":"en","type":"article","venue":"Journal of Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China","keywords":"Particle filter; Earth's magnetic field; Algorithm; Precise Point Positioning; Resampling; Filter (signal processing); Computer science; Matching (statistics); Divergence (linguistics); Blossom algorithm; Computer vision; Mathematics; Global Positioning System; Magnetic field; Physics; Statistics; GNSS applications","score_opus":0.008059673053482245,"score_gpt":0.23291041687592423,"score_spread":0.224850743822442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792091676","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017583516,0.00017858515,0.9970571,0.00005501657,0.00008613869,0.000018977442,0.00002675901,0.00022054196,0.0005985443],"genre_scores_gemma":[0.15137759,0.00076363265,0.8401303,0.00016952622,0.00023163756,0.00022184607,0.000414438,0.000094546245,0.006596393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991021,0.00014300941,0.000058900994,0.00021754818,0.00040520454,0.000073237854],"domain_scores_gemma":[0.9993604,0.00018768263,0.000055330092,0.000063685155,0.0003080854,0.000024866957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009403994,0.00081087434,0.0012258283,0.0008508713,0.00054026005,0.00083219446,0.0013641774,0.0014963406,0.0020775027],"category_scores_gemma":[0.0019432971,0.00040251247,0.0010190629,0.001270533,0.00043827156,0.0010807578,0.0007466812,0.0011831298,0.00097406376],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020078807,0.00007825785,0.0014972468,0.00023588032,0.00012660284,0.00016776391,0.00014924945,0.5534051,0.011350847,0.013914787,0.006260987,0.41261244],"study_design_scores_gemma":[0.000023099708,0.000029498016,0.00033647817,0.0000072291928,0.000014953287,0.000044556054,0.000006790311,0.9933211,0.0015833521,0.0011331842,0.0034853022,0.000014386198],"about_ca_topic_score_codex":0.013604983,"about_ca_topic_score_gemma":0.007820542,"teacher_disagreement_score":0.013604983,"about_ca_system_score_codex":0.0007505199,"about_ca_system_score_gemma":0.0015601217,"threshold_uncertainty_score":0.027051568},"labels":[],"label_agreement":null},{"id":"W2795168503","doi":"10.1145/3191739","title":"TagFree Activity Identification with RFIDs","year":2018,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Multipath propagation; Identification (biology); Computer science; Key (lock); SIGNAL (programming language); Internet of Things; Real-time computing; Data mining; Human–computer interaction; Telecommunications; Computer security","score_opus":0.0061779106187199164,"score_gpt":0.21857014275985898,"score_spread":0.21239223214113906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795168503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04232177,0.0008421742,0.9448746,0.00012503903,0.00018560213,0.000060457074,0.0002660804,0.0065398444,0.004784561],"genre_scores_gemma":[0.62382257,0.00083486736,0.3618457,0.00046360915,0.00010861959,0.00010186958,0.0009115125,0.00028295434,0.01162821],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99936026,0.000076344084,0.000035851674,0.00020690759,0.00024067872,0.000080023936],"domain_scores_gemma":[0.9995591,0.000095086674,0.00008230887,0.0001366476,0.000100967416,0.000025923606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033633164,0.00084087974,0.000644577,0.00084776897,0.000184052,0.00076297636,0.0011893059,0.00078873546,0.0017948535],"category_scores_gemma":[0.001487812,0.00037665747,0.00046455368,0.00084513065,0.00035351005,0.0014887218,0.0011986397,0.0006801922,0.0030195508],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004632028,0.0002151577,0.007085628,0.00040117826,0.00012584026,0.00037044514,0.00027207253,0.021375166,0.14814956,0.0051076934,0.004693858,0.8117402],"study_design_scores_gemma":[0.00006168297,0.0007841718,0.013564381,0.00017246802,0.00017354575,0.0026141738,0.00024149191,0.61597747,0.29872614,0.01759644,0.04986608,0.00022195019],"about_ca_topic_score_codex":0.000632408,"about_ca_topic_score_gemma":0.001033082,"teacher_disagreement_score":0.0017948535,"about_ca_system_score_codex":0.00026205787,"about_ca_system_score_gemma":0.00025214706,"threshold_uncertainty_score":0.0060043335},"labels":[],"label_agreement":null},{"id":"W2795232814","doi":"10.1145/3191742","title":"SiFi","year":2018,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Transmitter; Computer science; Synchronization (alternating current); Channel (broadcasting); MIMO; Time of arrival; Transmission (telecommunications); Distortion (music); Antenna (radio); Electronic engineering; Real-time computing; Telecommunications; Engineering","score_opus":0.007039213208057262,"score_gpt":0.22368979774010622,"score_spread":0.21665058453204897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795232814","genre_codex":"other","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01055539,0.002529975,0.3469121,0.004240915,0.0048962436,0.0004982287,0.0082934955,0.04491574,0.5771579],"genre_scores_gemma":[0.15737498,0.0035262718,0.15472342,0.003596833,0.0019475019,0.0008301859,0.038940325,0.004200112,0.6348604],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99872154,0.00015756297,0.000070306094,0.00029297252,0.000551686,0.00020585296],"domain_scores_gemma":[0.9977882,0.00025453177,0.0001119509,0.0005914335,0.0010241602,0.00022978216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013580195,0.0010790101,0.0006343872,0.001646309,0.0011668002,0.002643978,0.0022333018,0.0018726358,0.18754357],"category_scores_gemma":[0.0031518196,0.0004211192,0.00063036213,0.0012944549,0.0004967852,0.0025552232,0.0029750252,0.001630818,0.16710588],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034971777,0.00011379815,0.0026915504,0.00048965757,0.000050349034,0.0003475102,0.0002836527,0.0029206057,0.014212794,0.06048999,0.3845283,0.53352207],"study_design_scores_gemma":[0.000036437643,0.00012776059,0.00094344787,0.000073532814,0.000023192562,0.0004953362,0.000088890745,0.009390798,0.0067386026,0.007318089,0.974726,0.00003804467],"about_ca_topic_score_codex":0.0025998384,"about_ca_topic_score_gemma":0.0027672022,"teacher_disagreement_score":0.18754357,"about_ca_system_score_codex":0.0010576678,"about_ca_system_score_gemma":0.0018615216,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2795704652","doi":"10.23919/icmu.2017.8330076","title":"A three-dimensional smartphone positioning method using a spinning magnet marker","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"GLS Industries (Canada)","funders":"Japan Society for the Promotion of Science","keywords":"Spinning; Azimuth; Magnet; Position (finance); Computer science; Magnetic field; Gyroscope; Trajectory; Elevation (ballistics); Acoustics; Computer vision; Physics; Optics; Engineering; Mechanical engineering","score_opus":0.020830406295061336,"score_gpt":0.2734382157143528,"score_spread":0.25260780941929145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795704652","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017095644,0.0005823621,0.97874063,0.00010744482,0.0002565483,0.0000633919,0.00008897275,0.0016969813,0.0013679164],"genre_scores_gemma":[0.29902804,0.0007743543,0.6959825,0.000108300555,0.00012318425,0.00021205231,0.00026946794,0.00008865304,0.0034133303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992655,0.00014488467,0.00004381107,0.00015852564,0.00033574534,0.000051424133],"domain_scores_gemma":[0.99905163,0.00013230585,0.00014284354,0.00016645895,0.000436538,0.00007026471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034144244,0.00080169545,0.00067468284,0.0014866464,0.00043682402,0.00074670644,0.0012489597,0.0009793544,0.0014345762],"category_scores_gemma":[0.0014596852,0.0004790693,0.0007760755,0.0009989538,0.0002732716,0.0007491041,0.001085525,0.00065428956,0.0016527206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047082163,0.00012187082,0.006130639,0.0005960064,0.00016136686,0.00081652106,0.00063607097,0.016460028,0.2196246,0.0068329144,0.006189604,0.74195963],"study_design_scores_gemma":[0.0001807464,0.0012093473,0.013135931,0.00014729021,0.00024984212,0.0048254067,0.0003510111,0.74139076,0.1924873,0.0025300896,0.042924967,0.00056727335],"about_ca_topic_score_codex":0.0018574664,"about_ca_topic_score_gemma":0.0016491807,"teacher_disagreement_score":0.0018574664,"about_ca_system_score_codex":0.00024188295,"about_ca_system_score_gemma":0.0005024004,"threshold_uncertainty_score":0.004799068},"labels":[],"label_agreement":null},{"id":"W2796567768","doi":"10.1109/tnse.2018.2825144","title":"Channel Selective Activity Recognition with WiFi: A Deep Learning Approach Exploring Wideband Information","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Simon Fraser University","funders":"","keywords":"Computer science; Activity recognition; Channel (broadcasting); Spectrogram; Feature extraction; Hidden Markov model; Wideband; Context (archaeology); Deep learning; Bandwidth (computing); Artificial intelligence; Speech recognition; Computer network; Engineering; Electronic engineering","score_opus":0.015866780461194246,"score_gpt":0.18254906034636872,"score_spread":0.16668227988517448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796567768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073597185,0.0008491715,0.91946375,0.0003358825,0.00009798099,0.000048386384,0.00062835426,0.0020067738,0.002972454],"genre_scores_gemma":[0.83618087,0.0008772196,0.15509626,0.00034796,0.00008318562,0.000110014436,0.0012586459,0.00010690885,0.0059389705],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982446,0.00002639619,0.000008705826,0.000059420378,0.0000379196,0.000043168402],"domain_scores_gemma":[0.9998148,0.00007721393,0.000027135136,0.000026246153,0.00003870365,0.000015818669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025937107,0.0008560356,0.0004967933,0.0006575997,0.00017103285,0.0004613439,0.0009377791,0.0006072176,0.0011617551],"category_scores_gemma":[0.001037818,0.00029990185,0.00050639984,0.0007512537,0.00028692136,0.000854228,0.00083126395,0.0010170611,0.0005015193],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019159845,0.0002110455,0.0063521015,0.00013323504,0.00013491875,0.0002012756,0.00012147372,0.17496893,0.019985262,0.0029570411,0.004535029,0.7902081],"study_design_scores_gemma":[0.0000078015355,0.000063933636,0.0024565049,0.000018171144,0.000027774406,0.00008145404,0.00003558653,0.98628426,0.00540899,0.004148518,0.0014522476,0.0000146954135],"about_ca_topic_score_codex":0.004692106,"about_ca_topic_score_gemma":0.0073132874,"teacher_disagreement_score":0.004692106,"about_ca_system_score_codex":0.00035040418,"about_ca_system_score_gemma":0.00041711057,"threshold_uncertainty_score":0.009329617},"labels":[],"label_agreement":null},{"id":"W2797227892","doi":"10.1109/taes.2018.2826230","title":"TDOA Estimation With Compressive Sensing Measurements and Hadamard Matrix","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Royal Military College of Canada; Canadian Apheresis Group","funders":"Defence Research and Development Canada","keywords":"Multilateration; Compressed sensing; Hadamard transform; Matrix (chemical analysis); FDOA; Computer science; Signal reconstruction; Algorithm; SIGNAL (programming language); Property (philosophy); Acoustics; Signal processing; Mathematics; Telecommunications; Physics; Materials science; Mathematical analysis","score_opus":0.009556898881942083,"score_gpt":0.2181393096980737,"score_spread":0.20858241081613163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797227892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023042243,0.00011233899,0.9968796,0.00004101819,0.000027524182,0.000007910396,0.000013722388,0.00008129901,0.00053243234],"genre_scores_gemma":[0.23069388,0.00077610963,0.76653504,0.00010280904,0.00019555961,0.00007089255,0.00012919145,0.000039338654,0.001457074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994868,0.00012616513,0.000034695182,0.0001035544,0.00022044379,0.000028305365],"domain_scores_gemma":[0.99931073,0.00032069773,0.00009964137,0.00012032806,0.00012517594,0.000023413313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000505511,0.0006221395,0.0005021184,0.0004644826,0.00020897751,0.00059152703,0.0005736101,0.00071694946,0.0006301047],"category_scores_gemma":[0.0027763904,0.00034980522,0.0004102241,0.00074849074,0.00059453235,0.0012549877,0.0009146577,0.0009947683,0.00030844333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023218247,0.00007309137,0.00092076056,0.00038509653,0.00009547951,0.0003853425,0.00023565376,0.361975,0.15375485,0.10735239,0.0020497332,0.37254038],"study_design_scores_gemma":[0.000016841603,0.00007939709,0.00028230576,0.000018254104,0.0000123803675,0.00031488226,0.000021967224,0.96817905,0.015610162,0.01173601,0.0037021046,0.00002664181],"about_ca_topic_score_codex":0.0011806412,"about_ca_topic_score_gemma":0.0011326084,"teacher_disagreement_score":0.0011806412,"about_ca_system_score_codex":0.00025545247,"about_ca_system_score_gemma":0.0006190467,"threshold_uncertainty_score":0.0026733875},"labels":[],"label_agreement":null},{"id":"W2798760221","doi":"","title":"A Test-Bed for Localization and Tracking in Wireless Sensor Networks.","year":2009,"lang":"en","type":"article","venue":"NPARC","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Wireless sensor network; Computer science; Tracking (education); Wireless; Test (biology); Key distribution in wireless sensor networks; Wireless network; Computer network; Telecommunications","score_opus":0.008724487538851914,"score_gpt":0.22182893322381483,"score_spread":0.21310444568496292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798760221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27581486,0.0018915238,0.6911752,0.00059424044,0.0007801044,0.002391281,0.0018334886,0.015971906,0.009547349],"genre_scores_gemma":[0.6261609,0.0011550474,0.35383016,0.00036035402,0.00006908379,0.0019324033,0.004968754,0.00055460545,0.0109686125],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989102,0.00037479438,0.000063938714,0.00008229263,0.00048105235,0.00008771641],"domain_scores_gemma":[0.9980217,0.0006612294,0.00015196083,0.00044193718,0.00047555243,0.00024764624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010573978,0.00071442075,0.0004286473,0.00049625535,0.0004189151,0.00049203105,0.0019287915,0.0007498272,0.0031669708],"category_scores_gemma":[0.0028511377,0.0002934758,0.00034063242,0.0003769155,0.00036593887,0.00095101807,0.0006410543,0.00056902156,0.0013084618],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021510026,0.00290312,0.015596165,0.0028059073,0.0005624043,0.002658744,0.00042792404,0.100721784,0.39429617,0.011757431,0.031607572,0.43451184],"study_design_scores_gemma":[0.00043792458,0.013391026,0.01490946,0.00023648243,0.00025506574,0.0028596497,0.0003566048,0.31558672,0.5724727,0.0029945185,0.07637183,0.00012795457],"about_ca_topic_score_codex":0.0015044879,"about_ca_topic_score_gemma":0.0013584256,"teacher_disagreement_score":0.0031669708,"about_ca_system_score_codex":0.00029258765,"about_ca_system_score_gemma":0.0004699541,"threshold_uncertainty_score":0.010594606},"labels":[],"label_agreement":null},{"id":"W2799362944","doi":"10.1109/dinwc.2018.8356991","title":"Effect of UWB channel time delay parameters on TDOA localization","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Multilateration; Non-line-of-sight propagation; Computer science; Channel (broadcasting); Time of arrival; FDOA; Delay spread; Real-time computing; Line-of-sight; Sight; Line (geometry); Electronic engineering; Telecommunications; Acoustics; Wireless; Engineering; Mathematics; Multipath propagation; Physics","score_opus":0.004843699972772286,"score_gpt":0.21002322510808857,"score_spread":0.2051795251353163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799362944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3587636,0.0018123748,0.6331798,0.00019595462,0.00019308044,0.000034847988,0.0001720245,0.0009884271,0.0046598264],"genre_scores_gemma":[0.98101544,0.00054410094,0.01775452,0.00003293274,0.000015749518,0.000018683086,0.00006218383,0.00011123973,0.0004450574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990596,0.00024411347,0.00004485908,0.00018389188,0.00032945027,0.00013801135],"domain_scores_gemma":[0.99421173,0.0043267175,0.00044074914,0.00038569717,0.00057048735,0.000064623906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007195398,0.0006986544,0.0003672237,0.0006598007,0.00033802097,0.0006623234,0.00037467666,0.00053526065,0.00075672846],"category_scores_gemma":[0.0068422807,0.00027214762,0.00027897465,0.0006791108,0.00054751843,0.0012371559,0.000424044,0.0005614145,0.00021164023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011780395,0.000079677266,0.014082272,0.00070897024,0.00017266037,0.0013303667,0.0007907452,0.6477112,0.20230818,0.006649544,0.0005800383,0.12440831],"study_design_scores_gemma":[0.000071152295,0.0005857653,0.0113991415,0.00012765222,0.00038073238,0.0027044562,0.0007575744,0.6308731,0.34302613,0.0039948234,0.0058856397,0.00019376313],"about_ca_topic_score_codex":0.0017449686,"about_ca_topic_score_gemma":0.0013856014,"teacher_disagreement_score":0.0017449686,"about_ca_system_score_codex":0.00059052074,"about_ca_system_score_gemma":0.0004960579,"threshold_uncertainty_score":0.004284501},"labels":[],"label_agreement":null},{"id":"W2800009077","doi":"10.1139/tcsme-2013-0090","title":"INDOOR LOCALIZATION OF AN OMNI-DIRECTIONAL WHEELED MOBILE ROBOT","year":2013,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Odometry; Mobile robot; Beacon; Robot; Pose; Computer vision; Computer science; Encoder; Global Positioning System; Artificial intelligence; Sensor fusion; Orientation (vector space); Workspace; Position (finance); Real-time computing; Mathematics","score_opus":0.0059844990649136325,"score_gpt":0.18515952584674797,"score_spread":0.17917502678183433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800009077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027785871,0.00036017442,0.9670479,0.000042010382,0.00006579528,0.000041370946,0.000044510565,0.0020784256,0.0025338393],"genre_scores_gemma":[0.52921313,0.0007065372,0.46138144,0.00008792121,0.00007665122,0.00013497414,0.00027132826,0.000101983474,0.008026061],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997552,0.000038491606,0.000008922291,0.000053066007,0.000119392724,0.00002486734],"domain_scores_gemma":[0.9998431,0.000024845418,0.00002899194,0.000022236569,0.000064935746,0.000015836387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021076284,0.0004260819,0.0004738981,0.00042645598,0.0003350376,0.00037297976,0.000580817,0.00034865993,0.0010991404],"category_scores_gemma":[0.0003787684,0.00023837641,0.0002129237,0.00022711742,0.0002348553,0.000347782,0.0005276793,0.0003128967,0.0007788809],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030984156,0.00012681539,0.004473119,0.00047567225,0.0000696838,0.00056293904,0.0004501155,0.0338483,0.34712526,0.0054881563,0.004066055,0.603004],"study_design_scores_gemma":[0.00012538953,0.0015149415,0.016059944,0.00013930781,0.00021495743,0.0028226953,0.0002903488,0.50971055,0.37846315,0.0016346714,0.08885154,0.00017245798],"about_ca_topic_score_codex":0.0012880781,"about_ca_topic_score_gemma":0.0017035138,"teacher_disagreement_score":0.0012880781,"about_ca_system_score_codex":0.0001890437,"about_ca_system_score_gemma":0.00043834135,"threshold_uncertainty_score":0.0036770105},"labels":[],"label_agreement":null},{"id":"W2801720148","doi":"10.1109/dinwc.2018.8356990","title":"On performance study of TWR UWB ranging in underground mine","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Ranging; Computer science; Ultra-wideband; Distance measurement; Engineering; Electronic engineering; Telecommunications; Artificial intelligence","score_opus":0.009664073854416016,"score_gpt":0.2166740239716152,"score_spread":0.2070099501171992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801720148","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.877512,0.0016412702,0.10815766,0.00028692384,0.000072288516,0.0000525251,0.00018105294,0.0006316314,0.011464635],"genre_scores_gemma":[0.993968,0.0003354907,0.004436708,0.00004063096,0.0000137011375,0.000010069525,0.00009712,0.0000317185,0.0010664929],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987111,0.00027369751,0.00006185078,0.00020998472,0.0005677764,0.00017555106],"domain_scores_gemma":[0.9969247,0.0014549389,0.00033440546,0.00029109282,0.0009207229,0.00007415285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094491924,0.0003733859,0.00045333474,0.00056767376,0.00031627284,0.00047844113,0.00061092153,0.00074479514,0.0011834004],"category_scores_gemma":[0.004501242,0.00014473811,0.00019182285,0.0007467634,0.00032220603,0.00079830346,0.0004919005,0.00030394143,0.0005155155],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019853676,0.00021134851,0.040930957,0.0010695223,0.0001905382,0.0019278006,0.001096572,0.2503324,0.4978485,0.005482308,0.0013442799,0.19758037],"study_design_scores_gemma":[0.000059103033,0.0038384881,0.034378007,0.00013774722,0.00023924863,0.0051468867,0.0008401081,0.61196715,0.3338647,0.0020324653,0.0073641287,0.00013203571],"about_ca_topic_score_codex":0.0009042137,"about_ca_topic_score_gemma":0.00047245994,"teacher_disagreement_score":0.0011834004,"about_ca_system_score_codex":0.00039983733,"about_ca_system_score_gemma":0.00027109828,"threshold_uncertainty_score":0.0049972534},"labels":[],"label_agreement":null},{"id":"W2802810361","doi":"10.1007/s12200-018-0806-0","title":"Toward the implementation of a universal angle-based optical indoor positioning system","year":2018,"lang":"en","type":"article","venue":"Frontiers of Optoelectronics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Indoor positioning system; Positioning system; Global Positioning System; Angle of arrival; Real-time computing; Navigation system; Light-emitting diode; Elevation angle; Telecommunications; Optics; Acoustics; Physics; Azimuth","score_opus":0.005609935911105674,"score_gpt":0.2145244751563239,"score_spread":0.20891453924521822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802810361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009905615,0.0031574855,0.9750426,0.0003073922,0.0002706006,0.00006081669,0.000037057598,0.000880081,0.010338467],"genre_scores_gemma":[0.1771834,0.0032955804,0.8113536,0.00039009345,0.00016834673,0.000083071456,0.00010313456,0.00007945905,0.0073432443],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934894,0.000119320415,0.00003882355,0.000110466855,0.00031518636,0.00006715211],"domain_scores_gemma":[0.9992323,0.000096856835,0.00007386505,0.00008515529,0.00046680053,0.00004503295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000854449,0.00046516207,0.00029991614,0.00064113067,0.00029343046,0.0009172042,0.0012039223,0.000877628,0.0019991002],"category_scores_gemma":[0.0010468239,0.00029291477,0.00027018302,0.0005386701,0.00046293047,0.0011979344,0.0010085179,0.00081499503,0.0013797409],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018013795,0.000109385655,0.002828445,0.0008323948,0.000050655322,0.0002149619,0.00027259617,0.012635779,0.20344937,0.0743174,0.0045610345,0.7005479],"study_design_scores_gemma":[0.00007765263,0.001470149,0.0057104663,0.00065877236,0.00019117753,0.0018864876,0.00034810687,0.16696426,0.32583877,0.016323341,0.4803443,0.00018652304],"about_ca_topic_score_codex":0.0012949426,"about_ca_topic_score_gemma":0.0012847541,"teacher_disagreement_score":0.0019991002,"about_ca_system_score_codex":0.00052016537,"about_ca_system_score_gemma":0.0007281337,"threshold_uncertainty_score":0.0066877007},"labels":[],"label_agreement":null},{"id":"W2804028796","doi":"10.22215/etd/2008-07503","title":"Location estimation in wireless ad-hoc sensor networks","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Computer science; Wireless sensor network; Wireless ad hoc network; Telecommunications; Wireless; Computer network","score_opus":0.006142622795308006,"score_gpt":0.21721493052383403,"score_spread":0.21107230772852603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804028796","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022511054,0.008757663,0.96260655,0.00057108956,0.00046411564,0.000053883025,0.000074563,0.0005276567,0.0044333744],"genre_scores_gemma":[0.7406746,0.018677797,0.20654415,0.00016398015,0.0010009696,0.00019503493,0.000510082,0.000121070145,0.032112304],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995055,0.00014201432,0.000030545205,0.00010698172,0.0001781398,0.000036824942],"domain_scores_gemma":[0.9993284,0.0004050689,0.000040707622,0.000049096772,0.00015565172,0.000021061473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055425824,0.00040870183,0.00047713108,0.0005141682,0.00022935029,0.0005612845,0.00047843475,0.00038939223,0.0011791695],"category_scores_gemma":[0.0029201948,0.00027575588,0.00021245908,0.00077230815,0.00030919292,0.0011536523,0.00046441864,0.00046318275,0.00043557095],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019395619,0.00009573367,0.0018271196,0.0003226999,0.00008943244,0.00014913133,0.00016337205,0.45056787,0.008499343,0.013898049,0.007171157,0.5170221],"study_design_scores_gemma":[0.000020544338,0.000119790246,0.0014796425,0.000055943914,0.00003168932,0.00012215499,0.00010464595,0.97243834,0.004777611,0.011054519,0.009773923,0.000021290367],"about_ca_topic_score_codex":0.0025786613,"about_ca_topic_score_gemma":0.002208886,"teacher_disagreement_score":0.0025786613,"about_ca_system_score_codex":0.0002646044,"about_ca_system_score_gemma":0.00040885655,"threshold_uncertainty_score":0.0051273108},"labels":[],"label_agreement":null},{"id":"W2805929175","doi":"10.1109/comst.2018.2841901","title":"Localization Prediction in Vehicular Ad Hoc Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Vehicular ad hoc network; Wireless ad hoc network; Trajectory; Position (finance); Network topology; Set (abstract data type); Sensor fusion; Artificial intelligence; Computer network; Telecommunications; Wireless","score_opus":0.0256340918700639,"score_gpt":0.2589775161637297,"score_spread":0.2333434242936658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805929175","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021325845,0.0026960946,0.97185713,0.00054123596,0.00016831592,0.000037670747,0.00013845487,0.0006233213,0.0026119712],"genre_scores_gemma":[0.91782516,0.0051647727,0.0727165,0.00013000338,0.00025034347,0.00010492061,0.0004912625,0.000062641164,0.0032544066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934965,0.00019698609,0.00004004474,0.00013609556,0.00021298522,0.00006428123],"domain_scores_gemma":[0.9986272,0.00083912374,0.00015234883,0.000083019004,0.00027017135,0.000028233188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009234568,0.0005736256,0.0007238914,0.0006987313,0.0005289281,0.00091280456,0.00085327047,0.00073570677,0.0007409609],"category_scores_gemma":[0.00496793,0.00034795332,0.00033299328,0.0014969455,0.0004971718,0.0014270847,0.00067683,0.0007947823,0.00030783698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003323823,0.0000099023,0.0017402531,0.000058125057,0.00001936147,0.000072548515,0.000051930972,0.9323468,0.0004375494,0.009499466,0.0015373006,0.05419354],"study_design_scores_gemma":[0.0000023301086,0.000011215101,0.0002953429,0.0000092286255,0.000005854664,0.000021305204,0.00002286334,0.98981863,0.00022341733,0.00858107,0.0010027309,0.0000059598237],"about_ca_topic_score_codex":0.014550586,"about_ca_topic_score_gemma":0.0059293746,"teacher_disagreement_score":0.014550586,"about_ca_system_score_codex":0.0007248099,"about_ca_system_score_gemma":0.0006484295,"threshold_uncertainty_score":0.028931797},"labels":[],"label_agreement":null},{"id":"W2806663811","doi":"10.22215/etd/2017-12242","title":"Self-Stabilizing Switched Beam Offset Reflector (3SBOR) Antenna: 10GHz Prototype Antenna Solution for Mobile Radar and Communication Applications","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Periscope antenna; Antenna (radio); Beam waveguide antenna; Engineering; Directivity; Computer science; Electrical engineering; Omnidirectional antenna; Radar; Antenna gain; Electronic engineering; Telecommunications; Coaxial antenna","score_opus":0.013673527818752546,"score_gpt":0.27500360392612905,"score_spread":0.2613300761073765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806663811","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2975451,0.0016324274,0.65483755,0.0008400572,0.00061806425,0.00018826657,0.0003311121,0.0039391415,0.040068217],"genre_scores_gemma":[0.6905836,0.0013583852,0.22861058,0.00043154098,0.00012292608,0.00015648948,0.0005939186,0.00036397786,0.07777853],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999741,0.000035200588,0.000009864509,0.000044482953,0.0001314967,0.000037968213],"domain_scores_gemma":[0.999793,0.000023281409,0.000038186878,0.0000324222,0.00008748452,0.000025724941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024114564,0.00036510953,0.0002511681,0.00020918384,0.00013072317,0.0005877376,0.00077636103,0.0006794034,0.0035117574],"category_scores_gemma":[0.0002807648,0.00020360938,0.00038411663,0.0002281492,0.00017891858,0.0004174109,0.00020837346,0.00052281923,0.0033274894],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042216777,0.00010447054,0.00072089245,0.00021703956,0.000033641674,0.00019363008,0.00020353854,0.004150256,0.848904,0.0060110968,0.006064907,0.13297445],"study_design_scores_gemma":[0.00013791249,0.0030158875,0.003348336,0.00005989174,0.00008712776,0.0017866242,0.00016850272,0.028320396,0.8580194,0.0011567695,0.10382876,0.00007038032],"about_ca_topic_score_codex":0.0002759476,"about_ca_topic_score_gemma":0.0003971156,"teacher_disagreement_score":0.0035117574,"about_ca_system_score_codex":0.00029772107,"about_ca_system_score_gemma":0.0002850243,"threshold_uncertainty_score":0.011748016},"labels":[],"label_agreement":null},{"id":"W2806729898","doi":"10.1109/access.2018.2843325","title":"RSSI-Based Indoor Localization With the Internet of Things","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":544,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trilateration; Computer science; Global Positioning System; Bluetooth; Wireless; Real-time computing; Wireless sensor network; Bluetooth Low Energy; Hybrid positioning system; Wireless network; Embedded system; Computer network; Telecommunications; Positioning system; Node (physics)","score_opus":0.012140502835210811,"score_gpt":0.23777097901335564,"score_spread":0.22563047617814483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806729898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040910702,0.0027439622,0.9263043,0.00041374113,0.0007608797,0.0001783255,0.0005476869,0.008245594,0.019894777],"genre_scores_gemma":[0.71939546,0.0024928958,0.2635111,0.0005143986,0.00017472781,0.00025050264,0.0016148628,0.00032771684,0.011718421],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989446,0.0002466613,0.000055221746,0.00018771157,0.00048624177,0.00007956717],"domain_scores_gemma":[0.9996669,0.000059234168,0.000049513998,0.00007327919,0.00013889061,0.000012222042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051163795,0.0007472848,0.0006567708,0.0011504819,0.000324361,0.0007138885,0.00080389937,0.0007030911,0.0014475989],"category_scores_gemma":[0.0010798295,0.00022850321,0.00063466176,0.0018086876,0.0003000071,0.0011567147,0.00074656843,0.00040757874,0.0012221955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009400451,0.00027658988,0.0076116975,0.0011011728,0.00040834397,0.00097394886,0.0002996125,0.09696732,0.096529424,0.010132731,0.016443077,0.76831603],"study_design_scores_gemma":[0.0001644323,0.0023254436,0.018252185,0.000430946,0.00051546306,0.0042162933,0.0006026793,0.61930394,0.20187803,0.013570499,0.1381978,0.0005422415],"about_ca_topic_score_codex":0.0015931679,"about_ca_topic_score_gemma":0.0018786893,"teacher_disagreement_score":0.0015931679,"about_ca_system_score_codex":0.0002929807,"about_ca_system_score_gemma":0.00026469879,"threshold_uncertainty_score":0.004842639},"labels":[],"label_agreement":null},{"id":"W2806922942","doi":"10.1109/plans.2018.8373394","title":"Using a mobile range-camera motion capture system to evaluate the performance of integration of multiple low-cost wearable sensors and gait kinematics for pedestrian navigation in realistic environments","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Kinematics; Pedestrian; Wearable computer; Motion capture; Gait; Range (aeronautics); Computer vision; Motion (physics); Gait analysis; Simulation; Artificial intelligence; Physical medicine and rehabilitation; Engineering; Embedded system","score_opus":0.02090489618102303,"score_gpt":0.2540045076876121,"score_spread":0.2330996115065891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806922942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84235895,0.000508429,0.15314285,0.00006165415,0.00013653157,0.00022110784,0.00042232728,0.0011804656,0.001967618],"genre_scores_gemma":[0.9394811,0.00022877062,0.058412734,0.000047805577,0.000016003842,0.000096947515,0.00038497816,0.000027929833,0.0013036703],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997346,0.00003899779,0.000013979789,0.00007630672,0.00010052987,0.00003553313],"domain_scores_gemma":[0.99966,0.00007582098,0.000039153365,0.000035481753,0.00016537355,0.000024113246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030472048,0.00067570474,0.00040995592,0.0006503161,0.00017643349,0.00031178122,0.00035433366,0.000507729,0.0012922875],"category_scores_gemma":[0.0008943749,0.00017725112,0.00025136743,0.00046875817,0.00010287565,0.0004413095,0.00022529322,0.00017948826,0.00028498925],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023487,0.0011832129,0.037487328,0.0013039259,0.000428739,0.00088305824,0.00037384193,0.059295483,0.5702805,0.0010493438,0.0023771983,0.3229887],"study_design_scores_gemma":[0.00028235593,0.0086978525,0.16509873,0.00011138267,0.000444429,0.0016963328,0.00035885294,0.48536444,0.33115998,0.00032013492,0.0063010366,0.00016450697],"about_ca_topic_score_codex":0.004446872,"about_ca_topic_score_gemma":0.00768224,"teacher_disagreement_score":0.004446872,"about_ca_system_score_codex":0.00025844565,"about_ca_system_score_gemma":0.00027586185,"threshold_uncertainty_score":0.008841932},"labels":[],"label_agreement":null},{"id":"W2808565598","doi":"10.1109/icdcs.2018.00060","title":"Multiple Object Activity Identification Using RFIDs: A Multipath-Aware Deep Learning Solution","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Identification (biology); Object (grammar); Deep learning; Convolutional neural network; Decoupling (probability); Key (lock); Path (computing); Machine learning; Data mining; Computer network; Computer security","score_opus":0.018170016949584703,"score_gpt":0.24420303200709365,"score_spread":0.22603301505750895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808565598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023067692,0.00044737197,0.9720334,0.0004844481,0.00006736109,0.000024077826,0.0001682755,0.0014954511,0.0022120113],"genre_scores_gemma":[0.47456574,0.0006055424,0.50997216,0.0005414186,0.00014516588,0.00011520336,0.0011029077,0.00014337701,0.012808488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997993,0.000029462715,0.000008601395,0.00006759058,0.00004382494,0.00005118864],"domain_scores_gemma":[0.99976593,0.00006719379,0.00002727929,0.0000404898,0.00007209549,0.000026940272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047984123,0.00092191354,0.00070224365,0.00054421794,0.00031278425,0.00062140403,0.002206349,0.0014366016,0.0016057618],"category_scores_gemma":[0.0006911557,0.00039022887,0.00070468365,0.00085067394,0.00033397216,0.0012591651,0.0013526621,0.0014497201,0.00085963384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017780706,0.00027854025,0.0019556528,0.00009115623,0.00012217213,0.0001459995,0.000090240406,0.31114933,0.009841071,0.0054701143,0.0059514586,0.6647265],"study_design_scores_gemma":[0.000005111768,0.000032440217,0.00020493465,0.000006493651,0.000012738494,0.000022312064,0.000014585797,0.99271154,0.0019405932,0.004132973,0.00090888963,0.0000073972647],"about_ca_topic_score_codex":0.004015973,"about_ca_topic_score_gemma":0.006187202,"teacher_disagreement_score":0.004015973,"about_ca_system_score_codex":0.00057526084,"about_ca_system_score_gemma":0.0007753825,"threshold_uncertainty_score":0.007985234},"labels":[],"label_agreement":null},{"id":"W280876632","doi":"10.22260/isarc2013/0068","title":"Indoor Localization of RFID-Equipped Movable Assets Using Mobile Reader Based on Reference Tags Clustering","year":2013,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Radio-frequency identification; Cluster analysis; Context (archaeology); Asset management; Matching (statistics); Asset (computer security); Real-time locating system; Download; Real-time computing; Database; World Wide Web; Computer security; Artificial intelligence","score_opus":0.017234469149130547,"score_gpt":0.2247575740716269,"score_spread":0.20752310492249634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W280876632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06838243,0.00043517663,0.9271334,0.000049779585,0.000052464467,0.00003675483,0.00006133589,0.0013241877,0.0025244707],"genre_scores_gemma":[0.7149352,0.0005072466,0.27964318,0.000058058547,0.00004091994,0.00006148455,0.00033389492,0.00007756921,0.004342512],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956924,0.000079021156,0.000018406212,0.00012919899,0.00016038917,0.00004376533],"domain_scores_gemma":[0.99969435,0.000059739,0.000059305545,0.00006804637,0.00010332531,0.000015181053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022575789,0.00064293604,0.00078330183,0.0012451765,0.0002556681,0.0006226989,0.0011055334,0.0006664917,0.0009385764],"category_scores_gemma":[0.0006271531,0.00025117985,0.00070731126,0.0016486878,0.00024177322,0.0007849734,0.0006705802,0.0002669786,0.0013294308],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063529593,0.00016624296,0.007394519,0.00033846678,0.0002073931,0.0010470239,0.0004750577,0.1847534,0.2061935,0.0044094934,0.00268035,0.59169924],"study_design_scores_gemma":[0.000024072982,0.00036353403,0.006189223,0.000031531155,0.0001343986,0.0011852195,0.00022558481,0.9031302,0.08239376,0.0014349392,0.0047912775,0.000096277974],"about_ca_topic_score_codex":0.0014100429,"about_ca_topic_score_gemma":0.0013791485,"teacher_disagreement_score":0.0014100429,"about_ca_system_score_codex":0.00031777407,"about_ca_system_score_gemma":0.00021951362,"threshold_uncertainty_score":0.0031397939},"labels":[],"label_agreement":null},{"id":"W2809283576","doi":"10.1007/978-3-319-93034-3_26","title":"Human Identification via Unsupervised Feature Learning from UWB Radar Data","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Discriminative model; Radar; Cluster analysis; Classifier (UML); Identification (biology); Feature extraction; Pattern recognition (psychology); Feature (linguistics); Machine learning; Telecommunications","score_opus":0.022133597657297343,"score_gpt":0.23884305240708367,"score_spread":0.21670945474978634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809283576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034768444,0.00041567677,0.9621057,0.00006756939,0.000051479827,0.000024918387,0.00023731834,0.0010356647,0.0012932577],"genre_scores_gemma":[0.6000025,0.0008899871,0.3889581,0.00015751421,0.00019119568,0.00011861199,0.0020687985,0.00020379956,0.0074095074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996164,0.00009148767,0.000015770558,0.0001276248,0.00009113942,0.000057534555],"domain_scores_gemma":[0.9993886,0.00029644012,0.0000669559,0.00013377045,0.000098138094,0.000016133603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043992637,0.0005183509,0.0007658358,0.00074526516,0.0001756171,0.00052437215,0.0007083019,0.0006871239,0.0011091369],"category_scores_gemma":[0.0015680827,0.0002568592,0.0005778846,0.00081817637,0.00031533316,0.0007613193,0.0007343944,0.00070153666,0.0015576147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026824544,0.00014152132,0.0024842208,0.00011380616,0.00007635966,0.00016609467,0.00007329639,0.0414697,0.04511119,0.0016092685,0.0034551271,0.90503114],"study_design_scores_gemma":[0.000016378588,0.00017739505,0.008491858,0.00003313567,0.00005384209,0.0008802767,0.000074227646,0.940169,0.037336167,0.008808037,0.003921001,0.000038754493],"about_ca_topic_score_codex":0.0005824521,"about_ca_topic_score_gemma":0.0010767294,"teacher_disagreement_score":0.0011091369,"about_ca_system_score_codex":0.00012030178,"about_ca_system_score_gemma":0.00023824826,"threshold_uncertainty_score":0.0037103891},"labels":[],"label_agreement":null},{"id":"W2810060291","doi":"10.1016/j.eswa.2018.07.009","title":"Tracking objects within a smart home","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Trilateration; Random forest; Software deployment; Real-time computing; Tracking system; Ground truth; Radio-frequency identification; Classifier (UML); Data mining; Software; Artificial intelligence; Process (computing); Context (archaeology); Video tracking; Kalman filter; Object (grammar); Computer security","score_opus":0.010929263040730101,"score_gpt":0.2251402570885017,"score_spread":0.21421099404777158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810060291","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6071566,0.00036952694,0.38195202,0.00023705912,0.00011750307,0.000054097436,0.00039011083,0.0020278862,0.0076951636],"genre_scores_gemma":[0.93094134,0.00019193577,0.06302401,0.000055207536,0.000048165042,0.000014921175,0.00023451626,0.000049731225,0.0054402114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997937,0.00002499119,0.000010062151,0.00007762097,0.00006446872,0.00002924356],"domain_scores_gemma":[0.99975795,0.00006240979,0.000037491165,0.0000485162,0.00005296736,0.000040687773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001594924,0.00031391438,0.00044093168,0.00069818716,0.00035748692,0.00078393274,0.0004451223,0.00070365286,0.0013112569],"category_scores_gemma":[0.0006672554,0.00023845007,0.00019472919,0.00079079525,0.00020445779,0.00090109254,0.0008215514,0.0002925101,0.0005830528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015077239,0.00048365816,0.06848813,0.00021704653,0.0002102263,0.0032540793,0.0023448984,0.1197203,0.15226713,0.006237131,0.008074083,0.63719565],"study_design_scores_gemma":[0.00003413452,0.00037896872,0.04900411,0.000034617213,0.00014512172,0.0012808151,0.0013904527,0.89414424,0.03838391,0.00547569,0.009673509,0.000054529435],"about_ca_topic_score_codex":0.0034977216,"about_ca_topic_score_gemma":0.0050086323,"teacher_disagreement_score":0.0034977216,"about_ca_system_score_codex":0.00022131993,"about_ca_system_score_gemma":0.00022519528,"threshold_uncertainty_score":0.0069547296},"labels":[],"label_agreement":null},{"id":"W2810301152","doi":"10.1109/uic-atc.2017.8397547","title":"Scalable indoor navigation system based on proximity Bluetooth beacons using tools of AI","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scalability; Beacon; Bluetooth; Computer science; Autonomy; Population; Government (linguistics); Navigation system; Shopping mall; Telecommunications; Computer security; Human–computer interaction; Real-time computing; Wireless; Business; Database","score_opus":0.025129644560492104,"score_gpt":0.25506783819043016,"score_spread":0.22993819362993806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810301152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051076524,0.00074645696,0.91648304,0.00022167817,0.0003652719,0.0002105619,0.00029155947,0.018332237,0.012272613],"genre_scores_gemma":[0.6307323,0.00049728405,0.3485171,0.00022850568,0.00011549883,0.00044560482,0.00080726715,0.0002503923,0.018406175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996536,0.00005179115,0.000020608544,0.00009507081,0.00013570518,0.00004321011],"domain_scores_gemma":[0.9997044,0.000043233827,0.00003244484,0.000074033,0.00011529031,0.00003061956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026700582,0.00057697814,0.0006960562,0.0005716823,0.0003698392,0.00046603417,0.0016218812,0.00047871505,0.0040835934],"category_scores_gemma":[0.00061102695,0.00023675757,0.00036737794,0.00036852184,0.0001925757,0.00064363686,0.0009783272,0.00046161798,0.0018747484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007289257,0.00033711176,0.0049399515,0.0006659223,0.00015621158,0.0009532777,0.00071987306,0.015960295,0.3623065,0.008424243,0.01722194,0.5875857],"study_design_scores_gemma":[0.00056485407,0.0026387847,0.014167704,0.00020101664,0.00055465905,0.0037943886,0.00041146926,0.57757634,0.20603251,0.005814504,0.18792556,0.00031823033],"about_ca_topic_score_codex":0.0018377869,"about_ca_topic_score_gemma":0.0013989621,"teacher_disagreement_score":0.0040835934,"about_ca_system_score_codex":0.00018998695,"about_ca_system_score_gemma":0.0003857886,"threshold_uncertainty_score":0.013661027},"labels":[],"label_agreement":null},{"id":"W2810891502","doi":"10.1109/jsen.2018.2852494","title":"A Sensor Fusion-Based Framework for Floor Localization","year":2018,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RSS; Sensor fusion; Computer science; Real-time computing; Kalman filter; Wireless; Wearable computer; Algorithm; Heuristic; Artificial intelligence; Embedded system; Telecommunications","score_opus":0.014692946933724357,"score_gpt":0.2528694520777955,"score_spread":0.23817650514407113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810891502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045412744,0.00012252948,0.9986186,0.00003245999,0.000016797452,0.0000102114,0.000024014817,0.00017481245,0.00054638163],"genre_scores_gemma":[0.24494235,0.0011198105,0.74879116,0.00012951392,0.00015200191,0.0002257531,0.00047247525,0.00013438193,0.004032524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993099,0.00015860799,0.000031047195,0.00015529225,0.00026536136,0.00007974428],"domain_scores_gemma":[0.999726,0.000081801714,0.000040211584,0.000037023277,0.00009371594,0.000021378239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096737803,0.0009787234,0.0010959968,0.0010095461,0.00060738117,0.0012291184,0.002095467,0.0012752168,0.0022742276],"category_scores_gemma":[0.0013987888,0.0005143282,0.0012475508,0.0014541411,0.00085619267,0.0014993151,0.002077727,0.0012449228,0.0010057852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064906955,0.00003816418,0.00048638313,0.00013399885,0.000066842775,0.00020479085,0.00013567708,0.8028267,0.005917987,0.06839438,0.0026231473,0.11910697],"study_design_scores_gemma":[0.0000051913657,0.000028477703,0.00013097683,0.000012844221,0.000011846674,0.000052098978,0.000016317039,0.9834667,0.00082078343,0.012560148,0.002881276,0.000013343541],"about_ca_topic_score_codex":0.007927385,"about_ca_topic_score_gemma":0.007213096,"teacher_disagreement_score":0.007927385,"about_ca_system_score_codex":0.000809285,"about_ca_system_score_gemma":0.0015385286,"threshold_uncertainty_score":0.015762508},"labels":[],"label_agreement":null},{"id":"W2829549924","doi":"10.1109/i2mtc.2018.8409868","title":"Sensing instrumentation using smartphones: Securing impact and awareness","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Carleton University","keywords":"Instrumentation (computer programming); Computer science; Firmware; Embedded system; Accelerometer; Situation awareness; Computer security; Cloud computing; Scripting language; Participatory sensing; Real-time computing; Computer hardware; Human–computer interaction; Engineering; Operating system; Data science","score_opus":0.015415967092008587,"score_gpt":0.2701258677150117,"score_spread":0.2547099006230031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2829549924","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53528714,0.008720829,0.35914528,0.006404592,0.0008374919,0.0005080135,0.00037845504,0.0054699806,0.08324814],"genre_scores_gemma":[0.94943076,0.0022396669,0.040358596,0.00051738444,0.00017323368,0.00006330533,0.000101793,0.00007739454,0.007037774],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937433,0.00013164793,0.00004383055,0.0000753615,0.0002994872,0.00007530532],"domain_scores_gemma":[0.9983865,0.0003783662,0.00028667971,0.00024306585,0.00061269465,0.000092718066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046471442,0.0004862325,0.00031235782,0.0005648373,0.00029093676,0.00109095,0.0003293404,0.0005674306,0.0020102283],"category_scores_gemma":[0.0024474966,0.00023801054,0.00021141637,0.00030214296,0.00035781116,0.0017392874,0.0010581823,0.00047501747,0.0010494421],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037506252,0.00017775442,0.032385856,0.0007705058,0.000057780115,0.0010249235,0.0021091574,0.0024179893,0.21574736,0.008173339,0.007874171,0.728886],"study_design_scores_gemma":[0.00007227401,0.0040394985,0.12916325,0.0012450247,0.0005599585,0.011442757,0.0064486307,0.09843057,0.49927822,0.009996444,0.23902023,0.00030323627],"about_ca_topic_score_codex":0.00064332376,"about_ca_topic_score_gemma":0.0009376831,"teacher_disagreement_score":0.0020102283,"about_ca_system_score_codex":0.00020817546,"about_ca_system_score_gemma":0.00029389467,"threshold_uncertainty_score":0.006724894},"labels":[],"label_agreement":null},{"id":"W2833205538","doi":"10.23919/icins.2018.8405844","title":"Robust IMU/UWB integration for indoor pedestrian navigation","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Extended Kalman filter; Inertial measurement unit; Kalman filter; Computer science; Inertial navigation system; Covariance intersection; Covariance; Sensor fusion; Invariant extended Kalman filter; Computer vision; Control theory (sociology); Orientation (vector space); Artificial intelligence; Mathematics; Statistics","score_opus":0.027529251020772517,"score_gpt":0.23683369692497108,"score_spread":0.20930444590419855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2833205538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021114653,0.00054892077,0.97442484,0.000052943848,0.00013221081,0.000021303422,0.00008223276,0.0015861192,0.0020368048],"genre_scores_gemma":[0.7544863,0.0004750969,0.23940644,0.00008729386,0.000097486845,0.000090111076,0.0003648801,0.00008539735,0.0049070828],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961853,0.00007862408,0.000017216458,0.00010363257,0.00012916152,0.000052813204],"domain_scores_gemma":[0.9998023,0.000023215598,0.000029924548,0.000039001803,0.00009209554,0.000013452749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000402671,0.00072219496,0.0007095899,0.0005918691,0.00027160742,0.0005247504,0.00061808655,0.00054206897,0.0011244902],"category_scores_gemma":[0.0007680817,0.0002684529,0.0005176673,0.00057194795,0.00018509121,0.00052764034,0.0007846858,0.0003844159,0.00091911486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074040954,0.000101284866,0.00847452,0.00041301077,0.00019778675,0.000680421,0.0003128762,0.09522003,0.10097688,0.009732926,0.0058960128,0.7772539],"study_design_scores_gemma":[0.000037107828,0.0004719504,0.0065816646,0.00006431592,0.00019466285,0.0008385654,0.00016627483,0.84686947,0.12096369,0.0043890444,0.01933652,0.000086712585],"about_ca_topic_score_codex":0.0016752892,"about_ca_topic_score_gemma":0.002049369,"teacher_disagreement_score":0.0016752892,"about_ca_system_score_codex":0.00021130675,"about_ca_system_score_gemma":0.00046342757,"threshold_uncertainty_score":0.003761828},"labels":[],"label_agreement":null},{"id":"W283508369","doi":"","title":"STAR Localization Aids Enhancement Final Report: A Call-Up Under the Noise Monitoring Standing Offer","year":2005,"lang":"en","type":"article","venue":"Defense Technical Information Center (DTIC)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ellipse; Computer science; Software; Hyperbola; Algorithm; Operator (biology); Bin; Sensor fusion; Computer vision; Artificial intelligence; Mathematics; Programming language; Geometry","score_opus":0.022753577671305023,"score_gpt":0.25110847726844526,"score_spread":0.22835489959714023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W283508369","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075797625,0.0014359235,0.24454828,0.12857983,0.03299119,0.0073378906,0.010288777,0.074235305,0.42478526],"genre_scores_gemma":[0.06799865,0.0007444716,0.07388634,0.010747272,0.0041659107,0.00072388665,0.00636746,0.005885474,0.8294805],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978002,0.00024544838,0.00008005285,0.0002024141,0.0014094893,0.00026238107],"domain_scores_gemma":[0.9951668,0.0006000112,0.000075076714,0.00035284646,0.002637749,0.0011674528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046510156,0.00064989115,0.0004349285,0.00072023104,0.0019559425,0.0024677764,0.0016972641,0.0025870316,0.09908318],"category_scores_gemma":[0.004659615,0.00048606467,0.0006437779,0.00029121063,0.00046634368,0.0014633462,0.0019567795,0.0024765157,0.049350634],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036333274,0.00054189545,0.0017043133,0.00007319214,0.000015368747,0.0018815529,0.0005768381,0.00056237966,0.013245159,0.0019194656,0.8707049,0.10841161],"study_design_scores_gemma":[0.000085910135,0.00052259787,0.0022829666,0.000033746393,0.000017113704,0.0006113974,0.00061270606,0.0041964096,0.0044465666,0.0007533272,0.98638797,0.000049297167],"about_ca_topic_score_codex":0.006565531,"about_ca_topic_score_gemma":0.015655821,"teacher_disagreement_score":0.09908318,"about_ca_system_score_codex":0.0009058758,"about_ca_system_score_gemma":0.002742329,"threshold_uncertainty_score":0.33146626},"labels":[],"label_agreement":null},{"id":"W2849540937","doi":"10.18280/mmep.050206","title":"Investigation of wireless tracking performance in the tunnel-like environment with particle filter","year":2018,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tracking (education); Particle filter; Wireless; Particle (ecology); Computer science; Acoustics; Environmental science; Filter (signal processing); Aerospace engineering; Marine engineering; Engineering; Telecommunications; Electrical engineering; Physics; Geology; Psychology","score_opus":0.023242498090731818,"score_gpt":0.17324015889721298,"score_spread":0.14999766080648116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2849540937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73185366,0.00074511423,0.26007542,0.00039498217,0.00014556144,0.0000755773,0.00020408306,0.0008793586,0.0056261695],"genre_scores_gemma":[0.98345804,0.00025996997,0.015229755,0.000028641225,0.000008361933,0.000028472537,0.00014584411,0.000019008048,0.00082189747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995316,0.00012397776,0.000031662137,0.000085557906,0.00012500066,0.00010214932],"domain_scores_gemma":[0.9985128,0.00073968654,0.00016786587,0.00014897727,0.00036589862,0.000064797845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011621336,0.0006572069,0.0007221675,0.0006285755,0.0004289641,0.0006544605,0.0005824474,0.00106318,0.0006142869],"category_scores_gemma":[0.0030868878,0.00022692521,0.00064211304,0.0007773541,0.0004080058,0.0009460281,0.00060133624,0.00065994915,0.00018561381],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025682512,0.00014608385,0.011309444,0.00019469479,0.00009166627,0.0004529398,0.00020154957,0.9439371,0.0080770515,0.0022079428,0.0005642155,0.03256039],"study_design_scores_gemma":[0.000009081391,0.00018709169,0.0029556549,0.000011666619,0.000017380826,0.00008660868,0.00006823233,0.9934794,0.0025291268,0.0003958839,0.00024342621,0.000016396449],"about_ca_topic_score_codex":0.0087961005,"about_ca_topic_score_gemma":0.0031632413,"teacher_disagreement_score":0.0087961005,"about_ca_system_score_codex":0.00034824287,"about_ca_system_score_gemma":0.00057629007,"threshold_uncertainty_score":0.01748979},"labels":[],"label_agreement":null},{"id":"W2876971880","doi":"10.1007/978-981-13-0408-8_10","title":"LocSwayamwar: Finding a Suitable ML Algorithm for Wi-Fi Fingerprinting Based Indoor Positioning System","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"C4.5 algorithm; Computer science; Multilateration; Random forest; Algorithm; Multilayer perceptron; Perceptron; Classifier (UML); Angle of arrival; Artificial intelligence; Real-time computing; Machine learning; Data mining; Artificial neural network; Naive Bayes classifier; Telecommunications; Mathematics","score_opus":0.006615568229081985,"score_gpt":0.19556810604822336,"score_spread":0.18895253781914137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2876971880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002565752,0.00038455406,0.9940801,0.000057032805,0.00008216076,0.000017800041,0.000071557675,0.0017651233,0.00097585446],"genre_scores_gemma":[0.06289122,0.00049852347,0.9284395,0.000086879176,0.000103798164,0.000076332886,0.00056445174,0.0004411683,0.006898026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994868,0.00008912453,0.000030200868,0.00013969869,0.00020629694,0.000047936228],"domain_scores_gemma":[0.9996619,0.00010252211,0.000026660275,0.00007613922,0.00011725007,0.000015413974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047090003,0.0009658194,0.0008424837,0.000638999,0.00039509483,0.00095259823,0.0011267489,0.001170263,0.004563676],"category_scores_gemma":[0.001798257,0.0005063559,0.0006469061,0.00080818986,0.0003446103,0.0014077512,0.00091237505,0.0010508897,0.005072564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026281842,0.00006561757,0.00069835014,0.0002653338,0.000095230025,0.0002183605,0.000094400755,0.032902334,0.06667367,0.009754312,0.009393149,0.8795764],"study_design_scores_gemma":[0.00007491386,0.00034305116,0.001737013,0.0000945437,0.00010999302,0.0013957884,0.000091785594,0.7955027,0.13254178,0.015017468,0.0530085,0.00008243237],"about_ca_topic_score_codex":0.0006627982,"about_ca_topic_score_gemma":0.0008129811,"teacher_disagreement_score":0.004563676,"about_ca_system_score_codex":0.0002468612,"about_ca_system_score_gemma":0.0005302666,"threshold_uncertainty_score":0.015267074},"labels":[],"label_agreement":null},{"id":"W287923820","doi":"10.1007/978-3-319-01790-7_13","title":"Human Spatial Behavior, Sensor Informatics, and Disaggregate Data","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Global Positioning System; Pedestrian; Real-time computing; Tracking (education); Human–computer interaction; Data science; Artificial intelligence; Data mining; Telecommunications; Transport engineering","score_opus":0.01873355820278984,"score_gpt":0.23557690575437326,"score_spread":0.2168433475515834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W287923820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1536637,0.005841856,0.7517596,0.003236649,0.00023390821,0.000049502574,0.0045810877,0.0014364824,0.07919723],"genre_scores_gemma":[0.8884897,0.005118139,0.08905239,0.00030156443,0.00015124255,0.000055500343,0.0025115788,0.00020519418,0.014114691],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997625,0.000057343335,0.000013366277,0.00006533581,0.000083526575,0.000017962397],"domain_scores_gemma":[0.9992576,0.00034633477,0.00010108705,0.00017882012,0.00008831865,0.000027860513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032039266,0.00035104767,0.00032038073,0.00079192535,0.00017419983,0.0014475728,0.00032040087,0.00031604254,0.0032076552],"category_scores_gemma":[0.0019298457,0.00027984896,0.00023501323,0.0019512335,0.0006266808,0.0015167161,0.00071052986,0.00052742864,0.00060026656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029008862,0.00012187118,0.047945134,0.00064707117,0.00018519073,0.0003199513,0.0014492291,0.13579041,0.013794644,0.3240394,0.01908162,0.45633543],"study_design_scores_gemma":[0.000010306362,0.00015484856,0.07732612,0.00021331404,0.000098168064,0.0008623665,0.00171475,0.35565525,0.009302663,0.47701162,0.077565275,0.000085339685],"about_ca_topic_score_codex":0.002988184,"about_ca_topic_score_gemma":0.005498471,"teacher_disagreement_score":0.0032076552,"about_ca_system_score_codex":0.0004513401,"about_ca_system_score_gemma":0.0002970392,"threshold_uncertainty_score":0.010730684},"labels":[],"label_agreement":null},{"id":"W28820463","doi":"10.3390/ijerph14080930","title":"Mobile Terminal Tracking in Urban Scenarios Using Multipath Propagation.","year":2006,"lang":"en","type":"article","venue":"GI Jahrestagung (1)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Multipath propagation; Computer science; Terminal (telecommunication); Real-time computing; Base station; Ray tracing (physics); Tracking (education); Particle filter; Location-based service; Computer vision; Telecommunications; Filter (signal processing)","score_opus":0.010771290681763983,"score_gpt":0.2319907496934429,"score_spread":0.22121945901167892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W28820463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36456406,0.0012760332,0.620557,0.00065642426,0.00026993346,0.00015621672,0.0006990243,0.0017413751,0.010079922],"genre_scores_gemma":[0.9525996,0.0007434876,0.044275463,0.000040342522,0.000056899615,0.000038539398,0.00027812485,0.000024583445,0.0019430293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995647,0.00017816375,0.000010826856,0.000073529714,0.00008489555,0.000087931985],"domain_scores_gemma":[0.99902916,0.00057813845,0.000117962285,0.000053085867,0.00015660914,0.00006500458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009104624,0.0009058219,0.0004116987,0.0009789501,0.0005586764,0.0009373812,0.0005796653,0.0008479956,0.00084419904],"category_scores_gemma":[0.0025722366,0.00041458118,0.00033077147,0.0016631024,0.00042295136,0.0014215492,0.0009874739,0.0005424416,0.00038089574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026129567,0.0000656729,0.02352512,0.00013995476,0.00009669435,0.0014021707,0.00029212868,0.8824675,0.006525176,0.0072628562,0.002750186,0.07521119],"study_design_scores_gemma":[0.000009558481,0.0000662037,0.0034967642,0.000010496577,0.000024573284,0.00024000098,0.00011510827,0.9931798,0.0004353597,0.0015501429,0.0008564128,0.000015590413],"about_ca_topic_score_codex":0.013703746,"about_ca_topic_score_gemma":0.018250898,"teacher_disagreement_score":0.013703746,"about_ca_system_score_codex":0.0005978944,"about_ca_system_score_gemma":0.00068383274,"threshold_uncertainty_score":0.027247965},"labels":[],"label_agreement":null},{"id":"W2883537651","doi":"10.4236/pos.2018.93003","title":"New Strategy of Collaborative Acquisition for Connected GNSS Receivers in Deep Urban Environments","year":2018,"lang":"en","type":"article","venue":"Positioning","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; École de technologie supérieure","keywords":"GNSS applications; Computer science; Satellite system; Satellite navigation; Real-time computing; SIGNAL (programming language); Position (finance); Global Positioning System; Telecommunications","score_opus":0.005691251104985503,"score_gpt":0.2152555985678939,"score_spread":0.2095643474629084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883537651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026102528,0.00013199035,0.96919584,0.00013868292,0.000041205938,0.0000439669,0.000011924306,0.00025405944,0.0040798085],"genre_scores_gemma":[0.6926316,0.0001844106,0.29925704,0.00013886232,0.00006853093,0.00012707578,0.000052833715,0.000032170014,0.0075074993],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929726,0.00015542914,0.00003192453,0.00021458283,0.0002138537,0.00008698154],"domain_scores_gemma":[0.9995455,0.000097735785,0.00006647215,0.00010633206,0.00013245713,0.000051454146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004623582,0.00077579234,0.00056104147,0.0006412364,0.0006584084,0.0006589812,0.0012928304,0.00082617934,0.0010863148],"category_scores_gemma":[0.00071015686,0.0003147517,0.00047744703,0.0004806943,0.00076614856,0.0011781944,0.002088457,0.000646989,0.0005081352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037076158,0.00023514977,0.0059145633,0.00027273138,0.00018661687,0.0010896947,0.0011945992,0.17527086,0.22181253,0.06394203,0.0032580115,0.5264525],"study_design_scores_gemma":[0.000101656915,0.0009946302,0.0019279635,0.00003190228,0.00013965074,0.0015387037,0.00032578487,0.9188529,0.04705464,0.014337762,0.014593864,0.00010052912],"about_ca_topic_score_codex":0.0013598398,"about_ca_topic_score_gemma":0.0016062617,"teacher_disagreement_score":0.0013598398,"about_ca_system_score_codex":0.00042775436,"about_ca_system_score_gemma":0.00061058626,"threshold_uncertainty_score":0.0036340952},"labels":[],"label_agreement":null},{"id":"W2883735935","doi":"10.1109/ipin.2018.8533770","title":"LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inertial measurement unit; Inertial navigation system; Detector; Extended Kalman filter; Computer science; Kalman filter; Zero (linguistics); Control theory (sociology); Artificial intelligence; Stair climbing; Computer vision; Dead reckoning; Zero crossing; Inertial frame of reference; Physics; Global Positioning System","score_opus":0.020143394903848134,"score_gpt":0.23245689203014908,"score_spread":0.21231349712630093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883735935","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0275744,0.0003479125,0.96582717,0.00012208626,0.00018341359,0.000022180297,0.00024734673,0.004064746,0.0016107775],"genre_scores_gemma":[0.7188379,0.00030776946,0.2745744,0.00025001587,0.00009207403,0.00007954093,0.0009822673,0.00030912244,0.0045668413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969745,0.0000326624,0.000017790004,0.00011314483,0.00008843205,0.00005063094],"domain_scores_gemma":[0.9996408,0.00011435061,0.000048312915,0.000054873035,0.0001237621,0.000017848812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036421558,0.0009660536,0.00045759612,0.00062735577,0.00026267022,0.00054974505,0.0010539371,0.00073955493,0.0022341637],"category_scores_gemma":[0.0019548982,0.00037998648,0.00033792545,0.000706378,0.00037004284,0.0011448385,0.00089537626,0.0009144625,0.0011242105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023993083,0.00010986699,0.0027521786,0.00020030115,0.000098273034,0.00017241338,0.00015449553,0.13189322,0.067991346,0.0035848273,0.0071890145,0.7856141],"study_design_scores_gemma":[0.000010244678,0.00007486941,0.0011635515,0.000025750109,0.000027854561,0.00008953604,0.000021912476,0.9621862,0.03065846,0.0032188988,0.0025053108,0.000017358148],"about_ca_topic_score_codex":0.0044041313,"about_ca_topic_score_gemma":0.0067954944,"teacher_disagreement_score":0.0044041313,"about_ca_system_score_codex":0.0005246664,"about_ca_system_score_gemma":0.0008334975,"threshold_uncertainty_score":0.008756995},"labels":[],"label_agreement":null},{"id":"W2884705561","doi":"10.3390/s18082440","title":"A Privacy Preserving Scheme for Nearest Neighbor Query","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; k-nearest neighbors algorithm; Location-based service; Obfuscation; Data mining; Adaptability; Mobile device; Noise (video); Information privacy; Information retrieval; Computer network; Computer security; Machine learning; Artificial intelligence; World Wide Web","score_opus":0.014510829475756755,"score_gpt":0.23885217467122916,"score_spread":0.2243413451954724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884705561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009660093,0.0006760333,0.98648757,0.00030749937,0.00009744422,0.00013269157,0.00016218981,0.0005089489,0.0019675537],"genre_scores_gemma":[0.56756693,0.0011555941,0.4219728,0.00056025654,0.0003233391,0.00043394166,0.00083349337,0.000096586155,0.0070571243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930352,0.0017577399,0.00059498375,0.0011377686,0.0029340906,0.0005402566],"domain_scores_gemma":[0.99534297,0.001321438,0.00042637056,0.0018732086,0.0008898186,0.0001463528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026606522,0.00066341873,0.0016984643,0.0012634896,0.0019099204,0.0015345081,0.0028112999,0.0018640413,0.0018172493],"category_scores_gemma":[0.009709375,0.00036676467,0.001077151,0.0026945125,0.0013066708,0.0051489724,0.0043995907,0.002001801,0.0011386366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015929095,0.00035050744,0.001725504,0.00059320003,0.00019403879,0.0007537658,0.001481797,0.13662048,0.06530296,0.34244096,0.0133538265,0.43559006],"study_design_scores_gemma":[0.00019261501,0.00050742144,0.00044806593,0.000040061634,0.00008970484,0.001545215,0.00028873933,0.8583289,0.026901338,0.08658415,0.024916807,0.0001570253],"about_ca_topic_score_codex":0.0015008721,"about_ca_topic_score_gemma":0.0007997162,"teacher_disagreement_score":0.0028112999,"about_ca_system_score_codex":0.0012019926,"about_ca_system_score_gemma":0.001597367,"threshold_uncertainty_score":0.014070988},"labels":[],"label_agreement":null},{"id":"W2885250631","doi":"10.1109/icc.2018.8422349","title":"A Learning-Based Approach Towards Localization of Crowdsourced Motion-Data for Indoor Localization Applications","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Fingerprint (computing); Classifier (UML); Artificial intelligence; Motion (physics); Perspective (graphical); Machine learning; Data mining; Computer vision; Pattern recognition (psychology)","score_opus":0.022045778929565118,"score_gpt":0.25211273507534615,"score_spread":0.23006695614578104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885250631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053005256,0.00012735018,0.99183285,0.00014666097,0.000046921057,0.00010462681,0.0002511009,0.0015168608,0.00067295495],"genre_scores_gemma":[0.32310718,0.00025107723,0.66988426,0.0002884889,0.00020321853,0.0005086494,0.0019969211,0.00018475942,0.0035754626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980484,0.0004600993,0.00013366115,0.0007482899,0.000475797,0.00013381458],"domain_scores_gemma":[0.99722457,0.00093214604,0.00029183953,0.00072765356,0.0006799627,0.00014384456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018833929,0.0010976917,0.001256603,0.002220878,0.00102778,0.001350668,0.003142446,0.001439583,0.0017015022],"category_scores_gemma":[0.0075491383,0.00050319,0.0010416618,0.0023169862,0.0008424982,0.0019147852,0.0032731206,0.0015390979,0.0015273271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045664702,0.0005254331,0.0055728643,0.00041749194,0.00021674861,0.0003889587,0.00061459123,0.24011879,0.019288167,0.015658844,0.012936812,0.7038047],"study_design_scores_gemma":[0.000021121039,0.000094222014,0.0008083788,0.000027300117,0.000019178087,0.0000907907,0.00012657742,0.978112,0.0047543193,0.010888611,0.005032447,0.00002508671],"about_ca_topic_score_codex":0.006800771,"about_ca_topic_score_gemma":0.009446337,"teacher_disagreement_score":0.006800771,"about_ca_system_score_codex":0.0010070541,"about_ca_system_score_gemma":0.0012496528,"threshold_uncertainty_score":0.013522387},"labels":[],"label_agreement":null},{"id":"W2885559667","doi":"","title":"Étude et positionnement utilisant le réseau de capteur sans fil dans un environnement minier souterrain","year":2018,"lang":"fr","type":"dissertation","venue":"Depositum (Université du Québec en Abitibi-Témiscamingue)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Humanities; Geography; Physics; Political science; Art","score_opus":0.00291850298528076,"score_gpt":0.17330288672182656,"score_spread":0.1703843837365458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885559667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32148048,0.003930064,0.6366092,0.0006650237,0.0004920131,0.0003267466,0.00046665058,0.0057372865,0.030292545],"genre_scores_gemma":[0.80423486,0.0015715322,0.17005159,0.00012771605,0.00004406729,0.00017608049,0.00050726585,0.00048840494,0.02279841],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99873525,0.00015796024,0.00004485068,0.00028946498,0.00058110245,0.0001913741],"domain_scores_gemma":[0.99847025,0.0004721238,0.000116567,0.0002879901,0.00054639555,0.00010660178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000897495,0.0011506501,0.00096607656,0.0009017478,0.0012109914,0.0030243823,0.0014159462,0.0017042324,0.0045068734],"category_scores_gemma":[0.00264767,0.00054672547,0.00084921165,0.00091715396,0.0008387947,0.0023183478,0.0013364627,0.0010554101,0.0016963377],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091677933,0.00026297334,0.0108858375,0.0018878556,0.00014317167,0.0016384347,0.0026985388,0.23759456,0.27497017,0.011747997,0.0058282823,0.45142534],"study_design_scores_gemma":[0.00006225072,0.0012602346,0.014199656,0.00035404356,0.00023977866,0.0012486554,0.0029304074,0.6585688,0.22145839,0.0039339713,0.095452316,0.00029145723],"about_ca_topic_score_codex":0.011063569,"about_ca_topic_score_gemma":0.011201214,"teacher_disagreement_score":0.011063569,"about_ca_system_score_codex":0.0012139792,"about_ca_system_score_gemma":0.0013006662,"threshold_uncertainty_score":0.021998346},"labels":[],"label_agreement":null},{"id":"W2888301647","doi":"10.1145/3229434.3229447","title":"Mobiceil","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Ceiling (cloud); Computer science; Phone; Computer vision; Mobile phone; Table (database); Camera phone; Artificial intelligence; Real-time computing; Computer graphics (images); Telecommunications; Engineering; Data mining","score_opus":0.00425894005148099,"score_gpt":0.1833745842301475,"score_spread":0.1791156441786665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888301647","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0372308,0.0067076148,0.28437272,0.0025629103,0.0042070705,0.0020038623,0.030878799,0.21621343,0.41582277],"genre_scores_gemma":[0.33134156,0.004708361,0.2340916,0.0026687414,0.0023482875,0.0024029338,0.07210162,0.016549202,0.33378765],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952567,0.000074483425,0.000021599686,0.00010947614,0.00015848149,0.000110296256],"domain_scores_gemma":[0.99898475,0.00013095712,0.000088908484,0.00032956165,0.00019691754,0.00026882396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059752323,0.0014021986,0.00066696666,0.0015908122,0.0007868921,0.0018623113,0.0018782029,0.001040098,0.05646235],"category_scores_gemma":[0.0016540753,0.0004013102,0.00058979256,0.0006937793,0.00026813647,0.0022456592,0.0040279725,0.0010480523,0.041620538],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012173546,0.00024152365,0.0032078864,0.0010888764,0.000120389675,0.00049441407,0.000618837,0.0016922664,0.024559585,0.008616709,0.4927241,0.46541804],"study_design_scores_gemma":[0.00018192954,0.00026711862,0.0049329135,0.00022240171,0.00006194205,0.0006758922,0.00034202528,0.025610453,0.010107226,0.003710855,0.95377344,0.000113820264],"about_ca_topic_score_codex":0.0027714097,"about_ca_topic_score_gemma":0.004955702,"teacher_disagreement_score":0.05646235,"about_ca_system_score_codex":0.00034529073,"about_ca_system_score_gemma":0.0006585574,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2888349895","doi":"10.1109/jsen.2018.2866686","title":"ADMM-Based Sensor Network Localization Using Low-Rank Approximation","year":2018,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Semidefinite programming; Matrix completion; Scalability; Wireless sensor network; Euclidean distance matrix; Range (aeronautics); Scaling; Matrix (chemical analysis); Computer science; Algorithm; Distance matrix; Euclidean distance; Multidimensional scaling; Mathematical optimization; Mathematics; Artificial intelligence; Engineering; Machine learning","score_opus":0.015120306429026639,"score_gpt":0.2327125706401972,"score_spread":0.21759226421117056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888349895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093550456,0.00012391128,0.99818903,0.000065736334,0.000026458427,0.000009245742,0.00001598231,0.0001663498,0.00046785932],"genre_scores_gemma":[0.25354677,0.00078913296,0.7390719,0.00026479622,0.00015960007,0.00026367223,0.00051320734,0.00015398482,0.0052368916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992644,0.00025018357,0.000037346897,0.00017677307,0.00021833139,0.000052911488],"domain_scores_gemma":[0.9991019,0.00041778124,0.00012281592,0.00010009337,0.00022292686,0.000034490513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010467272,0.001413328,0.0013952631,0.0005731166,0.0004726417,0.0010498116,0.0015437345,0.0012682774,0.0018251765],"category_scores_gemma":[0.0027117159,0.0005443012,0.00076044735,0.0013088606,0.0007946448,0.0015991786,0.0013035608,0.0022285106,0.0008816553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007389305,0.000030127638,0.00026791287,0.00017292608,0.00004917984,0.00007387276,0.00006950857,0.8835412,0.0024222801,0.017246086,0.0029442285,0.09310881],"study_design_scores_gemma":[0.0000074164304,0.000019230061,0.000025953344,0.0000048166526,0.0000038674552,0.000018452745,0.0000060820757,0.99583143,0.00042589326,0.0028818466,0.00077056116,0.0000044531803],"about_ca_topic_score_codex":0.0031852152,"about_ca_topic_score_gemma":0.0027244566,"teacher_disagreement_score":0.0031852152,"about_ca_system_score_codex":0.00068216864,"about_ca_system_score_gemma":0.0012448652,"threshold_uncertainty_score":0.006333351},"labels":[],"label_agreement":null},{"id":"W2888724037","doi":"10.1080/03772063.2018.1497551","title":"TDOA and RSSD Based Hybrid Passive Source Localization with Unknown Transmit Power","year":2018,"lang":"en","type":"article","venue":"IETE Journal of Research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Multilateration; Cramér–Rao bound; Estimator; Upper and lower bounds; Computer science; FDOA; Transmitter power output; Algorithm; Power (physics); Mathematics; Statistics; Telecommunications; Physics; Transmitter","score_opus":0.014656052784265764,"score_gpt":0.2746083652859252,"score_spread":0.25995231250165945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888724037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01028562,0.00037646972,0.98705286,0.00007032069,0.00005459013,0.00001574864,0.000042395153,0.00047928008,0.0016227224],"genre_scores_gemma":[0.5583881,0.0009406426,0.43222347,0.00016637573,0.00012207033,0.00015579996,0.00027104188,0.00009408292,0.0076384125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939024,0.00012942273,0.000032571497,0.00015427805,0.00025840063,0.000035149602],"domain_scores_gemma":[0.99938345,0.0002170873,0.00010920448,0.000094038835,0.00017573661,0.000020391763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046993102,0.0008710818,0.0008575178,0.00086148904,0.00023448232,0.0009975543,0.00093312305,0.0006384706,0.0012075297],"category_scores_gemma":[0.00173829,0.00042487917,0.00059162796,0.0013281589,0.00048091728,0.0014854479,0.0013866309,0.0004286285,0.00095102756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046398962,0.00011137519,0.0035381448,0.0007369132,0.00022821127,0.00037659312,0.0003515993,0.27327248,0.12680867,0.014201834,0.002501998,0.5774082],"study_design_scores_gemma":[0.00003796899,0.00024268757,0.0010780906,0.000033464657,0.000061872735,0.0005751648,0.000064571475,0.97031873,0.018301742,0.0037386646,0.005499185,0.000047873822],"about_ca_topic_score_codex":0.00081867207,"about_ca_topic_score_gemma":0.0010207546,"teacher_disagreement_score":0.0012075297,"about_ca_system_score_codex":0.00032022104,"about_ca_system_score_gemma":0.00043588583,"threshold_uncertainty_score":0.0040395856},"labels":[],"label_agreement":null},{"id":"W2889158580","doi":"10.1109/icce-china.2018.8448941","title":"A GPS-Based Wander Management System for the Elderly","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Global Positioning System; Population ageing; Life expectancy; Categorization; Computer science; Gerontology; Population; Demography; Medicine; Telecommunications; Artificial intelligence; Sociology","score_opus":0.010738540326153855,"score_gpt":0.2152740596941562,"score_spread":0.20453551936800235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889158580","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5186948,0.0026005781,0.32526517,0.0008869832,0.0004674917,0.0019608836,0.009731205,0.11330444,0.0270884],"genre_scores_gemma":[0.91754836,0.0005781881,0.06526135,0.00031341607,0.0001193747,0.00059502077,0.003523558,0.00019304163,0.011867786],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998004,0.000041346277,0.000021844045,0.00005319103,0.00006359294,0.000019636118],"domain_scores_gemma":[0.9996909,0.000044360328,0.00004017598,0.00004908129,0.00012535845,0.00005013432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035076097,0.00048268982,0.0004178263,0.0013336722,0.0003562044,0.0003534101,0.00056097534,0.00043580853,0.0038010331],"category_scores_gemma":[0.0008089808,0.00013997001,0.0001765319,0.00090959755,0.000098190656,0.00051909307,0.0005035846,0.0002564562,0.0022568204],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017199821,0.00047330852,0.06542767,0.0005129341,0.00014999104,0.0013142525,0.0015008942,0.002067098,0.08039397,0.0010713617,0.053503606,0.7918649],"study_design_scores_gemma":[0.00091176626,0.0045682886,0.47032893,0.00038836634,0.0010425186,0.0053384774,0.002935058,0.23257467,0.07966049,0.0026917916,0.19897114,0.00058844476],"about_ca_topic_score_codex":0.0030341086,"about_ca_topic_score_gemma":0.0033779799,"teacher_disagreement_score":0.0038010331,"about_ca_system_score_codex":0.00019783698,"about_ca_system_score_gemma":0.0003393463,"threshold_uncertainty_score":0.012715757},"labels":[],"label_agreement":null},{"id":"W2889297752","doi":"10.1109/iwcmc.2018.8450418","title":"Utilization of Wavelet Packet Sensor De-noising for Accurate Positioning in Intelligent Road Services","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"GNSS applications; Computer science; Multipath propagation; Real-time computing; Inertial navigation system; Operability; Wavelet; GNSS augmentation; Satellite system; Network packet; Global Positioning System; Embedded system; Telecommunications; Inertial frame of reference; Computer network; Channel (broadcasting); Artificial intelligence","score_opus":0.024489993444882147,"score_gpt":0.27829491673219925,"score_spread":0.2538049232873171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889297752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30224878,0.00056670513,0.6919861,0.00020718903,0.0001778463,0.000051313116,0.000079990095,0.0006356246,0.004046453],"genre_scores_gemma":[0.89155555,0.00046715813,0.10601631,0.00007041721,0.000039116603,0.00002794626,0.00012420423,0.000027964605,0.001671307],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978215,0.000035528312,0.000011893256,0.00004396929,0.00009576109,0.00003077224],"domain_scores_gemma":[0.99985814,0.000029351426,0.00002018462,0.000025402074,0.0000609714,0.0000059350596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023089407,0.00035029565,0.00020900203,0.0002936692,0.00014010034,0.00026718105,0.00037438155,0.0003259059,0.00042953552],"category_scores_gemma":[0.00050728855,0.00012263494,0.00017487335,0.00038599092,0.00019359379,0.0005323671,0.00023884463,0.00029706725,0.0002246677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038006963,0.00020047638,0.00464717,0.0002048463,0.00004416289,0.0003300597,0.00017525075,0.04309993,0.54279554,0.0043752063,0.0013009541,0.4024464],"study_design_scores_gemma":[0.000025025362,0.0004951356,0.0071318746,0.000017406423,0.000050161176,0.00034102314,0.0000818443,0.63021857,0.35421205,0.0010357249,0.0063534942,0.000037633854],"about_ca_topic_score_codex":0.0006807324,"about_ca_topic_score_gemma":0.00085963093,"teacher_disagreement_score":0.0006807324,"about_ca_system_score_codex":0.00014607985,"about_ca_system_score_gemma":0.00020876402,"threshold_uncertainty_score":0.0014369488},"labels":[],"label_agreement":null},{"id":"W2889785251","doi":"10.5194/isprs-archives-xlii-4-253-2018","title":"INDOOR POSITIONING USING WLAN FINGERPRINT MATCHING AND PATH ASSESSMENT WITH RETROACTIVE ADJUSTMENT ON MOBILE DEVICES","year":2018,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Dead reckoning; Fingerprint (computing); Computer science; Real-time computing; Fingerprint recognition; Pedestrian; Matching (statistics); Indoor positioning system; Hybrid positioning system; Path (computing); Inertial measurement unit; Mobile device; Sensor fusion; Artificial intelligence; Positioning system; Computer vision; Global Positioning System; Accelerometer; Telecommunications; Computer network; Engineering; Transport engineering","score_opus":0.011994234007277324,"score_gpt":0.2524329836482493,"score_spread":0.24043874964097195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889785251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14487086,0.0004055924,0.84839994,0.00007750491,0.00013621022,0.00009710082,0.00012930854,0.0029130944,0.0029703092],"genre_scores_gemma":[0.7977277,0.00019725089,0.19951934,0.00005150064,0.000032710428,0.000062489555,0.00013603647,0.00004903486,0.0022238563],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992331,0.00015770068,0.000035736415,0.00018149696,0.0003145085,0.00007748206],"domain_scores_gemma":[0.99951494,0.000068462614,0.000092160175,0.0001266475,0.00017813145,0.00001971584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042320744,0.00069935876,0.00061486766,0.0011055514,0.00029761237,0.00061061385,0.0008294612,0.00055317656,0.0012801875],"category_scores_gemma":[0.0013644634,0.0002207486,0.00041915433,0.0013328297,0.0001854272,0.0008156127,0.00078380166,0.0003385641,0.00091911136],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006150396,0.00016281748,0.010491726,0.00021680622,0.00014141363,0.00029096514,0.00019735632,0.02977418,0.17641623,0.0012743102,0.0015219676,0.77889717],"study_design_scores_gemma":[0.00007623118,0.0013672048,0.03106906,0.000058391815,0.00025633918,0.002043569,0.00021426816,0.70214546,0.24947348,0.001377438,0.011745704,0.00017296486],"about_ca_topic_score_codex":0.0014506101,"about_ca_topic_score_gemma":0.0016999794,"teacher_disagreement_score":0.0014506101,"about_ca_system_score_codex":0.0002874224,"about_ca_system_score_gemma":0.00029047555,"threshold_uncertainty_score":0.0042826533},"labels":[],"label_agreement":null},{"id":"W2889917776","doi":"10.1109/msp.2018.2846804","title":"Microlocation for Smart Buildings in the Era of the Internet of Things: A Survey of Technologies, Techniques, and Approaches","year":2018,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Guelph","funders":"","keywords":"Beacon; Key (lock); Bluetooth; The Internet; Internet of Things; Cover (algebra); Bluetooth Low Energy; Process (computing); Building automation","score_opus":0.025061289362627387,"score_gpt":0.23203855411579646,"score_spread":0.20697726475316908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889917776","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014533112,0.3838475,0.540246,0.004677169,0.0008907349,0.00016134256,0.00015284595,0.00092205434,0.05456924],"genre_scores_gemma":[0.28149354,0.48153007,0.21194114,0.0017235744,0.0013675055,0.0001881147,0.0004026074,0.00017623976,0.02117726],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995396,0.00011511366,0.000026412514,0.000077644174,0.00019641455,0.000044791483],"domain_scores_gemma":[0.9995704,0.00017997825,0.00005532336,0.000057859514,0.0001131094,0.000023365856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000507774,0.00062872947,0.00059654395,0.0013620121,0.0004259138,0.0016182269,0.000739233,0.0010284308,0.0017700663],"category_scores_gemma":[0.00079398596,0.00033308915,0.0004987433,0.0019016069,0.00071243686,0.0028895617,0.0010208766,0.0010414252,0.0011052728],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054757194,0.00006261524,0.0017602335,0.00239968,0.000043122993,0.00026549256,0.00042353352,0.009393072,0.008659755,0.082021914,0.008727253,0.8861887],"study_design_scores_gemma":[0.000018040884,0.00031525147,0.0032512078,0.0015771573,0.00009380393,0.002581764,0.0019537555,0.0377898,0.013067745,0.068619624,0.8705598,0.00017211474],"about_ca_topic_score_codex":0.00087524706,"about_ca_topic_score_gemma":0.001167366,"teacher_disagreement_score":0.0017700663,"about_ca_system_score_codex":0.00060895924,"about_ca_system_score_gemma":0.0004939686,"threshold_uncertainty_score":0.005921483},"labels":[],"label_agreement":null},{"id":"W2890776497","doi":"10.1109/jiot.2018.2871445","title":"On Spatial Diversity in WiFi-Based Human Activity Recognition: A Deep Learning-Based Approach","year":2018,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":144,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Activity recognition; Transceiver; Classifier (UML); Antenna diversity; Communication source; Leverage (statistics); Key (lock); Profiling (computer programming); Spatial analysis; Artificial intelligence; Pattern recognition (psychology); Wireless; Telecommunications; Remote sensing","score_opus":0.024284465233960436,"score_gpt":0.23350298150030657,"score_spread":0.20921851626634613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890776497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08690439,0.0013917139,0.90747005,0.0005645407,0.00008749369,0.000040566763,0.00028609336,0.00081906834,0.00243604],"genre_scores_gemma":[0.9325704,0.0006968596,0.0630216,0.00029490946,0.00010172978,0.00005653455,0.00049395853,0.00004387871,0.0027201062],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959654,0.00007542867,0.00002338722,0.00012892975,0.000087987886,0.00008777266],"domain_scores_gemma":[0.9995178,0.0002194352,0.00006340694,0.000052718115,0.00010215113,0.000044497203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005895661,0.0008101105,0.0007025098,0.00086697086,0.00024983205,0.0004849094,0.0009959836,0.0006546467,0.0006888726],"category_scores_gemma":[0.0018280786,0.00034376702,0.0005979973,0.0008938327,0.00047638122,0.0010134364,0.0011438646,0.0012141932,0.0003417735],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022273531,0.00018845026,0.009995798,0.00010857079,0.00015165123,0.00019089926,0.00013175322,0.3918183,0.009943002,0.003936293,0.0028683895,0.5804442],"study_design_scores_gemma":[0.0000040810996,0.000027867454,0.0014649545,0.000007896027,0.00001107602,0.000039205595,0.000014000546,0.9945052,0.0013148215,0.0022319364,0.0003719768,0.000006866378],"about_ca_topic_score_codex":0.0059426464,"about_ca_topic_score_gemma":0.0067460374,"teacher_disagreement_score":0.0059426464,"about_ca_system_score_codex":0.00045046178,"about_ca_system_score_gemma":0.00046583166,"threshold_uncertainty_score":0.011816084},"labels":[],"label_agreement":null},{"id":"W2891046626","doi":"10.1109/jsyst.2018.2864794","title":"Quasi-Optimal Subcarrier Selection Dedicated for Localization With Multicarrier-Based Signals","year":2018,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Subcarrier; Upper and lower bounds; Multipath propagation; Cramér–Rao bound; Computer science; Transmitter power output; Algorithm; Selection (genetic algorithm); Dilution of precision; Pilot signal; Position (finance); Range (aeronautics); Mathematical optimization; Orthogonal frequency-division multiplexing; Estimation theory; Mathematics; Telecommunications; Engineering; Global Positioning System; Artificial intelligence; Transmitter","score_opus":0.01253414497590232,"score_gpt":0.235033612265771,"score_spread":0.22249946728986866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891046626","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054421306,0.0002803326,0.9432718,0.00011911943,0.000029853734,0.000018946512,0.000015598702,0.00009020448,0.0017527842],"genre_scores_gemma":[0.8220812,0.00024417602,0.17660902,0.00007892708,0.000038147642,0.000048508424,0.000027769045,0.000014972808,0.0008572317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962413,0.00012428271,0.000019538369,0.00007267149,0.00011026864,0.000049149443],"domain_scores_gemma":[0.99937963,0.0002652701,0.00012263375,0.00008619758,0.000118839846,0.000027401098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003623608,0.00039572758,0.00030271616,0.00026568028,0.0003325199,0.0004688554,0.00044037154,0.00035909624,0.0005575202],"category_scores_gemma":[0.0014436563,0.00014955192,0.00019389381,0.00031425455,0.00055243203,0.0006345423,0.000511206,0.00033935937,0.00018004808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070857443,0.00014716614,0.0033106857,0.00027209747,0.000066957524,0.00026036997,0.00038136207,0.31708792,0.20918706,0.091574416,0.0015270843,0.37547633],"study_design_scores_gemma":[0.000050178496,0.00030911615,0.00048128533,0.000017557772,0.000021883012,0.0002646099,0.000051633862,0.94688517,0.03773372,0.01119267,0.0029683386,0.000023881074],"about_ca_topic_score_codex":0.00029206785,"about_ca_topic_score_gemma":0.00038254875,"teacher_disagreement_score":0.0005575202,"about_ca_system_score_codex":0.00030166714,"about_ca_system_score_gemma":0.0004014858,"threshold_uncertainty_score":0.0021888018},"labels":[],"label_agreement":null},{"id":"W2891270324","doi":"10.5539/cis.v11n4p1","title":"3D Localization Algorithm Based on Linear Regression and Least Squares in NLOS Environments","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Algorithm; Base station; Terminal (telecommunication); Linear regression; Property (philosophy); Time of arrival; Mean squared error; Statistics; Mathematics; Telecommunications; Wireless; Machine learning","score_opus":0.006007791987898708,"score_gpt":0.211624041570119,"score_spread":0.20561624958222027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891270324","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022008724,0.00007053048,0.9964889,0.000045066256,0.000018979437,0.0000120995755,0.000013048147,0.0008143861,0.00033610399],"genre_scores_gemma":[0.15158354,0.00036776898,0.8444188,0.00012895606,0.000050069477,0.0002093471,0.00020791197,0.0002182582,0.002815384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989492,0.00024191437,0.00006109738,0.00027878318,0.00040809336,0.000060888564],"domain_scores_gemma":[0.99937564,0.00017072866,0.000076287324,0.000077282995,0.0002789679,0.000021078908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007189216,0.0009100899,0.0009819935,0.0010447282,0.0006353726,0.0008756809,0.0012741642,0.0009711254,0.001291802],"category_scores_gemma":[0.0019255156,0.00059099734,0.00081464683,0.0014445949,0.00051125616,0.0012019468,0.0013549874,0.00095135655,0.0011147175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017163435,0.0000557474,0.0018181085,0.00017954854,0.000103337086,0.00019195958,0.0003494194,0.3739297,0.029804712,0.011606747,0.0040934253,0.5776957],"study_design_scores_gemma":[0.000018776183,0.00004184819,0.000436576,0.000008499162,0.000017889519,0.00013995473,0.000031311833,0.98647004,0.007406675,0.0022233634,0.00316804,0.000037013404],"about_ca_topic_score_codex":0.0041745338,"about_ca_topic_score_gemma":0.0023620685,"teacher_disagreement_score":0.0041745338,"about_ca_system_score_codex":0.00045140547,"about_ca_system_score_gemma":0.0009949679,"threshold_uncertainty_score":0.008300483},"labels":[],"label_agreement":null},{"id":"W2893062876","doi":"10.5194/isprs-archives-xlii-1-379-2018","title":"EVALUATION OF DYNAMIC AD-HOC UWB INDOOR POSITIONING SYSTEM","year":2018,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Firmware; Node (physics); Computer science; Positioning system; Transceiver; Ultra-wideband; Multilateration; Indoor positioning system; Wireless ad hoc network; Real-time computing; Dynamic positioning; Range (aeronautics); Wireless; Engineering; Computer hardware; Telecommunications; Aerospace engineering","score_opus":0.014778021673807044,"score_gpt":0.25354758445257863,"score_spread":0.2387695627787716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893062876","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95445925,0.0010733504,0.034282938,0.00008844021,0.00015368291,0.00014553734,0.000245328,0.0007588468,0.008792629],"genre_scores_gemma":[0.9962321,0.00012503665,0.0027244883,0.000011363547,0.0000062732856,0.000017646105,0.00014935315,0.000007601303,0.0007260979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929833,0.00020897633,0.000040345076,0.000098216675,0.00028257378,0.00007156689],"domain_scores_gemma":[0.999017,0.0002914785,0.00007124239,0.00010238847,0.0004607097,0.00005711857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064133387,0.00045625697,0.00044646813,0.0006127794,0.00028322887,0.00040212876,0.00044439035,0.00029046505,0.0012155998],"category_scores_gemma":[0.0014031089,0.00007273979,0.00014581543,0.0004123859,0.00019563532,0.0004188961,0.00032601933,0.00014503418,0.00031104198],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003098072,0.0005665976,0.02468154,0.0011658855,0.00030390857,0.0009268273,0.00020945678,0.63013536,0.10409937,0.0017674543,0.0027171264,0.2303284],"study_design_scores_gemma":[0.00014671724,0.007202812,0.021310702,0.000041363335,0.00021104305,0.00077406224,0.00036187962,0.8886174,0.07643731,0.00039988445,0.0044456483,0.000051227933],"about_ca_topic_score_codex":0.0019716038,"about_ca_topic_score_gemma":0.001308493,"teacher_disagreement_score":0.0019716038,"about_ca_system_score_codex":0.00055413705,"about_ca_system_score_gemma":0.00030050232,"threshold_uncertainty_score":0.0040665865},"labels":[],"label_agreement":null},{"id":"W2895798777","doi":"10.5539/cis.v11n4p29","title":"Location Fingerprint Database Filter Algorithm Based on Multi-Mapping Data Structure","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Key (lock); Fingerprint (computing); Data mining; Process (computing); Filter (signal processing); Database; Position (finance); Matching (statistics); Vector map; Algorithm; Artificial intelligence; Computer vision","score_opus":0.024384716319381564,"score_gpt":0.24690575389301364,"score_spread":0.22252103757363206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895798777","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01398445,0.00029289263,0.9831881,0.00006649461,0.000069200585,0.0000677332,0.00016816969,0.0013658123,0.0007971417],"genre_scores_gemma":[0.3722489,0.0007906779,0.61758703,0.00017839973,0.00013510505,0.000288783,0.0016297153,0.00013462453,0.0070068217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985618,0.000112469126,0.00010259296,0.00041707762,0.0006777903,0.00012823215],"domain_scores_gemma":[0.99882084,0.0001642708,0.00008951816,0.0004123766,0.0004629467,0.000049953393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007525026,0.000665846,0.001246562,0.0021324686,0.0008401293,0.0015289772,0.00254071,0.0008814025,0.0028426747],"category_scores_gemma":[0.0022198015,0.00046379396,0.00075752963,0.0023765755,0.00031922307,0.0036418634,0.0014741822,0.0009770506,0.0012822651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047093455,0.00016907892,0.0038128644,0.00011939059,0.000090792935,0.00014597272,0.00016088483,0.021880118,0.044012547,0.0072268555,0.0043554,0.917555],"study_design_scores_gemma":[0.00020409982,0.00057030446,0.006704767,0.00003242533,0.00014175978,0.0017584071,0.0001633705,0.8332755,0.1228509,0.006519408,0.027634654,0.00014434084],"about_ca_topic_score_codex":0.005778189,"about_ca_topic_score_gemma":0.0030612273,"teacher_disagreement_score":0.005778189,"about_ca_system_score_codex":0.00080212177,"about_ca_system_score_gemma":0.0012971046,"threshold_uncertainty_score":0.011489093},"labels":[],"label_agreement":null},{"id":"W2895854662","doi":"10.1109/bsc.2018.8494692","title":"Optimized Physical Carrier Sensing Threshold in High Density CSMA/CA Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Stochastic geometry; Rayleigh fading; Computer science; Throughput; Path loss; Node (physics); Channel (broadcasting); Interference (communication); Computer network; Poisson point process; Probability density function; Poisson distribution; Shadow mapping; Fading; Topology (electrical circuits); Wireless; Mathematics; Telecommunications; Engineering; Statistics","score_opus":0.006897235838472175,"score_gpt":0.2081379201006103,"score_spread":0.20124068426213812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895854662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10749994,0.00060482183,0.88886243,0.00018513699,0.000037620932,0.000044620865,0.00001837254,0.00021546181,0.0025315909],"genre_scores_gemma":[0.9721729,0.00015440377,0.027248994,0.000025522815,0.000013480281,0.00002174737,0.0000069404973,0.000007292978,0.00034871182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993487,0.0002088986,0.000023696755,0.00011241379,0.00021913878,0.00008717935],"domain_scores_gemma":[0.99830014,0.001091895,0.00020903355,0.00012913664,0.00020717554,0.000062655185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010849796,0.00040013684,0.00045051094,0.0004660304,0.00045363975,0.0007207367,0.0007868156,0.0004527261,0.00035568082],"category_scores_gemma":[0.004522329,0.00022582163,0.00014679001,0.0005598862,0.0009833078,0.00093002274,0.00067102804,0.00038719364,0.00010595042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013086948,0.00007754204,0.0017303446,0.000069748414,0.00002439794,0.00016747994,0.00015894517,0.9045765,0.018029733,0.026648128,0.0006551573,0.047731146],"study_design_scores_gemma":[0.0000068356353,0.00004341273,0.00025206397,0.0000036655786,0.000007115797,0.000040398096,0.000021114862,0.99348676,0.002098994,0.0038444423,0.00018915322,0.000005922602],"about_ca_topic_score_codex":0.0025019776,"about_ca_topic_score_gemma":0.0026198518,"teacher_disagreement_score":0.0025019776,"about_ca_system_score_codex":0.0008876874,"about_ca_system_score_gemma":0.0010574708,"threshold_uncertainty_score":0.006440699},"labels":[],"label_agreement":null},{"id":"W2895879514","doi":"10.1109/tim.2018.2871808","title":"Accurate Step Length Estimation for Pedestrian Dead Reckoning Localization Using Stacked Autoencoders","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":114,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; China Scholarship Council","keywords":"Dead reckoning; Computer science; Estimation; Pedestrian; Independence (probability theory); Key (lock); Artificial intelligence; Phone; Machine learning; Computer vision; Data mining; Global Positioning System; Statistics; Mathematics; Engineering; Telecommunications","score_opus":0.057438367081008176,"score_gpt":0.27946852530231125,"score_spread":0.22203015822130306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895879514","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04798288,0.00036116363,0.9486895,0.00006563531,0.00008254721,0.000018828321,0.00013040859,0.001400246,0.0012686969],"genre_scores_gemma":[0.8085693,0.00045935865,0.18572123,0.00011617286,0.000053722673,0.00005571345,0.00063013873,0.00008496681,0.004309427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998435,0.000020544067,0.000010115997,0.000044993718,0.000054456166,0.000026409458],"domain_scores_gemma":[0.9997358,0.00007707122,0.000028699063,0.000034814675,0.000107813445,0.000015713737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020406008,0.00075496227,0.0005417601,0.00034401915,0.00018346451,0.00029443033,0.00070548896,0.0004093922,0.0009898306],"category_scores_gemma":[0.0008236863,0.0003419257,0.00040324987,0.00035143676,0.0001646762,0.00057585933,0.0005140097,0.0007694264,0.0005505573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027500387,0.00010482681,0.002689354,0.00011353597,0.000090478374,0.0001670084,0.00009732617,0.43230212,0.026992425,0.0013486968,0.0038121294,0.53200716],"study_design_scores_gemma":[0.000003495811,0.00002111411,0.0006754434,0.0000061302862,0.000009474314,0.000029751254,0.000008806649,0.9948508,0.0036209677,0.00039364537,0.00037335072,0.0000070738915],"about_ca_topic_score_codex":0.007138178,"about_ca_topic_score_gemma":0.011098391,"teacher_disagreement_score":0.007138178,"about_ca_system_score_codex":0.0002586527,"about_ca_system_score_gemma":0.0005013064,"threshold_uncertainty_score":0.014193296},"labels":[],"label_agreement":null},{"id":"W2896245277","doi":"10.1145/3277868.3277876","title":"ILOS","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"RSS; Fingerprint (computing); Computer science; Fingerprint recognition; Signal strength; Inertial measurement unit; Mobile phone; Smart phone; SIGNAL (programming language); Process (computing); Artificial intelligence; Pattern recognition (psychology); Wireless; Telecommunications","score_opus":0.005002795137475193,"score_gpt":0.184843040062857,"score_spread":0.1798402449253818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896245277","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007752484,0.0027427296,0.04462108,0.0042787613,0.003028567,0.00045988444,0.011828163,0.026223477,0.8990648],"genre_scores_gemma":[0.0945132,0.0032948873,0.039839476,0.002972056,0.0011746265,0.00064786995,0.02690734,0.0052397805,0.82541066],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99866295,0.00019278536,0.00008012566,0.00036604435,0.00047364875,0.00022434561],"domain_scores_gemma":[0.997919,0.00025548862,0.00015132928,0.0005251188,0.0007528489,0.0003961522],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012990655,0.0011139717,0.000664713,0.0017745087,0.0013958411,0.003994099,0.0015458056,0.0016351987,0.3563958],"category_scores_gemma":[0.0031105059,0.00046838188,0.00047891974,0.0014722632,0.000599679,0.0026663458,0.0031359864,0.0017154295,0.2746914],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011419099,0.00026353286,0.003547228,0.0008354964,0.00004670273,0.0004942056,0.0005090766,0.0012222623,0.01035332,0.046491608,0.45235544,0.48273924],"study_design_scores_gemma":[0.000051577077,0.00006008657,0.00062170037,0.00009974135,0.000013813836,0.0002114543,0.0001141482,0.0010921325,0.0023400078,0.0030727598,0.9923074,0.000015061334],"about_ca_topic_score_codex":0.0017463072,"about_ca_topic_score_gemma":0.0017004723,"teacher_disagreement_score":0.64360416,"about_ca_system_score_codex":0.0010587964,"about_ca_system_score_gemma":0.0018091127,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2898051988","doi":"10.1109/jsen.2018.2874453","title":"A Soft Range Limited K-Nearest Neighbors Algorithm for Indoor Localization Enhancement","year":2018,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"k-nearest neighbors algorithm; Computer science; Histogram; Fingerprint (computing); Range (aeronautics); Position (finance); Ambiguity; Algorithm; Pattern recognition (psychology); Signal strength; Ranking (information retrieval); Artificial intelligence; Data mining; Engineering; Image (mathematics)","score_opus":0.014357793883557662,"score_gpt":0.23984690769451528,"score_spread":0.22548911381095763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898051988","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0106809735,0.00060803606,0.98575926,0.00007051032,0.00008498244,0.000034543125,0.000042554544,0.0007744971,0.0019446461],"genre_scores_gemma":[0.3248315,0.0007629781,0.6667313,0.00021905683,0.000108200315,0.0001273572,0.00031707715,0.00017739882,0.0067251367],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883527,0.00020318621,0.000062375206,0.00024259624,0.0005641468,0.00009243812],"domain_scores_gemma":[0.9994493,0.00012630752,0.00006267846,0.000106240135,0.000233243,0.00002219739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055839313,0.0007312664,0.0009258192,0.0012390936,0.0006037312,0.0006422918,0.0014825164,0.0007918106,0.0016667291],"category_scores_gemma":[0.0019745033,0.00040513233,0.0006612445,0.0011112283,0.00046754436,0.0018013666,0.001162901,0.0006911603,0.0012823066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002961099,0.00010113282,0.0012516943,0.00015944346,0.000083389445,0.00017071496,0.00019119552,0.10412727,0.027860546,0.005708322,0.004256217,0.8557941],"study_design_scores_gemma":[0.000038082082,0.00012052725,0.001050873,0.000029558507,0.00004176655,0.00057860505,0.000097013864,0.9671957,0.018620443,0.003948945,0.008210403,0.000068073154],"about_ca_topic_score_codex":0.0035462927,"about_ca_topic_score_gemma":0.00510518,"teacher_disagreement_score":0.0035462927,"about_ca_system_score_codex":0.00039543005,"about_ca_system_score_gemma":0.0006207985,"threshold_uncertainty_score":0.007051289},"labels":[],"label_agreement":null},{"id":"W2898671709","doi":"10.3390/s18113690","title":"New Approach of High Sensitivity Techniques Using Collective Detection Method with Multiple GNSS Receivers","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"GNSS applications; Computer science; Weighting; Sensitivity (control systems); Dilution of precision; GPS signals; Real-time computing; Satellite navigation; Global Positioning System; Satellite; SIGNAL (programming language); Satellite system; GNSS augmentation; Electronic engineering; Remote sensing; Assisted GPS; Engineering; Telecommunications; Geography","score_opus":0.014196372551910204,"score_gpt":0.2359615327202323,"score_spread":0.2217651601683221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898671709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024247069,0.00044003813,0.97269183,0.00007207381,0.000037885522,0.000027518046,0.00001672754,0.00034380064,0.0021231412],"genre_scores_gemma":[0.29050726,0.00044020667,0.7039329,0.00006490575,0.00007107879,0.00006762583,0.00007841203,0.00007024426,0.004767401],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992663,0.00014127974,0.00002148359,0.00018534248,0.00034124043,0.000044315784],"domain_scores_gemma":[0.9996024,0.000090600945,0.000060765196,0.00006943423,0.00015271736,0.000024083749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052970764,0.00083125057,0.000555651,0.0012824489,0.000375739,0.00069607474,0.0009054599,0.0005986311,0.0010766432],"category_scores_gemma":[0.0006195135,0.00037851481,0.0005726173,0.0009805476,0.0004972609,0.0010792942,0.0010958236,0.00061412295,0.0005312068],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022344838,0.00009844306,0.0034284575,0.00031385128,0.00016193502,0.00017641732,0.00044726537,0.047892407,0.4029047,0.015835296,0.0015130879,0.5270047],"study_design_scores_gemma":[0.00006579143,0.0009520715,0.004477806,0.00006515741,0.00020803456,0.0011908308,0.00022224334,0.7156825,0.24169761,0.0075883367,0.027701592,0.00014803949],"about_ca_topic_score_codex":0.000670896,"about_ca_topic_score_gemma":0.001126068,"teacher_disagreement_score":0.0012824489,"about_ca_system_score_codex":0.0004742845,"about_ca_system_score_gemma":0.00042643482,"threshold_uncertainty_score":0.0036017299},"labels":[],"label_agreement":null},{"id":"W2899058086","doi":"10.1109/eit.2018.8500265","title":"A Hybrid Indoor Location Positioning System","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hybrid positioning system; Computer science; Indoor positioning system; Multilateration; Real-time computing; Positioning system; Mode (computer interface); Bluetooth; Range (aeronautics); Synchronization (alternating current); Bluetooth Low Energy; Embedded system; Wireless; Telecommunications; Acoustics; Engineering; Accelerometer","score_opus":0.004865704483601653,"score_gpt":0.18850017676055314,"score_spread":0.18363447227695148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899058086","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036912102,0.001370943,0.9289497,0.0002204895,0.00048782586,0.00020332698,0.0007121613,0.01151805,0.019625446],"genre_scores_gemma":[0.58644974,0.00086728355,0.35293728,0.00053865684,0.00029287045,0.0004803549,0.0018841805,0.00016150599,0.0563881],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992655,0.00010969277,0.000036577218,0.00019320656,0.0003185012,0.00007655833],"domain_scores_gemma":[0.99957734,0.000043119304,0.000045594297,0.00010642026,0.0001837573,0.00004377069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030527732,0.0006165604,0.00092979765,0.0009376036,0.00045515035,0.000841606,0.001485421,0.0013002154,0.006677943],"category_scores_gemma":[0.00037914142,0.00027078853,0.00039607307,0.0007609331,0.00019539858,0.00081130984,0.0014583763,0.0004707611,0.0062165386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007983847,0.0001663487,0.004411404,0.0007367343,0.00016907197,0.0011243148,0.0002865326,0.013826901,0.22359487,0.00678538,0.021241652,0.7268583],"study_design_scores_gemma":[0.0003222467,0.0036431246,0.020201005,0.00023185028,0.00052723306,0.009961352,0.0003862705,0.38250726,0.23420595,0.0043727253,0.34314135,0.0004995757],"about_ca_topic_score_codex":0.0010555725,"about_ca_topic_score_gemma":0.0012605422,"teacher_disagreement_score":0.006677943,"about_ca_system_score_codex":0.00025950785,"about_ca_system_score_gemma":0.0003533052,"threshold_uncertainty_score":0.02234},"labels":[],"label_agreement":null},{"id":"W2899127396","doi":"10.1109/lcomm.2018.2878704","title":"Enhanced Gaussian Process-Based Localization Using a Low Power Wide Area Network","year":2018,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"LPWAN; Computer science; Throughput; Network packet; Real-time computing; Computer network; Packet loss; Physical layer; Mean squared error; Wide area network; Telecommunications; Wireless; Statistics","score_opus":0.01840436064994636,"score_gpt":0.2579601780426334,"score_spread":0.23955581739268703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899127396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009076292,0.00006543077,0.9898956,0.00003786839,0.000016675644,0.000010607025,0.000009960616,0.00023765204,0.0006499416],"genre_scores_gemma":[0.6480441,0.0003674277,0.34775537,0.00012260533,0.00006137512,0.000077020195,0.000113882,0.000052362513,0.003405915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999298,0.00019455189,0.000020820551,0.00013086681,0.00029839098,0.00005729483],"domain_scores_gemma":[0.99953043,0.00018453506,0.00006110215,0.000066063396,0.00014066401,0.00001710993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006294997,0.0005430329,0.00054082955,0.0005551171,0.00023745817,0.00049483386,0.0009250728,0.00065522187,0.00077053806],"category_scores_gemma":[0.0016304199,0.0002460824,0.0005057477,0.0008746397,0.00045206954,0.0013083153,0.00094196695,0.00057408895,0.00044655794],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035199343,0.00011973952,0.002582387,0.000162824,0.0000964085,0.00027766507,0.00020887941,0.5375238,0.0671683,0.012701488,0.0013383711,0.37746817],"study_design_scores_gemma":[0.000012294922,0.000074191514,0.00040601622,0.000004276468,0.000014783611,0.00009196962,0.000013342742,0.9918829,0.0054357797,0.001106044,0.0009461141,0.000012275321],"about_ca_topic_score_codex":0.0016706358,"about_ca_topic_score_gemma":0.00232523,"teacher_disagreement_score":0.0016706358,"about_ca_system_score_codex":0.0004290379,"about_ca_system_score_gemma":0.000499687,"threshold_uncertainty_score":0.0033291578},"labels":[],"label_agreement":null},{"id":"W2900468917","doi":"10.1177/1550147718812722","title":"High-accuracy localization for indoor group users based on extended Kalman filter","year":2018,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Kalman filter; Pace; Wireless; Real-time computing; Extended Kalman filter; Filter (signal processing); Artificial intelligence; Telecommunications; Computer vision","score_opus":0.00957423256863624,"score_gpt":0.2408688208145869,"score_spread":0.23129458824595067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900468917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01060427,0.00017528435,0.98777604,0.000048608275,0.000032228305,0.000016480806,0.000017660832,0.0004713462,0.0008581184],"genre_scores_gemma":[0.83401126,0.0007430752,0.16106755,0.00007045383,0.000082786566,0.00012552318,0.00014422821,0.000060075003,0.003695005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994112,0.00011936576,0.000032211134,0.0001639574,0.00020886304,0.000064485976],"domain_scores_gemma":[0.9996455,0.00011569897,0.00004960415,0.000048694943,0.00012267502,0.000017972408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004932482,0.00070661365,0.0009702053,0.0005278161,0.0005059263,0.0004629344,0.00087714224,0.00064824376,0.0011537678],"category_scores_gemma":[0.0012208946,0.00030245332,0.0006521993,0.0007050484,0.00038524208,0.0013750867,0.00088594964,0.00054869126,0.0004675121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036659214,0.00008430871,0.0063541313,0.0003541965,0.0001997395,0.00035104822,0.00059135136,0.60122955,0.039529547,0.009742416,0.003121716,0.33807543],"study_design_scores_gemma":[0.000024532104,0.00007082182,0.00071709487,0.0000110918845,0.000042291053,0.00010026586,0.00004655837,0.9922504,0.0039505213,0.001406387,0.0013529988,0.000027014154],"about_ca_topic_score_codex":0.007872497,"about_ca_topic_score_gemma":0.0062484383,"teacher_disagreement_score":0.007872497,"about_ca_system_score_codex":0.0003737247,"about_ca_system_score_gemma":0.0006262437,"threshold_uncertainty_score":0.015653312},"labels":[],"label_agreement":null},{"id":"W2900741959","doi":"10.1016/j.measurement.2018.11.052","title":"Research on the improved method for dual foot-mounted Inertial/Magnetometer pedestrian positioning based on adaptive inequality constraints Kalman Filter algorithm","year":2018,"lang":"en","type":"article","venue":"Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Kalman filter; Inertial measurement unit; Computer science; Azimuth; Position (finance); Mathematics; Artificial intelligence","score_opus":0.12409588544027088,"score_gpt":0.3508628516076268,"score_spread":0.22676696616735592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900741959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034707605,0.0001932167,0.9950329,0.000037844882,0.000081235674,0.00001904356,0.000026415777,0.00020558045,0.00093289436],"genre_scores_gemma":[0.438039,0.0011622665,0.5494991,0.00017114336,0.00031163034,0.00024266104,0.0005043905,0.00013447591,0.009935278],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890256,0.00014278953,0.000074679316,0.0003768075,0.0004103976,0.00009283503],"domain_scores_gemma":[0.9992607,0.00016990604,0.00005753515,0.00007047263,0.000412391,0.000028967124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064298324,0.0008671308,0.001164301,0.0006977368,0.00059571187,0.00087150105,0.0013278674,0.0008837996,0.003377293],"category_scores_gemma":[0.001856436,0.00043363334,0.00073413656,0.0010528609,0.000403153,0.0015685792,0.0008739804,0.0011190344,0.00091158383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003686176,0.00014477488,0.0043336595,0.0006227435,0.00017885173,0.00019375699,0.00030254456,0.23578787,0.030938799,0.016649239,0.0049376255,0.70554155],"study_design_scores_gemma":[0.000028599055,0.00007652293,0.0013119426,0.000017458493,0.00004021269,0.000100653604,0.000038068614,0.9884321,0.004775658,0.0018776515,0.0032716624,0.00002943126],"about_ca_topic_score_codex":0.015884327,"about_ca_topic_score_gemma":0.008932681,"teacher_disagreement_score":0.015884327,"about_ca_system_score_codex":0.00044640832,"about_ca_system_score_gemma":0.0016568201,"threshold_uncertainty_score":0.031583786},"labels":[],"label_agreement":null},{"id":"W2901105223","doi":"10.1109/ipin.2018.8533819","title":"Assessing a UWB RTLS as a Means for Rapid WLAN Radio Map Generation","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Real-time locating system; Computer science; Wireless lan; Computer network; Real-time computing; Telecommunications; Wireless","score_opus":0.03086614800913556,"score_gpt":0.2726665875424609,"score_spread":0.24180043953332536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901105223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5383086,0.0004232348,0.45200226,0.00014119167,0.0000745245,0.00012159648,0.00025292143,0.0015602098,0.007115424],"genre_scores_gemma":[0.91532075,0.00015074773,0.08303406,0.000021897999,0.00001075853,0.000035057838,0.00010615049,0.000042563275,0.0012779582],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924845,0.00023127989,0.000019680194,0.00007176027,0.00039062946,0.000038202455],"domain_scores_gemma":[0.99900925,0.000353587,0.00012817087,0.00012634225,0.0003564365,0.000026162308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080797274,0.00040655915,0.00022709252,0.00077926065,0.00013780867,0.0007143956,0.00057986064,0.00038226513,0.0011489803],"category_scores_gemma":[0.0026006536,0.00016429351,0.00013332184,0.0005339811,0.0001777365,0.00070181984,0.00048655426,0.00020424773,0.0005156403],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011778185,0.00015139053,0.034040805,0.0004136161,0.00006753849,0.0006224682,0.00047919666,0.056018405,0.43132067,0.0039092046,0.0011706884,0.47062814],"study_design_scores_gemma":[0.00008060777,0.0030228538,0.05927945,0.00006387793,0.0001723698,0.0020916155,0.00078691734,0.24269111,0.6719706,0.0019740846,0.017751275,0.00011528562],"about_ca_topic_score_codex":0.00055248523,"about_ca_topic_score_gemma":0.0007839231,"teacher_disagreement_score":0.0011489803,"about_ca_system_score_codex":0.00021975675,"about_ca_system_score_gemma":0.00023233455,"threshold_uncertainty_score":0.0042729974},"labels":[],"label_agreement":null},{"id":"W2901666270","doi":"10.1109/ipin.2018.8533735","title":"Dynamic Beam Selection for Beam-RSRP Based Direction Finding in mmW 5G Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Beam (structure); Computer science; Telecommunications; Electrical engineering; Electronic engineering; Engineering; Physics; Artificial intelligence; Optics","score_opus":0.007060555348147044,"score_gpt":0.22621538113988435,"score_spread":0.2191548257917373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901666270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034910116,0.00040119438,0.9632469,0.000081794446,0.000028043467,0.000038536054,0.000038011294,0.00020638682,0.0010490419],"genre_scores_gemma":[0.779041,0.00042636064,0.21900688,0.00008270337,0.00007056778,0.00011078095,0.00012770708,0.000028210066,0.0011057609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888736,0.0005055873,0.00003973151,0.00016388443,0.00028630567,0.00011719153],"domain_scores_gemma":[0.99879175,0.0007164602,0.0001737018,0.00014904847,0.00013616392,0.00003282364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011629099,0.0005313263,0.0006334055,0.00049475644,0.000357759,0.000602784,0.0009195524,0.0005597563,0.0006637552],"category_scores_gemma":[0.004117194,0.00027290033,0.0003236166,0.0005951784,0.0005397291,0.00090881,0.0008832929,0.0003704159,0.00024118404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006163618,0.00009949891,0.0058421707,0.00012697384,0.000098195575,0.00017919576,0.00016680371,0.56897086,0.025129221,0.013308338,0.001049174,0.3844132],"study_design_scores_gemma":[0.000034809826,0.00017019994,0.0016280517,0.000011850135,0.000029137305,0.00016227804,0.000034173838,0.98730195,0.006638185,0.002729045,0.0012390338,0.00002129565],"about_ca_topic_score_codex":0.0017871949,"about_ca_topic_score_gemma":0.0021972635,"teacher_disagreement_score":0.0017871949,"about_ca_system_score_codex":0.0003697202,"about_ca_system_score_gemma":0.00065537734,"threshold_uncertainty_score":0.006150186},"labels":[],"label_agreement":null},{"id":"W2902751724","doi":"10.1177/1550147718815798","title":"A novel time difference of arrival localization algorithm using a neural network ensemble model","year":2018,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Government of Jiangsu Province; Qinglan Project of Jiangsu Province of China; National Science Foundation","keywords":"Computer science; Artificial neural network; Generalization; Algorithm; Stability (learning theory); Time delay neural network; Arrival time; Probabilistic neural network; Artificial intelligence; Machine learning; Mathematics","score_opus":0.014220312629266516,"score_gpt":0.23742862610462595,"score_spread":0.22320831347535944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902751724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007246267,0.00024043469,0.99112964,0.00010770023,0.000054013137,0.000016952183,0.000025870695,0.00029089546,0.00088817964],"genre_scores_gemma":[0.58361703,0.00087923865,0.40678796,0.00025101073,0.00018261463,0.0002679301,0.0003761547,0.00013291086,0.007505183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961495,0.000078342666,0.000027305023,0.00010538943,0.0001256793,0.000048270536],"domain_scores_gemma":[0.9994735,0.0001583137,0.000058734826,0.00003736732,0.0002411881,0.00003096643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007586003,0.0008024712,0.0010310546,0.0006240044,0.00046910884,0.00079670793,0.0016997302,0.0009872128,0.0012321562],"category_scores_gemma":[0.0017199118,0.0003872023,0.0008868635,0.000930806,0.0002885712,0.0015539267,0.0010100775,0.0014838983,0.00045791155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093372975,0.00004537401,0.0014352897,0.000056426234,0.00008872369,0.00006131467,0.00006988312,0.79187065,0.003060461,0.0054373676,0.0017775319,0.19600363],"study_design_scores_gemma":[0.000002651427,0.000010400337,0.00006175954,0.000002057126,0.0000061721294,0.000009104495,0.000002502848,0.9989207,0.0002581231,0.00045018693,0.0002735282,0.0000029703524],"about_ca_topic_score_codex":0.00933713,"about_ca_topic_score_gemma":0.0056540263,"teacher_disagreement_score":0.00933713,"about_ca_system_score_codex":0.0006571415,"about_ca_system_score_gemma":0.0008834436,"threshold_uncertainty_score":0.018565536},"labels":[],"label_agreement":null},{"id":"W2903437020","doi":"10.48550/arxiv.1811.10796","title":"Informative Path Planning for Location Fingerprint Collection","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Greedy algorithm; Fingerprint (computing); Constraint (computer-aided design); Data mining; Heuristic; Motion planning; Path (computing); Mathematical optimization; Artificial intelligence; Algorithm; Mathematics","score_opus":0.04901051946866406,"score_gpt":0.1836154339146826,"score_spread":0.13460491444601855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903437020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013618818,0.00017786447,0.9842519,0.00010784971,0.0000148482195,0.000043924567,0.00009448672,0.0003878809,0.0013024459],"genre_scores_gemma":[0.5358021,0.00037498894,0.46037275,0.00009045286,0.000030377014,0.00022289899,0.00046592948,0.00015669221,0.002483717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919635,0.00030577558,0.000027301787,0.00020095888,0.00016335068,0.000106316416],"domain_scores_gemma":[0.99838364,0.0010250093,0.00016829213,0.0002047245,0.00013504803,0.000083256724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008349562,0.0010400722,0.0007932482,0.0009254316,0.00060255855,0.00066291663,0.0011181836,0.00088874146,0.0023607477],"category_scores_gemma":[0.0039325673,0.00044036107,0.0005370222,0.0015429108,0.0008595958,0.0012848814,0.001210762,0.0011170598,0.0004355959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009781809,0.000078963734,0.0008287977,0.000092619994,0.000027664948,0.000089122506,0.00010559897,0.8885299,0.0019131819,0.013537032,0.0019600494,0.09273932],"study_design_scores_gemma":[0.000012705048,0.0000473957,0.00018333983,0.000009124158,0.00000998205,0.000051949057,0.00003701093,0.9806815,0.0012054071,0.016461898,0.0012896407,0.000010217452],"about_ca_topic_score_codex":0.0047521587,"about_ca_topic_score_gemma":0.005785878,"teacher_disagreement_score":0.0047521587,"about_ca_system_score_codex":0.00091394555,"about_ca_system_score_gemma":0.0013763235,"threshold_uncertainty_score":0.009449005},"labels":[],"label_agreement":null},{"id":"W2903442137","doi":"10.1108/sr-04-2018-0090","title":"An improved INS/PDR/UWB integrated positioning method for indoor foot-mounted pedestrians","year":2018,"lang":"en","type":"article","venue":"Sensor Review","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inertial navigation system; Dead reckoning; Kalman filter; Computer science; Positioning system; Gait; Inertial measurement unit; Pedestrian; STRIDE; Navigation system; GNSS applications; Real-time computing; Sensor fusion; Computer vision; Simulation; Artificial intelligence; Engineering; Inertial frame of reference; Global Positioning System; Telecommunications","score_opus":0.01534523553435728,"score_gpt":0.3177935564194004,"score_spread":0.3024483208850431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903442137","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0331078,0.00027860783,0.96335375,0.000038049933,0.00015975544,0.000034919947,0.000061138955,0.0013408741,0.0016251586],"genre_scores_gemma":[0.5166072,0.0003682532,0.47561857,0.00010554968,0.00008676618,0.00007005256,0.00025677076,0.000074167074,0.0068125874],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995896,0.00005422497,0.000022421405,0.00012514771,0.00017562842,0.00003292171],"domain_scores_gemma":[0.999696,0.000019713094,0.000036102865,0.00004892886,0.00018184983,0.000017457574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027362123,0.00057701004,0.00047358157,0.000713863,0.00026830524,0.00036114204,0.00073017937,0.00047606434,0.0014090147],"category_scores_gemma":[0.0005657271,0.0003181515,0.0004034183,0.00055955676,0.0001659568,0.00045033233,0.0004752351,0.0003208475,0.0009100856],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043010424,0.00008055396,0.0059399744,0.00033771314,0.00010481128,0.00038455182,0.00032148717,0.024881637,0.1873944,0.0022207105,0.0030119768,0.7748921],"study_design_scores_gemma":[0.00012946839,0.0014061256,0.023873618,0.000113140464,0.00042990665,0.0028899957,0.00032417342,0.71640193,0.22339648,0.0016459761,0.029180244,0.00020901546],"about_ca_topic_score_codex":0.0020230801,"about_ca_topic_score_gemma":0.0023414474,"teacher_disagreement_score":0.0020230801,"about_ca_system_score_codex":0.00018567454,"about_ca_system_score_gemma":0.0004070617,"threshold_uncertainty_score":0.0047136545},"labels":[],"label_agreement":null},{"id":"W2903512949","doi":"10.1109/apede.2018.8542363","title":"RFID Smart Shelf","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"March of Dimes Canada","funders":"","keywords":"Computer science; Off the shelf; Internet of Things; Embedded system; Systems design; Systems engineering; Software engineering; Engineering","score_opus":0.006689572604297734,"score_gpt":0.19756506250644623,"score_spread":0.1908754899021485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903512949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024559507,0.012294027,0.6227622,0.0016037583,0.003554366,0.00043097584,0.00074254256,0.0058470205,0.3282056],"genre_scores_gemma":[0.27427438,0.01081929,0.2720068,0.0035145814,0.0007577348,0.00032518533,0.0017241312,0.0004229636,0.4361549],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945563,0.000044167715,0.000037461174,0.000094557625,0.00032828873,0.00003997652],"domain_scores_gemma":[0.99968874,0.00003273384,0.000027475682,0.00008590103,0.00014128163,0.000023802317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025195506,0.00046999933,0.00044939702,0.00070892146,0.0005247041,0.0018179744,0.001239329,0.0017673227,0.020278238],"category_scores_gemma":[0.0004995429,0.00028595625,0.00044816197,0.0008559867,0.00051330484,0.0021314037,0.0010611714,0.0006755215,0.020302234],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022351817,0.00009305458,0.0018625094,0.0011453695,0.000043557895,0.0013473034,0.00048285865,0.0031329761,0.12658675,0.21479525,0.049756173,0.6005307],"study_design_scores_gemma":[0.000029916144,0.00032925257,0.0010708327,0.00011344674,0.00006466973,0.0035207465,0.00018943085,0.0077543305,0.069595404,0.012721473,0.9045524,0.000058034395],"about_ca_topic_score_codex":0.0004246686,"about_ca_topic_score_gemma":0.00042372316,"teacher_disagreement_score":0.020278238,"about_ca_system_score_codex":0.00036628862,"about_ca_system_score_gemma":0.00034005844,"threshold_uncertainty_score":0.06783748},"labels":[],"label_agreement":null},{"id":"W2903960665","doi":"10.1109/smartworld.2018.00183","title":"A Comparison of Inertial Data Acquisition Methods for a Position-Independent Soil Types Recognition","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Wearable computer; Computer science; Wearable technology; Mobile phone; Reliability (semiconductor); Data acquisition; Work (physics); Inertial frame of reference; Phone; Position (finance); Artificial intelligence; Mobile device; Computer vision; Engineering; Embedded system; Telecommunications","score_opus":0.07181738264281577,"score_gpt":0.3896086819777812,"score_spread":0.3177912993349654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903960665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31941077,0.009984339,0.65378344,0.0003283827,0.0011342013,0.00044277858,0.0009797515,0.005402212,0.008534166],"genre_scores_gemma":[0.6056492,0.0058825514,0.37879217,0.00020803735,0.00030922997,0.00028031462,0.0020796426,0.00035803122,0.006440774],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985909,0.00025217995,0.00013034689,0.0003082368,0.0006242606,0.000094088275],"domain_scores_gemma":[0.9980525,0.00070795964,0.00009395525,0.00022593829,0.0008479481,0.00007164294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015546113,0.00094343635,0.0008557184,0.0029827056,0.00026536942,0.00086288276,0.00088093645,0.0008538292,0.0020803658],"category_scores_gemma":[0.0035590813,0.0003135967,0.00060852815,0.0015643449,0.00023891545,0.0011525863,0.00054319366,0.0003865556,0.0014033836],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013462604,0.00026150155,0.010486387,0.0011094797,0.00036771188,0.00014697993,0.00022064216,0.0036837298,0.07592359,0.0004896127,0.0024050311,0.9035591],"study_design_scores_gemma":[0.00045871758,0.006204209,0.20543063,0.0006065753,0.0014794373,0.0035444833,0.0016238649,0.42634645,0.3048022,0.001711785,0.047254506,0.00053717714],"about_ca_topic_score_codex":0.0016160342,"about_ca_topic_score_gemma":0.0023527292,"teacher_disagreement_score":0.0029827056,"about_ca_system_score_codex":0.00018069733,"about_ca_system_score_gemma":0.00027784493,"threshold_uncertainty_score":0.008221626},"labels":[],"label_agreement":null},{"id":"W2906035660","doi":"10.4095/219959","title":"Towards Integrated Earth Sensing: The Role of In Situ Sensing","year":2002,"lang":"en","type":"report","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"In situ; Earth (classical element); Remote sensing; Earth observation; Environmental science; Geology; Geography; Engineering; Meteorology; Aerospace engineering; Physics; Satellite","score_opus":0.017507160633024724,"score_gpt":0.2264533217932148,"score_spread":0.2089461611601901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906035660","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01909137,0.016071018,0.932348,0.009324599,0.00049609947,0.000136842,0.0003048561,0.0011385402,0.021088712],"genre_scores_gemma":[0.18701303,0.02132742,0.78171545,0.002200367,0.0006385175,0.00013141854,0.00073754735,0.00020537888,0.0060308683],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981567,0.00070833846,0.000067436056,0.00031312852,0.0006475106,0.00010675761],"domain_scores_gemma":[0.9962459,0.0015261846,0.000302529,0.0005875191,0.0011308262,0.0002069763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046567162,0.0010268644,0.00092024956,0.0015160246,0.00048574558,0.0047017112,0.0018652519,0.002756538,0.0023536403],"category_scores_gemma":[0.004048214,0.00053271477,0.00039845725,0.0024896672,0.0024501965,0.008670173,0.003410101,0.0028372407,0.00079168467],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016137911,0.0002770715,0.00989798,0.0011239934,0.00014488323,0.00018860042,0.0014681122,0.019914135,0.032981936,0.20704225,0.01115653,0.71564317],"study_design_scores_gemma":[0.00005760846,0.0005285136,0.0073996102,0.00069149246,0.00013726007,0.000823326,0.0021647122,0.10645464,0.028508864,0.38067016,0.472366,0.00019790119],"about_ca_topic_score_codex":0.0026741186,"about_ca_topic_score_gemma":0.0026905176,"teacher_disagreement_score":0.0047017112,"about_ca_system_score_codex":0.000865712,"about_ca_system_score_gemma":0.0010131714,"threshold_uncertainty_score":0.024627388},"labels":[],"label_agreement":null},{"id":"W2907214697","doi":"10.1109/icarcv.2018.8581102","title":"Bluetooth Low Energy Based Activity Tracking of Patient","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Mitacs; Simon Fraser University","keywords":"Bluetooth Low Energy; RSS; Computer science; Bluetooth; Wearable computer; Network packet; Energy (signal processing); Noise (video); SIGNAL (programming language); Scanner; Quality (philosophy); Real-time computing; Embedded system; Artificial intelligence; Computer network; Wireless; Telecommunications","score_opus":0.00618998339988572,"score_gpt":0.19172001887796233,"score_spread":0.18553003547807662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907214697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3194501,0.002415678,0.65751797,0.00041487033,0.00035867107,0.00029842372,0.0011186287,0.004240217,0.014185435],"genre_scores_gemma":[0.89411646,0.0011064421,0.09651572,0.00024008617,0.00010721805,0.00020619926,0.0007105027,0.000071174574,0.0069262427],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996556,0.000091687354,0.000025709536,0.00008791793,0.000112815345,0.00002615665],"domain_scores_gemma":[0.9996049,0.00013438381,0.000072792376,0.000049210685,0.000119861645,0.000018889867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022372225,0.00040868935,0.00035068387,0.00096996356,0.00014379637,0.0004914236,0.00043301357,0.00049508316,0.0011824538],"category_scores_gemma":[0.0012890752,0.00013337689,0.00018685666,0.0007384496,0.00011319465,0.00040841766,0.0003705503,0.00025073742,0.0009881486],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012792302,0.00031595267,0.035552833,0.00070418045,0.00013468027,0.00072793034,0.00053064607,0.023328574,0.1351519,0.0018955139,0.0067265695,0.79365206],"study_design_scores_gemma":[0.00021364042,0.0026609201,0.121402204,0.00025474073,0.00027313878,0.0067278184,0.00046900846,0.6508984,0.17501216,0.003462636,0.03844957,0.00017581646],"about_ca_topic_score_codex":0.000365006,"about_ca_topic_score_gemma":0.00061905256,"teacher_disagreement_score":0.0011824538,"about_ca_system_score_codex":0.000116197116,"about_ca_system_score_gemma":0.00016990578,"threshold_uncertainty_score":0.003955722},"labels":[],"label_agreement":null},{"id":"W2908862527","doi":"10.21474/ijar01/8133","title":"A HYBRID APPROACH TOWARDS LOCALIZATION AND SECURITY IN WIRELESS NETWORKS.","year":2018,"lang":"en","type":"article","venue":"International Journal of Advanced Research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Universiti Sains Islam Malaysia","keywords":"Wireless; Wireless network; Network security; Science and engineering; Computer science; Computer security; Telecommunications; Computer network; Engineering; Engineering ethics","score_opus":0.019655582967165665,"score_gpt":0.3208642685465571,"score_spread":0.3012086855793914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908862527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018650624,0.0026395777,0.9855188,0.0007300566,0.00025473678,0.000043092896,0.00003052924,0.00017720278,0.008740942],"genre_scores_gemma":[0.30150345,0.007941037,0.6558471,0.0010313818,0.0009467335,0.00033707314,0.00022765352,0.00016970153,0.031995848],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982693,0.0005454251,0.000078120305,0.00028601012,0.0006753844,0.00014582802],"domain_scores_gemma":[0.99912184,0.00032433093,0.000058046502,0.00026492108,0.00016619005,0.00006479583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013979151,0.0009129616,0.0009794221,0.0014113394,0.0008162144,0.0024419914,0.00249262,0.002439661,0.0032591624],"category_scores_gemma":[0.0022590947,0.00042426374,0.0010531798,0.0012307938,0.0022641895,0.0048810495,0.005525514,0.001957451,0.0012668927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011024897,0.00006727312,0.0004932903,0.00036970552,0.00016385429,0.0003094339,0.00036961614,0.05806118,0.007772998,0.7912845,0.0077119637,0.13328597],"study_design_scores_gemma":[0.000037870082,0.0001998061,0.00028389404,0.00014003656,0.00008384004,0.00076763454,0.00022055702,0.45229286,0.0036873915,0.4877529,0.054467496,0.000065745946],"about_ca_topic_score_codex":0.0011078507,"about_ca_topic_score_gemma":0.0015787024,"teacher_disagreement_score":0.0032591624,"about_ca_system_score_codex":0.0008813894,"about_ca_system_score_gemma":0.00081774767,"threshold_uncertainty_score":0.010902941},"labels":[],"label_agreement":null},{"id":"W2908906402","doi":"10.3390/s19020424","title":"A Hybrid Method to Improve the BLE-Based Indoor Positioning in a Dense Bluetooth Environment","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Southwest Jiaotong University; Simon Fraser University; Tsinghua University","keywords":"Trilateration; Bluetooth; Computer science; Real-time computing; Beacon; Kalman filter; Dead reckoning; Hybrid positioning system; Sensor fusion; Indoor positioning system; Embedded system; Positioning system; Wireless; Computer vision; Artificial intelligence; Global Positioning System; Engineering; Telecommunications; Accelerometer","score_opus":0.004258112508648164,"score_gpt":0.20312561856144268,"score_spread":0.19886750605279452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908906402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008834518,0.00026826517,0.989122,0.000033474564,0.000075111406,0.00002065877,0.000016976803,0.00075480784,0.00087420107],"genre_scores_gemma":[0.40779603,0.00078776944,0.58468974,0.00013460814,0.00012286287,0.00009934568,0.0001979206,0.00015177703,0.006019928],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922407,0.00009391074,0.000056715664,0.00020427933,0.00035080675,0.00007024587],"domain_scores_gemma":[0.9993437,0.0001333248,0.000068853864,0.000118066964,0.0003064732,0.000029495835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058724626,0.0009294261,0.0007620368,0.0013478704,0.00046161513,0.00066604215,0.00096381083,0.00079383655,0.0014852054],"category_scores_gemma":[0.0016372522,0.00043344076,0.00090563507,0.0010036229,0.00031952417,0.0014139,0.0009849473,0.00056218717,0.0009314079],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024828894,0.00010177675,0.0023470127,0.00029129582,0.00012087927,0.00021819338,0.00037319004,0.08348869,0.111781746,0.0037690245,0.001941038,0.79531884],"study_design_scores_gemma":[0.000049308757,0.00045177262,0.0038036143,0.000037591017,0.00015238118,0.00064517366,0.00013836291,0.92872816,0.054510783,0.0015209822,0.0098545635,0.00010731391],"about_ca_topic_score_codex":0.0040095733,"about_ca_topic_score_gemma":0.0042758733,"teacher_disagreement_score":0.0040095733,"about_ca_system_score_codex":0.000301599,"about_ca_system_score_gemma":0.00054209295,"threshold_uncertainty_score":0.007972479},"labels":[],"label_agreement":null},{"id":"W2909282266","doi":"10.2514/6.2019-0374","title":"Ultrasonic Localization of a Quadrotor using a Portable Beacon","year":2019,"lang":"en","type":"article","venue":"AIAA Scitech 2019 Forum","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Ultrasonic sensor; Computer science; Acoustics; Remote sensing; Computer vision; Geology; Physics","score_opus":0.005349291471798489,"score_gpt":0.20570220835067687,"score_spread":0.20035291687887838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909282266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08160234,0.0012552588,0.9087371,0.00013536577,0.00022201371,0.00011778313,0.000097344964,0.0014590477,0.0063735996],"genre_scores_gemma":[0.6184323,0.0011139223,0.3648298,0.00009010672,0.000097060416,0.00014251536,0.00027683793,0.000096800664,0.014920596],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997125,0.00003620642,0.00001075283,0.000094846226,0.00012007948,0.000025580883],"domain_scores_gemma":[0.99976414,0.000048898793,0.00004095673,0.000045963112,0.000067694265,0.000032261312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002473885,0.00051494286,0.00050113414,0.0007660314,0.00032275481,0.00043985256,0.00083104975,0.00051407894,0.0018628273],"category_scores_gemma":[0.00046518448,0.0002833804,0.00026469602,0.00048551342,0.0003014866,0.00060067204,0.0007144848,0.00040635222,0.000996961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006279171,0.0001022648,0.0057374225,0.00036714826,0.000055889075,0.00068160833,0.0006122351,0.009231092,0.54749745,0.004520742,0.0024366758,0.42812946],"study_design_scores_gemma":[0.00046172677,0.0053152055,0.023206733,0.0002675845,0.00031775626,0.0059803016,0.0007195332,0.37485787,0.47098023,0.0027566233,0.114867695,0.0002687384],"about_ca_topic_score_codex":0.0020832643,"about_ca_topic_score_gemma":0.002309806,"teacher_disagreement_score":0.0020832643,"about_ca_system_score_codex":0.0003438229,"about_ca_system_score_gemma":0.0003046406,"threshold_uncertainty_score":0.006231785},"labels":[],"label_agreement":null},{"id":"W2909461389","doi":"10.1109/iemcon.2018.8614863","title":"Comparison of RSSI-Based Indoor Localization for Smart Buildings with Internet of Things","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Global Positioning System; Wireless; Bluetooth; Real-time computing; Bluetooth Low Energy; Internet of Things; Energy consumption; Power consumption; Workspace; Embedded system; Power (physics); Telecommunications; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.014945847648492458,"score_gpt":0.25999538925298404,"score_spread":0.24504954160449158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909461389","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8155462,0.008298092,0.14167714,0.00042752223,0.00069323793,0.00026156203,0.0020322436,0.004653835,0.026410103],"genre_scores_gemma":[0.9743061,0.0017494643,0.019790003,0.00007280774,0.00004730439,0.00008997323,0.0014886586,0.00007850524,0.002377222],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99798393,0.00048956875,0.00016457484,0.0002211296,0.0009315161,0.00020931437],"domain_scores_gemma":[0.9979674,0.000715646,0.00016852128,0.00022691426,0.00087234715,0.000048987564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010723814,0.0010492327,0.0007969418,0.0024882557,0.0003752856,0.00078066235,0.00083039043,0.0007859584,0.0019299595],"category_scores_gemma":[0.003369015,0.00019360504,0.0006540929,0.0027202182,0.000294983,0.0012931691,0.00054305437,0.00023563967,0.00083787],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004640496,0.000501174,0.050119128,0.0036280318,0.0010775651,0.0012482319,0.00050310197,0.17561442,0.050186146,0.0026340478,0.0066169864,0.7032306],"study_design_scores_gemma":[0.00041021773,0.009221392,0.29134473,0.00046855304,0.0018504674,0.0056666275,0.0032213242,0.50620633,0.13086541,0.0027276182,0.04732531,0.0006920177],"about_ca_topic_score_codex":0.0028319708,"about_ca_topic_score_gemma":0.004887198,"teacher_disagreement_score":0.0028319708,"about_ca_system_score_codex":0.00050301064,"about_ca_system_score_gemma":0.0002737697,"threshold_uncertainty_score":0.006456375},"labels":[],"label_agreement":null},{"id":"W2909518497","doi":"10.5539/cis.v12n1p44","title":"Research on an Economic Localization Approach","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Qinglan Project of Jiangsu Province of China; National Social Science Fund of China; Six Talent Peaks Project in Jiangsu Province; Government of Jiangsu Province","keywords":"Computer science; Hotspot (geology); Hybrid positioning system; Software deployment; Positioning technology; Location-based service; Wireless; Wireless network; Telecommunications; Real-time computing; Service (business); Positioning system; Computer network; Embedded system","score_opus":0.021344499871086244,"score_gpt":0.27404331279784105,"score_spread":0.25269881292675483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909518497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057000685,0.0029302554,0.95274705,0.0024410202,0.0003405928,0.00003701573,0.000074654694,0.00008543398,0.035643984],"genre_scores_gemma":[0.5660487,0.012608214,0.3633737,0.0010399993,0.001246375,0.00030246357,0.00026302235,0.00020421979,0.054913253],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991881,0.00027401795,0.000032441014,0.00021782184,0.00020937595,0.00007828697],"domain_scores_gemma":[0.99898356,0.00055485446,0.00008586582,0.00009396114,0.00024321908,0.00003853246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012877323,0.0008371534,0.00077378855,0.0015678438,0.00081201683,0.002078835,0.0019041544,0.0013303307,0.006832016],"category_scores_gemma":[0.004263906,0.0004142934,0.001009011,0.0020949077,0.0015027805,0.0047388873,0.0015269928,0.0017537627,0.0012692593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018571889,0.000025579808,0.00064077054,0.00014451703,0.000038340342,0.000081747494,0.0000784731,0.06515851,0.00036408668,0.8759838,0.0036902558,0.05377532],"study_design_scores_gemma":[0.000021998349,0.000059447168,0.00054806494,0.00009224901,0.000042398195,0.00020527266,0.00014254189,0.46683905,0.0005939614,0.49444824,0.036973976,0.000032815788],"about_ca_topic_score_codex":0.004607105,"about_ca_topic_score_gemma":0.0026127705,"teacher_disagreement_score":0.006832016,"about_ca_system_score_codex":0.0022090466,"about_ca_system_score_gemma":0.0013963943,"threshold_uncertainty_score":0.022855341},"labels":[],"label_agreement":null},{"id":"W2909910397","doi":"10.3390/s19020294","title":"Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Higher Education Discipline Innovation Project; Government of Jiangsu Province; China Postdoctoral Science Foundation; Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Dead reckoning; Inertial measurement unit; Heading (navigation); Kalman filter; Computer science; Inertial navigation system; Outlier; Extended Kalman filter; Computer vision; Artificial intelligence; Control theory (sociology); Engineering; Global Positioning System; Inertial frame of reference","score_opus":0.014666809061953875,"score_gpt":0.20389052152059178,"score_spread":0.1892237124586379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909910397","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014937561,0.0002770933,0.98278934,0.000043589713,0.00008478241,0.000020040934,0.000030038906,0.0008665147,0.00095121516],"genre_scores_gemma":[0.7511061,0.00077415264,0.24308729,0.00010539551,0.0001170996,0.00011434641,0.00029204265,0.000060082573,0.0043435185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999566,0.00006202712,0.00003493084,0.00013186948,0.000162257,0.00004292884],"domain_scores_gemma":[0.9997147,0.000048138765,0.000040069906,0.000039691633,0.00014680746,0.000010582936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037957338,0.0006667395,0.0006509297,0.00043472205,0.00037324635,0.00039425245,0.00065463857,0.0005211026,0.0010038146],"category_scores_gemma":[0.0007961896,0.0003193089,0.000533542,0.00043764865,0.00020397002,0.00071123644,0.00042379668,0.00047401423,0.00058898475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056441565,0.000114278155,0.004530932,0.0003924614,0.0001990389,0.0003509252,0.00033232,0.23973939,0.092255145,0.0040054466,0.0041607087,0.65335494],"study_design_scores_gemma":[0.00003671986,0.00022248126,0.0022288645,0.00001986622,0.00009488894,0.00019436459,0.00003563997,0.97289103,0.019829217,0.00049659226,0.0039018681,0.000048394835],"about_ca_topic_score_codex":0.005205305,"about_ca_topic_score_gemma":0.004481808,"teacher_disagreement_score":0.005205305,"about_ca_system_score_codex":0.00025814987,"about_ca_system_score_gemma":0.00054206094,"threshold_uncertainty_score":0.010349989},"labels":[],"label_agreement":null},{"id":"W2910075363","doi":"10.1109/apusncursinrsm.2018.8608674","title":"UWB Radar Sensor Development using Drone Arms for Sensing Applicaions","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Drone; Computer science; Radar; Antenna (radio); Ultra-wideband; Real-time computing; Electronic engineering; Engineering; Telecommunications","score_opus":0.023478269732796667,"score_gpt":0.24378777577323396,"score_spread":0.2203095060404373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910075363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4631905,0.004172669,0.51433754,0.00035373654,0.0003785525,0.00021999555,0.00010557736,0.0011199531,0.016121387],"genre_scores_gemma":[0.7769132,0.0016740012,0.20421448,0.00021140934,0.00006031616,0.00009017989,0.00016023315,0.00006180272,0.016614394],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996855,0.00005029486,0.000019957015,0.000068201225,0.00014749558,0.000028581271],"domain_scores_gemma":[0.99956626,0.00007217842,0.00008225124,0.00008143614,0.00016726289,0.000030623127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030414521,0.00036725224,0.00028205072,0.00027471935,0.00015520227,0.0005563351,0.00071230036,0.0007006283,0.0010790629],"category_scores_gemma":[0.00054179336,0.00023774971,0.000290611,0.00018084368,0.0002141851,0.0008008807,0.00044576987,0.0004660917,0.0007114408],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012881964,0.000043970846,0.0009839103,0.0002618364,0.00003719556,0.0002328426,0.00013277476,0.00340993,0.91444606,0.0015550844,0.000425212,0.07834241],"study_design_scores_gemma":[0.00001439699,0.0012797386,0.002439456,0.000053224412,0.00005871141,0.0014067168,0.000098903736,0.022016255,0.9435105,0.00025668534,0.028831732,0.00003370413],"about_ca_topic_score_codex":0.00014434545,"about_ca_topic_score_gemma":0.000319088,"teacher_disagreement_score":0.0010790629,"about_ca_system_score_codex":0.00018174255,"about_ca_system_score_gemma":0.00013509038,"threshold_uncertainty_score":0.0036097765},"labels":[],"label_agreement":null},{"id":"W2910183891","doi":"10.1109/smc.2018.00473","title":"A Miniature Multi-sensor Shoe-Mounted Platform for Accurate Positioning","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inertial measurement unit; USB; Computer science; Bluetooth; Sensor fusion; Real-time computing; Computer hardware; Embedded system; Units of measurement; Computer vision; Software; Wireless; Telecommunications","score_opus":0.021710009057020738,"score_gpt":0.2765057844352319,"score_spread":0.25479577537821113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910183891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05393155,0.0014180408,0.9360151,0.00018795476,0.00044350894,0.00028726802,0.00038065322,0.0018940608,0.005441884],"genre_scores_gemma":[0.4724526,0.0008286259,0.51191676,0.00023426225,0.0001766323,0.00031185392,0.00071433047,0.00011659321,0.0132483365],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962044,0.000059992173,0.000015564665,0.00008571988,0.00018669506,0.00003148893],"domain_scores_gemma":[0.9997079,0.0000446279,0.000044845576,0.00006957845,0.000103005776,0.000030101404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023571808,0.00077430636,0.00046724943,0.000586549,0.00023122829,0.00043252736,0.0011069509,0.00067589956,0.00519951],"category_scores_gemma":[0.0005143814,0.0003316743,0.00030651267,0.00042677624,0.00016474829,0.0006761173,0.00080801506,0.00044024983,0.0022378946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027697306,0.00014443845,0.0029870712,0.0010676464,0.00007389664,0.00066075876,0.00018532209,0.0064329575,0.5771572,0.003241072,0.006820656,0.40095192],"study_design_scores_gemma":[0.00024992332,0.006124504,0.047896553,0.00046304305,0.00029097477,0.008584589,0.00036810138,0.19883072,0.50457054,0.0033340992,0.22894529,0.00034164282],"about_ca_topic_score_codex":0.00035141033,"about_ca_topic_score_gemma":0.00065150444,"teacher_disagreement_score":0.00519951,"about_ca_system_score_codex":0.00012929097,"about_ca_system_score_gemma":0.0003564614,"threshold_uncertainty_score":0.017394125},"labels":[],"label_agreement":null},{"id":"W2910779736","doi":"10.3390/s19020324","title":"Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"RSS; Extended Kalman filter; Wireless; Computer science; Wireless sensor network; Artificial intelligence; Kalman filter; Artificial neural network; Real-time computing; Wireless network; Machine learning; Data mining; Telecommunications; Computer network","score_opus":0.004617575737699861,"score_gpt":0.1798695773789948,"score_spread":0.17525200164129495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910779736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051522385,0.00034475455,0.9465859,0.00008529632,0.00003124397,0.000021048992,0.000049897262,0.0004894894,0.0008700955],"genre_scores_gemma":[0.9535385,0.00024529034,0.045381773,0.000039187657,0.00003160387,0.000035212182,0.000085679465,0.000021275746,0.0006214377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992805,0.00014333372,0.000056688805,0.0002022197,0.00024674024,0.00007051954],"domain_scores_gemma":[0.9982503,0.00086646056,0.0002905704,0.00013702149,0.00041832318,0.000037355563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093561533,0.0007685858,0.00069113006,0.0009468668,0.00027633095,0.000669577,0.00064907223,0.0005494389,0.0004934782],"category_scores_gemma":[0.0042918297,0.00023393678,0.0004109328,0.0007015811,0.0003876893,0.0012302458,0.00071339007,0.00071496767,0.00018482545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012974719,0.000056928897,0.007937625,0.00008249452,0.0000549314,0.00011534827,0.00006769164,0.7920223,0.0047557103,0.0017718422,0.00052301335,0.19248246],"study_design_scores_gemma":[0.0000019426066,0.000017782664,0.00081134157,0.0000051334414,0.0000058154533,0.000022120312,0.0000055223254,0.9971269,0.0013952444,0.0005105809,0.00009181083,0.000005825594],"about_ca_topic_score_codex":0.0034582433,"about_ca_topic_score_gemma":0.0022712369,"teacher_disagreement_score":0.0034582433,"about_ca_system_score_codex":0.00048494292,"about_ca_system_score_gemma":0.00043342425,"threshold_uncertainty_score":0.0068762302},"labels":[],"label_agreement":null},{"id":"W2911526837","doi":"10.5430/air.v7n2p87","title":"Indoor Localization Based on Bluetooth","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Qinglan Project of Jiangsu Province of China; National Social Science Fund of China; Six Talent Peaks Project in Jiangsu Province; Government of Jiangsu Province","keywords":"Bluetooth; Hybrid positioning system; Fingerprint (computing); Global Positioning System; Computer science; Positioning technology; Real-time computing; Process (computing); Outlier; Terminal (telecommunication); Transmission (telecommunications); Indoor positioning system; Positioning system; Wireless; Computer vision; Artificial intelligence; Engineering; Telecommunications","score_opus":0.08013421044768386,"score_gpt":0.3464721814307017,"score_spread":0.2663379709830178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911526837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02985412,0.004351209,0.9372749,0.00020038488,0.00031142004,0.000086194574,0.00029476025,0.006702362,0.020924797],"genre_scores_gemma":[0.7796309,0.0050217668,0.19687021,0.00024013766,0.00029302266,0.00020507142,0.0010130558,0.0001709711,0.016554803],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994512,0.000094686235,0.00002698568,0.00012573256,0.00022412583,0.000077310586],"domain_scores_gemma":[0.99976987,0.00003766424,0.00003368896,0.000047133897,0.00009902322,0.000012576875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016643251,0.00057783996,0.0005563709,0.0013780909,0.00041448872,0.00074236916,0.0007299022,0.00045389432,0.0025707567],"category_scores_gemma":[0.0006057151,0.00020178802,0.0004556574,0.0015121412,0.00021482198,0.00077657116,0.0008417816,0.00031495342,0.0018114548],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029065443,0.000072681,0.0069381543,0.0006736471,0.00011188182,0.000602185,0.00020528154,0.01832968,0.051798772,0.007601505,0.01188053,0.90149504],"study_design_scores_gemma":[0.00021641424,0.0015319383,0.03109185,0.00055936124,0.0007458267,0.012189171,0.0006543533,0.57493734,0.12847324,0.010384893,0.23877504,0.00044050472],"about_ca_topic_score_codex":0.0016920246,"about_ca_topic_score_gemma":0.0013717356,"teacher_disagreement_score":0.0025707567,"about_ca_system_score_codex":0.00019203771,"about_ca_system_score_gemma":0.00031794255,"threshold_uncertainty_score":0.008599997},"labels":[],"label_agreement":null},{"id":"W2912199117","doi":"10.1109/access.2019.2899169","title":"A Novel Outlier Immune Multipath Fingerprinting Model for Indoor Single-Site Localization","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Jiangxi Provincial Department of Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Outlier; Pattern recognition (psychology); Multipath propagation; Artificial intelligence; Telecommunications","score_opus":0.02996522448085833,"score_gpt":0.25521642671666755,"score_spread":0.22525120223580922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912199117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011065073,0.00021414587,0.98718387,0.00011185041,0.00004034329,0.000019475076,0.00009046509,0.0003213591,0.0009533473],"genre_scores_gemma":[0.8600691,0.000826951,0.12988053,0.00018267367,0.0001127946,0.00018193772,0.00057097914,0.000116621624,0.008058519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994037,0.00009974867,0.000029687706,0.00016799722,0.00018745268,0.00011150779],"domain_scores_gemma":[0.999302,0.00021544774,0.00012856191,0.00006975066,0.0002470627,0.00003714073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008113497,0.00085025496,0.0011342727,0.000805304,0.00042468763,0.0008763415,0.0024334604,0.0012882572,0.0016436379],"category_scores_gemma":[0.0020341314,0.00037315456,0.00092335563,0.001358216,0.0007271909,0.0014849274,0.0010054758,0.0012915503,0.00059604895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114873765,0.00004105285,0.0017659777,0.000081388505,0.000053036933,0.00016325842,0.000089798596,0.91772383,0.004060345,0.01163016,0.001234928,0.063041314],"study_design_scores_gemma":[0.0000030612619,0.000018991834,0.00013521632,0.0000026686316,0.00000679901,0.000026993175,0.0000051237116,0.9979133,0.00029166008,0.0012941171,0.00029576212,0.000006241211],"about_ca_topic_score_codex":0.009520467,"about_ca_topic_score_gemma":0.0059073744,"teacher_disagreement_score":0.009520467,"about_ca_system_score_codex":0.00081131764,"about_ca_system_score_gemma":0.0009885668,"threshold_uncertainty_score":0.018930137},"labels":[],"label_agreement":null},{"id":"W2912423035","doi":"10.1109/apcc.2018.8633478","title":"A Velocity Based HMM Framework for Indoor Localization","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Hidden Markov model; Computer science; RSS; Kernel density estimation; Fingerprint (computing); Process (computing); Kernel (algebra); Real-time computing; SIGNAL (programming language); Transmission (telecommunications); Artificial intelligence; Pattern recognition (psychology); Algorithm; Data mining; Telecommunications; Mathematics","score_opus":0.014388359460744017,"score_gpt":0.2484742802156591,"score_spread":0.23408592075491508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912423035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025154122,0.00022050948,0.99555165,0.00005704173,0.000056027333,0.000010535027,0.00014902727,0.00043997567,0.000999755],"genre_scores_gemma":[0.5802988,0.0020217292,0.39297745,0.00016961925,0.00028439733,0.0002613276,0.0022624896,0.00033981504,0.021384379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964535,0.000081511665,0.000019221357,0.00012239034,0.00007900713,0.000052542833],"domain_scores_gemma":[0.9997198,0.000105321,0.000034418725,0.000040610088,0.000080835,0.000018986755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046687032,0.0006510206,0.00084738364,0.0006074332,0.00039917085,0.000715781,0.0016015177,0.0007867028,0.003587076],"category_scores_gemma":[0.0011848769,0.00047166846,0.0008443153,0.0010318363,0.00041911862,0.0010673378,0.0008434802,0.0012029221,0.001843261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014014948,0.00006286292,0.0026995733,0.0001864641,0.000102993625,0.00027173752,0.00020018725,0.74376434,0.00897393,0.06399602,0.0043748124,0.17522699],"study_design_scores_gemma":[0.0000038641724,0.000014002656,0.00033062586,0.000007105614,0.00001418571,0.0000380213,0.000014351708,0.9913954,0.0006308249,0.005750859,0.0017878016,0.0000129221135],"about_ca_topic_score_codex":0.018765267,"about_ca_topic_score_gemma":0.014238345,"teacher_disagreement_score":0.018765267,"about_ca_system_score_codex":0.0006094008,"about_ca_system_score_gemma":0.0009402287,"threshold_uncertainty_score":0.03731209},"labels":[],"label_agreement":null},{"id":"W2912561618","doi":"10.1109/tits.2019.2894522","title":"Integrated Positioning for Connected Vehicles","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; TD Bank Group; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pseudorange; Global Positioning System; GNSS applications; Multipath propagation; Computer science; Inertial navigation system; Precise Point Positioning; Positioning system; Hybrid positioning system; GPS/INS; Kalman filter; BeiDou Navigation Satellite System; Real-time computing; Inertial measurement unit; Assisted GPS; Engineering; Inertial frame of reference; Telecommunications; Artificial intelligence","score_opus":0.014032188072146989,"score_gpt":0.21852697580755465,"score_spread":0.20449478773540766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912561618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015504648,0.0014002341,0.9691821,0.00017664066,0.00038766424,0.00007170206,0.0001352825,0.002642703,0.010499021],"genre_scores_gemma":[0.75206316,0.0015057707,0.22276779,0.00024146962,0.00026128447,0.00017916062,0.00085679966,0.00015160003,0.021973033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992322,0.00012929813,0.000029729808,0.00017425868,0.0003580835,0.000076381555],"domain_scores_gemma":[0.99964964,0.000045282814,0.00003304728,0.000102455946,0.00015006373,0.000019599298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003462135,0.00088992866,0.0005237772,0.0006395458,0.00040361274,0.0009979507,0.0011346623,0.001200137,0.0038914017],"category_scores_gemma":[0.00124502,0.00033138477,0.00039835146,0.00094690063,0.0003553048,0.0010802993,0.0016190552,0.00064219476,0.002455433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002538881,0.00008264744,0.0033687165,0.00028456745,0.00018532366,0.00051713706,0.0002734365,0.26005688,0.02336249,0.050397225,0.010427739,0.65079004],"study_design_scores_gemma":[0.00006219867,0.00043904307,0.0019624783,0.000075199634,0.000129616,0.0004941828,0.00014162436,0.89672005,0.011510747,0.03126705,0.057138924,0.00005888611],"about_ca_topic_score_codex":0.004132704,"about_ca_topic_score_gemma":0.0039246893,"teacher_disagreement_score":0.004132704,"about_ca_system_score_codex":0.00060298137,"about_ca_system_score_gemma":0.00049719407,"threshold_uncertainty_score":0.013018072},"labels":[],"label_agreement":null},{"id":"W2912934789","doi":"10.1155/2019/6876925","title":"Study of Activity Tracking through Bluetooth Low Energy-Based Network","year":2019,"lang":"en","type":"article","venue":"Journal of Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Mitacs; Simon Fraser University","keywords":"Beacon; RSS; Bluetooth Low Energy; Computer science; Bluetooth; Scanner; Network packet; Energy (signal processing); Wearable computer; SIGNAL (programming language); Process (computing); Real-time computing; Computer vision; Artificial intelligence; Embedded system; Computer network; Wireless; Telecommunications; Mathematics","score_opus":0.0130362335757529,"score_gpt":0.22651192884462162,"score_spread":0.21347569526886873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912934789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44619235,0.0057033356,0.5334764,0.00056639494,0.00019987712,0.00010497126,0.00018104451,0.0003800703,0.013195493],"genre_scores_gemma":[0.97803307,0.0024301982,0.017027868,0.00005228676,0.00007468022,0.000043861888,0.00010142058,0.000016479315,0.0022200784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960524,0.00011721369,0.000016021084,0.00010314096,0.0001259507,0.00003250198],"domain_scores_gemma":[0.99960047,0.00021640531,0.0000573312,0.000028346702,0.000084306615,0.000013282108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031448025,0.00029880647,0.0002983186,0.0006121321,0.00015980218,0.00056577375,0.00049300544,0.00042448178,0.0005182773],"category_scores_gemma":[0.001369383,0.00012484583,0.00024156815,0.0007489435,0.00023592135,0.0010128639,0.00023804166,0.00020563709,0.00021934144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068671483,0.000327576,0.06433021,0.0011360453,0.00031868016,0.0025740145,0.0011873689,0.4040392,0.10672435,0.03525147,0.0028174322,0.38060698],"study_design_scores_gemma":[0.000012563001,0.00037312243,0.01754052,0.000054119875,0.00006590319,0.0012817055,0.00026891075,0.961901,0.01082949,0.0024676388,0.0051747765,0.000030322732],"about_ca_topic_score_codex":0.0010615785,"about_ca_topic_score_gemma":0.0005786416,"teacher_disagreement_score":0.0010615785,"about_ca_system_score_codex":0.00024660197,"about_ca_system_score_gemma":0.00015998253,"threshold_uncertainty_score":0.0021107793},"labels":[],"label_agreement":null},{"id":"W2913749846","doi":"10.1109/giis.2018.8635746","title":"Energy Consumption and Proximity Accuracy of BLE Beacons for Internet of Things Applications","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Beacon; Computer science; Bluetooth Low Energy; Scalability; Energy consumption; Kalman filter; Real-time computing; Bluetooth; The Internet; Embedded system; Telecommunications; Artificial intelligence; Wireless; Engineering; Electrical engineering; World Wide Web; Database","score_opus":0.01379846262284559,"score_gpt":0.24063591232508985,"score_spread":0.22683744970224426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913749846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79547536,0.0024911156,0.19326736,0.00036444422,0.00012592254,0.000052600568,0.0002668531,0.00090790825,0.0070484574],"genre_scores_gemma":[0.99219066,0.00024742473,0.006342841,0.000030429537,0.000008265332,0.000013518191,0.00012414061,0.000026704214,0.0010160039],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991825,0.00018586783,0.000045544268,0.0001397433,0.00035117057,0.00009515479],"domain_scores_gemma":[0.998365,0.00087792234,0.00016237375,0.00015492555,0.00041563477,0.000024271234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005996133,0.00041315833,0.0003584816,0.00063048943,0.00025906437,0.0004572102,0.00047833624,0.0005496541,0.000985488],"category_scores_gemma":[0.0036372775,0.0001464391,0.0002498128,0.0006293629,0.0001672027,0.00072561123,0.00029243398,0.00023610135,0.00035485544],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036970254,0.00043427,0.049528386,0.00073522894,0.00017107828,0.00060444576,0.0006768633,0.21858217,0.22937055,0.00365786,0.0032991534,0.48924297],"study_design_scores_gemma":[0.00006615666,0.0020461238,0.057896372,0.00008954923,0.00022033721,0.0010224596,0.00037337063,0.6781123,0.25122917,0.0011183554,0.007722789,0.00010301786],"about_ca_topic_score_codex":0.0016387883,"about_ca_topic_score_gemma":0.0019180302,"teacher_disagreement_score":0.0016387883,"about_ca_system_score_codex":0.0004270752,"about_ca_system_score_gemma":0.000157924,"threshold_uncertainty_score":0.003296733},"labels":[],"label_agreement":null},{"id":"W2917005499","doi":"10.1109/twc.2019.2914194","title":"Decimeter Ranging With Channel State Information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Huawei Technologies (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Channel state information; Computer science; Transmitter; Multipath propagation; Ranging; MIMO; Orthogonal frequency-division multiplexing; Algorithm; Rendering (computer graphics); Channel (broadcasting); Wireless; Correctness; Real-time computing; Telecommunications; Artificial intelligence","score_opus":0.00963863897880985,"score_gpt":0.20583802510158428,"score_spread":0.19619938612277443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917005499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049512245,0.0007299355,0.94385093,0.00017792442,0.000091863556,0.000021184142,0.0001611527,0.0005606364,0.0048941914],"genre_scores_gemma":[0.7610176,0.0009945688,0.2332769,0.00022854694,0.00013694305,0.000048131806,0.00043557552,0.00006814707,0.0037936033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944144,0.00010354852,0.000020666297,0.00015480224,0.00021182015,0.000067782734],"domain_scores_gemma":[0.9988558,0.0006691916,0.00012254876,0.00021998277,0.000110701454,0.000021783777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047030856,0.0006432082,0.0005681317,0.0004603823,0.00026344648,0.00074935355,0.00059015333,0.00079307513,0.0011260218],"category_scores_gemma":[0.003519617,0.0003353428,0.00021678906,0.000743638,0.0005037711,0.0021058235,0.001373885,0.00074594363,0.0005979041],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047744912,0.00010326634,0.0044051525,0.00039721536,0.00008989193,0.0002571232,0.00023856298,0.5559599,0.0659305,0.034151375,0.001353852,0.3366358],"study_design_scores_gemma":[0.000022502521,0.0001558253,0.0025657327,0.00004762344,0.00002957018,0.00047037264,0.0000920064,0.9386422,0.03392363,0.019637482,0.004348894,0.00006404261],"about_ca_topic_score_codex":0.00078628113,"about_ca_topic_score_gemma":0.0013833803,"teacher_disagreement_score":0.0011260218,"about_ca_system_score_codex":0.00028471602,"about_ca_system_score_gemma":0.0005471939,"threshold_uncertainty_score":0.0037668943},"labels":[],"label_agreement":null},{"id":"W2917631242","doi":"10.1109/glocom.2018.8647174","title":"Anomalous Path Detection for Spatial Crowdsourcing-Based Indoor Navigation System","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Crowdsourcing; Computer science; Hidden Markov model; Trajectory; Real-time computing; Scheme (mathematics); Path (computing); Data mining; Computer security; Artificial intelligence; Computer network; World Wide Web","score_opus":0.006210961457537821,"score_gpt":0.20040926745524842,"score_spread":0.1941983059977106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917631242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30917236,0.000440135,0.67782336,0.00034385463,0.00017599331,0.00021052295,0.00046438357,0.0075235628,0.003845798],"genre_scores_gemma":[0.9672954,0.00006086939,0.03147418,0.000049181872,0.000015920623,0.000038189843,0.0001833165,0.000019020454,0.00086386135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925584,0.00009583609,0.00004579151,0.0001583603,0.00031832993,0.00012589393],"domain_scores_gemma":[0.9989723,0.00015288536,0.00020149522,0.00023626091,0.00034176066,0.00009530639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043587963,0.0006045921,0.0006821537,0.0008821725,0.0006204055,0.00044151253,0.0010774975,0.0005167193,0.00052378594],"category_scores_gemma":[0.0018199674,0.00017761045,0.0003091876,0.00058800995,0.00039821534,0.00064393046,0.0010646081,0.00046986114,0.00041425097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014624477,0.00028529047,0.060450427,0.00032943347,0.0002581281,0.0021236858,0.0010200582,0.24626543,0.10966656,0.007093198,0.009457606,0.56158775],"study_design_scores_gemma":[0.000022925744,0.00012865064,0.005186862,0.000009701818,0.000034437486,0.00042084718,0.000121729216,0.9717177,0.017966287,0.0017609615,0.0025862167,0.000043585398],"about_ca_topic_score_codex":0.0070992643,"about_ca_topic_score_gemma":0.0065489835,"teacher_disagreement_score":0.0070992643,"about_ca_system_score_codex":0.0006965595,"about_ca_system_score_gemma":0.0009353346,"threshold_uncertainty_score":0.01411587},"labels":[],"label_agreement":null},{"id":"W2918278788","doi":"10.1109/globalsip.2018.8646581","title":"Enhanced Indoor Navigation System with Beacons and Kalman Filters","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Guelph","funders":"","keywords":"Beacon; Computer science; Kalman filter; Global Positioning System; Real-time computing; Bluetooth; Wireless; Usability; Embedded system; Telecommunications; Artificial intelligence; Human–computer interaction","score_opus":0.004651588858190171,"score_gpt":0.1883233516977646,"score_spread":0.18367176283957443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918278788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030496735,0.00048431932,0.9581293,0.00008270766,0.00019409583,0.000061027506,0.00015818073,0.006304143,0.004089462],"genre_scores_gemma":[0.7489013,0.00046029757,0.23635781,0.000117935386,0.00009117756,0.0001796589,0.00058654894,0.00014479263,0.013160545],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999567,0.00006493322,0.000027259635,0.00010941073,0.00018012615,0.00005128065],"domain_scores_gemma":[0.99957174,0.000058474274,0.000040950254,0.00006594735,0.00023843342,0.000024353652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035751233,0.000531994,0.0007166864,0.0005934442,0.00031445624,0.00037915749,0.0008658465,0.0005814374,0.0022140888],"category_scores_gemma":[0.00070338836,0.00026471468,0.00040618997,0.00047905996,0.0001409242,0.00062373746,0.00062659296,0.000474709,0.0013588618],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011776205,0.00023599085,0.006878219,0.0006040939,0.00014533666,0.00055979955,0.00062281056,0.083232,0.17545943,0.0057742707,0.008570791,0.71673965],"study_design_scores_gemma":[0.00023563471,0.0010660349,0.0071499585,0.00007860294,0.00028107778,0.00094637944,0.000084192034,0.863115,0.08219396,0.0013769346,0.04332009,0.00015206235],"about_ca_topic_score_codex":0.004953661,"about_ca_topic_score_gemma":0.0037152024,"teacher_disagreement_score":0.004953661,"about_ca_system_score_codex":0.00033023802,"about_ca_system_score_gemma":0.00041637855,"threshold_uncertainty_score":0.009849668},"labels":[],"label_agreement":null},{"id":"W2921408071","doi":"10.1109/lawp.2019.2904580","title":"Wireless Multifrequency Feature Set to Simplify Human 3-D Pose Estimation","year":2019,"lang":"en","type":"article","venue":"IEEE Antennas and Wireless Propagation Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Torso; Feature (linguistics); Computer science; Multipath propagation; Artificial intelligence; Computer vision; Channel (broadcasting); Feature extraction; Feature vector; Set (abstract data type); Pattern recognition (psychology); Data set; Doppler effect; SIGNAL (programming language); Wireless; Telecommunications; Physics","score_opus":0.008043742054474395,"score_gpt":0.22695804895378782,"score_spread":0.21891430689931343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921408071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05688931,0.00036668932,0.93495274,0.00012629006,0.00017449597,0.00013246424,0.0025362552,0.002634714,0.0021869976],"genre_scores_gemma":[0.5765926,0.0004318915,0.40793464,0.00021387247,0.00015211775,0.0007003128,0.008369522,0.00026416752,0.0053407596],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965,0.00004273258,0.000019992385,0.00009457149,0.00014118498,0.000051423125],"domain_scores_gemma":[0.99971277,0.000059486316,0.00003512254,0.00006455565,0.000111721754,0.000016345279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023541061,0.00087597175,0.0007608628,0.001143063,0.00026643925,0.0004436217,0.0006386765,0.00046326572,0.004166749],"category_scores_gemma":[0.0010787094,0.00019702395,0.0007025205,0.0010128326,0.00018076022,0.0004897787,0.00072454533,0.00049657386,0.0018352196],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002382522,0.00024354467,0.004449435,0.00014829391,0.00008244909,0.00020924465,0.0001160399,0.043142866,0.06077871,0.0023390504,0.01059088,0.8776612],"study_design_scores_gemma":[0.00006238805,0.00047905368,0.042889297,0.0000752012,0.00008981995,0.0012912684,0.00015042556,0.84117573,0.06847712,0.0048676985,0.040298663,0.00014332696],"about_ca_topic_score_codex":0.0028171958,"about_ca_topic_score_gemma":0.0034393491,"teacher_disagreement_score":0.004166749,"about_ca_system_score_codex":0.00021623788,"about_ca_system_score_gemma":0.00038880785,"threshold_uncertainty_score":0.013939202},"labels":[],"label_agreement":null},{"id":"W2921972197","doi":"10.1109/access.2019.2904843","title":"Locating the Nodes From Incomplete Euclidean Distance Matrix Using Bayesian Learning","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Jiangsu University of Technology; Government of Jiangsu Province; Jiangsu University; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Euclidean distance; Euclidean distance matrix; Node (physics); Wireless sensor network; Euclidean geometry; Multidimensional scaling; Computer science; Matrix completion; Distance matrix; Matrix (chemical analysis); Bayesian probability; Fraction (chemistry); Scaling; Rank (graph theory); Low-rank approximation; Artificial intelligence; Algorithm; Mathematics; Machine learning; Combinatorics; Geometry; Computer network","score_opus":0.016409863547858467,"score_gpt":0.27035488384674977,"score_spread":0.2539450202988913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921972197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004845137,0.0001357408,0.9944877,0.00007482684,0.00001048377,0.000011171085,0.00001726964,0.000111038564,0.000306592],"genre_scores_gemma":[0.50215113,0.0013499432,0.49286786,0.00020845802,0.00012717642,0.00017632547,0.0004393932,0.00008326119,0.0025965297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991893,0.00025766046,0.00004590253,0.00017459354,0.00028256534,0.00005006431],"domain_scores_gemma":[0.9987679,0.00063691876,0.00020118502,0.00013617286,0.00021691181,0.00004078541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010526181,0.0007905584,0.0008808177,0.0008378305,0.0003620357,0.0006068216,0.0010647648,0.000850308,0.00074505777],"category_scores_gemma":[0.004345032,0.0004509769,0.00059159135,0.0010790686,0.0007759657,0.0022347847,0.001209823,0.0010616523,0.00045555856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014855982,0.00006960303,0.001254705,0.00016314426,0.000061857645,0.00010051946,0.00013837952,0.7545136,0.007854174,0.01874607,0.0017365547,0.21521287],"study_design_scores_gemma":[0.000008386259,0.000033759803,0.00019176293,0.000007640284,0.000008835895,0.00004000902,0.000014181663,0.9921822,0.0013289714,0.005725112,0.00044711446,0.000011942123],"about_ca_topic_score_codex":0.0033814942,"about_ca_topic_score_gemma":0.003800107,"teacher_disagreement_score":0.0033814942,"about_ca_system_score_codex":0.0005114719,"about_ca_system_score_gemma":0.0009874084,"threshold_uncertainty_score":0.0067236423},"labels":[],"label_agreement":null},{"id":"W2921985399","doi":"10.1049/el.2018.7722","title":"Parametric/fingerprinting integrated angle and location estimation using RSS from single multi‐antenna access point","year":2019,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"RSS; Angle of arrival; Parametric statistics; Signal strength; Computer science; Point (geometry); Wireless; Antenna (radio); Telecommunications; Statistics; Mathematics; Geometry","score_opus":0.013678591051293994,"score_gpt":0.22736951634635683,"score_spread":0.21369092529506284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921985399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07419392,0.00038816908,0.9215044,0.000059937007,0.00011386494,0.000032744723,0.00013657042,0.0012581405,0.0023124134],"genre_scores_gemma":[0.77881205,0.00044904763,0.21744202,0.00006734652,0.00009328112,0.000052921867,0.0002863958,0.0000687227,0.0027282871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927896,0.0001127136,0.000047430916,0.00018132725,0.00031534862,0.000064275584],"domain_scores_gemma":[0.9988997,0.00016705015,0.00019831567,0.00043219796,0.00026666216,0.000036208672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003302456,0.0006087721,0.0007313044,0.0009605793,0.00022654343,0.0008655161,0.00076233456,0.0006081455,0.0009979055],"category_scores_gemma":[0.0018591143,0.00026916742,0.0004215253,0.0012126981,0.0002472595,0.0011666212,0.000825892,0.00047095434,0.0012402643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033036814,0.00012221489,0.009025293,0.00029435792,0.00010036934,0.00030803846,0.0001535666,0.04048655,0.15875328,0.0022781603,0.001560446,0.7865874],"study_design_scores_gemma":[0.00004967839,0.0009154298,0.01698436,0.00007645478,0.00015557051,0.0044563245,0.0002398816,0.63554674,0.32302806,0.0035520634,0.014821356,0.00017411297],"about_ca_topic_score_codex":0.0004513319,"about_ca_topic_score_gemma":0.0006506457,"teacher_disagreement_score":0.0009979055,"about_ca_system_score_codex":0.00015054818,"about_ca_system_score_gemma":0.0002490349,"threshold_uncertainty_score":0.003338337},"labels":[],"label_agreement":null},{"id":"W2922879336","doi":"10.1109/jiot.2019.2940368","title":"Recurrent Neural Networks for Accurate RSSI Indoor Localization","year":2019,"lang":"en","type":"preprint","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Recurrent neural network; Computer science; Trajectory; Probabilistic logic; Position (finance); Received signal strength indication; Artificial intelligence; Signal strength; Algorithm; Artificial neural network; Real-time computing; Wireless; Telecommunications","score_opus":0.020039521452000903,"score_gpt":0.2583800422731143,"score_spread":0.23834052082111337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922879336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023728779,0.0008171113,0.9718247,0.0001519694,0.000078903206,0.000013222075,0.00011652155,0.0017181325,0.0015505716],"genre_scores_gemma":[0.8512422,0.00066130585,0.14409338,0.00011567378,0.00006901323,0.000052197905,0.00040825768,0.00013258333,0.003225438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960226,0.000086161876,0.000031875672,0.000113195674,0.00011708285,0.00004943611],"domain_scores_gemma":[0.9995459,0.00018360768,0.000058131536,0.00005343003,0.00014789791,0.000011044369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059619895,0.00093191874,0.00065676897,0.00043934744,0.00023829257,0.00057840726,0.0009682674,0.0006875775,0.0011661408],"category_scores_gemma":[0.0022981076,0.000382691,0.00046234345,0.0006008888,0.00032498132,0.00095047,0.00051747984,0.0008579077,0.0005253547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011199128,0.000033626908,0.0011150554,0.000102964295,0.000073277064,0.00010791893,0.00006572066,0.8004284,0.008892287,0.005390454,0.0017533444,0.18192501],"study_design_scores_gemma":[0.0000019967513,0.000012052256,0.00015384678,0.0000039246042,0.000007111944,0.000014141699,0.0000040517493,0.99695873,0.0013885665,0.0011388751,0.0003121963,0.000004473477],"about_ca_topic_score_codex":0.008294974,"about_ca_topic_score_gemma":0.008455076,"teacher_disagreement_score":0.008294974,"about_ca_system_score_codex":0.0006606876,"about_ca_system_score_gemma":0.00047481217,"threshold_uncertainty_score":0.01649338},"labels":[],"label_agreement":null},{"id":"W2922887640","doi":"10.1109/jiot.2019.2906489","title":"An Adaptive Sampling Scheme via Approximate Volume Sampling for Fingerprint-Based Indoor Localization","year":2019,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Fingerprint (computing); Sampling (signal processing); Adaptive sampling; Scheme (mathematics); Fingerprint recognition; RSS; Wireless; Data mining; Algorithm; Real-time computing; Artificial intelligence; Computer vision; Mathematics; Statistics; Telecommunications; Monte Carlo method","score_opus":0.02095677382069503,"score_gpt":0.2548552377898855,"score_spread":0.23389846396919048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922887640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048519527,0.000118093405,0.9943639,0.000046774,0.00002330341,0.00001861251,0.00002773002,0.00022798164,0.00032161328],"genre_scores_gemma":[0.4320691,0.000521457,0.5644103,0.00013486943,0.00011662519,0.00014066984,0.0003494647,0.00010091982,0.0021565573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847573,0.0005202293,0.000078331934,0.00023387025,0.00055902544,0.00013280702],"domain_scores_gemma":[0.99831164,0.0006060897,0.0001827678,0.00044010824,0.00036362314,0.00009580747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011810226,0.0009471474,0.001103108,0.0007175787,0.00057392695,0.00073671,0.0015286241,0.0007760777,0.0015235367],"category_scores_gemma":[0.004715656,0.00035402807,0.00073879596,0.001336371,0.0007793278,0.0019691864,0.001972547,0.0010855348,0.00055209413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073319615,0.00011360861,0.0024066945,0.00024985397,0.00008373914,0.00023430304,0.00040020712,0.4784669,0.03872287,0.0434329,0.0038787872,0.43127695],"study_design_scores_gemma":[0.000012396558,0.000064762375,0.00013773389,0.00000609844,0.000008595046,0.000098902005,0.000020840607,0.9911675,0.0035100593,0.0039698207,0.0009865068,0.00001685755],"about_ca_topic_score_codex":0.0036533854,"about_ca_topic_score_gemma":0.0029328035,"teacher_disagreement_score":0.0036533854,"about_ca_system_score_codex":0.00062772,"about_ca_system_score_gemma":0.0008471527,"threshold_uncertainty_score":0.007264197},"labels":[],"label_agreement":null},{"id":"W2938373190","doi":"10.1109/icassp.2019.8683560","title":"Belief Condensation Filtering for RSSI-Based State Estimation in Indoor Localization","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Concordia University; University of Toronto","funders":"","keywords":"Beacon; Computer science; Multipath propagation; Kalman filter; Particle filter; Real-time computing; Gaussian; Filter (signal processing); Energy (signal processing); Algorithm; Telecommunications; Artificial intelligence; Computer vision; Mathematics","score_opus":0.00650974857785058,"score_gpt":0.21230130125013078,"score_spread":0.2057915526722802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938373190","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001116165,0.0001054114,0.9982685,0.00003665174,0.000020820971,0.000010062354,0.000014956106,0.00010023255,0.00032707493],"genre_scores_gemma":[0.4467494,0.0017202456,0.54373884,0.00026318952,0.00027226764,0.00025035668,0.00038118064,0.00013224203,0.0064923493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924564,0.00019929724,0.00004104142,0.00017648944,0.0002737187,0.000063743035],"domain_scores_gemma":[0.99871314,0.0007957097,0.00011606815,0.00011670615,0.00022498595,0.000033369975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001277949,0.00088778976,0.00083948317,0.00076453347,0.0004207338,0.0007054359,0.0012999332,0.000953336,0.0015081699],"category_scores_gemma":[0.0051026694,0.0005206135,0.000757934,0.001125415,0.0010805684,0.0014772395,0.0010828478,0.0011876504,0.00069472345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023774372,0.00006636285,0.0012627172,0.00027376454,0.00011309197,0.0001537058,0.00040996657,0.6647042,0.01375804,0.05395086,0.0026254791,0.26244405],"study_design_scores_gemma":[0.000007801528,0.000053316006,0.0002733631,0.000019409437,0.000017988017,0.000043722706,0.000017023309,0.9869307,0.0033297378,0.007137206,0.0021472392,0.000022440916],"about_ca_topic_score_codex":0.006337456,"about_ca_topic_score_gemma":0.006004503,"teacher_disagreement_score":0.006337456,"about_ca_system_score_codex":0.0009196225,"about_ca_system_score_gemma":0.00097498717,"threshold_uncertainty_score":0.012601137},"labels":[],"label_agreement":null},{"id":"W2938798429","doi":"10.1049/iet-wss.2018.5237","title":"Hybrid indoor location positioning system","year":2019,"lang":"en","type":"article","venue":"IET Wireless Sensor Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"RSS; Hybrid positioning system; Non-line-of-sight propagation; Computer science; Indoor positioning system; Robustness (evolution); Positioning system; Real-time computing; Signal strength; Wireless; Telecommunications; Acoustics","score_opus":0.004584018006843062,"score_gpt":0.1779164617426153,"score_spread":0.17333244373577225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938798429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02960209,0.0021535184,0.9199179,0.00023435877,0.00067558914,0.00018816894,0.0012167541,0.01734587,0.02866584],"genre_scores_gemma":[0.66793257,0.0013112775,0.25375855,0.0007480349,0.0004464638,0.0005732479,0.004553236,0.00023553526,0.070441104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913895,0.00013328299,0.000042143347,0.00025897648,0.0003133135,0.00011317051],"domain_scores_gemma":[0.99953175,0.00005173375,0.00004862685,0.00012591729,0.00020158826,0.000040450792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040724417,0.00072427513,0.0011479799,0.0011262884,0.0005668598,0.001158056,0.0018185992,0.0013762087,0.010277663],"category_scores_gemma":[0.00046734966,0.00024492166,0.0004155617,0.0011939468,0.00022735923,0.0010287738,0.0018031913,0.0005652283,0.009695983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082954665,0.00017067215,0.0043675136,0.00063742057,0.00021245194,0.00067812117,0.00022312235,0.022670759,0.101444416,0.008981089,0.033777665,0.82600737],"study_design_scores_gemma":[0.00029056563,0.0025612197,0.017500198,0.00017403034,0.0005168403,0.0049790437,0.00031133497,0.46659365,0.1371983,0.0068609277,0.36259657,0.0004173276],"about_ca_topic_score_codex":0.0015180318,"about_ca_topic_score_gemma":0.0014864263,"teacher_disagreement_score":0.010277663,"about_ca_system_score_codex":0.00039534256,"about_ca_system_score_gemma":0.00045626308,"threshold_uncertainty_score":0.034382164},"labels":[],"label_agreement":null},{"id":"W2939699006","doi":"10.3390/s19071678","title":"Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Component (thermodynamics); Scale (ratio); Computer science; Environmental science; Geography; Physics; Cartography","score_opus":0.013148581437981345,"score_gpt":0.21890889969915273,"score_spread":0.2057603182611714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2939699006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027397668,0.0056310426,0.9597587,0.00037577373,0.00010903154,0.000061952596,0.000072991046,0.00057978026,0.006013016],"genre_scores_gemma":[0.7824137,0.015971214,0.19468954,0.00024381233,0.0003089637,0.000111623085,0.00046221455,0.00011275816,0.0056861523],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996258,0.00008848754,0.000011740713,0.00007145133,0.00017431087,0.000028093604],"domain_scores_gemma":[0.9994796,0.0002007953,0.00004584957,0.00006967902,0.00019157483,0.000012541487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003857334,0.0004940649,0.00036531285,0.0006025473,0.00029094671,0.00056003884,0.00039299595,0.000351168,0.0008244008],"category_scores_gemma":[0.0013535293,0.0001717267,0.0003633217,0.0013519247,0.00032847997,0.00097597553,0.00032364746,0.00041192464,0.00036281568],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010851142,0.00010199744,0.005538391,0.0007899755,0.00014823339,0.0005805058,0.00018342824,0.1401206,0.038621534,0.019643912,0.0052589877,0.78890395],"study_design_scores_gemma":[0.000008535932,0.00024831077,0.017722704,0.00009766741,0.000117866264,0.0011342111,0.00023249505,0.9130943,0.026913965,0.009022958,0.031324774,0.00008231122],"about_ca_topic_score_codex":0.0017502968,"about_ca_topic_score_gemma":0.0014994214,"teacher_disagreement_score":0.0017502968,"about_ca_system_score_codex":0.00027349524,"about_ca_system_score_gemma":0.00042678538,"threshold_uncertainty_score":0.003480196},"labels":[],"label_agreement":null},{"id":"W2944147551","doi":"10.1051/itmconf/20192701003","title":"Impact of Initialization on Gradient Descent Method in Localization Using Received Signal Strength","year":2019,"lang":"en","type":"article","venue":"ITM Web of Conferences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Initialization; Trilateration; RSS; Gradient descent; Signal strength; Computer science; SIGNAL (programming language); Jackknife resampling; Selection (genetic algorithm); Algorithm; Descent direction; Artificial intelligence; Mathematics; Statistics; Artificial neural network; Telecommunications; Wireless; Estimator","score_opus":0.028418385228542453,"score_gpt":0.29642478509808834,"score_spread":0.2680063998695459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944147551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.104396306,0.0011164502,0.88935286,0.00026512658,0.0001393526,0.000094658004,0.00005543845,0.0015370395,0.0030428465],"genre_scores_gemma":[0.6380412,0.0007583891,0.35821855,0.000098757,0.00005081505,0.00007724952,0.00016812356,0.0004392791,0.0021476073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860686,0.00074225396,0.00008659298,0.00017758645,0.00030904976,0.00007772878],"domain_scores_gemma":[0.9956601,0.0028255545,0.0002694524,0.00045353794,0.0007066575,0.00008458199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017387948,0.00077216694,0.00061780127,0.00039565586,0.0003887765,0.0007925153,0.0005354436,0.00078959577,0.0012191574],"category_scores_gemma":[0.008744744,0.0002705114,0.0003538307,0.0004523299,0.00048561455,0.00089093315,0.0005189167,0.0008515316,0.00061698805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023968627,0.0004028499,0.008222632,0.00068867026,0.00022715374,0.0006795283,0.00037086057,0.41424835,0.121327,0.006045314,0.0021579193,0.44323283],"study_design_scores_gemma":[0.00008100703,0.00081760297,0.0062626554,0.00008676012,0.00010195364,0.0005978905,0.00008738057,0.88517576,0.10222002,0.0010796123,0.0034066094,0.0000827118],"about_ca_topic_score_codex":0.0019935216,"about_ca_topic_score_gemma":0.001736955,"teacher_disagreement_score":0.0019935216,"about_ca_system_score_codex":0.0002721585,"about_ca_system_score_gemma":0.0005863355,"threshold_uncertainty_score":0.009195745},"labels":[],"label_agreement":null},{"id":"W2945044524","doi":"10.1109/iccspa.2019.8713720","title":"Research on the Improved Data Processing Method for Foot-Mounted Inertial Pedestrian Positioning System","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Heading (navigation); Kalman filter; Position (finance); Computer science; Control theory (sociology); Inertial measurement unit; Inertial navigation system; Observability; Positioning system; Inertial frame of reference; Computer vision; Artificial intelligence; Engineering; Mathematics","score_opus":0.07291284755741742,"score_gpt":0.3723705647490279,"score_spread":0.2994577171916105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945044524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005170919,0.000357643,0.99238825,0.00010743574,0.000112998525,0.00004497965,0.000050957446,0.0006338066,0.0011330108],"genre_scores_gemma":[0.22497545,0.0016244994,0.76310074,0.0002208446,0.0003681921,0.00027671986,0.0007205116,0.00012623504,0.008586775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883765,0.00014352566,0.000088327346,0.00031544387,0.00053848233,0.00007666369],"domain_scores_gemma":[0.99927634,0.0000942249,0.000047040277,0.000077228084,0.00048152846,0.000023700599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000705208,0.00075997727,0.0007104886,0.0010620204,0.00059835386,0.0007903373,0.0008411959,0.0006368944,0.0036961772],"category_scores_gemma":[0.0019954504,0.00034702738,0.00073412043,0.0013575125,0.00028759893,0.0015392349,0.00061463384,0.00086629874,0.001402827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023155069,0.00006626545,0.0036763388,0.00038160454,0.00009365446,0.0001885237,0.00026566006,0.052511416,0.0400298,0.010965451,0.0052572484,0.8863325],"study_design_scores_gemma":[0.000089591216,0.00021834884,0.0037749931,0.000042990472,0.00009267303,0.00037782476,0.00012075203,0.94808674,0.024468025,0.00339355,0.019265722,0.00006875679],"about_ca_topic_score_codex":0.008481525,"about_ca_topic_score_gemma":0.004130991,"teacher_disagreement_score":0.008481525,"about_ca_system_score_codex":0.0004258495,"about_ca_system_score_gemma":0.0013720439,"threshold_uncertainty_score":0.0168643},"labels":[],"label_agreement":null},{"id":"W2945972880","doi":"10.1016/j.procs.2019.04.068","title":"Comparative Study on Range Free Localization Algorithms","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; King Fahd University of Petroleum and Minerals; Acadia University","keywords":"Computer science; Range (aeronautics); Centroid; Algorithm; Wireless sensor network; Node (physics); MATLAB; Scope (computer science); Position (finance); Artificial intelligence; Computer network","score_opus":0.016127823277977595,"score_gpt":0.24497356477435311,"score_spread":0.22884574149637552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945972880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11185576,0.05619098,0.77488303,0.0014897664,0.0009579415,0.00037347947,0.000802495,0.0029810932,0.050465364],"genre_scores_gemma":[0.6884496,0.026914705,0.27049527,0.000446759,0.00045082605,0.00026371583,0.0018708153,0.00063146855,0.010476863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959557,0.0011082849,0.0003073097,0.00048010732,0.0019024847,0.00024612926],"domain_scores_gemma":[0.9911708,0.005421762,0.00044430164,0.000709111,0.0021115078,0.00014253908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00261181,0.0011054792,0.0010079598,0.0045351023,0.0009851294,0.0021546716,0.0020200019,0.0014227823,0.004677589],"category_scores_gemma":[0.012963613,0.00029277935,0.0009067316,0.005012676,0.00056913815,0.0032769127,0.001058626,0.00060782593,0.001330211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010899123,0.00019834036,0.006589796,0.0018399815,0.00030378866,0.00032583374,0.00030907305,0.22466174,0.0062996685,0.028021686,0.010131636,0.72022843],"study_design_scores_gemma":[0.00017387391,0.0020973557,0.011185439,0.00050700596,0.00056916534,0.0034688662,0.0010755325,0.8633865,0.018403074,0.019849133,0.07906935,0.00021468043],"about_ca_topic_score_codex":0.002521741,"about_ca_topic_score_gemma":0.0014931962,"teacher_disagreement_score":0.004677589,"about_ca_system_score_codex":0.0010912472,"about_ca_system_score_gemma":0.0010239434,"threshold_uncertainty_score":0.015648127},"labels":[],"label_agreement":null},{"id":"W2946931672","doi":"10.1109/jsen.2019.2919899","title":"A Novel Subspace Approach for Bearing-Only Target Localization","year":2019,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subspace topology; Bearing (navigation); Algorithm; Scalar (mathematics); Dimension (graph theory); Nonlinear system; Upper and lower bounds; Computer science; Mathematics; Matrix (chemical analysis); Noise (video); Signal subspace; Mathematical optimization; Applied mathematics; Artificial intelligence; Mathematical analysis; Geometry; Combinatorics","score_opus":0.011932245078230262,"score_gpt":0.2143661797747627,"score_spread":0.20243393469653242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946931672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008346101,0.00008532835,0.99855095,0.000024536248,0.000017022967,0.0000061305827,0.000010092769,0.00014717132,0.00032412153],"genre_scores_gemma":[0.11965226,0.0007865287,0.8737425,0.00014153431,0.00015549915,0.000180164,0.0002970378,0.00015020999,0.004894268],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995204,0.000121466495,0.000019958628,0.00009500178,0.00020033125,0.000042853055],"domain_scores_gemma":[0.99964654,0.00008693546,0.000039433206,0.000059570077,0.00014010543,0.000027387827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000363409,0.0008424019,0.00096183765,0.0008048778,0.00041132426,0.00061128027,0.0009984429,0.0007297025,0.0020863833],"category_scores_gemma":[0.00094407075,0.00030417944,0.0006327083,0.0012178196,0.00049858127,0.0016622245,0.0012489937,0.00085180666,0.0014691959],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009125357,0.00009987752,0.00057487853,0.0002160558,0.00010181334,0.00014378385,0.00015355948,0.2633415,0.052256722,0.04659808,0.006238941,0.6301835],"study_design_scores_gemma":[0.0000075162816,0.00005599203,0.000100475576,0.0000052394203,0.000007670362,0.000103437975,0.000016936652,0.9844065,0.0038054448,0.006875122,0.0045974324,0.000018203486],"about_ca_topic_score_codex":0.0013353684,"about_ca_topic_score_gemma":0.0014405361,"teacher_disagreement_score":0.0020863833,"about_ca_system_score_codex":0.00022911424,"about_ca_system_score_gemma":0.00086727954,"threshold_uncertainty_score":0.0069795847},"labels":[],"label_agreement":null},{"id":"W2948076304","doi":"10.1007/978-981-13-8566-7_48","title":"Low-Cost Localization and Tracking System with Wireless Sensor Networks in Snowy Environments","year":2019,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université du Québec à Chicoutimi","funders":"","keywords":"Wireless sensor network; Software deployment; Computer science; Snow; Tracking (education); Real-time computing; Signal strength; Key distribution in wireless sensor networks; Wireless; Node (physics); Wireless network; Computer network; Telecommunications; Engineering; Geography; Meteorology","score_opus":0.00872805022187813,"score_gpt":0.17723879392135983,"score_spread":0.16851074369948169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948076304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06936091,0.0028834336,0.8913031,0.00045351248,0.0005067304,0.00007683694,0.00028645885,0.002827717,0.03230129],"genre_scores_gemma":[0.6675528,0.0032581768,0.22766222,0.000385889,0.0003034686,0.00015686973,0.0007921633,0.00019275791,0.099695645],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987006,0.000014096115,0.0000048453685,0.000031651172,0.00006219884,0.000017060596],"domain_scores_gemma":[0.9999261,0.000016854723,0.000008712825,0.000012740911,0.000028431594,0.0000071726286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012294627,0.0003667565,0.00040285854,0.00033138855,0.00034986442,0.000526451,0.0009288936,0.00052357104,0.0027209853],"category_scores_gemma":[0.00015113082,0.0002002791,0.00023092293,0.0005625305,0.00017348574,0.0010290737,0.00056112965,0.000344311,0.0011954044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036947237,0.00009342001,0.0025338044,0.00039203776,0.000060556198,0.0005206618,0.00030963894,0.024017816,0.26182964,0.011142872,0.022617284,0.67611283],"study_design_scores_gemma":[0.0000790315,0.00094957673,0.011763969,0.000115121606,0.00026642624,0.0021074957,0.000464602,0.55353135,0.20914596,0.010822638,0.21061929,0.0001346026],"about_ca_topic_score_codex":0.0019423343,"about_ca_topic_score_gemma":0.0027391333,"teacher_disagreement_score":0.0027209853,"about_ca_system_score_codex":0.00026308023,"about_ca_system_score_gemma":0.00033230064,"threshold_uncertainty_score":0.009102583},"labels":[],"label_agreement":null},{"id":"W2949072213","doi":"10.48550/arxiv.1310.7042","title":"On Convexification of Range Measurement Based Sensor and Source Localization Problems","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Convex optimization; Convexity; Minification; Range (aeronautics); Regular polygon; Proper convex function; Mathematical optimization; Computer science; Function (biology); Convex function; Noise (video); Convex set; Set (abstract data type); Convex analysis; Algorithm; Optimization problem; Mathematics; Artificial intelligence; Engineering; Geometry","score_opus":0.057067320206578225,"score_gpt":0.15581507832907904,"score_spread":0.0987477581225008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949072213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003249392,0.00043674983,0.9913174,0.0004539148,0.000040316954,0.00002838173,0.00006410981,0.00004059579,0.0043691],"genre_scores_gemma":[0.40536758,0.006188633,0.5640563,0.0008782837,0.0007388372,0.0007553771,0.0012173409,0.00071818335,0.02007958],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962447,0.0021532,0.00014519817,0.0005881272,0.0006926264,0.00017599667],"domain_scores_gemma":[0.99048996,0.00719723,0.0006217114,0.00046661476,0.0009997485,0.00022477182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005757743,0.002415952,0.00174503,0.001510169,0.0005904132,0.0027743382,0.0017721958,0.0018401404,0.0037377544],"category_scores_gemma":[0.014686526,0.0009626297,0.0017317375,0.0014984208,0.0031623647,0.004317688,0.0037220886,0.003882746,0.000977519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102776015,0.000056734494,0.0006570988,0.0004506661,0.00007414859,0.00030580393,0.00026642947,0.46961644,0.0028732622,0.4845744,0.004770187,0.03625207],"study_design_scores_gemma":[0.000011586247,0.00004583503,0.0002670714,0.000051842035,0.000014406342,0.00010468156,0.000052550968,0.8206347,0.0012517319,0.17403845,0.003506913,0.000020142146],"about_ca_topic_score_codex":0.0018133846,"about_ca_topic_score_gemma":0.0007576603,"teacher_disagreement_score":0.005757743,"about_ca_system_score_codex":0.0018748973,"about_ca_system_score_gemma":0.0009933832,"threshold_uncertainty_score":0.030450225},"labels":[],"label_agreement":null},{"id":"W2949335103","doi":"10.48550/arxiv.1207.1137","title":"Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Beijing University of Posts and Telecommunications","keywords":"RSS; Calibration; Background subtraction; Computer science; Baseline (sea); Artificial intelligence; Tracking (education); Similarity (geometry); Pixel; Mathematics; Image (mathematics); Statistics","score_opus":0.06722808966350476,"score_gpt":0.19801780235246083,"score_spread":0.13078971268895606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949335103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023700828,0.00017319375,0.9742755,0.000038254457,0.00003178012,0.000014862011,0.000026881613,0.00078181253,0.00095693255],"genre_scores_gemma":[0.54221827,0.00046579685,0.45439345,0.00007844423,0.00004521459,0.000072031995,0.00030656255,0.00029549035,0.0021247647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941266,0.00012807328,0.000022379521,0.00015579049,0.00021797056,0.00006302818],"domain_scores_gemma":[0.9991285,0.00039387486,0.00008881077,0.00015339974,0.00019882435,0.00003657775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081812614,0.000711999,0.0007096478,0.0009062488,0.0004519594,0.0007400866,0.0011004369,0.00065283146,0.0010765792],"category_scores_gemma":[0.0035935992,0.00032839668,0.00045051673,0.001149063,0.0005706798,0.0013056067,0.0008816696,0.00073212193,0.00072022656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004962093,0.00019052037,0.0036637955,0.00017976746,0.00007530256,0.00038883596,0.00027140687,0.33659852,0.1104232,0.0124762915,0.0021315112,0.5331046],"study_design_scores_gemma":[0.0000104034,0.00007037068,0.002219569,0.000012104706,0.000018850345,0.00023251056,0.000043443102,0.94252646,0.047266535,0.005102594,0.0024722477,0.00002493291],"about_ca_topic_score_codex":0.0022944643,"about_ca_topic_score_gemma":0.0025496145,"teacher_disagreement_score":0.0022944643,"about_ca_system_score_codex":0.0006246798,"about_ca_system_score_gemma":0.0005406755,"threshold_uncertainty_score":0.0045622587},"labels":[],"label_agreement":null},{"id":"W2949482420","doi":"10.1145/3307334.3326084","title":"Are RFID Sensing Systems Ready for the Real World?","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"RSS; Computer science; Identifier; Orientation (vector space); Radio-frequency identification; SIGNAL (programming language); Signal strength; Noise (video); Identification (biology); Radio frequency; Real-time computing; Wireless; Telecommunications; Artificial intelligence; Computer security; Computer network","score_opus":0.02259295689906578,"score_gpt":0.2377294378977709,"score_spread":0.2151364809987051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949482420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035381403,0.1711126,0.42483792,0.2716575,0.015267625,0.00014423487,0.00061224686,0.0022620494,0.078724466],"genre_scores_gemma":[0.60698116,0.1456567,0.16551164,0.040049963,0.007824664,0.00037900478,0.0007774371,0.00048469743,0.03233464],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959266,0.001319638,0.0002462153,0.0007967058,0.0013750057,0.00033573178],"domain_scores_gemma":[0.9891033,0.005209656,0.0010888601,0.0017169162,0.0025373062,0.0003438499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058902334,0.0009086576,0.0008937552,0.0012092256,0.0012158481,0.0073564737,0.0016778989,0.005312324,0.007948967],"category_scores_gemma":[0.022771696,0.00079666125,0.00073825545,0.0012947368,0.0065301573,0.023440173,0.001969521,0.0039088,0.0055760634],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028362806,0.00013606793,0.0074539743,0.0030682208,0.00015962162,0.00064257364,0.0019133333,0.0042491197,0.008039991,0.51614505,0.044280875,0.4136276],"study_design_scores_gemma":[0.00004275786,0.000377265,0.0060184062,0.0020921791,0.00015357953,0.002942318,0.0057259463,0.009341314,0.007865432,0.38338742,0.58172494,0.00032841496],"about_ca_topic_score_codex":0.0011105123,"about_ca_topic_score_gemma":0.00063598325,"teacher_disagreement_score":0.007948967,"about_ca_system_score_codex":0.0013807681,"about_ca_system_score_gemma":0.0012547798,"threshold_uncertainty_score":0.031150937},"labels":[],"label_agreement":null},{"id":"W2949593521","doi":"10.48550/arxiv.1207.1702","title":"Performance Study of Localization Techniques in Wireless Body Area Sensor Networks","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Wireless sensor network; Particle filter; Real-time computing; Global Positioning System; Tracking (education); Kalman filter; GSM; Wireless; Signal strength; Artificial intelligence; Computer network; Telecommunications","score_opus":0.033502829786406965,"score_gpt":0.17284259408133942,"score_spread":0.13933976429493244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949593521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37813082,0.029772596,0.56610924,0.0017802563,0.00052257,0.00017103755,0.000543857,0.0018256904,0.021143954],"genre_scores_gemma":[0.96254104,0.004508474,0.030306945,0.00009842755,0.0001229677,0.00006374663,0.00039091686,0.00007766844,0.0018897153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949079,0.0020663221,0.0002479013,0.0005083893,0.0018659484,0.00040350598],"domain_scores_gemma":[0.9898677,0.0067597805,0.0008545435,0.00067738356,0.0017416502,0.000098922574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034219087,0.00081408035,0.0007433082,0.0011772437,0.0005050631,0.0010524212,0.0008026892,0.0007972342,0.0011988082],"category_scores_gemma":[0.015642665,0.00021178398,0.00030046247,0.002432163,0.00044164952,0.0017385919,0.00067455944,0.00045185455,0.00041779305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009875881,0.00016472317,0.012128458,0.00088225264,0.00024629768,0.00022238551,0.00026132519,0.6591747,0.01448387,0.010332103,0.0038420889,0.2972742],"study_design_scores_gemma":[0.000018105718,0.0006886925,0.00458016,0.000077781515,0.00007333481,0.0004896638,0.0001942602,0.9789822,0.009138794,0.002243843,0.00348498,0.00002816638],"about_ca_topic_score_codex":0.0019506299,"about_ca_topic_score_gemma":0.0015371463,"teacher_disagreement_score":0.0034219087,"about_ca_system_score_codex":0.00096666266,"about_ca_system_score_gemma":0.000634017,"threshold_uncertainty_score":0.018096983},"labels":[],"label_agreement":null},{"id":"W2950069230","doi":"10.1007/978-3-030-21642-9_27","title":"Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-Based Classification and High-Level Heuristics","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Heuristics; Beacon; Bluetooth; Cluster analysis; Artificial intelligence; Real-time computing; Machine learning; Wireless; Telecommunications","score_opus":0.018484407098046662,"score_gpt":0.22899338626723037,"score_spread":0.2105089791691837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950069230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02940965,0.0012367668,0.9626841,0.00017650392,0.000103201346,0.00006394072,0.0002159484,0.0018080337,0.0043018125],"genre_scores_gemma":[0.34207177,0.0010564154,0.6510331,0.00008056745,0.000068925474,0.000076619624,0.00044504224,0.00015846896,0.0050091087],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964774,0.000068465175,0.000032838172,0.00011150013,0.00009727839,0.00004221325],"domain_scores_gemma":[0.99963653,0.00021278707,0.0000273338,0.000032655862,0.000072437346,0.000018324323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004350218,0.0005740517,0.0008447933,0.0007348305,0.00035705115,0.0009958792,0.0013629892,0.00070625177,0.0014184518],"category_scores_gemma":[0.0009548491,0.00041445182,0.0007626521,0.0012144343,0.00023316231,0.00071418355,0.00053394446,0.00047489727,0.00065963],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002812495,0.00013367792,0.005178103,0.00023546588,0.00012269212,0.00060596096,0.00024067651,0.24549566,0.013430059,0.005229722,0.0075187963,0.7215279],"study_design_scores_gemma":[0.000021565522,0.00017688409,0.0032666922,0.000054297576,0.000083600586,0.00053513236,0.00013988494,0.978962,0.0075947503,0.004225797,0.0049036844,0.000035732843],"about_ca_topic_score_codex":0.0053786486,"about_ca_topic_score_gemma":0.008984842,"teacher_disagreement_score":0.0053786486,"about_ca_system_score_codex":0.00042431938,"about_ca_system_score_gemma":0.00062425935,"threshold_uncertainty_score":0.010694742},"labels":[],"label_agreement":null},{"id":"W2951393576","doi":"10.48550/arxiv.1403.0503","title":"Distributed Cooperative Localization in Wireless Sensor Networks without NLOS Identification","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Non-line-of-sight propagation; Relaxation (psychology); Computer science; Mean squared error; Wireless sensor network; Outlier; Algorithm; Convex optimization; Function (biology); Convex function; A priori and a posteriori; Identification (biology); Iterative method; Mathematical optimization; Regular polygon; Mathematics; Wireless; Statistics; Artificial intelligence; Telecommunications","score_opus":0.02521034658161178,"score_gpt":0.17395117551017084,"score_spread":0.14874082892855905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951393576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054961075,0.0001364774,0.993893,0.000043871365,0.000010372484,0.000009305632,0.0000054624966,0.00013078461,0.00027462654],"genre_scores_gemma":[0.6749375,0.00038981362,0.32156286,0.0001093909,0.000070032416,0.00019030702,0.00008300747,0.000056693156,0.0026004198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990706,0.00029306707,0.000035983652,0.00026480242,0.00027104715,0.0000645628],"domain_scores_gemma":[0.9989104,0.0005046481,0.00019019798,0.0001670102,0.00019852462,0.000029224133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011693448,0.0007758496,0.0007959013,0.0004977652,0.00042475562,0.00059547654,0.0014713423,0.0010298466,0.00037120935],"category_scores_gemma":[0.002987505,0.00040604887,0.00048719282,0.0007617943,0.00088536483,0.0013353702,0.0013223516,0.00071231055,0.00024878333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012228654,0.000042336367,0.0004999514,0.00008640564,0.00004934597,0.00009172693,0.00016460975,0.87748027,0.008880773,0.009827775,0.0007638639,0.10199057],"study_design_scores_gemma":[0.000009105418,0.000046267553,0.00009882156,0.0000031775046,0.000006326845,0.000022555629,0.000011543372,0.9951291,0.0015448147,0.0025509128,0.0005715215,0.0000058697965],"about_ca_topic_score_codex":0.0019873495,"about_ca_topic_score_gemma":0.0015466912,"teacher_disagreement_score":0.0019873495,"about_ca_system_score_codex":0.00053312746,"about_ca_system_score_gemma":0.0006717725,"threshold_uncertainty_score":0.006184101},"labels":[],"label_agreement":null},{"id":"W2951523777","doi":"10.48550/arxiv.1507.08923","title":"On the Displacement for Covering a Unit Interval with Randomly Placed Sensors","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Displacement (psychology); RADIUS; Energy (signal processing); Range (aeronautics); Interval (graph theory); Power (physics); Energy consumption; Line (geometry); Focus (optics); Acceleration; Unit interval; Function (biology); Mathematics; Computer science; Electrical engineering; Geometry; Mathematical analysis; Engineering; Physics; Combinatorics; Statistics; Optics; Classical mechanics","score_opus":0.06101548770766273,"score_gpt":0.18173551368337362,"score_spread":0.1207200259757109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951523777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055681873,0.002923131,0.9203539,0.0032953022,0.00016422216,0.00018915848,0.0003090476,0.00024421915,0.016839176],"genre_scores_gemma":[0.7582607,0.0046370467,0.21790242,0.0011719466,0.00047839063,0.0010367748,0.00062677066,0.000627905,0.015258031],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975193,0.0010175597,0.00010400233,0.00048690342,0.0005376562,0.0003345462],"domain_scores_gemma":[0.9859136,0.0116568785,0.0007481156,0.0007561355,0.00047679816,0.00044852766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037546966,0.002015907,0.0014752622,0.001235881,0.00081247883,0.0016634349,0.002613776,0.0021767623,0.009868835],"category_scores_gemma":[0.024786295,0.0007341246,0.00084068783,0.0021126864,0.003850863,0.0059072115,0.003555939,0.002312272,0.0011466279],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006229141,0.00009482879,0.0016547372,0.00063108234,0.0000647274,0.0001822946,0.00039973293,0.8153343,0.0070598344,0.13699956,0.004187134,0.0327689],"study_design_scores_gemma":[0.00008258995,0.00040377368,0.0010154722,0.00015099075,0.00005355285,0.00020556392,0.00023822872,0.91520643,0.0035860746,0.074962094,0.0040451773,0.000050028833],"about_ca_topic_score_codex":0.0023388301,"about_ca_topic_score_gemma":0.0016453696,"teacher_disagreement_score":0.009868835,"about_ca_system_score_codex":0.0028975937,"about_ca_system_score_gemma":0.0010023304,"threshold_uncertainty_score":0.033014536},"labels":[],"label_agreement":null},{"id":"W2951656029","doi":"10.1007/s11277-019-06629-y","title":"Energy-Based Timing Estimation and Artificial Neural Network Based Ranging Error Mitigation in mm-Wave Ranging Systems Using Statistics Fingerprint Analysis","year":2019,"lang":"en","type":"article","venue":"Wireless Personal Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Ranging; Non-line-of-sight propagation; Computer science; Standard deviation; Artificial neural network; Kurtosis; Algorithm; Time of arrival; Skewness; Energy (signal processing); Multipath propagation; Statistics; Artificial intelligence; Telecommunications; Wireless; Mathematics","score_opus":0.035572082234093826,"score_gpt":0.2591491305330573,"score_spread":0.2235770482989635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951656029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11074674,0.00046257564,0.88662124,0.00013249805,0.000068529036,0.000015553716,0.000043930206,0.00030407414,0.0016048809],"genre_scores_gemma":[0.92522115,0.0002638458,0.07184059,0.00004490127,0.000035561516,0.000017199065,0.00006950181,0.000030977397,0.0024763336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997955,0.000041698422,0.000017363334,0.000045568384,0.00007722843,0.00002259907],"domain_scores_gemma":[0.9997087,0.000102848215,0.00004812654,0.000030420408,0.0001024364,0.0000074073528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031676865,0.00039245214,0.00032744132,0.00041398228,0.00019762848,0.00039210456,0.0004190379,0.0003998522,0.00056078186],"category_scores_gemma":[0.0011191122,0.00018957746,0.0002359776,0.0004571322,0.00017355407,0.00080780644,0.00041550625,0.00032143013,0.00016935052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005722934,0.00015870122,0.005342563,0.0001127685,0.00010197272,0.00011198797,0.00007748744,0.47089502,0.07815229,0.0037799675,0.0006707357,0.44002414],"study_design_scores_gemma":[0.0000046610776,0.000043241715,0.0017280794,0.000006210951,0.000015933852,0.00004806993,0.000009174317,0.982543,0.014815481,0.00049138453,0.00028595547,0.000008775459],"about_ca_topic_score_codex":0.0012156838,"about_ca_topic_score_gemma":0.0022076156,"teacher_disagreement_score":0.0012156838,"about_ca_system_score_codex":0.0002583885,"about_ca_system_score_gemma":0.00029678672,"threshold_uncertainty_score":0.0024172664},"labels":[],"label_agreement":null},{"id":"W2952652977","doi":"10.48550/arxiv.1409.8033","title":"On the Convergence of Alternating Direction Lagrangian Methods for Nonconvex Structured Optimization Problems","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Augmented Lagrangian method; Convergence (economics); Mathematical optimization; Penalty method; Quadratic equation; Optimization problem; Scalability; Function (biology); Computer science; Mathematics; Applied mathematics","score_opus":0.04326941817307656,"score_gpt":0.2123822878872143,"score_spread":0.16911286971413775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952652977","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036327075,0.0005806608,0.9927602,0.00023651928,0.000045409146,0.000030120884,0.000015636853,0.000048151625,0.0026507075],"genre_scores_gemma":[0.40331945,0.0042965375,0.5802841,0.0005064637,0.00037804426,0.00063622516,0.00033631615,0.00038530902,0.009857554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981851,0.0010414169,0.000073766525,0.00016441442,0.00045405285,0.00008122588],"domain_scores_gemma":[0.9895454,0.008300292,0.00052920816,0.00034715555,0.0010813184,0.00019656347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055312365,0.0019330969,0.0010428325,0.001332175,0.00062464975,0.0011815452,0.0009709594,0.0016792597,0.0020461231],"category_scores_gemma":[0.019161314,0.0006229972,0.001091216,0.0009488532,0.0026443503,0.002170017,0.0026154374,0.0031558687,0.0005774127],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011710456,0.00006795621,0.00069071574,0.00037030454,0.000081493265,0.00014898196,0.00023000297,0.802551,0.0032569852,0.14572956,0.0020779276,0.044677973],"study_design_scores_gemma":[0.000008806519,0.000044071832,0.000052525305,0.000025456226,0.0000044689914,0.000022320359,0.000012221814,0.97857326,0.00037735034,0.019999979,0.00087220134,0.0000073402707],"about_ca_topic_score_codex":0.0019942664,"about_ca_topic_score_gemma":0.0013941155,"teacher_disagreement_score":0.0055312365,"about_ca_system_score_codex":0.0007551278,"about_ca_system_score_gemma":0.0014347585,"threshold_uncertainty_score":0.02925235},"labels":[],"label_agreement":null},{"id":"W2953529749","doi":"10.1145/3322241","title":"Indoor Localization Improved by Spatial Context—A Survey","year":2019,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council","keywords":"Computer science; Spatial contextual awareness; Context (archaeology); Doors; Computer vision; Spatial analysis; Artificial intelligence; Location-based service; Remote sensing; Telecommunications; Geography","score_opus":0.044145676857707185,"score_gpt":0.29029304903867204,"score_spread":0.24614737218096486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953529749","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004609229,0.8043858,0.16269101,0.0012607428,0.0009485308,0.000074474076,0.00025144615,0.0008054512,0.024973387],"genre_scores_gemma":[0.061863936,0.88295823,0.04382414,0.00056213635,0.0010766494,0.000090036425,0.00064149115,0.00015277142,0.008830684],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991893,0.00018815532,0.000056749184,0.0001805335,0.0003048827,0.00008034278],"domain_scores_gemma":[0.9983706,0.0008773774,0.00008688613,0.00012876844,0.0004972865,0.0000392137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079895527,0.0012995247,0.0013200854,0.0022623667,0.0004119117,0.0012686386,0.001865212,0.0010615102,0.005394859],"category_scores_gemma":[0.002510447,0.00058753433,0.0009097518,0.004682447,0.00054836186,0.0027784822,0.0013243917,0.0008072305,0.003240693],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039009054,0.00005025824,0.0009213035,0.005098666,0.000058343958,0.00011935175,0.00011296851,0.0047831093,0.0011869171,0.008166054,0.015032519,0.96443164],"study_design_scores_gemma":[0.000028325238,0.00038704657,0.0048784213,0.005490177,0.0005000689,0.003399097,0.0010514633,0.03143811,0.0057329354,0.014209816,0.93269616,0.00018832283],"about_ca_topic_score_codex":0.0042153876,"about_ca_topic_score_gemma":0.0036498813,"teacher_disagreement_score":0.005394859,"about_ca_system_score_codex":0.00050499954,"about_ca_system_score_gemma":0.0009061029,"threshold_uncertainty_score":0.018047571},"labels":[],"label_agreement":null},{"id":"W2955099080","doi":"10.22260/isarc2019/0110","title":"Using BIM and Sensing Mats to Improve IMU-based Indoor Positioning Accuracy","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Inertial measurement unit; Computer science; Real-time computing; Computer vision; Artificial intelligence","score_opus":0.00941061756488226,"score_gpt":0.22110910611583615,"score_spread":0.21169848855095388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955099080","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06348376,0.0005838342,0.9282387,0.0001479711,0.000232075,0.000048936476,0.00014738999,0.002350896,0.004766493],"genre_scores_gemma":[0.63939846,0.0004940461,0.35668656,0.00013632122,0.000118329546,0.000079686266,0.00035554718,0.00021259442,0.0025184287],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988978,0.00018009794,0.000062554616,0.00015936724,0.00061333674,0.00008688709],"domain_scores_gemma":[0.99904686,0.00017459494,0.00012148933,0.00022048548,0.0003980903,0.000038413524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085535034,0.0012720835,0.0007098858,0.0018937187,0.00031361077,0.0011559344,0.0010475393,0.00065316656,0.0018312826],"category_scores_gemma":[0.0025500609,0.00044141532,0.00057137996,0.0017984665,0.00035945847,0.0015983916,0.0018343122,0.00050799694,0.0013780757],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004186284,0.00015645982,0.011660655,0.00045089296,0.00012436222,0.00035206234,0.00038045002,0.09889398,0.1638929,0.0042767297,0.0037448509,0.7156481],"study_design_scores_gemma":[0.00003976222,0.00036253547,0.013720382,0.000077593,0.0001629477,0.0005259943,0.00024108532,0.8236066,0.13764486,0.0022420497,0.021269897,0.00010626705],"about_ca_topic_score_codex":0.0011306249,"about_ca_topic_score_gemma":0.0011026463,"teacher_disagreement_score":0.0018937187,"about_ca_system_score_codex":0.00025304826,"about_ca_system_score_gemma":0.00038516204,"threshold_uncertainty_score":0.0061262846},"labels":[],"label_agreement":null},{"id":"W2955459433","doi":"10.1002/9780470396360.ch11","title":"Localization Systems for Wireless Sensor Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Wireless sensor network; Computer science; Tracking (education); Event (particle physics); Key distribution in wireless sensor networks; Position (finance); Wireless; Real-time computing; Computation; Wireless network; Artificial intelligence; Computer network; Telecommunications; Algorithm","score_opus":0.013421573606665932,"score_gpt":0.1980963027921266,"score_spread":0.18467472918546066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955459433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015991767,0.03730158,0.9211395,0.0015742988,0.0033519447,0.00015720955,0.00048054443,0.003135264,0.031260513],"genre_scores_gemma":[0.08782354,0.12909971,0.5670851,0.0012052704,0.004449994,0.00071944087,0.003870796,0.0009570454,0.2047892],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995403,0.000105586165,0.00004130967,0.000098838515,0.00018621565,0.000027692031],"domain_scores_gemma":[0.99975437,0.000057586745,0.000018695839,0.000050153045,0.000110862755,0.0000083110435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035250347,0.0009196776,0.0008174049,0.0008515019,0.00053360686,0.0017822456,0.0010886317,0.0010852705,0.02106086],"category_scores_gemma":[0.0013119952,0.0004697852,0.0004378932,0.0021472108,0.0004366113,0.0025275021,0.0012637344,0.0011918509,0.011844367],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053475254,0.000028828812,0.0003094522,0.0014764483,0.000051438128,0.00013253439,0.00020349723,0.033384513,0.008463677,0.120728925,0.09544071,0.7397265],"study_design_scores_gemma":[0.000027247732,0.00011857589,0.000579703,0.0005728132,0.0000616845,0.00040332205,0.00019201208,0.11592825,0.005813393,0.09182608,0.78441644,0.000060564373],"about_ca_topic_score_codex":0.0016915922,"about_ca_topic_score_gemma":0.0016259885,"teacher_disagreement_score":0.02106086,"about_ca_system_score_codex":0.00057246967,"about_ca_system_score_gemma":0.00048678755,"threshold_uncertainty_score":0.07045555},"labels":[],"label_agreement":null},{"id":"W2955592451","doi":"10.1145/3328938","title":"Learning to Recognize Unmodified Lights with Invisible Features","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Computer vision; Discriminative model; Android (operating system); Deep learning; Deep neural networks","score_opus":0.004282129545466528,"score_gpt":0.2030721953180769,"score_spread":0.19879006577261038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955592451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13166364,0.0002137443,0.8610016,0.00017420763,0.00010486759,0.00005873379,0.0002314987,0.0033692988,0.0031824203],"genre_scores_gemma":[0.8245831,0.0001746005,0.16942361,0.00029594504,0.000058257392,0.00006320681,0.00068102876,0.0001923588,0.004527793],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996722,0.000038435002,0.000010234366,0.00013600128,0.00007549771,0.00006762435],"domain_scores_gemma":[0.9994955,0.000114661794,0.000062326624,0.0001202278,0.000172947,0.000034242836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003069616,0.0007660526,0.0005749725,0.0005820894,0.00023101474,0.000641768,0.00096373423,0.00052176556,0.0015215825],"category_scores_gemma":[0.001541426,0.00043456815,0.0005432084,0.0004139987,0.00048245728,0.0012718823,0.0011993621,0.0009462517,0.0010936575],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032259666,0.00020664348,0.007529498,0.0001647163,0.00008428722,0.00015284891,0.0001740534,0.10084871,0.075911425,0.0037144623,0.007163,0.80372775],"study_design_scores_gemma":[0.000014792447,0.000099791665,0.0019206594,0.000014121864,0.00001714727,0.00010603557,0.000064938526,0.97359425,0.019104395,0.0030868798,0.001961199,0.000015811982],"about_ca_topic_score_codex":0.003645533,"about_ca_topic_score_gemma":0.0076430957,"teacher_disagreement_score":0.003645533,"about_ca_system_score_codex":0.0004323025,"about_ca_system_score_gemma":0.0004999778,"threshold_uncertainty_score":0.00724864},"labels":[],"label_agreement":null},{"id":"W2956646111","doi":"10.1109/icc.2019.8761085","title":"Localization Sensitivity Under RSSI Quantization","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Quantization (signal processing); Computer science; Algorithm; Mathematics","score_opus":0.005861284207622043,"score_gpt":0.192035311666191,"score_spread":0.18617402745856895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956646111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7660239,0.0032102424,0.21736425,0.0009907291,0.0003216078,0.00017716497,0.0006111419,0.001717829,0.00958314],"genre_scores_gemma":[0.9897226,0.00038323805,0.008789918,0.0001513427,0.000030072597,0.000032434466,0.00019735759,0.00005280483,0.00064019277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99414164,0.0017850683,0.00034653407,0.0010723956,0.0019685326,0.0006857299],"domain_scores_gemma":[0.9731663,0.019224789,0.0017858248,0.002608353,0.0028606395,0.0003541152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040193694,0.0010004638,0.0011054518,0.0010914635,0.0005792055,0.0011667479,0.0007425192,0.0014495129,0.0010665805],"category_scores_gemma":[0.037435226,0.00037491997,0.00042400055,0.0013892216,0.0014595934,0.002229344,0.0016480569,0.0009109561,0.00037403306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001154935,0.000113368405,0.0127863735,0.000421831,0.00016023929,0.00039777523,0.00029905993,0.89079344,0.026313363,0.0033409528,0.0013185408,0.06290015],"study_design_scores_gemma":[0.00006744302,0.0012507471,0.026262894,0.0001568193,0.00016797785,0.0011525857,0.0005128884,0.9134986,0.048212107,0.0063943905,0.0021582558,0.00016525663],"about_ca_topic_score_codex":0.00483148,"about_ca_topic_score_gemma":0.0022078624,"teacher_disagreement_score":0.00483148,"about_ca_system_score_codex":0.001209759,"about_ca_system_score_gemma":0.0006945871,"threshold_uncertainty_score":0.021256685},"labels":[],"label_agreement":null},{"id":"W2957030275","doi":"10.1109/compsac.2019.10272","title":"Security Features for Proximity Verification","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Mitacs","keywords":"Eavesdropping; Computer science; Global Positioning System; Physical security; Feature (linguistics); Mobile device; Identification (biology); Relay; Computer security; Embedded system; Telecommunications","score_opus":0.004731507559976575,"score_gpt":0.20003988838664594,"score_spread":0.19530838082666935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957030275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07091924,0.0011328992,0.9142859,0.00042808676,0.00023831072,0.00037655293,0.00039974347,0.0027184254,0.009500733],"genre_scores_gemma":[0.8140377,0.0004201458,0.18222757,0.0001018445,0.00013508786,0.00022049638,0.00040001707,0.000057947065,0.0023992052],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969891,0.00080283463,0.00028701875,0.00042851875,0.0012969308,0.00019563518],"domain_scores_gemma":[0.99291444,0.0021531207,0.001405232,0.0019899672,0.0013506203,0.00018661866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014613004,0.0008174092,0.0007753389,0.0015061853,0.00076265604,0.00097199465,0.0007915353,0.0009851974,0.0034547793],"category_scores_gemma":[0.009065778,0.00023099426,0.0006126994,0.0009663766,0.000989682,0.0024206145,0.0018081658,0.00080171874,0.0013375016],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017187272,0.00028726584,0.02270679,0.0010941891,0.00018757254,0.0011406917,0.000503665,0.052207503,0.13658047,0.07610236,0.009213966,0.69825685],"study_design_scores_gemma":[0.00025357306,0.0039987243,0.033818815,0.00033907517,0.0003295407,0.011832962,0.0005439211,0.67850596,0.15286823,0.045685306,0.07145298,0.00037096444],"about_ca_topic_score_codex":0.0005033411,"about_ca_topic_score_gemma":0.0004003471,"teacher_disagreement_score":0.0034547793,"about_ca_system_score_codex":0.00046311493,"about_ca_system_score_gemma":0.0005366842,"threshold_uncertainty_score":0.0115574},"labels":[],"label_agreement":null},{"id":"W2957104866","doi":"10.3390/s19143140","title":"Using Step Size and Lower Limb Segment Orientation from Multiple Low-Cost Wearable Inertial/Magnetic Sensors for Pedestrian Navigation","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Kinematics; Orientation (vector space); STRIDE; Motion capture; Computer science; Computer vision; Inertial measurement unit; Step detection; Simulation; Dead reckoning; Artificial intelligence; Geodesy; Mathematics; Motion (physics); Physics; Global Positioning System; Geometry; Geology; Telecommunications","score_opus":0.011695895905981784,"score_gpt":0.22756781377758628,"score_spread":0.2158719178716045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957104866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25173616,0.00038513224,0.7443863,0.000052993295,0.00008417096,0.0000441152,0.00016233076,0.0009985599,0.0021502492],"genre_scores_gemma":[0.8814248,0.00026707823,0.11688863,0.000021322463,0.000018944671,0.000027609478,0.0001714502,0.000025646368,0.00115451],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999045,0.000013869639,0.000005491062,0.0000264514,0.00004092305,0.000008787606],"domain_scores_gemma":[0.9998871,0.000019657382,0.000027185797,0.0000156979,0.000041791383,0.000008539873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012555055,0.0005861684,0.00030021966,0.00044398048,0.00012980845,0.00026815513,0.00027963452,0.0001939833,0.00079287036],"category_scores_gemma":[0.00044311793,0.00018040724,0.0002392859,0.00036610183,0.00009026902,0.00028586277,0.00024547923,0.00013645596,0.0003381138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005046447,0.00013621326,0.026349347,0.0004006223,0.00011348486,0.00033404655,0.00019638723,0.07863904,0.24533084,0.0011529821,0.0015033104,0.6453391],"study_design_scores_gemma":[0.000043225096,0.00069645245,0.06255588,0.00008463701,0.00017571398,0.00094424404,0.00018652818,0.8108349,0.117715195,0.0012909092,0.005384619,0.000087784494],"about_ca_topic_score_codex":0.002197942,"about_ca_topic_score_gemma":0.00639785,"teacher_disagreement_score":0.002197942,"about_ca_system_score_codex":0.00011684578,"about_ca_system_score_gemma":0.00024886685,"threshold_uncertainty_score":0.004370272},"labels":[],"label_agreement":null},{"id":"W2961025739","doi":"10.1109/iccw.2019.8756989","title":"Optimization of BLE Beacon Density for RSSI-Based Indoor Localization","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Trilateration; Beacon; Computer science; Bluetooth; Kalman filter; Real-time computing; Interference (communication); Standard deviation; Computer vision; Algorithm; Artificial intelligence; Wireless; Telecommunications; Mathematics; Statistics; Triangulation","score_opus":0.005868968182819353,"score_gpt":0.19475151085718578,"score_spread":0.18888254267436644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961025739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19783677,0.0006530559,0.79647255,0.00014319191,0.000049053706,0.000078014855,0.00008913418,0.0012426624,0.0034354918],"genre_scores_gemma":[0.88531506,0.00022622405,0.11301529,0.00002457371,0.000008393859,0.00005867114,0.0000956947,0.000039389903,0.0012166777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930847,0.00021860417,0.00003758279,0.00012413961,0.00022948947,0.000081843384],"domain_scores_gemma":[0.9980312,0.0009078864,0.00028368662,0.00015617839,0.0005734411,0.00004768717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007792424,0.00064490666,0.0005182069,0.001058804,0.0002739872,0.00047104325,0.00064792944,0.00043989636,0.0007729421],"category_scores_gemma":[0.0036968025,0.0002941974,0.0003226259,0.0006606369,0.000263229,0.00078598235,0.00031960342,0.00027280772,0.00041576143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010064272,0.00035664628,0.016623365,0.0003720948,0.00008813054,0.000226629,0.00029021694,0.4419859,0.14699282,0.0032919482,0.0013848552,0.38738096],"study_design_scores_gemma":[0.000077848366,0.0010293169,0.013702445,0.000043289532,0.00010682022,0.00036851625,0.00021040702,0.8685369,0.110560596,0.0011311343,0.004166347,0.000066397384],"about_ca_topic_score_codex":0.0018254877,"about_ca_topic_score_gemma":0.0028284048,"teacher_disagreement_score":0.0018254877,"about_ca_system_score_codex":0.0005643355,"about_ca_system_score_gemma":0.0003567043,"threshold_uncertainty_score":0.004121065},"labels":[],"label_agreement":null},{"id":"W2962678776","doi":"10.1109/ccece.2016.7726814","title":"Novel velocity model to improve indoor localization using inertial navigation with sensors on a smartphone","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); GNSS applications; Computer science; Inertial navigation system; Heading (navigation); Inertial frame of reference; Gaussian; Computer vision; Artificial intelligence; Inertial measurement unit; Variance (accounting); Algorithm; Simulation; Control theory (sociology); Engineering; Global Positioning System; Physics; Telecommunications","score_opus":0.015585543431057029,"score_gpt":0.2229400334785028,"score_spread":0.20735449004744577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962678776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012279785,0.00026081616,0.9855871,0.000065513326,0.0001127832,0.000022860717,0.000044923883,0.00086332735,0.0007629063],"genre_scores_gemma":[0.65480727,0.0009240282,0.33882502,0.000100571895,0.00013297537,0.00012513546,0.00041623664,0.00020444086,0.004464335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971825,0.000044103464,0.000019129035,0.00007587009,0.00011099184,0.0000315886],"domain_scores_gemma":[0.9997168,0.00005189524,0.000037366943,0.000040613533,0.00013895264,0.000014249486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023419499,0.000724572,0.00053149246,0.000522969,0.00019882829,0.0004475208,0.00079576025,0.000504668,0.00086169346],"category_scores_gemma":[0.0009316151,0.00024855003,0.00048398512,0.00050635036,0.0002017957,0.0008592379,0.00046562037,0.0004498207,0.00051758956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018082984,0.000090278656,0.0059761303,0.0003345531,0.00008697726,0.0003027347,0.00021933728,0.47707456,0.051438827,0.010227537,0.0050356463,0.44903263],"study_design_scores_gemma":[0.000015916252,0.00010448825,0.001228482,0.00001547597,0.000024439587,0.0001424557,0.000020728776,0.9877394,0.005883308,0.00079310854,0.004008939,0.000023238983],"about_ca_topic_score_codex":0.009445712,"about_ca_topic_score_gemma":0.008030285,"teacher_disagreement_score":0.009445712,"about_ca_system_score_codex":0.0003001484,"about_ca_system_score_gemma":0.00068319566,"threshold_uncertainty_score":0.018781483},"labels":[],"label_agreement":null},{"id":"W2962902933","doi":"10.1109/thms.2017.2693242","title":"Qualitative Action Recognition by Wireless Radio Signals in Human–Machine Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Quality (philosophy); Action (physics); Key (lock); Artificial neural network; Artificial intelligence; Identification (biology); Variety (cybernetics); SIGNAL (programming language); Wireless; Human–computer interaction; Machine learning; Telecommunications; Computer security","score_opus":0.07680294279126616,"score_gpt":0.35059736358463295,"score_spread":0.2737944207933668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962902933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25394064,0.0009844687,0.74095774,0.0003182283,0.000083429666,0.00005598196,0.00016496467,0.0006755596,0.0028189702],"genre_scores_gemma":[0.97565776,0.00016486023,0.023313457,0.000046875775,0.000024922307,0.000020094812,0.00005541809,0.000016545237,0.0007000935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953306,0.0001593222,0.00002613883,0.00012098132,0.000114932576,0.00004552376],"domain_scores_gemma":[0.99920565,0.0004455039,0.00015178866,0.000064906286,0.00009590643,0.000036362282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006298747,0.00044723912,0.0004125489,0.00069845264,0.0001852019,0.0007517558,0.00041528625,0.00051450054,0.00083942467],"category_scores_gemma":[0.0028787942,0.00020369209,0.00028919286,0.0005290421,0.00094777433,0.00097017427,0.0005681127,0.00042160365,0.00020301077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005048673,0.00019487081,0.023181984,0.00046934164,0.00018905776,0.00059433497,0.0008995662,0.5262286,0.08860416,0.03002872,0.0017717126,0.32733276],"study_design_scores_gemma":[0.000007993165,0.000120963356,0.014867628,0.000015709613,0.00002022999,0.000110560286,0.000117431824,0.9618718,0.008078538,0.013892968,0.00086557964,0.000030482382],"about_ca_topic_score_codex":0.0022892198,"about_ca_topic_score_gemma":0.0012082586,"teacher_disagreement_score":0.0022892198,"about_ca_system_score_codex":0.00046304057,"about_ca_system_score_gemma":0.00019081862,"threshold_uncertainty_score":0.004551828},"labels":[],"label_agreement":null},{"id":"W2962906349","doi":"10.3934/ipi.2018029","title":"On a gesture-computing technique using electromagnetic waves","year":2018,"lang":"en","type":"article","venue":"Inverse Problems and Imaging","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Gesture; Electromagnetic radiation; Acoustics; Artificial intelligence; Physics; Optics","score_opus":0.008773334623270755,"score_gpt":0.21005211373152743,"score_spread":0.20127877910825667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962906349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008242206,0.00033712614,0.9856388,0.00017869324,0.00005535975,0.000030664276,0.000016542988,0.0002523598,0.005248231],"genre_scores_gemma":[0.21415763,0.0011628544,0.77214956,0.0002265501,0.00010188991,0.00011593503,0.00006225151,0.00007699346,0.011946403],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972874,0.000058686717,0.000014959525,0.000058360045,0.00012097293,0.000018256274],"domain_scores_gemma":[0.9998429,0.0000645659,0.000013494465,0.000039720126,0.000028308996,0.000010984752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020122564,0.0003917431,0.00034017145,0.00044774692,0.00042913112,0.00076978863,0.0006424133,0.0005688794,0.0033559785],"category_scores_gemma":[0.0005694645,0.00017599728,0.00039685523,0.000615752,0.0011077547,0.001291083,0.0011012602,0.00069930515,0.0009778268],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023225487,0.00008086002,0.000550049,0.00041380618,0.000035789217,0.0006061559,0.0007322955,0.030604543,0.24167275,0.2912733,0.003006163,0.43079194],"study_design_scores_gemma":[0.00008075176,0.00052620965,0.0011410522,0.00016255667,0.00007313383,0.0023937859,0.00035543146,0.64960855,0.18461351,0.076254606,0.084650695,0.00013987745],"about_ca_topic_score_codex":0.00054139615,"about_ca_topic_score_gemma":0.00047628133,"teacher_disagreement_score":0.0033559785,"about_ca_system_score_codex":0.00023531074,"about_ca_system_score_gemma":0.00027032266,"threshold_uncertainty_score":0.0112268925},"labels":[],"label_agreement":null},{"id":"W2963374770","doi":"10.3934/ipi.2019040","title":"Two gesture-computing approaches by using electromagnetic waves","year":2019,"lang":"en","type":"article","venue":"Inverse Problems and Imaging","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Trajectory; Electromagnetic field; Gesture; Point (geometry); Field (mathematics); Inverse problem; Artificial intelligence; Computer vision; Mathematics; Physics; Mathematical analysis","score_opus":0.012859654785122182,"score_gpt":0.19241595987920973,"score_spread":0.17955630509408754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963374770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056453845,0.00047806647,0.9896293,0.00020707119,0.00007459896,0.000038721995,0.000025891317,0.00018286782,0.0037180386],"genre_scores_gemma":[0.2283648,0.0013495537,0.7563986,0.0002025896,0.0001161946,0.00021152064,0.00010191566,0.00006832461,0.013186528],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964225,0.000057863122,0.000024923005,0.000103434584,0.00013555281,0.000035973488],"domain_scores_gemma":[0.9997888,0.000055667,0.000025929661,0.00007341488,0.000037455153,0.000018755612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002494239,0.00053393096,0.00050303363,0.00059568917,0.00035822587,0.0011664784,0.0010414103,0.0010299835,0.0020297861],"category_scores_gemma":[0.00096361845,0.00027860835,0.00063165603,0.00066396873,0.00094832445,0.0015798084,0.0012077494,0.00074830034,0.00079930364],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020662628,0.00016104906,0.0011557686,0.0004310414,0.00008351236,0.00044538284,0.0005417655,0.08910239,0.067680456,0.17781699,0.0025267683,0.6598483],"study_design_scores_gemma":[0.000039070554,0.00022209916,0.001450249,0.000064867774,0.000049272567,0.0010153194,0.00019820331,0.882892,0.045046072,0.037522916,0.03139165,0.000108262],"about_ca_topic_score_codex":0.0013626401,"about_ca_topic_score_gemma":0.0018400161,"teacher_disagreement_score":0.0020297861,"about_ca_system_score_codex":0.00036977834,"about_ca_system_score_gemma":0.00053724746,"threshold_uncertainty_score":0.0067902803},"labels":[],"label_agreement":null},{"id":"W2963884633","doi":"10.1049/iet-its.2019.0178","title":"Fast and robust map‐matching algorithm based on a global measure and dynamic programming for sparse probe data","year":2019,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Nunavut","funders":"","keywords":"Measure (data warehouse); Computer science; Map matching; Dynamic programming; Matching (statistics); Matching pursuit; Algorithm; Data mining; Artificial intelligence; Mathematics; Compressed sensing; Global Positioning System","score_opus":0.022523838342666278,"score_gpt":0.23384825622187203,"score_spread":0.21132441787920575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963884633","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00723991,0.000060490955,0.9918555,0.00003920902,0.000018683104,0.000020695088,0.000014960586,0.00034185217,0.0004087365],"genre_scores_gemma":[0.326697,0.00021815338,0.6691726,0.000100920486,0.000062615705,0.00023189641,0.00028339232,0.00015654515,0.0030769135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929357,0.00010180391,0.000040035844,0.00020962964,0.00027636485,0.00007853281],"domain_scores_gemma":[0.99944574,0.00021865692,0.00006850425,0.000071627845,0.00016435274,0.0000312132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006272586,0.0006908801,0.0012276962,0.0013178318,0.00055347034,0.000989501,0.001480726,0.0008467944,0.0013901707],"category_scores_gemma":[0.0018970907,0.00043949025,0.0008896608,0.0016635426,0.00059122295,0.0017580002,0.0015506897,0.0012725779,0.00043899304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002814548,0.00016431272,0.0012976445,0.000097799,0.00012577369,0.00009291534,0.000116101764,0.36228856,0.0177779,0.015269302,0.00251637,0.5999719],"study_design_scores_gemma":[0.0000130034605,0.00006240864,0.0002473024,0.000002792446,0.000009366791,0.00004314563,0.000015224175,0.99336046,0.0029273445,0.0025025192,0.0008042739,0.000012198636],"about_ca_topic_score_codex":0.005019178,"about_ca_topic_score_gemma":0.0024090493,"teacher_disagreement_score":0.005019178,"about_ca_system_score_codex":0.00063198176,"about_ca_system_score_gemma":0.0013210174,"threshold_uncertainty_score":0.009979963},"labels":[],"label_agreement":null},{"id":"W2963971397","doi":"10.1109/wf-iot.2019.8767206","title":"Simulation-Based Deployment Configuration of Smart Indoor Spaces","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Software deployment; Computer science; Task (project management); Real-time computing; Distributed computing; Wireless sensor network; Systems engineering; Embedded system; Engineering; Computer network; Software engineering","score_opus":0.00971890987417735,"score_gpt":0.22241792117712414,"score_spread":0.2126990113029468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963971397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42006013,0.0002177328,0.5652183,0.0002553101,0.00008818306,0.00047969838,0.0006957526,0.0024986577,0.010486287],"genre_scores_gemma":[0.92943716,0.00013273383,0.06921688,0.000022569644,0.000005695788,0.00019379593,0.00026446054,0.00007062595,0.0006561927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999201,0.0003778695,0.00004794913,0.00010188713,0.00017660664,0.000094573385],"domain_scores_gemma":[0.9984794,0.00074713747,0.00017068197,0.0002902042,0.000206448,0.000106149586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010417755,0.00074238406,0.00054705725,0.00061470125,0.00040508385,0.000996377,0.001080731,0.00064499024,0.0013541423],"category_scores_gemma":[0.002842203,0.00039843502,0.00040786853,0.0005579088,0.0005472748,0.0008667066,0.0007483795,0.00041095194,0.00024259108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004465468,0.00003712826,0.0019026424,0.000044127493,0.000015173789,0.00006364311,0.00005519252,0.98973864,0.0018624909,0.0017177955,0.00020692252,0.0043116794],"study_design_scores_gemma":[0.000010275518,0.000041867002,0.00045045384,0.000006752875,0.00000840832,0.0000202022,0.000040946365,0.99608445,0.0020821784,0.0006767361,0.0005690421,0.000008711642],"about_ca_topic_score_codex":0.00351677,"about_ca_topic_score_gemma":0.0063384473,"teacher_disagreement_score":0.00351677,"about_ca_system_score_codex":0.0008604734,"about_ca_system_score_gemma":0.00091765966,"threshold_uncertainty_score":0.0069925785},"labels":[],"label_agreement":null},{"id":"W2964229939","doi":"10.1109/ipin.2017.8115897","title":"TuRF: Fast data collection for fingerprint-based indoor localization","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Fingerprint (computing); Computer science; Fingerprint recognition; Process (computing); Trajectory; Gaussian process; Artificial intelligence; Data collection; Real-time computing; Path (computing); Gaussian network model; Gaussian; Data mining; Computer vision; Computer network; Statistics; Mathematics","score_opus":0.03937463307296051,"score_gpt":0.27638937376875516,"score_spread":0.23701474069579465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964229939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018837852,0.00022175325,0.95721716,0.000074225216,0.000070954375,0.00013856281,0.0012188941,0.021259919,0.0009607156],"genre_scores_gemma":[0.47237563,0.00040942334,0.5181763,0.00015646925,0.00007699311,0.00042311047,0.0046384963,0.00070960255,0.0030339765],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923146,0.00012175548,0.0000332272,0.000167297,0.00033437082,0.00011189472],"domain_scores_gemma":[0.9985607,0.00029356492,0.00015975603,0.0005826877,0.00032234297,0.000080879625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008542227,0.0008093605,0.0007986789,0.0016334832,0.0004150789,0.0006252427,0.0017832018,0.00065396295,0.003574965],"category_scores_gemma":[0.003465366,0.00035052147,0.00033708612,0.0016387758,0.0003148177,0.0012432495,0.0014742244,0.00066603837,0.0023989824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007022304,0.00020018821,0.011678841,0.00027989844,0.00013018449,0.0004375566,0.00026606812,0.03149851,0.0554384,0.0041096113,0.024495883,0.87076277],"study_design_scores_gemma":[0.00017687272,0.00075341825,0.019169757,0.00007128239,0.000076592354,0.0020780754,0.00023750446,0.8177028,0.101948746,0.005946829,0.051636286,0.00020182996],"about_ca_topic_score_codex":0.0034001023,"about_ca_topic_score_gemma":0.004138034,"teacher_disagreement_score":0.003574965,"about_ca_system_score_codex":0.0003693503,"about_ca_system_score_gemma":0.00059608335,"threshold_uncertainty_score":0.011959434},"labels":[],"label_agreement":null},{"id":"W2964526071","doi":"10.48550/arxiv.1907.12475","title":"Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mathematical optimization; Mobile edge computing; Optimization problem; Edge computing; Probabilistic logic; Regularization (linguistics); Convex optimization; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Algorithm; Mathematics; Regular polygon","score_opus":0.04347074322987002,"score_gpt":0.18173293683165886,"score_spread":0.13826219360178885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964526071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038085398,0.000041327312,0.9952236,0.00007123293,0.000009170924,0.000007709947,0.000020250569,0.00010809385,0.00071004237],"genre_scores_gemma":[0.5502137,0.00027868978,0.44382897,0.00022498834,0.00008045552,0.000104434,0.00023115447,0.0001117109,0.0049258876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996791,0.000083330015,0.000011774159,0.00008240138,0.00009492195,0.000048428617],"domain_scores_gemma":[0.9995964,0.00018733421,0.000045088513,0.000070964634,0.00008003827,0.000020131245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005971009,0.00078067835,0.00054885703,0.0002928053,0.00033750513,0.00064278446,0.0013029452,0.0007773468,0.0017317152],"category_scores_gemma":[0.0018545797,0.0002984631,0.0004855391,0.00055221387,0.0006566115,0.0013610176,0.0011115003,0.0012996106,0.0004984449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009526765,0.000073688214,0.0005060623,0.00006484853,0.000036754143,0.000093922594,0.00007222961,0.85265225,0.011301284,0.028603397,0.0026915614,0.103808805],"study_design_scores_gemma":[0.0000022698316,0.000009909245,0.00003315849,0.0000016622273,0.0000026864689,0.000008847032,0.000005115818,0.9938061,0.0014854298,0.004331559,0.00031086674,0.0000023423217],"about_ca_topic_score_codex":0.0025379418,"about_ca_topic_score_gemma":0.004150418,"teacher_disagreement_score":0.0025379418,"about_ca_system_score_codex":0.0004762098,"about_ca_system_score_gemma":0.00069364044,"threshold_uncertainty_score":0.005793214},"labels":[],"label_agreement":null},{"id":"W2965983834","doi":"10.1109/tnet.2019.2924152","title":"Network Navigation With Scheduling: Distributed Algorithms","year":2019,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tellabs (Canada)","funders":"Office of Naval Research","keywords":"Computer science; Wireless network; Asynchronous communication; Distributed computing; Scheduling (production processes); Wireless; Wireless sensor network; Real-time computing; Computer network; Algorithm; Telecommunications","score_opus":0.012333443045676077,"score_gpt":0.2108393640984207,"score_spread":0.19850592105274462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965983834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030041377,0.00054783496,0.9932247,0.00022763317,0.00010090195,0.000053009873,0.000023695184,0.00035804723,0.0024600204],"genre_scores_gemma":[0.45003363,0.0019173544,0.5399132,0.000272761,0.00053298665,0.0005444238,0.00020363266,0.00026910158,0.0063129524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989849,0.00038706776,0.00005014987,0.00021763718,0.00025166236,0.00010866143],"domain_scores_gemma":[0.99776137,0.0014452202,0.00021613945,0.0001864995,0.00029283352,0.00009797793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019915218,0.0012022039,0.0011039906,0.00068265543,0.0008407848,0.0015756419,0.0015154342,0.0011937727,0.002534123],"category_scores_gemma":[0.0068791923,0.00050189235,0.00041105365,0.0014359136,0.0010512703,0.0015160367,0.0013102706,0.0013065123,0.00075819984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007536385,0.00004954696,0.00029930493,0.00006491134,0.000019635421,0.000020776099,0.0000469917,0.90844476,0.00055014365,0.028891869,0.0026097766,0.05892688],"study_design_scores_gemma":[0.000030525352,0.00001465588,0.000029002125,0.0000062649697,0.0000047305452,0.000008696324,0.000008976516,0.98376507,0.00020596512,0.014547036,0.0013753811,0.0000037517348],"about_ca_topic_score_codex":0.0059541445,"about_ca_topic_score_gemma":0.0045076376,"teacher_disagreement_score":0.0059541445,"about_ca_system_score_codex":0.0017397362,"about_ca_system_score_gemma":0.0025419125,"threshold_uncertainty_score":0.012622774},"labels":[],"label_agreement":null},{"id":"W2969903088","doi":"10.1109/dcoss.2019.00026","title":"Thermal Piloting: A Novel Approach for Sensor Localization in Data Center Monitoring","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Multipath propagation; Bijection; Real-time computing; Set (abstract data type); Matching (statistics); Resilience (materials science); Software deployment; Data mining; Channel (broadcasting); Mathematics; Telecommunications","score_opus":0.04137120944526495,"score_gpt":0.2500793389609286,"score_spread":0.20870812951566364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969903088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074718967,0.000058624097,0.991606,0.000028235332,0.000019114384,0.000022836597,0.000012702773,0.00038653772,0.00039413702],"genre_scores_gemma":[0.38648644,0.00016387811,0.6107884,0.0000946399,0.00008056456,0.00014368187,0.00014054944,0.000100074496,0.0020017973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918014,0.00020702087,0.000037147816,0.00024468903,0.00023579883,0.000095157295],"domain_scores_gemma":[0.9992331,0.00022762822,0.00011291262,0.00019954424,0.00018170808,0.000045098826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006545559,0.0006749569,0.00075173785,0.0008699666,0.00048854586,0.00048713846,0.0015274247,0.0008193526,0.001189412],"category_scores_gemma":[0.0025305962,0.0004545507,0.00063920516,0.0012354578,0.00061281095,0.0015463032,0.001443373,0.0007037561,0.0006789895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041161073,0.00026125906,0.0020969477,0.00014141612,0.00006573049,0.00022409223,0.00036957735,0.2713099,0.0571653,0.010411355,0.0027446684,0.6547981],"study_design_scores_gemma":[0.000011417615,0.00011345666,0.00034566922,0.000005530341,0.000011335234,0.00011032901,0.00003406256,0.98784435,0.007177808,0.002865025,0.0014680577,0.000012948035],"about_ca_topic_score_codex":0.001309542,"about_ca_topic_score_gemma":0.0013269641,"teacher_disagreement_score":0.0015274247,"about_ca_system_score_codex":0.00021565887,"about_ca_system_score_gemma":0.00051861804,"threshold_uncertainty_score":0.0039789677},"labels":[],"label_agreement":null},{"id":"W2976075487","doi":"10.1109/tnse.2019.2943816","title":"Informative Path Planning for Location Fingerprint Collection","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Greedy algorithm; Fingerprint (computing); Motion planning; Data mining; Constraint (computer-aided design); Artificial intelligence; Algorithm; Mathematical optimization; Mathematics; Robot","score_opus":0.006846594532282774,"score_gpt":0.20008909247636741,"score_spread":0.19324249794408463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976075487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014076296,0.00017258196,0.98400116,0.000083265506,0.00001438742,0.000047933878,0.00008334511,0.0004025174,0.0011184228],"genre_scores_gemma":[0.55223966,0.00034318873,0.4445756,0.00008162209,0.000025405549,0.0002245611,0.00042436618,0.00013009147,0.0019555434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993006,0.0002429443,0.000024936973,0.00017665778,0.00015208853,0.000102752914],"domain_scores_gemma":[0.9986234,0.0008428146,0.00015886358,0.0001675858,0.0001338101,0.00007362445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007832193,0.0010262658,0.0008004546,0.0009239682,0.00058881077,0.0005657937,0.0011075895,0.00078840303,0.0019263587],"category_scores_gemma":[0.003283323,0.0004262099,0.000548703,0.0014723318,0.0007461395,0.0011805254,0.0010872517,0.0010152651,0.00035284305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090610345,0.00007904004,0.0008341352,0.00008522564,0.00002602239,0.00008328697,0.00009132118,0.8898585,0.0021984112,0.008797159,0.001527423,0.0963289],"study_design_scores_gemma":[0.000011495854,0.000051217794,0.00018912427,0.0000074440577,0.000009330415,0.000046399837,0.000031207783,0.9883663,0.0011519911,0.009148567,0.0009771094,0.000009720574],"about_ca_topic_score_codex":0.00523303,"about_ca_topic_score_gemma":0.0064423345,"teacher_disagreement_score":0.00523303,"about_ca_system_score_codex":0.00080879463,"about_ca_system_score_gemma":0.0014643185,"threshold_uncertainty_score":0.010405123},"labels":[],"label_agreement":null},{"id":"W2977323247","doi":"10.1109/actea.2019.8851085","title":"Gradient Descent Localization Algorithm Based on Received Signal Strength Technique in a Noisy Wireless Sensor Network","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"RSS; Wireless sensor network; Gradient descent; Noise (video); Computer science; Algorithm; Node (physics); Signal strength; Noise measurement; Wireless; Artificial intelligence; Artificial neural network; Noise reduction; Computer network; Telecommunications; Acoustics","score_opus":0.0051095640056050134,"score_gpt":0.18887263475748173,"score_spread":0.1837630707518767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977323247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045110686,0.00013274835,0.99447083,0.000068625835,0.000027967208,0.000014076427,0.000008763706,0.00023953363,0.00052641146],"genre_scores_gemma":[0.3093485,0.00076429313,0.6829023,0.00013078307,0.00010189662,0.00019533696,0.00013133815,0.00012299708,0.00630257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952483,0.00013328108,0.000026212781,0.00009746607,0.00018673827,0.000031458156],"domain_scores_gemma":[0.99968827,0.00008606638,0.00004960552,0.000031824406,0.00013163038,0.000012631185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057634333,0.000543884,0.0008554534,0.0004246605,0.00030954627,0.00060209754,0.0008990647,0.00067270023,0.0007180048],"category_scores_gemma":[0.0012214199,0.00028095866,0.00039491314,0.00055351784,0.00042229384,0.0009020125,0.00046222095,0.00059583795,0.0005556623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013557334,0.000059774156,0.001342126,0.00018130646,0.00009415514,0.00023282654,0.00015438195,0.7551351,0.023660943,0.0134673165,0.0026298435,0.20290656],"study_design_scores_gemma":[0.0000091487655,0.000045532295,0.00021004466,0.0000055676014,0.000009264361,0.000051554125,0.00000846421,0.9949705,0.0019301252,0.0014803052,0.0012715942,0.000007927388],"about_ca_topic_score_codex":0.0031531367,"about_ca_topic_score_gemma":0.0018172251,"teacher_disagreement_score":0.0031531367,"about_ca_system_score_codex":0.00034982074,"about_ca_system_score_gemma":0.00075395824,"threshold_uncertainty_score":0.006269574},"labels":[],"label_agreement":null},{"id":"W2977625447","doi":"10.1109/jiot.2019.2945054","title":"Asynchronous Acoustic Localization and Tracking for Mobile Targets","year":2019,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Real-time computing; Asynchronous communication; Testbed; Multipath propagation; Latency (audio); Multilateration; Channel (broadcasting); Computer network; Telecommunications; Acoustics","score_opus":0.006392945763772613,"score_gpt":0.21830189550795873,"score_spread":0.21190894974418611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977625447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04591296,0.00025099382,0.9496493,0.000113520975,0.00009127444,0.000031209067,0.000059138165,0.0016219954,0.0022697723],"genre_scores_gemma":[0.74721855,0.0005420948,0.24563132,0.00014363999,0.00013775231,0.00012554889,0.0003049397,0.00009900564,0.0057971976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999445,0.00009228644,0.000020960091,0.00013317894,0.00026735538,0.00004118344],"domain_scores_gemma":[0.9991522,0.0002710602,0.000130506,0.00013574748,0.00026371487,0.00004681145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056178763,0.00050224934,0.00035540404,0.00056645443,0.0003595683,0.00067289866,0.0007786022,0.0006856622,0.0013296467],"category_scores_gemma":[0.0020314085,0.00021758674,0.00022689924,0.0005248152,0.00035698255,0.0010242859,0.00084100274,0.00048402595,0.0009447694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006424719,0.00015102253,0.007446975,0.00031889672,0.00006236914,0.00043272146,0.0003904024,0.24842882,0.22089255,0.011012771,0.0054334286,0.5047875],"study_design_scores_gemma":[0.000046927114,0.00026770425,0.002713827,0.000018083821,0.000031216226,0.00021121523,0.000056625042,0.94691306,0.03901951,0.0027347896,0.007959454,0.000027592498],"about_ca_topic_score_codex":0.001965028,"about_ca_topic_score_gemma":0.0020830748,"teacher_disagreement_score":0.001965028,"about_ca_system_score_codex":0.00039295474,"about_ca_system_score_gemma":0.0004936636,"threshold_uncertainty_score":0.0044481754},"labels":[],"label_agreement":null},{"id":"W2978489487","doi":"10.1109/jsen.2019.2944412","title":"Robust Data-Driven Zero-Velocity Detection for Foot-Mounted Inertial Navigation","year":2019,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial navigation system; Detector; Computer science; Artificial intelligence; Inertial frame of reference; Zero (linguistics); Computer vision; Physics","score_opus":0.02904519433023986,"score_gpt":0.250744242576194,"score_spread":0.22169904824595413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978489487","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025214653,0.00026092923,0.97078216,0.000075319374,0.0001514192,0.000038584192,0.00024106106,0.0022501785,0.0009856799],"genre_scores_gemma":[0.6458432,0.00022936323,0.34932786,0.00015389931,0.00009998264,0.000112000875,0.0009988939,0.0002042476,0.003030524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996209,0.0000432237,0.000017302718,0.0001157661,0.00014597645,0.000056810524],"domain_scores_gemma":[0.99944454,0.0001584842,0.00008128052,0.000091487214,0.00019756414,0.000026626074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041300379,0.00086052465,0.00066499214,0.00075193704,0.00023238042,0.00048289666,0.0012511839,0.00057539484,0.001557261],"category_scores_gemma":[0.002336227,0.00038773404,0.0003077337,0.00065697235,0.00030809565,0.00083114597,0.0010406505,0.0009540579,0.0008235513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004456914,0.0001862069,0.007242687,0.00024270284,0.00010525608,0.00022059475,0.00015492046,0.092733495,0.096978895,0.0043058177,0.007875204,0.78950846],"study_design_scores_gemma":[0.000019833114,0.000119749035,0.0030644396,0.000032409618,0.00002043094,0.00012859699,0.00003165909,0.95406467,0.036789753,0.0025645138,0.0031313777,0.000032483757],"about_ca_topic_score_codex":0.0028894467,"about_ca_topic_score_gemma":0.0071034105,"teacher_disagreement_score":0.0028894467,"about_ca_system_score_codex":0.00027856932,"about_ca_system_score_gemma":0.0006378742,"threshold_uncertainty_score":0.0057452917},"labels":[],"label_agreement":null},{"id":"W2979334546","doi":"10.1109/iccids.2019.8862117","title":"Crowdsensing-based WiFi Indoor Localization using Feed-forward Multilayer Perceptron Regressor","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; RSS; Multilayer perceptron; Probabilistic logic; Data mining; A priori and a posteriori; Crowdsensing; Artificial intelligence; Artificial neural network; Crowdsourcing; Wireless sensor network; Mobile phone; Pattern recognition (psychology); Real-time computing; Machine learning; Computer network","score_opus":0.009991793609650072,"score_gpt":0.21962177616146816,"score_spread":0.20962998255181808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979334546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07688012,0.00039661225,0.9164225,0.00024135856,0.00013337884,0.000055638873,0.00013736179,0.0035191388,0.0022139046],"genre_scores_gemma":[0.94875145,0.00015600282,0.048293743,0.00007684966,0.00004624109,0.00005081303,0.00014293648,0.0000318914,0.002450048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995523,0.00009863067,0.000023100178,0.00013716357,0.000119197284,0.00006962071],"domain_scores_gemma":[0.99955314,0.00018777291,0.000062528954,0.000045455694,0.0001288412,0.000022277445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065801165,0.00083253783,0.00090028153,0.0005273512,0.00032458839,0.0004592175,0.0010869716,0.000641991,0.000999257],"category_scores_gemma":[0.0014295656,0.00038633952,0.0006199571,0.0006079621,0.00033943573,0.0006472601,0.00076593255,0.00071084884,0.00053107494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005532513,0.00019907989,0.004081015,0.0001768763,0.00013780923,0.00030122988,0.00014122023,0.7237948,0.013688566,0.001976684,0.0019147291,0.25303474],"study_design_scores_gemma":[0.000006671824,0.000027340679,0.00029350023,0.000003286639,0.000007998572,0.000017704378,0.0000061146843,0.9976554,0.0014469696,0.00035801623,0.000170825,0.0000060508387],"about_ca_topic_score_codex":0.011100165,"about_ca_topic_score_gemma":0.0076540075,"teacher_disagreement_score":0.011100165,"about_ca_system_score_codex":0.0006010483,"about_ca_system_score_gemma":0.0005090672,"threshold_uncertainty_score":0.022071123},"labels":[],"label_agreement":null},{"id":"W2982416128","doi":"10.1109/apusncursinrsm.2019.8888443","title":"A new Gradient Descent Positioning Method in Wireless Sensor Network Based on Received Signal Strength","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Signal strength; Wireless sensor network; Computer science; Gradient descent; SIGNAL (programming language); Hybrid positioning system; Computer network; Artificial intelligence; Artificial neural network; Acoustics; Positioning system; Physics","score_opus":0.006480165419062519,"score_gpt":0.2133212212430008,"score_spread":0.20684105582393827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982416128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020844783,0.00022843255,0.9968257,0.000071482864,0.000060807815,0.000015208422,0.000013055207,0.0002126281,0.00048828434],"genre_scores_gemma":[0.13791998,0.00085710746,0.85389996,0.00017126901,0.00015913835,0.00019585276,0.00015307806,0.00018799462,0.0064556166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994874,0.00013558909,0.000024780507,0.0001092871,0.00021803698,0.000024845913],"domain_scores_gemma":[0.9997421,0.000056475237,0.00002674977,0.000022006847,0.00013768204,0.000015036577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005737864,0.00075466087,0.00073400745,0.00060379395,0.00035549505,0.0005238014,0.0009997017,0.0007047514,0.0007629512],"category_scores_gemma":[0.0009478984,0.0003613442,0.0005327178,0.00068310474,0.00042960697,0.0010139337,0.00053726946,0.0007000674,0.0005999303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015034543,0.00009001532,0.0015340368,0.00036055403,0.00016290732,0.0002049995,0.00020521328,0.47040346,0.047680385,0.021821542,0.0066612903,0.45072526],"study_design_scores_gemma":[0.000014271622,0.000063511536,0.0003203415,0.0000101783435,0.000014738336,0.00008343205,0.000008989649,0.9896764,0.0038240042,0.0015860936,0.0043779043,0.00002018205],"about_ca_topic_score_codex":0.0032196182,"about_ca_topic_score_gemma":0.0026972715,"teacher_disagreement_score":0.0032196182,"about_ca_system_score_codex":0.00039286428,"about_ca_system_score_gemma":0.0007810162,"threshold_uncertainty_score":0.0064017177},"labels":[],"label_agreement":null},{"id":"W2983035018","doi":"10.1109/tvt.2019.2951022","title":"Accurate Indoor Localization Assisted With Optimizing Array Orientations and Receiver Positions","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Shaanxi Province Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Multipath propagation; Angle of arrival; Realization (probability); Real-time computing; Orientation (vector space); Calibration; Electronic engineering; Channel (broadcasting); Engineering; Antenna (radio); Telecommunications","score_opus":0.005777989695196722,"score_gpt":0.20083897719576713,"score_spread":0.1950609875005704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983035018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01630082,0.00014701516,0.9790213,0.0000857814,0.000045615892,0.000018965819,0.000040625277,0.0018632577,0.0024766908],"genre_scores_gemma":[0.5748751,0.00029901916,0.4211013,0.00015270527,0.00006444487,0.00009461126,0.00020499452,0.0001675339,0.0030402301],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933785,0.0001840605,0.000027656231,0.00014428857,0.00020369384,0.00010240768],"domain_scores_gemma":[0.9991641,0.00015267415,0.00017973926,0.0002362425,0.00022985405,0.000037432572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044570272,0.0011717471,0.00053063117,0.00048411574,0.00033838468,0.000571249,0.0006683558,0.00070254726,0.001115435],"category_scores_gemma":[0.0017238132,0.0003759366,0.00024297349,0.0006183102,0.0004918168,0.0009982061,0.0012566204,0.00070041773,0.0018100344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036765455,0.00011640766,0.0039699078,0.00026345166,0.00004791693,0.0003517162,0.00041709136,0.23539379,0.25798312,0.011866246,0.0056729536,0.4835497],"study_design_scores_gemma":[0.0000671174,0.00047913997,0.0031385613,0.000048700545,0.0000624153,0.000717285,0.00018078389,0.7602201,0.20864671,0.0043854974,0.021924093,0.0001296235],"about_ca_topic_score_codex":0.00067828776,"about_ca_topic_score_gemma":0.0011731533,"teacher_disagreement_score":0.0011717471,"about_ca_system_score_codex":0.00025425747,"about_ca_system_score_gemma":0.0006331967,"threshold_uncertainty_score":0.0037315488},"labels":[],"label_agreement":null},{"id":"W2983248265","doi":"10.1109/vtcfall.2019.8891169","title":"RSS-Based Positioning in Distributed Massive MIMO under Unknown Transmit Power and Pathloss Exponent","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"RSS; Telecommunications link; MIMO; Exponent; Computer science; Transmitter power output; Power (physics); Algorithm; Real-time computing; Control theory (sociology); Telecommunications; Transmitter; Artificial intelligence; Physics; Channel (broadcasting)","score_opus":0.0038804097818105005,"score_gpt":0.18671861885847232,"score_spread":0.18283820907666182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983248265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0716456,0.00022011175,0.92579406,0.00013205993,0.000036046444,0.000022168508,0.000057910504,0.0002972122,0.0017947754],"genre_scores_gemma":[0.9600338,0.00021882304,0.0379671,0.00003583745,0.00003237477,0.000026389067,0.00005245451,0.000015138829,0.0016181297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995784,0.00013072665,0.000016223896,0.000109253386,0.000114333656,0.00005104085],"domain_scores_gemma":[0.9991468,0.00049472024,0.00013354373,0.000083429295,0.00011166533,0.000029826375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060074043,0.0007233701,0.00057746284,0.00025923856,0.00022829391,0.00051149965,0.00064656895,0.0006001882,0.00044625858],"category_scores_gemma":[0.0022287427,0.00033048054,0.00031896116,0.0005742709,0.00078794523,0.00070032594,0.00075207104,0.00039538593,0.00022383471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038441187,0.000007912259,0.0004675605,0.000032158383,0.000013014467,0.0001181152,0.000032937387,0.9865302,0.0027721364,0.0024986833,0.000104941486,0.0073839044],"study_design_scores_gemma":[0.0000044705093,0.000028874858,0.0002797955,0.0000024568146,0.000004822961,0.000039038263,0.000011357618,0.99782443,0.00070182275,0.001028576,0.00006842541,0.000005975759],"about_ca_topic_score_codex":0.003059525,"about_ca_topic_score_gemma":0.002711222,"teacher_disagreement_score":0.003059525,"about_ca_system_score_codex":0.00052384334,"about_ca_system_score_gemma":0.00037677432,"threshold_uncertainty_score":0.006083429},"labels":[],"label_agreement":null},{"id":"W2984768289","doi":"10.1109/igarss.2019.8898669","title":"Slam-Based Multi-Sensor Backpack Lidar Systems in Gnss-Denied Environments","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backpack; Lidar; GNSS applications; Computer science; Inertial measurement unit; Remote sensing; Real-time computing; Computer vision; Artificial intelligence; Global Positioning System; Engineering; Geography; Telecommunications","score_opus":0.010274735151158844,"score_gpt":0.19944719759470186,"score_spread":0.18917246244354302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984768289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12653184,0.0006513746,0.8656434,0.0002714875,0.0001659117,0.00008946774,0.00012658107,0.0026397028,0.003880226],"genre_scores_gemma":[0.7803565,0.00023543672,0.2166977,0.00013629775,0.0000455302,0.00006914115,0.00016202424,0.00005644654,0.0022409009],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942124,0.00012684087,0.000028543265,0.00007929812,0.00028207674,0.00006195074],"domain_scores_gemma":[0.9996697,0.000052622792,0.000048442755,0.00008846156,0.00011483521,0.000025868432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045781533,0.0004119539,0.00069320766,0.0005665603,0.00041199397,0.0008047585,0.00078722654,0.0006706786,0.0011918203],"category_scores_gemma":[0.00081881246,0.00038154662,0.00032635612,0.00067953166,0.00032941892,0.0013189461,0.0017809939,0.0006960568,0.00071242126],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006552181,0.00022335337,0.004607628,0.00040882546,0.000118131225,0.0005359487,0.0007626896,0.07990637,0.31895277,0.00780413,0.0039357576,0.58208907],"study_design_scores_gemma":[0.000112177775,0.0006748039,0.004268607,0.00006125545,0.00004744939,0.0006830384,0.00032194844,0.8758698,0.10247489,0.0045370585,0.010845231,0.00010375094],"about_ca_topic_score_codex":0.00132364,"about_ca_topic_score_gemma":0.0017529675,"teacher_disagreement_score":0.00132364,"about_ca_system_score_codex":0.00021853107,"about_ca_system_score_gemma":0.0005329524,"threshold_uncertainty_score":0.003987074},"labels":[],"label_agreement":null},{"id":"W2989663650","doi":"10.1109/ipin.2019.8911816","title":"Trilateration With BLE RSSI Accounting for Pathloss Due to Human Obstacles","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Trilateration; Bluetooth Low Energy; Computer science; Signal strength; Bluetooth; Received signal strength indication; Ranging; Real-time computing; SIGNAL (programming language); Path loss; Wireless; Telecommunications; Engineering","score_opus":0.007030374742422817,"score_gpt":0.20840458080434812,"score_spread":0.2013742060619253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989663650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026953667,0.00036743502,0.9673708,0.000046938567,0.0001482882,0.0000621119,0.0001308619,0.0028384503,0.0020813416],"genre_scores_gemma":[0.3427675,0.00088298274,0.6446212,0.00014539206,0.0001077959,0.00021587376,0.0011262249,0.00063769094,0.009495358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916446,0.0001054709,0.00006410111,0.00019480583,0.00040472194,0.000066503744],"domain_scores_gemma":[0.9990396,0.0001622639,0.00018194405,0.00025868023,0.00032250464,0.00003497099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058187975,0.0014101618,0.00074028585,0.0020502938,0.00040622347,0.000990867,0.0011161716,0.0006332256,0.0026919483],"category_scores_gemma":[0.0025634689,0.0004038783,0.0007704576,0.0020483052,0.0003492471,0.0012896617,0.0009068158,0.00080813287,0.0030500193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000519961,0.00017084567,0.007239779,0.00042218302,0.00012313864,0.00034343914,0.00030946953,0.05239475,0.11028627,0.0029814162,0.003677461,0.82153136],"study_design_scores_gemma":[0.00005225484,0.0008028471,0.021290071,0.00009101741,0.00015538807,0.0024392332,0.00021173245,0.787088,0.1624983,0.0029931292,0.02218535,0.0001927096],"about_ca_topic_score_codex":0.0013078686,"about_ca_topic_score_gemma":0.0028343513,"teacher_disagreement_score":0.0026919483,"about_ca_system_score_codex":0.0003189876,"about_ca_system_score_gemma":0.00046647285,"threshold_uncertainty_score":0.009005487},"labels":[],"label_agreement":null},{"id":"W2989703840","doi":"10.3390/s19235204","title":"Tag Localization with Asynchronous Inertial-Based Shifting and Trilateration","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Trilateration; Computer science; Scalability; Real-time computing; Crowdsourcing; GNSS applications; Global Positioning System; Leverage (statistics); Asynchronous communication; Dead reckoning; Computer network; Artificial intelligence; Engineering; Telecommunications; Node (physics)","score_opus":0.0027281074095130914,"score_gpt":0.1632904839487941,"score_spread":0.16056237653928102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989703840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024101978,0.00028367236,0.97208136,0.00011409413,0.00009307279,0.00004099941,0.000025820105,0.0006651255,0.0025938794],"genre_scores_gemma":[0.77338165,0.00045626643,0.22078042,0.00020924797,0.00014652092,0.0001279743,0.0001415011,0.00007447118,0.0046819644],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925834,0.00012207375,0.000036298432,0.00021298465,0.0003019522,0.00006827248],"domain_scores_gemma":[0.9993856,0.00012654637,0.00013597397,0.00015842165,0.00016126131,0.000032130385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005109753,0.00073062937,0.0005323848,0.00077445235,0.00061802124,0.00057217316,0.0014397174,0.0007157353,0.0007596981],"category_scores_gemma":[0.0014426187,0.0002909437,0.0005587256,0.0011484622,0.0007525632,0.0013767843,0.0015650883,0.0006038564,0.0007123588],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073781295,0.00014989871,0.0037968687,0.00035272242,0.0001053899,0.0008206424,0.0011580348,0.2136094,0.1692601,0.014100536,0.0038349559,0.5920737],"study_design_scores_gemma":[0.00008651603,0.0005014397,0.0017315554,0.000028900751,0.000060977727,0.0008751741,0.00018936113,0.9269345,0.051320113,0.0072552445,0.010934963,0.00008125151],"about_ca_topic_score_codex":0.0016388241,"about_ca_topic_score_gemma":0.0016331862,"teacher_disagreement_score":0.0016388241,"about_ca_system_score_codex":0.00043408043,"about_ca_system_score_gemma":0.0004990496,"threshold_uncertainty_score":0.003258586},"labels":[],"label_agreement":null},{"id":"W2991134859","doi":"10.1109/access.2019.2954330","title":"Deep Sea TDOA Localization Method Based on Improved OMP Algorithm","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Qinglan Project of Jiangsu Province of China; Government of Jiangsu Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Multilateration; Computer science; Algorithm; Multipath propagation; Matching pursuit; Synchronization (alternating current); Least-squares function approximation; Interference (communication); Stability (learning theory); Compressed sensing; Acceleration; Real-time computing; Mathematics; Telecommunications; Azimuth","score_opus":0.009839874242796684,"score_gpt":0.26641986974476795,"score_spread":0.25657999550197125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991134859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057798503,0.00019265292,0.99273807,0.00005885514,0.000045096287,0.0000114679515,0.000020441357,0.00030693825,0.0008465592],"genre_scores_gemma":[0.263069,0.00076597836,0.72991854,0.000097609336,0.00011449035,0.00009820196,0.00025479496,0.00011322676,0.0055680876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961936,0.000060250924,0.000023084021,0.000089299196,0.00017612778,0.000031867887],"domain_scores_gemma":[0.99967253,0.00006107204,0.000042078766,0.00003949957,0.00016987363,0.000015002615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025855235,0.000658823,0.00048232166,0.0007840366,0.00024827436,0.0005339353,0.0006818056,0.00047099407,0.001314722],"category_scores_gemma":[0.0008636634,0.00022816249,0.00048208126,0.0010393373,0.00025976755,0.0011077409,0.0007777054,0.00067984156,0.0006092199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002858016,0.000070114445,0.0015196233,0.00025453354,0.0000863629,0.00020243177,0.0002202449,0.15541027,0.10539034,0.014715012,0.0029171773,0.71892816],"study_design_scores_gemma":[0.000029123592,0.00010531408,0.00056744623,0.000011898334,0.000020806947,0.000279085,0.00003676781,0.9751915,0.016191104,0.0024494969,0.005087608,0.000029830904],"about_ca_topic_score_codex":0.002317805,"about_ca_topic_score_gemma":0.0016484335,"teacher_disagreement_score":0.002317805,"about_ca_system_score_codex":0.00021887013,"about_ca_system_score_gemma":0.0007042469,"threshold_uncertainty_score":0.0046085715},"labels":[],"label_agreement":null},{"id":"W2991310872","doi":"10.3390/app9235027","title":"A Novel Localization Technique Using Luminous Flux","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario","funders":"Yanshan University; National Natural Science Foundation of China","keywords":"Dilution of precision; Computer science; Optical wireless; Satellite system; Non-line-of-sight propagation; Mean squared error; RSS; Wireless; GNSS applications; Global Positioning System; Real-time computing; Telecommunications; Mathematics; Statistics","score_opus":0.016195917252544684,"score_gpt":0.22637583597366523,"score_spread":0.21017991872112055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991310872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03091132,0.0011513899,0.9598254,0.00021224888,0.00024138702,0.000045863137,0.00006024124,0.0012840457,0.006268138],"genre_scores_gemma":[0.52443033,0.0012632828,0.45963007,0.00030674887,0.00016845971,0.00010625056,0.00014044615,0.00009421684,0.0138602415],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996934,0.000047835107,0.000011415858,0.0001011621,0.00011476633,0.000031420823],"domain_scores_gemma":[0.9997732,0.00005297105,0.000049341965,0.000040530078,0.00007207842,0.0000118091075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020998738,0.00045170143,0.00041976594,0.00077065144,0.0003818439,0.0006149652,0.00092414644,0.00076260883,0.0018153371],"category_scores_gemma":[0.00050984713,0.00020784921,0.00037795413,0.0007588852,0.00038258667,0.001050628,0.00079704507,0.00038049128,0.0011994479],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031141238,0.00010131271,0.0018423672,0.00044675267,0.000051621704,0.0003935959,0.00027626587,0.0073769647,0.43083504,0.011096559,0.0027258082,0.5445423],"study_design_scores_gemma":[0.0001538067,0.0012269868,0.0049183536,0.00015686045,0.00019666117,0.004656208,0.00035303368,0.3069013,0.58463025,0.0058709187,0.090711094,0.00022454829],"about_ca_topic_score_codex":0.00054215756,"about_ca_topic_score_gemma":0.0006382132,"teacher_disagreement_score":0.0018153371,"about_ca_system_score_codex":0.00034005777,"about_ca_system_score_gemma":0.00031487053,"threshold_uncertainty_score":0.0060729384},"labels":[],"label_agreement":null},{"id":"W2991365221","doi":"","title":"Pedestrian Dead-Reckoning Using Multiple Low-Cost Inertial/Magnetic Sensors on Lower Limbs","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Dead reckoning; Pedestrian; Computer science; Inertial measurement unit; Computer vision; Artificial intelligence; Engineering; Telecommunications; Global Positioning System; Transport engineering","score_opus":0.011577305266755211,"score_gpt":0.209655912191689,"score_spread":0.1980786069249338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991365221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4366875,0.0016986826,0.5446033,0.00029788664,0.00055726344,0.0001224119,0.0009212775,0.0029954875,0.012116233],"genre_scores_gemma":[0.94254327,0.00045099348,0.047355283,0.00015485835,0.00009128456,0.000059178685,0.00042904893,0.000033230765,0.008882821],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973935,0.000031358326,0.000012073073,0.00007840608,0.00009433061,0.000044422126],"domain_scores_gemma":[0.99985754,0.000010567646,0.000022975628,0.000017388375,0.00007768517,0.00001375846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011464888,0.00084297196,0.00073094835,0.0006127657,0.00031535933,0.00039564853,0.00055166805,0.00048605868,0.002280506],"category_scores_gemma":[0.00027913987,0.00031724133,0.00034043065,0.0006984929,0.00013119633,0.00046483736,0.00052090763,0.00024880754,0.0016573393],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011431632,0.00022298629,0.019666435,0.00045620682,0.0001779318,0.0007230497,0.0002701546,0.007134942,0.2391335,0.000638969,0.007885656,0.722547],"study_design_scores_gemma":[0.00021797125,0.0029918277,0.27784726,0.00028473878,0.00094607484,0.0067231823,0.001157593,0.38703603,0.2834331,0.0028101103,0.036231596,0.0003205602],"about_ca_topic_score_codex":0.0026967726,"about_ca_topic_score_gemma":0.007004104,"teacher_disagreement_score":0.0026967726,"about_ca_system_score_codex":0.00014732145,"about_ca_system_score_gemma":0.00036772294,"threshold_uncertainty_score":0.007629037},"labels":[],"label_agreement":null},{"id":"W2995054110","doi":"10.1109/taes.2019.2958397","title":"Multiemitter Two-Dimensional Angle-of-Arrival Estimator via Compressive Sensing","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Compressed sensing; Estimator; Bandwidth (computing); Algorithm; Angle of arrival; Computer science; A priori and a posteriori; Direction of arrival; Acoustics; Electronic engineering; Mathematics; Antenna (radio); Engineering; Telecommunications; Statistics; Physics","score_opus":0.004720931939007709,"score_gpt":0.19915187977361515,"score_spread":0.19443094783460743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995054110","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071050907,0.00010402812,0.991981,0.000052758267,0.000024611998,0.000018621784,0.000023421499,0.00014755822,0.00054298737],"genre_scores_gemma":[0.2081141,0.00029224154,0.7893169,0.00011319084,0.000078789075,0.00010713587,0.00019249987,0.000039009377,0.0017461153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992188,0.00024017581,0.000034045825,0.00013048486,0.0003243142,0.000052115574],"domain_scores_gemma":[0.9991702,0.0003819202,0.00012916565,0.0001243965,0.00015009307,0.000044347962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077466836,0.0006769971,0.0006149706,0.00062835775,0.00026917743,0.00055706577,0.0006876345,0.0006522814,0.00091676385],"category_scores_gemma":[0.0024464966,0.00037763017,0.0004589149,0.00057959656,0.0003957468,0.0013784984,0.0012353561,0.0007662858,0.00047721452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049654976,0.00014110221,0.0030822244,0.00021950503,0.00011487281,0.0001644274,0.00024006184,0.3498879,0.10394434,0.028428469,0.0023474237,0.51093316],"study_design_scores_gemma":[0.000020361185,0.00011974775,0.00045814962,0.000011767619,0.000009751288,0.00015510616,0.000016344999,0.9846239,0.010228377,0.0023305153,0.0019999342,0.000026002495],"about_ca_topic_score_codex":0.00089964917,"about_ca_topic_score_gemma":0.0012577878,"teacher_disagreement_score":0.00091676385,"about_ca_system_score_codex":0.00026623686,"about_ca_system_score_gemma":0.00065725297,"threshold_uncertainty_score":0.0040968657},"labels":[],"label_agreement":null},{"id":"W2995335241","doi":"10.3390/s20010078","title":"LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Node (physics); Computer science; Ranging; Wireless ad hoc network; Computer network; Transceiver; Range (aeronautics); Positioning system; Ultra-wideband; Network topology; Real-time computing; Topology (electrical circuits); Distributed computing; Wireless; Engineering; Telecommunications; Electrical engineering","score_opus":0.0037162927433570052,"score_gpt":0.18877460018069284,"score_spread":0.18505830743733584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995335241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064415656,0.00074403256,0.916469,0.00010248979,0.0001607088,0.00020811234,0.00024738593,0.013793475,0.0038592638],"genre_scores_gemma":[0.6662137,0.0004406207,0.32272905,0.00027117468,0.00012632557,0.00037513257,0.0009883342,0.0002703792,0.008585376],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929535,0.00009645394,0.000045944675,0.00018647652,0.00029661242,0.00007908966],"domain_scores_gemma":[0.99921954,0.00012469926,0.0001265758,0.00022890537,0.00021748683,0.00008267316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004382536,0.00071192574,0.001062359,0.0010005407,0.0004460349,0.0005174643,0.0019643267,0.0008186412,0.0018077897],"category_scores_gemma":[0.0009850848,0.000326405,0.0003175539,0.0004893109,0.00034220863,0.0013757809,0.0016615283,0.000604474,0.0011823091],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010000264,0.00024763393,0.0064166184,0.00058588985,0.00015198906,0.0009115597,0.00063920434,0.018917276,0.21060556,0.0046542953,0.011660416,0.7442095],"study_design_scores_gemma":[0.0006158442,0.0035466228,0.019293094,0.0001807542,0.0004445762,0.006391433,0.00058773335,0.62851286,0.2229502,0.0043362645,0.11258401,0.0005566276],"about_ca_topic_score_codex":0.0012582468,"about_ca_topic_score_gemma":0.0014934877,"teacher_disagreement_score":0.0019643267,"about_ca_system_score_codex":0.00031377675,"about_ca_system_score_gemma":0.00048359038,"threshold_uncertainty_score":0.0060476065},"labels":[],"label_agreement":null},{"id":"W2995502867","doi":"10.1109/tvt.2019.2959308","title":"Learning RSSI Feature via Ranking Model for Wi-Fi Fingerprinting Localization","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China","keywords":"Computer science; Received signal strength indication; Boosting (machine learning); Artificial intelligence; Signal strength; Pattern recognition (psychology); k-nearest neighbors algorithm; Function (biology); Gradient boosting; Data mining; Wireless; Random forest; Telecommunications","score_opus":0.005544197147146781,"score_gpt":0.2016449867393047,"score_spread":0.19610078959215793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995502867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01711768,0.0004756185,0.980471,0.00012564882,0.00004904671,0.00002695953,0.000113907234,0.00096022355,0.0006598273],"genre_scores_gemma":[0.79345906,0.0007734009,0.19789524,0.00026453045,0.00016635872,0.00021735787,0.0010336696,0.00015607891,0.006034337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943846,0.00014551522,0.000027384998,0.00016800931,0.00012555697,0.00009506985],"domain_scores_gemma":[0.9993808,0.0002554701,0.000078745245,0.00006596715,0.0001906621,0.000028355342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009449953,0.0010474115,0.0016230119,0.0009190496,0.00032828146,0.000664064,0.0017969086,0.0010483743,0.0015853642],"category_scores_gemma":[0.0021131348,0.0003985216,0.0008204968,0.0011960901,0.0004063255,0.0009954063,0.00056337914,0.0011545407,0.0010237391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015199755,0.00015066777,0.0025531324,0.00010437408,0.000088287976,0.00010763439,0.00003890493,0.7637308,0.0048121405,0.0030652157,0.0034867525,0.22171007],"study_design_scores_gemma":[0.000003897977,0.000018814353,0.00020312257,0.000003123742,0.000006905419,0.000016665226,0.000002612818,0.99850816,0.0003745596,0.000673204,0.00018417042,0.000004695002],"about_ca_topic_score_codex":0.0064693163,"about_ca_topic_score_gemma":0.005857206,"teacher_disagreement_score":0.0064693163,"about_ca_system_score_codex":0.0005163941,"about_ca_system_score_gemma":0.0007597625,"threshold_uncertainty_score":0.012863278},"labels":[],"label_agreement":null},{"id":"W2995957663","doi":"10.3390/s19245532","title":"Direction of Arrival Estimation of GPS Narrowband Jammers Using High-Resolution Techniques","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Jamming; Global Positioning System; Narrowband; Computer science; Direction of arrival; GPS signals; Assisted GPS; Telecommunications; Antenna (radio); Physics","score_opus":0.0072515333243267244,"score_gpt":0.21519474768885696,"score_spread":0.20794321436453023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995957663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27458993,0.00034461136,0.72104156,0.00008695951,0.00005602734,0.000039256738,0.00009380864,0.0009479551,0.002799957],"genre_scores_gemma":[0.78729266,0.0003417844,0.21058324,0.000033498756,0.00003586113,0.000028425984,0.0001570908,0.000040810322,0.0014866592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997732,0.000053656055,0.000009489897,0.00003396801,0.00010798109,0.000021713106],"domain_scores_gemma":[0.99954754,0.00010762643,0.00011024978,0.00004328108,0.00017172971,0.000019567806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024005002,0.00040932526,0.00024218755,0.0009084191,0.00016356792,0.00033534822,0.00023336467,0.00032803745,0.00065413467],"category_scores_gemma":[0.0012710026,0.0001712014,0.00021484328,0.0005730947,0.0001162604,0.00045500632,0.00034973604,0.00032522398,0.000390473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064702175,0.00016773738,0.010823978,0.00034338824,0.000086738226,0.00021089343,0.0003626363,0.09730077,0.36019266,0.0023561688,0.0014650888,0.5260429],"study_design_scores_gemma":[0.00008138542,0.00060460763,0.024170738,0.000042697546,0.000092473645,0.0008265047,0.00021786308,0.8200342,0.14808977,0.0010860501,0.004674116,0.00007960559],"about_ca_topic_score_codex":0.0007647492,"about_ca_topic_score_gemma":0.0010942202,"teacher_disagreement_score":0.0009084191,"about_ca_system_score_codex":0.00014241665,"about_ca_system_score_gemma":0.00033408808,"threshold_uncertainty_score":0.0021882653},"labels":[],"label_agreement":null},{"id":"W2998805216","doi":"10.1109/access.2020.2966688","title":"Robust Localization of Mobile Robot in Industrial Environments With Non-Line-of-Sight Situation","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Non-line-of-sight propagation; Particle filter; Computer science; Mobile robot; Resampling; Artificial intelligence; Computer vision; Genetic algorithm; Process (computing); Robot; Real-time computing; Filter (signal processing); Wireless; Machine learning","score_opus":0.05242575843962191,"score_gpt":0.23969503716835996,"score_spread":0.18726927872873805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998805216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012327045,0.00037917428,0.9861059,0.000060870283,0.0000386074,0.000011554097,0.000014220568,0.00050523557,0.00055726076],"genre_scores_gemma":[0.58779013,0.0008946421,0.40820128,0.00012221062,0.00011487163,0.00006359096,0.00014026785,0.00010078016,0.002572215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994862,0.000073701485,0.00002668387,0.00015525908,0.00021617513,0.00004203038],"domain_scores_gemma":[0.99970055,0.0000768764,0.000084063264,0.00004986986,0.00007546666,0.000013307122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038361564,0.0007136338,0.00073147303,0.00062771013,0.00029318998,0.0004893368,0.00095264,0.0008208202,0.00039481398],"category_scores_gemma":[0.00094652764,0.00026808408,0.0004422381,0.0005754546,0.00047906747,0.0009606558,0.000623768,0.0004933331,0.00031612723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026293428,0.00008129055,0.0033294482,0.0003888584,0.0001675748,0.0006229152,0.00026789375,0.3054513,0.116256215,0.008262466,0.0026671782,0.5622419],"study_design_scores_gemma":[0.000025273663,0.00017872667,0.0018453808,0.0000151212425,0.00004722544,0.00042323748,0.000049464557,0.96474695,0.026520843,0.00195598,0.0041522435,0.000039564216],"about_ca_topic_score_codex":0.0017053109,"about_ca_topic_score_gemma":0.001359507,"teacher_disagreement_score":0.0017053109,"about_ca_system_score_codex":0.00026505964,"about_ca_system_score_gemma":0.0004084409,"threshold_uncertainty_score":0.0033907294},"labels":[],"label_agreement":null},{"id":"W2998949556","doi":"10.1109/jiot.2020.2965583","title":"Improving BLE Beacon Proximity Estimation Accuracy Through Bayesian Filtering","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Bluetooth; Beacon; Mean squared error; Kalman filter; Context (archaeology); Recursive Bayesian estimation; Real-time computing; Bayesian probability; Wireless; Data mining; Telecommunications; Artificial intelligence; Statistics; Mathematics","score_opus":0.016474733903240953,"score_gpt":0.23362659279008213,"score_spread":0.2171518588868412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998949556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018133791,0.00020289824,0.9796083,0.00009617314,0.000040251336,0.000020256868,0.000029045334,0.0006120216,0.0012573453],"genre_scores_gemma":[0.6239317,0.0006290227,0.37118828,0.00019469573,0.00008210136,0.00009816776,0.0002689626,0.000116863965,0.0034902028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883205,0.00023181825,0.00007044777,0.00022059516,0.00052792014,0.00011705715],"domain_scores_gemma":[0.99781966,0.0010023618,0.00024960967,0.00021674533,0.00066865166,0.00004305621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014727799,0.0006987902,0.00074619637,0.0010912701,0.00041368644,0.0007813378,0.0008967713,0.0010540753,0.0008723543],"category_scores_gemma":[0.00828112,0.0004144293,0.00063759886,0.00073196396,0.00033765286,0.001375363,0.00081497093,0.0009215187,0.0006946629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055670814,0.00021807879,0.012638313,0.00024524916,0.00010095263,0.00016689622,0.0004374892,0.31453627,0.052877773,0.007154341,0.0028975427,0.6081704],"study_design_scores_gemma":[0.0000216526,0.00014685962,0.004085653,0.000029271114,0.000043089643,0.00012012537,0.000044694967,0.9794845,0.011838483,0.00170391,0.0024494242,0.000032364253],"about_ca_topic_score_codex":0.007791481,"about_ca_topic_score_gemma":0.007787113,"teacher_disagreement_score":0.007791481,"about_ca_system_score_codex":0.0005833738,"about_ca_system_score_gemma":0.00075594935,"threshold_uncertainty_score":0.01549226},"labels":[],"label_agreement":null},{"id":"W2999369619","doi":"10.3390/electronics9010120","title":"A Low Cost Civil Vehicular Seamless Navigation Technology Based on Enhanced RISS/GPS between the Outdoors and an Underground Garage","year":2020,"lang":"en","type":"article","venue":"Electronics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Queen's University","funders":"Fundamental Research Funds for the Central Universities; Chongqing Municipal Education Commission; National Natural Science Foundation of China","keywords":"Global Positioning System; Inertial navigation system; Odometer; Dead reckoning; Inertial measurement unit; Navigation system; Real-time computing; Computer science; Wind triangle; Satellite navigation; Embedded system; Simulation; Engineering; Inertial frame of reference; Telecommunications; Artificial intelligence; Mobile robot; Robot","score_opus":0.008276303511402397,"score_gpt":0.22546334009777905,"score_spread":0.21718703658637664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999369619","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14696151,0.0013511332,0.83010864,0.0002848184,0.00037026222,0.00010246688,0.00023101953,0.004627208,0.015962988],"genre_scores_gemma":[0.8661422,0.0005876378,0.12214917,0.00015372872,0.00007217402,0.000049202878,0.00036277247,0.000059576832,0.010423543],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972945,0.000029842373,0.000016091584,0.00006408636,0.00012284081,0.00003758317],"domain_scores_gemma":[0.99987674,0.000007456144,0.000019625299,0.00002629398,0.00005681813,0.000012962935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012357933,0.0004130089,0.00035245542,0.0005882681,0.00021734228,0.00035621307,0.0008430009,0.00032954675,0.0014655294],"category_scores_gemma":[0.00014910653,0.00017206746,0.00037958135,0.00039050687,0.00022679067,0.00058053975,0.0004829671,0.00034646445,0.00085591694],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040036344,0.00008886497,0.0077039385,0.0005662157,0.00008096534,0.00068274495,0.00029511377,0.006564238,0.50124085,0.015108096,0.006483158,0.46078557],"study_design_scores_gemma":[0.00012422331,0.0026309753,0.014334486,0.00012674929,0.0005409607,0.0055589913,0.00047505493,0.13341412,0.682974,0.0027874236,0.15682568,0.00020735868],"about_ca_topic_score_codex":0.0012815465,"about_ca_topic_score_gemma":0.001884693,"teacher_disagreement_score":0.0014655294,"about_ca_system_score_codex":0.00031096785,"about_ca_system_score_gemma":0.00043923935,"threshold_uncertainty_score":0.004902661},"labels":[],"label_agreement":null},{"id":"W2999749128","doi":"10.1109/taes.2020.2966095","title":"Compressive TDOA Estimation: Cramér–Rao Bound and Incoherent Processing","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Multilateration; FDOA; Cramér–Rao bound; Algorithm; Sampling (signal processing); Computer science; Signal processing; Time–frequency analysis; Upper and lower bounds; Statistics; Estimation theory; Mathematics; Speech recognition; Acoustics; Telecommunications; Physics; Mathematical analysis","score_opus":0.009657263901768694,"score_gpt":0.20487537700074318,"score_spread":0.1952181130989745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999749128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014570107,0.001988111,0.99478936,0.00014273498,0.000068461355,0.00001151741,0.00002774289,0.0000550799,0.00145994],"genre_scores_gemma":[0.27616194,0.0139198145,0.70333546,0.00039806473,0.001025531,0.00024831062,0.00038525136,0.00017502617,0.0043505933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981255,0.00038772597,0.00008087198,0.00034439805,0.0009798504,0.00008162123],"domain_scores_gemma":[0.99637246,0.0020637864,0.00033567837,0.0003582245,0.000799812,0.000070045004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001396409,0.0014160763,0.0010325664,0.0012405998,0.00042727936,0.001736793,0.0011073443,0.0013746702,0.0011626014],"category_scores_gemma":[0.011131053,0.00059179665,0.00059530407,0.0023101147,0.0016461578,0.0022386762,0.0015506137,0.0021462506,0.0007741031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000245951,0.000073689036,0.0010592223,0.00060678716,0.0001145796,0.0002618752,0.00027916534,0.49437964,0.021984756,0.15134035,0.004794237,0.32485974],"study_design_scores_gemma":[0.000010000127,0.00006667989,0.00049063965,0.0000846424,0.00002751398,0.0002868729,0.000047269816,0.95689136,0.0056782668,0.031231714,0.0051340833,0.00005097213],"about_ca_topic_score_codex":0.0035285903,"about_ca_topic_score_gemma":0.0025611985,"teacher_disagreement_score":0.0035285903,"about_ca_system_score_codex":0.0008660062,"about_ca_system_score_gemma":0.0014312316,"threshold_uncertainty_score":0.007384956},"labels":[],"label_agreement":null},{"id":"W2999843304","doi":"10.1109/sensors43011.2019.8956541","title":"Device Mobility Detection Based on Optical Flow and Multi-Receiver Consensus","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Signal strength; Computer science; Real-time computing; Wireless; SIGNAL (programming language); Optical flow; Received signal strength indication; Tracking (education); Artificial intelligence; Computer vision; Telecommunications","score_opus":0.009304614466175821,"score_gpt":0.20846027476436882,"score_spread":0.199155660298193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999843304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04082496,0.0002144377,0.9569757,0.00015281126,0.00009989098,0.00006725886,0.000048711026,0.00036950523,0.001246633],"genre_scores_gemma":[0.8399906,0.00021964902,0.15732206,0.000088563356,0.00012446902,0.00010120447,0.00014062243,0.000039054317,0.0019737293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992575,0.0001504688,0.00003847758,0.00022637208,0.00021089193,0.00011630786],"domain_scores_gemma":[0.9985423,0.0004941009,0.00024580598,0.00013462068,0.00048675895,0.000096469266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012546165,0.00094477297,0.0012955335,0.0020699804,0.00059741363,0.0007223891,0.00153372,0.0009570286,0.000845508],"category_scores_gemma":[0.0038753208,0.0004770333,0.0008289116,0.0011421628,0.00063683576,0.0016062649,0.0011420344,0.0007840037,0.00029520076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003851806,0.00024170558,0.008057165,0.00019828175,0.00015817808,0.00031851567,0.00042774362,0.5278825,0.023691978,0.015040484,0.0023925568,0.42120573],"study_design_scores_gemma":[0.0000064027604,0.00003391909,0.00059638894,0.000004302736,0.000005631254,0.000042996537,0.000014654639,0.9962752,0.0013272371,0.0014805581,0.00019938433,0.000013346293],"about_ca_topic_score_codex":0.0048058787,"about_ca_topic_score_gemma":0.00390367,"teacher_disagreement_score":0.0048058787,"about_ca_system_score_codex":0.0008350648,"about_ca_system_score_gemma":0.00092901307,"threshold_uncertainty_score":0.009555817},"labels":[],"label_agreement":null},{"id":"W2999864345","doi":"10.1109/jsen.2020.2972850","title":"Semi-Sequential Probabilistic Model for Indoor Localization Enhancement","year":2020,"lang":"en","type":"preprint","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Probabilistic logic; Computer science; Position (finance); Channel (broadcasting); Statistical model; Probabilistic method; Probabilistic analysis of algorithms; Channel state information; Bounded function; Signal strength; Artificial intelligence; Mathematics; Wireless; Telecommunications","score_opus":0.039933545519918706,"score_gpt":0.26660329714715814,"score_spread":0.22666975162723943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999864345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038299125,0.00017026969,0.9944674,0.0000723609,0.000030512223,0.000011788317,0.000054399883,0.00032153665,0.0010418722],"genre_scores_gemma":[0.7881871,0.0012530787,0.20127358,0.00023720221,0.00018143129,0.00018432955,0.000452918,0.00019037895,0.008040013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992824,0.00016861883,0.000039331724,0.00017924697,0.00025675038,0.00007373847],"domain_scores_gemma":[0.99868554,0.0006237303,0.00013977292,0.00018710927,0.00032245458,0.00004132523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094301027,0.0009562015,0.0009808855,0.00060205365,0.00032480972,0.0007679614,0.0021921655,0.0008273153,0.0024572848],"category_scores_gemma":[0.0023521876,0.00059582916,0.0009775641,0.0010149548,0.00063338253,0.00202337,0.0012761179,0.0010877402,0.0009435586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013377411,0.000042778796,0.0007190278,0.00013963685,0.000048525977,0.00012323672,0.000088313274,0.91333634,0.0047734585,0.021273477,0.0012610513,0.05806034],"study_design_scores_gemma":[0.0000046311975,0.000032345946,0.00009379232,0.000003792088,0.000011557988,0.000051049323,0.000005101878,0.99424297,0.00063731254,0.0041641016,0.0007456145,0.00000773487],"about_ca_topic_score_codex":0.0049750414,"about_ca_topic_score_gemma":0.0043402775,"teacher_disagreement_score":0.0049750414,"about_ca_system_score_codex":0.0005622499,"about_ca_system_score_gemma":0.0008472763,"threshold_uncertainty_score":0.009892166},"labels":[],"label_agreement":null},{"id":"W3000594500","doi":"10.1109/sensors43011.2019.8956942","title":"An Indoor Navigation System using Stereo Vision, IMU and UWB Sensor Fusion","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Odometry; Inertial measurement unit; Ranging; Gyroscope; GNSS applications; Extended Kalman filter; Artificial intelligence; Sensor fusion; Computer vision; Fuse (electrical); Accelerometer; Kalman filter; Multipath propagation; Global Positioning System; Real-time computing; Mobile robot; Engineering; Robot; Telecommunications","score_opus":0.005791909104769503,"score_gpt":0.2244042846058672,"score_spread":0.2186123755010977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000594500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037124038,0.00043581182,0.9509675,0.00011062893,0.0002161987,0.000070909686,0.00023342304,0.005171109,0.0056703626],"genre_scores_gemma":[0.70238197,0.00033415787,0.28967813,0.00022306477,0.00011692779,0.0001579681,0.0006215712,0.00007040779,0.006415956],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995246,0.000049768445,0.000025151307,0.000115857765,0.00022781843,0.000056779052],"domain_scores_gemma":[0.99981004,0.000012123472,0.000032550768,0.000028885173,0.00009870334,0.000017699705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030029324,0.00071841135,0.0007104212,0.00067082956,0.00043420543,0.0005206339,0.0007654688,0.00066084915,0.0012532412],"category_scores_gemma":[0.0003814195,0.00024057797,0.0004579742,0.0006536274,0.00020170875,0.00067318155,0.0010393814,0.0004496743,0.0009553582],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042816883,0.0001923998,0.005472356,0.00039574527,0.00016718903,0.00052218157,0.0002929338,0.062927864,0.19812466,0.0042718328,0.007208473,0.7199962],"study_design_scores_gemma":[0.00011528276,0.0010917214,0.011452599,0.00009249362,0.00029223043,0.001115042,0.00021193878,0.8170206,0.1303862,0.0049669934,0.03309376,0.00016113953],"about_ca_topic_score_codex":0.0029063025,"about_ca_topic_score_gemma":0.0029404506,"teacher_disagreement_score":0.0029063025,"about_ca_system_score_codex":0.0003375782,"about_ca_system_score_gemma":0.00070465176,"threshold_uncertainty_score":0.0057787895},"labels":[],"label_agreement":null},{"id":"W3001565854","doi":"10.1177/1550147719900093","title":"In-vehicle localization based on multi-channel Bluetooth Low Energy received signal strength indicator","year":2020,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Real-time computing; Multipath propagation; Bluetooth; Standard deviation; Ranging; Channel (broadcasting); Simulation; Wireless; Telecommunications","score_opus":0.00951907764172564,"score_gpt":0.21484475714626333,"score_spread":0.2053256795045377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001565854","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07952601,0.00073151634,0.9118098,0.0001360065,0.00016098855,0.000070071066,0.00017967966,0.0034213837,0.0039646057],"genre_scores_gemma":[0.8952882,0.00053491484,0.10036337,0.00008801513,0.00006874684,0.00008155314,0.00032859412,0.000058646932,0.0031880343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938786,0.00011168961,0.000025920599,0.00014231389,0.00026447684,0.000067614405],"domain_scores_gemma":[0.9996326,0.00005754317,0.00007983235,0.000055287237,0.00015283593,0.00002186297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023638722,0.0006184717,0.00055521,0.0013435232,0.0002166936,0.0005099273,0.00083939475,0.00042668835,0.0006768781],"category_scores_gemma":[0.0008911084,0.00024629672,0.0003420147,0.0008927585,0.00017650245,0.00071429106,0.0006642773,0.00039845912,0.0006911353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004851903,0.00020235541,0.015441279,0.00052296586,0.00016200125,0.0006314661,0.0003076282,0.06981179,0.12578377,0.0025474757,0.0049588336,0.7791453],"study_design_scores_gemma":[0.00005360757,0.0005125762,0.017246135,0.00004588194,0.00014292967,0.0014385416,0.00017084643,0.88881314,0.08082567,0.0014633707,0.009172075,0.0001151911],"about_ca_topic_score_codex":0.0014494598,"about_ca_topic_score_gemma":0.0015156094,"teacher_disagreement_score":0.0014494598,"about_ca_system_score_codex":0.0002126662,"about_ca_system_score_gemma":0.000292905,"threshold_uncertainty_score":0.0028820634},"labels":[],"label_agreement":null},{"id":"W3003824496","doi":"10.1109/iros40897.2019.8968057","title":"Observability Analysis of Position Estimation for Quadrotors With Modified Dynamics and Range Measurements","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Observability; Unobservable; Control theory (sociology); Range (aeronautics); Nonlinear system; Position (finance); Inertial navigation system; Computer science; Aerodynamics; Estimator; Inertial frame of reference; Mathematics; Engineering; Aerospace engineering; Applied mathematics; Physics; Artificial intelligence","score_opus":0.01707499467502591,"score_gpt":0.2240693853745694,"score_spread":0.2069943906995435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003824496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11532359,0.00020846259,0.88233626,0.000098054596,0.000015818057,0.000017944969,0.000052023137,0.00010083313,0.0018469897],"genre_scores_gemma":[0.99382716,0.00010608774,0.005456405,0.000012285527,0.000011060819,0.000013958882,0.00004421586,0.000010846996,0.000517914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995241,0.00009568298,0.000022011975,0.00012206964,0.00017546692,0.000060539027],"domain_scores_gemma":[0.9975922,0.0014534332,0.0005066335,0.00014066693,0.00027203956,0.000035070978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061315123,0.00045487407,0.0003310327,0.00030626848,0.00019659348,0.00047146954,0.00034478054,0.00031347954,0.000632873],"category_scores_gemma":[0.0043709874,0.00019175374,0.0003539626,0.00017524189,0.00069350796,0.00071583013,0.000646215,0.00052191253,0.000075185446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012199317,0.000024938814,0.00431999,0.000117427924,0.00004824875,0.0002984869,0.00016037382,0.9434062,0.016293477,0.017476995,0.00019014807,0.017541671],"study_design_scores_gemma":[0.000002513803,0.000029349745,0.0012252427,0.0000032384435,0.0000047618378,0.000026477535,0.000012033091,0.9959364,0.0012554994,0.0013860097,0.000113722555,0.0000048850047],"about_ca_topic_score_codex":0.005343405,"about_ca_topic_score_gemma":0.0023818677,"teacher_disagreement_score":0.005343405,"about_ca_system_score_codex":0.0004990803,"about_ca_system_score_gemma":0.0004485582,"threshold_uncertainty_score":0.010624588},"labels":[],"label_agreement":null},{"id":"W3006072199","doi":"10.1109/jiot.2020.2972746","title":"Short-Baseline High-Precision DGPS for Smart Snow Blower","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Western University","funders":"National Natural Science Foundation of China","keywords":"Global Positioning System; Computer science; Multipath propagation; Differential GPS; Multipath mitigation; Multipath interference; Remote sensing; Real-time computing; Noise (video); Telecommunications; Artificial intelligence; GNSS applications","score_opus":0.01830995942612861,"score_gpt":0.2355612435120151,"score_spread":0.21725128408588648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006072199","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52244484,0.0009189426,0.45276818,0.0008632543,0.00038076888,0.00015324097,0.0013053411,0.0067736385,0.014391816],"genre_scores_gemma":[0.94473976,0.00017703162,0.051829863,0.000097233904,0.000027219867,0.00004438894,0.000515298,0.000051137955,0.0025181274],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977714,0.000026329548,0.0000060514144,0.000047846734,0.000122068755,0.000020617912],"domain_scores_gemma":[0.99986255,0.000014260115,0.000018636532,0.000028136621,0.00006477396,0.00001172769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016377533,0.00048801178,0.0003399602,0.0003145309,0.00027645638,0.00030185358,0.00044537376,0.0005466872,0.0014524028],"category_scores_gemma":[0.00033675818,0.00013622585,0.00021065417,0.00044284662,0.00020017526,0.0004955945,0.00033452758,0.0003239395,0.0006835978],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009018417,0.00008362938,0.025582472,0.000647903,0.00009859128,0.0010599829,0.00045055954,0.037958764,0.5358011,0.002080271,0.009709934,0.38562503],"study_design_scores_gemma":[0.00036743857,0.0012694421,0.08336208,0.00012427614,0.00033269185,0.001287656,0.0008470513,0.45520693,0.38417804,0.0034297956,0.0694142,0.00018040388],"about_ca_topic_score_codex":0.0025078896,"about_ca_topic_score_gemma":0.005594605,"teacher_disagreement_score":0.0025078896,"about_ca_system_score_codex":0.00035329256,"about_ca_system_score_gemma":0.00035780246,"threshold_uncertainty_score":0.0049865246},"labels":[],"label_agreement":null},{"id":"W3007160888","doi":"10.1109/globecom38437.2019.9013565","title":"Neighborhood Discovery Approach in WSN for Star Topology Using a Switched Beam Antenna","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Star (game theory); Topology (electrical circuits); Computer science; Antenna (radio); Network topology; Telecommunications; Computer network; Engineering; Physics; Electrical engineering","score_opus":0.013414127276440636,"score_gpt":0.2251025536608362,"score_spread":0.21168842638439556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007160888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026198912,0.00066671026,0.9694975,0.00016836423,0.00006902779,0.000071133356,0.00004392102,0.00028774719,0.002996725],"genre_scores_gemma":[0.54166377,0.001391256,0.44870067,0.00014436612,0.00008103736,0.00020388883,0.00023610942,0.000053564927,0.007525332],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999691,0.00009290025,0.000021859496,0.000086783926,0.00007601244,0.00003143864],"domain_scores_gemma":[0.9997621,0.00007343399,0.000031005282,0.000043090564,0.00006650761,0.000023919438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043272038,0.0002523065,0.00048650368,0.00057102606,0.0006102185,0.00050542236,0.0010039667,0.00046579013,0.0006708477],"category_scores_gemma":[0.00061677926,0.00015798696,0.0004920496,0.0005700171,0.00037361428,0.000860735,0.00090552156,0.00028462076,0.0002765415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041543905,0.00017973788,0.0059259622,0.00067646254,0.00020008479,0.0012853148,0.001334384,0.32923067,0.070530586,0.13217862,0.0076223584,0.4504204],"study_design_scores_gemma":[0.00002784527,0.00029912856,0.0012167762,0.00003356603,0.000071575516,0.0010407676,0.0003392059,0.95224977,0.012689767,0.016431868,0.015555815,0.00004391413],"about_ca_topic_score_codex":0.0014925168,"about_ca_topic_score_gemma":0.0019064113,"teacher_disagreement_score":0.0014925168,"about_ca_system_score_codex":0.00031517088,"about_ca_system_score_gemma":0.00041694462,"threshold_uncertainty_score":0.0029676557},"labels":[],"label_agreement":null},{"id":"W3009228778","doi":"10.3390/s20051350","title":"Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trilateration; Bluetooth Low Energy; Computer science; Received signal strength indication; Wireless; Bluetooth; Real-time computing; Signal strength; Artificial intelligence; Simulation; Engineering; Telecommunications","score_opus":0.07147044553420495,"score_gpt":0.277422457597863,"score_spread":0.20595201206365804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009228778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10493391,0.00033285815,0.8909842,0.00012333665,0.00006046514,0.000031587235,0.000030969048,0.0010017584,0.0025008852],"genre_scores_gemma":[0.8253556,0.00029891895,0.17155612,0.00014932662,0.00003080677,0.000047184836,0.00011605704,0.0000602328,0.0023857285],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969363,0.000060629383,0.000014904133,0.0000735134,0.000114318274,0.000043067685],"domain_scores_gemma":[0.9996462,0.00011878133,0.000073662544,0.0000433462,0.00010067198,0.000017359782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036849445,0.0006375463,0.00055160257,0.0008492157,0.0003017756,0.0005279082,0.00069769553,0.00068779837,0.0006558257],"category_scores_gemma":[0.0011693103,0.00021831969,0.0004184469,0.0005353114,0.00038257154,0.0006508159,0.0006142831,0.00048466542,0.0003947658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039768033,0.00023460509,0.007750047,0.0001745698,0.000101846745,0.0002844261,0.0002812556,0.27785945,0.07265363,0.0017993673,0.0012822369,0.6371809],"study_design_scores_gemma":[0.00000980748,0.00019198188,0.004345014,0.0000142550825,0.000026296588,0.00020723476,0.00006819319,0.9773128,0.015841097,0.0009043695,0.0010547349,0.000024230265],"about_ca_topic_score_codex":0.0017784322,"about_ca_topic_score_gemma":0.0027184002,"teacher_disagreement_score":0.0017784322,"about_ca_system_score_codex":0.00019671986,"about_ca_system_score_gemma":0.00027357636,"threshold_uncertainty_score":0.0035361648},"labels":[],"label_agreement":null},{"id":"W3010195964","doi":"10.23919/cnsm46954.2019.9012737","title":"Machine Learning for Location and Orientation Fingerprinting in MIMO WLANs","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Orientation (vector space); Computer science; MIMO; Network packet; Telecommunications link; Wireless; Wi-Fi; Real-time computing; Physical layer; Wireless network; Computer network; Channel (broadcasting); Telecommunications","score_opus":0.005543820865016199,"score_gpt":0.2079551656066907,"score_spread":0.2024113447416745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010195964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057360757,0.00035159106,0.9396817,0.00016598859,0.000047552126,0.000040483937,0.000092814604,0.00081971224,0.001439359],"genre_scores_gemma":[0.8494386,0.00026710023,0.14834118,0.00007593483,0.000044733668,0.00007591231,0.00015726381,0.000026132762,0.0015731939],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994504,0.00017165522,0.00003697303,0.0001135414,0.00014993797,0.00007765405],"domain_scores_gemma":[0.99843794,0.0010107823,0.0001322488,0.0001307624,0.00025643824,0.00003183496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009193119,0.000454757,0.0005068144,0.000544011,0.000299465,0.00061164045,0.0005411263,0.00054764317,0.00093060645],"category_scores_gemma":[0.003557279,0.00020235023,0.0003030358,0.00059163646,0.00028573553,0.00062480633,0.00043358415,0.00060441723,0.00046035988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021591042,0.00015859847,0.0043588574,0.00008797852,0.00005173623,0.000091154,0.00005870075,0.614017,0.009839043,0.0044849217,0.0009796866,0.3656565],"study_design_scores_gemma":[0.000003166169,0.000032620745,0.0006275685,0.0000042004813,0.0000031847087,0.000023273677,0.00001041179,0.9960431,0.0020002488,0.0010578717,0.00018930924,0.000005029466],"about_ca_topic_score_codex":0.0024892217,"about_ca_topic_score_gemma":0.002412641,"teacher_disagreement_score":0.0024892217,"about_ca_system_score_codex":0.0004940988,"about_ca_system_score_gemma":0.00036372544,"threshold_uncertainty_score":0.0049494505},"labels":[],"label_agreement":null},{"id":"W3010234400","doi":"10.1109/globecom38437.2019.9014129","title":"Experimental Comparison of Energy Consumption and Proximity Accuracy of BLE Beacons","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Beacon; Energy consumption; Computer science; Consumption (sociology); Energy (signal processing); Real-time computing; Statistics; Mathematics; Electrical engineering; Engineering","score_opus":0.01640341178547937,"score_gpt":0.26186869262701695,"score_spread":0.24546528084153757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010234400","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9404518,0.0009326561,0.05265323,0.00013137277,0.00016463778,0.000096432326,0.00052016054,0.00067325076,0.0043764687],"genre_scores_gemma":[0.9854624,0.00032944584,0.010756578,0.000042116255,0.00001797075,0.00007089169,0.0003429027,0.00004737137,0.0029302682],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990435,0.00024397988,0.000079233534,0.00017445289,0.00033479003,0.00012407544],"domain_scores_gemma":[0.9962559,0.0020677152,0.00026718888,0.0003422877,0.0009855871,0.00008132706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065226736,0.00057375414,0.0004240927,0.00092955196,0.00028671083,0.0003271068,0.0007581782,0.0006929946,0.0028910472],"category_scores_gemma":[0.004012589,0.0002170209,0.00024254437,0.0008555354,0.0003024173,0.00061293534,0.0003476844,0.00034527673,0.0005362196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0071542496,0.0010511128,0.015887126,0.0015913857,0.00024269866,0.0006614205,0.0009123138,0.038094435,0.70224756,0.0014319449,0.002182945,0.22854285],"study_design_scores_gemma":[0.00020521422,0.009610586,0.050904017,0.000120940786,0.00037246256,0.0014287694,0.0009497342,0.10599316,0.8221908,0.00072925433,0.007382334,0.000112684385],"about_ca_topic_score_codex":0.0005404696,"about_ca_topic_score_gemma":0.0008220197,"teacher_disagreement_score":0.0028910472,"about_ca_system_score_codex":0.0002277606,"about_ca_system_score_gemma":0.00010061513,"threshold_uncertainty_score":0.00967145},"labels":[],"label_agreement":null},{"id":"W3010420271","doi":"10.1109/globecom38437.2019.9013801","title":"ZeeFi: Zero-Effort Floor Identification with Deep Learning for Indoor Localization","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Software deployment; Computer science; Identification (biology); Deep learning; Artificial intelligence; Floor plan; Machine learning; Real-time computing; Engineering","score_opus":0.00494041797080076,"score_gpt":0.19560917975385406,"score_spread":0.1906687617830533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010420271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039360512,0.00036817437,0.94400585,0.00013228995,0.000110812616,0.000059143356,0.00069014926,0.012075341,0.0031977035],"genre_scores_gemma":[0.63751966,0.0002865968,0.35114044,0.00031812777,0.00004529114,0.00013028101,0.0024845512,0.00021020648,0.007864924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997838,0.000022964581,0.000008289086,0.00005852211,0.00006572192,0.000060737868],"domain_scores_gemma":[0.9998159,0.000039711176,0.00002589873,0.00004170309,0.000053214477,0.00002373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023565089,0.00095533184,0.0006289479,0.0006740585,0.0002280098,0.0003795043,0.0014727546,0.0006431319,0.002272431],"category_scores_gemma":[0.00071880996,0.0003309617,0.00032711055,0.00049399777,0.00025744567,0.00081667345,0.0014444746,0.0009113103,0.001120222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032039368,0.00032064156,0.0063732266,0.00017580285,0.000094681825,0.00025969095,0.000109817025,0.115539655,0.031258933,0.0021784806,0.01629999,0.8270687],"study_design_scores_gemma":[0.00001750023,0.00007776971,0.0019170452,0.000015915339,0.000014488787,0.0001165016,0.000027059587,0.98257947,0.011306226,0.0014412283,0.0024656488,0.000021134445],"about_ca_topic_score_codex":0.004437667,"about_ca_topic_score_gemma":0.0108739855,"teacher_disagreement_score":0.004437667,"about_ca_system_score_codex":0.00045169215,"about_ca_system_score_gemma":0.00063427293,"threshold_uncertainty_score":0.008823633},"labels":[],"label_agreement":null},{"id":"W3010876645","doi":"10.23919/fusion43075.2019.9011323","title":"Coursa Venue: Indoor Navigation Platform Using Fusion of Inertial Sensors with Magnetic and Radio Fingerprinting","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TD Bank Group","funders":"","keywords":"Computer science; GNSS applications; Real-time computing; Inertial measurement unit; Global Positioning System; Android (operating system); Indoor positioning system; Fingerprint recognition; Multipath propagation; Accelerometer; Fingerprint (computing); Telecommunications; Computer security; Artificial intelligence","score_opus":0.006047294925542352,"score_gpt":0.1898707166681399,"score_spread":0.18382342174259755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010876645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11307706,0.00428666,0.7387849,0.00044573806,0.0010263434,0.0008995671,0.0032399737,0.08801745,0.05022224],"genre_scores_gemma":[0.63565695,0.0009535808,0.31440055,0.00055618136,0.0001777676,0.00046534595,0.0051569412,0.00091840926,0.041714236],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995579,0.000053988522,0.000017748227,0.00012512214,0.0001671827,0.000078076264],"domain_scores_gemma":[0.99968636,0.000029289287,0.00003607464,0.0000709449,0.00011735721,0.000059911963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039670337,0.0011193505,0.0008514978,0.00085733394,0.0003320932,0.0006659803,0.0016988743,0.0007486716,0.006126343],"category_scores_gemma":[0.0007028644,0.00033174574,0.00056993397,0.00051177986,0.00024493027,0.0007822485,0.0020058565,0.00058286724,0.0032279247],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026170562,0.00039504535,0.019197091,0.0010653075,0.00053760427,0.0023418982,0.0006755375,0.016651608,0.15140478,0.008422548,0.06644401,0.7302475],"study_design_scores_gemma":[0.0009090207,0.004337041,0.03238551,0.00044083656,0.000538157,0.005691918,0.00064912764,0.411882,0.12571494,0.005401538,0.41129264,0.0007573772],"about_ca_topic_score_codex":0.0055948202,"about_ca_topic_score_gemma":0.009188148,"teacher_disagreement_score":0.006126343,"about_ca_system_score_codex":0.00025815357,"about_ca_system_score_gemma":0.0007294689,"threshold_uncertainty_score":0.02049464},"labels":[],"label_agreement":null},{"id":"W3011442853","doi":"10.23919/fusion43075.2019.9011367","title":"Multiple Model BLE-based Tracking via Validation of RSSI Fluctuations under Different Conditions","year":2019,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Computer science; Bluetooth Low Energy; Bluetooth; Real-time computing; Tracking (education); Sensor fusion; Tracking system; The Internet; Internet of Things; Embedded system; Wireless; Artificial intelligence; Kalman filter; Telecommunications; World Wide Web","score_opus":0.015763298833998657,"score_gpt":0.226477891418611,"score_spread":0.21071459258461234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011442853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60551584,0.00029914532,0.3800064,0.0001821742,0.00019397654,0.00014627072,0.0015333858,0.004292673,0.00783014],"genre_scores_gemma":[0.98018026,0.00008914451,0.017121596,0.000037349833,0.0000082056085,0.000066630404,0.0010104696,0.000074071744,0.0014122961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990841,0.00017588279,0.00006469804,0.00028797294,0.00029478798,0.00009252184],"domain_scores_gemma":[0.9991848,0.00023351997,0.00009258148,0.0002345943,0.00022798522,0.000026506983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000973343,0.00089894695,0.0006632604,0.0008879001,0.00040374685,0.0009813743,0.00089833257,0.000958805,0.0013068862],"category_scores_gemma":[0.0025329178,0.00019426798,0.0005928902,0.0008930904,0.0003659903,0.0009114836,0.00063690194,0.00059200113,0.0008596977],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091625995,0.0005492668,0.032214563,0.00040582463,0.0002534057,0.00033035275,0.00035507104,0.79798526,0.058837324,0.0027378132,0.0020176282,0.10339727],"study_design_scores_gemma":[0.00002320139,0.00030618752,0.009870023,0.000019992494,0.000039875795,0.00014448205,0.00007964992,0.95832753,0.029088793,0.00082808366,0.0012303612,0.000041863586],"about_ca_topic_score_codex":0.0051206434,"about_ca_topic_score_gemma":0.004657132,"teacher_disagreement_score":0.0051206434,"about_ca_system_score_codex":0.00048032834,"about_ca_system_score_gemma":0.00044641708,"threshold_uncertainty_score":0.010181665},"labels":[],"label_agreement":null},{"id":"W3011916804","doi":"10.1109/jiot.2020.2979523","title":"Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Mobile edge computing; Mathematical optimization; Optimization problem; Probabilistic logic; Regularization (linguistics); Edge computing; Artificial intelligence; Algorithm; Enhanced Data Rates for GSM Evolution; Mathematics","score_opus":0.02037098207662308,"score_gpt":0.23307424168221444,"score_spread":0.21270325960559136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011916804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035758973,0.000034451106,0.9954776,0.00006305423,0.000008410703,0.000008363958,0.000016916394,0.00010471638,0.0007104732],"genre_scores_gemma":[0.4870878,0.0002649961,0.50732255,0.00020564007,0.00007606702,0.00011660001,0.00020098852,0.00010026953,0.0046251216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963295,0.00009130666,0.000014648226,0.00009372715,0.000112403206,0.00005500516],"domain_scores_gemma":[0.99953735,0.00021357242,0.000048876882,0.000086804364,0.00009302017,0.000020389321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064849807,0.0007561114,0.00056134525,0.00031349447,0.0003995386,0.0006528109,0.0013162933,0.00078084134,0.0017979215],"category_scores_gemma":[0.002027066,0.00031485915,0.0005079255,0.00059957645,0.0006555479,0.0015877412,0.0011737003,0.0012955207,0.0005351969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001093919,0.00009233323,0.00053407985,0.00007185655,0.000040021965,0.00011163289,0.000098678545,0.8087684,0.016522814,0.037173238,0.0029506155,0.13352695],"study_design_scores_gemma":[0.0000028316822,0.000012501297,0.0000373023,0.000001805359,0.0000034458099,0.00001159748,0.000006357446,0.9925895,0.002076899,0.0048513957,0.0004034177,0.0000029831583],"about_ca_topic_score_codex":0.0025309308,"about_ca_topic_score_gemma":0.0044206735,"teacher_disagreement_score":0.0025309308,"about_ca_system_score_codex":0.00051078055,"about_ca_system_score_gemma":0.0007940317,"threshold_uncertainty_score":0.006014645},"labels":[],"label_agreement":null},{"id":"W3012591950","doi":"10.48550/arxiv.2003.09371","title":"Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"","keywords":"Resource (disambiguation); Computer science; Mobile robot; Robot; Wideband; Artificial intelligence; Human–computer interaction; Electronic engineering; Engineering; Computer network","score_opus":0.04725769958057272,"score_gpt":0.18139410065241587,"score_spread":0.13413640107184316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012591950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010639862,0.0001301767,0.9882416,0.000035797566,0.000028029131,0.000009186626,0.000011235462,0.00051660434,0.00038749893],"genre_scores_gemma":[0.6353333,0.00043596214,0.36035612,0.00017891442,0.00008891003,0.00011169943,0.00020153563,0.00022856807,0.0030650566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949276,0.000084854466,0.000026001931,0.00013978669,0.00019841145,0.00005825131],"domain_scores_gemma":[0.99918073,0.00022510589,0.00018032291,0.00014908872,0.00023344555,0.000031222844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004824412,0.00060665194,0.0005670005,0.00053147675,0.00028514254,0.00045004243,0.00091153046,0.00051148405,0.0007411446],"category_scores_gemma":[0.0025981548,0.00024833734,0.00033523666,0.00064737606,0.00044176384,0.00072266936,0.0011788814,0.0007726972,0.0006166312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001794712,0.000077391494,0.0032685257,0.00013366799,0.0000810476,0.00018452638,0.00021917521,0.28457877,0.061229702,0.00539492,0.0017060107,0.6429469],"study_design_scores_gemma":[0.000018240265,0.00008622573,0.0015168032,0.000020579357,0.000022038688,0.0001953014,0.000039764294,0.96429014,0.025296228,0.0048783137,0.0036097323,0.000026516127],"about_ca_topic_score_codex":0.0013820812,"about_ca_topic_score_gemma":0.0015847537,"teacher_disagreement_score":0.0013820812,"about_ca_system_score_codex":0.0003993722,"about_ca_system_score_gemma":0.00059206516,"threshold_uncertainty_score":0.0028976202},"labels":[],"label_agreement":null},{"id":"W3013229757","doi":"10.5383/juspn.06.01.005","title":"End-to-End Safety Solution for Children Enabled by a Wearable Sensor Vest","year":2015,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Tekes","keywords":"VEST; Wearable computer; Computer science; Cloud computing; Human–computer interaction; Multimedia; Computer security; Embedded system; Operating system","score_opus":0.011413794159475658,"score_gpt":0.21404964253396577,"score_spread":0.2026358483744901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013229757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19177191,0.0007980223,0.77756834,0.0009577573,0.00030363014,0.00036193867,0.0005575558,0.008401914,0.01927887],"genre_scores_gemma":[0.80994385,0.00078248343,0.1682251,0.00043959863,0.00007804306,0.00031754427,0.000608938,0.00019114288,0.019413281],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996197,0.000071531416,0.000029213776,0.000063832675,0.00014436395,0.00007134413],"domain_scores_gemma":[0.9997334,0.00003332426,0.000027939488,0.00003235145,0.000095768024,0.000077167686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000310182,0.0004783034,0.00041886157,0.00041079687,0.00035062924,0.00055551407,0.0010198345,0.00091746723,0.0031389752],"category_scores_gemma":[0.00060365815,0.00020968147,0.0005050328,0.00021657317,0.00025483387,0.0009648959,0.001717427,0.0005889295,0.0010023429],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013904629,0.00077975215,0.02098195,0.0013129375,0.00026496933,0.011731712,0.0049588173,0.015056005,0.36655173,0.02886952,0.031023974,0.5170781],"study_design_scores_gemma":[0.0002632749,0.003351229,0.027009407,0.0005085342,0.00053389027,0.023751544,0.0040067444,0.24667823,0.30421937,0.011208481,0.3780946,0.00037473845],"about_ca_topic_score_codex":0.00048022444,"about_ca_topic_score_gemma":0.0006843683,"teacher_disagreement_score":0.0031389752,"about_ca_system_score_codex":0.0001753987,"about_ca_system_score_gemma":0.0004209432,"threshold_uncertainty_score":0.0105009675},"labels":[],"label_agreement":null},{"id":"W3014081040","doi":"10.1109/icnc47757.2020.9049799","title":"Linear FMCW Radar System for Accurate Indoor Localization and Trajectory Detection","year":2020,"lang":"en","type":"article","venue":"2020 International Conference on Computing, Networking and Communications (ICNC)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Constant false alarm rate; Computer science; Continuous-wave radar; Radar; Kalman filter; Detector; Trajectory; Position (finance); Doppler effect; Doppler frequency; Computer vision; Doppler radar; Pulse-Doppler radar; Radar imaging; Artificial intelligence; Telecommunications; Physics","score_opus":0.05047648642916045,"score_gpt":0.2681436908502404,"score_spread":0.21766720442107998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014081040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01555385,0.0008807987,0.97834295,0.00018386926,0.000084321044,0.000035444,0.00006464304,0.0019039871,0.002950147],"genre_scores_gemma":[0.4901045,0.0010318513,0.49913687,0.0005736822,0.00022180661,0.00011884137,0.00035702737,0.000083619336,0.008371756],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945027,0.00012988331,0.00002045487,0.00011622584,0.0002333469,0.000049726368],"domain_scores_gemma":[0.99963856,0.00008973494,0.00006312624,0.000060835588,0.00013222688,0.000015436783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043483463,0.0004371633,0.00040087063,0.00040742647,0.00019715042,0.00032954267,0.00059652404,0.0006246018,0.00217756],"category_scores_gemma":[0.0007845612,0.0001757707,0.00021254641,0.0006119381,0.00021544678,0.0007743023,0.0005137657,0.0005455113,0.0017944528],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020321838,0.00011186262,0.0018175105,0.00046615364,0.00005078483,0.00021414629,0.00017093848,0.018978147,0.41681132,0.0073257727,0.006160186,0.5476899],"study_design_scores_gemma":[0.00013270575,0.00142218,0.0072049415,0.00009630337,0.00012147352,0.002062324,0.000113830334,0.57847285,0.3469499,0.003243685,0.06002993,0.00014989109],"about_ca_topic_score_codex":0.0006232888,"about_ca_topic_score_gemma":0.00056100986,"teacher_disagreement_score":0.00217756,"about_ca_system_score_codex":0.00028025798,"about_ca_system_score_gemma":0.00030546307,"threshold_uncertainty_score":0.007284701},"labels":[],"label_agreement":null},{"id":"W3015193621","doi":"10.18280/i2m.190109","title":"A Wi-Fi Positioning System for Material Transport in Greenhouses","year":2020,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Greenhouse; Environmental science; Agricultural engineering; Engineering; Agronomy; Biology","score_opus":0.017426692861187528,"score_gpt":0.2305300916447169,"score_spread":0.21310339878352938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015193621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058499914,0.00056065386,0.9265898,0.00029142576,0.00032198837,0.00014175691,0.00024734967,0.0045876736,0.008759457],"genre_scores_gemma":[0.7181889,0.00053480495,0.26409927,0.00019998415,0.00010490563,0.00026220342,0.00051107205,0.00006208403,0.016036829],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997768,0.00003350231,0.00001125151,0.00006028507,0.00009401997,0.00002419118],"domain_scores_gemma":[0.9998754,0.000013019723,0.000016366128,0.000021686063,0.00006283694,0.000010707731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021822106,0.00055079587,0.00028023074,0.00045843175,0.0004690932,0.00032927762,0.0008215193,0.00058648025,0.0018786677],"category_scores_gemma":[0.0003302667,0.00017337792,0.00019732138,0.0003858123,0.00018525647,0.0007364956,0.00039623506,0.00032459488,0.0011305624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003584491,0.00010909459,0.0067737405,0.0003070153,0.00006210173,0.0006777957,0.00027438987,0.021037234,0.4819766,0.007550415,0.008759295,0.4721139],"study_design_scores_gemma":[0.00017528182,0.00196816,0.025140159,0.00010755898,0.00029164602,0.002542786,0.00030199197,0.46234092,0.34423757,0.002744705,0.15986536,0.00028393394],"about_ca_topic_score_codex":0.0024059971,"about_ca_topic_score_gemma":0.0022672466,"teacher_disagreement_score":0.0024059971,"about_ca_system_score_codex":0.00039182993,"about_ca_system_score_gemma":0.0006150077,"threshold_uncertainty_score":0.006284833},"labels":[],"label_agreement":null},{"id":"W3015994947","doi":"10.1109/icmim48759.2020.9299052","title":"Deep Open Space Segmentation using Automotive Radar","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Radar; Software deployment; Computer science; Artificial intelligence; Segmentation; Radar imaging; Computer vision; Real-time computing; Automotive industry; Deep learning; Engineering; Telecommunications; Aerospace engineering","score_opus":0.037750045666447324,"score_gpt":0.2825259257407056,"score_spread":0.2447758800742583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015994947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13691029,0.0012646378,0.84332556,0.00039213616,0.00024657606,0.000075938704,0.0021594926,0.010665697,0.0049595907],"genre_scores_gemma":[0.8099729,0.000530948,0.17609966,0.0003798129,0.00013771492,0.0000664156,0.0073279627,0.000432675,0.0050520664],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967504,0.000037051235,0.000010729164,0.00012062383,0.000060511455,0.00009606969],"domain_scores_gemma":[0.99975723,0.000054512206,0.000036247395,0.000058588117,0.000064077016,0.000029321493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000261725,0.0011458572,0.0008301843,0.001329629,0.00024070828,0.0011291035,0.0010562757,0.0010091945,0.0015632615],"category_scores_gemma":[0.00065913104,0.0004272531,0.0008079946,0.0011374853,0.00035680275,0.0012466718,0.0013743391,0.0009880606,0.0017528806],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062738295,0.0003458902,0.009376093,0.0002558608,0.00020338627,0.00049818214,0.00022323983,0.37296346,0.051711373,0.0043360237,0.014595962,0.5448631],"study_design_scores_gemma":[0.000014223777,0.00007543324,0.002203649,0.000025988804,0.000028692486,0.00014196018,0.000074061405,0.9754623,0.013509876,0.0046950066,0.0037461317,0.00002258418],"about_ca_topic_score_codex":0.0033012014,"about_ca_topic_score_gemma":0.0055745067,"teacher_disagreement_score":0.0033012014,"about_ca_system_score_codex":0.00035924846,"about_ca_system_score_gemma":0.0005904827,"threshold_uncertainty_score":0.0065639615},"labels":[],"label_agreement":null},{"id":"W3017995847","doi":"10.1109/twc.2020.2987990","title":"A Downscaled Faster-RCNN Framework for Signal Detection and Time-Frequency Localization in Wideband RF Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Radio frequency; Wideband; Interference (communication); SIGNAL (programming language); Bluetooth; Wireless; Artificial intelligence; Radio spectrum; Feature extraction; Signal-to-noise ratio (imaging); Noise (video); Detection theory; Time–frequency analysis; Speech recognition; Pattern recognition (psychology); Electronic engineering; Telecommunications; Detector; Engineering; Radar","score_opus":0.019277183968542,"score_gpt":0.23415989395902737,"score_spread":0.21488270999048537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017995847","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022067063,0.000528003,0.97188985,0.00013308719,0.00008699667,0.000038030066,0.0001716527,0.0032162135,0.0018690506],"genre_scores_gemma":[0.5517524,0.00057584286,0.43827748,0.00037986133,0.00009633887,0.0001594468,0.001039823,0.00044154708,0.00727733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997527,0.000026698675,0.0000107381775,0.0000976977,0.00007157758,0.000040605115],"domain_scores_gemma":[0.99971443,0.000069535585,0.000029521356,0.000057109188,0.000116258234,0.0000130183225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042894116,0.0010608146,0.0006580593,0.0005579612,0.00022616249,0.00052144786,0.0015604211,0.0006927553,0.0024185807],"category_scores_gemma":[0.0010324838,0.00041683923,0.0006509868,0.00049228396,0.0003452616,0.00091259985,0.0007125316,0.0010389279,0.0009981542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011611109,0.00008334887,0.00084356416,0.000114999166,0.00007065919,0.00014989368,0.00006510883,0.5932537,0.033743173,0.0032408356,0.0037253744,0.3645933],"study_design_scores_gemma":[0.000004214349,0.000026872916,0.00023751514,0.000006262903,0.000011791691,0.00003207736,0.0000061688747,0.9939176,0.003641963,0.0009103753,0.0011968421,0.000008259419],"about_ca_topic_score_codex":0.013715694,"about_ca_topic_score_gemma":0.015350364,"teacher_disagreement_score":0.013715694,"about_ca_system_score_codex":0.0006105373,"about_ca_system_score_gemma":0.00073009275,"threshold_uncertainty_score":0.027271688},"labels":[],"label_agreement":null},{"id":"W3019515151","doi":"10.1109/jiot.2020.2989501","title":"Landmark Graph-Based Indoor Localization","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China","keywords":"Landmark; Computer science; Graph; Artificial intelligence; Computer vision; Location-based service; Spatial analysis; Data mining; Theoretical computer science; Computer network","score_opus":0.01238444621135133,"score_gpt":0.2050927273687971,"score_spread":0.1927082811574458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019515151","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048102657,0.00036940933,0.9887129,0.000107016924,0.0001180827,0.00004003876,0.00028330268,0.003115875,0.0024430663],"genre_scores_gemma":[0.47338128,0.0017439259,0.5122058,0.00026091628,0.00019868251,0.0001929954,0.0029168364,0.00059015065,0.008509447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940586,0.00011636208,0.000017440643,0.00015693212,0.00024003665,0.00006340975],"domain_scores_gemma":[0.9995493,0.00010091964,0.00005871173,0.00010609939,0.00015188762,0.000032942113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021134804,0.0012918586,0.0011064353,0.002000681,0.00055355235,0.0007155307,0.0015543619,0.0006733667,0.002932522],"category_scores_gemma":[0.0012122073,0.0003981438,0.0008645428,0.0029246677,0.0005390468,0.0011340291,0.0015182674,0.00077220623,0.0024533733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031815743,0.0000812939,0.002367215,0.0004886327,0.00015698858,0.00050225254,0.00024926398,0.3048535,0.02428451,0.023438044,0.026496112,0.61676407],"study_design_scores_gemma":[0.000036477988,0.00008783037,0.0013712657,0.00003336967,0.000070896276,0.000541521,0.00013172119,0.95478386,0.013397944,0.012818035,0.016650818,0.00007624202],"about_ca_topic_score_codex":0.009189361,"about_ca_topic_score_gemma":0.013957108,"teacher_disagreement_score":0.009189361,"about_ca_system_score_codex":0.00051748403,"about_ca_system_score_gemma":0.00088036124,"threshold_uncertainty_score":0.018271744},"labels":[],"label_agreement":null},{"id":"W3022617222","doi":"10.1177/0361198120917382","title":"Development of a Positioning Technique for Traffic Data Collection Using Wireless Signal Scanners","year":2020,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Beacon; Bluetooth; Intersection (aeronautics); Electric beacon; Wireless; Computer science; Data collection; Real-time computing; Hybrid positioning system; Positioning system; Received signal strength indication; Engineering; Telecommunications; Mathematics; Statistics","score_opus":0.14672362421861546,"score_gpt":0.3686691330530949,"score_spread":0.22194550883447944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022617222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025564404,0.00014565275,0.972531,0.00005867061,0.000045337867,0.00009957437,0.000024102976,0.00056634657,0.00096496486],"genre_scores_gemma":[0.12753333,0.00021592317,0.87049794,0.000028788543,0.000022752816,0.00012481322,0.000063133055,0.00003282172,0.0014806106],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923956,0.00013436595,0.000039928524,0.0001219702,0.00043449542,0.000029687246],"domain_scores_gemma":[0.9990445,0.0002521552,0.00009364455,0.00013023533,0.0004529536,0.00002663075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006663196,0.00028431104,0.0003313232,0.00069666206,0.00024613357,0.00031907673,0.00086603215,0.00056805613,0.0011183971],"category_scores_gemma":[0.0019146183,0.0003937529,0.00033790612,0.00082757074,0.00033538323,0.00082305155,0.00043207261,0.00053207,0.0006282188],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014297757,0.00008319773,0.00538162,0.00031752014,0.000048561305,0.00035742705,0.000419749,0.01461668,0.4170054,0.008006414,0.0011802379,0.5524403],"study_design_scores_gemma":[0.00013829784,0.002631182,0.015060516,0.00013377557,0.00020120299,0.005667003,0.00043627882,0.39079556,0.5148526,0.003135068,0.06673245,0.00021616594],"about_ca_topic_score_codex":0.00079906604,"about_ca_topic_score_gemma":0.0008379012,"teacher_disagreement_score":0.0011183971,"about_ca_system_score_codex":0.00019739024,"about_ca_system_score_gemma":0.0006776869,"threshold_uncertainty_score":0.0037413836},"labels":[],"label_agreement":null},{"id":"W3025790845","doi":"10.48550/arxiv.2005.06394","title":"A CNN-LSTM Quantifier for Single Access Point CSI Indoor Localization","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Router; Normalization (sociology); Convolutional neural network; Laptop; Artificial intelligence; Contrast (vision); Filter (signal processing); Algorithm; Pattern recognition (psychology); Real-time computing; Computer vision; Computer network","score_opus":0.12212262888191834,"score_gpt":0.20606208384964556,"score_spread":0.08393945496772721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025790845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012911234,0.00032994617,0.98131424,0.00022138456,0.000092909475,0.000032224732,0.00028154734,0.0027692912,0.0020472433],"genre_scores_gemma":[0.6551675,0.00038846288,0.33275172,0.00047639332,0.00012769797,0.0001154443,0.0009374253,0.00024169762,0.009793581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999691,0.000028573852,0.000015434765,0.000122036945,0.000092518996,0.000050414954],"domain_scores_gemma":[0.99966836,0.00007461743,0.0000552184,0.00005133714,0.00012914861,0.000021308617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005470406,0.0011630838,0.0006242656,0.0007366781,0.0003300857,0.0005910779,0.0019220327,0.00080761244,0.0034231239],"category_scores_gemma":[0.0015782011,0.00043202363,0.00045321314,0.0006551563,0.0005668865,0.001851106,0.0011241141,0.0010639303,0.0006869376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002160838,0.00011545634,0.0019496176,0.00022410075,0.00012888625,0.00021371774,0.00008769712,0.46817145,0.028109184,0.026782185,0.008234847,0.46576685],"study_design_scores_gemma":[0.0000058616506,0.0000369796,0.00023180526,0.0000092451555,0.000016632792,0.00003675626,0.0000065537843,0.99002963,0.0048576863,0.0035040814,0.0012557647,0.000009073985],"about_ca_topic_score_codex":0.014456872,"about_ca_topic_score_gemma":0.024657663,"teacher_disagreement_score":0.014456872,"about_ca_system_score_codex":0.0016950131,"about_ca_system_score_gemma":0.0011968515,"threshold_uncertainty_score":0.028745413},"labels":[],"label_agreement":null},{"id":"W3027700670","doi":"10.1007/s00779-020-01370-x","title":"Performance evaluation of range-free localization algorithms for wireless sensor networks","year":2020,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Wireless sensor network; Computer science; Key distribution in wireless sensor networks; Wireless ad hoc network; Topology control; Mobile wireless sensor network; Range (aeronautics); Visual sensor network; Centroid; Computer network; Algorithm; Distributed computing; Real-time computing; Wireless; Wireless network; Artificial intelligence; Telecommunications","score_opus":0.030001594235684843,"score_gpt":0.24175433031174884,"score_spread":0.21175273607606399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027700670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6246353,0.007487206,0.3505093,0.0008451753,0.00039897943,0.00023205909,0.0005306154,0.0037557308,0.011605704],"genre_scores_gemma":[0.9554814,0.00094855344,0.0406448,0.00009574624,0.00006047054,0.00005890469,0.0006135738,0.00012517258,0.0019714613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99578166,0.0014409969,0.0002570373,0.00046338444,0.0016721151,0.0003848198],"domain_scores_gemma":[0.98611253,0.009910309,0.0006386361,0.00082652125,0.002311679,0.00020026628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033992103,0.0011952257,0.0007948576,0.0017453914,0.00071752124,0.0010863133,0.0014352293,0.0010610967,0.0018288515],"category_scores_gemma":[0.017814223,0.00028749806,0.00034307374,0.0016853574,0.000602719,0.0021108645,0.0011300537,0.00046144967,0.00042825358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002544463,0.0004275728,0.005214858,0.00043997072,0.00022887417,0.00009511097,0.00012656675,0.7609822,0.011695435,0.003710956,0.00286492,0.21166913],"study_design_scores_gemma":[0.00006798423,0.0005262583,0.001905337,0.000014487092,0.000055040866,0.00015163394,0.00007089211,0.9883426,0.0074771354,0.00080843404,0.0005587224,0.000021542333],"about_ca_topic_score_codex":0.005619525,"about_ca_topic_score_gemma":0.0043832585,"teacher_disagreement_score":0.005619525,"about_ca_system_score_codex":0.0015279375,"about_ca_system_score_gemma":0.0012075641,"threshold_uncertainty_score":0.01797694},"labels":[],"label_agreement":null},{"id":"W3028223633","doi":"10.1109/tim.2020.2995281","title":"Improving Accuracy and Robustness in HF-RFID-Based Indoor Positioning With Kalman Filtering and Tukey Smoothing","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Robustness (evolution); Kalman filter; Computer science; Smoothing; Positioning system; Observational error; Measurement uncertainty; Indoor positioning system; Real-time computing; Computer vision; Artificial intelligence; Accelerometer; Engineering; Mathematics; Statistics","score_opus":0.021007624482778216,"score_gpt":0.2046960675556115,"score_spread":0.1836884430728333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028223633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033184968,0.00025322515,0.96466357,0.000041204326,0.000059871494,0.000018538838,0.000033512944,0.0012066743,0.0005385224],"genre_scores_gemma":[0.6500488,0.00031829186,0.3476492,0.00006529334,0.000084232124,0.00004214404,0.00014192736,0.00011938473,0.0015307806],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984567,0.00029217114,0.00010323578,0.00042147946,0.0006001501,0.00012627813],"domain_scores_gemma":[0.9986626,0.00041344977,0.00018937227,0.00026270884,0.00043656278,0.000035228473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011360061,0.0008063998,0.0007999368,0.0005950774,0.00039690745,0.0005345629,0.0009639431,0.00064821535,0.0008246672],"category_scores_gemma":[0.0040987204,0.00037020814,0.0005802142,0.00081466977,0.00039618654,0.0012048624,0.000882513,0.000518342,0.0005929309],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010822834,0.0001602244,0.00681492,0.000364368,0.00021415709,0.00023066813,0.00036614694,0.1675709,0.20217787,0.0033338952,0.001482961,0.61620164],"study_design_scores_gemma":[0.00005633834,0.0005272759,0.006932974,0.00001690181,0.0001243938,0.00028916882,0.00008152228,0.89078057,0.096078835,0.0012157117,0.003805454,0.00009089327],"about_ca_topic_score_codex":0.0049463687,"about_ca_topic_score_gemma":0.0050542406,"teacher_disagreement_score":0.0049463687,"about_ca_system_score_codex":0.00035861233,"about_ca_system_score_gemma":0.0005984551,"threshold_uncertainty_score":0.009835184},"labels":[],"label_agreement":null},{"id":"W3033713916","doi":"10.1007/978-981-15-3707-3_68","title":"Reliable Localization Using Multi-sensor Fusion for Automated Valet Parking Applications","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Trusted Positioning (Canada)","funders":"","keywords":"Lidar; Computer science; Point cloud; Radar; Real-time computing; Key (lock); Computer vision; Ranging; Sensor fusion; Artificial intelligence; Set (abstract data type); Remote sensing; Geography","score_opus":0.014986232634184918,"score_gpt":0.22867219393860522,"score_spread":0.2136859613044203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033713916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023632918,0.001372737,0.96576256,0.00016085766,0.00019138936,0.000027905058,0.00016032759,0.0023446316,0.0063465666],"genre_scores_gemma":[0.7528298,0.0009244799,0.23255186,0.000112287315,0.000110019115,0.000050444258,0.00065010804,0.00017463729,0.012596425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971503,0.000050319708,0.000011382003,0.00005621519,0.0001329578,0.00003391515],"domain_scores_gemma":[0.99978286,0.00005197242,0.00002272257,0.00005381272,0.00008077298,0.000007742799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029138505,0.00054674374,0.00046692215,0.0007111599,0.00025197546,0.0006627331,0.00090653205,0.0007423324,0.00222359],"category_scores_gemma":[0.00050995767,0.00033697626,0.00032383512,0.0008780717,0.00023818784,0.001163709,0.00080244214,0.00049785303,0.0013197045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035400156,0.000091025824,0.0013353013,0.0002841115,0.000068557776,0.00019990925,0.00013389286,0.11732712,0.119494915,0.004754999,0.013148207,0.742808],"study_design_scores_gemma":[0.000018569508,0.00022375095,0.0027262254,0.00004233591,0.000039978313,0.00034547635,0.00008945895,0.9109542,0.060409103,0.005668384,0.019439097,0.000043438613],"about_ca_topic_score_codex":0.00090912794,"about_ca_topic_score_gemma":0.0017918207,"teacher_disagreement_score":0.00222359,"about_ca_system_score_codex":0.00020378691,"about_ca_system_score_gemma":0.0003002019,"threshold_uncertainty_score":0.0074386},"labels":[],"label_agreement":null},{"id":"W3034012802","doi":"10.18280/i2m.190203","title":"Integrated Navigation by a Greenhouse Robot Based on an Odometer/Lidar","year":2020,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Odometer; Lidar; Greenhouse; Computer science; Computer vision; Environmental science; Artificial intelligence; Robot; Remote sensing; Geography","score_opus":0.02049154263473762,"score_gpt":0.24698422188189886,"score_spread":0.22649267924716124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034012802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18984766,0.00019334232,0.79274637,0.00011409689,0.00015271085,0.00021596435,0.00030397097,0.009235285,0.007190506],"genre_scores_gemma":[0.6297128,0.000083631436,0.364422,0.00008272919,0.000021563177,0.000250197,0.00039486052,0.00010197061,0.0049302992],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997676,0.00002475065,0.000010993555,0.00006899944,0.00009528236,0.000032500222],"domain_scores_gemma":[0.9998405,0.000017726446,0.000016206914,0.000036372687,0.00006133031,0.000027812635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018445644,0.00052915857,0.0004041772,0.00039716065,0.00035377385,0.0003059603,0.00073069514,0.00038160753,0.0018574869],"category_scores_gemma":[0.00023887513,0.0002579946,0.0002831792,0.00022996144,0.00021015703,0.00045560827,0.00089091953,0.00032457197,0.0007132867],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051777763,0.00024294949,0.008414297,0.000277771,0.0000904037,0.0005577741,0.00049022347,0.04400621,0.57707334,0.003372661,0.0041943532,0.36076233],"study_design_scores_gemma":[0.00030373328,0.0024896997,0.023484152,0.000078857774,0.0002449057,0.001356776,0.00044275023,0.63661355,0.26808882,0.0022823084,0.064363584,0.000250806],"about_ca_topic_score_codex":0.0036726014,"about_ca_topic_score_gemma":0.004095469,"teacher_disagreement_score":0.0036726014,"about_ca_system_score_codex":0.00018040724,"about_ca_system_score_gemma":0.0008456671,"threshold_uncertainty_score":0.007302463},"labels":[],"label_agreement":null},{"id":"W3035362115","doi":"10.1109/ipsn48710.2020.00017","title":"Robust Dynamic Hand Gesture Interaction using LTE Terminals","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"CHIST-ERA; Peking University","keywords":"Gesture; Computer science; Gesture recognition; Terminal (telecommunication); Base station; Computer vision; Artificial intelligence; Human–computer interaction; Computer network","score_opus":0.048266333377957896,"score_gpt":0.2685778070996333,"score_spread":0.22031147372167542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035362115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34150365,0.0008149134,0.6456633,0.00016278119,0.00007972085,0.00012829529,0.00015183748,0.004822059,0.006673462],"genre_scores_gemma":[0.9229871,0.00014747446,0.07229803,0.000078447636,0.000018063392,0.00006621665,0.00010871088,0.00006753528,0.0042283605],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949133,0.000099950536,0.000037168636,0.000106943175,0.00019228709,0.0000723164],"domain_scores_gemma":[0.9996464,0.00010807959,0.000058262598,0.00007655918,0.000069710324,0.000040959956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003000148,0.00064834795,0.00044653093,0.00029680482,0.00028938535,0.0005362337,0.0005744669,0.0005977337,0.0013258822],"category_scores_gemma":[0.0009933416,0.00018504959,0.0003481448,0.00019873354,0.00040608968,0.0006060318,0.00074223476,0.000284477,0.0006777983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083837827,0.000069660644,0.0035236925,0.00022369732,0.00004448385,0.00086782384,0.00050894415,0.03645749,0.69793385,0.001568412,0.0016768854,0.25628662],"study_design_scores_gemma":[0.00007793763,0.0015424787,0.015108076,0.000071617935,0.000103624436,0.0024324078,0.00023713581,0.51950693,0.44387367,0.0014991376,0.015365245,0.00018180916],"about_ca_topic_score_codex":0.0018019181,"about_ca_topic_score_gemma":0.0018480371,"teacher_disagreement_score":0.0018019181,"about_ca_system_score_codex":0.00029208485,"about_ca_system_score_gemma":0.00026688672,"threshold_uncertainty_score":0.0044354796},"labels":[],"label_agreement":null},{"id":"W3036094796","doi":"10.1007/1345_2020_120","title":"Assessment of a GNSS/INS/Wi-Fi Tight-Integration Method Using Support Vector Machine and Extended Kalman Filter","year":2020,"lang":"en","type":"book-chapter","venue":"International Association of Geodesy symposia","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"GNSS applications; Computer science; Kalman filter; Extended Kalman filter; Inertial navigation system; Context (archaeology); Kinematics; Real-time computing; Global Positioning System; Artificial intelligence; Inertial frame of reference; Telecommunications; Geography","score_opus":0.012180227756684162,"score_gpt":0.26900812393419243,"score_spread":0.2568278961775083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036094796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17648183,0.00036999758,0.8191384,0.00014983924,0.00011682786,0.00006689406,0.00007012116,0.0015435765,0.0020625289],"genre_scores_gemma":[0.8391566,0.00010485864,0.15906766,0.000035126464,0.000021719605,0.000046644484,0.00015099705,0.0000640051,0.0013524507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928147,0.00018713577,0.000049744383,0.00014836973,0.0002480026,0.00008521091],"domain_scores_gemma":[0.99902713,0.00029083452,0.00009374881,0.00008102203,0.00045933857,0.000047903857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017391491,0.00072371966,0.0007022691,0.00087407196,0.00032890833,0.0007285085,0.00051562284,0.00082996004,0.0013627944],"category_scores_gemma":[0.0036175717,0.00027390258,0.00047518394,0.00054721866,0.0002595138,0.0007302747,0.0007565107,0.0006051138,0.00059447484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006871756,0.00030189514,0.0132072745,0.00020086113,0.00018779763,0.00016061145,0.00014214317,0.4921735,0.021126546,0.0020292127,0.001188416,0.46859458],"study_design_scores_gemma":[0.0000069770053,0.000057978064,0.0020650194,0.0000074626964,0.000014361476,0.000013709046,0.000020505797,0.99519914,0.0021310367,0.00016754014,0.00031032253,0.000006072779],"about_ca_topic_score_codex":0.008288685,"about_ca_topic_score_gemma":0.004126462,"teacher_disagreement_score":0.008288685,"about_ca_system_score_codex":0.00035809132,"about_ca_system_score_gemma":0.0008390453,"threshold_uncertainty_score":0.016480863},"labels":[],"label_agreement":null},{"id":"W3037812697","doi":"10.3390/technologies8030037","title":"Autonomous Smart White Cane Navigation System for Indoor Usage","year":2020,"lang":"en","type":"article","venue":"Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Sight; Cloud computing; Computer science; Visual impairment; Internet of Things; Cane; The Internet; Visually impaired; Software; Human–computer interaction; Computer security; World Wide Web; Medicine","score_opus":0.013496370251488143,"score_gpt":0.20564411038098424,"score_spread":0.1921477401294961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037812697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.116190426,0.0016684777,0.7196389,0.0008232398,0.0009529107,0.0007736388,0.002602137,0.06192573,0.09542468],"genre_scores_gemma":[0.8014099,0.0007672435,0.11120199,0.0006662705,0.000120721204,0.0006983824,0.002970206,0.00040711524,0.081758104],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966764,0.00003547749,0.000013579207,0.0000772856,0.00015228985,0.00005373852],"domain_scores_gemma":[0.9997596,0.000020486175,0.00001549485,0.00004171578,0.00013278592,0.000029931889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020255349,0.0007613356,0.00056619645,0.0006825949,0.0005271733,0.0005146818,0.001208544,0.0007001584,0.011596095],"category_scores_gemma":[0.00047327782,0.00017535055,0.00038539397,0.0003839398,0.00017222043,0.0007829936,0.0009227018,0.00038034725,0.0051880907],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011864844,0.0006111739,0.011281634,0.00092216063,0.00019273524,0.0019530446,0.0010443078,0.014992882,0.1496128,0.008495346,0.1236372,0.6860702],"study_design_scores_gemma":[0.00046005906,0.00178763,0.02150287,0.00022128136,0.00038710108,0.0036834062,0.0006280147,0.39026594,0.11791186,0.0050109993,0.4577752,0.00036566416],"about_ca_topic_score_codex":0.0044588046,"about_ca_topic_score_gemma":0.005986422,"teacher_disagreement_score":0.011596095,"about_ca_system_score_codex":0.00022710377,"about_ca_system_score_gemma":0.00060509855,"threshold_uncertainty_score":0.03879279},"labels":[],"label_agreement":null},{"id":"W3037950529","doi":"10.1109/twc.2020.3003775","title":"Covert Localization in Wireless Networks: Feasibility and Performance Analysis","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Covert; Computer science; Transmitter power output; Wireless; Noise (video); Power (physics); Transmission (telecommunications); Statistical power; Mathematical optimization; Wireless network; Algorithm; Mathematics; Artificial intelligence; Telecommunications; Statistics; Transmitter","score_opus":0.026439724545802925,"score_gpt":0.24060949868170484,"score_spread":0.2141697741359019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037950529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024054449,0.0015338728,0.96808046,0.0008148505,0.000056629407,0.000083297884,0.000061439576,0.00019759423,0.0051174047],"genre_scores_gemma":[0.91029966,0.0027769692,0.08441768,0.0002313235,0.00017249754,0.0002783283,0.00015318448,0.00009466646,0.0015756676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961462,0.0015637879,0.000103160855,0.00052083185,0.0012147446,0.0004513072],"domain_scores_gemma":[0.97986525,0.015327413,0.0016439977,0.0009983372,0.0018398009,0.00032512652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046345023,0.0014860556,0.0014072373,0.001392315,0.0009109592,0.0017826875,0.0015614235,0.001988483,0.002001234],"category_scores_gemma":[0.02843353,0.00057938305,0.00083385425,0.0015438793,0.0029294437,0.0040203314,0.003121376,0.0018412205,0.00035502613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016827107,0.000043730837,0.00089599186,0.00024481615,0.00003902542,0.00020724755,0.00012361162,0.9250124,0.002777752,0.04289558,0.0011072631,0.026484359],"study_design_scores_gemma":[0.000008008731,0.00008103975,0.00012966056,0.0000202363,0.000007939483,0.000119894,0.000027103784,0.9883022,0.00089079776,0.01004856,0.00035483704,0.000009641543],"about_ca_topic_score_codex":0.0014882932,"about_ca_topic_score_gemma":0.0008429286,"teacher_disagreement_score":0.0046345023,"about_ca_system_score_codex":0.0020204172,"about_ca_system_score_gemma":0.0012568963,"threshold_uncertainty_score":0.024509847},"labels":[],"label_agreement":null},{"id":"W3038116752","doi":"10.1109/tvt.2020.3004175","title":"Multi-Target Device-Free Wireless Sensing Based on Multiplexing Mechanisms","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Liaoning Revitalization Talents Program; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Multiplexing; Wireless; Computer science; Time-division multiplexing; Electronic engineering; Exploit; Frequency-division multiplexing; Orthogonal frequency-division multiplexing; Real-time computing; Engineering; Telecommunications; Channel (broadcasting)","score_opus":0.015935877232935682,"score_gpt":0.21252176601256362,"score_spread":0.19658588877962793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038116752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12439961,0.0018195662,0.86475164,0.00026651873,0.00019279018,0.00014535409,0.000055944747,0.0006229899,0.00774553],"genre_scores_gemma":[0.85584706,0.0011849379,0.13936931,0.0002168234,0.00012200982,0.000109522334,0.00004706653,0.000027235084,0.0030760197],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994276,0.00009768606,0.000030260057,0.00013701365,0.00024729653,0.00005999576],"domain_scores_gemma":[0.9995097,0.00018327041,0.00011239759,0.00008049964,0.00008238757,0.00003178536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045672298,0.00083442405,0.00039060312,0.0005878813,0.00032852287,0.00057962525,0.0010308056,0.0006151731,0.0008629378],"category_scores_gemma":[0.00078838837,0.0003340728,0.00041135645,0.0004717926,0.0005681609,0.0018681457,0.0010715374,0.000462158,0.00029490763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027965638,0.0000845064,0.0013536144,0.00031504265,0.000057661666,0.00035926848,0.00023799973,0.013051382,0.83115214,0.022655273,0.0006806443,0.12977283],"study_design_scores_gemma":[0.000050182396,0.00080702076,0.0015194783,0.00004823748,0.00008509344,0.001719557,0.00011617436,0.30771235,0.6670214,0.008231944,0.012557955,0.00013059966],"about_ca_topic_score_codex":0.00024216657,"about_ca_topic_score_gemma":0.00033204097,"teacher_disagreement_score":0.0010308056,"about_ca_system_score_codex":0.00032018806,"about_ca_system_score_gemma":0.00020885283,"threshold_uncertainty_score":0.0028867722},"labels":[],"label_agreement":null},{"id":"W3038788938","doi":"10.1109/vtc2020-spring48590.2020.9128759","title":"Indoor Localization Using Channel State Information With Regression Artificial Neural Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial neural network; Perceptron; Artificial intelligence; Novelty; Network packet; Channel (broadcasting); Pattern recognition (psychology); Feature extraction; Channel state information; Multilayer perceptron; Regression; Matching (statistics); Data mining; Machine learning; Statistics; Mathematics; Telecommunications; Computer network; Wireless","score_opus":0.0167873517257543,"score_gpt":0.20365072308817608,"score_spread":0.1868633713624218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038788938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035777114,0.0005753798,0.95948243,0.00009976028,0.00007941048,0.000020824938,0.00011429154,0.001954432,0.0018963501],"genre_scores_gemma":[0.83291316,0.000507336,0.16175807,0.00008063882,0.00009005224,0.00005434018,0.00037470792,0.000091617374,0.0041301553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996352,0.000095036805,0.000019527559,0.00011244677,0.00009197045,0.00004578522],"domain_scores_gemma":[0.99948263,0.00020211464,0.00009518683,0.000059541966,0.00014655826,0.000013922189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045266567,0.0009083369,0.0006116454,0.00087524124,0.00023561048,0.0005983998,0.0007643127,0.00061954424,0.0009201111],"category_scores_gemma":[0.001597491,0.00037156817,0.00054616167,0.0012719132,0.00023924118,0.00092712295,0.00052818604,0.0007546055,0.0006185678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021447823,0.00008574299,0.00268179,0.00011193259,0.00011876873,0.00012180773,0.00007542124,0.64121366,0.0077374005,0.0016287107,0.0011910317,0.3448192],"study_design_scores_gemma":[0.000003586867,0.000019580391,0.00044400335,0.0000059415347,0.00001195082,0.000021080245,0.000007507151,0.9969715,0.0017282139,0.0004590371,0.0003196466,0.00000780746],"about_ca_topic_score_codex":0.0044569783,"about_ca_topic_score_gemma":0.004177409,"teacher_disagreement_score":0.0044569783,"about_ca_system_score_codex":0.00041064413,"about_ca_system_score_gemma":0.00033472345,"threshold_uncertainty_score":0.008862078},"labels":[],"label_agreement":null},{"id":"W3038849551","doi":"10.1109/lcomm.2020.3007191","title":"Robust Recursive RSSD Based Source Localization in Gaussian Mixture Channels","year":2020,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Cramér–Rao bound; Estimator; Gaussian noise; Algorithm; Gaussian; Mathematics; Noise (video); Upper and lower bounds; Noise measurement; Benchmark (surveying); Signal-to-noise ratio (imaging); Mathematical optimization; Computer science; Applied mathematics; Estimation theory; Noise reduction; Statistics; Artificial intelligence; Mathematical analysis","score_opus":0.03282035298952111,"score_gpt":0.2222320983360919,"score_spread":0.1894117453465708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038849551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0080362335,0.00013758232,0.9907946,0.00004266754,0.000010288449,0.000008188839,0.000018477274,0.00054987497,0.00040209937],"genre_scores_gemma":[0.49932382,0.00038501213,0.49688762,0.000080286634,0.000030812884,0.000051003026,0.00019378439,0.00013421204,0.0029135074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949634,0.00013322657,0.000023406945,0.00011869627,0.00018444835,0.000043935634],"domain_scores_gemma":[0.99966013,0.00015161443,0.0000520802,0.000050786944,0.00007464535,0.000010846937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000576888,0.000644593,0.0009127354,0.00054122123,0.00019459464,0.00068511744,0.0009028788,0.00065558724,0.000794677],"category_scores_gemma":[0.0013936037,0.00048403104,0.0005599052,0.00069677026,0.0005613677,0.0008899112,0.0010448581,0.0005681736,0.00058904424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022500282,0.000049159873,0.00101034,0.00016998363,0.000096057986,0.00015363582,0.00014743891,0.72936845,0.05109876,0.01238192,0.0013161763,0.20398304],"study_design_scores_gemma":[0.000005547499,0.000017676144,0.00016888899,0.0000029258897,0.000007394285,0.000037114358,0.0000057077173,0.99312395,0.005021232,0.0010611165,0.0005378936,0.000010563981],"about_ca_topic_score_codex":0.00271029,"about_ca_topic_score_gemma":0.002967601,"teacher_disagreement_score":0.00271029,"about_ca_system_score_codex":0.0005085119,"about_ca_system_score_gemma":0.0006586155,"threshold_uncertainty_score":0.0053890347},"labels":[],"label_agreement":null},{"id":"W3041201212","doi":"10.1109/jsen.2020.3008393","title":"Joint Estimation of Location and Orientation in Wireless Sensor Networks Using Directional Antennas","year":2020,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Directivity; Computer science; Orientation (vector space); Antenna (radio); Directional antenna; Transmitter; Omnidirectional antenna; Wireless sensor network; Sensor array; Electronic engineering; Algorithm; Telecommunications; Engineering; Mathematics; Computer network","score_opus":0.020793793439266253,"score_gpt":0.23115873085067157,"score_spread":0.21036493741140533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041201212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01742888,0.00018431613,0.9818147,0.000026045134,0.000016148366,0.000008381466,0.000009362675,0.0002304756,0.0002817479],"genre_scores_gemma":[0.66193914,0.0005203327,0.33584085,0.000042190808,0.000052138006,0.000057701054,0.000112442416,0.00004496678,0.0013902083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994469,0.00018464656,0.00003288686,0.00012182669,0.00016999475,0.000043741948],"domain_scores_gemma":[0.9995028,0.00017700317,0.000113151924,0.000073715004,0.000113634764,0.000019661937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053478434,0.0007390904,0.00065363204,0.0005861505,0.00026301455,0.00047755876,0.00059065945,0.00046380304,0.00020557731],"category_scores_gemma":[0.0018180973,0.00028280792,0.0003638399,0.0007712793,0.00036444078,0.0009969315,0.000713024,0.00041596775,0.00025543832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002746568,0.000046732617,0.0031698258,0.00010194618,0.000078400306,0.0000926126,0.00011039611,0.4946533,0.033818327,0.004925392,0.0006556532,0.46207273],"study_design_scores_gemma":[0.00000899775,0.000075216994,0.0008021497,0.000006291964,0.000017761447,0.00007607707,0.000026551645,0.9870198,0.009097883,0.0020389128,0.0008171384,0.000013234252],"about_ca_topic_score_codex":0.000981862,"about_ca_topic_score_gemma":0.0012776422,"teacher_disagreement_score":0.000981862,"about_ca_system_score_codex":0.0002166188,"about_ca_system_score_gemma":0.00029238884,"threshold_uncertainty_score":0.0028282404},"labels":[],"label_agreement":null},{"id":"W3043809140","doi":"10.48550/arxiv.2007.06727","title":"Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial navigation system; Computer science; Inertial measurement unit; Sensor fusion; Artificial intelligence; Big data; Inertial frame of reference; Systems engineering; Real-time computing; Data mining; Engineering","score_opus":0.18318829414089643,"score_gpt":0.21414148354961468,"score_spread":0.030953189408718246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043809140","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00308901,0.51521724,0.042515524,0.38092056,0.009480226,0.000037578517,0.0001529728,0.0003786301,0.048208196],"genre_scores_gemma":[0.12797119,0.6978916,0.05742829,0.05326313,0.045346025,0.00017183402,0.0002574074,0.00028209106,0.017388482],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99648714,0.0014836169,0.00019826318,0.00035183315,0.0012342299,0.00024488982],"domain_scores_gemma":[0.9902666,0.00706268,0.00025311706,0.0006590871,0.0013814821,0.0003770207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006294461,0.0008871779,0.0014515187,0.0021596465,0.00117734,0.007816218,0.0015075096,0.0047698566,0.0053412896],"category_scores_gemma":[0.010461644,0.00043171505,0.0005723903,0.0023061924,0.0072583677,0.020817695,0.003306391,0.007011437,0.0032904577],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006904477,0.00006981695,0.00082026207,0.0020695957,0.00008729692,0.00017301693,0.000707448,0.0008346343,0.00084703724,0.535722,0.08681785,0.37178203],"study_design_scores_gemma":[0.000017318387,0.000078842706,0.0005830773,0.0013932127,0.0000295738,0.00032842075,0.0017240179,0.0031151776,0.00051302876,0.5505516,0.44160798,0.000057798854],"about_ca_topic_score_codex":0.0009601713,"about_ca_topic_score_gemma":0.000979131,"teacher_disagreement_score":0.007816218,"about_ca_system_score_codex":0.0014346534,"about_ca_system_score_gemma":0.002219622,"threshold_uncertainty_score":0.033288658},"labels":[],"label_agreement":null},{"id":"W3044627453","doi":"10.1109/iccworkshops49005.2020.9145198","title":"Neural-Network-Switched Kalman Filters as Novel Trackers for Multipath Channels","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Agencia Estatal de Investigación; Universidad Carlos III de Madrid","keywords":"Kalman filter; Orthogonal frequency-division multiplexing; Multipath propagation; Computer science; Channel (broadcasting); Electronic engineering; Algorithm; Telecommunications; Engineering; Artificial intelligence","score_opus":0.028050522623341182,"score_gpt":0.23089761602115302,"score_spread":0.20284709339781185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044627453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014910046,0.00056552765,0.9823136,0.00010100025,0.00009119497,0.00001866721,0.000035684137,0.00032429933,0.0016399315],"genre_scores_gemma":[0.8914937,0.0007643714,0.10210296,0.00013168134,0.000102687016,0.00008896403,0.000118888296,0.000039204042,0.005157534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974173,0.00005987233,0.000016886028,0.000072123315,0.00007305565,0.00003622813],"domain_scores_gemma":[0.99943787,0.00028926306,0.00008890039,0.000035313296,0.00012699417,0.000021638238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069016614,0.00046406547,0.00047495615,0.00034128106,0.00024216196,0.0005619764,0.0007945558,0.0008060187,0.00094712037],"category_scores_gemma":[0.0019390049,0.0002573367,0.000399889,0.00042712546,0.00054235966,0.000986738,0.000598044,0.00087367115,0.00023135068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015178899,0.000047400463,0.0016125842,0.00010477612,0.00008032375,0.000060740873,0.00009949829,0.8324407,0.0051368657,0.014771411,0.0009246658,0.14456922],"study_design_scores_gemma":[0.0000040731225,0.00001953446,0.00011892733,0.000004678025,0.000009003852,0.000010681058,0.0000034236616,0.9972914,0.0007328134,0.0013296582,0.00047061912,0.000005187008],"about_ca_topic_score_codex":0.0060190866,"about_ca_topic_score_gemma":0.0055829557,"teacher_disagreement_score":0.0060190866,"about_ca_system_score_codex":0.00070574216,"about_ca_system_score_gemma":0.00065256143,"threshold_uncertainty_score":0.011968076},"labels":[],"label_agreement":null},{"id":"W3044964533","doi":"10.1016/j.artmed.2020.101931","title":"Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristics; Identification (biology); Artificial intelligence; Machine learning; Process (computing); Health care; Analytics; Semantics (computer science); Human–computer interaction; Data science","score_opus":0.06088236808318261,"score_gpt":0.3656424298976679,"score_spread":0.3047600618144853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044964533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058501042,0.0004514952,0.93073183,0.0006207148,0.00011097621,0.000118940254,0.00035661613,0.0009656776,0.008142758],"genre_scores_gemma":[0.75482446,0.00042053085,0.24024475,0.00021383454,0.000096905555,0.0000959425,0.00045274693,0.000046186877,0.003604694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947935,0.00013098473,0.00004242074,0.0001289085,0.00014250512,0.00007589509],"domain_scores_gemma":[0.9988887,0.00045714568,0.00015589931,0.00009644264,0.00034632513,0.000055579872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005115774,0.000714952,0.0007766759,0.0026734015,0.00059791666,0.0014868355,0.0011838496,0.0009864278,0.0017848858],"category_scores_gemma":[0.002389036,0.000245347,0.0008369245,0.0016765561,0.000372292,0.0010273717,0.00077419233,0.00066995324,0.00083710195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033166909,0.0006663377,0.031566285,0.0003929708,0.00023428675,0.0008002473,0.0006456443,0.29518875,0.017223265,0.010649972,0.005849156,0.63645136],"study_design_scores_gemma":[0.000013629478,0.00008273491,0.0053775576,0.00004689745,0.000063624815,0.00027860448,0.0004365555,0.9825822,0.0039604115,0.0054832962,0.0016425457,0.000031949945],"about_ca_topic_score_codex":0.009264506,"about_ca_topic_score_gemma":0.010283423,"teacher_disagreement_score":0.009264506,"about_ca_system_score_codex":0.00073775434,"about_ca_system_score_gemma":0.0013906348,"threshold_uncertainty_score":0.018421173},"labels":[],"label_agreement":null},{"id":"W3046106220","doi":"10.1109/icc40277.2020.9148958","title":"A Ferrous-Selective Proximity Sensor for Industrial Internet of Things","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Alberta Energy; Queen's University","funders":"","keywords":"Resistor; Wireless sensor network; Ferrous; Wireless; Internet of Things; Computer science; Magnet; The Internet; Electrical engineering; Materials science; Embedded system; Engineering; Telecommunications; Computer network","score_opus":0.03659813960645634,"score_gpt":0.22384816043287614,"score_spread":0.1872500208264198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046106220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14100389,0.009075482,0.8000933,0.0019642198,0.0016110457,0.0003519987,0.00034822608,0.0033469244,0.042204853],"genre_scores_gemma":[0.71349806,0.0023210656,0.25547525,0.0012777755,0.00027557986,0.00015441288,0.00023393024,0.000071281436,0.02669263],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976474,0.000031678945,0.000008783587,0.000052409974,0.00012649818,0.000015935782],"domain_scores_gemma":[0.99989676,0.000021172342,0.00002111113,0.000011687249,0.00003569085,0.000013622118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013876522,0.0003242018,0.00026562889,0.00032897544,0.00033570168,0.00025248626,0.00054666505,0.00074015255,0.0013721897],"category_scores_gemma":[0.00022341403,0.0001433975,0.0001769436,0.00026854812,0.00020005275,0.00059276336,0.00037724013,0.00042597653,0.00063894386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013321216,0.000054452452,0.0007765177,0.00039981317,0.000022809372,0.0004261051,0.0000680631,0.0008626713,0.86354595,0.004730081,0.0042962367,0.124683954],"study_design_scores_gemma":[0.0000438206,0.0010843858,0.0032602993,0.0000588533,0.00007253171,0.0055542104,0.00009949692,0.026775228,0.8390741,0.0019700062,0.12194116,0.0000658995],"about_ca_topic_score_codex":0.00016400302,"about_ca_topic_score_gemma":0.0004594027,"teacher_disagreement_score":0.0013721897,"about_ca_system_score_codex":0.0002180216,"about_ca_system_score_gemma":0.00016924574,"threshold_uncertainty_score":0.0045904517},"labels":[],"label_agreement":null},{"id":"W3047059496","doi":"10.5194/isprs-annals-v-1-2020-317-2020","title":"MULTILATERATION UNDER FLIP AMBIGUITY FOR UAV POSITIONING USING ULTRAWIDE-BAND","year":2020,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Multilateration; FDOA; Ambiguity; Computer science; Ranging; Multipath propagation; Robustness (evolution); GNSS applications; Position (finance); Algorithm; Real-time computing; Global Positioning System; Engineering; Telecommunications","score_opus":0.07778878510124347,"score_gpt":0.298101558837882,"score_spread":0.2203127737366385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047059496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022625271,0.000085768756,0.97634673,0.00003684375,0.00002092406,0.000013165162,0.000008631364,0.00029757174,0.0005650957],"genre_scores_gemma":[0.45233977,0.00012830061,0.5454987,0.00006417292,0.00004299016,0.000045096203,0.00011175966,0.000089818976,0.0016793914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967396,0.000080968784,0.000015576235,0.00008138971,0.00011326718,0.000034866745],"domain_scores_gemma":[0.99961215,0.00013858188,0.00007442149,0.000060218204,0.0000936555,0.000020976437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033695746,0.00068851386,0.0006372778,0.0004225699,0.0003768877,0.00051517854,0.000563994,0.0005623933,0.0011316156],"category_scores_gemma":[0.0016740622,0.00026965546,0.0005376606,0.0004966375,0.0004174799,0.000615782,0.0010440587,0.00066253764,0.00052485446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025306462,0.000073331816,0.002138722,0.00013569287,0.00009050565,0.00028866276,0.0003773285,0.3758146,0.05511099,0.007680149,0.001437007,0.55660003],"study_design_scores_gemma":[0.000020624357,0.000097644646,0.0005138369,0.000008159219,0.000023071789,0.00014893418,0.00004249448,0.9848899,0.011254693,0.0016641982,0.0013193057,0.000017056971],"about_ca_topic_score_codex":0.0016603756,"about_ca_topic_score_gemma":0.0016514151,"teacher_disagreement_score":0.0016603756,"about_ca_system_score_codex":0.00024722933,"about_ca_system_score_gemma":0.0005555334,"threshold_uncertainty_score":0.0037856698},"labels":[],"label_agreement":null},{"id":"W3047357263","doi":"10.5194/isprs-annals-v-4-2020-57-2020","title":"WI-FI RSS FINGERPRINTING FOR INDOOR LOCALIZATION USING AUGMENTED REALITY","year":2020,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RSS; Fingerprint (computing); Computer science; Floor plan; Signal strength; Fingerprint recognition; Augmented reality; Partition (number theory); Real-time computing; Artificial intelligence; Wireless; Telecommunications; Engineering; Engineering drawing; Mathematics","score_opus":0.08777183650087812,"score_gpt":0.3061238054042272,"score_spread":0.21835196890334907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047357263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070079975,0.0005415377,0.9182717,0.00011327725,0.00013601272,0.00006752119,0.0005605009,0.0059233336,0.0043060775],"genre_scores_gemma":[0.6782813,0.00047774336,0.31697562,0.000075386815,0.00004020949,0.00009390456,0.0006271984,0.0001198881,0.0033088848],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944466,0.000130247,0.000033875433,0.00010089243,0.0002398931,0.00005048604],"domain_scores_gemma":[0.99942005,0.00010222055,0.00007807019,0.00017862777,0.00019755647,0.00002360709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003066676,0.00070854084,0.00042186232,0.0014382051,0.0002362072,0.0008392683,0.00064258784,0.00056356605,0.0039827786],"category_scores_gemma":[0.0011223085,0.00029176395,0.00043890372,0.0012461809,0.00021054051,0.00078859687,0.0007347731,0.00041350012,0.0020019542],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005384355,0.00018672389,0.008174831,0.00039901285,0.00014632287,0.0005117628,0.00029143546,0.033018313,0.15266792,0.0023647381,0.005895665,0.7958048],"study_design_scores_gemma":[0.00008279599,0.00068363134,0.025825813,0.0001378675,0.00025325498,0.0019322323,0.00030432592,0.72767574,0.21045433,0.0024406482,0.03002119,0.00018823704],"about_ca_topic_score_codex":0.0016467442,"about_ca_topic_score_gemma":0.0022586884,"teacher_disagreement_score":0.0039827786,"about_ca_system_score_codex":0.0002161348,"about_ca_system_score_gemma":0.00023179645,"threshold_uncertainty_score":0.013323724},"labels":[],"label_agreement":null},{"id":"W3047770534","doi":"10.1109/newcas49341.2020.9159784","title":"Collaborative Localization and Tracking with Minimal Infrastructure","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Tracking (education); Computer science; Automation; The Internet; Location tracking; Robotics; Tracking system; Wireless; Artificial intelligence; Internet of Things; Real-time computing; Computer vision; Robot; Embedded system; Telecommunications; Engineering; World Wide Web; Kalman filter","score_opus":0.006236393935791722,"score_gpt":0.205477271991639,"score_spread":0.19924087805584728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047770534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01354157,0.00012312467,0.983158,0.00009192819,0.000022989107,0.000044668985,0.000038583494,0.00054108683,0.0024380488],"genre_scores_gemma":[0.70283115,0.00030382676,0.28786257,0.00011189897,0.00009470387,0.00028737498,0.0002725104,0.00009326266,0.008142687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764735,0.0005548145,0.000087987995,0.00078593026,0.00060326525,0.000320636],"domain_scores_gemma":[0.9977591,0.0007559605,0.00027013427,0.00090038416,0.00017669186,0.00013760944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092240295,0.0010774169,0.0013908744,0.00075273606,0.00096153945,0.0016689872,0.0023932217,0.0018579245,0.0021497684],"category_scores_gemma":[0.0034823173,0.0006027555,0.00092281954,0.0008828217,0.001130878,0.0023399778,0.004970377,0.00096964947,0.0014012494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050269545,0.00021208849,0.0018739839,0.00037048376,0.00015066689,0.0009792732,0.00052406423,0.7108122,0.04484417,0.045634065,0.003626376,0.19046997],"study_design_scores_gemma":[0.00007471876,0.00034735707,0.000987442,0.00003207949,0.00005253523,0.0006115135,0.00014293188,0.9522706,0.013690762,0.02413553,0.0076105464,0.000043998138],"about_ca_topic_score_codex":0.0021991932,"about_ca_topic_score_gemma":0.0017879426,"teacher_disagreement_score":0.0023932217,"about_ca_system_score_codex":0.0006093732,"about_ca_system_score_gemma":0.0007232694,"threshold_uncertainty_score":0.007191658},"labels":[],"label_agreement":null},{"id":"W3047914583","doi":"10.5194/isprs-archives-xliii-b1-2020-557-2020","title":"GEOMAGNETIC FIELD-BASED INDOOR POSITIONING USING BACK-PROPAGATION NEURAL NETWORKS","year":2020,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Precise Point Positioning; Computer science; Artificial neural network; Earth's magnetic field; Positioning technology; Similarity (geometry); Positioning system; Fingerprint (computing); Cluster analysis; Global Positioning System; Artificial intelligence; Dynamic positioning; Field (mathematics); Indoor positioning system; Geographic coordinate system; Real-time computing; Computer vision; Data mining; Pattern recognition (psychology); Point (geometry); Geodesy; Mathematics; Geography; Telecommunications; Image (mathematics); Engineering; Accelerometer; Magnetic field; GNSS applications","score_opus":0.015309160856306365,"score_gpt":0.23003588800117608,"score_spread":0.2147267271448697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047914583","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04417129,0.0006385844,0.95029056,0.00015841535,0.00017136609,0.000029676077,0.00008699356,0.0019408851,0.0025123316],"genre_scores_gemma":[0.8872344,0.00050886546,0.10683919,0.00012291671,0.00008101984,0.000069055845,0.00031100758,0.000066896886,0.004766559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999705,0.000040355302,0.000018532068,0.00009789972,0.00010021599,0.00003791681],"domain_scores_gemma":[0.9996747,0.00008897455,0.000046388293,0.000031418076,0.00014409184,0.000014496093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003402939,0.0008513827,0.00068291865,0.0006467529,0.00034231629,0.0005358656,0.0009912112,0.00067116204,0.0011618757],"category_scores_gemma":[0.0010043599,0.00035417525,0.00056430965,0.0008048053,0.000249451,0.00071044994,0.00045527032,0.00073669036,0.00052017503],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020438395,0.00007903071,0.0021704077,0.00008661063,0.000081481034,0.00008782802,0.000053250576,0.64032596,0.0064407126,0.0008457324,0.0015682016,0.34805644],"study_design_scores_gemma":[0.000004765182,0.000019565156,0.0004055356,0.000004830253,0.000011031292,0.000014327257,0.0000048715974,0.99784267,0.0012261571,0.00025600492,0.00020468462,0.0000055378805],"about_ca_topic_score_codex":0.015642915,"about_ca_topic_score_gemma":0.010654777,"teacher_disagreement_score":0.015642915,"about_ca_system_score_codex":0.0006075789,"about_ca_system_score_gemma":0.00052284193,"threshold_uncertainty_score":0.03110373},"labels":[],"label_agreement":null},{"id":"W3048448329","doi":"10.1109/infocomwkshps50562.2020.9162727","title":"Handling Device Heterogeneity in Wi-Fi based Indoor Positioning Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"RSS; Computer science; Software deployment; Signal strength; Process (computing); Artificial neural network; Gaussian process; Real-time computing; Indoor positioning system; Kriging; Gaussian; Artificial intelligence; Hybrid positioning system; Machine learning; Data mining; Wireless sensor network; Positioning system; Computer network; Node (physics); Accelerometer; Engineering","score_opus":0.02319992987267694,"score_gpt":0.21687643051939606,"score_spread":0.19367650064671912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048448329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07340896,0.00028558797,0.92439723,0.00015792255,0.000048561276,0.00002937334,0.00006173918,0.0005868962,0.0010237569],"genre_scores_gemma":[0.97245276,0.00017906228,0.026280632,0.00007208021,0.00003786382,0.0000321896,0.00009617834,0.000038888178,0.00081021024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99874324,0.00032158938,0.000057605965,0.00035551217,0.00035514196,0.0001669103],"domain_scores_gemma":[0.9980636,0.000855652,0.00026929314,0.0005366772,0.00021688498,0.000057842364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013157185,0.0008451403,0.0009054065,0.0004708336,0.0005454731,0.0006618876,0.0014989375,0.0009136386,0.0005157698],"category_scores_gemma":[0.0038811327,0.00042689996,0.00041707003,0.00075836823,0.00078435196,0.0017117718,0.0023462668,0.0009702002,0.00029657513],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020037247,0.000048039106,0.007320557,0.000096368516,0.00008298015,0.0006458407,0.00019420098,0.88064116,0.014944264,0.006394595,0.0010531965,0.08837841],"study_design_scores_gemma":[0.000006859169,0.000067089684,0.0017924861,0.000008151385,0.000020496002,0.00025218894,0.000048956288,0.98845786,0.0052593634,0.0029558735,0.0011142469,0.000016347898],"about_ca_topic_score_codex":0.0029871871,"about_ca_topic_score_gemma":0.0026608203,"teacher_disagreement_score":0.0029871871,"about_ca_system_score_codex":0.0005333582,"about_ca_system_score_gemma":0.00037784965,"threshold_uncertainty_score":0.006958246},"labels":[],"label_agreement":null},{"id":"W3054027031","doi":"10.1109/access.2020.3017730","title":"Channel State Information Based Indoor Localization Error Bound Leveraging Pedestrian Random Motion","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Electronic Science and Technology of China; Natural Science Foundation of Chongqing; Chongqing University of Posts and Telecommunications; University of Toronto; Harbin Institute of Technology; Chongqing University; National Natural Science Foundation of China","keywords":"Pedestrian; Computer science; Channel (broadcasting); Motion (physics); Channel state information; State (computer science); Artificial intelligence; Computer network; Telecommunications; Algorithm; Wireless; Engineering; Transport engineering","score_opus":0.03153276964838448,"score_gpt":0.24621379459167644,"score_spread":0.21468102494329197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3054027031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024081456,0.0011849967,0.96955127,0.00017168887,0.00012688043,0.000029996705,0.00009151066,0.0007432193,0.0040189708],"genre_scores_gemma":[0.90090805,0.0015145226,0.09397753,0.00020674031,0.000087211214,0.0000910346,0.00030528955,0.0001342734,0.0027753476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99681914,0.00067338784,0.00018535677,0.00055479683,0.0013470089,0.00042024275],"domain_scores_gemma":[0.9945815,0.0023661891,0.0005683745,0.0007595812,0.0016154591,0.00010887209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019226858,0.0012023344,0.0009081124,0.00118591,0.00056853256,0.001368961,0.001020233,0.0010508568,0.0013447315],"category_scores_gemma":[0.012266242,0.0003081162,0.00042142003,0.0009635707,0.001006907,0.0019562142,0.0021300612,0.0010122291,0.0006271854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047848717,0.00010250634,0.0036695471,0.0004464333,0.00009318402,0.00025899019,0.00022400418,0.8201095,0.024452884,0.02593463,0.002563113,0.12166676],"study_design_scores_gemma":[0.000012402326,0.00021118698,0.0010314093,0.00006402404,0.000048637205,0.00027935204,0.000075138916,0.9698454,0.021500077,0.0042811357,0.0025897922,0.0000613775],"about_ca_topic_score_codex":0.0031884187,"about_ca_topic_score_gemma":0.0032879815,"teacher_disagreement_score":0.0031884187,"about_ca_system_score_codex":0.00096283754,"about_ca_system_score_gemma":0.0015562923,"threshold_uncertainty_score":0.010168254},"labels":[],"label_agreement":null},{"id":"W3083085256","doi":"10.1109/lwc.2020.3021991","title":"RSS Localization Under Gaussian Distributed Path Loss Exponent Model","year":2020,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"RSS; Estimator; Mathematics; Maximum a posteriori estimation; Algorithm; Gaussian; Cramér–Rao bound; Statistics; Random variable; Node (physics); Computer science; Mathematical optimization; Maximum likelihood","score_opus":0.02766037570336851,"score_gpt":0.23041915226067752,"score_spread":0.202758776557309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083085256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027128967,0.0006285651,0.96916807,0.00025930887,0.00004660489,0.000028798771,0.00018491506,0.00037049502,0.0021842858],"genre_scores_gemma":[0.92254746,0.002696266,0.06483143,0.00015785008,0.00015636916,0.00010095182,0.00043181435,0.00010235789,0.008975481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990439,0.00028182616,0.000029847088,0.00025770577,0.00023404705,0.00015260032],"domain_scores_gemma":[0.99907684,0.00041535738,0.00016017036,0.000120020406,0.00020005224,0.000027530607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011400199,0.0011409281,0.0009854091,0.00066700927,0.00025548582,0.000903498,0.0017104164,0.0016198934,0.00095631165],"category_scores_gemma":[0.0030101102,0.00040123292,0.0007356647,0.0015868918,0.0012747458,0.001769427,0.0010903183,0.0007939901,0.0008211702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090352376,0.000024403038,0.0014974922,0.00010387104,0.000036588855,0.0005124911,0.00008413515,0.95749664,0.0039001992,0.024273701,0.0009764782,0.011003688],"study_design_scores_gemma":[0.000007747065,0.00003406768,0.000293176,0.000003887542,0.000010355355,0.000089339985,0.000016798207,0.9939015,0.00044592464,0.004823826,0.0003606517,0.000012680196],"about_ca_topic_score_codex":0.0058910185,"about_ca_topic_score_gemma":0.0032769705,"teacher_disagreement_score":0.0058910185,"about_ca_system_score_codex":0.0006403344,"about_ca_system_score_gemma":0.0006296109,"threshold_uncertainty_score":0.011713445},"labels":[],"label_agreement":null},{"id":"W3085410529","doi":"10.2196/19874","title":"Measuring Mobility and Room Occupancy in Clinical Settings: System Development and Implementation","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Commonwealth Scientific and Industrial Research Organisation","keywords":"Occupancy; Computer science; Environmental science; Engineering; Architectural engineering","score_opus":0.07498379658850528,"score_gpt":0.36150622186108494,"score_spread":0.28652242527257965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3085410529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38577747,0.0009063264,0.5897813,0.0021235798,0.00022764517,0.0043216357,0.0017315161,0.011123465,0.0040070424],"genre_scores_gemma":[0.6014419,0.0005492451,0.39261925,0.0002887748,0.000087053035,0.0023353247,0.0016335596,0.0001273409,0.0009175581],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99694437,0.0012969777,0.0002819267,0.00059273123,0.0007122933,0.0001717493],"domain_scores_gemma":[0.9941506,0.002048128,0.00035338316,0.00066556875,0.002394339,0.0003880148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005507367,0.0006511118,0.000621519,0.0013136012,0.00044811436,0.001511312,0.0019487585,0.0010326141,0.00177056],"category_scores_gemma":[0.012092568,0.00047672508,0.0004275715,0.0011775829,0.00047982266,0.001528339,0.0013967574,0.0007892125,0.0012064017],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001300203,0.0018044278,0.18476191,0.00087152503,0.0002885335,0.0004700848,0.0016154714,0.033844598,0.0326944,0.0016597268,0.008001168,0.732688],"study_design_scores_gemma":[0.0010110871,0.006468045,0.18261828,0.0006962795,0.00048062284,0.0016052902,0.0023948757,0.70188355,0.069101796,0.0038359377,0.029480547,0.00042376685],"about_ca_topic_score_codex":0.0046526627,"about_ca_topic_score_gemma":0.0029145358,"teacher_disagreement_score":0.005507367,"about_ca_system_score_codex":0.0010685704,"about_ca_system_score_gemma":0.0016538185,"threshold_uncertainty_score":0.029126048},"labels":[],"label_agreement":null},{"id":"W3087978450","doi":"10.1002/rob.21988","title":"Radio propagation models for differential GNSS based on dense point clouds","year":2020,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Computer science; Geolocation; Remote sensing; Radio propagation; Real-time computing; Satellite system; USable; Precise Point Positioning; Satellite; Global Positioning System; Telecommunications; Geography; Engineering; Aerospace engineering","score_opus":0.02099891088341088,"score_gpt":0.2213785956624911,"score_spread":0.20037968477908022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087978450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16891332,0.00024505967,0.8251215,0.00022308786,0.00007020815,0.000092550144,0.00045648366,0.0007989941,0.0040787887],"genre_scores_gemma":[0.9613733,0.0002808921,0.034765817,0.000049947925,0.00002805715,0.000114756425,0.0005108846,0.00005430303,0.002821973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974257,0.000053742493,0.000013709662,0.000056978923,0.0000909735,0.000042029493],"domain_scores_gemma":[0.9993162,0.00031046523,0.000108863875,0.00006958728,0.00015514907,0.00003975171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036728464,0.0008871695,0.00049899874,0.0007543209,0.0003741739,0.00080787076,0.001096282,0.0008195755,0.0009791758],"category_scores_gemma":[0.0015113868,0.0006574334,0.0007756715,0.000872943,0.0006839826,0.00079194497,0.0008040948,0.0007604985,0.00044078336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041743847,0.0000036192916,0.00022597061,0.0000029082712,0.0000024573621,0.000011575217,0.0000062896765,0.99821424,0.00021620806,0.00023269498,0.000040912117,0.0010390475],"study_design_scores_gemma":[0.0000016021031,0.0000025753434,0.0001449363,0.0000011051756,9.1841497e-7,0.0000032637706,0.0000022152733,0.99951684,0.000051784213,0.00022227966,0.00005052909,0.0000018424239],"about_ca_topic_score_codex":0.05101597,"about_ca_topic_score_gemma":0.02164297,"teacher_disagreement_score":0.05101597,"about_ca_system_score_codex":0.0008623045,"about_ca_system_score_gemma":0.0006865204,"threshold_uncertainty_score":0.101438046},"labels":[],"label_agreement":null},{"id":"W3088842518","doi":"10.7557/3.5585","title":"The Geometer: A New Device for Recording Angles in Visual Surveys","year":2020,"lang":"en","type":"article","venue":"NAMMCO Scientific Publications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada; Bayer (Canada)","funders":"","keywords":"USB; Software; Computer science; Mobile device; Data collection; Computer hardware; Tracking (education); Computer graphics (images); Embedded system; Operating system; Statistics; Mathematics","score_opus":0.041647113830778175,"score_gpt":0.27324419017076323,"score_spread":0.23159707633998505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088842518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030253643,0.0018634037,0.8906779,0.000405211,0.000658631,0.0006940258,0.008568686,0.033123035,0.033755448],"genre_scores_gemma":[0.120521404,0.0014328414,0.81687933,0.00041998838,0.0002657065,0.0008067842,0.006242975,0.0020771818,0.05135373],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988864,0.00017143358,0.000059925023,0.00029038507,0.0005346538,0.000057233563],"domain_scores_gemma":[0.99904877,0.00021985894,0.000120867124,0.0002587285,0.00025529024,0.000096400996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006617246,0.0007051925,0.0005901263,0.0017278122,0.0003551844,0.0010957214,0.0010031817,0.0006258544,0.018379077],"category_scores_gemma":[0.0017797599,0.0005708642,0.0003694427,0.0017455843,0.0004434021,0.0016149437,0.0015739818,0.0006135964,0.008355687],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007053421,0.00007220458,0.007492803,0.0008822093,0.000080424754,0.00034013402,0.0008930083,0.001415924,0.2373603,0.007922265,0.07380774,0.6690276],"study_design_scores_gemma":[0.0002798655,0.0008483264,0.04126161,0.00036832315,0.000174899,0.0029594623,0.00033798523,0.018735018,0.091491,0.0027097585,0.84038574,0.00044800606],"about_ca_topic_score_codex":0.0019757363,"about_ca_topic_score_gemma":0.0044156983,"teacher_disagreement_score":0.018379077,"about_ca_system_score_codex":0.00034427142,"about_ca_system_score_gemma":0.00048899173,"threshold_uncertainty_score":0.0614841},"labels":[],"label_agreement":null},{"id":"W3089639240","doi":"10.1109/icra40945.2020.9197345","title":"Accurate position tracking with a single UWB anchor","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Inertial measurement unit; Computer science; Extended Kalman filter; Ranging; Robot; Ultra-wideband; Tracking (education); Simultaneous localization and mapping; Computer vision; Global Positioning System; Kalman filter; Artificial intelligence; Orientation (vector space); Tracking system; Mobile robot; Real-time computing; Telecommunications","score_opus":0.020301918104470837,"score_gpt":0.1954887862530715,"score_spread":0.17518686814860068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089639240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03038499,0.00025987998,0.967157,0.00008211488,0.00008584722,0.0000099809795,0.000017643428,0.0005623218,0.0014402248],"genre_scores_gemma":[0.74468446,0.00041401607,0.25034314,0.00007733013,0.0000756169,0.00004190795,0.00007897039,0.00006440914,0.0042201895],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950135,0.000053294676,0.000026966813,0.0002003387,0.00017624929,0.000041813884],"domain_scores_gemma":[0.9995216,0.000078611585,0.00013605798,0.00013683495,0.00009836931,0.00002843777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033487985,0.0005016288,0.0008003825,0.0003734024,0.0003441909,0.00054333755,0.0006810361,0.0010597389,0.0005763645],"category_scores_gemma":[0.0011214729,0.00034111092,0.00030525462,0.0004968056,0.0004015503,0.0011720764,0.0012318528,0.00060133764,0.0005922235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003502054,0.000112320326,0.0035842129,0.00027946598,0.00008387546,0.0008667568,0.00044429142,0.27353534,0.2947135,0.012541608,0.002296528,0.41119197],"study_design_scores_gemma":[0.000035408695,0.00035320182,0.0018512893,0.000034539997,0.00005534176,0.0009662591,0.0000931922,0.9128972,0.07305202,0.004038805,0.006564139,0.000058642],"about_ca_topic_score_codex":0.0006634017,"about_ca_topic_score_gemma":0.00045073923,"teacher_disagreement_score":0.0010597389,"about_ca_system_score_codex":0.0002000293,"about_ca_system_score_gemma":0.00029100192,"threshold_uncertainty_score":0.0019281507},"labels":[],"label_agreement":null},{"id":"W3089771850","doi":"","title":"RSSI-based Indoor Localization with LTE-A Ultra-Dense Networks","year":2020,"lang":"en","type":"article","venue":"International Symposium on Performance Evaluation of Computer and Telecommunication Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Signal strength; Real-time computing; Mobile telephony; User equipment; Computer network; Mobile radio; Base station; Wireless sensor network","score_opus":0.020020531197588915,"score_gpt":0.23676197302346505,"score_spread":0.21674144182587612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089771850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055062898,0.0015318138,0.9366788,0.00021653819,0.00008253619,0.000044873894,0.00012962446,0.0014037677,0.004849102],"genre_scores_gemma":[0.95747465,0.0009873491,0.03960343,0.00005719675,0.000042696374,0.000040505845,0.00012060082,0.000028371966,0.0016451925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991666,0.0002923167,0.000035069424,0.00011632346,0.00029283625,0.00009685306],"domain_scores_gemma":[0.99942386,0.00017843893,0.00009734328,0.00011385977,0.00016240342,0.000024083718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062690093,0.0008110088,0.0006493027,0.00062553294,0.00039005314,0.0007932179,0.0009713003,0.0006209343,0.00046657602],"category_scores_gemma":[0.0014123847,0.00029022616,0.00053643534,0.0012384853,0.0005405832,0.0012246316,0.00082092657,0.00045746897,0.00026475053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001797215,0.000050473718,0.0029346326,0.00011880667,0.00007057977,0.00019164014,0.00010753697,0.9231521,0.007166504,0.009020785,0.0011303625,0.05587692],"study_design_scores_gemma":[0.000008908733,0.00008482523,0.0007589883,0.0000101241585,0.00002794738,0.00010977222,0.000025503958,0.99457365,0.0018903689,0.0014764932,0.0010152435,0.000018106104],"about_ca_topic_score_codex":0.012711609,"about_ca_topic_score_gemma":0.010280147,"teacher_disagreement_score":0.012711609,"about_ca_system_score_codex":0.0011155445,"about_ca_system_score_gemma":0.0006238513,"threshold_uncertainty_score":0.02527523},"labels":[],"label_agreement":null},{"id":"W3092034864","doi":"10.1109/pimrc48278.2020.9217356","title":"Fingerprinting Localization Method Based on Clustering and Gaussian Process Regression in Distributed Massive MIMO Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"MIMO; Kriging; Cluster analysis; Computer science; RSS; Ground-penetrating radar; Computational complexity theory; Gaussian process; Mean squared error; Algorithm; Telecommunications link; Gaussian; Data mining; Artificial intelligence; Pattern recognition (psychology); Statistics; Machine learning; Mathematics; Radar","score_opus":0.01327861393589627,"score_gpt":0.251295320555648,"score_spread":0.23801670661975174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092034864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011240252,0.00013916299,0.9875781,0.0000555777,0.000025218045,0.000012138945,0.000016927883,0.00035813966,0.0005744957],"genre_scores_gemma":[0.72687846,0.000586887,0.2694263,0.00011812495,0.000067440116,0.00007219993,0.000104153674,0.000063988664,0.002682484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994466,0.00013031086,0.000025244477,0.00014528149,0.00019727177,0.00005530437],"domain_scores_gemma":[0.9995067,0.00015085637,0.00006809856,0.00007606448,0.00017415005,0.00002400683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051009184,0.00060712965,0.0006478862,0.00059162435,0.00034137737,0.00048242,0.00089876447,0.00059778756,0.0005573998],"category_scores_gemma":[0.0016292708,0.0002629243,0.0004799988,0.0009988957,0.0003484858,0.0008224953,0.00066413486,0.00057365187,0.00037942434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020577357,0.00006150865,0.0023003991,0.0001538459,0.00007341618,0.00022364956,0.00017455617,0.66226774,0.033324752,0.0120586455,0.0017669997,0.28738865],"study_design_scores_gemma":[0.0000075551984,0.000036964317,0.0004591927,0.000004351763,0.000011120165,0.00008731577,0.000015578691,0.9938538,0.0037420753,0.0012292285,0.00053721044,0.00001564205],"about_ca_topic_score_codex":0.004370008,"about_ca_topic_score_gemma":0.0024326467,"teacher_disagreement_score":0.004370008,"about_ca_system_score_codex":0.0004372894,"about_ca_system_score_gemma":0.0005163451,"threshold_uncertainty_score":0.008689165},"labels":[],"label_agreement":null},{"id":"W3092314206","doi":"10.3390/robotics9040082","title":"Directional-Sensor Network Deployment Planning for Mobile-Target Search","year":2020,"lang":"en","type":"article","venue":"Robotics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software deployment; Probabilistic logic; Computer science; Wireless sensor network; Real-time computing; Artificial intelligence; Computer network","score_opus":0.03094136154079036,"score_gpt":0.25473760929459455,"score_spread":0.2237962477538042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092314206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006306184,0.00013908293,0.99194133,0.00004715768,0.000010711023,0.000018203373,0.000020675769,0.000097341785,0.0014192305],"genre_scores_gemma":[0.5868287,0.00060742756,0.4090437,0.00007033566,0.000026478148,0.000120859586,0.00015686774,0.000060934803,0.0030847096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980766,0.00007712773,0.0000071955587,0.00004317665,0.000045592973,0.00001930839],"domain_scores_gemma":[0.9997985,0.000096555625,0.000032485266,0.000020970125,0.000037370348,0.000014032417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031830408,0.0005510818,0.00026572513,0.0003388145,0.00020292755,0.00024233262,0.0005688147,0.0003155482,0.0010884056],"category_scores_gemma":[0.0008172912,0.00024014733,0.00025527523,0.00039051526,0.00026817227,0.0004576639,0.0005713105,0.0003076674,0.0002634314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046530335,0.000026364234,0.0004114773,0.00007355322,0.000015498852,0.000064260574,0.00005044649,0.9204148,0.005808258,0.013251835,0.0011599993,0.058676936],"study_design_scores_gemma":[0.000005089152,0.00003411725,0.0000933581,0.0000032116143,0.000003871457,0.00004440123,0.000017549866,0.9942603,0.0011953991,0.0028838366,0.0014548178,0.000004103359],"about_ca_topic_score_codex":0.0018064994,"about_ca_topic_score_gemma":0.002863419,"teacher_disagreement_score":0.0018064994,"about_ca_system_score_codex":0.00038723976,"about_ca_system_score_gemma":0.0006540477,"threshold_uncertainty_score":0.003641069},"labels":[],"label_agreement":null},{"id":"W3092317128","doi":"10.3390/rs12193271","title":"Indoor and Outdoor Low-Cost Seamless Integrated Navigation System Based on the Integration of INS/GNSS/LIDAR System","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"GNSS applications; Lidar; Computer science; Inertial navigation system; Navigation system; Air navigation; Dead reckoning; Remote sensing; Satellite system; Global Positioning System; Ranging; Real-time computing; Satellite navigation; Geography; Telecommunications; Inertial frame of reference","score_opus":0.017164271316068923,"score_gpt":0.2089769428844158,"score_spread":0.19181267156834686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092317128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12550505,0.0005798621,0.8486266,0.0001694312,0.00027620493,0.00019499607,0.00022573306,0.011563443,0.012858664],"genre_scores_gemma":[0.8257276,0.00024734795,0.16509525,0.0001872144,0.00007183259,0.0001433215,0.00055873214,0.000097676195,0.007871039],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993648,0.00004738634,0.000028016793,0.00014274448,0.00034193636,0.000075220196],"domain_scores_gemma":[0.9997029,0.000015582227,0.00003181261,0.00004713038,0.00017197047,0.00003060953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002263886,0.000611535,0.0005218417,0.0007101078,0.00048072962,0.0005494109,0.0010858068,0.00040284893,0.002308347],"category_scores_gemma":[0.00034662517,0.0002510338,0.00029709077,0.00045629134,0.00019294402,0.00066506787,0.0006729615,0.00035760502,0.0010676245],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048411678,0.00024634897,0.01442059,0.00036762515,0.00009549773,0.0004415547,0.0003721502,0.021385022,0.21916431,0.0042683207,0.009168643,0.72958577],"study_design_scores_gemma":[0.00031754424,0.0022390254,0.025926923,0.000114711685,0.00044381822,0.0029618384,0.00045924212,0.65146446,0.2478919,0.0026115435,0.06529317,0.0002757336],"about_ca_topic_score_codex":0.004366686,"about_ca_topic_score_gemma":0.0036790962,"teacher_disagreement_score":0.004366686,"about_ca_system_score_codex":0.00030573548,"about_ca_system_score_gemma":0.0008010623,"threshold_uncertainty_score":0.008682549},"labels":[],"label_agreement":null},{"id":"W3093310284","doi":"10.1155/2020/7274181","title":"Evaluation on Nonholonomic Constraints and Rauch–Tung–Striebel Filter-Enhanced UWB/INS Integration","year":2020,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Non-line-of-sight propagation; GNSS applications; Inertial navigation system; Nonholonomic system; Computer science; Kalman filter; Satellite system; Ultra-wideband; Real-time computing; Position (finance); Dynamic positioning; Engineering; Global Positioning System; Inertial frame of reference; Artificial intelligence; Wireless; Telecommunications; Mobile robot","score_opus":0.025888029660495,"score_gpt":0.22239982225930383,"score_spread":0.19651179259880883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093310284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14459546,0.000738038,0.8501335,0.00012322859,0.00009311083,0.00006829593,0.00006933016,0.0006897138,0.0034893446],"genre_scores_gemma":[0.8732686,0.00036186512,0.12392913,0.00007601319,0.0000368528,0.000079075966,0.00027665315,0.000085141204,0.0018866349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896944,0.0001778011,0.00006610394,0.00015873853,0.0005038781,0.00012414304],"domain_scores_gemma":[0.9983582,0.00073691737,0.00015591343,0.00010966323,0.000572418,0.00006687552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015457305,0.0010151061,0.0008709664,0.0006030389,0.00036148253,0.00091009855,0.0006296449,0.0009916688,0.001808529],"category_scores_gemma":[0.004476832,0.00028013886,0.0006951824,0.00055049825,0.00035608286,0.0011018456,0.00067886815,0.00061394216,0.0003625318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010505522,0.00023340421,0.00842841,0.0006509605,0.00021848177,0.00042756138,0.00025658563,0.6715189,0.04501786,0.004861804,0.0012037534,0.26613173],"study_design_scores_gemma":[0.000019156936,0.00028231431,0.0024017608,0.00001089548,0.000037063415,0.00008372234,0.00004490391,0.9883145,0.007820481,0.00030607777,0.0006577941,0.00002124303],"about_ca_topic_score_codex":0.006196871,"about_ca_topic_score_gemma":0.0038582012,"teacher_disagreement_score":0.006196871,"about_ca_system_score_codex":0.0004481334,"about_ca_system_score_gemma":0.001091004,"threshold_uncertainty_score":0.012321591},"labels":[],"label_agreement":null},{"id":"W3093540758","doi":"10.18280/i2m.190404","title":"Positioning of Wireless Sensor Network under Emergency Communication Environment","year":2020,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wireless sensor network; Computer science; Kalman filter; Real-time computing; Wireless; Node (physics); Positioning technology; Wireless network; Computer network; Distributed computing; Engineering; Telecommunications; Artificial intelligence","score_opus":0.018082840235513303,"score_gpt":0.22813433741164668,"score_spread":0.2100514971761334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093540758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06036558,0.00060409197,0.9345513,0.00018174907,0.00014272127,0.000027010365,0.00004454879,0.00052176573,0.003561175],"genre_scores_gemma":[0.9091919,0.0008610185,0.08600014,0.000046673613,0.000047338133,0.00005184817,0.00011374102,0.000029930936,0.003657441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996917,0.00007437881,0.000015435364,0.00008999682,0.000095556396,0.000032837146],"domain_scores_gemma":[0.99985576,0.000034259097,0.000026922042,0.000018857236,0.000054249547,0.00000999346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023851986,0.00042015422,0.00030814647,0.00037650397,0.00041507828,0.00040949075,0.0004907196,0.0004569582,0.0004794364],"category_scores_gemma":[0.0008093575,0.00013926774,0.00016202757,0.0004855521,0.00025949697,0.0007761768,0.0005232829,0.0002751482,0.0002157716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027644204,0.00003950546,0.009914765,0.00031970674,0.000060825314,0.00079450075,0.00050292135,0.51961404,0.09508791,0.015196106,0.0035720617,0.35462123],"study_design_scores_gemma":[0.000017394634,0.00018798912,0.0034719964,0.000021943628,0.000026458974,0.00036651173,0.00024917483,0.9546836,0.029336,0.0039533037,0.0076527414,0.000032924265],"about_ca_topic_score_codex":0.0016733165,"about_ca_topic_score_gemma":0.0012248444,"teacher_disagreement_score":0.0016733165,"about_ca_system_score_codex":0.0002529062,"about_ca_system_score_gemma":0.0003606031,"threshold_uncertainty_score":0.0033271909},"labels":[],"label_agreement":null},{"id":"W3096488186","doi":"10.1109/jiot.2020.3034342","title":"Probabilistic Source Localization Based on Time-of-Arrival Measurements","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Probabilistic logic; Probability density function; Gaussian; A priori and a posteriori; SIGNAL (programming language); Maximum a posteriori estimation; Time of arrival; Algorithm; Non-line-of-sight propagation; Mathematics; Statistics; Telecommunications; Artificial intelligence; Wireless; Physics; Maximum likelihood","score_opus":0.022331382521032863,"score_gpt":0.21500119843814894,"score_spread":0.19266981591711607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096488186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021225056,0.00013386583,0.9969445,0.000041697134,0.000020916985,0.000008985154,0.000019035944,0.00016732208,0.00054122624],"genre_scores_gemma":[0.5673017,0.0017931368,0.4261199,0.00013075958,0.00020844309,0.00017537927,0.00040155934,0.00013068627,0.003738587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884343,0.00035928586,0.00005373202,0.00019721141,0.00047401225,0.0000723284],"domain_scores_gemma":[0.99805355,0.0012079395,0.00021533233,0.00015478997,0.0003335724,0.00003472647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009641952,0.0008253999,0.0008543175,0.0010947243,0.00040374033,0.0010583708,0.0012985189,0.0010420901,0.0008710797],"category_scores_gemma":[0.0057877954,0.000667602,0.00079959544,0.0018815902,0.00082001224,0.002103459,0.0013617559,0.001626234,0.00055158755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013361259,0.000033607932,0.0010244692,0.00018885426,0.000067237306,0.00013141624,0.00015942194,0.8528582,0.009732883,0.043786347,0.001231387,0.09065271],"study_design_scores_gemma":[0.0000058505834,0.000024386964,0.00019915254,0.000010043155,0.0000081125345,0.00006377084,0.000009413326,0.9913573,0.001370656,0.006011635,0.00092482223,0.000014755738],"about_ca_topic_score_codex":0.0026024326,"about_ca_topic_score_gemma":0.0023225734,"teacher_disagreement_score":0.0026024326,"about_ca_system_score_codex":0.00059586804,"about_ca_system_score_gemma":0.0009609569,"threshold_uncertainty_score":0.005174637},"labels":[],"label_agreement":null},{"id":"W3101503627","doi":"10.1109/jiot.2020.3037067","title":"Cognitive Neighbor Discovery With Directional Antennas in Self-Organizing IoT Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Neighbor Discovery Protocol; Computer science; k-nearest neighbors algorithm; Imperfect; Transmission (telecommunications); Ideal (ethics); Perfect information; Algorithm; Data mining; Artificial intelligence; Mathematics; Telecommunications; The Internet","score_opus":0.008819922184537487,"score_gpt":0.19718833525818732,"score_spread":0.18836841307364985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101503627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042495348,0.0005152299,0.9548829,0.0001592667,0.000036539226,0.000030591276,0.000017787546,0.00015111356,0.0017112246],"genre_scores_gemma":[0.95067996,0.00039975293,0.047721673,0.0000638761,0.000045695662,0.000051853454,0.000027012791,0.000019293355,0.000990836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893755,0.00041665757,0.00004088667,0.00018449227,0.0002788554,0.00014156254],"domain_scores_gemma":[0.99712807,0.0019853795,0.00035690307,0.00023574807,0.00020136428,0.00009249159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016985153,0.0003594893,0.00063406926,0.0005716256,0.00071554846,0.00087688357,0.0012628289,0.00066340074,0.00037562582],"category_scores_gemma":[0.005221329,0.00037267286,0.00045660787,0.0008389402,0.0010731636,0.001654261,0.0008312719,0.0005233148,0.00008513394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014993269,0.00006429642,0.0015896315,0.00010707288,0.000069492504,0.00014501145,0.00021153518,0.87148845,0.0037955353,0.07477566,0.00077678973,0.046826623],"study_design_scores_gemma":[0.000010159879,0.0000499733,0.00023933804,0.0000036935726,0.000016333574,0.00004860829,0.000036344634,0.98593175,0.0007817773,0.012363758,0.000508046,0.000010344438],"about_ca_topic_score_codex":0.0020148407,"about_ca_topic_score_gemma":0.0019382087,"teacher_disagreement_score":0.0020148407,"about_ca_system_score_codex":0.0010094004,"about_ca_system_score_gemma":0.00079327315,"threshold_uncertainty_score":0.008982658},"labels":[],"label_agreement":null},{"id":"W3104168494","doi":"","title":"Energy-efficient localization and tracking of mobile devices in wireless sensor networks","year":2017,"lang":"en","type":"article","venue":"ResearchOnline at James Cook University (James Cook University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Wireless sensor network; Efficient energy use; Energy consumption; Computer science; Key distribution in wireless sensor networks; Tracking (education); Real-time computing; Energy (signal processing); Wireless; Interference (communication); Embedded system; Transmission (telecommunications); Channel (broadcasting); Wireless network; Engineering; Computer network; Telecommunications; Electrical engineering","score_opus":0.011702457331980588,"score_gpt":0.21713228492883338,"score_spread":0.2054298275968528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104168494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09222859,0.0022075835,0.89948463,0.00026254755,0.00009310752,0.000070210095,0.00005664846,0.00095336547,0.0046434198],"genre_scores_gemma":[0.85790133,0.002733607,0.13298482,0.00013975226,0.00005473268,0.000080370846,0.00014630136,0.00005495984,0.0059041125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996563,0.00008288215,0.00001926846,0.000053005027,0.00015527869,0.00003318843],"domain_scores_gemma":[0.9998159,0.00006532921,0.000037558006,0.000030718158,0.00004193296,0.000008643227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030785517,0.0003686262,0.00031346577,0.0005223172,0.00038235204,0.00040905262,0.000521822,0.00038716337,0.00047750436],"category_scores_gemma":[0.00083801977,0.00016950624,0.00018033157,0.00062495196,0.00027146787,0.0009737455,0.00060895435,0.00022246047,0.0002806508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029648578,0.00014633888,0.004423996,0.00040557297,0.000069653135,0.00058225216,0.00029101656,0.39622724,0.11096196,0.016197257,0.004170998,0.46622732],"study_design_scores_gemma":[0.000026104983,0.00024878792,0.0030983,0.00005660913,0.000040679333,0.00045337688,0.00012445336,0.9214651,0.054607924,0.008006834,0.011829955,0.000041937106],"about_ca_topic_score_codex":0.0014894628,"about_ca_topic_score_gemma":0.0022576475,"teacher_disagreement_score":0.0014894628,"about_ca_system_score_codex":0.00029702135,"about_ca_system_score_gemma":0.0003299026,"threshold_uncertainty_score":0.002961576},"labels":[],"label_agreement":null},{"id":"W3105951068","doi":"","title":"Performance Limits and Geometric Properties of Array Localization","year":2015,"lang":"en","type":"article","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":125,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Cramér–Rao bound; Computer science; Antenna (radio); Bandwidth (computing); Fisher information; Upper and lower bounds; Wireless; SIGNAL (programming language); Angle of arrival; Antenna array; Topology (electrical circuits); Algorithm; Mathematics; Telecommunications; Mathematical analysis; Estimation theory; Combinatorics","score_opus":0.0225535303604312,"score_gpt":0.20729303832959448,"score_spread":0.18473950796916327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105951068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055383753,0.0039742445,0.9082682,0.002617596,0.00016303983,0.000054074335,0.00033735376,0.0010345278,0.028167224],"genre_scores_gemma":[0.92869544,0.0037374168,0.06262903,0.00045006268,0.00041097944,0.0002522725,0.00052240083,0.00025473142,0.003047624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954774,0.0013348152,0.00013196382,0.00080539624,0.0017644431,0.00048610973],"domain_scores_gemma":[0.9578515,0.03325619,0.0026439847,0.0027468328,0.0031111862,0.00039031182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040833782,0.0010777151,0.00091061933,0.0019146394,0.001005368,0.002199935,0.0017241398,0.001994291,0.0024012122],"category_scores_gemma":[0.057730496,0.0007800678,0.0004267807,0.00197869,0.0032978894,0.006062324,0.0030955768,0.001712732,0.0015530296],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002704167,0.00005366087,0.00402608,0.00043784513,0.00006399682,0.0003220675,0.00036337203,0.67035884,0.013681811,0.23515968,0.003098828,0.07216341],"study_design_scores_gemma":[0.000025730513,0.00023215284,0.0019001316,0.00010389075,0.000031182324,0.0010073754,0.0001757027,0.81923544,0.010044682,0.16197452,0.005190826,0.000078303594],"about_ca_topic_score_codex":0.0010205626,"about_ca_topic_score_gemma":0.00033935727,"teacher_disagreement_score":0.0040833782,"about_ca_system_score_codex":0.0014688148,"about_ca_system_score_gemma":0.00080651813,"threshold_uncertainty_score":0.02159524},"labels":[],"label_agreement":null},{"id":"W3108914020","doi":"10.1109/access.2020.3039271","title":"A Survey of Machine Learning for Indoor Positioning","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":258,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Scalability; Adaptability; Non-line-of-sight propagation; Software deployment; Wireless; Machine learning; Artificial intelligence; Telecommunications","score_opus":0.04411765217212168,"score_gpt":0.2787286757262707,"score_spread":0.234611023554149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108914020","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038653163,0.27839735,0.6943922,0.0030465282,0.0014090302,0.000091422735,0.000665546,0.0010812448,0.017051347],"genre_scores_gemma":[0.12097794,0.49785602,0.355238,0.0019813997,0.005760241,0.0004242532,0.0032921508,0.00039589783,0.014074172],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99853754,0.00043955215,0.00015513391,0.00032355203,0.00046990105,0.000074320014],"domain_scores_gemma":[0.9976503,0.0014712212,0.000096798896,0.00023199797,0.0005058688,0.000043739296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016766726,0.0014254249,0.0016253601,0.0022194975,0.00051223737,0.0016597852,0.0018881964,0.0017069946,0.0040820576],"category_scores_gemma":[0.005206742,0.00059765653,0.0011919582,0.004957225,0.0006307677,0.0028670114,0.0010965838,0.0020872015,0.0036960037],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047391364,0.0000762135,0.001700078,0.0018304267,0.00011949304,0.00010525671,0.000081922815,0.032289132,0.00088112714,0.02204247,0.020495825,0.9203307],"study_design_scores_gemma":[0.000029785871,0.00035573388,0.0047510457,0.0018747888,0.00016651118,0.0009292378,0.00021099494,0.426796,0.004116796,0.09203297,0.46857396,0.00016225339],"about_ca_topic_score_codex":0.0027417308,"about_ca_topic_score_gemma":0.0019509344,"teacher_disagreement_score":0.0040820576,"about_ca_system_score_codex":0.0007871606,"about_ca_system_score_gemma":0.0010637819,"threshold_uncertainty_score":0.013655841},"labels":[],"label_agreement":null},{"id":"W3109081127","doi":"10.1109/ccece47787.2020.9255727","title":"Wireless Positioning Network Location Prediction Based on Machine Learning Techniques","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Wireless; Wireless network; Wireless sensor network; Artificial neural network; Node (physics); Key distribution in wireless sensor networks; Position (finance); Computer network; Artificial intelligence; Real-time computing; Wi-Fi array; Machine learning; Telecommunications; Engineering","score_opus":0.006840212832025952,"score_gpt":0.18338265763628592,"score_spread":0.17654244480425996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109081127","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08672225,0.00059381936,0.90782845,0.00023106125,0.00011457194,0.000051853924,0.00013527827,0.0013449597,0.0029777288],"genre_scores_gemma":[0.89079213,0.0004845071,0.10544806,0.00005872645,0.0000629542,0.00007358126,0.00026281396,0.00003332957,0.002783969],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996137,0.00009029136,0.00002881117,0.000100607765,0.00013217657,0.00003446842],"domain_scores_gemma":[0.9992042,0.00033560346,0.00010983857,0.00006359758,0.0002702017,0.000016577183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045612804,0.00056852004,0.00044780117,0.0009647167,0.0002871589,0.0004250377,0.00055360515,0.000478531,0.000957814],"category_scores_gemma":[0.0020447527,0.0001675503,0.00031927635,0.00081568304,0.00018618119,0.0007871242,0.0002955277,0.00054293923,0.00053989864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013224536,0.000104883504,0.009443537,0.000086349566,0.00006386508,0.00010633989,0.00005644126,0.6293951,0.0050860927,0.001470297,0.0017358874,0.3523189],"study_design_scores_gemma":[0.000002506852,0.00002661679,0.0010762605,0.0000065695967,0.0000070593555,0.000021579493,0.000008836178,0.9966466,0.0015231429,0.000379032,0.00029717502,0.000004634109],"about_ca_topic_score_codex":0.005229911,"about_ca_topic_score_gemma":0.004731541,"teacher_disagreement_score":0.005229911,"about_ca_system_score_codex":0.00044244216,"about_ca_system_score_gemma":0.0003699372,"threshold_uncertainty_score":0.010398984},"labels":[],"label_agreement":null},{"id":"W3110671580","doi":"10.1109/lcomm.2020.3043974","title":"Error Analysis of Localization Based on Minimum-Error Entropy With Fiducial Points","year":2020,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Non-line-of-sight propagation; Computer science; Algorithm; Fiducial marker; Entropy (arrow of time); Gaussian; Context (archaeology); Variance (accounting); Artificial intelligence; Wireless; Telecommunications","score_opus":0.02699204944762141,"score_gpt":0.24778490621889712,"score_spread":0.22079285677127572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110671580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020442314,0.00041688513,0.9773744,0.00014542266,0.00002667959,0.000012332782,0.00002935301,0.00010234923,0.0014503602],"genre_scores_gemma":[0.9164655,0.00056982855,0.08121257,0.00007029049,0.000054279513,0.000058183236,0.0001022939,0.000079277015,0.0013876923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988889,0.00042278934,0.000047531244,0.00013960633,0.0004080006,0.000093197275],"domain_scores_gemma":[0.9948372,0.003928431,0.0004026749,0.00027569447,0.00048924115,0.00006680217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020842152,0.00056817016,0.0006908064,0.00082554267,0.00031319304,0.00085399527,0.00091777724,0.00073407573,0.00095524674],"category_scores_gemma":[0.010652199,0.00024857398,0.0004283899,0.00069071224,0.0011775369,0.0015464224,0.001348982,0.0008012818,0.00018434708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010437514,0.000010968741,0.0010145444,0.000075274016,0.000026983911,0.00006117354,0.00006709961,0.9499879,0.0022000053,0.026261253,0.00034123403,0.019849082],"study_design_scores_gemma":[0.000002112099,0.000019287976,0.00022893085,0.000009721068,0.0000032546104,0.000028752434,0.000007098073,0.9945703,0.0009920268,0.003975516,0.00015453024,0.0000083699015],"about_ca_topic_score_codex":0.0015668283,"about_ca_topic_score_gemma":0.00074039237,"teacher_disagreement_score":0.0020842152,"about_ca_system_score_codex":0.0008625297,"about_ca_system_score_gemma":0.00062790606,"threshold_uncertainty_score":0.011022508},"labels":[],"label_agreement":null},{"id":"W3112893815","doi":"10.3390/s20247003","title":"Indoor Positioning System Using Dynamic Model Estimation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Samsung; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universidade Federal do Amazonas","keywords":"Computer science; Computation; Node (physics); Position (finance); SIGNAL (programming language); Bluetooth; RSS; Real-time computing; Set (abstract data type); Scale (ratio); Simulation; Algorithm; Wireless; Engineering; Telecommunications","score_opus":0.013765885562010546,"score_gpt":0.21448787437234454,"score_spread":0.20072198881033398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112893815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004148407,0.00011153845,0.99252254,0.00003569239,0.0000394716,0.000015433881,0.00009901116,0.0018929447,0.0011349325],"genre_scores_gemma":[0.44340444,0.00050879427,0.549281,0.000120223434,0.00009334303,0.00013602496,0.0014592939,0.0002225601,0.004774314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945086,0.00010045276,0.000024434778,0.00017176323,0.00021001724,0.00004240602],"domain_scores_gemma":[0.99962425,0.00007174852,0.000052509466,0.000113168906,0.00012349537,0.000014835459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031222412,0.0010346043,0.0010132263,0.0009294057,0.00037899218,0.0007400559,0.0009522653,0.000712014,0.0019110215],"category_scores_gemma":[0.0014924998,0.00040919497,0.0006597875,0.0013343391,0.00021985442,0.0011970687,0.0011254541,0.00077466393,0.0019730548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015083882,0.00008492301,0.0029594016,0.00018007991,0.00016281437,0.00015505408,0.00008750035,0.4529163,0.027411342,0.0064470833,0.005519231,0.50392544],"study_design_scores_gemma":[0.00001820086,0.000066069675,0.0010079688,0.000012905728,0.000028114253,0.0001822477,0.000020892898,0.98492277,0.005810714,0.0028692242,0.005032042,0.000028907554],"about_ca_topic_score_codex":0.0038196542,"about_ca_topic_score_gemma":0.0039133197,"teacher_disagreement_score":0.0038196542,"about_ca_system_score_codex":0.00034821048,"about_ca_system_score_gemma":0.0005417345,"threshold_uncertainty_score":0.007594824},"labels":[],"label_agreement":null},{"id":"W3114645126","doi":"10.23919/eusipco47968.2020.9287489","title":"Orientation-Matched Multiple Modeling for RSSI-based Indoor Localization via BLE Sensors","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Inertial measurement unit; Computer science; Orientation (vector space); Sensor fusion; Bluetooth Low Energy; Real-time computing; Artificial intelligence; Signal strength; Wireless sensor network; Computer vision; Bluetooth; Telecommunications; Wireless; Computer network; Mathematics","score_opus":0.01973168945791653,"score_gpt":0.22116623264156834,"score_spread":0.2014345431836518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114645126","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01166892,0.00011689491,0.98687404,0.000058372894,0.00002338761,0.000014692118,0.00004848323,0.00049165665,0.00070359436],"genre_scores_gemma":[0.86864537,0.0005289873,0.12609236,0.000110804394,0.000060382903,0.00011681421,0.00045043236,0.00013737522,0.0038575372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995889,0.000112837835,0.000020415355,0.0001028665,0.0001309289,0.000044022836],"domain_scores_gemma":[0.9996718,0.00009793858,0.00007090579,0.00005420678,0.00008662928,0.000018461158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000521594,0.00081843295,0.00059021264,0.0006038613,0.000232415,0.00062827306,0.0010694095,0.0005628477,0.0009134367],"category_scores_gemma":[0.00137505,0.00030972576,0.00081729586,0.00074455736,0.0003313898,0.00093052484,0.00066149,0.0007140243,0.00076162076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010529582,0.00007085871,0.0030727403,0.00006888945,0.00007000754,0.00010899421,0.00008843008,0.89404845,0.006607005,0.0063857012,0.00077714387,0.08859644],"study_design_scores_gemma":[0.0000023330217,0.000024505474,0.00038214514,0.000004010612,0.0000094910765,0.000027732956,0.0000086704185,0.99685067,0.0010141813,0.0011267472,0.00054252846,0.000007097785],"about_ca_topic_score_codex":0.0053593726,"about_ca_topic_score_gemma":0.0061333585,"teacher_disagreement_score":0.0053593726,"about_ca_system_score_codex":0.0004780614,"about_ca_system_score_gemma":0.0004305056,"threshold_uncertainty_score":0.010656357},"labels":[],"label_agreement":null},{"id":"W3116592636","doi":"10.1109/itsc45102.2020.9294440","title":"Benchmark Dataset of Ultra-Wideband Radio Based UAV Positioning","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Benchmark (surveying); Computer science; Drone; Ranging; Multipath propagation; Real-time computing; Ultra-wideband; Inertial measurement unit; Global Positioning System; Multilateration; Noise (video); Ground truth; Artificial intelligence; Telecommunications; Engineering","score_opus":0.010718590153513797,"score_gpt":0.20039868545706735,"score_spread":0.18968009530355356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116592636","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18505849,0.0072755436,0.03438512,0.001218534,0.0013484624,0.0008206128,0.7366096,0.017479932,0.015803704],"genre_scores_gemma":[0.09227187,0.00066796143,0.022809673,0.00018471337,0.000079145146,0.0002847722,0.88097763,0.0002664437,0.0024578404],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99820566,0.00028685288,0.00015678312,0.00053759373,0.0006009911,0.00021204159],"domain_scores_gemma":[0.9984175,0.00034439433,0.00014511765,0.00042004883,0.0005445754,0.00012848621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009224838,0.0029722801,0.0016383429,0.0024568243,0.0008905398,0.001366455,0.0034757624,0.0021891056,0.0031810591],"category_scores_gemma":[0.0032908642,0.0003340489,0.0012372615,0.003628856,0.00065288437,0.00089620805,0.0015492205,0.0011826504,0.0049380367],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013979475,0.0013174837,0.023026947,0.0041388306,0.0008356861,0.0017337548,0.00022647978,0.12869614,0.011892101,0.0025796976,0.60886854,0.2152864],"study_design_scores_gemma":[0.0009312512,0.0019435476,0.097200565,0.0010111105,0.00040281305,0.003315838,0.0014765532,0.38676384,0.034014154,0.0064224633,0.46613488,0.00038300938],"about_ca_topic_score_codex":0.020260876,"about_ca_topic_score_gemma":0.036894042,"teacher_disagreement_score":0.020260876,"about_ca_system_score_codex":0.0010231053,"about_ca_system_score_gemma":0.001218498,"threshold_uncertainty_score":0.040285885},"labels":[],"label_agreement":null},{"id":"W3116869432","doi":"10.3390/su122410627","title":"Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Linear discriminant analysis; Naive Bayes classifier; Bluetooth; Decision tree; Support vector machine; Bluetooth Low Energy; Artificial intelligence; Trilateration; Real-time computing; Machine learning; Wireless; Recursive Bayesian estimation; Data mining; Bayesian probability; Telecommunications; Engineering","score_opus":0.009116049326240515,"score_gpt":0.24010661052930754,"score_spread":0.23099056120306702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116869432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07200573,0.0003355415,0.9195733,0.0001479755,0.000090038535,0.000038029575,0.000109487424,0.003606274,0.004093629],"genre_scores_gemma":[0.8617685,0.00021777833,0.1327979,0.00008879425,0.000033429285,0.00005301899,0.00019128028,0.00007013694,0.004779288],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970764,0.00006281171,0.000013016831,0.00008870966,0.00010120358,0.000026673297],"domain_scores_gemma":[0.9997656,0.000057375568,0.00003217624,0.000034093573,0.00009852919,0.000012204436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002860498,0.00038535596,0.00047180022,0.000806424,0.0002970984,0.0003836193,0.00049805234,0.00034535344,0.0012542546],"category_scores_gemma":[0.00067718036,0.00017034684,0.00027501181,0.0005930742,0.00016946941,0.0006574926,0.00039571268,0.00022743031,0.0010581021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049040955,0.00025016742,0.007208471,0.0001885025,0.000088100045,0.00029897862,0.00020294756,0.058684967,0.10119615,0.0029241692,0.004428557,0.8240385],"study_design_scores_gemma":[0.000035085308,0.00029403192,0.0049960646,0.000028539498,0.000056178127,0.0004988119,0.00006549487,0.9505886,0.03644336,0.0014452607,0.0054860073,0.00006261662],"about_ca_topic_score_codex":0.0017477027,"about_ca_topic_score_gemma":0.002281102,"teacher_disagreement_score":0.0017477027,"about_ca_system_score_codex":0.00023441012,"about_ca_system_score_gemma":0.00017823017,"threshold_uncertainty_score":0.0041959286},"labels":[],"label_agreement":null},{"id":"W3117750865","doi":"10.3390/s21010050","title":"An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Interoperability; Geospatial analysis; Computer science; World Wide Web; Reuse; Computer security; Data science; Engineering","score_opus":0.03793967303771522,"score_gpt":0.31258557389045366,"score_spread":0.27464590085273843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117750865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3110161,0.000769779,0.5925332,0.0059131775,0.0003808748,0.0016097559,0.00032030596,0.0023983805,0.0850584],"genre_scores_gemma":[0.76034045,0.000595732,0.22565614,0.0007621105,0.000033707285,0.00045413038,0.0005445769,0.00025192642,0.0113613345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977471,0.0006818246,0.00018485465,0.00021131389,0.00076735887,0.00040747088],"domain_scores_gemma":[0.9981382,0.00039786915,0.00014150378,0.0005448144,0.00047289266,0.00030475372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032760703,0.00041122924,0.00024679388,0.0007205328,0.0016524572,0.003041926,0.0014320783,0.0031576362,0.001227881],"category_scores_gemma":[0.002886339,0.00028754192,0.00074394455,0.0009719326,0.001938527,0.0048754243,0.0045443834,0.0020601372,0.00045414252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036325282,0.0011495207,0.028571054,0.0008298344,0.0001072448,0.020878086,0.015129713,0.06979378,0.05556206,0.5831373,0.020498551,0.20397964],"study_design_scores_gemma":[0.00010887984,0.0009838918,0.0138619095,0.000753805,0.00016741295,0.009210138,0.017123112,0.24450304,0.047658894,0.09925696,0.5660671,0.00030490014],"about_ca_topic_score_codex":0.0056023407,"about_ca_topic_score_gemma":0.006880518,"teacher_disagreement_score":0.0056023407,"about_ca_system_score_codex":0.0017281809,"about_ca_system_score_gemma":0.0019807194,"threshold_uncertainty_score":0.0173257},"labels":[],"label_agreement":null},{"id":"W3118569766","doi":"10.1007/978-3-030-62800-0_11","title":"A Comparison of Indoor Positioning Approaches with UWB, IMU, WiFi and Magnetic Fingerprinting","year":2020,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Computer science; Dead reckoning; RSS; Real-time computing; Inertial navigation system; Indoor positioning system; Signal strength; Trajectory; Global Positioning System; Accelerometer; Inertial frame of reference; Wireless; Artificial intelligence; Telecommunications; Physics","score_opus":0.0440098426428637,"score_gpt":0.2540633953285481,"score_spread":0.2100535526856844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118569766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10699818,0.057894398,0.66623485,0.0007322295,0.001257865,0.00018660047,0.0019140078,0.004958327,0.15982364],"genre_scores_gemma":[0.56471413,0.029833343,0.33811438,0.0003116053,0.00042448498,0.00010879117,0.0019778912,0.00047119748,0.064044096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927133,0.00017524429,0.000024133427,0.00009337069,0.00037364452,0.00006213983],"domain_scores_gemma":[0.9992156,0.00041460036,0.000052719366,0.000076265074,0.00022263394,0.000018061548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006539268,0.000633634,0.00056183984,0.0013294708,0.00038420508,0.0012235362,0.0009749565,0.00070046674,0.0072423266],"category_scores_gemma":[0.0017401569,0.0001954856,0.0005080734,0.0029855843,0.00023447182,0.0010892827,0.00058793253,0.00031075446,0.00302661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041955087,0.000048093047,0.0036417956,0.00091761415,0.000114372764,0.0001452078,0.00014023378,0.012659218,0.010230528,0.0047188853,0.0061535654,0.96081096],"study_design_scores_gemma":[0.00016846738,0.004420027,0.09167295,0.0019937991,0.0023885623,0.010280888,0.0035647645,0.320354,0.124439456,0.017779034,0.42243525,0.00050276174],"about_ca_topic_score_codex":0.003056378,"about_ca_topic_score_gemma":0.0066371667,"teacher_disagreement_score":0.0072423266,"about_ca_system_score_codex":0.0003651978,"about_ca_system_score_gemma":0.00035114688,"threshold_uncertainty_score":0.024227977},"labels":[],"label_agreement":null},{"id":"W3119651306","doi":"10.1155/2021/6660990","title":"Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Fingerprint (computing); Artificial intelligence","score_opus":0.014196284599636988,"score_gpt":0.25225071241892844,"score_spread":0.23805442781929145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119651306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03615857,0.00042740253,0.95938003,0.00011303287,0.00005337482,0.000016776532,0.00012112903,0.00233441,0.0013952951],"genre_scores_gemma":[0.83650035,0.00046770883,0.15812697,0.0001812852,0.000047564175,0.000050324663,0.00049010577,0.0000732552,0.004062318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997999,0.000031818796,0.0000101511,0.00005932866,0.000060819297,0.000038075246],"domain_scores_gemma":[0.999775,0.000057928006,0.000031245643,0.00003623604,0.000086092405,0.0000136254985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028966027,0.00070216774,0.00053454994,0.00046390516,0.00019004493,0.00032085512,0.0008626941,0.0005083639,0.0011921279],"category_scores_gemma":[0.0007956749,0.00028111058,0.00040248886,0.0005320297,0.0002389806,0.0008679934,0.0006469061,0.0006502483,0.00058397965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025153646,0.00012450284,0.0041578216,0.0001291905,0.000120665485,0.00016842388,0.000096857184,0.3252887,0.028053068,0.0025302854,0.0034394716,0.6356395],"study_design_scores_gemma":[0.0000049281048,0.00003276125,0.0005829868,0.0000065205954,0.000012819205,0.000043722364,0.000008765939,0.9927625,0.0054275943,0.00056533073,0.0005446027,0.000007417778],"about_ca_topic_score_codex":0.0063652685,"about_ca_topic_score_gemma":0.007896881,"teacher_disagreement_score":0.0063652685,"about_ca_system_score_codex":0.00043113946,"about_ca_system_score_gemma":0.00046377472,"threshold_uncertainty_score":0.01265645},"labels":[],"label_agreement":null},{"id":"W3119750220","doi":"10.5539/nct.v5n2p34","title":"Indoor Localization Based on Optimized KNN","year":2020,"lang":"en","type":"article","venue":"Network and Communication Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Fingerprint (computing); Hybrid positioning system; Hotspot (geology); Real-time computing; Positioning technology; Indoor positioning system; Wireless; Signal strength; Received signal strength indication; RSS; Positioning system; Artificial intelligence; Accelerometer; Telecommunications; Engineering; Node (physics)","score_opus":0.011578293337162827,"score_gpt":0.19910371742433003,"score_spread":0.18752542408716721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119750220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012150247,0.0004346287,0.98360574,0.00007870743,0.000091609225,0.000033565666,0.000060178114,0.00078724587,0.0027581258],"genre_scores_gemma":[0.61927634,0.00078727445,0.36919063,0.00021369704,0.00013319794,0.00015029688,0.0005274446,0.00019808656,0.009523161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989911,0.00013246298,0.00006204022,0.0003452392,0.00030655184,0.00016261634],"domain_scores_gemma":[0.9995334,0.00010123691,0.000057474666,0.000047334892,0.00023735505,0.000023262226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043707623,0.00089825894,0.0011150743,0.0010418097,0.00067400554,0.0007639494,0.0012139231,0.00091239717,0.0022898733],"category_scores_gemma":[0.001692612,0.0004287,0.0006126822,0.0014212818,0.0005431258,0.0013369177,0.0009809266,0.00061087316,0.0010833049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020985598,0.00004286101,0.0017284942,0.00013078659,0.00006290098,0.00009368153,0.0001026307,0.6286975,0.0068875705,0.004511871,0.0029228837,0.35460898],"study_design_scores_gemma":[0.000011658736,0.000040942276,0.00042611436,0.000012231636,0.000017648865,0.000082793034,0.000034542743,0.9937336,0.0020559444,0.0019724518,0.0015947212,0.000017329505],"about_ca_topic_score_codex":0.015369635,"about_ca_topic_score_gemma":0.012914545,"teacher_disagreement_score":0.015369635,"about_ca_system_score_codex":0.0007908575,"about_ca_system_score_gemma":0.00096930435,"threshold_uncertainty_score":0.030560315},"labels":[],"label_agreement":null},{"id":"W3120074481","doi":"10.1109/jiot.2021.3050436","title":"MotionBeep: Enabling Fitness Game for Collocated Players With Acoustic-Enabled IoT Devices","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Internet of Things; Computer network; Human–computer interaction; Computer security","score_opus":0.011756879659643593,"score_gpt":0.2229096513195775,"score_spread":0.2111527716599339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120074481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12251171,0.0014399117,0.8219305,0.00044578334,0.0003145449,0.0011756471,0.0006422231,0.019892965,0.031646647],"genre_scores_gemma":[0.82402366,0.0007752247,0.15100847,0.00063473434,0.000069084475,0.0008933995,0.0010265849,0.00066118606,0.020907633],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970955,0.000046486453,0.000018247638,0.0000511077,0.000115216055,0.00005939786],"domain_scores_gemma":[0.9997348,0.000068593756,0.000033657885,0.000035513825,0.00007316237,0.000054236727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033028022,0.0011344219,0.00034702424,0.00054265285,0.00020257744,0.00046867438,0.0014928245,0.00053581677,0.0048816884],"category_scores_gemma":[0.0010765443,0.0003053169,0.00029359697,0.0002104815,0.00026834465,0.0010358602,0.0014213672,0.000702059,0.0010018909],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022379337,0.000931655,0.01261002,0.001256249,0.00030219092,0.0021368496,0.0009893422,0.025784291,0.25582507,0.016450588,0.035646673,0.6458291],"study_design_scores_gemma":[0.0006092621,0.0037019928,0.019911475,0.00026506878,0.0003137538,0.0038368867,0.0003746799,0.6338869,0.12652121,0.008599415,0.20168497,0.00029442215],"about_ca_topic_score_codex":0.0016233348,"about_ca_topic_score_gemma":0.0024970851,"teacher_disagreement_score":0.0048816884,"about_ca_system_score_codex":0.00023399165,"about_ca_system_score_gemma":0.00027137576,"threshold_uncertainty_score":0.016330838},"labels":[],"label_agreement":null},{"id":"W3120403041","doi":"10.2514/6.2021-1398","title":"A Geometric Model for Estimating Time Difference of Arrival (TDOA) Performance","year":2021,"lang":"en","type":"article","venue":"AIAA Scitech 2021 Forum","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Multilateration; Cramér–Rao bound; Hyperbola; Computer science; Intersection (aeronautics); FDOA; Ranging; Dwell time; Time of arrival; Common emitter; Dilation (metric space); Algorithm; Statistics; Mathematics; Estimation theory; Geometry; Electronic engineering; Telecommunications; Engineering; Wireless","score_opus":0.011140450782523868,"score_gpt":0.2144674473376576,"score_spread":0.20332699655513373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120403041","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026001432,0.00013353657,0.993588,0.00010764946,0.000045680175,0.000035346904,0.00016881071,0.0004939899,0.002826898],"genre_scores_gemma":[0.49560487,0.0019714586,0.48211145,0.00032094785,0.00025453282,0.00068275403,0.0019881844,0.0009411265,0.01612461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838006,0.00032341926,0.000063624735,0.00039776313,0.0006882694,0.00014685691],"domain_scores_gemma":[0.9979512,0.0007283963,0.0002837091,0.00028836052,0.0007045056,0.00004384634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012010152,0.0017249384,0.00081512827,0.0019595667,0.00039886468,0.0016479754,0.0021015082,0.001344176,0.0046904273],"category_scores_gemma":[0.006839899,0.0006407535,0.0011145099,0.002427942,0.0009722483,0.0022777652,0.0012362477,0.0015211015,0.0049755205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006636106,0.000035784466,0.0020932897,0.00010487341,0.000040497984,0.00012545087,0.00011381459,0.88745403,0.0070575643,0.036632918,0.0036537088,0.06262162],"study_design_scores_gemma":[0.0000068480185,0.000070869086,0.000628754,0.000021064214,0.000018809482,0.00022726669,0.000024007044,0.98282844,0.002216085,0.008602502,0.005325487,0.000029939314],"about_ca_topic_score_codex":0.0057303933,"about_ca_topic_score_gemma":0.003794025,"teacher_disagreement_score":0.0057303933,"about_ca_system_score_codex":0.0014340094,"about_ca_system_score_gemma":0.0010202418,"threshold_uncertainty_score":0.015691102},"labels":[],"label_agreement":null},{"id":"W3120984923","doi":"10.5539/nct.v5n2p40","title":"Indoor Localization Based on Fingerprint Clustering","year":2020,"lang":"en","type":"article","venue":"Network and Communication Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"RSS; Computer science; Cluster analysis; Fingerprint (computing); Gaussian; Fingerprint recognition; Data mining; Artificial intelligence; SIGNAL (programming language); Pattern recognition (psychology)","score_opus":0.012704379530852692,"score_gpt":0.2002165457740009,"score_spread":0.1875121662431482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120984923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020150617,0.0004964363,0.9722891,0.0000895952,0.0000889005,0.000056133213,0.00017613199,0.0029654,0.0036876695],"genre_scores_gemma":[0.71881133,0.0011667274,0.27328336,0.000104922685,0.0001176306,0.00013936884,0.0008130445,0.00016252292,0.0054010986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989491,0.00016885462,0.000045114826,0.00030073497,0.00040392895,0.00013227764],"domain_scores_gemma":[0.99948186,0.00007528973,0.00007394384,0.000114678376,0.00022823685,0.000025905736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034483412,0.0008363433,0.0009330466,0.0021219633,0.0006090817,0.00077232363,0.0010041762,0.0007333726,0.001806589],"category_scores_gemma":[0.0012425178,0.00030302483,0.00066067156,0.002843979,0.00034857885,0.001159466,0.00095046556,0.00039244056,0.0014730563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044628535,0.00010672219,0.006424959,0.00030757056,0.00011657174,0.00029026205,0.00021424002,0.1479628,0.04964404,0.0049602417,0.0066734683,0.78285277],"study_design_scores_gemma":[0.000044132525,0.00020124277,0.0066859243,0.00005333323,0.000103300765,0.0011145667,0.00017013964,0.94035625,0.037069757,0.003787402,0.010290677,0.00012321156],"about_ca_topic_score_codex":0.00600526,"about_ca_topic_score_gemma":0.0033484865,"teacher_disagreement_score":0.00600526,"about_ca_system_score_codex":0.000503647,"about_ca_system_score_gemma":0.0005892417,"threshold_uncertainty_score":0.0119405985},"labels":[],"label_agreement":null},{"id":"W3121348583","doi":"10.1109/globecom42002.2020.9322264","title":"Cooperative Positioning in Vehicular Networks using Angle of Arrival Estimation through mmWave","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Angle of arrival; Global Positioning System; Computer science; Position (finance); Pairwise comparison; Extremely high frequency; Multidimensional scaling; Multiple signal classification; Scaling; Real-time computing; Artificial intelligence; Telecommunications; Machine learning; Antenna (radio); Mathematics","score_opus":0.01888362547186283,"score_gpt":0.23019209087850434,"score_spread":0.2113084654066415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121348583","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019997535,0.00060941273,0.9775857,0.000101252925,0.00008367666,0.00001702058,0.000015877382,0.0003256419,0.0012638301],"genre_scores_gemma":[0.7927235,0.00082708505,0.20368779,0.00014288287,0.00016625997,0.00006717337,0.00007632202,0.000036707188,0.0022723558],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991227,0.00025325516,0.00003191204,0.00015988604,0.00034218593,0.00009007126],"domain_scores_gemma":[0.99909914,0.0003003012,0.00013878776,0.0001826479,0.0002473013,0.000031981635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066931156,0.0009247847,0.0005803491,0.00093281723,0.0005114738,0.00056802284,0.0011652801,0.0007713711,0.0005093237],"category_scores_gemma":[0.0020266878,0.00031373248,0.00047732485,0.0011378094,0.00045471388,0.0012434887,0.0015435889,0.00060478115,0.00045858053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037865713,0.00009741186,0.004803515,0.00027170766,0.00018542357,0.0004987699,0.0005673388,0.2892236,0.09532569,0.014563769,0.0023590606,0.591725],"study_design_scores_gemma":[0.00004394822,0.00045927358,0.0015557827,0.000025026378,0.000094335766,0.0006585337,0.00018244928,0.95219135,0.033315167,0.0046638423,0.0067519434,0.000058275262],"about_ca_topic_score_codex":0.001343904,"about_ca_topic_score_gemma":0.001525612,"teacher_disagreement_score":0.001343904,"about_ca_system_score_codex":0.0002857971,"about_ca_system_score_gemma":0.00038936347,"threshold_uncertainty_score":0.003539741},"labels":[],"label_agreement":null},{"id":"W3122790281","doi":"10.1002/navi.445","title":"Data‐driven protection levels for camera and 3D map‐based safe urban localization","year":2021,"lang":"en","type":"preprint","venue":"NAVIGATION Journal of the Institute of Navigation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"U.S. Geological Survey; Canadian Institute for Advanced Research; National Science Foundation","keywords":"GNSS applications; Computer science; Outlier; Position (finance); Weighting; Lidar; Artificial intelligence; Gaussian; Probabilistic logic; Artificial neural network; Data mining; Computer vision; Algorithm; Pattern recognition (psychology); Global Positioning System; Remote sensing; Geography","score_opus":0.042569719368520505,"score_gpt":0.272000307019412,"score_spread":0.22943058765089153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122790281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018012894,0.00008754245,0.9803535,0.00006198077,0.000013792031,0.000015751864,0.00008790998,0.00075048755,0.0006162301],"genre_scores_gemma":[0.75651556,0.00011475112,0.24144416,0.0000685849,0.000017329488,0.00006477129,0.00047078545,0.00014036465,0.0011636483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994398,0.00008808406,0.000025634656,0.000117261814,0.0002759975,0.00005321212],"domain_scores_gemma":[0.9991615,0.00020303075,0.0001499185,0.00017137827,0.00026742741,0.000046773595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006024773,0.0007045634,0.00042192047,0.0009485214,0.00025077688,0.00079509366,0.0011054482,0.00074751343,0.0015348411],"category_scores_gemma":[0.0036645525,0.00037615743,0.00039412506,0.0009879834,0.0005934363,0.001575344,0.0016819496,0.00089885015,0.0005534996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015900818,0.00006564601,0.00396861,0.00009988166,0.0000494484,0.000077834215,0.00009436434,0.8006484,0.011592024,0.00872641,0.0012994038,0.17321894],"study_design_scores_gemma":[0.0000038706685,0.000018816887,0.0006460833,0.000007007492,0.0000032104642,0.000026969501,0.000012425442,0.9917115,0.0038988474,0.003165069,0.0004967395,0.000009388737],"about_ca_topic_score_codex":0.0046932665,"about_ca_topic_score_gemma":0.007146801,"teacher_disagreement_score":0.0046932665,"about_ca_system_score_codex":0.0010434829,"about_ca_system_score_gemma":0.0009310911,"threshold_uncertainty_score":0.009331882},"labels":[],"label_agreement":null},{"id":"W3124238078","doi":"10.20944/preprints201704.0114.v1","title":"Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data","year":2017,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"RSS; Crowdsourcing; Computer science; Exploit; Signal strength; Graph; Constraint (computer-aided design); Data mining; Artificial intelligence; Pattern recognition (psychology); Machine learning; Mathematics; Theoretical computer science; Computer network; Wireless sensor network","score_opus":0.1773712907338744,"score_gpt":0.34812819848381893,"score_spread":0.17075690774994454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124238078","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012135486,0.00016469164,0.9858397,0.00017795087,0.000034693458,0.00005165368,0.0001285874,0.00090404705,0.00056314684],"genre_scores_gemma":[0.69816357,0.0003598718,0.29470515,0.00037377857,0.00022335391,0.0004418959,0.0017600907,0.00033367856,0.0036385662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854374,0.0005210697,0.00007177644,0.00047879972,0.0002610229,0.00012356076],"domain_scores_gemma":[0.9949062,0.0031272776,0.000499319,0.0005995653,0.00068745436,0.00018020919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016487474,0.0015449103,0.0018983913,0.0014516708,0.000769168,0.0009893964,0.0030202786,0.0015522112,0.0017532486],"category_scores_gemma":[0.006987598,0.0007513947,0.0012337492,0.0015190829,0.0013863912,0.0015468716,0.0022647087,0.0018946212,0.0007527694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019319709,0.00013013744,0.0010827953,0.00020493205,0.0001005749,0.00012227334,0.00022582526,0.85348034,0.0020607158,0.00665462,0.003182839,0.13256179],"study_design_scores_gemma":[0.0000068685404,0.000011815633,0.00008329975,0.0000043138475,0.0000038093108,0.000008554715,0.000011470314,0.995047,0.00028469076,0.0043339655,0.00019927419,0.0000049431155],"about_ca_topic_score_codex":0.010568614,"about_ca_topic_score_gemma":0.010003847,"teacher_disagreement_score":0.010568614,"about_ca_system_score_codex":0.0011787596,"about_ca_system_score_gemma":0.0014528206,"threshold_uncertainty_score":0.021014214},"labels":[],"label_agreement":null},{"id":"W3124510331","doi":"10.11575/prism/38564","title":"Determining Speed and Stride Length using an Ultrawide Bandwidth Local Positioning System","year":2021,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs","keywords":"STRIDE; Bandwidth (computing); Computer science; Real-time computing; Telecommunications; Computer security","score_opus":0.026513635752732755,"score_gpt":0.2810963346247384,"score_spread":0.25458269887200563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124510331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2593509,0.0071620466,0.71470505,0.00030773788,0.00034518033,0.00022207297,0.0013588001,0.0017674649,0.014780787],"genre_scores_gemma":[0.68693316,0.0060851467,0.29051295,0.00021862105,0.00015336664,0.0002807693,0.0012780987,0.00013434472,0.014403494],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994843,0.000086123786,0.00003415374,0.00015332944,0.00021322754,0.000028751887],"domain_scores_gemma":[0.99951017,0.00011287964,0.00009347285,0.000047951067,0.00021976526,0.000015889573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045718867,0.0004717062,0.00043436335,0.0012416956,0.00017687141,0.00089230726,0.00049300166,0.00056669634,0.0024816783],"category_scores_gemma":[0.0011828988,0.00018630516,0.00029565196,0.0016968829,0.00015521159,0.0007307721,0.00046175552,0.00031731089,0.0016925676],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036471066,0.00011598138,0.035019957,0.0015693896,0.00018369278,0.00023891704,0.0005656787,0.010232925,0.13478214,0.0024606858,0.0035759087,0.81089],"study_design_scores_gemma":[0.00009683465,0.0028486787,0.3426073,0.0017786828,0.00093056296,0.0037672746,0.0032555833,0.24657513,0.28832257,0.008355735,0.10103391,0.00042780206],"about_ca_topic_score_codex":0.0012658401,"about_ca_topic_score_gemma":0.003070342,"teacher_disagreement_score":0.0024816783,"about_ca_system_score_codex":0.00019571651,"about_ca_system_score_gemma":0.00027574116,"threshold_uncertainty_score":0.008302033},"labels":[],"label_agreement":null},{"id":"W3127276349","doi":"10.1109/nemo49486.2020.9343496","title":"Recent Advances in Deep Neural Network Technique for High-Dimensional Microwave Modeling","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial neural network; Deep learning; Computer science; Sigmoid function; Artificial intelligence; Microwave imaging; Deep neural networks; Time delay neural network; Microwave; Telecommunications","score_opus":0.013932211603491288,"score_gpt":0.22093003403634562,"score_spread":0.20699782243285433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127276349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003499557,0.008611645,0.9825775,0.00046641004,0.00013436413,0.000013496537,0.00009872549,0.00055837416,0.004039918],"genre_scores_gemma":[0.2833211,0.052234467,0.644769,0.0006994743,0.00066089374,0.00019380264,0.0012312193,0.00055999745,0.016330078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972016,0.00006147665,0.000025990514,0.00004614956,0.00012655254,0.000019658319],"domain_scores_gemma":[0.99963236,0.00015866036,0.000033485463,0.000041777188,0.000117095755,0.000016538703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006006019,0.00073354767,0.0006510643,0.0006263613,0.000200035,0.0007595627,0.0010327091,0.0007235177,0.003333336],"category_scores_gemma":[0.0011215038,0.00043239997,0.0008422935,0.0010158835,0.00035298924,0.0014597428,0.00077297824,0.001464769,0.00093254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009689323,0.00006332658,0.0015084852,0.0008681126,0.00021026918,0.00020844679,0.00009399349,0.42078933,0.01203302,0.043194786,0.008722666,0.5122107],"study_design_scores_gemma":[0.000005661524,0.000023430945,0.00026769252,0.000045058274,0.000028728104,0.000062852574,0.000011338518,0.96857655,0.0031352215,0.01128287,0.016542489,0.000018220682],"about_ca_topic_score_codex":0.0033335371,"about_ca_topic_score_gemma":0.0032419038,"teacher_disagreement_score":0.0033335371,"about_ca_system_score_codex":0.00050454825,"about_ca_system_score_gemma":0.00064451125,"threshold_uncertainty_score":0.011151075},"labels":[],"label_agreement":null},{"id":"W3128159412","doi":"10.1109/tsp.2022.3168363","title":"New Closed-Form Joint Localization and Synchronization Using Sequential One-Way TOAs","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Synchronization (alternating current); Joint (building); Computer science; Algorithm; Telecommunications; Engineering; Channel (broadcasting)","score_opus":0.025941748941957436,"score_gpt":0.23054926162365708,"score_spread":0.20460751268169963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128159412","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002250698,0.0000762481,0.9966428,0.00004239449,0.000025061316,0.000012615473,0.000019953774,0.00020458245,0.0007255952],"genre_scores_gemma":[0.29703668,0.0005577313,0.6952372,0.00018003349,0.00010691482,0.00024587044,0.000328949,0.00015847436,0.006148188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895716,0.00022055127,0.000056109184,0.00028774884,0.00038483815,0.000093514194],"domain_scores_gemma":[0.999106,0.00032145684,0.00014489568,0.00011483407,0.00027116842,0.00004157987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009589677,0.0011909498,0.0010546789,0.0006225274,0.0005060985,0.001357223,0.0015101208,0.0011718392,0.0020910786],"category_scores_gemma":[0.0029468953,0.0006407147,0.0009672517,0.0012755856,0.0008194201,0.0020985703,0.0019010748,0.001081045,0.00092022464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017557562,0.00006781378,0.00151225,0.00029528968,0.00009791659,0.00026588203,0.00035171566,0.7115098,0.024143536,0.06745664,0.0042943177,0.18982926],"study_design_scores_gemma":[0.000014719072,0.00003738916,0.00008599404,0.0000094455745,0.000009307394,0.00006886724,0.000019992582,0.99033785,0.0018700946,0.0053839106,0.0021463325,0.00001621337],"about_ca_topic_score_codex":0.003671773,"about_ca_topic_score_gemma":0.0039919936,"teacher_disagreement_score":0.003671773,"about_ca_system_score_codex":0.0005563367,"about_ca_system_score_gemma":0.0017490466,"threshold_uncertainty_score":0.007300794},"labels":[],"label_agreement":null},{"id":"W3128989612","doi":"10.1109/jiot.2020.3024234","title":"Device-Free Wireless Sensing for Human Detection: The Deep Learning Perspective","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":136,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Wireless; Wireless network; Artificial intelligence; Deep learning; Field (mathematics); Wireless sensor network; Telecommunications; Computer network","score_opus":0.01676452595402647,"score_gpt":0.24006298718084382,"score_spread":0.22329846122681735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128989612","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015634162,0.03471683,0.9268963,0.006251414,0.00031776103,0.00004067034,0.0002665837,0.0005254092,0.015350893],"genre_scores_gemma":[0.66476643,0.09170343,0.21730341,0.0024989746,0.001459077,0.00017522629,0.0007850788,0.00015716309,0.021151127],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998023,0.000048916147,0.000011643116,0.000049834143,0.000059713948,0.000027590984],"domain_scores_gemma":[0.99965644,0.00019769651,0.000029332634,0.000031326734,0.0000651983,0.00002008382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061870983,0.0006881426,0.0005374805,0.00051021855,0.00018310921,0.0011988218,0.001294177,0.001218665,0.001676841],"category_scores_gemma":[0.0015236326,0.00033987174,0.00046236417,0.0007622794,0.0008572519,0.0020636693,0.00088524166,0.0018562682,0.00058517954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000104186765,0.00015438934,0.002966849,0.0011075621,0.00015816938,0.00018248103,0.00023613965,0.23208643,0.007667397,0.15772957,0.0132727595,0.5843341],"study_design_scores_gemma":[0.000006021688,0.000065042295,0.0009867848,0.00014272882,0.000035637553,0.00010052364,0.000045603687,0.906031,0.0036966233,0.07078487,0.01807701,0.000028190923],"about_ca_topic_score_codex":0.0036062184,"about_ca_topic_score_gemma":0.0024027615,"teacher_disagreement_score":0.0036062184,"about_ca_system_score_codex":0.00075093773,"about_ca_system_score_gemma":0.00064727117,"threshold_uncertainty_score":0.0071704984},"labels":[],"label_agreement":null},{"id":"W3129924891","doi":"10.1109/ieeeconf35879.2020.9329810","title":"A Comparative Study of Corrugated Ground Planes for GNSS Multipath Mitigation","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"GNSS applications; Multipath propagation; Multipath mitigation; Satellite system; Computer science; Interference (communication); Antenna (radio); Satellite navigation; Ground plane; Remote sensing; Multipath interference; Global Positioning System; Satellite; Electronic engineering; Telecommunications; Engineering; Geology; Aerospace engineering","score_opus":0.04081926961881271,"score_gpt":0.26051129381197125,"score_spread":0.21969202419315853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129924891","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8789042,0.0012835893,0.10825144,0.00016617352,0.00012321534,0.00006867024,0.00014741883,0.00053977984,0.010515624],"genre_scores_gemma":[0.9703901,0.00067097467,0.026954409,0.000035337634,0.000015793963,0.000012983722,0.00012015227,0.000022537692,0.001777615],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995447,0.000117200776,0.00002114695,0.000048020178,0.00019042334,0.00007853494],"domain_scores_gemma":[0.99854434,0.00034121968,0.00023968841,0.0001864354,0.0006308204,0.000057510133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031832958,0.00065178017,0.00027105844,0.0007822191,0.00018397708,0.0005768555,0.00039472026,0.000379756,0.001468791],"category_scores_gemma":[0.0016431998,0.00011012349,0.00025212517,0.0010821497,0.00021687796,0.0005168267,0.000247038,0.00019803458,0.0003907927],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036627708,0.00030317288,0.017631782,0.00052633527,0.00027594148,0.0008080609,0.00029020835,0.15797424,0.32118547,0.0048033143,0.002228223,0.4903104],"study_design_scores_gemma":[0.00030498716,0.019291503,0.051949125,0.0001733319,0.001033629,0.003497004,0.0013918481,0.31482673,0.5707399,0.002235498,0.034353044,0.00020327923],"about_ca_topic_score_codex":0.0014396817,"about_ca_topic_score_gemma":0.0017586137,"teacher_disagreement_score":0.001468791,"about_ca_system_score_codex":0.00031798592,"about_ca_system_score_gemma":0.00023708511,"threshold_uncertainty_score":0.0049135685},"labels":[],"label_agreement":null},{"id":"W3129987514","doi":"10.1109/vtc2020-fall49728.2020.9348731","title":"Testing Vehicle-to-Vehicle Relative Position and Attitude Estimation using Multiple UWB Ranging","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Ranging; Ultra-wideband; Chipset; Global Positioning System; Computer science; Real-time computing; Position (finance); Heading (navigation); Hybrid positioning system; Kalman filter; Wireless; Frame (networking); Positioning system; Engineering; Telecommunications; Artificial intelligence; Aerospace engineering","score_opus":0.02790405533554612,"score_gpt":0.23308389880474295,"score_spread":0.20517984346919682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129987514","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9146097,0.000107792206,0.08289461,0.00004161614,0.00006500668,0.00011803294,0.00016818584,0.0007981791,0.0011969365],"genre_scores_gemma":[0.97477424,0.000048513073,0.023870377,0.00001732235,0.00000874988,0.000056518773,0.00015779558,0.00002318484,0.0010433533],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992305,0.0001243338,0.00003690406,0.000183693,0.00032500905,0.000099549135],"domain_scores_gemma":[0.99870944,0.0005134457,0.00012295843,0.00019877547,0.0003610828,0.00009429016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005869248,0.00064025045,0.00048349288,0.00068759883,0.00026204062,0.00030957194,0.0010140361,0.001007583,0.0014102658],"category_scores_gemma":[0.0018605429,0.00028021605,0.00030881347,0.00043343456,0.0003255724,0.0007030499,0.00062432146,0.0003805945,0.00040134136],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003003136,0.0017524923,0.034046195,0.0007286859,0.00037011298,0.00091102073,0.0007234759,0.24860466,0.43204108,0.0012913297,0.00083279,0.275695],"study_design_scores_gemma":[0.00024083215,0.011406389,0.031174736,0.000038559185,0.00014657297,0.0007275998,0.0006206423,0.54640734,0.40662816,0.00049621466,0.0020122249,0.000100750134],"about_ca_topic_score_codex":0.0028151819,"about_ca_topic_score_gemma":0.001670499,"teacher_disagreement_score":0.0028151819,"about_ca_system_score_codex":0.0002598369,"about_ca_system_score_gemma":0.00031351272,"threshold_uncertainty_score":0.0055975914},"labels":[],"label_agreement":null},{"id":"W3129991564","doi":"10.1109/vtc2020-fall49728.2020.9348603","title":"A Fingerprint Localization Method in Collocated Massive MIMO-OFDM Systems Using Clustering and Gaussian Process Regression","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Cluster analysis; Computational complexity theory; MIMO; Kriging; Orthogonal frequency-division multiplexing; Algorithm; Channel state information; Gaussian process; Gaussian; Artificial intelligence; Channel (broadcasting); Wireless; Machine learning; Telecommunications","score_opus":0.0237562259433943,"score_gpt":0.2747229638959561,"score_spread":0.25096673795256175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129991564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008891101,0.00010272872,0.9901918,0.00005062142,0.000024943687,0.000012196669,0.000013072618,0.00025223027,0.00046133678],"genre_scores_gemma":[0.5314103,0.00042459066,0.46382034,0.00012080435,0.00007319744,0.0000659209,0.000106305095,0.000072306866,0.003906275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996132,0.00008567488,0.000015116185,0.000102767415,0.00014133404,0.00004185152],"domain_scores_gemma":[0.9995921,0.00011829019,0.00006350364,0.000059477377,0.00014296088,0.000023584576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042986174,0.0006331429,0.0005724233,0.00056434947,0.0004410204,0.00047478208,0.00090775767,0.0006713725,0.0007732342],"category_scores_gemma":[0.0015180194,0.00026903555,0.0005135542,0.000918884,0.00037675028,0.0008476013,0.0007445808,0.00067041884,0.00046991603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024112502,0.000096372496,0.0023265576,0.00014805899,0.00009632049,0.00026927082,0.00022570023,0.4906535,0.044766415,0.015486882,0.0025882658,0.44310144],"study_design_scores_gemma":[0.000006746277,0.000043016276,0.00032527494,0.000003558814,0.0000103926695,0.000101830636,0.00001637354,0.99372673,0.0040978873,0.0010451082,0.0006089334,0.000014121822],"about_ca_topic_score_codex":0.0038345468,"about_ca_topic_score_gemma":0.0030995246,"teacher_disagreement_score":0.0038345468,"about_ca_system_score_codex":0.00047953028,"about_ca_system_score_gemma":0.0006729141,"threshold_uncertainty_score":0.0076244473},"labels":[],"label_agreement":null},{"id":"W3130272883","doi":"10.1109/globecom42002.2020.9348254","title":"Accurate Distance Estimation for RSS Localization With Statistical Path Loss Exponent Model","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Exponent; Function (biology); Exponential function; Path (computing); Algorithm; Mathematics; Transmission (telecommunications); Computer science; Mathematical optimization; Applied mathematics; Mathematical analysis; Telecommunications","score_opus":0.015992098516998152,"score_gpt":0.226773995520781,"score_spread":0.21078189700378286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130272883","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006557168,0.00024119447,0.99266994,0.00004885488,0.000012679734,0.0000063882544,0.000017330018,0.00013488877,0.00031156885],"genre_scores_gemma":[0.63839185,0.001442149,0.35696682,0.00011439114,0.00010309612,0.00008726022,0.00019423789,0.00010546521,0.0025947075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942845,0.00020790381,0.000031255615,0.000110498826,0.0001795435,0.000042317002],"domain_scores_gemma":[0.9989434,0.0006583663,0.00013832616,0.00011903178,0.00012314333,0.000017762519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009010436,0.0008237766,0.0007912729,0.0005120909,0.00017906689,0.000497858,0.0010187004,0.0008947925,0.0006914483],"category_scores_gemma":[0.003866073,0.00041314162,0.00048833684,0.0007129629,0.00047311504,0.001572558,0.0007986242,0.0008283278,0.00042618223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006988504,0.000021520102,0.0008992114,0.00013909755,0.000039235616,0.00012688043,0.00007050628,0.9390601,0.007751941,0.010625727,0.0006545234,0.0405414],"study_design_scores_gemma":[0.0000024522656,0.00001810642,0.000116889394,0.000002955352,0.0000034358422,0.000041411633,0.0000056949552,0.9975605,0.00064374506,0.0013669672,0.00023261689,0.000005202568],"about_ca_topic_score_codex":0.0018312834,"about_ca_topic_score_gemma":0.0014102195,"teacher_disagreement_score":0.0018312834,"about_ca_system_score_codex":0.00038487097,"about_ca_system_score_gemma":0.0005311126,"threshold_uncertainty_score":0.0047652125},"labels":[],"label_agreement":null},{"id":"W3131055141","doi":"10.1109/vtc2020-fall49728.2020.9348513","title":"A Classification Algorithm for Blind UAV Detection in Wideband RF Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Spectrogram; Artificial intelligence; Algorithm; Support vector machine; Feature extraction; Sliding window protocol; Histogram; Pattern recognition (psychology); Histogram of oriented gradients; Statistical classification; Robustness (evolution); Window (computing)","score_opus":0.028043713454398524,"score_gpt":0.23268847670818832,"score_spread":0.2046447632537898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131055141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011121713,0.00020048533,0.9863987,0.00010000532,0.00007641242,0.00007010332,0.00006745449,0.0012754767,0.00068950135],"genre_scores_gemma":[0.24784595,0.000253279,0.7463432,0.00027420276,0.00014365299,0.0002548132,0.0005067365,0.000107630636,0.004270526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999111,0.00010456521,0.00008162098,0.00027220673,0.0002908023,0.0001397762],"domain_scores_gemma":[0.99920696,0.00024204119,0.00010667921,0.000071796305,0.0003316578,0.000040835064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078240095,0.000991156,0.0011555573,0.0013554048,0.00075182883,0.0010494541,0.0014610335,0.0018284153,0.0025046507],"category_scores_gemma":[0.0027097305,0.0003027066,0.00073014805,0.00090680114,0.00045365703,0.0010774389,0.0008262041,0.0011894867,0.0018245174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031849227,0.00016683002,0.0018054349,0.00009106386,0.000048084446,0.0001233339,0.000058387184,0.08605604,0.025274245,0.0030138183,0.0046171686,0.87842697],"study_design_scores_gemma":[0.00002137004,0.000082101185,0.0007757525,0.000012852489,0.0000123274385,0.00012325165,0.000023272585,0.9880042,0.007401936,0.0018803288,0.0016499282,0.000012763371],"about_ca_topic_score_codex":0.0035963545,"about_ca_topic_score_gemma":0.0030007157,"teacher_disagreement_score":0.0035963545,"about_ca_system_score_codex":0.0006455074,"about_ca_system_score_gemma":0.0009299164,"threshold_uncertainty_score":0.008378863},"labels":[],"label_agreement":null},{"id":"W3133056604","doi":"10.1109/iros45743.2020.9340971","title":"Kalman Filter based Range Estimation and Clock Synchronization for Ultra Wide Band Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Clock drift; Clock synchronization; Self-clocking signal; Computer science; Kalman filter; Synchronization (alternating current); Digital clock manager; Real-time computing; Clock skew; Filter (signal processing); Control theory (sociology); Clock signal; Telecommunications; Artificial intelligence; Jitter; Computer vision","score_opus":0.009841227969035434,"score_gpt":0.19750567482477407,"score_spread":0.18766444685573863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133056604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025195319,0.00017804727,0.9962644,0.000024145813,0.00002486263,0.000006699476,0.0000154263,0.00029074526,0.0006761358],"genre_scores_gemma":[0.5714642,0.0014434318,0.41946855,0.00009876896,0.00012757881,0.00014509252,0.00028469734,0.00012624143,0.006841402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995005,0.00008774381,0.000030288738,0.00014251765,0.0001959173,0.000043088283],"domain_scores_gemma":[0.99965644,0.00010584061,0.00006850156,0.000052720097,0.000106918116,0.000009541984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042004712,0.0003981823,0.00047879224,0.00050645607,0.00028951315,0.0004980227,0.0007034969,0.00046421547,0.0012000317],"category_scores_gemma":[0.0015556322,0.00025174927,0.00035002423,0.0006156866,0.0002742708,0.001174829,0.00051904307,0.00062471227,0.0005375471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001246041,0.000042894553,0.0015437276,0.00014718795,0.00005752184,0.00010989963,0.00015182719,0.55657476,0.028496053,0.028167352,0.0023521362,0.3822321],"study_design_scores_gemma":[0.0000073069127,0.000036164718,0.00051023904,0.00001226028,0.000013024963,0.000049105325,0.000014362837,0.9871111,0.0065105446,0.0024988153,0.003221773,0.000015247603],"about_ca_topic_score_codex":0.0054700426,"about_ca_topic_score_gemma":0.004024326,"teacher_disagreement_score":0.0054700426,"about_ca_system_score_codex":0.0005037032,"about_ca_system_score_gemma":0.0007062252,"threshold_uncertainty_score":0.010876417},"labels":[],"label_agreement":null},{"id":"W3133202092","doi":"10.1109/ieeeconf35879.2020.9330310","title":"Simple Dipole Scatter for Filling a Shadow Region","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Shadow (psychology); Diffraction; Polarization (electrochemistry); Dipole; Scattering; Shadow mapping; Optics; Wavelength; Simple (philosophy); Transmitter; Line-of-sight; SIGNAL (programming language); Computer science; Physics; Telecommunications; Artificial intelligence","score_opus":0.02475745882420278,"score_gpt":0.2169681086388921,"score_spread":0.19221064981468933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133202092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48694068,0.00083984947,0.49454135,0.00040339172,0.00018102354,0.000071137576,0.0001653,0.0011283263,0.015728941],"genre_scores_gemma":[0.9415808,0.0002518515,0.05469246,0.00007192341,0.000022294098,0.000023316336,0.000057144287,0.000041034342,0.0032591445],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999105,0.000017361308,0.0000034890445,0.000019085224,0.00003112923,0.000018525114],"domain_scores_gemma":[0.9998241,0.000051106643,0.00003334312,0.000045206827,0.00002727164,0.000018999912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007638618,0.0003035563,0.0002495031,0.00014791165,0.00013051642,0.00035545556,0.00025965215,0.00037845693,0.001702391],"category_scores_gemma":[0.0002560428,0.00011369107,0.00021958082,0.00017352992,0.0004250588,0.0002570606,0.0005923084,0.00024998648,0.00059280545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063866255,0.000104981606,0.0030549758,0.0002560306,0.00005826074,0.0008002762,0.00023490275,0.17578952,0.74080724,0.0197157,0.0018348481,0.056704532],"study_design_scores_gemma":[0.00020035203,0.0011558643,0.003909681,0.000051093495,0.000096024494,0.0025894034,0.00024769682,0.5758518,0.38425684,0.010237993,0.021304535,0.00009875069],"about_ca_topic_score_codex":0.00035351998,"about_ca_topic_score_gemma":0.00043556935,"teacher_disagreement_score":0.001702391,"about_ca_system_score_codex":0.00016937424,"about_ca_system_score_gemma":0.00019926386,"threshold_uncertainty_score":0.0056951046},"labels":[],"label_agreement":null},{"id":"W3133531253","doi":"10.5539/nct.v6n1p1","title":"An Improved Indoor Positioning Method Based on Nearest Neighbor Interpolation","year":2021,"lang":"en","type":"article","venue":"Network and Communication Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fingerprint (computing); Computer science; Interpolation (computer graphics); k-nearest neighbors algorithm; Sampling (signal processing); Sample (material); Field (mathematics); Artificial intelligence; Computer vision; Data mining; Mathematics; Image (mathematics)","score_opus":0.007005076333734036,"score_gpt":0.24190782011407055,"score_spread":0.23490274378033651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133531253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006566276,0.0004733798,0.98915535,0.00006527175,0.00019197431,0.00003704192,0.000052602718,0.0010924402,0.0023656194],"genre_scores_gemma":[0.23809904,0.0011091452,0.7484387,0.00009691225,0.00020182572,0.00013945784,0.00038762682,0.0001707782,0.011356488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876934,0.00015803493,0.000048499947,0.0002579846,0.00068650977,0.00007960862],"domain_scores_gemma":[0.99958974,0.000058225192,0.000035288092,0.00005579244,0.00023985906,0.000021130203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004777516,0.00066272676,0.0011339297,0.0012998113,0.0006284078,0.0005623842,0.0014390663,0.00064764725,0.0025992028],"category_scores_gemma":[0.0010599191,0.00039136852,0.0008684405,0.0018157656,0.00025959796,0.0010356338,0.0007152345,0.00077749044,0.0014479264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002757823,0.0000757048,0.0015390457,0.0002822359,0.00008230108,0.00019474565,0.00022432834,0.07778778,0.037207954,0.0061572543,0.004704495,0.87146837],"study_design_scores_gemma":[0.000050941446,0.00015283359,0.0016226773,0.000026300098,0.000060263563,0.00067330204,0.00006290627,0.9568299,0.020575754,0.0014756656,0.018361056,0.00010843025],"about_ca_topic_score_codex":0.006805172,"about_ca_topic_score_gemma":0.0042415406,"teacher_disagreement_score":0.006805172,"about_ca_system_score_codex":0.00040194133,"about_ca_system_score_gemma":0.0008593784,"threshold_uncertainty_score":0.013531148},"labels":[],"label_agreement":null},{"id":"W3135177025","doi":"10.48550/arxiv.2103.01885","title":"Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"Technische Universiteit Delft","keywords":"Multilateration; Computer science; Ultra-wideband; Outlier; Real-time computing; Key (lock); Trajectory; Robot; Quadcopter; Artificial intelligence; Azimuth; Engineering; Telecommunications; Mathematics","score_opus":0.029743450735261676,"score_gpt":0.17251702574325545,"score_spread":0.14277357500799376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135177025","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009846607,0.000110570465,0.9892352,0.000041688465,0.000022897833,0.000008495266,0.000011038592,0.0003841536,0.0003392731],"genre_scores_gemma":[0.5984712,0.00037494462,0.3980991,0.00016471076,0.000078220626,0.00009963761,0.00016067641,0.00015968851,0.0023917942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962354,0.000080461985,0.000019882491,0.00010383203,0.00013265977,0.000039665618],"domain_scores_gemma":[0.9993648,0.00019451015,0.00013605531,0.00012101425,0.000161064,0.000022629203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005183625,0.00062296604,0.00051644776,0.00041008627,0.00029077314,0.0004228171,0.0008853956,0.00054806663,0.0006912671],"category_scores_gemma":[0.0030102546,0.00030105433,0.0003623553,0.0005690584,0.0004369371,0.0006757991,0.0009687851,0.00075791345,0.0005627625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018861608,0.00007283638,0.0025617022,0.00010930084,0.00007812989,0.00012735168,0.0001497823,0.46248987,0.043623164,0.005566266,0.0016765409,0.48335642],"study_design_scores_gemma":[0.000011418683,0.00005298958,0.0007679972,0.000011137004,0.000011955498,0.000076122706,0.000015877768,0.9821201,0.012355394,0.00285862,0.0017039209,0.0000144808255],"about_ca_topic_score_codex":0.0022957427,"about_ca_topic_score_gemma":0.0022262153,"teacher_disagreement_score":0.0022957427,"about_ca_system_score_codex":0.0004226114,"about_ca_system_score_gemma":0.00063959725,"threshold_uncertainty_score":0.004564762},"labels":[],"label_agreement":null},{"id":"W3136990667","doi":"10.1109/bmsb49480.2020.9379587","title":"Indoor Object Localization and Tracking Using Deep Learning over Received Signal Strength","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Received signal strength indication; Scheme (mathematics); Signal strength; Real-time computing; Computer vision; Perceptron; Multilayer perceptron; Artificial neural network; Trajectory; Object detection; Mobile device; SIGNAL (programming language); Pattern recognition (psychology); Wireless sensor network; Wireless; Telecommunications; Mathematics; Computer network","score_opus":0.016445807084924883,"score_gpt":0.2195448749053153,"score_spread":0.20309906782039042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136990667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01580517,0.00022122072,0.9815367,0.00007144107,0.000028704211,0.000012263597,0.0000639677,0.0011776141,0.001083003],"genre_scores_gemma":[0.7614102,0.00048185096,0.23288414,0.00015626592,0.00006271987,0.00006501019,0.00044546297,0.000078574005,0.0044158157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968743,0.000046191464,0.000014320479,0.000108167136,0.000085761276,0.000058163616],"domain_scores_gemma":[0.99969125,0.000079874364,0.000059366816,0.00005844441,0.000089044275,0.000022096976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042667415,0.00080234883,0.00059974333,0.0007129041,0.0002750215,0.00056449254,0.001158744,0.0006678053,0.0009697825],"category_scores_gemma":[0.00093043793,0.0003651682,0.0004963862,0.0009497635,0.00036053645,0.0010766207,0.0011358969,0.0007250299,0.00052386057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014992211,0.0000983898,0.0033453903,0.00009930864,0.000090304864,0.00011294439,0.00008171173,0.5569463,0.014486981,0.00396457,0.00177406,0.41885003],"study_design_scores_gemma":[0.000003502495,0.000022487407,0.0004292112,0.0000058762357,0.000009505164,0.00002362637,0.0000062895556,0.99539644,0.002314584,0.00127674,0.00050616695,0.000005605881],"about_ca_topic_score_codex":0.006318854,"about_ca_topic_score_gemma":0.0075243027,"teacher_disagreement_score":0.006318854,"about_ca_system_score_codex":0.0006555308,"about_ca_system_score_gemma":0.0006258195,"threshold_uncertainty_score":0.012564182},"labels":[],"label_agreement":null},{"id":"W3139149656","doi":"10.3390/geomatics1020010","title":"S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices","year":2021,"lang":"en","type":"article","venue":"Geomatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Dead reckoning; Computer science; Inertial measurement unit; Real-time computing; Inertial navigation system; Pedestrian; Mobile device; Navigation system; Reliability (semiconductor); Step detection; Fuse (electrical); Noise (video); Artificial intelligence; Computer vision; Embedded system; Simulation; Global Positioning System; Engineering; Telecommunications; Inertial frame of reference","score_opus":0.013897757348680222,"score_gpt":0.22798957074716636,"score_spread":0.21409181339848612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139149656","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046978034,0.0008018719,0.9430524,0.0000980463,0.00015961741,0.00012266345,0.00023009208,0.005587949,0.0029692785],"genre_scores_gemma":[0.4010845,0.00052917236,0.5883435,0.0001268786,0.00004712896,0.0001705452,0.0010202841,0.00020729842,0.0084707355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980897,0.000016843704,0.000010760371,0.0000456442,0.00009801463,0.000019861274],"domain_scores_gemma":[0.9998678,0.000019501353,0.000017519085,0.00001927456,0.00006632556,0.00000960212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001568006,0.0006139442,0.00041770106,0.0005036547,0.00022646633,0.00031093849,0.0007026554,0.00036212252,0.0019753098],"category_scores_gemma":[0.0006460333,0.0002048498,0.00033735108,0.00040467182,0.00012912613,0.0003465657,0.0004251966,0.00045955114,0.0012495742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048499313,0.00007462883,0.0028470315,0.00017388565,0.00007542425,0.00027742865,0.00011666584,0.04747602,0.04339907,0.0011664416,0.008415047,0.8954932],"study_design_scores_gemma":[0.000073077514,0.00026687956,0.0049964646,0.000029368495,0.00003526435,0.0005106922,0.00006719298,0.95071226,0.031016542,0.00057616766,0.011679804,0.000036274487],"about_ca_topic_score_codex":0.005230809,"about_ca_topic_score_gemma":0.00662427,"teacher_disagreement_score":0.005230809,"about_ca_system_score_codex":0.00021810122,"about_ca_system_score_gemma":0.0005000217,"threshold_uncertainty_score":0.0104007125},"labels":[],"label_agreement":null},{"id":"W3141312746","doi":"10.1109/iccspa49915.2021.9385713","title":"Mass Flow Meter and Vehicle Information DR Land Vehicles Navigation System in Indoor Environment","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Heading (navigation); Odometer; Computer science; Global Positioning System; Remote sensing; Dead reckoning; Environmental science; Real-time computing; Simulation; Geodesy; Artificial intelligence; Telecommunications; Geography","score_opus":0.004457416774989791,"score_gpt":0.16680055166835311,"score_spread":0.16234313489336333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141312746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26984107,0.001162661,0.6982889,0.00038421422,0.0004969304,0.00023769194,0.0010438628,0.015263546,0.013281155],"genre_scores_gemma":[0.9057661,0.00022204088,0.08314434,0.00017659305,0.00007029788,0.00010642253,0.00068520085,0.00005082699,0.009778225],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997094,0.000048006295,0.000013589517,0.000084446496,0.000112536974,0.00003205319],"domain_scores_gemma":[0.9997931,0.000025267396,0.00003490653,0.00003236826,0.00010106602,0.000013324127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022575016,0.00053533464,0.00044755876,0.000591132,0.000225629,0.00032237903,0.0007130383,0.0005106776,0.0019918384],"category_scores_gemma":[0.00041679438,0.00013451066,0.00017201809,0.0004202386,0.00012339634,0.0005146374,0.00034959454,0.00025578783,0.0011826161],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006877754,0.00023123575,0.026609767,0.00046598338,0.00007996287,0.00047468353,0.00026824174,0.027512727,0.19729257,0.002639296,0.012067323,0.7316704],"study_design_scores_gemma":[0.0002781573,0.0019595043,0.050676312,0.00011060947,0.0002657635,0.0016680018,0.00031175054,0.6795406,0.20639223,0.002071358,0.05656605,0.00015960682],"about_ca_topic_score_codex":0.0023817488,"about_ca_topic_score_gemma":0.0028511703,"teacher_disagreement_score":0.0023817488,"about_ca_system_score_codex":0.00026342913,"about_ca_system_score_gemma":0.00038084167,"threshold_uncertainty_score":0.006663382},"labels":[],"label_agreement":null},{"id":"W3141622885","doi":"10.1109/access.2021.3069793","title":"Time Difference of Arrival Based Indoor Positioning System Using Visible Light Communication","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Sultan Qaboos University","keywords":"Computer science; Visible light communication; Indoor positioning system; Multilateration; Real-time computing; Optics; Light-emitting diode; Physics; Azimuth","score_opus":0.01748066655225314,"score_gpt":0.2526229024616228,"score_spread":0.23514223590936964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141622885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08485292,0.00030089772,0.897079,0.00012925846,0.00017715909,0.000105064144,0.00046967765,0.009674006,0.0072120293],"genre_scores_gemma":[0.85760576,0.00028480726,0.13609165,0.000053249634,0.000028944423,0.00015819368,0.0006813594,0.00005767738,0.0050384477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968195,0.0000657109,0.000014016927,0.000060450155,0.00014225264,0.000035539797],"domain_scores_gemma":[0.99969184,0.000046143323,0.000049189537,0.00004957373,0.00014117715,0.000021967642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003142087,0.00041070662,0.000505371,0.0004586267,0.0002837524,0.00061221403,0.00086313393,0.0004323567,0.0017819267],"category_scores_gemma":[0.00054255564,0.00016924429,0.0003795003,0.0005986771,0.0001380395,0.00047997045,0.0005531468,0.0004273802,0.00089810067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009343113,0.0003217158,0.013385086,0.0006490968,0.00029383795,0.0004276088,0.0003688764,0.4341102,0.14321694,0.007931772,0.007906163,0.3904544],"study_design_scores_gemma":[0.000102442056,0.0005598814,0.0028943706,0.000021852416,0.000081970764,0.00020015906,0.000043955486,0.9545583,0.032536194,0.00078646204,0.008154053,0.00006027515],"about_ca_topic_score_codex":0.0035602977,"about_ca_topic_score_gemma":0.0034993743,"teacher_disagreement_score":0.0035602977,"about_ca_system_score_codex":0.0003946787,"about_ca_system_score_gemma":0.00075407897,"threshold_uncertainty_score":0.007079184},"labels":[],"label_agreement":null},{"id":"W3143247426","doi":"10.1109/twc.2021.3067957","title":"Model-Based Learning Network for 3-D Localization in mmWave Communications","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Scientific Research Foundation of the Graduate School of Southeast University; Engineering and Physical Sciences Research Council; Ministry of Science and Technology, Taiwan; Qualcomm; Queen's University Belfast; Department for the Economy; Queen's University; Southeast University; National Natural Science Foundation of China","keywords":"Robustness (evolution); Computer science; Estimator; Cramér–Rao bound; Artificial neural network; Base station; Algorithm; Mean squared error; Artificial intelligence; Telecommunications; Estimation theory; Mathematics; Statistics","score_opus":0.03332283685218557,"score_gpt":0.26422652850077694,"score_spread":0.23090369164859137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143247426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010624342,0.00048224532,0.98716533,0.0001583288,0.000039050195,0.000012798517,0.00003508752,0.00036533907,0.0011174994],"genre_scores_gemma":[0.8598946,0.0009518491,0.13385735,0.000243283,0.00007141145,0.00015651218,0.00028026602,0.00006341879,0.0044813296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973136,0.000056940586,0.00001613255,0.00009182145,0.000066841734,0.000036970447],"domain_scores_gemma":[0.99972016,0.000120483026,0.000043216085,0.000026243742,0.00007658199,0.000013182561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004620582,0.0006276037,0.0007511843,0.00041764093,0.00040612838,0.0006795077,0.0010328926,0.0009746514,0.0010306883],"category_scores_gemma":[0.0014308235,0.00040907192,0.00063135085,0.0006405965,0.00050780404,0.0010608875,0.0009838701,0.0011182214,0.00029524296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002707805,0.000018326782,0.0005088921,0.000029306006,0.000019057492,0.000027440223,0.000028427932,0.95140564,0.0008985367,0.003049872,0.00050449674,0.04348299],"study_design_scores_gemma":[0.000001053213,0.000006003371,0.00004339531,0.0000016515696,0.000002150186,0.0000036255112,0.0000019350337,0.9989146,0.00013043951,0.0007611319,0.00013211966,0.0000019158142],"about_ca_topic_score_codex":0.013244216,"about_ca_topic_score_gemma":0.008667103,"teacher_disagreement_score":0.013244216,"about_ca_system_score_codex":0.0008362381,"about_ca_system_score_gemma":0.00077215035,"threshold_uncertainty_score":0.026334226},"labels":[],"label_agreement":null},{"id":"W3145377020","doi":"10.1109/jsen.2021.3070144","title":"Pseudo-Zero Velocity Re-Detection Double Threshold Zero-Velocity Update (ZUPT) for Inertial Sensor-Based Pedestrian Navigation","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Inertial measurement unit; Kalman filter; Inertial navigation system; Computer science; Dead reckoning; Control theory (sociology); Tracking (education); Tracking system; Simulation; Global Positioning System; Artificial intelligence; Inertial frame of reference; Telecommunications","score_opus":0.022959572123524848,"score_gpt":0.25227209051927413,"score_spread":0.2293125183957493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145377020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015074916,0.00049482606,0.9818627,0.000044937595,0.00022492725,0.00004459886,0.00004372273,0.0012777972,0.0009315421],"genre_scores_gemma":[0.50182605,0.0007180278,0.49213895,0.00012507808,0.000104884166,0.00012119701,0.0003729805,0.00021281958,0.0043799477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993343,0.00007635404,0.000042428015,0.00014751435,0.00035012796,0.000049290164],"domain_scores_gemma":[0.99928206,0.0001131511,0.00010898834,0.000119587094,0.00034679755,0.000029405825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041305358,0.0007824121,0.00056691945,0.00095055293,0.00033240902,0.0005364919,0.000967009,0.00059509644,0.001193172],"category_scores_gemma":[0.0020930076,0.00037484223,0.00041817373,0.00077248336,0.0002930114,0.0009586562,0.00067424384,0.00075276254,0.00059805997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003808849,0.00006931992,0.004667418,0.00027096918,0.000058643844,0.00021047344,0.00020584367,0.024394266,0.06495197,0.0035463108,0.003889871,0.8973541],"study_design_scores_gemma":[0.000082526065,0.00048357312,0.009745931,0.00006868487,0.00011920824,0.0010723025,0.00009998976,0.8537748,0.111322135,0.0021568232,0.020930506,0.00014350409],"about_ca_topic_score_codex":0.003229605,"about_ca_topic_score_gemma":0.0029403165,"teacher_disagreement_score":0.003229605,"about_ca_system_score_codex":0.0002921578,"about_ca_system_score_gemma":0.0007059547,"threshold_uncertainty_score":0.0064216256},"labels":[],"label_agreement":null},{"id":"W3147441693","doi":"10.1109/iccspa49915.2021.9385733","title":"Evaluation of 5G Cell Densification for Autonomous Vehicles Positioning in Urban Settings","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trilateration; Computer science; Base station; Real-time computing; Global Positioning System; Multilateration; Hybrid positioning system; Kinematics; Simulation; Wireless; Positioning system; Telecommunications; Engineering","score_opus":0.015974240458648314,"score_gpt":0.23881136779499967,"score_spread":0.22283712733635136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3147441693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98593605,0.00022569997,0.011133513,0.00010459062,0.000030125084,0.00010728065,0.00012508717,0.000114661925,0.0022229915],"genre_scores_gemma":[0.9969619,0.000064758926,0.00269699,0.00001107299,0.000003614475,0.000014233819,0.00006624212,0.000003120883,0.00017813827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991767,0.000296773,0.000035804667,0.000082292994,0.0002731658,0.00013527472],"domain_scores_gemma":[0.9974312,0.0013948539,0.00021550304,0.00019169852,0.00064179406,0.00012496207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014035017,0.00058569707,0.00032774924,0.00046144726,0.00028094367,0.00052357104,0.00061781926,0.00050505705,0.00065312925],"category_scores_gemma":[0.004283217,0.000119419434,0.00018716692,0.00055390096,0.00044937007,0.00069717516,0.00050573534,0.00024554218,0.000113896254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012929797,0.00033531614,0.035141278,0.00020711994,0.0000864421,0.00044767998,0.00015483671,0.895232,0.017723614,0.0015303384,0.0005810385,0.047267254],"study_design_scores_gemma":[0.000114192175,0.0056235855,0.044711694,0.00005683421,0.00017550918,0.00035614832,0.00096516707,0.91157335,0.03391872,0.00059888937,0.0018586137,0.00004728052],"about_ca_topic_score_codex":0.007511285,"about_ca_topic_score_gemma":0.007045811,"teacher_disagreement_score":0.007511285,"about_ca_system_score_codex":0.0009207772,"about_ca_system_score_gemma":0.00038524685,"threshold_uncertainty_score":0.014935136},"labels":[],"label_agreement":null},{"id":"W3149744105","doi":"10.1109/ipsn.2007.4379664","title":"Localization in Wireless Sensor Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer science; Range (aeronautics); Wireless; Upper and lower bounds; Measure (data warehouse); Real-time computing; SIGNAL (programming language); Algorithm; Computer network; Mathematics; Telecommunications; Engineering; Data mining","score_opus":0.005446138333896764,"score_gpt":0.20192027313614766,"score_spread":0.1964741348022509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149744105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034827036,0.019170215,0.9669377,0.0017531583,0.0007432687,0.000061362254,0.00010453532,0.0006670603,0.007080027],"genre_scores_gemma":[0.4398499,0.06326452,0.47606242,0.0014618129,0.0023998492,0.0005380063,0.00072554196,0.00028545002,0.015412532],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99829286,0.0006254897,0.00011331314,0.00026404046,0.0006178686,0.00008640937],"domain_scores_gemma":[0.99880135,0.0006848277,0.00013062253,0.0001487375,0.00019817536,0.000036355403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013133461,0.000777049,0.00096681324,0.0008522641,0.00064655195,0.0015048347,0.0010293067,0.0015437132,0.002137436],"category_scores_gemma":[0.0063089575,0.00041205875,0.00036272334,0.0020720107,0.0010602146,0.003107234,0.0013139741,0.0011971542,0.001268478],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010573959,0.000038608967,0.0009026495,0.00149205,0.00008144992,0.00025551138,0.00016866165,0.29012373,0.0045358012,0.17645223,0.019222176,0.5066214],"study_design_scores_gemma":[0.00004187971,0.00015489296,0.0005737654,0.0002749594,0.00005173407,0.0006353167,0.00014400044,0.65630805,0.0036400387,0.23105627,0.1070598,0.000059263875],"about_ca_topic_score_codex":0.001024301,"about_ca_topic_score_gemma":0.000781429,"teacher_disagreement_score":0.002137436,"about_ca_system_score_codex":0.00059523934,"about_ca_system_score_gemma":0.00054604193,"threshold_uncertainty_score":0.0071504116},"labels":[],"label_agreement":null},{"id":"W3149875662","doi":"10.21203/rs.3.rs-178416/v1","title":"An Accurate, Robust and Low Dimensionality Deep Learning Localization Approach in DM-MIMO Systems Based on RSS","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RSS; MIMO; Computer science; Cluster analysis; Principal component analysis; Algorithm; Artificial neural network; Mean squared error; Multipath propagation; Redundancy (engineering); Data mining; Artificial intelligence; Pattern recognition (psychology); Mathematics; Telecommunications; Statistics","score_opus":0.042395546522935826,"score_gpt":0.3141003222997898,"score_spread":0.271704775776854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149875662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02485715,0.00023563328,0.97291315,0.00013397484,0.000032482385,0.000012286268,0.00003358682,0.00045050343,0.0013311638],"genre_scores_gemma":[0.8974022,0.000288835,0.098888315,0.00015908903,0.000053099382,0.00004246683,0.00012807603,0.000033885965,0.0030040937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999734,0.000065363805,0.00001546841,0.000059136033,0.00007533385,0.000050632065],"domain_scores_gemma":[0.99971265,0.000099109486,0.00003869253,0.000038105303,0.00009248421,0.000018880484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003899747,0.0006426016,0.0006783543,0.0003576649,0.00026644586,0.00052216335,0.0007831346,0.0005527628,0.0008666071],"category_scores_gemma":[0.00086518266,0.00032135594,0.0004786311,0.0004550809,0.00035587754,0.0006513163,0.0007965622,0.00079989946,0.0003145381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116040406,0.00004133395,0.0012934206,0.00007569387,0.00005487726,0.00013687812,0.000062360225,0.8486356,0.010022139,0.004491806,0.0011877128,0.13388215],"study_design_scores_gemma":[0.0000017340506,0.0000126250725,0.00010739881,0.0000021650737,0.0000035505902,0.000012169896,0.000004383967,0.99849665,0.00068212074,0.0005479578,0.00012591496,0.0000033025397],"about_ca_topic_score_codex":0.0044339146,"about_ca_topic_score_gemma":0.004147565,"teacher_disagreement_score":0.0044339146,"about_ca_system_score_codex":0.00043510392,"about_ca_system_score_gemma":0.00056287274,"threshold_uncertainty_score":0.008816183},"labels":[],"label_agreement":null},{"id":"W3151632264","doi":"10.18280/ijsse.110102","title":"Received-Signal-Strength-Based Approach for Detection and 2D Indoor Localization of Evil Twin Rogue Access Point in 802.11","year":2021,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Trilateration; Computer science; Signal strength; Centroid; SIGNAL (programming language); Point (geometry); Received signal strength indication; Wireless; Real-time computing; Computer network; Computer security; Artificial intelligence; Telecommunications; Mathematics; Triangulation","score_opus":0.008199853544559137,"score_gpt":0.22471009366685046,"score_spread":0.21651024012229134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151632264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038536977,0.00022729565,0.95853025,0.000063985426,0.000054326152,0.000033043438,0.000034504283,0.0010449848,0.0014745672],"genre_scores_gemma":[0.631677,0.00035624404,0.3653116,0.000094340816,0.00005185982,0.00009970526,0.00013764012,0.000040838924,0.002230829],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993967,0.00013671612,0.00002849035,0.00014822162,0.00023342608,0.000056547113],"domain_scores_gemma":[0.99968004,0.000056976172,0.000056558936,0.000059066166,0.00012900401,0.000018270572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033342748,0.0008226591,0.0006232662,0.0012901274,0.00028388202,0.0005574575,0.0007493429,0.0006437727,0.0006964244],"category_scores_gemma":[0.0007640237,0.00025827248,0.00038676866,0.00082050223,0.00038115613,0.0006335815,0.0007654199,0.00051980256,0.0005671362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055715395,0.000269782,0.008527847,0.00032905207,0.00017310992,0.00079602096,0.00047642636,0.13270195,0.27498838,0.0063331523,0.0023319959,0.57251513],"study_design_scores_gemma":[0.00003511585,0.00046911777,0.0062308772,0.000021577274,0.000062772335,0.0014767809,0.00015223198,0.9291142,0.057588905,0.0014348228,0.0033377828,0.0000757728],"about_ca_topic_score_codex":0.0009163399,"about_ca_topic_score_gemma":0.0016008537,"teacher_disagreement_score":0.0012901274,"about_ca_system_score_codex":0.00031006077,"about_ca_system_score_gemma":0.00035428215,"threshold_uncertainty_score":0.0023297668},"labels":[],"label_agreement":null},{"id":"W3154784371","doi":"10.1109/infocom42981.2021.9488669","title":"Multi-Robot Path Planning for Mobile Sensing through Deep Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reinforcement learning; Computer science; Mobile robot; Motion planning; Robot; Scalability; Real-time computing; Artificial intelligence; Baseline (sea); RSS; Path (computing); Data collection; Machine learning; Computer network","score_opus":0.02268754140055075,"score_gpt":0.2624999235518939,"score_spread":0.23981238215134315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154784371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039277326,0.00035012438,0.95667416,0.00028400568,0.000048407772,0.000051313684,0.0000622769,0.0010909854,0.0021613657],"genre_scores_gemma":[0.8965069,0.00012399587,0.100965045,0.00014515093,0.00002155948,0.00011960944,0.000120605495,0.000066260916,0.0019307177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978036,0.00004975261,0.000009477122,0.00006445384,0.00004461671,0.000051369436],"domain_scores_gemma":[0.9992187,0.00045514677,0.00010180783,0.000052908006,0.00011041118,0.000060984614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067687995,0.00095382886,0.00076405425,0.00037843472,0.00031618058,0.0005028093,0.0013365523,0.0008797726,0.0021575717],"category_scores_gemma":[0.0019877998,0.00046631522,0.0004117256,0.00035473652,0.0008047218,0.00074843655,0.0007874555,0.0014167601,0.00027877177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003077035,0.000034344586,0.0003911528,0.000021207894,0.000013305258,0.000023957575,0.000019420671,0.9781691,0.00058510684,0.0013842798,0.00042202245,0.01890535],"study_design_scores_gemma":[0.0000034618752,0.0000076966,0.000022170872,0.0000014807266,0.0000012468576,0.0000020010934,0.000002036341,0.9991629,0.000114054434,0.0006220907,0.000059937385,9.05062e-7],"about_ca_topic_score_codex":0.011391598,"about_ca_topic_score_gemma":0.014209288,"teacher_disagreement_score":0.011391598,"about_ca_system_score_codex":0.0011549213,"about_ca_system_score_gemma":0.0015014955,"threshold_uncertainty_score":0.0226506},"labels":[],"label_agreement":null},{"id":"W3155584426","doi":"10.11591/eei.v10i3.2763","title":"Indoor positioning system based on magnetic fingerprinting-images","year":2021,"lang":"en","type":"article","venue":"Bulletin of Electrical Engineering and Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Università degli Studi di Padova","keywords":"Inertial measurement unit; Computer science; Global Positioning System; Dead reckoning; RSS; Computer vision; Real-time computing; Path (computing); Artificial intelligence; SIGNAL (programming language); Telecommunications","score_opus":0.0021911738919770155,"score_gpt":0.1518972565107338,"score_spread":0.1497060826187568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155584426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117910735,0.0012528766,0.8395889,0.00017418945,0.0003907093,0.00021366717,0.003420703,0.015043251,0.022005051],"genre_scores_gemma":[0.808446,0.00082261255,0.17559138,0.00011656072,0.00013281536,0.00023844058,0.0027822626,0.00018305705,0.011686839],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996294,0.00004738203,0.000021120808,0.00009426672,0.00016867962,0.000039106377],"domain_scores_gemma":[0.9998124,0.000015748039,0.00002793032,0.000043149932,0.0000897805,0.000011120366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015263584,0.0005326868,0.00044053214,0.0012653803,0.00020846765,0.0005779072,0.0004782077,0.00037068754,0.003941204],"category_scores_gemma":[0.00046292096,0.00012971355,0.0002697465,0.0010332488,0.00011027733,0.00055017904,0.0004951771,0.00023715835,0.002558722],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006067022,0.000106659485,0.011007205,0.0005601235,0.00008717048,0.00038013878,0.00020008383,0.0141590405,0.16421604,0.0025461577,0.010564634,0.795566],"study_design_scores_gemma":[0.00016527482,0.001458247,0.0921688,0.00029843443,0.00045991887,0.0050743795,0.0007050754,0.4202242,0.375,0.004452013,0.0996425,0.00035130346],"about_ca_topic_score_codex":0.001691027,"about_ca_topic_score_gemma":0.0016746847,"teacher_disagreement_score":0.003941204,"about_ca_system_score_codex":0.00016606317,"about_ca_system_score_gemma":0.00019279728,"threshold_uncertainty_score":0.013184667},"labels":[],"label_agreement":null},{"id":"W3158945143","doi":"10.48550/arxiv.2104.13296","title":"CAIM: Cooperative Angle of Arrival Estimation using the Ising Method","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Compressed sensing; Monte Carlo method; Mathematical optimization; Energy (signal processing); Algorithm; Minification; Markov chain; Channel (broadcasting); Norm (philosophy); Angle of arrival; Cross-entropy method; Mathematics; Bayesian probability; Optimization problem; Statistics; Artificial intelligence; Machine learning; Telecommunications","score_opus":0.06812528635332347,"score_gpt":0.2142906213540749,"score_spread":0.1461653350007514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158945143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025254241,0.00012617237,0.9960918,0.000055648918,0.000028452898,0.00001652805,0.00002548245,0.00024132387,0.0008891226],"genre_scores_gemma":[0.30687127,0.0006186644,0.68672943,0.00021251058,0.00019626353,0.00019280234,0.00031040062,0.00012903784,0.004739677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991111,0.00027447255,0.000032247986,0.00016432637,0.0003538533,0.00006394127],"domain_scores_gemma":[0.9988888,0.0005568373,0.00012554669,0.00017427804,0.00020140383,0.000053260035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095100084,0.0008705068,0.0008287118,0.0010514902,0.0005276793,0.0007248154,0.0016732584,0.001075486,0.0016014292],"category_scores_gemma":[0.0034695016,0.00037173595,0.00063777814,0.0014765939,0.0007430968,0.0012783562,0.0015220534,0.0013166305,0.0008160376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034099928,0.00010734112,0.001591549,0.00015028937,0.00012447704,0.00020275118,0.00017802851,0.5895979,0.015609443,0.04686178,0.0049989433,0.3402365],"study_design_scores_gemma":[0.000015299667,0.00003487161,0.00017855618,0.000009261318,0.000012423141,0.000076116354,0.000010832783,0.98728174,0.00263701,0.0074187596,0.0023068893,0.000018251414],"about_ca_topic_score_codex":0.003569922,"about_ca_topic_score_gemma":0.0032420764,"teacher_disagreement_score":0.003569922,"about_ca_system_score_codex":0.0005980699,"about_ca_system_score_gemma":0.0012889863,"threshold_uncertainty_score":0.007098317},"labels":[],"label_agreement":null},{"id":"W3159701637","doi":"10.3390/s21092896","title":"Measuring Gait Velocity and Stride Length with an Ultrawide Bandwidth Local Positioning System and an Inertial Measurement Unit","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Inertial measurement unit; STRIDE; Global Positioning System; Gait; Computer science; Step detection; Wearable computer; Simulation; Gait analysis; Units of measurement; Physical medicine and rehabilitation; Artificial intelligence; Telecommunications; Physics; Medicine; Embedded system","score_opus":0.022088582734206115,"score_gpt":0.20261481478219542,"score_spread":0.18052623204798932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159701637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8533693,0.0007243953,0.14088225,0.00009111093,0.00009495991,0.00030410654,0.00097557594,0.00038530614,0.0031731813],"genre_scores_gemma":[0.85919195,0.0004516335,0.13649672,0.000114751296,0.00005085341,0.0005648515,0.00065154315,0.000038975162,0.0024386928],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914813,0.00025607084,0.00008870771,0.00017825127,0.0002838414,0.000044926164],"domain_scores_gemma":[0.9988998,0.0002446118,0.00023805126,0.00012788907,0.00041471684,0.000074839954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079492014,0.00047442672,0.000554736,0.00100297,0.00021499308,0.00046246315,0.0004003457,0.00044837504,0.002512945],"category_scores_gemma":[0.0022539245,0.00023499058,0.00023280132,0.0009071447,0.00021907056,0.00044184073,0.00052128837,0.00026958555,0.0008468368],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022864426,0.0006587954,0.3238352,0.00096784026,0.00048086658,0.00022764862,0.0011107143,0.0017663062,0.20655881,0.00079285126,0.002110022,0.4592045],"study_design_scores_gemma":[0.00013654085,0.0034730267,0.936539,0.00013420377,0.00025559595,0.001272245,0.001107118,0.012956686,0.03941918,0.00072903046,0.0038863148,0.00009108515],"about_ca_topic_score_codex":0.0009392635,"about_ca_topic_score_gemma":0.0027722835,"teacher_disagreement_score":0.002512945,"about_ca_system_score_codex":0.00012516338,"about_ca_system_score_gemma":0.00020224434,"threshold_uncertainty_score":0.0084065795},"labels":[],"label_agreement":null},{"id":"W3161097911","doi":"10.3390/s21103472","title":"A People-Counting and Speed-Estimation System Using Wi-Fi Signals","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Fuzhou University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Subcarrier; Outlier; Computer science; Statistics; Variance (accounting); Estimation; Filter (signal processing); Dynamic time warping; Real-time computing; Simulation; Artificial intelligence; Mathematics; Computer vision; Engineering; Estimator","score_opus":0.010979301212375654,"score_gpt":0.2134409662752489,"score_spread":0.20246166506287325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161097911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16190685,0.00042393678,0.8119252,0.0002088925,0.0003213076,0.0003301708,0.0007669215,0.018964201,0.00515251],"genre_scores_gemma":[0.70563656,0.0003623009,0.28212515,0.00027234506,0.00023248715,0.00042032232,0.0012000654,0.00014304921,0.00960774],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995584,0.000055060886,0.000033585962,0.00014004405,0.00015639486,0.000056386507],"domain_scores_gemma":[0.99952316,0.00007227954,0.000058741833,0.00006181819,0.00022964473,0.0000544314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004524629,0.0007781353,0.0008723168,0.0014287838,0.00049662543,0.000593094,0.0011092954,0.00083261245,0.0019396796],"category_scores_gemma":[0.0008689392,0.00029136005,0.00027986336,0.0007251114,0.00019408282,0.0010820244,0.0006712903,0.0004591433,0.0016645122],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011022474,0.0007309873,0.02447803,0.00035678686,0.00020191011,0.0005974264,0.00040502532,0.01162703,0.16779818,0.002157507,0.012477002,0.7780679],"study_design_scores_gemma":[0.00027432086,0.0013982133,0.040823974,0.000106018226,0.00035687996,0.0020656155,0.00033456925,0.7026973,0.216966,0.0025832672,0.032055136,0.00033879856],"about_ca_topic_score_codex":0.0028417355,"about_ca_topic_score_gemma":0.002292619,"teacher_disagreement_score":0.0028417355,"about_ca_system_score_codex":0.00022302942,"about_ca_system_score_gemma":0.0004477582,"threshold_uncertainty_score":0.0064889193},"labels":[],"label_agreement":null},{"id":"W3161458415","doi":"10.1109/icassp39728.2021.9413455","title":"Bluetooth Low Energy and CNN-Based Angle of Arrival Localization in Presence of Rayleigh Fading","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Angle of arrival; Computer science; Rayleigh fading; Fading; Additive white Gaussian noise; Non-line-of-sight propagation; Path loss; Real-time computing; Time of arrival; Beacon; Gaussian noise; Electronic engineering; Algorithm; Channel (broadcasting); Wireless; Telecommunications; Antenna (radio); Engineering","score_opus":0.005954464926880999,"score_gpt":0.19068094336996058,"score_spread":0.18472647844307957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161458415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2934122,0.0006848909,0.6945187,0.0003616375,0.00014776363,0.00004810046,0.00019255768,0.0016322541,0.009001815],"genre_scores_gemma":[0.9714382,0.00016272141,0.025932495,0.000064380554,0.000018940433,0.000022145572,0.00014600712,0.000014488947,0.0022006854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998728,0.000016916207,0.0000054675825,0.000036464862,0.000035631976,0.000032701544],"domain_scores_gemma":[0.99982435,0.00004459949,0.000032081865,0.000020477675,0.000065103675,0.000013461387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022325412,0.00048893463,0.00028165357,0.00023290834,0.00014203596,0.0003064595,0.0006958828,0.00039058525,0.0005584142],"category_scores_gemma":[0.0008376062,0.00016391983,0.00022904719,0.00022582723,0.00023140252,0.00039791426,0.00046048363,0.00032554287,0.0001960794],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023571335,0.00006642776,0.007150414,0.00008506503,0.000070582224,0.00035766652,0.000078104,0.7742367,0.024739759,0.0027221644,0.0012508051,0.18900661],"study_design_scores_gemma":[0.0000027905157,0.00002625514,0.0009209231,0.0000033220783,0.000008172409,0.00004417377,0.0000073284605,0.9955883,0.0028920732,0.00026644717,0.00023646964,0.0000038072053],"about_ca_topic_score_codex":0.010971151,"about_ca_topic_score_gemma":0.012159506,"teacher_disagreement_score":0.010971151,"about_ca_system_score_codex":0.00047180537,"about_ca_system_score_gemma":0.00045296265,"threshold_uncertainty_score":0.021814585},"labels":[],"label_agreement":null},{"id":"W3162503496","doi":"10.1109/icassp39728.2021.9414174","title":"Online Dynamic Window (ODW) Assisted 2-Stage LSTM Indoor Localization for Smart Phones","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Inertial measurement unit; Artificial intelligence; Rendering (computer graphics); Real-time computing; Scalability; Dead reckoning; Software deployment; Computer vision","score_opus":0.014212322252561421,"score_gpt":0.2457753670917847,"score_spread":0.2315630448392233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162503496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06921146,0.0013264195,0.91626626,0.0003363778,0.00029609943,0.000048783055,0.00044720556,0.009758379,0.0023090187],"genre_scores_gemma":[0.83159477,0.00055630616,0.16025971,0.00029136133,0.00009559891,0.0001371893,0.0010791433,0.00024861493,0.005737385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986124,0.000018021827,0.000008639862,0.000052559742,0.000027254013,0.00003230244],"domain_scores_gemma":[0.9998418,0.000054821558,0.000016268817,0.000023635168,0.00005062468,0.000012845782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000255452,0.0010385761,0.00052649813,0.00027218368,0.0002113696,0.00043547922,0.001017248,0.00068932964,0.0025046922],"category_scores_gemma":[0.00078207295,0.00033379486,0.00044316333,0.00036965855,0.00019887589,0.0010133273,0.0007023471,0.0010194771,0.0012179856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041516198,0.000116334886,0.0010341668,0.00020042007,0.000092393064,0.0002537538,0.00015810321,0.26837847,0.041658595,0.0018592045,0.0074630664,0.6783704],"study_design_scores_gemma":[0.000010070607,0.000066605164,0.00028806692,0.000009006287,0.000016673217,0.000037617996,0.000020627002,0.9905978,0.0069270106,0.0010083341,0.0010080802,0.000010068207],"about_ca_topic_score_codex":0.006190423,"about_ca_topic_score_gemma":0.011067676,"teacher_disagreement_score":0.006190423,"about_ca_system_score_codex":0.00033440642,"about_ca_system_score_gemma":0.0006166703,"threshold_uncertainty_score":0.012308776},"labels":[],"label_agreement":null},{"id":"W3162789749","doi":"10.1109/wcnc49053.2021.9417253","title":"Missing Data Inference for Crowdsourced Radio Map Construction: An Adversarial Auto-Encoder Method","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Inference; Missing data; Crowdsourcing; Scheme (mathematics); Data mining; Encoder; Deep learning; Artificial intelligence; Adversarial system; Machine learning; Quality (philosophy); Matrix completion","score_opus":0.04066670652942805,"score_gpt":0.3122847258207017,"score_spread":0.27161801929127366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162789749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073975907,0.00023094588,0.9907799,0.00025096064,0.00004146729,0.000033125427,0.0001172921,0.00041211682,0.00073659717],"genre_scores_gemma":[0.6854156,0.00047616506,0.30511788,0.0007013221,0.00018017937,0.00025340493,0.0011428888,0.00021853266,0.0064939894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990558,0.00033475712,0.000043157757,0.0002536503,0.00019822828,0.00011446039],"domain_scores_gemma":[0.99654144,0.002379128,0.00023605244,0.0003311837,0.0003662455,0.00014596527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024730996,0.0013404335,0.0014872064,0.0006847095,0.00052465784,0.0008269969,0.0024443702,0.0014998512,0.0023125734],"category_scores_gemma":[0.0065186475,0.00090014987,0.0009721043,0.00085518695,0.0013906042,0.0017083562,0.002820603,0.0031893512,0.000644479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015353423,0.000054716074,0.0010789606,0.0001037233,0.00006687103,0.000118857344,0.000128348,0.91352206,0.0017439476,0.011391126,0.0026882326,0.06894962],"study_design_scores_gemma":[0.000006279437,0.000011449689,0.000056354118,0.0000064571846,0.0000047294875,0.000012580974,0.0000058256855,0.99560213,0.000362606,0.0037113237,0.00021563181,0.0000045749885],"about_ca_topic_score_codex":0.0074706995,"about_ca_topic_score_gemma":0.0071749697,"teacher_disagreement_score":0.0074706995,"about_ca_system_score_codex":0.0009831716,"about_ca_system_score_gemma":0.0017037166,"threshold_uncertainty_score":0.014854431},"labels":[],"label_agreement":null},{"id":"W3165471237","doi":"10.3390/s21113706","title":"GPS Swept Anti-Jamming Technique Based on Fast Orthogonal Search (FOS)","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Global Positioning System; Jamming; Computer science; GPS signals; Precision Lightweight GPS Receiver; Assisted GPS; Time to first fix; SIGNAL (programming language); Interference (communication); Real-time computing; Telecommunications; Gps receiver","score_opus":0.010258985696331229,"score_gpt":0.2225929232804146,"score_spread":0.21233393758408337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165471237","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05518583,0.00040632018,0.9419094,0.000055584776,0.000047350786,0.000035394027,0.000035736797,0.00042292342,0.001901606],"genre_scores_gemma":[0.50249976,0.0006024328,0.49406835,0.000062886786,0.00004300342,0.00007932208,0.00010114082,0.000038216604,0.0025049609],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979013,0.00004990897,0.000009367981,0.00003119657,0.0001010449,0.000018289024],"domain_scores_gemma":[0.99974066,0.00008685978,0.000058997626,0.000019948904,0.000084640946,0.000008863788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002477161,0.0004552404,0.0002574361,0.0006803296,0.00019529632,0.00023108789,0.00030876757,0.00029825434,0.00070052914],"category_scores_gemma":[0.0008369939,0.00013635255,0.0002547651,0.00043631648,0.0002394525,0.0005628574,0.00027238225,0.0002416808,0.00023298185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005041814,0.000099868426,0.0024672565,0.0003410852,0.000081058235,0.0002339216,0.00033625428,0.0931769,0.25223976,0.011961182,0.0012921402,0.6372664],"study_design_scores_gemma":[0.00004499066,0.00068601547,0.0028083655,0.00004925524,0.00006484131,0.00092785404,0.000104570114,0.85047024,0.13383503,0.0025468424,0.008397822,0.00006415584],"about_ca_topic_score_codex":0.00081554503,"about_ca_topic_score_gemma":0.0012738634,"teacher_disagreement_score":0.00081554503,"about_ca_system_score_codex":0.00017896398,"about_ca_system_score_gemma":0.0003642088,"threshold_uncertainty_score":0.0023435354},"labels":[],"label_agreement":null},{"id":"W3165482683","doi":"10.3390/s21113615","title":"Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Inertial measurement unit; Heading (navigation); Computer science; Extended Kalman filter; Sensor fusion; Inertial navigation system; Kalman filter; Calibration; Dead reckoning; Artificial intelligence; Computer vision; Pedestrian; Simulation; Real-time computing; Global Positioning System; Inertial frame of reference; Engineering; Telecommunications; Aerospace engineering; Physics","score_opus":0.020925970490885636,"score_gpt":0.29154042474274966,"score_spread":0.27061445425186403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165482683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01349483,0.0002482126,0.9849133,0.00002991722,0.000051382383,0.000016614751,0.000019837766,0.00043074452,0.00079513824],"genre_scores_gemma":[0.6166393,0.0004949397,0.38049236,0.0000768155,0.00009572369,0.00007665268,0.00012369057,0.000045359717,0.0019551606],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960476,0.00007742798,0.000023182783,0.00011180391,0.00013620315,0.00004661227],"domain_scores_gemma":[0.9998198,0.000025216434,0.000029745222,0.000032269443,0.00008096986,0.00001202695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048296482,0.0006488817,0.00051981123,0.00065195194,0.00035358063,0.00037077186,0.00052988814,0.0005411421,0.0007947395],"category_scores_gemma":[0.0005989242,0.00026424517,0.0006297559,0.0006821098,0.0002423821,0.00081111817,0.0010031272,0.00036813013,0.00035075226],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002687534,0.00008933641,0.004606686,0.00022284412,0.00012423482,0.00027721358,0.00040813402,0.17393205,0.06803743,0.007986576,0.0014634051,0.7425834],"study_design_scores_gemma":[0.000014995619,0.00027124712,0.004175636,0.00003027809,0.00007161184,0.00025756413,0.00010323105,0.9585851,0.026215803,0.0038586052,0.0063663344,0.00004963019],"about_ca_topic_score_codex":0.0018293479,"about_ca_topic_score_gemma":0.0014208381,"teacher_disagreement_score":0.0018293479,"about_ca_system_score_codex":0.00022083196,"about_ca_system_score_gemma":0.00041759457,"threshold_uncertainty_score":0.0036373734},"labels":[],"label_agreement":null},{"id":"W3165528186","doi":"10.1186/s43020-021-00041-3","title":"Indoor navigation: state of the art and future trends","year":2021,"lang":"en","type":"article","venue":"Satellite Navigation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":251,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Canada Research Chairs","keywords":"GNSS applications; Computer science; Plan (archaeology); Real-time computing; Inertial navigation system; Lidar; Sensor fusion; State (computer science); Global Positioning System; Enhanced Data Rates for GSM Evolution; Mobile mapping; Systems engineering; Artificial intelligence; Telecommunications; Remote sensing; Geography; Engineering; Inertial frame of reference","score_opus":0.005247880054686253,"score_gpt":0.20589258380181943,"score_spread":0.20064470374713317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165528186","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021314488,0.96445423,0.011981781,0.005166022,0.0015346379,0.000017381235,0.00009216938,0.00016785075,0.014454448],"genre_scores_gemma":[0.03461229,0.9446671,0.011156041,0.0020261824,0.0025905876,0.000029084373,0.00033028037,0.0000411329,0.0045472975],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99907815,0.00021406818,0.000079507874,0.00018956311,0.00036256848,0.00007608852],"domain_scores_gemma":[0.9977323,0.0009139761,0.00016341059,0.00008342026,0.0009890423,0.000117816344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016052466,0.0005083641,0.0005343357,0.0018014798,0.00036756694,0.002185516,0.0010324362,0.0013599207,0.0049754074],"category_scores_gemma":[0.0017232816,0.00026573805,0.00045983598,0.0027960713,0.0007849296,0.0038776023,0.0009653171,0.0013116541,0.0021882984],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006939262,0.00006167423,0.0020846257,0.0040512276,0.00003410584,0.00007818737,0.0001276311,0.0023139734,0.0011811825,0.0183349,0.02205951,0.94960356],"study_design_scores_gemma":[0.000008308505,0.00023611072,0.002179875,0.0040134727,0.000101870144,0.00083022687,0.00066634774,0.006876956,0.0012831762,0.013589742,0.9701395,0.000074359894],"about_ca_topic_score_codex":0.0023280082,"about_ca_topic_score_gemma":0.002368686,"teacher_disagreement_score":0.0049754074,"about_ca_system_score_codex":0.0009649865,"about_ca_system_score_gemma":0.0012932043,"threshold_uncertainty_score":0.016644418},"labels":[],"label_agreement":null},{"id":"W3166438672","doi":"10.1109/jsen.2021.3087954","title":"Efficient Wi-Fi Fingerprint Crowdsourcing for Indoor Localization","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"RSS; Crowdsourcing; Fingerprint (computing); Computer science; Fingerprint recognition; Process (computing); Signal strength; Software deployment; Gaussian process; Artificial intelligence; Path (computing); Data mining; Pattern recognition (psychology); Gaussian; Real-time computing; Wireless sensor network; Computer network","score_opus":0.011388076653776948,"score_gpt":0.22429601274698832,"score_spread":0.21290793609321138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166438672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04182112,0.00028833558,0.95335424,0.00012282915,0.00008069473,0.00012107788,0.0001663502,0.0018796016,0.0021657096],"genre_scores_gemma":[0.82411766,0.00022924267,0.17165212,0.00012150743,0.00007076491,0.00020385826,0.0003360984,0.00010915576,0.0031596986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899894,0.00024229655,0.00002722028,0.0002269169,0.00034357375,0.00016101962],"domain_scores_gemma":[0.9990307,0.00029067416,0.00010197046,0.00027669562,0.0002040996,0.00009583058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006928533,0.0012072627,0.001245994,0.0010177948,0.000729301,0.00063727354,0.0019866687,0.0007679328,0.0013039663],"category_scores_gemma":[0.0022903308,0.0003388691,0.0006355286,0.0012741017,0.0005020002,0.000765989,0.0022164509,0.00059052237,0.0011731241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010546319,0.0003369373,0.005169383,0.00039914553,0.00016875703,0.0012917506,0.00066500215,0.26077026,0.11020801,0.007127047,0.0074080057,0.6054011],"study_design_scores_gemma":[0.000066285065,0.0001939038,0.0019649125,0.000020497217,0.00004224778,0.00037991468,0.00023321842,0.9664678,0.019615822,0.0058037294,0.0051415376,0.00007018373],"about_ca_topic_score_codex":0.0045904284,"about_ca_topic_score_gemma":0.0054621235,"teacher_disagreement_score":0.0045904284,"about_ca_system_score_codex":0.00055498804,"about_ca_system_score_gemma":0.00079331885,"threshold_uncertainty_score":0.009127378},"labels":[],"label_agreement":null},{"id":"W3167446093","doi":"10.22215/etd/2020-14432","title":"Hybrid Localization for UAV-based Charging of Wireless Sensor Networks","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Node (physics); Wireless sensor network; Position (finance); Wireless; Fading; Computer science; Signal strength; Real-time computing; Key distribution in wireless sensor networks; Scheme (mathematics); Computer network; Wireless network; Engineering; Telecommunications","score_opus":0.008229770474678646,"score_gpt":0.21580897745325966,"score_spread":0.207579206978581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167446093","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03703786,0.0013874657,0.947249,0.00017013817,0.00020742763,0.00005675003,0.000049034807,0.0007944635,0.013047835],"genre_scores_gemma":[0.8992821,0.00096627447,0.08585849,0.00008968837,0.00004351792,0.000079554884,0.00012251492,0.0001065724,0.013451231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998499,0.000031542,0.0000052730875,0.000034335826,0.0000524672,0.00002651345],"domain_scores_gemma":[0.9999329,0.00001778522,0.0000069526013,0.0000121880175,0.000024917583,0.000005257767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015977642,0.00032077194,0.0004376038,0.00023025543,0.0002512232,0.0005072624,0.000462291,0.00030245248,0.002232077],"category_scores_gemma":[0.00037365864,0.0001321217,0.00035799848,0.00031828406,0.00023230622,0.000552928,0.0007659975,0.00026042073,0.000694931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035601473,0.000097034936,0.0013593001,0.00040122974,0.000080163496,0.00036284782,0.00032209844,0.4409748,0.068456106,0.05042253,0.006038503,0.4311294],"study_design_scores_gemma":[0.000024293378,0.000164233,0.0005739811,0.00002590776,0.000018062365,0.00018166544,0.000075764605,0.9581117,0.017245827,0.010706064,0.01285338,0.000019086048],"about_ca_topic_score_codex":0.0011595229,"about_ca_topic_score_gemma":0.0011963506,"teacher_disagreement_score":0.002232077,"about_ca_system_score_codex":0.00040955847,"about_ca_system_score_gemma":0.0002001949,"threshold_uncertainty_score":0.0074670315},"labels":[],"label_agreement":null},{"id":"W3167888726","doi":"10.1109/lwc.2021.3087581","title":"Improved RSSD-Based Source Localization With Unknown Sensor Position Errors","year":2021,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Semidefinite programming; Robustness (evolution); Mathematics; Estimator; Mean squared error; Mathematical optimization; Convex optimization; Minimax; Noise measurement; Nonlinear programming; Algorithm; Computer science; Nonlinear system; Regular polygon; Statistics","score_opus":0.010202895442946515,"score_gpt":0.2124274257161078,"score_spread":0.20222453027316128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167888726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034310895,0.00011452398,0.995539,0.000055519675,0.00002155025,0.000007002496,0.0000140652855,0.00020011413,0.0006170124],"genre_scores_gemma":[0.37919,0.00048464743,0.6150254,0.00016841643,0.00007926719,0.00009481713,0.00023024331,0.00012615793,0.0046011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924576,0.00020111394,0.000038521262,0.00013780952,0.0003412588,0.000035573106],"domain_scores_gemma":[0.99945456,0.00019598471,0.000058750407,0.000088929264,0.0001865984,0.00001523863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084469275,0.0006777234,0.00077775354,0.00054532656,0.00017178436,0.0006511227,0.0010474529,0.0005960038,0.0008500358],"category_scores_gemma":[0.001983574,0.0004353655,0.0005620176,0.00074747927,0.00053196884,0.0009209258,0.0011837475,0.00071800547,0.00064133725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018474422,0.00006417762,0.00067660917,0.00023489307,0.0000587016,0.00024360974,0.0001386915,0.7366217,0.04031569,0.017234705,0.0024263046,0.20180011],"study_design_scores_gemma":[0.000008766173,0.000040336818,0.0000920251,0.0000057887946,0.0000055616597,0.000062845196,0.000005242444,0.993145,0.0043137916,0.0010557614,0.0012558011,0.000009109763],"about_ca_topic_score_codex":0.0008145828,"about_ca_topic_score_gemma":0.0007903178,"teacher_disagreement_score":0.0010474529,"about_ca_system_score_codex":0.00037654507,"about_ca_system_score_gemma":0.00059029646,"threshold_uncertainty_score":0.0044671893},"labels":[],"label_agreement":null},{"id":"W3169059668","doi":"10.1109/percom50583.2021.9439133","title":"Mobility Improves Accuracy: Precise Robot Manipulation with COTS RFID Systems","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Kalman filter; Artificial intelligence; Position (finance); Robotics; Process (computing); Robot; Real-time computing; Key (lock); Computer vision","score_opus":0.012314671769779545,"score_gpt":0.21133650510254748,"score_spread":0.19902183333276793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169059668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1049648,0.0053805253,0.8728136,0.0009757811,0.0004119151,0.00005511268,0.00018574775,0.0076213856,0.0075912126],"genre_scores_gemma":[0.7619666,0.0013583669,0.23037484,0.00040321713,0.00016806698,0.000041751042,0.00024793675,0.00024052979,0.0051987274],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904364,0.00012839134,0.000050944072,0.00022302996,0.00042826222,0.00012566378],"domain_scores_gemma":[0.9986505,0.00032736614,0.00021747516,0.00040409248,0.00035008063,0.000050454055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006808252,0.00090167695,0.0005606517,0.00076347124,0.00042992382,0.0007369646,0.0010472354,0.00076598726,0.0025230953],"category_scores_gemma":[0.0024215218,0.00045129168,0.00043810697,0.00062005967,0.0006192163,0.0020380917,0.0018031585,0.00084694655,0.0011356891],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047603648,0.000080812715,0.008053163,0.0007969925,0.00010120845,0.0005396891,0.0005413288,0.07629903,0.2176779,0.011639461,0.008686392,0.6751081],"study_design_scores_gemma":[0.00013888141,0.0015983118,0.015646512,0.0003193232,0.00020574553,0.0045906967,0.00034078152,0.6326392,0.21580799,0.009461393,0.1189352,0.00031588695],"about_ca_topic_score_codex":0.0025500997,"about_ca_topic_score_gemma":0.002837705,"teacher_disagreement_score":0.0025500997,"about_ca_system_score_codex":0.00050776236,"about_ca_system_score_gemma":0.00046755193,"threshold_uncertainty_score":0.008440614},"labels":[],"label_agreement":null},{"id":"W3171100995","doi":"10.3390/s21124152","title":"Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Wireless sensor network; Hop (telecommunications); Algorithm; Computer science; Signal strength; Computation; Wireless; Distance measurement; Artificial intelligence; Computer network; Telecommunications","score_opus":0.010516637327522673,"score_gpt":0.19947968225147655,"score_spread":0.18896304492395388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171100995","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026460555,0.00036880712,0.99592716,0.000045305485,0.000030599207,0.000013452183,0.000012442304,0.00034448394,0.00061161484],"genre_scores_gemma":[0.25402945,0.0012414864,0.7398971,0.00008411424,0.00007250244,0.00013160499,0.00027773972,0.00009108754,0.0041749734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937576,0.00016147658,0.000033330616,0.00013119217,0.00026577653,0.000032411666],"domain_scores_gemma":[0.99966,0.0001078606,0.00004207366,0.00005037888,0.00012918239,0.000010523021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044232598,0.0005941933,0.0007103525,0.0006502886,0.0003194578,0.0005407404,0.0013381961,0.0006167296,0.0008637168],"category_scores_gemma":[0.0014226265,0.00024393445,0.0004639224,0.0011540377,0.00033331278,0.0008565056,0.0005849278,0.0006170213,0.00056380697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014032194,0.000055727327,0.0010998214,0.0002771187,0.00007117975,0.00015974196,0.00017631124,0.30648246,0.035384145,0.017172046,0.0033065057,0.63567466],"study_design_scores_gemma":[0.00002380482,0.00011837975,0.000509201,0.000014575204,0.000024943929,0.00029417308,0.000028393044,0.97816044,0.009610219,0.0030344215,0.008150426,0.000031026735],"about_ca_topic_score_codex":0.002364274,"about_ca_topic_score_gemma":0.0021440357,"teacher_disagreement_score":0.002364274,"about_ca_system_score_codex":0.0003911701,"about_ca_system_score_gemma":0.0005947239,"threshold_uncertainty_score":0.0047010183},"labels":[],"label_agreement":null},{"id":"W3173061206","doi":"10.1109/vtc2021-spring51267.2021.9448857","title":"Deep Learning based Localization of LTE eNodeBs from Large Crowdsourced Smartphone Datasets","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Global Positioning System; Deep learning; Transfer of learning; Classifier (UML); Cellular network; Baseline (sea); Metropolitan area; Machine learning; Heuristic; Artificial neural network; Data mining; Telecommunications; Geography","score_opus":0.005541564707458832,"score_gpt":0.19715395155879098,"score_spread":0.19161238685133214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173061206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82661986,0.0026300978,0.13439515,0.0014475392,0.00046623178,0.00020662781,0.018825678,0.006833506,0.008575335],"genre_scores_gemma":[0.9481265,0.0003349652,0.024796924,0.00017976617,0.00008376271,0.00009523474,0.023064263,0.00006934801,0.0032492443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995347,0.00010971124,0.000023678213,0.00014488722,0.000090310925,0.00009668585],"domain_scores_gemma":[0.9993042,0.00024620286,0.00008055704,0.00013272709,0.0001903432,0.000045974273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006456299,0.00126263,0.0007513118,0.001239225,0.00036930206,0.00055755023,0.0012510158,0.00091025204,0.0008830829],"category_scores_gemma":[0.0027013987,0.00021469666,0.00051183644,0.0011071048,0.00038123468,0.0007214847,0.0011036851,0.0007916024,0.0009522417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011894103,0.00057189923,0.07546757,0.00067138043,0.00036821305,0.0015413383,0.00047217266,0.5133113,0.00828041,0.0022913457,0.059676275,0.33615863],"study_design_scores_gemma":[0.00005183282,0.0000896618,0.012015148,0.000054164208,0.000032044776,0.00011532176,0.00030981755,0.9762818,0.003792304,0.002332782,0.004898691,0.000026410067],"about_ca_topic_score_codex":0.031806033,"about_ca_topic_score_gemma":0.054810263,"teacher_disagreement_score":0.031806033,"about_ca_system_score_codex":0.0008351205,"about_ca_system_score_gemma":0.0006737762,"threshold_uncertainty_score":0.06324178},"labels":[],"label_agreement":null},{"id":"W3173320109","doi":"10.1145/3463526","title":"Unlocking the Beamforming Potential of LoRa for Long-range Multi-target Respiration Sensing","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Youth Innovation Promotion Association of the Chinese Academy of Sciences; CHIST-ERA; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Chinese Academy of Sciences; Youth Innovation Promotion Association; Agence Nationale de la Recherche","keywords":"Beamforming; Computer science; Transmitter; Key (lock); Synchronization (alternating current); SIGNAL (programming language); Channel state information; Real-time computing; Channel (broadcasting); Electronic engineering; Wireless; Telecommunications; Engineering; Computer security","score_opus":0.012250043478199881,"score_gpt":0.2413049221978834,"score_spread":0.22905487871968352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173320109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022057444,0.00094262685,0.9708566,0.0006381415,0.00011032217,0.00004836074,0.000049446622,0.0014538756,0.0038432314],"genre_scores_gemma":[0.5591582,0.0014743442,0.43454406,0.00085625955,0.00021672893,0.00020332438,0.000146396,0.00018220476,0.0032184983],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99933165,0.00025192433,0.000027871427,0.000104897765,0.00022283186,0.00006087366],"domain_scores_gemma":[0.99887234,0.0004884695,0.00013698559,0.00017251474,0.00025710312,0.00007264199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008254561,0.00068422646,0.00050622947,0.0004898758,0.00033171,0.0006384145,0.00064774416,0.0005801567,0.001389522],"category_scores_gemma":[0.0018961113,0.00033497534,0.00041514123,0.00044477812,0.00063543464,0.0012124579,0.0013476695,0.0009841706,0.0013111214],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003598554,0.00011741852,0.0033554751,0.0005088346,0.00009798175,0.00046587246,0.00055778265,0.082721464,0.3621705,0.019854901,0.004040287,0.5257496],"study_design_scores_gemma":[0.00008213913,0.00067025074,0.002964299,0.00010304441,0.00006752084,0.0012400474,0.00028976408,0.83105314,0.10929305,0.020255439,0.033803914,0.00017745486],"about_ca_topic_score_codex":0.000789108,"about_ca_topic_score_gemma":0.0013618428,"teacher_disagreement_score":0.001389522,"about_ca_system_score_codex":0.0002850947,"about_ca_system_score_gemma":0.0004082011,"threshold_uncertainty_score":0.004648447},"labels":[],"label_agreement":null},{"id":"W3173477196","doi":"10.21203/rs.3.rs-227194/v1","title":"A novel approach of localization with Single Mobile Anchor using Salp Swarm Algorithm in Wireless Sensor Networks","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Swarm behaviour; Wireless sensor network; Wireless; Mobile wireless; Algorithm; Artificial intelligence; Computer network; Telecommunications","score_opus":0.04498888480902922,"score_gpt":0.3063136137023464,"score_spread":0.26132472889331715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173477196","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011545558,0.00024708387,0.98666257,0.00010533309,0.000061346625,0.000023708164,0.000008423011,0.00016958293,0.0011763253],"genre_scores_gemma":[0.64881355,0.00064800616,0.3463721,0.00010328031,0.00010196915,0.00018786325,0.0000940472,0.000053493404,0.0036257203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970704,0.00009830925,0.000013417899,0.00006248659,0.00009504137,0.000023629053],"domain_scores_gemma":[0.99979156,0.00007061484,0.00003181155,0.000022639879,0.00006725481,0.000016116175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038400022,0.00055815745,0.00067815813,0.0006397628,0.0004884113,0.00060816575,0.00081777497,0.0006486801,0.0008791005],"category_scores_gemma":[0.00077563967,0.0002491888,0.00053147017,0.0006427269,0.0005128388,0.0008702509,0.0009036047,0.00047821636,0.0002732341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014672693,0.00006969087,0.0019115387,0.00018346797,0.000085960935,0.0002976511,0.00025203353,0.78231543,0.017411685,0.022282805,0.0023959847,0.17264703],"study_design_scores_gemma":[0.0000073744986,0.0000363397,0.00007879692,0.0000036179304,0.000005981513,0.000033310916,0.00001573852,0.997036,0.00075040513,0.0013317495,0.00069629645,0.0000043938235],"about_ca_topic_score_codex":0.0022605474,"about_ca_topic_score_gemma":0.001123906,"teacher_disagreement_score":0.0022605474,"about_ca_system_score_codex":0.00035561607,"about_ca_system_score_gemma":0.00056745904,"threshold_uncertainty_score":0.0044947863},"labels":[],"label_agreement":null},{"id":"W3174181545","doi":"10.1109/ipdps49936.2021.00057","title":"Code Generation for Room Acoustics Simulations with Complex Boundary Conditions","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Code (set theory); Finite-difference time-domain method; Kernel (algebra); Implementation; Perfectly matched layer; Domain (mathematical analysis); Software; Layer (electronics); Supercomputer; Computer engineering; Parallel computing; Computer architecture; Computational science; Programming language; Set (abstract data type)","score_opus":0.03327872343706034,"score_gpt":0.2642872894095108,"score_spread":0.23100856597245045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174181545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044262715,0.00005262087,0.93997526,0.00012535493,0.00010604142,0.00012753425,0.0002895429,0.009012344,0.0060486514],"genre_scores_gemma":[0.28048292,0.00010471766,0.7072348,0.0001495385,0.000026817348,0.00047420713,0.0011304817,0.004955273,0.0054412107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997259,0.00005301812,0.000018895073,0.00003216227,0.00013686057,0.000033109867],"domain_scores_gemma":[0.99897975,0.00042188267,0.00006762148,0.00021344799,0.0002511576,0.000066085966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050264876,0.00044980738,0.00029777642,0.0003968683,0.00040038393,0.0007221035,0.0012056131,0.00075354957,0.005805536],"category_scores_gemma":[0.003102977,0.00033844984,0.00044308687,0.0003136661,0.00047427163,0.0006602344,0.0010894301,0.0011279742,0.001851595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030819382,0.00029066112,0.0056658117,0.0003416151,0.00007614544,0.0006839803,0.0008489993,0.7030185,0.08659434,0.06360931,0.019165149,0.119397335],"study_design_scores_gemma":[0.000027277669,0.000019221692,0.00016508785,0.000012146832,0.0000034173834,0.000048339873,0.000022781915,0.97351426,0.016069034,0.003991683,0.0061150296,0.000011630294],"about_ca_topic_score_codex":0.0015475183,"about_ca_topic_score_gemma":0.002040768,"teacher_disagreement_score":0.005805536,"about_ca_system_score_codex":0.00045173953,"about_ca_system_score_gemma":0.001018141,"threshold_uncertainty_score":0.019421399},"labels":[],"label_agreement":null},{"id":"W3175439978","doi":"10.1109/lwc.2021.3091896","title":"An Efficient Information Sampling Method for Multi-Category RFID Systems","year":2021,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Coding (social sciences); Sampling (signal processing); Identification (biology); Data mining; Object (grammar); Construct (python library); Information retrieval; Artificial intelligence; Computer network; Telecommunications; Mathematics; Statistics","score_opus":0.04829854963668167,"score_gpt":0.31039410719299726,"score_spread":0.2620955575563156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175439978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016485194,0.00025683115,0.98142,0.00012270016,0.000047059402,0.000041931573,0.000032196134,0.00023485585,0.0013592349],"genre_scores_gemma":[0.66972136,0.00037942766,0.3261677,0.00018785537,0.00009646534,0.0001376973,0.00016331262,0.000033066328,0.0031132428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993649,0.00014979168,0.000036246576,0.00009250875,0.0002936299,0.00006290436],"domain_scores_gemma":[0.9993654,0.00022185063,0.00006981379,0.00012636208,0.00018501574,0.000031593954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046541245,0.00047265255,0.0004551918,0.00060141674,0.0004363388,0.000419352,0.0008026004,0.0004708859,0.0011610598],"category_scores_gemma":[0.0015420493,0.00018968359,0.00031038348,0.0009194714,0.00048432528,0.0013432731,0.00094301795,0.0005247199,0.00029479153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070692063,0.00012763287,0.001283522,0.00027586744,0.00007294258,0.00033501722,0.000352564,0.18080147,0.117654085,0.068935595,0.004427213,0.6250271],"study_design_scores_gemma":[0.000029868877,0.00019786276,0.00025388255,0.000013162995,0.000020130672,0.00030275094,0.000048380494,0.9644304,0.020394249,0.010238217,0.004041667,0.000029449668],"about_ca_topic_score_codex":0.0008983176,"about_ca_topic_score_gemma":0.0008013735,"teacher_disagreement_score":0.0011610598,"about_ca_system_score_codex":0.00039222307,"about_ca_system_score_gemma":0.00056132674,"threshold_uncertainty_score":0.0038841367},"labels":[],"label_agreement":null},{"id":"W3176790016","doi":"10.1109/tmc.2021.3093259","title":"Acoustic Software Defined Platform: A Versatile Sensing and General Benchmarking Platform","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Benchmarking; Software; Usability; Interface (matter); User interface; Focus (optics); Ubiquitous computing; Embedded system; Graphical user interface; Human–computer interaction; Software engineering; Operating system","score_opus":0.012017022441308268,"score_gpt":0.21554543594089662,"score_spread":0.20352841349958836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176790016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07035136,0.0010454877,0.75860816,0.00068220636,0.00056981505,0.001744637,0.0022541676,0.14980052,0.014943596],"genre_scores_gemma":[0.6123695,0.00089360203,0.34502307,0.00085071987,0.00016958266,0.002587846,0.011477301,0.017867506,0.008760889],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9946673,0.0011572983,0.0005241862,0.0009288694,0.0019810796,0.0007412694],"domain_scores_gemma":[0.9959019,0.0008109362,0.00029773105,0.0013273751,0.001083185,0.0005787939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052268403,0.002049689,0.00092620385,0.0019036097,0.0005958226,0.0023826791,0.0039249873,0.001124171,0.0037484628],"category_scores_gemma":[0.009530503,0.0009581941,0.0009239581,0.00093562517,0.0010751899,0.0036730815,0.004136692,0.003073803,0.002216495],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004931975,0.002166722,0.02142292,0.0031221323,0.0006676912,0.0020246382,0.0023876007,0.078228824,0.20892689,0.08155886,0.101924926,0.49263677],"study_design_scores_gemma":[0.000752464,0.0029037085,0.010003954,0.0003846862,0.00030031306,0.0014308203,0.00049083825,0.54463476,0.18015169,0.018889243,0.23955181,0.00050574634],"about_ca_topic_score_codex":0.0023043314,"about_ca_topic_score_gemma":0.0009638279,"teacher_disagreement_score":0.0052268403,"about_ca_system_score_codex":0.0008901453,"about_ca_system_score_gemma":0.0021581908,"threshold_uncertainty_score":0.027642488},"labels":[],"label_agreement":null},{"id":"W3176852835","doi":"10.3390/app11136079","title":"A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Geolocation; Computer science; Satellite; GNSS applications; Wireless; Tracking (education); Satellite system; Kalman filter; Algorithm; Communications satellite; Global Positioning System; Real-time computing; Telecommunications; Engineering; Artificial intelligence; Aerospace engineering","score_opus":0.026367361425190902,"score_gpt":0.25923909335069906,"score_spread":0.23287173192550814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176852835","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041856705,0.23126046,0.747691,0.00060329813,0.0010834829,0.00009898835,0.00029326105,0.0011192849,0.013664562],"genre_scores_gemma":[0.0744461,0.43316004,0.47245815,0.0007449091,0.0020509171,0.0002929794,0.0021092568,0.00032202713,0.014415687],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991985,0.00014161276,0.00012409409,0.00016735961,0.00031641818,0.00005201233],"domain_scores_gemma":[0.99900645,0.0004290385,0.00007958516,0.0000981627,0.00036684837,0.000020015026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008537279,0.001282535,0.0011443638,0.0024211337,0.0004734078,0.0014204964,0.0013124095,0.0011919979,0.0039649433],"category_scores_gemma":[0.0022625108,0.00050319696,0.0009613722,0.0042111203,0.00044920517,0.002115357,0.0008247085,0.0010896216,0.0033311774],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077633544,0.000055308963,0.0010579696,0.0029117428,0.00007291113,0.00013414909,0.00015530403,0.015882354,0.0042827674,0.012310003,0.012072228,0.95098764],"study_design_scores_gemma":[0.00004122807,0.00051224267,0.0044100066,0.0022460297,0.00036594685,0.0024061704,0.00045489124,0.19426939,0.012481941,0.024650687,0.7579411,0.00022032142],"about_ca_topic_score_codex":0.0024188648,"about_ca_topic_score_gemma":0.0015919481,"teacher_disagreement_score":0.0039649433,"about_ca_system_score_codex":0.00044363862,"about_ca_system_score_gemma":0.001010687,"threshold_uncertainty_score":0.013264},"labels":[],"label_agreement":null},{"id":"W3185810774","doi":"10.1155/2021/7057513","title":"Improved Height Estimation Using Extended Kalman Filter on UWB‐Barometer 3D Indoor Positioning System","year":2021,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Barometer; Computer science; Kalman filter; Extended Kalman filter; Estimation; Geodesy; Real-time computing; Remote sensing; Artificial intelligence; Meteorology; Geology; Geography","score_opus":0.014900324476036638,"score_gpt":0.24802806245642578,"score_spread":0.23312773798038913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185810774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042511772,0.0003264712,0.95518094,0.0000612011,0.00007829039,0.000015174055,0.00005227977,0.00071000477,0.0010639416],"genre_scores_gemma":[0.8205383,0.00052807503,0.17622499,0.00006936561,0.000082144164,0.000049590297,0.00021164489,0.000037016376,0.002258859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950683,0.000086564374,0.000028712368,0.00011426399,0.00021697086,0.000046674424],"domain_scores_gemma":[0.99969184,0.000066014734,0.000043997614,0.000044344553,0.00014154548,0.000012277229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042268654,0.000492675,0.00063067727,0.00040874915,0.00029987012,0.00037408868,0.0005441043,0.00047278928,0.0006971665],"category_scores_gemma":[0.00095657463,0.00025211868,0.0004926062,0.000589042,0.00018011643,0.0007745248,0.0006462119,0.0004554976,0.0003124022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033009495,0.000058519992,0.005658321,0.00028752896,0.0001386062,0.00024996346,0.00029805445,0.40739402,0.08542695,0.0037383102,0.0019328329,0.49448684],"study_design_scores_gemma":[0.000026558082,0.00010422192,0.0028779781,0.000010155498,0.000042653937,0.00008575924,0.000024274153,0.9857049,0.009251277,0.0005476474,0.0012946426,0.000030000661],"about_ca_topic_score_codex":0.0069492785,"about_ca_topic_score_gemma":0.0043250266,"teacher_disagreement_score":0.0069492785,"about_ca_system_score_codex":0.00024083363,"about_ca_system_score_gemma":0.0004883801,"threshold_uncertainty_score":0.013817668},"labels":[],"label_agreement":null},{"id":"W3187487380","doi":"10.1109/icuas51884.2021.9476831","title":"Velocity estimation for UAVs using ultra wide-band system","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Computer science; Kalman filter; Position (finance); Range (aeronautics); Noise (video); Ranging; Motion capture; Computer vision; Artificial intelligence; Simulation; Motion (physics); Engineering; Aerospace engineering; Telecommunications","score_opus":0.015683570391642095,"score_gpt":0.22664918290891906,"score_spread":0.21096561251727697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187487380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022440773,0.000184544,0.9758902,0.000044785083,0.000037567905,0.00001056329,0.000014627807,0.00034631215,0.0010306047],"genre_scores_gemma":[0.7036152,0.00032073434,0.293037,0.000058825186,0.000038020728,0.00005098645,0.00013657023,0.000050332786,0.002692415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998319,0.000030875013,0.000008136078,0.00004763745,0.00005966864,0.00002171496],"domain_scores_gemma":[0.999889,0.000024163826,0.000024013767,0.000014732152,0.000040323022,0.000007869004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016384687,0.0004986721,0.00036269706,0.0004022902,0.00022004085,0.0004125825,0.00037049394,0.00041741107,0.00060358393],"category_scores_gemma":[0.00046711066,0.00018330348,0.00025993006,0.0002639269,0.00015863546,0.00041876693,0.0004431266,0.00034708314,0.00036116992],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001854708,0.000053372027,0.0037047246,0.00017423958,0.000071180446,0.00023312996,0.00024720255,0.4485538,0.105520576,0.006993046,0.0017948078,0.4324684],"study_design_scores_gemma":[0.000015337408,0.00009669332,0.0013002013,0.000014936664,0.000014747844,0.00012188748,0.000042030246,0.982402,0.012047768,0.0009805752,0.0029472592,0.000016622516],"about_ca_topic_score_codex":0.002594326,"about_ca_topic_score_gemma":0.0024780508,"teacher_disagreement_score":0.002594326,"about_ca_system_score_codex":0.00021235236,"about_ca_system_score_gemma":0.00035911112,"threshold_uncertainty_score":0.005158484},"labels":[],"label_agreement":null},{"id":"W3188122282","doi":"10.1109/iwcmc51323.2021.9498987","title":"A Mobile Node Assisted Localization System for Wireless Sensor Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province","keywords":"Drone; Computer science; Wireless sensor network; Real-time computing; Node (physics); Hybrid positioning system; Positioning system; Sensor node; Wireless; Key distribution in wireless sensor networks; Wireless network; Computer network; Telecommunications; Engineering","score_opus":0.009132940176190962,"score_gpt":0.2115161943514763,"score_spread":0.20238325417528535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188122282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032454163,0.0017056464,0.94582593,0.00032694518,0.0005534213,0.00025998984,0.00021289708,0.0084769735,0.010184079],"genre_scores_gemma":[0.6177645,0.0016094618,0.34023926,0.00046980564,0.00024503362,0.0005259707,0.0009176671,0.00015149865,0.038076825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998011,0.00003808942,0.0000113457645,0.000045925208,0.0000880917,0.000015548605],"domain_scores_gemma":[0.999877,0.000012436093,0.000016009762,0.000017744193,0.00006357749,0.000013266904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016739078,0.0005200531,0.00040552585,0.00047502204,0.00041396593,0.00036520112,0.0007359452,0.00054903707,0.0027003838],"category_scores_gemma":[0.00029644396,0.00012939014,0.00023057662,0.00037326894,0.00016132029,0.00045933985,0.000494205,0.00037639795,0.0017210796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004631494,0.00012323889,0.0020291305,0.0005114381,0.00008138496,0.0007958434,0.0003267817,0.017990874,0.28135023,0.010311716,0.016781129,0.669235],"study_design_scores_gemma":[0.00022596338,0.0026753226,0.00558342,0.0001143807,0.00022825005,0.0027351873,0.00014712822,0.521843,0.18274127,0.002858745,0.28065878,0.00018853409],"about_ca_topic_score_codex":0.000989238,"about_ca_topic_score_gemma":0.0010607424,"teacher_disagreement_score":0.0027003838,"about_ca_system_score_codex":0.00023122168,"about_ca_system_score_gemma":0.0003617066,"threshold_uncertainty_score":0.00903368},"labels":[],"label_agreement":null},{"id":"W3188272423","doi":"10.1109/lra.2021.3102300","title":"Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Heading (navigation); RSS; Gyroscope; Orientation (vector space); Compass; Robot; Signal strength; Gaussian; Position (finance)","score_opus":0.012184573701099504,"score_gpt":0.2228633227354646,"score_spread":0.2106787490343651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188272423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0671784,0.00014045753,0.9309807,0.00008358007,0.000033709744,0.000010272178,0.000021772266,0.00046385478,0.0010872283],"genre_scores_gemma":[0.92004555,0.00024118238,0.07817032,0.00005464001,0.00003854758,0.0000123938125,0.00006845215,0.000033615608,0.0013352926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997222,0.00005295395,0.000010481987,0.00007750853,0.00010471183,0.00003211011],"domain_scores_gemma":[0.9996228,0.00012980218,0.00008112355,0.000043989545,0.000107321604,0.000014847893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003966287,0.00028646243,0.00029720747,0.00044270375,0.00014666031,0.00048426964,0.00028565517,0.00033290786,0.0002549131],"category_scores_gemma":[0.0013878953,0.00019372877,0.0002708864,0.00047288038,0.00036754872,0.0006262252,0.00050172827,0.00045094016,0.00017478883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038197715,0.00011603486,0.008440533,0.0001312129,0.00009927874,0.0003060466,0.00040557,0.48325902,0.09055342,0.01579709,0.0012060005,0.39930388],"study_design_scores_gemma":[0.00001537005,0.000072849856,0.0024787656,0.0000074234326,0.000016184946,0.00007665438,0.000026234755,0.9852481,0.0088922,0.002208137,0.0009380568,0.000020084692],"about_ca_topic_score_codex":0.002559495,"about_ca_topic_score_gemma":0.0022717135,"teacher_disagreement_score":0.002559495,"about_ca_system_score_codex":0.00029933968,"about_ca_system_score_gemma":0.00037509855,"threshold_uncertainty_score":0.005089164},"labels":[],"label_agreement":null},{"id":"W3189493599","doi":"10.1364/ofc.2021.tu5e.1","title":"Passive Positioning using Visible Light Systems","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Indoor positioning system; Visible light communication; Position (finance); Object detection; Object (grammar); Position error; Pattern recognition (psychology); Light-emitting diode; Optics; Orientation (vector space); Physics; Mathematics","score_opus":0.00907943147594691,"score_gpt":0.21041296277893445,"score_spread":0.20133353130298753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189493599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013624105,0.0010370254,0.97274846,0.0002955691,0.00019237927,0.000020543423,0.00011886328,0.0013884928,0.010574611],"genre_scores_gemma":[0.820036,0.0014768939,0.15681142,0.0004129473,0.00032324722,0.000053163814,0.00037977533,0.00014849001,0.020358028],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99892443,0.00016151142,0.000026924476,0.00034986174,0.0004320778,0.00010523831],"domain_scores_gemma":[0.99914753,0.00024628095,0.00011016709,0.00020038565,0.00025476047,0.0000408868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039159262,0.00087301456,0.00066633575,0.00057350344,0.0005307839,0.0015025043,0.0014837204,0.0017633992,0.0035277656],"category_scores_gemma":[0.0019802472,0.00059065444,0.0004147239,0.00073972414,0.00071057456,0.0023120025,0.0019515156,0.0011584188,0.0027197085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008840918,0.0001901816,0.0034893556,0.00066914974,0.00014620968,0.00036790324,0.0003263227,0.18426971,0.214521,0.069408715,0.01160805,0.5141193],"study_design_scores_gemma":[0.00007667566,0.00050459435,0.0022318251,0.0001235033,0.000078004065,0.00068123126,0.00007884832,0.86935896,0.07343997,0.025533186,0.027772794,0.00012032958],"about_ca_topic_score_codex":0.001833429,"about_ca_topic_score_gemma":0.0020126875,"teacher_disagreement_score":0.0035277656,"about_ca_system_score_codex":0.0007604173,"about_ca_system_score_gemma":0.00044096762,"threshold_uncertainty_score":0.011801541},"labels":[],"label_agreement":null},{"id":"W3189744191","doi":"10.23977/acss.2021.050107","title":"Improved Algorithm for RFID Indoor Positioning","year":2021,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Algorithm; Interpolation (computer graphics); Indoor positioning system; Centroid; Computer vision; Artificial intelligence; Accelerometer; Image (mathematics)","score_opus":0.007698284900553788,"score_gpt":0.23041319780692024,"score_spread":0.22271491290636644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189744191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011088344,0.00025771034,0.9968155,0.000046468464,0.00009940338,0.000015756857,0.000020995214,0.00045063675,0.0011846317],"genre_scores_gemma":[0.064291,0.00053969596,0.9281022,0.00009161056,0.00012552035,0.00011142089,0.00022978327,0.00008582501,0.006422907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883324,0.00021291841,0.00006446282,0.00025077036,0.00056062354,0.000078040866],"domain_scores_gemma":[0.99942255,0.000093247865,0.00004063869,0.00011995333,0.0003109051,0.000012650417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007459963,0.000823861,0.000835023,0.0012472318,0.0005170669,0.00081966957,0.0011780193,0.0010303918,0.0028738722],"category_scores_gemma":[0.0019062037,0.00030982733,0.00069566094,0.0020634523,0.00042412506,0.0013439173,0.0010660493,0.0010579157,0.0030603558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017602909,0.000044366978,0.0008423225,0.00020251937,0.00005543508,0.00015943797,0.00015313608,0.1028064,0.02317254,0.03628843,0.0077332794,0.82836604],"study_design_scores_gemma":[0.000055691482,0.00013533243,0.00081533985,0.000035780715,0.000044873123,0.0007907946,0.000041639134,0.9138244,0.0208605,0.00800982,0.05530748,0.00007840332],"about_ca_topic_score_codex":0.001588563,"about_ca_topic_score_gemma":0.0011532584,"teacher_disagreement_score":0.0028738722,"about_ca_system_score_codex":0.0005184522,"about_ca_system_score_gemma":0.0006532684,"threshold_uncertainty_score":0.00961405},"labels":[],"label_agreement":null},{"id":"W3190199533","doi":"10.1109/icc42927.2021.9500783","title":"A Scalable BP Method for Joint Localization and Synchronization in Dense Wireless Sensor Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Wireless sensor network; Computer science; Robustness (evolution); Scalability; Belief propagation; Synchronization (alternating current); Computation; Wireless; Covariance; Computational complexity theory; Algorithm; Real-time computing; Computer network; Mathematics; Decoding methods","score_opus":0.010171623812818319,"score_gpt":0.2288692869996453,"score_spread":0.21869766318682696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190199533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00148503,0.00005905608,0.99803966,0.000024546056,0.000016617976,0.0000074281425,0.000008493332,0.00014327292,0.0002160056],"genre_scores_gemma":[0.26109412,0.0003473819,0.73581946,0.00009931662,0.00011440651,0.00014858643,0.00015329459,0.00011396266,0.0021094638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993749,0.000129745,0.000027143018,0.00012047715,0.0003061696,0.000041578453],"domain_scores_gemma":[0.9991345,0.0003956017,0.000084683496,0.00010984715,0.00023357192,0.000041824012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008388894,0.000618737,0.0006838317,0.00043656406,0.0003760221,0.0004211208,0.0011455609,0.00057452027,0.0010840601],"category_scores_gemma":[0.0027279698,0.00035340458,0.00042558266,0.00089789403,0.0004741164,0.0010490537,0.001064878,0.0011757638,0.00045851892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001529079,0.00005174063,0.0005823165,0.00015044241,0.00008330729,0.00013000488,0.00013345007,0.6343285,0.02339471,0.023199327,0.0023795937,0.31541374],"study_design_scores_gemma":[0.0000072964554,0.00001750973,0.00006912248,0.0000033279846,0.000005295224,0.000018811732,0.000005649276,0.9953164,0.0012498995,0.0027198005,0.00058198,0.0000048977677],"about_ca_topic_score_codex":0.0048910836,"about_ca_topic_score_gemma":0.003615625,"teacher_disagreement_score":0.0048910836,"about_ca_system_score_codex":0.00041976472,"about_ca_system_score_gemma":0.0010487145,"threshold_uncertainty_score":0.009725273},"labels":[],"label_agreement":null},{"id":"W3194854450","doi":"10.1049/smt2.12078","title":"A novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM","year":2021,"lang":"en","type":"article","venue":"IET Science Measurement & Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Six Talent Peaks Project in Jiangsu Province","keywords":"Computer science","score_opus":0.033017633811244715,"score_gpt":0.2276689806387679,"score_spread":0.19465134682752316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194854450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15098554,0.0002480553,0.8415081,0.00024629277,0.0001509065,0.00005728989,0.00018203893,0.0015723095,0.0050494378],"genre_scores_gemma":[0.84851426,0.00014839967,0.1485307,0.00012162438,0.000021491001,0.00007647198,0.00016122742,0.000038401147,0.0023874317],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996728,0.00004411391,0.000015668753,0.000097422686,0.00013898457,0.000031178963],"domain_scores_gemma":[0.99979275,0.000030721985,0.000034180477,0.000033819044,0.000091874914,0.000016617409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034698655,0.00060684996,0.00042162553,0.000643577,0.00023276181,0.00059796596,0.0008408172,0.0007959271,0.0013296595],"category_scores_gemma":[0.000623489,0.0002992866,0.00040366527,0.0007067699,0.0002957918,0.0011459946,0.00070775056,0.00043550145,0.0003634255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005125721,0.00019376146,0.018499965,0.00026759744,0.00011142405,0.00069177675,0.00027701075,0.31546065,0.2336915,0.005280784,0.0030570019,0.42195603],"study_design_scores_gemma":[0.000008807644,0.00004918801,0.0027899877,0.0000098402,0.000013801914,0.00011559993,0.000027986976,0.96848035,0.027046846,0.00054299075,0.0008895788,0.000024892797],"about_ca_topic_score_codex":0.0029036163,"about_ca_topic_score_gemma":0.0028484548,"teacher_disagreement_score":0.0029036163,"about_ca_system_score_codex":0.00065567164,"about_ca_system_score_gemma":0.00038574685,"threshold_uncertainty_score":0.0057734847},"labels":[],"label_agreement":null},{"id":"W3195801301","doi":"10.1109/jiot.2021.3066243","title":"Distributed TDMA for Mobile UWB Network Localization","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Council for the Arts","keywords":"Time division multiple access; Computer science; Network topology; Computer network; Ranging; Global Positioning System; Scalability; Synchronization (alternating current); Distributed computing; Schedule; Real-time computing; Scheduling (production processes); Topology (electrical circuits); Telecommunications; Channel (broadcasting); Engineering","score_opus":0.008680460302758273,"score_gpt":0.22490947961105126,"score_spread":0.216229019308293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195801301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026154881,0.002490514,0.9819754,0.00036636807,0.00059279334,0.00006217307,0.00006688524,0.0010465096,0.010784004],"genre_scores_gemma":[0.3544747,0.005100011,0.5892429,0.00074139255,0.0007180544,0.0004840858,0.00036717363,0.00020951653,0.048662096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996983,0.00006983291,0.000016918111,0.00007714834,0.000115161914,0.000022699596],"domain_scores_gemma":[0.9998373,0.000048504415,0.000022268145,0.000033081807,0.00004896042,0.000009885236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021241189,0.00036743766,0.00025838896,0.00040579712,0.0003424679,0.00048072342,0.0005885866,0.000568025,0.0045901756],"category_scores_gemma":[0.00073030044,0.00012693756,0.00020315134,0.0006453681,0.00041619662,0.00046343045,0.00035269288,0.0008291253,0.002064904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026707968,0.00007155583,0.00067427155,0.00050502224,0.000048670794,0.00060154084,0.00032760348,0.04504246,0.09583083,0.22610536,0.024177738,0.6063479],"study_design_scores_gemma":[0.00014281349,0.0004315015,0.0008591713,0.00017558421,0.00007459866,0.0017976479,0.000119378514,0.45389482,0.0306482,0.07006426,0.44169828,0.00009370529],"about_ca_topic_score_codex":0.000938538,"about_ca_topic_score_gemma":0.0012723015,"teacher_disagreement_score":0.0045901756,"about_ca_system_score_codex":0.0005529649,"about_ca_system_score_gemma":0.00045887002,"threshold_uncertainty_score":0.015355706},"labels":[],"label_agreement":null},{"id":"W3195923156","doi":"10.1177/03611981211035759","title":"Research on Roadside Unit-Assisted Cooperative Positioning Method for a Connected Vehicle Environment","year":2021,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Global Positioning System; RSS; Ranging; Computer science; Real-time computing; GNSS applications; Position (finance); Reliability (semiconductor); Hybrid positioning system; Positioning technology; Sensor fusion; Positioning system; Simulation; Engineering; Artificial intelligence; Telecommunications","score_opus":0.101363482411825,"score_gpt":0.3941172577054605,"score_spread":0.29275377529363555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195923156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026729682,0.0005249159,0.9701084,0.00006315679,0.00007281319,0.00003308187,0.00001944666,0.00047885746,0.00196969],"genre_scores_gemma":[0.6893247,0.0009199167,0.3027489,0.00008779007,0.00012680588,0.000110257686,0.00013951349,0.00005044904,0.0064916634],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912876,0.00016300817,0.000028292285,0.0002765286,0.00034585595,0.000057487407],"domain_scores_gemma":[0.99944717,0.00010242337,0.000049406473,0.000092494396,0.00028274095,0.000025760814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000427912,0.00066851306,0.0005344801,0.0006963186,0.0003803955,0.00046479818,0.0011560841,0.0006094458,0.0009684846],"category_scores_gemma":[0.0010993917,0.0002831411,0.0005303255,0.00090340205,0.0003169115,0.0010905348,0.00066806184,0.00042894096,0.0005704454],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025440115,0.000111590896,0.0053880946,0.0002778614,0.0001913478,0.000361291,0.00064672704,0.13180621,0.10586435,0.010215285,0.0022255788,0.7426571],"study_design_scores_gemma":[0.000039536564,0.0005067654,0.0027066784,0.00001900498,0.00009886804,0.00062807987,0.00018519947,0.95851624,0.027056346,0.0019908263,0.008184358,0.000068093665],"about_ca_topic_score_codex":0.003299073,"about_ca_topic_score_gemma":0.0020825753,"teacher_disagreement_score":0.003299073,"about_ca_system_score_codex":0.00028382626,"about_ca_system_score_gemma":0.0005567123,"threshold_uncertainty_score":0.006559789},"labels":[],"label_agreement":null},{"id":"W3197013133","doi":"10.1109/tsp.2022.3171678","title":"DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Defence Research and Development Canada; Concordia University","funders":"","keywords":"Beacon; Non-line-of-sight propagation; Computer science; Multilateration; Node (physics); Real-time computing; RSS; Wireless; Artificial intelligence; Telecommunications; Engineering","score_opus":0.009969674578276248,"score_gpt":0.2189438162547185,"score_spread":0.20897414167644227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197013133","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018281337,0.00047857538,0.97842556,0.00032812005,0.00005946789,0.00004176761,0.00009755538,0.00087219407,0.001415529],"genre_scores_gemma":[0.8370986,0.00033824207,0.15571308,0.00079127745,0.000076122065,0.00018505435,0.0003654807,0.00011072949,0.005321429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946386,0.00014846573,0.00002467809,0.0001352434,0.00011409803,0.000113619506],"domain_scores_gemma":[0.99874216,0.0007398427,0.000102202765,0.00007175183,0.0002565242,0.00008752444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001395012,0.00077118154,0.0009013935,0.0004238217,0.0003268724,0.00065278227,0.0020468633,0.0011326418,0.002932904],"category_scores_gemma":[0.0032653536,0.00039040972,0.00035763285,0.000483942,0.00071489444,0.0011542537,0.0014611635,0.0013960241,0.0005194123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018946925,0.00017005939,0.0018037974,0.000112852875,0.00004657357,0.00011149917,0.000086705484,0.80814785,0.0030700548,0.006186282,0.004045815,0.17602907],"study_design_scores_gemma":[0.000011490559,0.00003831348,0.00006932569,0.0000042835327,0.000003816642,0.000011011145,0.000005900753,0.99739,0.00040221805,0.0017746828,0.0002858683,0.0000030434785],"about_ca_topic_score_codex":0.0055976696,"about_ca_topic_score_gemma":0.006373401,"teacher_disagreement_score":0.0055976696,"about_ca_system_score_codex":0.0008467972,"about_ca_system_score_gemma":0.0014784884,"threshold_uncertainty_score":0.011130214},"labels":[],"label_agreement":null},{"id":"W3198896842","doi":"10.1007/s11265-022-01752-9","title":"Online Dynamic Window (ODW) Assisted Two-Stage LSTM Frameworks For Indoor Localization","year":2022,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Inertial measurement unit; Context (archaeology); Artificial intelligence; Window (computing); Heading (navigation); Field (mathematics); Sliding window protocol; Real-time computing; Engineering","score_opus":0.013587254431149475,"score_gpt":0.26004785592222374,"score_spread":0.24646060149107427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198896842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0086400155,0.0005334561,0.98637044,0.00011176612,0.0001398543,0.000017448267,0.00021984613,0.002677175,0.0012898943],"genre_scores_gemma":[0.50926614,0.0009360197,0.47651052,0.00036774596,0.00020376469,0.00015650426,0.0013677004,0.0005825346,0.01060909],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997614,0.000041381703,0.000013922568,0.00007131472,0.00005776261,0.000054166056],"domain_scores_gemma":[0.9997694,0.000074831565,0.000015916765,0.00004004612,0.00008386846,0.00001585955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003711515,0.0010644307,0.00078147254,0.00040702467,0.00024660572,0.00064222387,0.0014797174,0.0010002847,0.005437325],"category_scores_gemma":[0.0010029639,0.00038760746,0.0005719158,0.00073002843,0.00021428702,0.0015464622,0.0011908471,0.0013853085,0.0021346675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038211606,0.00013978065,0.0004117281,0.00019810643,0.00009066548,0.00013532533,0.00010336377,0.12679814,0.033965237,0.005160528,0.008666204,0.8239489],"study_design_scores_gemma":[0.000009711381,0.000043820848,0.00015454681,0.000011164244,0.000018315444,0.000037295576,0.000019856952,0.9895432,0.0060866848,0.0023650546,0.0016997014,0.0000106921925],"about_ca_topic_score_codex":0.0058802464,"about_ca_topic_score_gemma":0.011752884,"teacher_disagreement_score":0.0058802464,"about_ca_system_score_codex":0.0002868169,"about_ca_system_score_gemma":0.000820588,"threshold_uncertainty_score":0.018189609},"labels":[],"label_agreement":null},{"id":"W3199955931","doi":"10.1109/jsen.2021.3114206","title":"Device-Free Activity Detection and Wireless Localization Based on CNN Using Channel State Information Measurement","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Channel state information; Channel (broadcasting); Convolutional neural network; Position (finance); Feature extraction; Artificial intelligence; Scheme (mathematics); Wireless; Feature (linguistics); Joint (building); Frequency domain; Pattern recognition (psychology); Algorithm; Electronic engineering; Computer vision; Telecommunications; Engineering; Mathematics","score_opus":0.022393830985434703,"score_gpt":0.21439437942199183,"score_spread":0.19200054843655712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199955931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032253876,0.000421307,0.9632399,0.00011325366,0.00009445801,0.000041687897,0.000119952936,0.0013678481,0.002347566],"genre_scores_gemma":[0.8378578,0.000440951,0.15529944,0.00016981745,0.00005999593,0.00009233871,0.00043435054,0.000056284847,0.0055890214],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999691,0.00003875943,0.000016986018,0.000104953506,0.00009528042,0.000052889613],"domain_scores_gemma":[0.999668,0.00008210067,0.000057276706,0.000081267535,0.00008805538,0.00002326234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031250317,0.0008765219,0.0005632135,0.00043355356,0.00021409955,0.00050307275,0.0013265578,0.00058413215,0.0013149111],"category_scores_gemma":[0.0009642425,0.00034885146,0.00038313578,0.0005132884,0.0003633574,0.0013084006,0.0010542033,0.0006006251,0.00049703434],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005799309,0.00021466696,0.004638047,0.0003516724,0.00021053459,0.00038763494,0.00014089633,0.1763023,0.109785065,0.008942691,0.0032951673,0.6951514],"study_design_scores_gemma":[0.000012626824,0.000074768985,0.0013180787,0.000012164832,0.00003764719,0.00012858742,0.000014086334,0.97245705,0.02326517,0.0013630048,0.0012992395,0.000017483442],"about_ca_topic_score_codex":0.004024347,"about_ca_topic_score_gemma":0.00597316,"teacher_disagreement_score":0.004024347,"about_ca_system_score_codex":0.00051318156,"about_ca_system_score_gemma":0.0006105199,"threshold_uncertainty_score":0.008001864},"labels":[],"label_agreement":null},{"id":"W3202292180","doi":"10.1109/bmsb53066.2021.9547072","title":"Novel Device-Free Indoor Human Localization using Wireless Radio-Frequency Fingerprinting","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Fingerprint (computing); Computer science; FEKO; Transceiver; Wireless; Real-time computing; Trilateration; Fingerprint recognition; Random forest; Received signal strength indication; Channel (broadcasting); Tracking (education); Radio frequency; Artificial intelligence; Computer vision; Antenna (radio); Telecommunications; Acoustics","score_opus":0.024066790719638475,"score_gpt":0.2436791134432031,"score_spread":0.21961232272356462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202292180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016010718,0.00020897534,0.98196375,0.00003535473,0.00003014097,0.000016266333,0.000044183387,0.0010865527,0.000604191],"genre_scores_gemma":[0.530067,0.00041688103,0.46686172,0.0001218435,0.00005996589,0.00008447857,0.00022897591,0.00005974755,0.0020993224],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99951863,0.00009575384,0.00001665448,0.00011391825,0.0001889421,0.000066024826],"domain_scores_gemma":[0.99966466,0.00008631283,0.000055192766,0.00008042487,0.00008964413,0.000023856737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038450735,0.0005266959,0.00074217183,0.0007748776,0.00034436467,0.00044785943,0.0011909356,0.00066696474,0.0008441283],"category_scores_gemma":[0.0008724445,0.00023114277,0.00044223343,0.00088468345,0.0002750653,0.0011131549,0.0007908036,0.00037722522,0.0008452563],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033068817,0.0001936441,0.0068932185,0.00027406294,0.00010608341,0.00042410957,0.0001991321,0.056041785,0.114020176,0.0032498047,0.0030101778,0.81525713],"study_design_scores_gemma":[0.000062320825,0.00041951376,0.0053995475,0.0000457332,0.00010415476,0.0018743738,0.000059511993,0.90153235,0.081287205,0.0023169718,0.0067960485,0.00010222929],"about_ca_topic_score_codex":0.0012970323,"about_ca_topic_score_gemma":0.0017489592,"teacher_disagreement_score":0.0012970323,"about_ca_system_score_codex":0.00018333914,"about_ca_system_score_gemma":0.00036755853,"threshold_uncertainty_score":0.0028238893},"labels":[],"label_agreement":null},{"id":"W3203691443","doi":"10.1109/bmsb53066.2021.9547179","title":"Novel Indoor Device-Free Human Tracking Using Learning Systems with Hidden Markov Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Viterbi algorithm; Hidden Markov model; Computer science; Discriminative model; Artificial intelligence; Classifier (UML); Decision tree; Markov chain; Pattern recognition (psychology); Machine learning; Boosting (machine learning); Computer vision","score_opus":0.03507088450501152,"score_gpt":0.23463663858823508,"score_spread":0.19956575408322355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203691443","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016851798,0.00021686518,0.98111,0.00006338485,0.00003815677,0.000017932067,0.00003379584,0.0008807209,0.00078735204],"genre_scores_gemma":[0.7960243,0.00027301334,0.20072567,0.00013773766,0.000072195435,0.00006974901,0.00021108527,0.00006471606,0.0024214091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993197,0.00016625307,0.000034557102,0.0002158106,0.0001763659,0.00008744294],"domain_scores_gemma":[0.99928916,0.00030569462,0.000102701575,0.00011352351,0.00014261747,0.000046265362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009235431,0.000594589,0.00091697613,0.000499913,0.00051045156,0.0006716995,0.0012674454,0.0006787666,0.00072377553],"category_scores_gemma":[0.0017885648,0.0004775665,0.0006413507,0.00067151873,0.0004092006,0.0010795519,0.0010945988,0.00083759904,0.0005634986],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003167295,0.00015279363,0.0059315218,0.0001295698,0.00014696221,0.000198786,0.00023120598,0.60050315,0.011857287,0.009203824,0.0021587124,0.3691695],"study_design_scores_gemma":[0.0000059466906,0.000023573173,0.0002808715,0.0000031206202,0.00000967694,0.000025679698,0.0000038851977,0.9972887,0.0010656309,0.0009966805,0.00028918218,0.0000070175065],"about_ca_topic_score_codex":0.005782414,"about_ca_topic_score_gemma":0.005720358,"teacher_disagreement_score":0.005782414,"about_ca_system_score_codex":0.000532732,"about_ca_system_score_gemma":0.0007541487,"threshold_uncertainty_score":0.011497557},"labels":[],"label_agreement":null},{"id":"W3204093416","doi":"10.1109/jsen.2021.3116930","title":"Toward Developing an Indoor Localization System for MAVs Using Two or Three RF Range Anchors: An Observability Based Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Observability; Unobservable; Range (aeronautics); Control theory (sociology); Nonlinear system; Trajectory; Inertial navigation system; Computer science; Inertial frame of reference; Engineering; Mathematics; Aerospace engineering; Physics; Applied mathematics; Artificial intelligence","score_opus":0.10785047180066892,"score_gpt":0.2947275086884602,"score_spread":0.18687703688779128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204093416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011956213,0.00007409176,0.9866263,0.0000541004,0.000009687669,0.000010548509,0.000009172494,0.0001308509,0.0011290552],"genre_scores_gemma":[0.859832,0.00036407204,0.13731427,0.000058200385,0.00003759108,0.00007705513,0.00007797726,0.000041713865,0.0021971322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998491,0.00003176215,0.0000071376458,0.000040798655,0.00005144455,0.000019733174],"domain_scores_gemma":[0.999765,0.00007038613,0.0000561257,0.000031265903,0.00006634291,0.000010835751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024681978,0.00048607998,0.0002646849,0.0002601542,0.00025737032,0.0005037678,0.00048505433,0.0003702184,0.00073342223],"category_scores_gemma":[0.0006449137,0.00019632748,0.00041820164,0.0001721863,0.00049561576,0.0007374901,0.0007754527,0.0005179269,0.00019859165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000683145,0.000037733287,0.0028731623,0.00016808162,0.000043045533,0.0002991238,0.0002543823,0.84980845,0.04882783,0.029663417,0.0004988702,0.06745763],"study_design_scores_gemma":[0.0000041591397,0.00005159168,0.00042658288,0.00000673542,0.000009179295,0.00003809501,0.0000416136,0.9925097,0.003583605,0.0022700848,0.0010511312,0.0000074412374],"about_ca_topic_score_codex":0.0032295128,"about_ca_topic_score_gemma":0.002499262,"teacher_disagreement_score":0.0032295128,"about_ca_system_score_codex":0.00035209706,"about_ca_system_score_gemma":0.0005315867,"threshold_uncertainty_score":0.006421387},"labels":[],"label_agreement":null},{"id":"W3208086732","doi":"10.1109/ccece53047.2021.9569057","title":"Satellite Image and Received Signal-based Outdoor Localization using Deep Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Global Positioning System; Artificial neural network; Geographic coordinate system; Artificial intelligence; Satellite; Deep learning; Computer vision; Remote sensing; Channel (broadcasting); Real-time computing; Telecommunications; Geography; Geodesy; Engineering","score_opus":0.01042961533521887,"score_gpt":0.21300643332810631,"score_spread":0.20257681799288746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208086732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11258711,0.00062720635,0.8783058,0.00019047088,0.000117139534,0.000022767428,0.0002790709,0.0027261223,0.00514436],"genre_scores_gemma":[0.90654844,0.00023908181,0.08758282,0.000119368706,0.00006588672,0.00002395877,0.0005276276,0.00005549821,0.004837281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998597,0.000019853645,0.0000058362098,0.000042974803,0.00004058832,0.000030940133],"domain_scores_gemma":[0.9998671,0.000026842,0.000028447112,0.000019234221,0.00004958743,0.0000087318995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015077242,0.0005648381,0.00031471567,0.00052317994,0.00016803204,0.00035762636,0.00055200624,0.0004127609,0.0010756652],"category_scores_gemma":[0.00048812345,0.00016065178,0.00030718642,0.0007465062,0.0002018113,0.00048361553,0.00039355346,0.00037657216,0.00049539167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003216875,0.00014208596,0.0071294587,0.00013477443,0.00011473522,0.00026290218,0.00011859404,0.34494135,0.04136659,0.002887238,0.0034464716,0.59913415],"study_design_scores_gemma":[0.000007952225,0.000042051706,0.0022514993,0.000010870855,0.000019313047,0.000060137045,0.000023422335,0.9863789,0.009030745,0.0009092039,0.0012551879,0.0000107584265],"about_ca_topic_score_codex":0.0073654004,"about_ca_topic_score_gemma":0.0106428815,"teacher_disagreement_score":0.0073654004,"about_ca_system_score_codex":0.00035545317,"about_ca_system_score_gemma":0.00029340334,"threshold_uncertainty_score":0.01464504},"labels":[],"label_agreement":null},{"id":"W3208087516","doi":"10.1109/pimrc50174.2021.9569433","title":"Efficient Non-Line-of-Sight Identification in Localization Using a Bank of Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Identification (biology); Artificial neural network; Line (geometry); Wireless; Algorithm; Set (abstract data type); Nonlinear system; Line-of-sight; Artificial intelligence; Machine learning; Telecommunications; Mathematics; Engineering","score_opus":0.012771364713384041,"score_gpt":0.2332389059742325,"score_spread":0.22046754126084844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208087516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042022992,0.00033862394,0.95478857,0.00017986615,0.00005811599,0.00003228155,0.00003040839,0.00069834595,0.0018508324],"genre_scores_gemma":[0.8423293,0.00028705664,0.15349364,0.00016276627,0.0000512877,0.00011440216,0.00009483322,0.000031112995,0.0034355887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969447,0.00007521387,0.000021509262,0.0000784913,0.00007681065,0.000053508502],"domain_scores_gemma":[0.99917775,0.00038306846,0.00009125282,0.000061361185,0.0002649445,0.00002165618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007479114,0.0005311417,0.00060786674,0.00032342476,0.00045389557,0.00060841563,0.0009027377,0.00095975015,0.0012621195],"category_scores_gemma":[0.0018982873,0.00038049146,0.0003005576,0.00041201813,0.0004646287,0.0009831229,0.0005875239,0.0009413129,0.00039735908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015958655,0.00011193888,0.0012748668,0.000056515542,0.000046694324,0.00007005825,0.000045888002,0.81878453,0.005171613,0.0027692725,0.00097196014,0.17053713],"study_design_scores_gemma":[0.0000019899326,0.00001264488,0.00008743343,0.0000021454057,0.0000032181788,0.00000789604,0.0000028729457,0.99876034,0.00075687794,0.00028141827,0.00008093476,0.0000022279921],"about_ca_topic_score_codex":0.0059854575,"about_ca_topic_score_gemma":0.007124825,"teacher_disagreement_score":0.0059854575,"about_ca_system_score_codex":0.00054372457,"about_ca_system_score_gemma":0.0008093806,"threshold_uncertainty_score":0.011901259},"labels":[],"label_agreement":null},{"id":"W3210294081","doi":"10.1145/3488281","title":"Wireless Localization with Spatial-Temporal Robust Fingerprints","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; RSS; Fingerprint (computing); Ambiguity; Multipath propagation; Software deployment; Key (lock); Wireless; Real-time computing; Fingerprint recognition; Representation (politics); Wireless sensor network; Artificial intelligence; Data mining; Telecommunications; Computer security; Computer network; World Wide Web","score_opus":0.011563743726465393,"score_gpt":0.19582652673206855,"score_spread":0.18426278300560314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210294081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02127952,0.0004272769,0.9750381,0.000103091734,0.00004536856,0.00002316731,0.000087413166,0.0014148913,0.0015811153],"genre_scores_gemma":[0.69037765,0.0007509573,0.3050436,0.0001299072,0.000097635544,0.0000592368,0.00022758647,0.000094026815,0.0032193777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936396,0.00014007371,0.000028699502,0.00011630872,0.0002684005,0.000082508275],"domain_scores_gemma":[0.99941945,0.00012517151,0.0001233024,0.00018430529,0.00012388041,0.000023878833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042587932,0.0005054252,0.0004314896,0.00063762226,0.00023724294,0.0006875801,0.0008894361,0.0005310063,0.00091241853],"category_scores_gemma":[0.0022836744,0.00026490758,0.00033787376,0.0011482041,0.00040065675,0.001342778,0.001496982,0.00046026552,0.0006779475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047403265,0.00007961112,0.0030571239,0.00026621117,0.00008252274,0.00033104178,0.00020429755,0.13452965,0.13614288,0.021981983,0.0034923872,0.6993584],"study_design_scores_gemma":[0.00007162611,0.00051090727,0.003258278,0.00006244363,0.000086971464,0.0017497606,0.00012519807,0.87732226,0.08610456,0.01166971,0.018925475,0.0001128181],"about_ca_topic_score_codex":0.001215275,"about_ca_topic_score_gemma":0.0015980212,"teacher_disagreement_score":0.001215275,"about_ca_system_score_codex":0.00024034227,"about_ca_system_score_gemma":0.00039992502,"threshold_uncertainty_score":0.0030523539},"labels":[],"label_agreement":null},{"id":"W3210566209","doi":"10.1109/icc45855.2022.9838844","title":"Self-Supervised Radio-Visual Representation Learning for 6G Sensing","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Leverage (statistics); Scalability; Artificial intelligence; Machine learning; Representation (politics); Radio frequency; Benchmark (surveying); Feature learning; Telecommunications","score_opus":0.07512378077141116,"score_gpt":0.3386144584261083,"score_spread":0.26349067765469714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210566209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032724623,0.00025172255,0.96351266,0.00024527588,0.000057952362,0.000042658186,0.00012634056,0.0014853848,0.0015533131],"genre_scores_gemma":[0.8517878,0.00015148855,0.14392544,0.00031809544,0.0001094725,0.00009832199,0.00072592386,0.00015793864,0.0027255826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992029,0.0002613901,0.000029675288,0.00024056557,0.00016190457,0.00010364481],"domain_scores_gemma":[0.9980901,0.00079484144,0.00023662025,0.0004402977,0.00034533397,0.00009276376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013304353,0.0009555753,0.0008677945,0.0005583605,0.00035381634,0.0008651081,0.0020022048,0.0011547793,0.0012590021],"category_scores_gemma":[0.0047718356,0.00037482864,0.00060770917,0.00063025934,0.0008915399,0.001662473,0.0014580882,0.0015502261,0.00070403586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003994096,0.0003286619,0.0027676371,0.0001803593,0.00010256238,0.00011357341,0.00020283295,0.6333757,0.01430476,0.00821955,0.0070341486,0.33297077],"study_design_scores_gemma":[0.0000053842996,0.000028347424,0.00020272762,0.000004471632,0.000004319295,0.000019117968,0.0000098753835,0.99440396,0.0018642929,0.0031330704,0.00031885717,0.0000056228328],"about_ca_topic_score_codex":0.0027507178,"about_ca_topic_score_gemma":0.0032414016,"teacher_disagreement_score":0.0027507178,"about_ca_system_score_codex":0.0006697403,"about_ca_system_score_gemma":0.00065542985,"threshold_uncertainty_score":0.00703609},"labels":[],"label_agreement":null},{"id":"W3215177466","doi":"10.23977/jaip.2020.040108","title":"Ultra-wideband (UWB) precise location problem under signal interference based on Shark optimization algorithm","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ultra-wideband; Multipath propagation; Interference (communication); Computer science; Algorithm; Optimization problem; Electronic engineering; Engineering; Telecommunications","score_opus":0.02770249681916532,"score_gpt":0.2786256198608083,"score_spread":0.250923123041643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215177466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011135437,0.00015108663,0.98724735,0.00011925294,0.000017628316,0.0000144279065,0.00001726997,0.00007422172,0.0012233098],"genre_scores_gemma":[0.65299654,0.0007511934,0.33759898,0.00014616142,0.00004965527,0.00024887035,0.00021182642,0.00011189776,0.007884796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996045,0.00012109617,0.000023550818,0.00010776003,0.00010341135,0.00003958905],"domain_scores_gemma":[0.99957925,0.00022540388,0.00005408197,0.000017080863,0.00010921514,0.0000149366615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007546239,0.00066923565,0.0011677308,0.0004522626,0.00043306968,0.000855017,0.000624876,0.0008352789,0.001146474],"category_scores_gemma":[0.0011945075,0.00043971612,0.0006280668,0.0007718494,0.0006838474,0.0010658457,0.00078961236,0.0007428876,0.00022344725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036244845,0.000017278697,0.00048466722,0.000054467375,0.00002629611,0.000045700563,0.000043853797,0.9690148,0.0011133465,0.006136121,0.0006255859,0.022401663],"study_design_scores_gemma":[0.0000032232474,0.000012952058,0.00006960172,0.0000024157082,0.0000032599917,0.000008798042,0.000009141914,0.9982919,0.0001729057,0.0012835134,0.00013902057,0.0000033405354],"about_ca_topic_score_codex":0.006094826,"about_ca_topic_score_gemma":0.0023459261,"teacher_disagreement_score":0.006094826,"about_ca_system_score_codex":0.00060230703,"about_ca_system_score_gemma":0.001032438,"threshold_uncertainty_score":0.012118697},"labels":[],"label_agreement":null},{"id":"W3215291346","doi":"10.3390/s21238011","title":"A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voronoi diagram; Software deployment; Computer science; Genetic algorithm; Wireless sensor network; CMA-ES; Adaptation (eye); Real-time computing; Evolution strategy; Optimization problem; Distributed computing; Evolutionary algorithm; Artificial intelligence; Algorithm; Machine learning; Computer network","score_opus":0.015565399974670479,"score_gpt":0.24256489336369133,"score_spread":0.22699949338902084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215291346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032097395,0.000136486,0.99565095,0.000039808278,0.000014620337,0.000021627828,0.000029027804,0.000118409436,0.00077928556],"genre_scores_gemma":[0.29526764,0.00045026606,0.70079505,0.000076389275,0.000043682772,0.00032305674,0.00019421188,0.00014948196,0.00270019],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996935,0.00010137863,0.000013444001,0.00005542449,0.00010622759,0.000030080482],"domain_scores_gemma":[0.9995983,0.0002100833,0.00004137586,0.000020908521,0.000102535436,0.000026858366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005441362,0.0005829919,0.00077304523,0.000932421,0.00042064578,0.00062515895,0.0010826038,0.0006720815,0.0018858829],"category_scores_gemma":[0.0013464034,0.000347851,0.0007341981,0.0009402618,0.00040245763,0.0007206584,0.0008391072,0.0004536257,0.00037407386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028525901,0.000013932969,0.0003783751,0.000051958472,0.00001943128,0.000032062133,0.000043841843,0.9505816,0.0017072284,0.006948218,0.00070521166,0.039489653],"study_design_scores_gemma":[0.0000037608077,0.0000073262295,0.000036227902,0.000002447727,0.0000020777136,0.0000122074425,0.0000052304504,0.99821484,0.00027405168,0.0008922353,0.0005460962,0.0000034482623],"about_ca_topic_score_codex":0.008402646,"about_ca_topic_score_gemma":0.008787532,"teacher_disagreement_score":0.008402646,"about_ca_system_score_codex":0.0008376717,"about_ca_system_score_gemma":0.00120145,"threshold_uncertainty_score":0.01670748},"labels":[],"label_agreement":null},{"id":"W3215381541","doi":"10.1145/3479243.3487303","title":"Autonomous Vehicle Navigation and Communication by Passive Radio Frequency (RFID) Tags","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Ultra high frequency; Radio-frequency identification; Computer science; Global Positioning System; Antenna (radio); Radio frequency; Track (disk drive); Real-time computing; Telecommunications","score_opus":0.004404066221144034,"score_gpt":0.1918990283213434,"score_spread":0.18749496210019936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215381541","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4953795,0.0014424208,0.49128267,0.00012231339,0.00012714177,0.000060552862,0.000076735014,0.0009994078,0.010509219],"genre_scores_gemma":[0.94360673,0.00041778391,0.050091874,0.00005430285,0.000033202392,0.000024935694,0.00010776757,0.00002485516,0.005638674],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941564,0.00015628843,0.000019288032,0.000117456635,0.00023803225,0.000053268916],"domain_scores_gemma":[0.9993594,0.00021997848,0.000115678,0.00008516352,0.00019807043,0.000021650741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040595577,0.0003832505,0.00021747289,0.00039500915,0.00022098298,0.00069522817,0.00056107296,0.00066083565,0.0008488783],"category_scores_gemma":[0.0009900088,0.00016331264,0.00020949182,0.00035159846,0.00035534953,0.0014413066,0.00044176384,0.00016549838,0.0005733966],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009012066,0.00019432201,0.037648026,0.000558797,0.000104567356,0.0003738332,0.00061681366,0.036929775,0.43765825,0.006847177,0.0012880584,0.4768793],"study_design_scores_gemma":[0.00009973459,0.006530645,0.037618447,0.00013720899,0.00032602067,0.0033320186,0.0011042125,0.27009037,0.5973628,0.0046842285,0.07853908,0.00017521303],"about_ca_topic_score_codex":0.0010451245,"about_ca_topic_score_gemma":0.0011392051,"teacher_disagreement_score":0.0010451245,"about_ca_system_score_codex":0.00020921795,"about_ca_system_score_gemma":0.00021705609,"threshold_uncertainty_score":0.0028398037},"labels":[],"label_agreement":null},{"id":"W3216259880","doi":"10.1109/5gwf52925.2021.00047","title":"Long Short-Term Memory for Indoor Localization Using WI-FI Received Signal Strength and Channel State Information","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; CMC Microsystems","keywords":"RSS; Non-line-of-sight propagation; Computer science; Channel state information; Channel (broadcasting); Range (aeronautics); Signal strength; Term (time); SIGNAL (programming language); Speech recognition; Matrix (chemical analysis); Confusion; State (computer science); Artificial intelligence; Algorithm; Wireless; Telecommunications; Engineering","score_opus":0.014609158977597047,"score_gpt":0.2260062260818615,"score_spread":0.21139706710426445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216259880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23049052,0.0018533523,0.75759166,0.0007011212,0.0002741475,0.0000450477,0.0010822805,0.0039598714,0.0040019816],"genre_scores_gemma":[0.92979616,0.0005217322,0.065390125,0.00016221963,0.000042308435,0.00005726952,0.0012058364,0.00008478848,0.0027395743],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998276,0.000028930399,0.000013229673,0.000050647242,0.000043594246,0.000035903144],"domain_scores_gemma":[0.999461,0.00024147736,0.000054606862,0.00007865113,0.00014322008,0.000021066056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006003472,0.00072380796,0.00040746725,0.00035069627,0.0002776858,0.00052663294,0.0008621842,0.00070298056,0.0022324924],"category_scores_gemma":[0.0019760937,0.00021076023,0.00029799397,0.00061994087,0.00029323835,0.0012412226,0.00064348197,0.0009886223,0.00086353876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004936764,0.0001692639,0.0051035658,0.0002697755,0.00013040616,0.00023714747,0.00012500798,0.4682195,0.029512445,0.0035100724,0.0041899052,0.48803926],"study_design_scores_gemma":[0.000008055762,0.000115951596,0.0012768375,0.0000214556,0.000024926247,0.000074283016,0.00002718003,0.9820258,0.013246706,0.0023139054,0.00084906426,0.000015809976],"about_ca_topic_score_codex":0.006402356,"about_ca_topic_score_gemma":0.0104466,"teacher_disagreement_score":0.006402356,"about_ca_system_score_codex":0.00047076808,"about_ca_system_score_gemma":0.00068349164,"threshold_uncertainty_score":0.012730181},"labels":[],"label_agreement":null},{"id":"W3217472497","doi":"10.1109/lsp.2021.3130504","title":"Uncertainty Estimation via Monte Carlo Dropout in CNN-Based mmWave MIMO Localization","year":2021,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Monte Carlo method; MIMO; Convolutional neural network; Expectation propagation; Algorithm; Bayesian inference; Inference; Bayesian probability; Artificial intelligence; Beamforming; Mathematics; Telecommunications; Statistics","score_opus":0.00826032944837046,"score_gpt":0.21246265854265614,"score_spread":0.20420232909428568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217472497","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040927283,0.00026549268,0.95692486,0.00020207463,0.000025202218,0.000021836842,0.00005423496,0.0003792182,0.001199769],"genre_scores_gemma":[0.945173,0.00019470167,0.052867897,0.00014349323,0.00002777359,0.000060280112,0.00012465031,0.00005463614,0.0013536103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962294,0.000112661786,0.000021721233,0.000072108836,0.00011224846,0.000058377304],"domain_scores_gemma":[0.9982457,0.0011369524,0.00022961036,0.000099292665,0.00022582865,0.000062655774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014200514,0.0005913219,0.0007141566,0.00043515782,0.00038472863,0.0006092652,0.0012278132,0.00081246876,0.00092212483],"category_scores_gemma":[0.004493424,0.00052262895,0.00040190303,0.00041629738,0.0009238216,0.0011483314,0.0010965122,0.00095576106,0.00013648863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007851029,0.000010902907,0.0009279627,0.000025789524,0.0000146488155,0.000048075424,0.000023877948,0.9829496,0.0008336732,0.003250746,0.00016467429,0.0116715],"study_design_scores_gemma":[0.0000010858379,0.000003996371,0.00006873505,0.000001761689,0.0000015070882,0.0000050885183,0.0000012557582,0.9990426,0.0003307968,0.00050489727,0.000036317364,0.0000019027501],"about_ca_topic_score_codex":0.012912363,"about_ca_topic_score_gemma":0.011737866,"teacher_disagreement_score":0.012912363,"about_ca_system_score_codex":0.0016561986,"about_ca_system_score_gemma":0.0009763095,"threshold_uncertainty_score":0.025674403},"labels":[],"label_agreement":null},{"id":"W4200017925","doi":"10.1016/j.ins.2021.12.022","title":"WSN optimization for sampling-based signal estimation using semi-binarized variational autoencoder","year":2021,"lang":"en","type":"article","venue":"Information Sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"Autoencoder; Computer science; SIGNAL (programming language); Sampling (signal processing); Pattern recognition (psychology); Mean squared error; Artificial intelligence; Feature (linguistics); Noise (video); Encoding (memory); Data mining; Algorithm; Deep learning; Statistics; Mathematics; Computer vision; Image (mathematics)","score_opus":0.03700843849503497,"score_gpt":0.28215579410997427,"score_spread":0.2451473556149393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200017925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049265935,0.000116403404,0.994393,0.00005076803,0.000014486547,0.0000110772635,0.000024054485,0.00007931249,0.00038427618],"genre_scores_gemma":[0.5828327,0.0006554816,0.40970537,0.00014786015,0.00007903417,0.00027553664,0.00049615995,0.00022338997,0.0055844537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996855,0.00010058333,0.00001768124,0.00008102758,0.00008132548,0.000033949254],"domain_scores_gemma":[0.99934596,0.00041201126,0.000052412106,0.000037295416,0.00013114272,0.000021185757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093931984,0.00084861997,0.0010246909,0.00039962315,0.0002538561,0.00062713906,0.00092311285,0.0010097553,0.0014038774],"category_scores_gemma":[0.002371507,0.00067092676,0.00086414575,0.000602888,0.0007217144,0.0009319147,0.0011077109,0.001036575,0.00030012324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042895783,0.000017735785,0.000329507,0.000064493026,0.000036829326,0.000022068773,0.000031476473,0.9715848,0.0022935783,0.0041846214,0.00042454194,0.02096745],"study_design_scores_gemma":[0.0000010198003,0.0000041115545,0.000033826323,0.0000016916986,0.0000016278063,0.0000028718312,0.0000018600932,0.99923205,0.0001575813,0.0004958373,0.00006631342,0.0000013315957],"about_ca_topic_score_codex":0.008989311,"about_ca_topic_score_gemma":0.0066109193,"teacher_disagreement_score":0.008989311,"about_ca_system_score_codex":0.0006548776,"about_ca_system_score_gemma":0.0011475512,"threshold_uncertainty_score":0.017873943},"labels":[],"label_agreement":null},{"id":"W4200100360","doi":"10.32920/17303813","title":"Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Strong","keywords":"Software deployment; Flexibility (engineering); Computer science; Context (archaeology); Indoor positioning system; Real-time computing; Artificial neural network; Scalability; Artificial intelligence; Accelerometer","score_opus":0.0373299826625816,"score_gpt":0.2534180924300665,"score_spread":0.21608810976748494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200100360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00623329,0.00008132266,0.9912513,0.00008734556,0.000024800946,0.000026699192,0.00001669568,0.00041910875,0.0018594505],"genre_scores_gemma":[0.61986154,0.00022198557,0.37024185,0.00013344188,0.000056683104,0.00017645785,0.000115426505,0.00006862889,0.00912396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996037,0.00007184414,0.000023543882,0.00013505649,0.00012000894,0.000045877397],"domain_scores_gemma":[0.9997446,0.000056368408,0.000048011094,0.0000365269,0.000104160805,0.00001026827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040101702,0.00065851945,0.00038829443,0.00032026958,0.0004338883,0.00072698586,0.0008731954,0.00085546245,0.0030212037],"category_scores_gemma":[0.00068341277,0.0002924022,0.0004252347,0.00025396503,0.00042020212,0.0006564492,0.00071886205,0.0007576773,0.0009055673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016989834,0.000070454116,0.0011758255,0.00020089067,0.000081203936,0.00026193124,0.00016199294,0.6119317,0.06708994,0.0116118705,0.0019316814,0.3053126],"study_design_scores_gemma":[0.0000074600657,0.000062395266,0.00027469842,0.000010353948,0.000016285581,0.00006329757,0.00001724369,0.98742175,0.009193561,0.0012671968,0.0016569286,0.00000886082],"about_ca_topic_score_codex":0.0032264797,"about_ca_topic_score_gemma":0.0038262878,"teacher_disagreement_score":0.0032264797,"about_ca_system_score_codex":0.0006585358,"about_ca_system_score_gemma":0.00062542775,"threshold_uncertainty_score":0.010106981},"labels":[],"label_agreement":null},{"id":"W4200248959","doi":"10.5121/csit.2021.112201","title":"Anchor Density Minimization for Localization in Wireless Sensor Network (WSN)","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Wireless sensor network; Software deployment; Computer science; Flexibility (engineering); Computer network; Network topology; Minification; Key distribution in wireless sensor networks; Reduction (mathematics); Position (finance); A priori and a posteriori; Distributed computing; Real-time computing; Wireless; Topology (electrical circuits); Wireless network; Engineering; Mathematics; Telecommunications","score_opus":0.009456430539975759,"score_gpt":0.21066459006356725,"score_spread":0.2012081595235915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200248959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011109896,0.0007884465,0.9862363,0.0001469308,0.0000359323,0.000031413303,0.000041752934,0.0002178391,0.0013915116],"genre_scores_gemma":[0.5458661,0.003058215,0.4446928,0.00014056796,0.000117420044,0.00029502818,0.00033834085,0.0002121042,0.0052795126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999326,0.0002679379,0.000026617863,0.00010722857,0.00022497958,0.000047268506],"domain_scores_gemma":[0.9992455,0.0004369053,0.00009388062,0.00007167607,0.0001321013,0.000019935813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009345132,0.0009223441,0.00080101046,0.00084394286,0.00037806403,0.00047673506,0.00085196696,0.0007409824,0.001240861],"category_scores_gemma":[0.0036236336,0.00037554724,0.00057661365,0.0011217057,0.0006256907,0.0012322102,0.0011017565,0.00055913,0.0005428557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015368365,0.00006402144,0.0013503818,0.00040226485,0.00007286628,0.00014085625,0.00017926891,0.81160265,0.012660494,0.01817931,0.00299185,0.15220244],"study_design_scores_gemma":[0.000007343632,0.00009683224,0.00052089855,0.000025542875,0.000017753642,0.00010896563,0.000045609326,0.9866859,0.0037457456,0.0063959677,0.0023377433,0.000011781155],"about_ca_topic_score_codex":0.0017158125,"about_ca_topic_score_gemma":0.0011355982,"teacher_disagreement_score":0.0017158125,"about_ca_system_score_codex":0.000599467,"about_ca_system_score_gemma":0.00044103005,"threshold_uncertainty_score":0.0049422383},"labels":[],"label_agreement":null},{"id":"W4200263087","doi":"10.1109/vtc2021-fall52928.2021.9625387","title":"Rigid Body Localization and Environment Sensing with 5G Millimeter Wave MIMO","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Singular value decomposition; Specular reflection; Rigid body; Computer vision; Position (finance); Channel (broadcasting); Compressed sensing; Artificial intelligence; Algorithm; Wireless; Telecommunications; Physics; Optics","score_opus":0.008840740403978978,"score_gpt":0.18469389803806951,"score_spread":0.17585315763409054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200263087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032070704,0.00047682005,0.9617506,0.00024552178,0.00010819883,0.000030423596,0.00006909519,0.00069012784,0.004558457],"genre_scores_gemma":[0.8261949,0.00065714726,0.16828899,0.000248724,0.000099357465,0.00006492723,0.00018112718,0.000025096502,0.004239749],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956626,0.000114721974,0.000015187926,0.00007295595,0.00015681577,0.00007408159],"domain_scores_gemma":[0.99981433,0.000041744086,0.00003750533,0.000046156023,0.000045856923,0.000014368593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002484901,0.00074340566,0.00047157382,0.000460248,0.00039498167,0.00050568546,0.00053549284,0.00055877946,0.00092782156],"category_scores_gemma":[0.00070303923,0.00024332215,0.0004340378,0.0006582067,0.00040470442,0.0008018501,0.0011800318,0.00052883965,0.00049477536],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004592067,0.0000932915,0.0046259956,0.00031323524,0.00013426189,0.0009759837,0.00034340375,0.23489046,0.15043387,0.02383478,0.004844587,0.5790509],"study_design_scores_gemma":[0.000041787247,0.00033257512,0.0022929395,0.000025775995,0.000061354134,0.00075325987,0.00013397729,0.94982046,0.03576032,0.004879493,0.005843453,0.000054700748],"about_ca_topic_score_codex":0.0026995724,"about_ca_topic_score_gemma":0.0035978546,"teacher_disagreement_score":0.0026995724,"about_ca_system_score_codex":0.0002450569,"about_ca_system_score_gemma":0.00041268382,"threshold_uncertainty_score":0.0053676963},"labels":[],"label_agreement":null},{"id":"W4200294516","doi":"10.1109/iscc53001.2021.9631433","title":"Machine Learning-Enabled Localization in 5G using LIDAR and RSS Data","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Symposium on Computers and Communications (ISCC)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"RSS; Lidar; Computer science; Convolutional neural network; Global Positioning System; Signal strength; Bandwidth (computing); Artificial intelligence; Real-time computing; Remote sensing; Computer vision; Data mining; Telecommunications; Geography; Wireless","score_opus":0.028021652267999186,"score_gpt":0.2502637704874839,"score_spread":0.2222421182194847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200294516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27578425,0.0013166189,0.7043807,0.0009516323,0.0003206255,0.0000724473,0.000492381,0.0069056894,0.009775588],"genre_scores_gemma":[0.95003533,0.00024643398,0.047791936,0.0001299353,0.000029176354,0.000018166354,0.00024744147,0.000023450879,0.0014781205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997904,0.000040161627,0.000009903919,0.000039495942,0.000067400746,0.000052615796],"domain_scores_gemma":[0.99979013,0.00006724535,0.000027613494,0.000029897314,0.00007583981,0.000009255115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003224079,0.00054372224,0.00035794528,0.0005324033,0.00021447036,0.00038569153,0.0006416891,0.0006360181,0.0013566596],"category_scores_gemma":[0.00081352703,0.00015583428,0.0003152535,0.00064282765,0.0002287506,0.0005905528,0.0003966338,0.0003789723,0.00050019496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037819316,0.0001885239,0.011453151,0.00019659495,0.00013239366,0.00061921874,0.000079012825,0.53050154,0.031858224,0.0032837212,0.0041500586,0.41715935],"study_design_scores_gemma":[0.00001005001,0.00009892147,0.0027482265,0.000018409466,0.000021983442,0.000113878115,0.000019842833,0.98394233,0.010663951,0.0009047335,0.0014439522,0.000013774359],"about_ca_topic_score_codex":0.010542085,"about_ca_topic_score_gemma":0.017377937,"teacher_disagreement_score":0.010542085,"about_ca_system_score_codex":0.00042244766,"about_ca_system_score_gemma":0.00038299814,"threshold_uncertainty_score":0.020961463},"labels":[],"label_agreement":null},{"id":"W4200353083","doi":"10.32920/17303813.v1","title":"Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Strong","keywords":"Software deployment; Flexibility (engineering); Computer science; Context (archaeology); Real-time computing; Indoor positioning system; Scalability; Artificial neural network; Hybrid positioning system; Positioning system; Artificial intelligence; Engineering; Accelerometer","score_opus":0.0373299826625816,"score_gpt":0.2534180924300665,"score_spread":0.21608810976748494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200353083","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00623329,0.00008132266,0.9912513,0.00008734556,0.000024800946,0.000026699192,0.00001669568,0.00041910875,0.0018594505],"genre_scores_gemma":[0.61986154,0.00022198557,0.37024185,0.00013344188,0.000056683104,0.00017645785,0.000115426505,0.00006862889,0.00912396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996037,0.00007184414,0.000023543882,0.00013505649,0.00012000894,0.000045877397],"domain_scores_gemma":[0.9997446,0.000056368408,0.000048011094,0.0000365269,0.000104160805,0.00001026827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040101702,0.00065851945,0.00038829443,0.00032026958,0.0004338883,0.00072698586,0.0008731954,0.00085546245,0.0030212037],"category_scores_gemma":[0.00068341277,0.0002924022,0.0004252347,0.00025396503,0.00042020212,0.0006564492,0.00071886205,0.0007576773,0.0009055673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016989834,0.000070454116,0.0011758255,0.00020089067,0.000081203936,0.00026193124,0.00016199294,0.6119317,0.06708994,0.0116118705,0.0019316814,0.3053126],"study_design_scores_gemma":[0.0000074600657,0.000062395266,0.00027469842,0.000010353948,0.000016285581,0.00006329757,0.00001724369,0.98742175,0.009193561,0.0012671968,0.0016569286,0.00000886082],"about_ca_topic_score_codex":0.0032264797,"about_ca_topic_score_gemma":0.0038262878,"teacher_disagreement_score":0.0032264797,"about_ca_system_score_codex":0.0006585358,"about_ca_system_score_gemma":0.00062542775,"threshold_uncertainty_score":0.010106981},"labels":[],"label_agreement":null},{"id":"W4200356239","doi":"10.1109/iros51168.2021.9636461","title":"Map-Aided Train Navigation with IMU Measurements","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inertial measurement unit; Computer science; Estimator; GNSS applications; Dead reckoning; Acceleration; Track (disk drive); Sensor fusion; Computer vision; Constraint (computer-aided design); Global Positioning System; Real-time computing; Artificial intelligence; Simulation; Engineering; Mathematics; Telecommunications; Statistics","score_opus":0.06689787562250461,"score_gpt":0.2691267012699457,"score_spread":0.2022288256474411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200356239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06789179,0.00020895756,0.9206809,0.00008281643,0.00008833587,0.000048186397,0.00037496464,0.0027696202,0.007854546],"genre_scores_gemma":[0.85107946,0.00015746456,0.14319882,0.0000406912,0.000043031338,0.00010784969,0.00044782393,0.00009134772,0.004833472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999788,0.00003931051,0.000008028691,0.000040021365,0.00009926414,0.000025448348],"domain_scores_gemma":[0.9998323,0.000033406635,0.000024489696,0.000040934214,0.000060798648,0.0000081376475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014983112,0.00068170915,0.00047739016,0.0005203766,0.00031873622,0.0006195339,0.00056722836,0.0004242447,0.0016614817],"category_scores_gemma":[0.00066828565,0.00023665669,0.00025453104,0.0005632006,0.00016678043,0.0006225173,0.0010020584,0.00031795955,0.0013446016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029576235,0.00007133884,0.0052854777,0.00023099274,0.00010479542,0.00020318969,0.00022362146,0.47219786,0.045999553,0.0043893666,0.0036558176,0.46734226],"study_design_scores_gemma":[0.000015815907,0.00010439943,0.0038242503,0.0000123602385,0.000026043615,0.00009686866,0.00006439589,0.97300875,0.015053244,0.0013648144,0.0064026215,0.000026409907],"about_ca_topic_score_codex":0.004318988,"about_ca_topic_score_gemma":0.0058438657,"teacher_disagreement_score":0.004318988,"about_ca_system_score_codex":0.00017137993,"about_ca_system_score_gemma":0.00057325314,"threshold_uncertainty_score":0.008587658},"labels":[],"label_agreement":null},{"id":"W4200403323","doi":"10.3390/rs14010019","title":"Wits: An Efficient Wi-Fi Based Indoor Positioning and Tracking System","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Tracking (education); Channel (broadcasting); Interference (communication); Track (disk drive); Wireless; Point (geometry); Channel state information; Path (computing); Phase (matter); Real-time computing; Indoor positioning system; Tracking system; Computer vision; Simulation; Artificial intelligence; Telecommunications; Kalman filter; Mathematics","score_opus":0.008586487539154392,"score_gpt":0.20412042854667165,"score_spread":0.19553394100751725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200403323","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04575109,0.0005127666,0.8816701,0.00018837588,0.00027179212,0.000327261,0.001733126,0.052127264,0.017418157],"genre_scores_gemma":[0.5737835,0.0006238998,0.3692783,0.00040490966,0.00017215694,0.0005375907,0.0074704303,0.0006486443,0.04708049],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952316,0.000048575912,0.000024902145,0.00009349671,0.00026578657,0.00004409301],"domain_scores_gemma":[0.9997825,0.000017190241,0.000037267964,0.00004823845,0.00009252093,0.000022327784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033151652,0.00075485976,0.0009417932,0.00110991,0.00038743735,0.0005213435,0.0016474082,0.00048407025,0.007525745],"category_scores_gemma":[0.00053950766,0.00033527054,0.0003505933,0.0013107649,0.0002656213,0.0012016429,0.0010822043,0.00043846376,0.0044735395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010485292,0.0003104224,0.0050390973,0.0006156933,0.0001402324,0.0007557828,0.00018541825,0.02703861,0.13790336,0.0063032703,0.06434933,0.75631034],"study_design_scores_gemma":[0.0005548287,0.001921177,0.012187348,0.00010893236,0.00023993629,0.0034516328,0.00019701323,0.72915566,0.103051245,0.004154254,0.14462224,0.0003556575],"about_ca_topic_score_codex":0.0020283079,"about_ca_topic_score_gemma":0.0024190743,"teacher_disagreement_score":0.007525745,"about_ca_system_score_codex":0.00030827252,"about_ca_system_score_gemma":0.00058245903,"threshold_uncertainty_score":0.025176108},"labels":[],"label_agreement":null},{"id":"W4200592094","doi":"10.1049/sil2.12091","title":"Underwater source localization using time difference of arrival and frequency difference of arrival measurements based on an improved invasive weed optimization algorithm","year":2021,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Cramér–Rao bound; Algorithm; Computer science; Position (finance); Mean squared error; Time of arrival; Underwater; Noise (video); Gaussian; Gaussian noise; Upper and lower bounds; Mathematics; Control theory (sociology); Estimation theory; Artificial intelligence; Statistics; Physics; Telecommunications","score_opus":0.021243461222240797,"score_gpt":0.22510503022311631,"score_spread":0.20386156900087551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200592094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015312631,0.00006431165,0.98371744,0.000051865154,0.000017770086,0.0000106594025,0.000009214385,0.00018387321,0.0006321392],"genre_scores_gemma":[0.5412226,0.00015635123,0.4556749,0.0000690774,0.00003401372,0.0000884601,0.00007659439,0.000050338913,0.0026277474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997371,0.000057504305,0.0000131882925,0.000057669502,0.00011307585,0.000021346083],"domain_scores_gemma":[0.9997795,0.00007413676,0.000044479322,0.000020032958,0.00007089402,0.000010895366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000357559,0.0005728629,0.00056254823,0.0004981452,0.00022506286,0.00033212552,0.0007026507,0.00045218997,0.00058902544],"category_scores_gemma":[0.0008965053,0.00021641278,0.00042354,0.0005383011,0.00034192653,0.00069324166,0.0005891366,0.00048637987,0.00016955797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013906191,0.00006185464,0.0016859429,0.000096721,0.00006965304,0.00015704025,0.00014896331,0.7264077,0.049411356,0.010388467,0.0011002754,0.21033293],"study_design_scores_gemma":[0.000008052614,0.000023494427,0.00017420469,0.0000022693955,0.000005791409,0.000021600594,0.000006116305,0.9969392,0.001849753,0.00059691194,0.00036667308,0.000005946177],"about_ca_topic_score_codex":0.0027932527,"about_ca_topic_score_gemma":0.0019842235,"teacher_disagreement_score":0.0027932527,"about_ca_system_score_codex":0.00031789648,"about_ca_system_score_gemma":0.0006699294,"threshold_uncertainty_score":0.005553961},"labels":[],"label_agreement":null},{"id":"W4200597688","doi":"10.1109/camad52502.2021.9617769","title":"A Neural Network Based Recursive Least Square Multilateration Technique for Indoor Positioning","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Multilateration; Mean squared error; Computer science; Multipath propagation; Indoor positioning system; Artificial neural network; Algorithm; FDOA; Recursive least squares filter; Artificial intelligence; Mathematics; Statistics; Telecommunications; Adaptive filter; Azimuth","score_opus":0.00967459973482485,"score_gpt":0.22193975040389524,"score_spread":0.2122651506690704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200597688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005092915,0.00021661635,0.99300086,0.00005689655,0.000052608266,0.000012665709,0.000021151684,0.00061420276,0.0009320304],"genre_scores_gemma":[0.3230207,0.00044473866,0.669209,0.00013824632,0.000080396094,0.000088108325,0.00020110059,0.00010165714,0.0067160134],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996724,0.00007161,0.000018589148,0.00007813363,0.00013454047,0.000024742467],"domain_scores_gemma":[0.9997012,0.00009697107,0.00004250565,0.000034292505,0.000117598036,0.000007277701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003411368,0.0005890583,0.00042045454,0.00033751302,0.00024313407,0.00030241182,0.00072832033,0.00057448354,0.0014526595],"category_scores_gemma":[0.0009461377,0.00025012714,0.00048446518,0.00061874057,0.00020957015,0.00059418,0.00039592246,0.00077551114,0.00077927916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014760139,0.000067778856,0.0010409502,0.00017069074,0.00010982589,0.00018934437,0.0001399652,0.23954053,0.05978587,0.0049654823,0.0033109419,0.690531],"study_design_scores_gemma":[0.000007913094,0.000081114355,0.00039350716,0.0000093546305,0.000020869309,0.00012875511,0.000010811317,0.9846978,0.0110206045,0.00065024017,0.0029634284,0.000015687916],"about_ca_topic_score_codex":0.0026064783,"about_ca_topic_score_gemma":0.0042727273,"teacher_disagreement_score":0.0026064783,"about_ca_system_score_codex":0.00027226337,"about_ca_system_score_gemma":0.000390743,"threshold_uncertainty_score":0.0051825643},"labels":[],"label_agreement":null},{"id":"W4205241350","doi":"10.1109/ipin51156.2021.9662632","title":"Comparing and Evaluating Indoor Positioning Techniques","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Trilateration; Beacon; Computer science; Global Positioning System; Real-time computing; Measure (data warehouse); Suite; Set (abstract data type); Artificial intelligence; Data mining; Telecommunications; Triangulation","score_opus":0.023172495972049265,"score_gpt":0.2621312320921712,"score_spread":0.23895873612012195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205241350","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3075772,0.014493933,0.64438504,0.000653133,0.0009859615,0.00071308314,0.002220202,0.006657596,0.0223139],"genre_scores_gemma":[0.6958758,0.005036551,0.29160902,0.00013151772,0.00021421115,0.0002106939,0.0029826635,0.00027908443,0.0036603687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918526,0.0026382548,0.0005847391,0.0011280776,0.003420501,0.00037588875],"domain_scores_gemma":[0.99010605,0.0052234232,0.00082186644,0.001156784,0.0025233028,0.00016856616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037547534,0.0016486924,0.0010828096,0.0041155913,0.00060481194,0.0016680921,0.0017991157,0.0012256929,0.001863847],"category_scores_gemma":[0.015961,0.0002748886,0.0008655253,0.004445042,0.0005019672,0.0020476934,0.0012400711,0.0004560676,0.0010573706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006270506,0.00024095707,0.014265428,0.0020111925,0.0004899457,0.00017619316,0.0003811702,0.10288929,0.008030393,0.003307364,0.0044734483,0.86310756],"study_design_scores_gemma":[0.00018051377,0.0043832106,0.043387353,0.0008093435,0.0010351754,0.0022450364,0.002228747,0.8579072,0.04575431,0.004154181,0.03757834,0.00033671208],"about_ca_topic_score_codex":0.0064479467,"about_ca_topic_score_gemma":0.0073321084,"teacher_disagreement_score":0.0064479467,"about_ca_system_score_codex":0.0009227783,"about_ca_system_score_gemma":0.0008312973,"threshold_uncertainty_score":0.019857228},"labels":[],"label_agreement":null},{"id":"W4205315723","doi":"10.22215/etd/2021-14683","title":"Autonomous Vehicle Navigation and Communication by Radio Frequency Identity Tags","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Global Positioning System; Radio-frequency identification; Computer science; Engineering; Track (disk drive); Identity (music); Radio frequency; Electrical engineering; Telecommunications; Real-time computing; Acoustics; Computer security","score_opus":0.004720831898348321,"score_gpt":0.22277692663655632,"score_spread":0.218056094738208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205315723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31644508,0.0059789806,0.5374962,0.00067221146,0.00041749384,0.00013473173,0.00015170543,0.00086407387,0.13783942],"genre_scores_gemma":[0.8595799,0.0030132392,0.053878717,0.00013093071,0.000064263055,0.00006148619,0.00021804552,0.00003537316,0.08301803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974495,0.0000484017,0.0000067666983,0.000052808908,0.00011457518,0.00003252401],"domain_scores_gemma":[0.99985886,0.000037094578,0.000018587067,0.000019305771,0.000057571673,0.000008507078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019330402,0.00020664954,0.00012979534,0.00031483153,0.0002643336,0.00095376844,0.000311058,0.00041689567,0.00195505],"category_scores_gemma":[0.00038750347,0.000106874,0.00014763376,0.0004258513,0.00026322276,0.0008325855,0.0003881029,0.00024898883,0.001154319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025466518,0.00014385593,0.0073809,0.00040444435,0.00002787631,0.00040430864,0.0012737632,0.032659043,0.1526821,0.056885786,0.005699257,0.742184],"study_design_scores_gemma":[0.000046761794,0.0011957521,0.0104296,0.00019994641,0.00008071912,0.0017009114,0.0020733925,0.13385354,0.3327127,0.015530699,0.50207484,0.00010115913],"about_ca_topic_score_codex":0.0010919226,"about_ca_topic_score_gemma":0.00089240895,"teacher_disagreement_score":0.00195505,"about_ca_system_score_codex":0.00024759,"about_ca_system_score_gemma":0.00032865288,"threshold_uncertainty_score":0.006540358},"labels":[],"label_agreement":null},{"id":"W4206109262","doi":"10.1002/9780470512494.ch6","title":"Location Awareness","year":2007,"lang":"en","type":"other","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Geography; Computer science; Unit (ring theory); Cartography; Data science; Information retrieval; World Wide Web; Mathematics; Mathematics education","score_opus":0.009881417976355104,"score_gpt":0.2326864195493612,"score_spread":0.2228050015730061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206109262","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050274995,0.010436507,0.19905455,0.0047562085,0.002384738,0.00020337694,0.001158644,0.0034317411,0.7735467],"genre_scores_gemma":[0.20022516,0.031657603,0.13146757,0.003183772,0.0022229787,0.00029905947,0.004647725,0.0008578029,0.62543833],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99945444,0.00007394533,0.000025140742,0.0001324116,0.00025125965,0.000062789935],"domain_scores_gemma":[0.99924767,0.00017692983,0.000040898816,0.00026835594,0.0002106561,0.00005565162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040473105,0.0006415704,0.00045294513,0.00139401,0.0007740655,0.0036190795,0.0013136644,0.0009367936,0.056290273],"category_scores_gemma":[0.0023059018,0.00036297986,0.00039430958,0.001966667,0.00060363516,0.005874209,0.0023115615,0.0010229493,0.024908267],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004213127,0.000055371318,0.0005264576,0.00037431272,0.000018627547,0.00011326039,0.0003974826,0.0026654433,0.0027073289,0.3038245,0.09065162,0.5986235],"study_design_scores_gemma":[0.000007808242,0.000030431058,0.00043008185,0.00021470047,0.000022834995,0.0003965464,0.000352806,0.0041894214,0.0023701235,0.07549312,0.91647226,0.000019808025],"about_ca_topic_score_codex":0.00092096796,"about_ca_topic_score_gemma":0.0011160158,"teacher_disagreement_score":0.056290273,"about_ca_system_score_codex":0.0006218582,"about_ca_system_score_gemma":0.0007630054,"threshold_uncertainty_score":0.18830973},"labels":[],"label_agreement":null},{"id":"W4206149286","doi":"10.3390/s22010276","title":"An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Fuzhou University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Angle of arrival; Cluster analysis; Multipath propagation; Computer science; Algorithm; Tracking (education); Channel (broadcasting); Least-squares function approximation; Path (computing); SIGNAL (programming language); Phase angle (astronomy); Artificial intelligence; Mathematics; Antenna (radio); Statistics","score_opus":0.0329187215215515,"score_gpt":0.24744953876558123,"score_spread":0.21453081724402973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206149286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071778907,0.00013226457,0.99104977,0.000033455563,0.000046224453,0.00003086645,0.0000524526,0.0008425713,0.000634485],"genre_scores_gemma":[0.2171113,0.00044577304,0.77813345,0.00008984319,0.00007181343,0.00019756483,0.0006364891,0.00012739855,0.0031863144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993223,0.000055249737,0.00006024876,0.00020835448,0.00028403604,0.00006986993],"domain_scores_gemma":[0.99945194,0.00007563254,0.00006760301,0.00007214024,0.0003073928,0.000025386329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034081563,0.000713943,0.0007300169,0.0013456169,0.00046480683,0.0006466192,0.001208552,0.00052269443,0.0011689467],"category_scores_gemma":[0.001563522,0.0003469644,0.0006157377,0.0016162297,0.00024952306,0.0010305493,0.0006976234,0.0007415437,0.0008897574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020709082,0.00007137557,0.0031148824,0.00007259166,0.000058127956,0.000070955735,0.0000876585,0.07725221,0.031437263,0.0033581764,0.003328294,0.88094145],"study_design_scores_gemma":[0.000033358952,0.00010919771,0.003185832,0.000012261718,0.000037259877,0.0003396045,0.00006513482,0.9620523,0.026518892,0.0016808434,0.005921263,0.00004416086],"about_ca_topic_score_codex":0.0060942746,"about_ca_topic_score_gemma":0.0048886384,"teacher_disagreement_score":0.0060942746,"about_ca_system_score_codex":0.00044822268,"about_ca_system_score_gemma":0.00083923514,"threshold_uncertainty_score":0.012117624},"labels":[],"label_agreement":null},{"id":"W4206542241","doi":"10.1109/lcomm.2022.3140271","title":"NOMA Empowered Integrated Sensing and Communication","year":2022,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":227,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Noma; Computer science; Base station; Throughput; Beamforming; Single antenna interference cancellation; Power (physics); Mathematical optimization; Computer network; Wireless; Telecommunications; Telecommunications link; Channel (broadcasting); Mathematics","score_opus":0.013824821486929971,"score_gpt":0.21752816697317226,"score_spread":0.20370334548624228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206542241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075473026,0.000466296,0.9871022,0.00017205172,0.00009511826,0.00003219646,0.00004506371,0.00011823712,0.004421478],"genre_scores_gemma":[0.73927695,0.00088298664,0.24981244,0.0003408979,0.0002175609,0.00018484484,0.00012233098,0.000039098286,0.009122793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886334,0.00041646537,0.000029267296,0.00019649592,0.00032222178,0.0001722155],"domain_scores_gemma":[0.99913245,0.00035143504,0.0001314826,0.00012461966,0.00019903117,0.000060936098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093103613,0.001329786,0.0009273739,0.0004532769,0.0005614568,0.0011667713,0.0013866336,0.00089100195,0.0015104582],"category_scores_gemma":[0.00177198,0.00037221448,0.00048531863,0.00077053654,0.000876238,0.0013032202,0.001796834,0.0012016337,0.00052328716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029117576,0.00015130005,0.0009960855,0.00039925217,0.00014771443,0.0007150708,0.00018997176,0.6545919,0.034799542,0.15666991,0.0050897514,0.14595829],"study_design_scores_gemma":[0.000009176543,0.00010124572,0.000118247735,0.000011421829,0.000015410582,0.00016227433,0.000024418376,0.9852909,0.0024221523,0.009464682,0.0023644902,0.000015651653],"about_ca_topic_score_codex":0.0011337758,"about_ca_topic_score_gemma":0.0025266225,"teacher_disagreement_score":0.0015104582,"about_ca_system_score_codex":0.00046392196,"about_ca_system_score_gemma":0.0012547552,"threshold_uncertainty_score":0.0050530434},"labels":[],"label_agreement":null},{"id":"W4206631577","doi":"10.5815/ijwmt.2021.03.05","title":"A Node Localization Algorithm based on WoaBp Optimization","year":2021,"lang":"en","type":"article","venue":"International Journal of Wireless and Microwave Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Artificial neural network; Real-time computing; Global Positioning System; Node (physics); Positioning technology; Positioning system; Wireless sensor network; Hybrid positioning system; Received signal strength indication; Algorithm; Artificial intelligence; Wireless; Engineering; Computer network; Telecommunications","score_opus":0.005516957282453784,"score_gpt":0.20884578940283563,"score_spread":0.20332883212038186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206631577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006286893,0.00018750892,0.9899657,0.00014686605,0.000055293804,0.00005216284,0.0000341699,0.00040707024,0.0028642719],"genre_scores_gemma":[0.4249333,0.0005995956,0.5608621,0.00023316148,0.00009299352,0.0006414892,0.00033909557,0.00021201995,0.012086122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965024,0.0000684839,0.000018507755,0.00009713179,0.00011453832,0.00005116114],"domain_scores_gemma":[0.9997397,0.00008848539,0.000030054574,0.00001423722,0.000113457776,0.000014129188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056515686,0.00096830755,0.0011872851,0.0008882991,0.00072515296,0.0008656712,0.0012003542,0.0011654595,0.0035472433],"category_scores_gemma":[0.0015433554,0.00046384038,0.0006307397,0.0010340031,0.000545014,0.0009848498,0.0009735433,0.0009227001,0.00088327937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006090601,0.000026255728,0.0005761708,0.000067065856,0.000030324929,0.000055055127,0.00005610872,0.90074164,0.0019264414,0.0060316026,0.002063047,0.088365294],"study_design_scores_gemma":[0.000007349429,0.000011867923,0.00006476247,0.0000044131007,0.0000037718212,0.000010020066,0.00000615762,0.998376,0.0002640947,0.0007415497,0.0005067217,0.0000032528872],"about_ca_topic_score_codex":0.014914453,"about_ca_topic_score_gemma":0.007867791,"teacher_disagreement_score":0.014914453,"about_ca_system_score_codex":0.00075899874,"about_ca_system_score_gemma":0.0014781253,"threshold_uncertainty_score":0.029655278},"labels":[],"label_agreement":null},{"id":"W4206643886","doi":"10.1109/ipin51156.2021.9662564","title":"Periodic Extended Kalman Filter to Estimate Rowing Motion Indoors Using a Wearable Ultra-Wideband Ranging Positioning System","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Rowing; Ranging; Kalman filter; Computer science; Motion capture; Extended Kalman filter; Tracking system; Simulation; Motion (physics); Computer vision; Artificial intelligence; Telecommunications","score_opus":0.010136471457174185,"score_gpt":0.23608409510249845,"score_spread":0.22594762364532425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206643886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022176178,0.0002578858,0.9755999,0.000041525152,0.00006550391,0.000029984301,0.00004827966,0.00064905983,0.0011316588],"genre_scores_gemma":[0.80120337,0.00066599686,0.19215447,0.00010058784,0.00007414895,0.00014379591,0.00036210674,0.000047789177,0.005247766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970263,0.00004294716,0.000024368459,0.000109735745,0.00009013807,0.000030300671],"domain_scores_gemma":[0.99978155,0.000048060927,0.000038545408,0.000027223916,0.00009697239,0.000007720434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003289676,0.0005052465,0.0005525624,0.00033449187,0.00024252725,0.00037437567,0.00049366825,0.0005056893,0.0012152265],"category_scores_gemma":[0.0008413249,0.00029043708,0.00050534046,0.00034155758,0.00014530009,0.0004633554,0.00032169317,0.00046747932,0.0005527937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024011094,0.000078699275,0.00484981,0.00031494128,0.00017491831,0.00026497833,0.00028711878,0.46966937,0.047533475,0.003078037,0.0021353934,0.47137308],"study_design_scores_gemma":[0.000014728572,0.0000923803,0.0022711132,0.000018032579,0.000039044717,0.00007218371,0.000022119359,0.9913551,0.004317156,0.00040008224,0.00138199,0.000016011702],"about_ca_topic_score_codex":0.007908661,"about_ca_topic_score_gemma":0.005211407,"teacher_disagreement_score":0.007908661,"about_ca_system_score_codex":0.00022339889,"about_ca_system_score_gemma":0.0005341552,"threshold_uncertainty_score":0.015725255},"labels":[],"label_agreement":null},{"id":"W4206978992","doi":"10.1109/gcwkshps52748.2021.9682026","title":"CAIM: Cooperative Angle of Arrival Estimation using the Ising Model","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Globecom Workshops (GC Wkshps)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Compressed sensing; Markov chain Monte Carlo; Computer science; Markov chain; Ising model; Algorithm; Energy (signal processing); Monte Carlo method; Channel (broadcasting); Minification; Norm (philosophy); Mathematical optimization; Mathematics; Artificial intelligence; Statistical physics; Statistics; Bayesian probability; Machine learning; Physics; Telecommunications","score_opus":0.020227488958665402,"score_gpt":0.25223228742300324,"score_spread":0.23200479846433783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206978992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005887215,0.00018736992,0.9923849,0.0000843574,0.000029467628,0.00001921647,0.000028265813,0.00026323993,0.0011159198],"genre_scores_gemma":[0.61213857,0.0007779944,0.38130087,0.0002499177,0.0001865188,0.00015605215,0.00029104415,0.00008791798,0.0048110667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999172,0.00024667507,0.00003088499,0.00015336838,0.00031090807,0.000086296626],"domain_scores_gemma":[0.99890995,0.00057472516,0.00013235601,0.00014745789,0.0001878116,0.000047763948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095776963,0.00078710396,0.00088037015,0.00085324614,0.0005418548,0.0007528275,0.0017487429,0.0010232033,0.000924375],"category_scores_gemma":[0.0030286626,0.0003892931,0.00068440416,0.0014223299,0.0007416656,0.0014989981,0.0014609596,0.0012061048,0.00047089733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026403443,0.00008696392,0.0015311397,0.00012082741,0.00011291347,0.00019696158,0.00018076434,0.77704656,0.009321512,0.033239014,0.002862827,0.17503645],"study_design_scores_gemma":[0.000012752561,0.000035923647,0.00013063455,0.0000059642284,0.00001364114,0.000064076994,0.000010752588,0.99303484,0.001532059,0.004034181,0.0011120135,0.000013174145],"about_ca_topic_score_codex":0.0057515553,"about_ca_topic_score_gemma":0.004795991,"teacher_disagreement_score":0.0057515553,"about_ca_system_score_codex":0.00062920246,"about_ca_system_score_gemma":0.0013905745,"threshold_uncertainty_score":0.011436164},"labels":[],"label_agreement":null},{"id":"W4210269033","doi":"10.1109/globecom46510.2021.9685151","title":"Learning K-Nearest Neighbour Regression for Noisy Dataset with Application in Indoor Localization","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Global Communications Conference (GLOBECOM)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Regression; Intuition; Noisy data; Noise (video); k-nearest neighbors algorithm; Data mining; Nearest neighbour; Machine learning; Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.023061310121454456,"score_gpt":0.2798857173768979,"score_spread":0.2568244072554434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210269033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020399017,0.0004990903,0.97677135,0.00024045016,0.00008361032,0.00004940876,0.00016726393,0.0012717434,0.00051822694],"genre_scores_gemma":[0.43593207,0.00051361386,0.5590585,0.00027123326,0.00019432606,0.00022072578,0.0016130559,0.00027160122,0.0019248211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640685,0.0012702609,0.00024897803,0.0011275541,0.0007371947,0.00020913612],"domain_scores_gemma":[0.9916898,0.004334785,0.0006729747,0.0013201007,0.0018459469,0.00013634328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004662433,0.0012762028,0.0021511805,0.0014522362,0.0010295586,0.0012545629,0.0026950878,0.002573,0.0007192454],"category_scores_gemma":[0.02620965,0.0005206639,0.0011309227,0.0025401202,0.0011202904,0.001881952,0.0015447381,0.0021614188,0.0010879624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023036552,0.00019232604,0.005423305,0.00019543878,0.0001500094,0.00023314235,0.00017044376,0.7755876,0.0033582554,0.0038650224,0.0046659256,0.20592818],"study_design_scores_gemma":[0.000007881999,0.000026595153,0.000466301,0.000008773388,0.0000070946685,0.000054885317,0.000031680232,0.99472296,0.0011517585,0.0029275294,0.0005805064,0.000014158659],"about_ca_topic_score_codex":0.007606326,"about_ca_topic_score_gemma":0.00632016,"teacher_disagreement_score":0.007606326,"about_ca_system_score_codex":0.0008998918,"about_ca_system_score_gemma":0.0008127603,"threshold_uncertainty_score":0.024657607},"labels":[],"label_agreement":null},{"id":"W4211208223","doi":"10.21203/rs.3.rs-187259/v1","title":"An Improved and Low-dimensional Fingerprint-based Localization Method in Collocated Massive MIMO-OFDM Systems","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; MIMO; Computational complexity theory; Cluster analysis; Orthogonal frequency-division multiplexing; Kriging; Algorithm; Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Channel (broadcasting); Machine learning; Telecommunications","score_opus":0.025202426276372654,"score_gpt":0.3382371450937671,"score_spread":0.3130347188173945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211208223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015115964,0.00012645636,0.9835565,0.00006811648,0.000031818527,0.000012922335,0.000016390602,0.00026994804,0.0008018742],"genre_scores_gemma":[0.70476544,0.00030266328,0.29144922,0.00009759628,0.00005122433,0.000053248525,0.00007812178,0.000045298963,0.0031571612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995933,0.00010229114,0.000016993154,0.000085982654,0.00016390145,0.00003750148],"domain_scores_gemma":[0.99963105,0.000101414276,0.000057455123,0.00005585922,0.00013482533,0.000019418429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032128673,0.0005806864,0.0005347658,0.00038003703,0.00037063332,0.00049533404,0.0008573871,0.00051575486,0.0008667099],"category_scores_gemma":[0.0010690745,0.0002577227,0.00045365226,0.00061719154,0.0003055956,0.0006839862,0.0006260492,0.00054940535,0.00045240403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021534764,0.00006955841,0.0018638751,0.00012606576,0.000064076354,0.00024567652,0.00018520893,0.6700823,0.037669495,0.010335983,0.0023693156,0.27677307],"study_design_scores_gemma":[0.000003343569,0.000023496796,0.00019108018,0.0000022741049,0.0000051686247,0.000042829735,0.000008132939,0.99708146,0.001846417,0.00046872097,0.0003207816,0.0000063264056],"about_ca_topic_score_codex":0.0040105754,"about_ca_topic_score_gemma":0.0029396974,"teacher_disagreement_score":0.0040105754,"about_ca_system_score_codex":0.0004040703,"about_ca_system_score_gemma":0.00054536184,"threshold_uncertainty_score":0.007974505},"labels":[],"label_agreement":null},{"id":"W4211222620","doi":"10.1109/jiot.2022.3150794","title":"CRB Weighted Source Localization Method Based on Deep Neural Networks in Multi-UAV Network","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Japan Society for the Promotion of Science; National Natural Science Foundation of China","keywords":"Computer science; Direction of arrival; Multi-source; Artificial neural network; Fusion center; Artificial intelligence; Algorithm; Real-time computing; Telecommunications; Wireless; Mathematics","score_opus":0.01152914207252195,"score_gpt":0.23952208413837353,"score_spread":0.22799294206585158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211222620","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008993171,0.00040403305,0.9888094,0.00012478244,0.000051733983,0.00001482957,0.000025514519,0.00042476333,0.001151834],"genre_scores_gemma":[0.7030824,0.000998535,0.28694868,0.00031887044,0.00012862697,0.00013432151,0.00032515093,0.00018315067,0.007880293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997528,0.00004987128,0.000015843325,0.0000653886,0.000075944095,0.00004011625],"domain_scores_gemma":[0.9996842,0.000102681435,0.00004090104,0.000023106399,0.00012953638,0.000019655063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047345815,0.00082052656,0.00070916035,0.0005416279,0.0003091272,0.00064009003,0.0011716472,0.0008255163,0.0015582637],"category_scores_gemma":[0.0012125833,0.0003858901,0.00059862935,0.0005273854,0.00036163107,0.0010156522,0.00084763643,0.0010013946,0.00036344505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017390028,0.000047556747,0.0010544432,0.00013498055,0.00008858905,0.00017957692,0.00010566109,0.74425304,0.009755564,0.009161739,0.002893836,0.23215108],"study_design_scores_gemma":[0.0000035510864,0.000008930555,0.00006514617,0.0000036104148,0.000005235158,0.000013699062,0.00000420916,0.99815434,0.0006515825,0.0008355621,0.00025045683,0.0000036274707],"about_ca_topic_score_codex":0.010622225,"about_ca_topic_score_gemma":0.0070013953,"teacher_disagreement_score":0.010622225,"about_ca_system_score_codex":0.0006059697,"about_ca_system_score_gemma":0.0006889113,"threshold_uncertainty_score":0.021120787},"labels":[],"label_agreement":null},{"id":"W4213089608","doi":"10.1109/aps/ursi47566.2021.9704309","title":"Physics-Informed Convolutional Neural Network for Indoor Localization","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Convolutional neural network; Computer science; Signal strength; Wireless; Radio propagation; Artificial neural network; Artificial intelligence; k-nearest neighbors algorithm; Real-time computing; Ray tracing (physics); Machine learning; Computer engineering; Data mining; Telecommunications","score_opus":0.014326286249220232,"score_gpt":0.2506401370257762,"score_spread":0.23631385077655598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213089608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025976313,0.0007991413,0.9675087,0.0003437231,0.00007014035,0.000018159884,0.0001989074,0.0013156515,0.0037691838],"genre_scores_gemma":[0.87465954,0.00077376026,0.116078384,0.0001586092,0.000061127015,0.0000801762,0.00049572723,0.00010164957,0.007591016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999,0.000020039539,0.0000042065208,0.000027524819,0.000029433944,0.000018870753],"domain_scores_gemma":[0.9997869,0.00010050111,0.000027039214,0.000020692178,0.00005551405,0.000009292106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003103616,0.000563002,0.00041820007,0.00037986482,0.00022379351,0.00041582325,0.00089786365,0.0006690731,0.0016172988],"category_scores_gemma":[0.0010485847,0.00032130847,0.0003959651,0.0005319217,0.00035134229,0.00058935705,0.00055204594,0.0008828608,0.00044109987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031682957,0.000017539176,0.00047150106,0.000026834541,0.00002533822,0.000032313306,0.000012132267,0.9533791,0.0015146328,0.0045131124,0.00079560804,0.03918012],"study_design_scores_gemma":[6.84096e-7,0.0000024288813,0.000059103437,0.0000012433808,0.0000016336516,0.000003066575,6.8809305e-7,0.99864763,0.00019393304,0.0009531719,0.00013512754,0.0000012510181],"about_ca_topic_score_codex":0.015729018,"about_ca_topic_score_gemma":0.0144854,"teacher_disagreement_score":0.015729018,"about_ca_system_score_codex":0.0009992175,"about_ca_system_score_gemma":0.0007851458,"threshold_uncertainty_score":0.031274915},"labels":[],"label_agreement":null},{"id":"W4213121407","doi":"10.1109/aps/ursi47566.2021.9704192","title":"In-Package Integrated Dielectric Lens Paired with a MIMO mm-Wave Radar for Corridor Gait Monitoring","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Radar; Computer science; Antenna (radio); Cadence; MIMO; Electronic engineering; Acoustics; Engineering; Telecommunications; Physics","score_opus":0.01634539976842787,"score_gpt":0.23464011711618576,"score_spread":0.2182947173477579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213121407","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31360683,0.0012562835,0.6704606,0.00040145,0.0004185765,0.00015797566,0.00048352816,0.005392873,0.0078219],"genre_scores_gemma":[0.78431976,0.00043163486,0.2052193,0.00036068278,0.00015739686,0.000083061524,0.00041983026,0.0001327365,0.008875661],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996505,0.000053693493,0.000014713069,0.00008737451,0.00013864602,0.000055243323],"domain_scores_gemma":[0.99957734,0.000049377864,0.00013200282,0.00005609965,0.00012751714,0.000057651985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000203331,0.0007198519,0.00044023053,0.00049277383,0.000121065794,0.00058418507,0.0010365641,0.0005286983,0.0021023003],"category_scores_gemma":[0.00037435995,0.0002873279,0.0003598956,0.00038103084,0.00014616358,0.0007678896,0.0004802219,0.00031545045,0.0019160196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005471995,0.0002355375,0.008089803,0.00037050166,0.00013084918,0.0006838937,0.00011017565,0.0027444814,0.821876,0.0013163363,0.0035542506,0.160341],"study_design_scores_gemma":[0.000107868094,0.0024391238,0.014006277,0.000032649496,0.00020984933,0.0031578126,0.00014510547,0.06294574,0.89452255,0.00031310008,0.022015635,0.00010423971],"about_ca_topic_score_codex":0.000444374,"about_ca_topic_score_gemma":0.00082089874,"teacher_disagreement_score":0.0021023003,"about_ca_system_score_codex":0.00031741345,"about_ca_system_score_gemma":0.00028554027,"threshold_uncertainty_score":0.0070328712},"labels":[],"label_agreement":null},{"id":"W4213219791","doi":"10.32920/ryerson.14654106.v1","title":"Location and sensing network for industrial automation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wireless sensor network; Latency (audio); Computer science; Efficient energy use; Computer network; Protocol (science); Automation; Real-time computing; Throughput; Energy (signal processing); Wireless; Embedded system; Telecommunications; Engineering","score_opus":0.02596843939380738,"score_gpt":0.22980513504242728,"score_spread":0.2038366956486199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213219791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022794466,0.012698476,0.8669439,0.0028870674,0.0021166792,0.00020139311,0.00061553455,0.0049292357,0.08681323],"genre_scores_gemma":[0.53406435,0.011466745,0.2708005,0.0008308381,0.001137691,0.00035890666,0.0019962671,0.0002755215,0.1790691],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996544,0.00006172615,0.000016720018,0.00011034098,0.00012774364,0.000029051173],"domain_scores_gemma":[0.99977547,0.000048170717,0.000027491802,0.00006981227,0.00006497545,0.000014125462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002606244,0.000405063,0.0003340887,0.000377386,0.00043108768,0.0008946328,0.0005016642,0.0007295892,0.0143711595],"category_scores_gemma":[0.00076074636,0.00015759548,0.0002517668,0.00067626004,0.00031180205,0.0010285485,0.0008625544,0.00068051607,0.0068314984],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022813641,0.00008774087,0.0010519184,0.00040818786,0.000037917685,0.0003857937,0.00013537571,0.03161364,0.023262562,0.13690731,0.04759734,0.75828403],"study_design_scores_gemma":[0.00007414405,0.00037470227,0.0033686243,0.00024359474,0.00007141409,0.0014180145,0.00015716488,0.2107454,0.023304613,0.11592518,0.64425534,0.000061778286],"about_ca_topic_score_codex":0.0011408596,"about_ca_topic_score_gemma":0.0010238669,"teacher_disagreement_score":0.0143711595,"about_ca_system_score_codex":0.00052370055,"about_ca_system_score_gemma":0.0006681615,"threshold_uncertainty_score":0.04807633},"labels":[],"label_agreement":null},{"id":"W4213358579","doi":"10.1109/aps/ursi47566.2021.9704654","title":"Physics- Informed Machine Learning Models for Indoor Wi-Fi Access Point Placement","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Benchmark (surveying); Computer science; Deep learning; Generative adversarial network; Point (geometry); Generative model; Signal strength; Power point; Generative grammar; Computer engineering; Artificial intelligence; Machine learning; Simulation; Real-time computing; Computer network; Wireless sensor network; Mathematics; Geometry; Operating system","score_opus":0.025693628678237695,"score_gpt":0.27091597768922016,"score_spread":0.24522234901098247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213358579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019839268,0.000295643,0.9763323,0.00035548944,0.00005119921,0.000020850448,0.00020789383,0.00059545285,0.0023019114],"genre_scores_gemma":[0.9166186,0.0005498761,0.07307843,0.00024991517,0.0000963046,0.00012488176,0.0006178565,0.00011832401,0.008545828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999803,0.000056135865,0.000006793318,0.00005362589,0.000046383368,0.00003411007],"domain_scores_gemma":[0.99929357,0.00045506863,0.000069707254,0.000054207976,0.000097156044,0.000030237132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005212839,0.00092411786,0.0007308508,0.00054241624,0.00028130764,0.00068869436,0.0015765872,0.0011060744,0.002363957],"category_scores_gemma":[0.0021259887,0.0006582227,0.00063401036,0.00064703624,0.00085506553,0.001019839,0.0010565775,0.0019806665,0.00069699413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001271193,0.000008929131,0.00022589149,0.000009242518,0.00000711344,0.000012314246,0.000007982417,0.99064714,0.00025017362,0.0029109041,0.00027287973,0.0056347186],"study_design_scores_gemma":[0.000001058701,0.0000028571899,0.00004511639,0.0000013189801,0.0000010972242,0.000003823095,9.714612e-7,0.99819046,0.00007853206,0.0015799736,0.0000933092,0.0000013869023],"about_ca_topic_score_codex":0.008261911,"about_ca_topic_score_gemma":0.009133311,"teacher_disagreement_score":0.008261911,"about_ca_system_score_codex":0.0010965352,"about_ca_system_score_gemma":0.00075962534,"threshold_uncertainty_score":0.016427636},"labels":[],"label_agreement":null},{"id":"W4214547634","doi":"10.1109/jsen.2022.3153629","title":"Unknown Transmit Power RSSD-Based Localization in a Gaussian Mixture Channel","year":2022,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"China Scholarship Council; China Postdoctoral Science Foundation; Shanghai Maritime University; National Natural Science Foundation of China","keywords":"Cramér–Rao bound; Gaussian; Noise measurement; Benchmark (surveying); Upper and lower bounds; Computer science; Gaussian noise; Algorithm; Transmitter power output; Signal-to-noise ratio (imaging); Mathematics; Mathematical optimization; Channel (broadcasting); Artificial intelligence; Estimation theory; Noise reduction; Telecommunications; Physics","score_opus":0.006819279004045344,"score_gpt":0.19923710191739388,"score_spread":0.19241782291334855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214547634","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007167254,0.00014400948,0.9918332,0.0000755673,0.000018292156,0.000009848933,0.0000137464685,0.00014529879,0.0005928589],"genre_scores_gemma":[0.7320604,0.0009904534,0.26170942,0.00013866992,0.00007171057,0.00011239078,0.0001814398,0.00009045378,0.0046450533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992631,0.00023442303,0.000030686595,0.00015832673,0.00026175784,0.000051708746],"domain_scores_gemma":[0.9993711,0.00032876508,0.00009508085,0.000054542103,0.00012708198,0.00002346847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008286005,0.0009443716,0.0011445917,0.00048111676,0.0003054558,0.00087511324,0.0010966836,0.0010798244,0.00062876544],"category_scores_gemma":[0.0022108646,0.0004992887,0.0008328378,0.0009436988,0.0012119937,0.0013797068,0.0013108067,0.000904205,0.00028989595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016559396,0.00002505494,0.00055971014,0.00012726618,0.000043128366,0.00013339086,0.000117972355,0.9421307,0.008286018,0.00842547,0.00058549,0.039400317],"study_design_scores_gemma":[0.0000041110056,0.000016799275,0.00006950852,0.0000029484418,0.000006317814,0.000025510057,0.0000062811673,0.9977597,0.0010818358,0.00083707337,0.00018398165,0.0000060001544],"about_ca_topic_score_codex":0.003980075,"about_ca_topic_score_gemma":0.002463751,"teacher_disagreement_score":0.003980075,"about_ca_system_score_codex":0.0006489145,"about_ca_system_score_gemma":0.0009000316,"threshold_uncertainty_score":0.007913828},"labels":[],"label_agreement":null},{"id":"W4214828252","doi":"10.1109/jsac.2022.3155496","title":"Active Sensing for Communications by Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Science and Engineering Research Council; Huawei Technologies","keywords":"Computer science; Channel state information; Frame (networking); Wireless; Channel (broadcasting); Deep learning; Beamforming; Artificial intelligence; Exploit; Machine learning; Telecommunications","score_opus":0.02444263985850341,"score_gpt":0.27502468229122135,"score_spread":0.2505820424327179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214828252","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034403114,0.0012761289,0.990567,0.00065327174,0.00012508439,0.0000181535,0.000031214007,0.00018137226,0.0037074292],"genre_scores_gemma":[0.737065,0.004370167,0.24261773,0.0011780683,0.00067737803,0.00027542617,0.00020197783,0.00014500781,0.013469248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993917,0.00018649682,0.000030480473,0.00014086207,0.00018544987,0.00006493496],"domain_scores_gemma":[0.9986846,0.00089551293,0.00009872,0.00013912182,0.00014507066,0.000036903253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011331894,0.0009384852,0.00074800407,0.00039839308,0.00038513687,0.001278755,0.0013111358,0.0015660438,0.0029123724],"category_scores_gemma":[0.00341352,0.00040890466,0.00052733364,0.0005752213,0.0017423658,0.00270595,0.0015448852,0.0023202202,0.0004612663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014437403,0.00006402418,0.0005099327,0.00028937715,0.00008128494,0.000104347746,0.000154091,0.601758,0.0064963396,0.24874651,0.0049711927,0.13668053],"study_design_scores_gemma":[0.000009334372,0.000027748347,0.000053945845,0.000019112782,0.000006661312,0.000021476602,0.000010587524,0.93640876,0.0009652682,0.059452713,0.0030153026,0.000009154885],"about_ca_topic_score_codex":0.0022639493,"about_ca_topic_score_gemma":0.0020087834,"teacher_disagreement_score":0.0029123724,"about_ca_system_score_codex":0.0009213757,"about_ca_system_score_gemma":0.0007573709,"threshold_uncertainty_score":0.009742856},"labels":[],"label_agreement":null},{"id":"W4214948608","doi":"10.1155/2022/3815306","title":"A Practical and Economical Ultra-wideband Base Station Placement Approach for Indoor Autonomous Driving Systems","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":268,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; Shanghai Municipal Education Commission","keywords":"Multilateration; Base station; Software deployment; Computer science; Real-time computing; Ultra-wideband; Dilution of precision; Base (topology); Real-time locating system; Wideband; Simulation; Engineering; Electronic engineering; Telecommunications; Global Positioning System","score_opus":0.011950709390903591,"score_gpt":0.23812966057926466,"score_spread":0.22617895118836107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214948608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005167541,0.0000975882,0.9927713,0.000038113336,0.000012636097,0.000031607102,0.000017210594,0.00023166246,0.0016323982],"genre_scores_gemma":[0.53432417,0.00043108166,0.4600531,0.000054276432,0.0000345078,0.00016339107,0.000115607545,0.000041006977,0.00478292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996234,0.00009260056,0.000019019104,0.00007107994,0.00015038758,0.000043454274],"domain_scores_gemma":[0.9998191,0.000031114283,0.000028591905,0.000028531273,0.000081092985,0.000011681695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027190408,0.00071004895,0.00036365128,0.00063023536,0.00055206515,0.0005816987,0.0007970022,0.0005260236,0.0018255103],"category_scores_gemma":[0.00045741163,0.00036496788,0.00040712536,0.0005793311,0.00026356505,0.0006096988,0.00068376074,0.00037654635,0.00068715366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009557663,0.000048920065,0.0016036142,0.00021090309,0.000040515064,0.00039347252,0.00020250269,0.56224024,0.04969917,0.013697345,0.0023556254,0.36941212],"study_design_scores_gemma":[0.000028628943,0.00028081116,0.0012179619,0.000023234405,0.000046819536,0.00054588466,0.00019255065,0.9662317,0.015049643,0.0062686577,0.01007147,0.000042606378],"about_ca_topic_score_codex":0.0033246363,"about_ca_topic_score_gemma":0.0051424108,"teacher_disagreement_score":0.0033246363,"about_ca_system_score_codex":0.0006142245,"about_ca_system_score_gemma":0.001019682,"threshold_uncertainty_score":0.0066105723},"labels":[],"label_agreement":null},{"id":"W42166874","doi":"10.1007/978-3-319-05582-4_8","title":"A Convex Fuzzy Range-Free Sensor Network Localization Method","year":2014,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Range (aeronautics); Maxima and minima; Fuzzy logic; Regular polygon; Euclidean geometry; Computer science; Fuzzy set; Set (abstract data type); Algorithm; Mathematics; Artificial intelligence; Engineering","score_opus":0.01089911257178175,"score_gpt":0.24096252009193486,"score_spread":0.2300634075201531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W42166874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000808099,0.000108511995,0.9977366,0.000041690088,0.00002716401,0.000009255946,0.000014308326,0.00007474581,0.0011796464],"genre_scores_gemma":[0.19072777,0.0006286066,0.7961174,0.00019269374,0.00015407622,0.00017209175,0.00020541721,0.00020433204,0.011597692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947447,0.000109639994,0.000022412623,0.00012933245,0.00022888648,0.000035299374],"domain_scores_gemma":[0.9995084,0.00019710476,0.000030682833,0.000053300082,0.00018883153,0.000021653308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006753461,0.00076455495,0.00095351355,0.0006755511,0.00041941134,0.000733633,0.0018349218,0.00082418445,0.002898561],"category_scores_gemma":[0.0015914183,0.00044475181,0.00085274345,0.00071108155,0.0006629561,0.0011360229,0.0014473923,0.0012094179,0.0008186178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014419554,0.00005074267,0.00025525107,0.00023453441,0.00006882212,0.00009891539,0.00012371677,0.57120126,0.0154343285,0.0646898,0.0065424335,0.34115598],"study_design_scores_gemma":[0.0000054828697,0.000019980134,0.000051309977,0.000008049134,0.000008117604,0.00003877112,0.000005784148,0.9918794,0.0013177743,0.0048869075,0.0017689858,0.000009456731],"about_ca_topic_score_codex":0.0038126677,"about_ca_topic_score_gemma":0.0023958154,"teacher_disagreement_score":0.0038126677,"about_ca_system_score_codex":0.0009060493,"about_ca_system_score_gemma":0.0007697355,"threshold_uncertainty_score":0.009696722},"labels":[],"label_agreement":null},{"id":"W4220715374","doi":"10.3390/machines10030218","title":"Improved Extreme Learning Machine Based UWB Positioning for Mobile Robots with Signal Interference","year":2022,"lang":"en","type":"article","venue":"Machines","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Interference (communication); Extreme learning machine; Computer science; Mean squared error; SIGNAL (programming language); Genetic algorithm; Ultra-wideband; Artificial intelligence; Positioning system; Compensation (psychology); Algorithm; Mathematics; Telecommunications; Acoustics; Statistics; Machine learning; Artificial neural network; Physics","score_opus":0.00924588718433074,"score_gpt":0.20246130052189437,"score_spread":0.19321541333756365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220715374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014913251,0.000114144466,0.98377496,0.000042978998,0.000025611806,0.000008006423,0.000009659837,0.00041738042,0.00069405325],"genre_scores_gemma":[0.6947948,0.00019313347,0.30195534,0.000106717445,0.000034212153,0.000080767095,0.000092356604,0.00005213786,0.00269049],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956733,0.00009854865,0.00002711171,0.00010333465,0.0001631491,0.000040443207],"domain_scores_gemma":[0.99968433,0.00010245042,0.000053407573,0.00004611254,0.000102807164,0.000010897281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041415106,0.00051473477,0.00067346485,0.0005092674,0.0002606137,0.0005097555,0.0008732175,0.0007700822,0.0006494824],"category_scores_gemma":[0.0012897422,0.00022022701,0.0004886313,0.00054974767,0.00034596308,0.0006370883,0.0005187495,0.00054954,0.00028835176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001498695,0.00004657251,0.002264483,0.00015533868,0.000073301686,0.00020213472,0.00013831454,0.61381286,0.02914666,0.005027069,0.00097254093,0.34801084],"study_design_scores_gemma":[0.000006370367,0.000048585945,0.00053242384,0.0000059893046,0.000009704807,0.00007935376,0.000011572389,0.9926604,0.0050453553,0.00096936245,0.0006179883,0.000012865563],"about_ca_topic_score_codex":0.0015943471,"about_ca_topic_score_gemma":0.0011646127,"teacher_disagreement_score":0.0015943471,"about_ca_system_score_codex":0.00029539163,"about_ca_system_score_gemma":0.0003200486,"threshold_uncertainty_score":0.0031701922},"labels":[],"label_agreement":null},{"id":"W4220716396","doi":"10.1109/jsac.2022.3155523","title":"Rethinking Doppler Effect for Accurate Velocity Estimation With Commodity WiFi Devices","year":2022,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"China Postdoctoral Science Foundation; Chinese Academy of Sciences; National Natural Science Foundation of China; Nanyang Technological University","keywords":"Computer science; Doppler effect; Telecommunications; Commodity; Estimation; Real-time computing","score_opus":0.030598049709833913,"score_gpt":0.28100655394572893,"score_spread":0.250408504235895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220716396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01909438,0.0003277727,0.97816944,0.00015614412,0.00009575369,0.000032360957,0.00004161761,0.00084230106,0.0012401527],"genre_scores_gemma":[0.5480178,0.0006237584,0.44896477,0.00014516558,0.00013779095,0.000088139954,0.00014405059,0.000086943364,0.001791608],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990772,0.00023193043,0.000054205495,0.00016481172,0.00037081435,0.000101068996],"domain_scores_gemma":[0.9984738,0.0008372368,0.00012093289,0.000325369,0.00021037715,0.000032350625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009466661,0.00067350466,0.00070748315,0.000572882,0.00044278285,0.00087467604,0.0011025654,0.00085750106,0.001793757],"category_scores_gemma":[0.006119111,0.00041386118,0.0003473675,0.0006565208,0.00048027225,0.0019198194,0.0012460967,0.0011132143,0.0007553091],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003623256,0.00009146438,0.006452315,0.0003690644,0.00008196969,0.0002684401,0.00035889758,0.1558597,0.103670515,0.026731016,0.0046060756,0.70114815],"study_design_scores_gemma":[0.00007110655,0.00024105761,0.0029436916,0.000061556486,0.000058341346,0.0004369338,0.00007928273,0.9315759,0.045917973,0.008883802,0.009653388,0.00007689279],"about_ca_topic_score_codex":0.0026337774,"about_ca_topic_score_gemma":0.0030629581,"teacher_disagreement_score":0.0026337774,"about_ca_system_score_codex":0.000425886,"about_ca_system_score_gemma":0.0006688846,"threshold_uncertainty_score":0.006000638},"labels":[],"label_agreement":null},{"id":"W4220745968","doi":"10.1061/9780784483961.037","title":"Feasibility Assessment and Enhancement of TOF-Based UWB RTLS for Non-Line-of-Sight Conditions on Construction Sites","year":2022,"lang":"en","type":"article","venue":"Construction Research Congress 2022","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Non-line-of-sight propagation; Real-time locating system; Computer science; Multipath propagation; Residual; Line (geometry); Kalman filter; Multilateration; Ultra-wideband; Line-of-sight; Real-time computing; Engineering; Wireless; Telecommunications; Algorithm; Artificial intelligence; Mathematics; Node (physics)","score_opus":0.05858282337471942,"score_gpt":0.3769091979142025,"score_spread":0.3183263745394831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220745968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5937626,0.0004450841,0.40127248,0.00029364714,0.00009607836,0.00011380908,0.00011742525,0.00056289317,0.0033359937],"genre_scores_gemma":[0.9578568,0.00018385191,0.041210067,0.000026616657,0.0000129973205,0.000041738444,0.00005408113,0.000012214977,0.0006016237],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990984,0.00029185694,0.00003538864,0.0000951486,0.0003882242,0.000090964844],"domain_scores_gemma":[0.99834204,0.00041438662,0.00023069358,0.00013693662,0.0008250994,0.00005092587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012842696,0.0005823419,0.00035970233,0.00048359478,0.00019993797,0.0004388903,0.0008476481,0.0007458168,0.0007665338],"category_scores_gemma":[0.002928991,0.00016639127,0.00034956922,0.00028828002,0.00028172717,0.00092373753,0.0006029351,0.00026844753,0.0003428586],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021437097,0.0002563037,0.04972949,0.0011000292,0.000073336436,0.0012415645,0.000886249,0.11635556,0.51641315,0.002166645,0.0009923313,0.30864167],"study_design_scores_gemma":[0.000117062664,0.0066229315,0.042667292,0.000119152915,0.00024316584,0.0017629311,0.0013454101,0.5258119,0.4119839,0.0010896237,0.008113885,0.00012274183],"about_ca_topic_score_codex":0.0007941747,"about_ca_topic_score_gemma":0.00088768016,"teacher_disagreement_score":0.0012842696,"about_ca_system_score_codex":0.00027934407,"about_ca_system_score_gemma":0.00043452095,"threshold_uncertainty_score":0.0067919493},"labels":[],"label_agreement":null},{"id":"W4220947191","doi":"10.1016/j.simpat.2022.102543","title":"Machine learning-based indoor localization and occupancy estimation using 5G ultra-dense networks","year":2022,"lang":"en","type":"article","venue":"Simulation Modelling Practice and Theory","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Occupancy; Computer science; Estimation; Building model; Real-time computing; Building automation; Occupancy grid mapping; Artificial intelligence; Data mining; Machine learning; Simulation; Engineering; Architectural engineering; Mobile robot","score_opus":0.016976095080916905,"score_gpt":0.25305656256217174,"score_spread":0.23608046748125483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220947191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1028463,0.00042915522,0.8933517,0.00022712498,0.000055682838,0.000031958618,0.00009483467,0.00074235507,0.0022208486],"genre_scores_gemma":[0.95239514,0.00027529435,0.046241216,0.000044738983,0.000026210077,0.000035625075,0.0001329577,0.000015274329,0.0008336113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956244,0.00015014094,0.00002683935,0.0000889806,0.00011163542,0.00005996707],"domain_scores_gemma":[0.99935955,0.00031677418,0.00009549751,0.000062945364,0.00013716499,0.000028013816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000545954,0.000503708,0.000676203,0.000606031,0.0002927012,0.0005964601,0.00072587305,0.00039249667,0.0005697197],"category_scores_gemma":[0.0020198836,0.00028021526,0.00044759506,0.00089063187,0.00030568274,0.0008792007,0.00063964666,0.00038001096,0.00016075291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072882336,0.000045565517,0.0037131424,0.000039744085,0.00003961172,0.00005555912,0.000048713122,0.929925,0.0009211912,0.002777224,0.00043444827,0.061926905],"study_design_scores_gemma":[0.0000016094239,0.0000075267876,0.00033442085,0.0000020100404,0.0000035039684,0.000009862434,0.000004890768,0.99884075,0.00017944339,0.0005177943,0.00009581318,0.0000023820814],"about_ca_topic_score_codex":0.012131599,"about_ca_topic_score_gemma":0.009932118,"teacher_disagreement_score":0.012131599,"about_ca_system_score_codex":0.00083085574,"about_ca_system_score_gemma":0.0005280008,"threshold_uncertainty_score":0.024122},"labels":[],"label_agreement":null},{"id":"W4220954749","doi":"10.3390/fi14040111","title":"Location Transparency Call (LTC) System: An Intelligent Phone Dialing System Based on the Phone of Things (PoT) Architecture","year":2022,"lang":"en","type":"article","venue":"Future Internet","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Phone; Transparency (behavior); Scalability; Implementation; Architecture; Systems architecture; Usability; Telecommunications; Computer security; Computer network; Database; Human–computer interaction; Software engineering","score_opus":0.008336472758480616,"score_gpt":0.18761902288814197,"score_spread":0.17928255012966135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220954749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12239552,0.0008220628,0.7075986,0.00062841823,0.000515558,0.0006494162,0.0012989166,0.13275756,0.033333912],"genre_scores_gemma":[0.87385523,0.00032325226,0.09881617,0.00091411994,0.00013849708,0.00033459367,0.00198342,0.0011934863,0.022441149],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944335,0.000087440836,0.000049320373,0.0001110602,0.00022893785,0.000079866055],"domain_scores_gemma":[0.9991744,0.00015024678,0.000105696796,0.000229327,0.00019605913,0.00014427984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041112106,0.00049322145,0.00046458156,0.00068106985,0.0005161387,0.00093505747,0.0008267137,0.0007866979,0.005271251],"category_scores_gemma":[0.0013260968,0.00023579947,0.00026236387,0.00037183802,0.0002755677,0.0013939297,0.0014218518,0.0007941463,0.0026374187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002201748,0.0005179302,0.01453969,0.0006225639,0.00016368752,0.0023252277,0.0023528994,0.0056166956,0.19053228,0.015720695,0.09712181,0.6682848],"study_design_scores_gemma":[0.0007100487,0.0022338862,0.027811907,0.0002252618,0.00052158884,0.008522531,0.0011581647,0.30938488,0.21238111,0.011823621,0.42452705,0.00069996674],"about_ca_topic_score_codex":0.0010508432,"about_ca_topic_score_gemma":0.0010309372,"teacher_disagreement_score":0.005271251,"about_ca_system_score_codex":0.00029695596,"about_ca_system_score_gemma":0.00049812975,"threshold_uncertainty_score":0.017634094},"labels":[],"label_agreement":null},{"id":"W4221122615","doi":"10.1109/jsen.2022.3150130","title":"Guest Editorial Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation","year":2022,"lang":"en","type":"editorial","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Standardization; Computer science; Field (mathematics); Tracking (education); Wireless sensor network; Sensor fusion; Real-time computing; Artificial intelligence; Computer network","score_opus":0.00473048680060626,"score_gpt":0.23285449286668386,"score_spread":0.2281240060660776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221122615","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034281453,0.0036906986,0.00015269987,0.016218776,0.9776037,0.000017568733,0.000044466342,0.000060603896,0.0021771248],"genre_scores_gemma":[0.00033176335,0.0039436575,0.00010067562,0.009076911,0.9747048,0.000015390566,0.00004292778,0.00004062158,0.011743236],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970682,0.00034350035,0.0002906002,0.00037820314,0.0017036948,0.00021575295],"domain_scores_gemma":[0.98933625,0.002493558,0.00066221395,0.00028642762,0.0050918227,0.0021296996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035267249,0.003435775,0.0025984107,0.0029313287,0.0018908125,0.0066908924,0.0022621558,0.009378775,0.025460828],"category_scores_gemma":[0.0103804935,0.00086494075,0.0018909456,0.0010765437,0.0014421159,0.0041386234,0.0012981973,0.011995104,0.024351982],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042273492,0.000013498038,0.000023817785,0.00017304582,0.000009310053,0.00010458031,0.0000053745994,0.000020287609,0.00010102494,0.00019242032,0.9926853,0.0066290246],"study_design_scores_gemma":[0.00003979117,0.0000341261,0.00018731947,0.00022043417,0.00002649233,0.00031609967,0.00002160248,0.00012717857,0.00017350938,0.0005866987,0.9982545,0.000012254983],"about_ca_topic_score_codex":0.00063895236,"about_ca_topic_score_gemma":0.0020840554,"teacher_disagreement_score":0.025460828,"about_ca_system_score_codex":0.0016718316,"about_ca_system_score_gemma":0.0014356552,"threshold_uncertainty_score":0.08517498},"labels":[],"label_agreement":null},{"id":"W4221140877","doi":"10.1007/s11042-023-17487-z","title":"A prospective approach for human-to-human interaction recognition from Wi-Fi channel data using attention bidirectional gated recurrent neural network with GUI application implementation","year":2024,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mutual information; Artificial intelligence; Recurrent neural network; Executable; Pattern recognition (psychology); Artificial neural network; Machine learning; Human–computer interaction","score_opus":0.07889820510792529,"score_gpt":0.33733427080751777,"score_spread":0.2584360656995925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221140877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011802713,0.00013928105,0.9837346,0.00007891309,0.00004689061,0.00004464504,0.000076320015,0.0028853666,0.0011912095],"genre_scores_gemma":[0.5347684,0.00029122524,0.45512402,0.00022179887,0.000062310864,0.00015593464,0.0005042732,0.00016695175,0.008705141],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981743,0.00003598137,0.000009820431,0.00005692214,0.000050205068,0.00002973911],"domain_scores_gemma":[0.9998109,0.00003810912,0.000008028921,0.000028940482,0.000099805206,0.000014405782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036754014,0.00043644174,0.00036272855,0.00024794697,0.00019072896,0.00041814943,0.0010289027,0.0004999104,0.0044545247],"category_scores_gemma":[0.0006719771,0.00018242383,0.00038874388,0.00029633997,0.0001851463,0.000641307,0.0005049047,0.00051081856,0.0011356317],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045367316,0.00036139772,0.0026817168,0.00012976922,0.00016442365,0.00027693724,0.00011559781,0.09213606,0.100456215,0.00863217,0.00601855,0.78857344],"study_design_scores_gemma":[0.00001039405,0.00008306724,0.0007630745,0.000004661203,0.00002194222,0.00005103578,0.000018659879,0.9822481,0.01458276,0.0010284451,0.0011773078,0.000010549781],"about_ca_topic_score_codex":0.010752129,"about_ca_topic_score_gemma":0.013449897,"teacher_disagreement_score":0.010752129,"about_ca_system_score_codex":0.00031825856,"about_ca_system_score_gemma":0.00060478714,"threshold_uncertainty_score":0.021379113},"labels":[],"label_agreement":null},{"id":"W4223997090","doi":"10.21203/rs.3.rs-1527636/v1","title":"Augmented Reality indoor tracking using Placenote","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Association of Universities and Colleges of Canada","funders":"","keywords":"Augmented reality; Computer science; Global Positioning System; Tracking (education); Real-time computing; Tracking system; Destinations; Computer vision; Artificial intelligence; Geography; Telecommunications; Tourism","score_opus":0.13933341749048828,"score_gpt":0.4093233766460258,"score_spread":0.26998995915553753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223997090","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03496925,0.00019797159,0.95835865,0.000084196276,0.00009685573,0.00003233567,0.00011559183,0.0027479474,0.0033971493],"genre_scores_gemma":[0.89584315,0.00033097452,0.09962789,0.00005371181,0.00003192716,0.000034418597,0.00023566128,0.000059984115,0.0037822586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964225,0.00006135905,0.000018527067,0.000114726645,0.000119642966,0.000043497217],"domain_scores_gemma":[0.99966943,0.000059647507,0.000055074826,0.0001069805,0.00008522472,0.000023657272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000281364,0.00058696227,0.000552806,0.0005512885,0.00033904472,0.0011959015,0.000936282,0.0006459191,0.0022121936],"category_scores_gemma":[0.0006832985,0.00027792176,0.00056966324,0.0009960833,0.00033853104,0.0011747053,0.00093093544,0.00049206417,0.00090287405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005767485,0.0001954049,0.005486013,0.00020048942,0.0000935592,0.0005550304,0.00026235453,0.5673074,0.038813256,0.011815559,0.003984958,0.37070918],"study_design_scores_gemma":[0.00001685555,0.00014024507,0.0010136431,0.000017675266,0.000028485641,0.00020787871,0.000035513214,0.97946554,0.012873283,0.0010845743,0.005086641,0.000029765024],"about_ca_topic_score_codex":0.0049028574,"about_ca_topic_score_gemma":0.0029670661,"teacher_disagreement_score":0.0049028574,"about_ca_system_score_codex":0.00053148816,"about_ca_system_score_gemma":0.0005457896,"threshold_uncertainty_score":0.009748638},"labels":[],"label_agreement":null},{"id":"W4224213717","doi":"10.3390/s22093135","title":"FPGA-Based Autonomous GPS-Disciplined Oscillatorsfor Wireless Sensor Network Nodes","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Field-programmable gate array; Global Positioning System; Computer science; Gate array; Context (archaeology); Embedded system; SIGNAL (programming language); Wireless sensor network; GPS signals; Assisted GPS; Precision Lightweight GPS Receiver; Real-time computing; Wireless; Computer hardware; Telecommunications; Computer network; Gps receiver","score_opus":0.008303739136767572,"score_gpt":0.20407918331306577,"score_spread":0.1957754441762982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224213717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3096646,0.0023297307,0.6585961,0.0002834802,0.0006239724,0.0003337013,0.00047876718,0.006750554,0.020939017],"genre_scores_gemma":[0.9028412,0.00029412392,0.09039475,0.000107532665,0.00004560851,0.00007864018,0.00018348653,0.000044720306,0.0060098837],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998616,0.000020661386,0.000008707977,0.000035164034,0.000052530395,0.000021350126],"domain_scores_gemma":[0.99989307,0.000022982274,0.000021811142,0.000018058563,0.000035713463,0.000008292498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001121821,0.00027506793,0.00014809934,0.00025789425,0.00013480471,0.00020074924,0.0005893624,0.00016215633,0.002704769],"category_scores_gemma":[0.00025004463,0.00010292766,0.00008673812,0.00016280361,0.00010882031,0.00028963268,0.00016618466,0.00018726582,0.0004966245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081181404,0.00018199107,0.006053966,0.00080773025,0.00010714642,0.00047824226,0.0002415495,0.034201477,0.5355848,0.0093752835,0.008456609,0.4036994],"study_design_scores_gemma":[0.00042053388,0.002572601,0.011077711,0.00012267586,0.00019534685,0.0019235559,0.000098066026,0.3002829,0.58619434,0.0017968816,0.09520863,0.00010680089],"about_ca_topic_score_codex":0.00077240204,"about_ca_topic_score_gemma":0.001799078,"teacher_disagreement_score":0.002704769,"about_ca_system_score_codex":0.0002046694,"about_ca_system_score_gemma":0.0002466892,"threshold_uncertainty_score":0.009048343},"labels":[],"label_agreement":null},{"id":"W4224237118","doi":"10.1145/3530682","title":"A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open Issues","year":2022,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Granularity; Identification (biology); Task (project management); Human–computer interaction; Wireless; Ranging; Artificial intelligence; Real-time computing; Telecommunications; Systems engineering","score_opus":0.13172137550989402,"score_gpt":0.36528622726662374,"score_spread":0.23356485175672972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224237118","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042627696,0.891672,0.07905861,0.0039543165,0.0019279344,0.00015354136,0.0002877984,0.0005064921,0.018176602],"genre_scores_gemma":[0.035177834,0.9276859,0.026579903,0.0024180233,0.002598201,0.00020585868,0.0007602784,0.00009521772,0.004478878],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978098,0.00052809203,0.0002410237,0.0005095227,0.0007466425,0.00016496188],"domain_scores_gemma":[0.9927862,0.005044215,0.00025293045,0.00040692152,0.0013520251,0.0001576393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027986038,0.0015880421,0.0016026365,0.0031638502,0.000731404,0.0029903657,0.0023576522,0.0023196451,0.005098021],"category_scores_gemma":[0.0070718033,0.00087113987,0.00090356864,0.0059379865,0.0009843155,0.0068832072,0.0018210402,0.0015655779,0.0026668988],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001467042,0.00009593244,0.002207633,0.013017543,0.00008658595,0.00023579458,0.00033266307,0.0025384875,0.0037199017,0.02587572,0.031815786,0.9199272],"study_design_scores_gemma":[0.000028373812,0.00046777815,0.004217482,0.008769373,0.00028441584,0.0025474278,0.0018965352,0.024433332,0.0065654204,0.03608176,0.9144972,0.00021082986],"about_ca_topic_score_codex":0.0015367644,"about_ca_topic_score_gemma":0.0012231424,"teacher_disagreement_score":0.005098021,"about_ca_system_score_codex":0.00072770496,"about_ca_system_score_gemma":0.0013194744,"threshold_uncertainty_score":0.017054558},"labels":[],"label_agreement":null},{"id":"W4224315682","doi":"10.5194/isprs-archives-xlvi-3-w1-2022-61-2022","title":"CONVOLUTIONAL NEURAL NETWORKS BASED GNSS SIGNAL CLASSIFICATION USING CORRELATOR-LEVEL MEASUREMENTS","year":2022,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Non-line-of-sight propagation; Computer science; Convolutional neural network; SIGNAL (programming language); Support vector machine; Pattern recognition (psychology); Artificial intelligence; Multipath propagation; k-nearest neighbors algorithm; Global Positioning System; Telecommunications","score_opus":0.040599591456193164,"score_gpt":0.25005986949166453,"score_spread":0.20946027803547138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224315682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36517277,0.00080341223,0.6234892,0.00029222638,0.00026669985,0.00007466494,0.00050109223,0.003050993,0.0063488507],"genre_scores_gemma":[0.9518303,0.00023310266,0.042779386,0.00008381523,0.000036950783,0.000035147896,0.0006284602,0.000025460187,0.0043474208],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999821,0.000020380234,0.000010484673,0.000057485333,0.000056015586,0.00003463054],"domain_scores_gemma":[0.9997564,0.000056179306,0.00004328484,0.000026925374,0.00010506252,0.000012123631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002944741,0.00066599774,0.00032916776,0.00061849377,0.00017564121,0.0004254644,0.0005446622,0.0004958218,0.0011221049],"category_scores_gemma":[0.0007705527,0.00019171223,0.0003583326,0.00054985663,0.00019591533,0.00048041352,0.00031330623,0.00044721548,0.0005226209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032303474,0.00019414644,0.012726426,0.00010767311,0.00015644921,0.00016496853,0.000051020375,0.39867738,0.032263707,0.0012758818,0.0028070137,0.55125225],"study_design_scores_gemma":[0.0000029619778,0.000024678924,0.001793935,0.0000049444016,0.000011819606,0.000016586306,0.0000047534613,0.992008,0.0057124663,0.00016690977,0.00024840707,0.000004660236],"about_ca_topic_score_codex":0.008924765,"about_ca_topic_score_gemma":0.00771131,"teacher_disagreement_score":0.008924765,"about_ca_system_score_codex":0.00055317604,"about_ca_system_score_gemma":0.0004179346,"threshold_uncertainty_score":0.017745614},"labels":[],"label_agreement":null},{"id":"W4224918263","doi":"10.1109/icassp43922.2022.9746803","title":"LiteHAR: Lightweight Human Activity Recognition from WIFI Signals with Random Convolution Kernels","year":2022,"lang":"en","type":"article","venue":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computational complexity theory; Activity recognition; Feature extraction; Benchmark (surveying); Artificial intelligence; Convolution (computer science); Classifier (UML); Deep learning; Pattern recognition (psychology); Wireless; Channel state information; Machine learning; Artificial neural network; Algorithm","score_opus":0.037102443668402894,"score_gpt":0.2654851081899215,"score_spread":0.2283826645215186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224918263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06539822,0.0007428423,0.9167034,0.00021059044,0.00012938688,0.00012865367,0.0019138918,0.012398205,0.0023748078],"genre_scores_gemma":[0.7272522,0.00080100977,0.25402638,0.0004096618,0.000103414226,0.00032790776,0.007705948,0.0002636218,0.009109873],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996823,0.000050509654,0.000017999862,0.0001044347,0.000086705906,0.000058062175],"domain_scores_gemma":[0.99979717,0.00005671573,0.000032808366,0.000054997145,0.000040932584,0.000017475364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034560027,0.00091660843,0.0008032156,0.00065057963,0.00014465643,0.0005076192,0.001227767,0.00059067324,0.0020991156],"category_scores_gemma":[0.0012447567,0.0002548489,0.00054111885,0.000618594,0.000188549,0.000841689,0.0010156478,0.000735862,0.0014833736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048237463,0.0003921545,0.0054205004,0.00020466471,0.000209253,0.00027471135,0.0000646089,0.07626588,0.021561705,0.0023444134,0.014909221,0.87787044],"study_design_scores_gemma":[0.000031272666,0.00016505906,0.0038931803,0.000019324125,0.00003005679,0.0003351992,0.0000234423,0.97831213,0.012113338,0.0023426937,0.002706892,0.00002743601],"about_ca_topic_score_codex":0.0031654823,"about_ca_topic_score_gemma":0.0046184855,"teacher_disagreement_score":0.0031654823,"about_ca_system_score_codex":0.00029661754,"about_ca_system_score_gemma":0.00043719105,"threshold_uncertainty_score":0.0070222616},"labels":[],"label_agreement":null},{"id":"W4225109868","doi":"10.5772/intechopen.103893","title":"Localization Context-Aware Models for Wireless Sensor Network","year":2022,"lang":"en","type":"book-chapter","venue":"IntechOpen eBooks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Wireless sensor network; Global Positioning System; Computer science; Context (archaeology); Location awareness; Variety (cybernetics); Key (lock); Wireless; Routing (electronic design automation); Intrusion detection system; Geographic routing; Context awareness; Computer network; Ubiquitous computing; Distributed computing; Computer security; Routing protocol; Telecommunications; Human–computer interaction; Geography; Link-state routing protocol; Artificial intelligence","score_opus":0.022616362479859944,"score_gpt":0.21517231279200508,"score_spread":0.19255595031214512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225109868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004004127,0.009991924,0.9713046,0.0010082645,0.0003754161,0.00006458834,0.00036190113,0.00042988092,0.012459231],"genre_scores_gemma":[0.5579198,0.054370474,0.32733914,0.0009865615,0.0014644845,0.0012553678,0.0025152785,0.00057608495,0.05357285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996049,0.00014769494,0.000021893162,0.000076885684,0.0001142351,0.000034359313],"domain_scores_gemma":[0.9995034,0.00028828863,0.0000466235,0.000046739828,0.000093426584,0.000021623027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058885844,0.0011489169,0.0007806743,0.00062265055,0.0003390305,0.0014960314,0.0017534656,0.0011469639,0.003143332],"category_scores_gemma":[0.0020136463,0.00039779666,0.00076677446,0.0012111795,0.0006081348,0.0022711947,0.0011931838,0.0017038276,0.0013971765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046375997,0.00003949924,0.00046681968,0.0002847036,0.000048062022,0.00014049726,0.00017779719,0.7073209,0.0011132596,0.21949178,0.008606345,0.062263913],"study_design_scores_gemma":[0.000004491662,0.000017661214,0.00009859215,0.000048014157,0.00001197885,0.000049921156,0.000025560461,0.9232212,0.00012725209,0.065798104,0.010587184,0.000010063047],"about_ca_topic_score_codex":0.004057863,"about_ca_topic_score_gemma":0.003980383,"teacher_disagreement_score":0.004057863,"about_ca_system_score_codex":0.0009310805,"about_ca_system_score_gemma":0.0006818882,"threshold_uncertainty_score":0.010515511},"labels":[],"label_agreement":null},{"id":"W4225298526","doi":"10.1109/icassp43922.2022.9747619","title":"Direct Localization: An Ising Model Approach","year":2022,"lang":"en","type":"article","venue":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Ising model; Markov chain Monte Carlo; Computer science; Monte Carlo method; Markov chain; Planar; Multipath propagation; Algorithm; Energy (signal processing); Compressed sensing; Norm (philosophy); Mathematical optimization; Theoretical computer science; Artificial intelligence; Mathematics; Statistical physics; Bayesian probability; Machine learning; Physics; Statistics","score_opus":0.04232976235872387,"score_gpt":0.2736876734956003,"score_spread":0.23135791113687643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225298526","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010203523,0.00030182605,0.9853952,0.00030677093,0.000039447932,0.00002478567,0.000058307214,0.00020120316,0.0034690257],"genre_scores_gemma":[0.69579345,0.0016367824,0.2879643,0.00045588936,0.00018958736,0.00017853973,0.0003230496,0.00012837582,0.013329993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958533,0.00010583439,0.000018562518,0.000083310515,0.00016502626,0.000041885247],"domain_scores_gemma":[0.9991279,0.0005172377,0.00008958993,0.00010486938,0.00012390311,0.000036499183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054655067,0.0006380052,0.0008052288,0.0008740162,0.0004147846,0.0007596912,0.0014862304,0.0014647244,0.0024050532],"category_scores_gemma":[0.0022844262,0.00047396205,0.0005808444,0.0010283975,0.00097333465,0.0016551213,0.0012388764,0.0010675223,0.0005436479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007670037,0.0000538622,0.000977745,0.00015402792,0.00005261539,0.000301173,0.000118076256,0.82732713,0.0040751053,0.1072705,0.0019983335,0.05759476],"study_design_scores_gemma":[0.000009531134,0.000013408941,0.000096320546,0.0000074093387,0.000009812906,0.00008472183,0.000009636018,0.9811003,0.00053637614,0.0174698,0.00065016584,0.00001244235],"about_ca_topic_score_codex":0.0058783316,"about_ca_topic_score_gemma":0.005602495,"teacher_disagreement_score":0.0058783316,"about_ca_system_score_codex":0.00077721605,"about_ca_system_score_gemma":0.0011347067,"threshold_uncertainty_score":0.011688232},"labels":[],"label_agreement":null},{"id":"W4225906403","doi":"10.2139/ssrn.4081104","title":"An Innovative Machine Learning Based Technology for an Indoor Positioning System","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Engineering; Artificial intelligence; Engineering management","score_opus":0.005252687475700659,"score_gpt":0.21857367015227877,"score_spread":0.21332098267657812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225906403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002730252,0.00026633404,0.9933461,0.00015171566,0.00021668237,0.000023665243,0.000058065314,0.0010848454,0.0021225116],"genre_scores_gemma":[0.19758435,0.00074333884,0.7868041,0.00039028688,0.00036960942,0.00017574639,0.0002988596,0.00011136209,0.0135224145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930596,0.00013614414,0.000039700917,0.0001484563,0.00032846726,0.00004130581],"domain_scores_gemma":[0.9995276,0.00013366497,0.00004951796,0.000093142204,0.00017493451,0.000021173568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000446273,0.0005451714,0.000589679,0.0006602854,0.00045020142,0.00089832355,0.0009448112,0.0010288581,0.0034516728],"category_scores_gemma":[0.0010155438,0.000240037,0.00043070674,0.0009047558,0.00037586098,0.0011831637,0.000796935,0.000945547,0.0024502245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020773905,0.00012323048,0.0016812979,0.00027042523,0.00010274937,0.00025975454,0.00013044101,0.0399993,0.102745846,0.035428707,0.007126044,0.81192446],"study_design_scores_gemma":[0.000033926473,0.00041717803,0.0029162453,0.00007262685,0.000116499155,0.0009016831,0.000053599248,0.86297953,0.06390066,0.015063321,0.05345105,0.00009371091],"about_ca_topic_score_codex":0.0011218159,"about_ca_topic_score_gemma":0.0011121326,"teacher_disagreement_score":0.0034516728,"about_ca_system_score_codex":0.00041476838,"about_ca_system_score_gemma":0.0004694242,"threshold_uncertainty_score":0.011547029},"labels":[],"label_agreement":null},{"id":"W4225998828","doi":"10.48550/arxiv.2112.04550","title":"NOMA Empowered Integrated Sensing and Communication","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Noma; Computer science; Base station; Beamforming; Throughput; Power (physics); SIGNAL (programming language); Single antenna interference cancellation; Dual (grammatical number); Distributed computing; Computer network; Wireless; Telecommunications; Telecommunications link; Channel (broadcasting)","score_opus":0.03618196746500894,"score_gpt":0.16042903822564558,"score_spread":0.12424707076063664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225998828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073690894,0.0003912485,0.9877051,0.00017167872,0.00009005669,0.000029097268,0.00004519404,0.00010391809,0.0040946202],"genre_scores_gemma":[0.7419923,0.00087477337,0.2465651,0.00032563682,0.0002266353,0.00017992698,0.00012929656,0.00003919503,0.009667154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987839,0.00047083665,0.000031322677,0.0002171502,0.00032674108,0.00017012679],"domain_scores_gemma":[0.99906904,0.00039495795,0.00013656472,0.0001339854,0.000202354,0.00006304041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009448594,0.0013123994,0.00092936895,0.00046673729,0.00056759885,0.0012130066,0.0013266891,0.00092485466,0.001559187],"category_scores_gemma":[0.0019906843,0.00037918388,0.0004977427,0.0008153368,0.00090015546,0.0013149381,0.0019042678,0.0012686887,0.0005362196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028048488,0.00014618886,0.0009952891,0.0003783799,0.0001418907,0.0006480488,0.00017978075,0.6657506,0.029042454,0.16105734,0.004782935,0.13659663],"study_design_scores_gemma":[0.000008978685,0.00009321964,0.00012919957,0.000011806278,0.000015864147,0.00015075099,0.000026074613,0.98348665,0.002219788,0.011481751,0.002360739,0.000015203038],"about_ca_topic_score_codex":0.0011567841,"about_ca_topic_score_gemma":0.0023709992,"teacher_disagreement_score":0.001559187,"about_ca_system_score_codex":0.00048229247,"about_ca_system_score_gemma":0.0012258611,"threshold_uncertainty_score":0.005216062},"labels":[],"label_agreement":null},{"id":"W4226050909","doi":"10.1109/comst.2022.3145856","title":"Ubiquitous Acoustic Sensing on Commodity IoT Devices: A Survey","year":2022,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Transceiver; Spawn (biology); Exploit; Key (lock); Signal processing; Internet of Things; Computer hardware; Telecommunications; Wireless; Embedded system; Digital signal processing; Operating system","score_opus":0.06446879189553352,"score_gpt":0.2864616461087042,"score_spread":0.22199285421317072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226050909","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015712006,0.9205566,0.037947707,0.0009548981,0.00051487546,0.00011214571,0.00020385471,0.0002651616,0.023732753],"genre_scores_gemma":[0.049102146,0.92668,0.01770635,0.0006132377,0.0006873541,0.000103394384,0.00044719077,0.0000564025,0.004603829],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99908435,0.00013774798,0.00010782769,0.00020571103,0.00037891406,0.000085526226],"domain_scores_gemma":[0.9985537,0.00081159885,0.00011694509,0.00008497711,0.00035585256,0.00007701975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007909525,0.0008808597,0.00096605724,0.0034007633,0.00048804854,0.0017433413,0.0010996986,0.0012385708,0.0024156764],"category_scores_gemma":[0.001794985,0.0007734874,0.00066243793,0.0063477852,0.000401336,0.004418437,0.0012173668,0.0008684595,0.0011865111],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008946858,0.000103759936,0.004090637,0.007017402,0.00008037553,0.00041690006,0.00035623866,0.0028632022,0.004091384,0.007852873,0.012265791,0.960772],"study_design_scores_gemma":[0.000017619863,0.0005473243,0.014729075,0.005126306,0.00025995672,0.004305698,0.0017216296,0.01811698,0.0067434427,0.01066843,0.9375997,0.00016379697],"about_ca_topic_score_codex":0.0016117962,"about_ca_topic_score_gemma":0.0014184349,"teacher_disagreement_score":0.0034007633,"about_ca_system_score_codex":0.00045698104,"about_ca_system_score_gemma":0.00073616294,"threshold_uncertainty_score":0.008081257},"labels":[],"label_agreement":null},{"id":"W4226182768","doi":"10.1145/3508072.3508111","title":"Towards Optimal Placement of Cloud Edge Gateways","year":2021,"lang":"en","type":"article","venue":"The 5th International Conference on Future Networks &amp; Distributed Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Cloud computing; Software deployment; Enhanced Data Rates for GSM Evolution; Key (lock); Global Positioning System; Distributed computing; Architecture; Field (mathematics); Real-time computing; The Internet; Edge computing; Computer network; Computer security; Telecommunications; Operating system","score_opus":0.03300617404750839,"score_gpt":0.26713071159346785,"score_spread":0.23412453754595947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226182768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07722329,0.00020445911,0.91640925,0.00033841914,0.00006858025,0.0001554052,0.00017340176,0.00066320854,0.0047639557],"genre_scores_gemma":[0.40567684,0.00016208075,0.5917154,0.00010657509,0.000019610416,0.00010365632,0.00020793537,0.00010532944,0.0019025574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997018,0.00007045273,0.000018246732,0.00006637643,0.000064721135,0.000078348436],"domain_scores_gemma":[0.99937755,0.00020376373,0.00009765871,0.000065372595,0.00018749498,0.000068087174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051939895,0.00097144186,0.0008445896,0.000941307,0.00054005143,0.0011055983,0.00081279984,0.00090947916,0.001928663],"category_scores_gemma":[0.0026119507,0.0005174073,0.00038904604,0.00067680905,0.00055271486,0.0007206615,0.0011315203,0.00054461695,0.00073637516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015779182,0.000089608504,0.0026624033,0.00007964315,0.000018686695,0.00015257813,0.000110261964,0.9039991,0.007746052,0.0117319375,0.004081174,0.069170855],"study_design_scores_gemma":[0.00001958422,0.000030488756,0.0002734552,0.000010177781,0.0000044887915,0.00002782768,0.000051887953,0.99292386,0.002009258,0.0037703023,0.00087314803,0.0000054004067],"about_ca_topic_score_codex":0.0055593727,"about_ca_topic_score_gemma":0.0077109486,"teacher_disagreement_score":0.0055593727,"about_ca_system_score_codex":0.0008981075,"about_ca_system_score_gemma":0.0018711471,"threshold_uncertainty_score":0.011054039},"labels":[],"label_agreement":null},{"id":"W4229548260","doi":"10.1007/978-3-319-23519-6_629-2","title":"Indoor Positioning with Wireless Local Area Networks (WLAN)","year":2016,"lang":"en","type":"book-chapter","venue":"Encyclopedia of GIS","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Wi-Fi; Computer network; Computer science; Local area network; Wireless network; Wireless; Wireless lan; Geography; Telecommunications","score_opus":0.004774676568567232,"score_gpt":0.17103754621732278,"score_spread":0.16626286964875556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229548260","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028927107,0.0631681,0.55881923,0.00096145837,0.0028439963,0.000089668894,0.0005572059,0.0038654855,0.36680216],"genre_scores_gemma":[0.07685019,0.12138625,0.21682118,0.001163875,0.0028964493,0.00017605264,0.0017939275,0.00079401926,0.578118],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995846,0.00006174338,0.00002367867,0.00009937559,0.00019739162,0.00003314019],"domain_scores_gemma":[0.9998529,0.00004160722,0.000014224947,0.000033120898,0.00004930079,0.000008776482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021678596,0.0012164396,0.0006773072,0.0009806757,0.00038192855,0.0018336184,0.0011262863,0.00093767367,0.02418822],"category_scores_gemma":[0.0005851388,0.00041137703,0.00041945252,0.0030957682,0.00044240992,0.0019082205,0.001281181,0.0010659015,0.02358834],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042298958,0.000029605537,0.00039676158,0.0006565536,0.000023362643,0.00018319399,0.00017820725,0.0046852035,0.0070868107,0.034751285,0.05994611,0.8920206],"study_design_scores_gemma":[0.0000069453495,0.00010575996,0.00082761206,0.00036620352,0.000046666933,0.0012676003,0.00015100991,0.0072149304,0.005393979,0.0132027855,0.9713767,0.00003988187],"about_ca_topic_score_codex":0.0010549804,"about_ca_topic_score_gemma":0.0013493983,"teacher_disagreement_score":0.02418822,"about_ca_system_score_codex":0.00040251308,"about_ca_system_score_gemma":0.0004188502,"threshold_uncertainty_score":0.08091766},"labels":[],"label_agreement":null},{"id":"W4230206305","doi":"10.4018/978-1-7998-2454-1.ch071","title":"A Comparative Study of Range-Free and Range-Based Localization Protocols for Wireless Sensor Network","year":2020,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"RSS; Computer science; Wireless sensor network; Range (aeronautics); Computer network; Centroid; Node (physics); Distance-vector routing protocol; Signal strength; Routing protocol; Topology (electrical circuits); Real-time computing; Routing (electronic design automation); Artificial intelligence; Engineering; Wireless Routing Protocol; Electrical engineering","score_opus":0.02964519499063292,"score_gpt":0.2642424513901338,"score_spread":0.23459725639950088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230206305","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07093353,0.19880295,0.52624714,0.0014737725,0.002140525,0.0004498052,0.00057508936,0.0024350095,0.19694221],"genre_scores_gemma":[0.46259534,0.15742493,0.27585492,0.0008841605,0.0009817228,0.00043907834,0.0021445828,0.0007051216,0.09897014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991198,0.00021308474,0.00004677468,0.000088176035,0.0004879433,0.000044195847],"domain_scores_gemma":[0.9981931,0.0010551594,0.000087585926,0.00015036383,0.00047542207,0.000038472757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083713065,0.00051826617,0.00044537697,0.0012759623,0.00032602058,0.0012161742,0.0010603676,0.00050473196,0.0038663135],"category_scores_gemma":[0.002573021,0.0001792873,0.000337221,0.0027246845,0.00027153516,0.0027856417,0.0004545202,0.00051360705,0.0013743509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026628227,0.0001788118,0.0011552937,0.0014637718,0.00008266726,0.00023395347,0.0002461534,0.025953587,0.013038638,0.060592625,0.018119462,0.8786687],"study_design_scores_gemma":[0.00006421157,0.0031490405,0.006907613,0.0012692637,0.00043080698,0.0065914253,0.0009857048,0.36765835,0.038575962,0.04799743,0.52615094,0.00021923809],"about_ca_topic_score_codex":0.00049741473,"about_ca_topic_score_gemma":0.0005961353,"teacher_disagreement_score":0.0038663135,"about_ca_system_score_codex":0.0006178548,"about_ca_system_score_gemma":0.00041044556,"threshold_uncertainty_score":0.012934089},"labels":[],"label_agreement":null},{"id":"W4230706346","doi":"10.1002/wcm.523","title":"Performance of a MANET directional MAC protocol with angle‐of‐arrival estimation","year":2007,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Global Positioning System; Protocol (science); Mobile ad hoc network; Computer network; Node (physics); Throughput; Real-time computing; Wireless ad hoc network; Position (finance); Assisted GPS; Omnidirectional antenna; Wireless; Telecommunications; Network packet; Antenna (radio)","score_opus":0.008737972771375536,"score_gpt":0.24984131702569795,"score_spread":0.24110334425432242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230706346","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8766319,0.0012928778,0.11320663,0.00056279317,0.0002576441,0.00021503457,0.00021525574,0.0014771803,0.0061407737],"genre_scores_gemma":[0.9933984,0.00014203903,0.0056660967,0.000043213622,0.000017661767,0.000045020715,0.00009841609,0.0000162474,0.0005728045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981779,0.0006486826,0.00013393702,0.00019386363,0.00061631185,0.00022928863],"domain_scores_gemma":[0.99088895,0.0050386526,0.0010238158,0.00075195293,0.0019953179,0.00030134965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003686455,0.0008928631,0.0007358014,0.0010981545,0.00060122536,0.001182486,0.0007357572,0.0006555005,0.00083299767],"category_scores_gemma":[0.0097369775,0.00027679314,0.0002227693,0.00074148044,0.0006658124,0.0008304355,0.00084970903,0.0006830208,0.00018602966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005078479,0.00070539373,0.015978513,0.00036677485,0.00043111097,0.0005748329,0.00028895788,0.8195852,0.057204284,0.009821866,0.0031612904,0.08680327],"study_design_scores_gemma":[0.000051977215,0.00071253045,0.0010467842,0.000007462727,0.000051379844,0.00013928262,0.00003894711,0.98773533,0.009366268,0.00044429922,0.0003858839,0.000019886715],"about_ca_topic_score_codex":0.0024113297,"about_ca_topic_score_gemma":0.001266565,"teacher_disagreement_score":0.003686455,"about_ca_system_score_codex":0.000855354,"about_ca_system_score_gemma":0.000997007,"threshold_uncertainty_score":0.019496083},"labels":[],"label_agreement":null},{"id":"W4232390248","doi":"10.32920/ryerson.14643726","title":"Integration Of RFID With WLAN","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer network; Computer science; Wi-Fi; Access control; Throughput; Interference (communication); Channel (broadcasting); Radio-frequency identification; Wireless lan; Wireless; IEEE 802.11; Physical layer; Media access control; Wireless network; Telecommunications; Computer security","score_opus":0.010116996895363434,"score_gpt":0.20268502328122448,"score_spread":0.19256802638586104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232390248","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08612003,0.0048194975,0.8495537,0.0007692418,0.0003628509,0.00015838273,0.000058103666,0.0022222481,0.05593583],"genre_scores_gemma":[0.78956294,0.0032566593,0.1807329,0.00039546288,0.00022370912,0.00009762564,0.0001884844,0.00008796012,0.02545425],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987349,0.00034146642,0.0000615008,0.00022533335,0.00044616088,0.00019069304],"domain_scores_gemma":[0.99946517,0.0001304339,0.00005709582,0.0001325002,0.00018004357,0.0000348264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006898465,0.000500729,0.00040713084,0.00042654973,0.00034024913,0.0014799852,0.00079080136,0.0008093459,0.0018272279],"category_scores_gemma":[0.0011741258,0.00037518187,0.00044974097,0.0006899169,0.00025425118,0.0017120033,0.0013676306,0.0007998256,0.0014119106],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038315274,0.00041024206,0.010506319,0.0006707903,0.00025014798,0.0016489996,0.0004254572,0.072400555,0.12281311,0.061167046,0.0047845524,0.7245397],"study_design_scores_gemma":[0.000068138484,0.0014510788,0.008808958,0.00026554064,0.00048776023,0.0051376075,0.00058851443,0.61551666,0.14680848,0.023122687,0.19757791,0.00016661317],"about_ca_topic_score_codex":0.00073877984,"about_ca_topic_score_gemma":0.0007115975,"teacher_disagreement_score":0.0018272279,"about_ca_system_score_codex":0.00033458084,"about_ca_system_score_gemma":0.00046526676,"threshold_uncertainty_score":0.0061127543},"labels":[],"label_agreement":null},{"id":"W4233333049","doi":"10.1007/s10796-008-9124-1","title":"Monitoring user activities in smart home environments","year":2008,"lang":"en","type":"article","venue":"Information Systems Frontiers","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Bedroom; Scalability; Wireless sensor network; Context (archaeology); Home automation; Architecture; Ubiquitous computing; Activity recognition; Ambient intelligence; Context awareness; Human–computer interaction; Real-time computing; Computer network; Telecommunications; Database; Artificial intelligence","score_opus":0.007434234030408974,"score_gpt":0.17327179875987994,"score_spread":0.16583756472947098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233333049","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9848236,0.00014874489,0.012119666,0.000052461935,0.000011170685,0.000031487925,0.00061685976,0.00042444596,0.0017717248],"genre_scores_gemma":[0.9940584,0.00011712968,0.004488443,0.000021962998,0.000012062561,0.000023073111,0.0003298485,0.000016283331,0.0009327876],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998006,0.000068077024,0.000012064306,0.000042979642,0.00004919048,0.00002710369],"domain_scores_gemma":[0.99952865,0.0001851141,0.00007248606,0.00003407724,0.00009901482,0.00008070155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001525017,0.00031913453,0.00043696893,0.0006670735,0.0001894965,0.00045519526,0.00021893546,0.00032878734,0.0012088664],"category_scores_gemma":[0.0008228037,0.0001402245,0.00011019597,0.00052818656,0.00009563016,0.00042959733,0.00028333932,0.0001924595,0.0004683339],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029471242,0.0006478289,0.5980801,0.00032081606,0.0002783173,0.0006289953,0.002285958,0.008728468,0.05135875,0.00044892717,0.0046303435,0.32964438],"study_design_scores_gemma":[0.00008686202,0.0013952091,0.83163357,0.00004893408,0.00032724254,0.001404541,0.0032398186,0.12706299,0.030033803,0.00091641344,0.0037965851,0.000054042797],"about_ca_topic_score_codex":0.0021934833,"about_ca_topic_score_gemma":0.004841477,"teacher_disagreement_score":0.0021934833,"about_ca_system_score_codex":0.00013148651,"about_ca_system_score_gemma":0.0001140705,"threshold_uncertainty_score":0.0043615103},"labels":[],"label_agreement":null},{"id":"W4234726511","doi":"10.22215/etd/2005-08029","title":"Synthetic doppler for precise indoor geolocation","year":2005,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Geolocation; Computer science; Geography; World Wide Web","score_opus":0.007128436805685441,"score_gpt":0.229016431334667,"score_spread":0.22188799452898156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234726511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012195874,0.0005828528,0.9761633,0.00021217953,0.00042019883,0.00001811899,0.00027278866,0.0013675526,0.008767139],"genre_scores_gemma":[0.52889544,0.0011295928,0.44846568,0.00016808866,0.00034229318,0.000081897546,0.0017798942,0.00032182035,0.018815344],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999683,0.00008133822,0.000009604485,0.000056458975,0.00013274436,0.00003676323],"domain_scores_gemma":[0.9995333,0.00017281686,0.00002320196,0.000117190495,0.00013520264,0.000018248527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035443128,0.00047311865,0.00037995933,0.0005870722,0.00025324497,0.0005497204,0.0003984793,0.00038369358,0.0063412916],"category_scores_gemma":[0.001965043,0.00024240889,0.00025631464,0.00067984255,0.00026770096,0.0008482877,0.00068516383,0.0005011449,0.0025499566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004636643,0.000048524016,0.0012068653,0.00019727432,0.000041089374,0.00013160342,0.00020628834,0.15334253,0.0655694,0.046696432,0.020781402,0.7113149],"study_design_scores_gemma":[0.0000642839,0.00016352563,0.0018684022,0.000067136134,0.000028989785,0.00038978987,0.000116418414,0.8739086,0.036066554,0.018629313,0.06865293,0.00004412344],"about_ca_topic_score_codex":0.0011201693,"about_ca_topic_score_gemma":0.0015673424,"teacher_disagreement_score":0.0063412916,"about_ca_system_score_codex":0.00024343886,"about_ca_system_score_gemma":0.00038712777,"threshold_uncertainty_score":0.02121371},"labels":[],"label_agreement":null},{"id":"W4237291893","doi":"10.1109/milcom.2010.5680201","title":"Distributed combined authentication and intrusion detection with data fusion in high security mobile ad-hoc networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Carleton University","funders":"","keywords":"Computer science; Intrusion detection system; Authentication (law); Biometrics; Wireless ad hoc network; Mobile ad hoc network; Sensor fusion; Scheme (mathematics); Mobile device; Computer network; Computer security; Artificial intelligence; Wireless; Telecommunications","score_opus":0.0042122564743308595,"score_gpt":0.19419198851031277,"score_spread":0.18997973203598192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237291893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05198046,0.0017874275,0.94490963,0.00018864992,0.00007331685,0.000038713526,0.0000075290327,0.00018069023,0.0008335398],"genre_scores_gemma":[0.9270677,0.0007835882,0.070945255,0.000058843198,0.000110012115,0.000047352958,0.000018247087,0.00000978672,0.0009592582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99722666,0.001185715,0.00012997122,0.00039970397,0.0008685074,0.00018943299],"domain_scores_gemma":[0.99820375,0.0010091271,0.00021028717,0.00017977862,0.00034281015,0.000054295506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024182631,0.0007767125,0.0011832701,0.0007141019,0.0005996904,0.0009974821,0.0010614228,0.001013924,0.0003380661],"category_scores_gemma":[0.002933205,0.0003780143,0.00062891014,0.0010263049,0.0010876987,0.0028586972,0.0014189119,0.00072215305,0.00014641733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081622775,0.00025958027,0.0034698017,0.0002839485,0.00035851653,0.00042195476,0.00044871512,0.63530105,0.02965821,0.01530165,0.00084861426,0.31283173],"study_design_scores_gemma":[0.000022226968,0.00033093695,0.0006049149,0.0000099312665,0.000050684805,0.00016552459,0.00006469111,0.98565257,0.007834757,0.0044925967,0.00075040525,0.00002087644],"about_ca_topic_score_codex":0.00091346796,"about_ca_topic_score_gemma":0.0006729247,"teacher_disagreement_score":0.0024182631,"about_ca_system_score_codex":0.0006524648,"about_ca_system_score_gemma":0.00041346074,"threshold_uncertainty_score":0.01278913},"labels":[],"label_agreement":null},{"id":"W4237370188","doi":"10.22215/etd/2010-09021","title":"Investigation of a direction finding antenna array for radio frequency identification","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Radio-frequency identification; Antenna (radio); Antenna array; Telecommunications; Computer science; Computer security","score_opus":0.014266590193255263,"score_gpt":0.23725172572908781,"score_spread":0.22298513553583255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237370188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06065722,0.0004862544,0.9128579,0.000534248,0.00020360325,0.00015905255,0.0001366839,0.0009868816,0.02397816],"genre_scores_gemma":[0.5226507,0.00054707244,0.4554241,0.0002861215,0.0000750539,0.00013793063,0.00027641203,0.00008585896,0.020516716],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956924,0.00011427919,0.000013703221,0.00008350246,0.0001695683,0.000049750073],"domain_scores_gemma":[0.99907494,0.00027378317,0.000066999906,0.00014231248,0.00039046258,0.00005145288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034358288,0.0003183083,0.00032802994,0.00033885924,0.00022365442,0.00080463424,0.00056939234,0.0007400569,0.0036240283],"category_scores_gemma":[0.0013888406,0.0002685059,0.00029093702,0.0004470132,0.00025166958,0.0007676921,0.00027293232,0.00039922795,0.0018365487],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087486405,0.0002636404,0.006973874,0.00028105514,0.00012602212,0.0006867473,0.00026594265,0.045814853,0.5472009,0.027352398,0.004494177,0.36566547],"study_design_scores_gemma":[0.00017446086,0.0025290512,0.006984603,0.00010259967,0.0001757388,0.00384824,0.00037395992,0.5418615,0.38424408,0.005356979,0.05425115,0.00009767867],"about_ca_topic_score_codex":0.00034875883,"about_ca_topic_score_gemma":0.0004262926,"teacher_disagreement_score":0.0036240283,"about_ca_system_score_codex":0.00029161377,"about_ca_system_score_gemma":0.0006628095,"threshold_uncertainty_score":0.012123585},"labels":[],"label_agreement":null},{"id":"W4237939710","doi":"10.22215/etd/2008-08488","title":"A hybrid location identification method in wireless Ad Hoc/Sensor networks","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Wireless sensor network; Computer science; Wireless ad hoc network; Computer network; Identification (biology); Wireless; Telecommunications","score_opus":0.008515620120764306,"score_gpt":0.24743521317622003,"score_spread":0.2389195930554557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237939710","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009318537,0.0003205576,0.98851717,0.000052749165,0.00009676304,0.00002369944,0.000019682222,0.0007340351,0.00091680005],"genre_scores_gemma":[0.33121014,0.00056781253,0.64956504,0.00013133766,0.00020860227,0.00015229304,0.00020325938,0.00016495914,0.017796602],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995198,0.00015015488,0.00002615786,0.00009092635,0.00018288134,0.000030035959],"domain_scores_gemma":[0.9994974,0.00018520295,0.00003180316,0.00007284553,0.00019167126,0.000021043512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047929384,0.0003844284,0.0005044869,0.0007735353,0.00031716604,0.00054657826,0.00071097625,0.0005715534,0.0022299234],"category_scores_gemma":[0.0010127433,0.00026763,0.00028980695,0.00056671794,0.00032784735,0.0011408416,0.00067592115,0.0005040639,0.0012441569],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025489763,0.00012262625,0.0010650994,0.0001206541,0.00008524074,0.00010815736,0.00009822945,0.108793706,0.0484636,0.0076506794,0.003381855,0.8298552],"study_design_scores_gemma":[0.00001772625,0.000094872135,0.0004980768,0.000010294252,0.000016506803,0.00013481427,0.00003262309,0.9823188,0.01076496,0.0018134532,0.004279627,0.000018121225],"about_ca_topic_score_codex":0.0014095274,"about_ca_topic_score_gemma":0.0014411983,"teacher_disagreement_score":0.0022299234,"about_ca_system_score_codex":0.0002166279,"about_ca_system_score_gemma":0.0003607353,"threshold_uncertainty_score":0.007459879},"labels":[],"label_agreement":null},{"id":"W4239741103","doi":"10.22215/etd/2013-06296","title":"Minimum movement relocation strategies for barrier coverage on a line segment","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Heritage; Library and Archives Canada; Carleton University; ASTER","funders":"","keywords":"Relocation; Humanities; Computer science; Art; Operating system","score_opus":0.011593504240613586,"score_gpt":0.2450165657143206,"score_spread":0.233423061473707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239741103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.208007,0.0005141724,0.78005886,0.00025213594,0.00005742962,0.00013834289,0.00018008005,0.0006133241,0.010178644],"genre_scores_gemma":[0.92036873,0.00017929757,0.07348546,0.000028280476,0.000015384892,0.0000948319,0.0001873663,0.00009413062,0.005546588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978715,0.00005860379,0.0000091840275,0.000033638724,0.0000505455,0.000060895483],"domain_scores_gemma":[0.9994716,0.00025456835,0.00007399119,0.00005513008,0.000085917105,0.00005880814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026130016,0.00052066066,0.0006078882,0.00052486395,0.0003470972,0.0004506136,0.0011967015,0.00039731083,0.003861008],"category_scores_gemma":[0.0016701468,0.00022437186,0.0003600068,0.00045595152,0.00023199832,0.00060167245,0.0007087505,0.00041547316,0.00049322966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080492184,0.00018777823,0.0014593074,0.00017452054,0.00006125634,0.00016007376,0.00028233274,0.81667924,0.02208165,0.012817705,0.0049129683,0.14037828],"study_design_scores_gemma":[0.000035908142,0.00021373597,0.00047930502,0.000011632769,0.000014331098,0.00004500713,0.00006128663,0.9949321,0.0017483669,0.0014428891,0.0010058993,0.000009523426],"about_ca_topic_score_codex":0.0042669033,"about_ca_topic_score_gemma":0.0046040337,"teacher_disagreement_score":0.0042669033,"about_ca_system_score_codex":0.00048198772,"about_ca_system_score_gemma":0.0004747848,"threshold_uncertainty_score":0.012916386},"labels":[],"label_agreement":null},{"id":"W4240079786","doi":"10.1007/978-3-540-74853-3_11","title":"An Exploration of Location Error Estimation","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Estimation; Computer science; Engineering; Systems engineering","score_opus":0.033827221528103024,"score_gpt":0.2736534265513825,"score_spread":0.23982620502327945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240079786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001311636,0.01904562,0.967951,0.00094060553,0.00043079176,0.000010523889,0.00007045895,0.0001434867,0.010095921],"genre_scores_gemma":[0.16463383,0.06478492,0.71267045,0.0009342594,0.0027779704,0.000114099304,0.00044126346,0.0003589668,0.053284243],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988451,0.00049971597,0.00006422182,0.0002033555,0.0003408542,0.000046741177],"domain_scores_gemma":[0.9963366,0.002849588,0.00009525913,0.00026913913,0.00041245334,0.000036898673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001963607,0.001126597,0.0013913024,0.0013450467,0.000410533,0.0026861911,0.0020369813,0.0017105687,0.005702843],"category_scores_gemma":[0.009302345,0.0010139424,0.0011057442,0.0032043345,0.0017079535,0.004679194,0.0022092042,0.0024058048,0.0019324586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083275896,0.000050320476,0.0005775831,0.0007246602,0.00010772427,0.00030709454,0.00028258085,0.09141126,0.0011706251,0.55029106,0.015464136,0.3395297],"study_design_scores_gemma":[0.0000139488775,0.00008345416,0.00043951676,0.00038505328,0.000051633866,0.0006069216,0.000152656,0.4505425,0.0012883305,0.49731547,0.049072318,0.00004813953],"about_ca_topic_score_codex":0.0027139348,"about_ca_topic_score_gemma":0.001889893,"teacher_disagreement_score":0.005702843,"about_ca_system_score_codex":0.0008893857,"about_ca_system_score_gemma":0.0006402719,"threshold_uncertainty_score":0.019077957},"labels":[],"label_agreement":null},{"id":"W4240342581","doi":"10.1017/cbo9780511978784","title":"WLAN Positioning Systems","year":2012,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto; Holland Bloorview Kids Rehabilitation Hospital","funders":"","keywords":"Computer science; Estimator; Key (lock); Parametric statistics; Positioning system; Estimation; Hybrid positioning system; Perspective (graphical); Data science; Real-time computing; Systems engineering; Artificial intelligence; Engineering; Computer security; Mathematics; Statistics","score_opus":0.011483088785777472,"score_gpt":0.16581626320411072,"score_spread":0.15433317441833325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240342581","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013438168,0.04994822,0.30430695,0.0029019688,0.0074184136,0.0004434693,0.0025611296,0.0059574777,0.6251186],"genre_scores_gemma":[0.011555527,0.05109125,0.105823,0.00227461,0.0019420993,0.00035292274,0.0033698373,0.0006605959,0.8229302],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992299,0.00009315879,0.000052744563,0.00017575771,0.00038993478,0.00005844547],"domain_scores_gemma":[0.9996221,0.000078421865,0.000021229174,0.000071066024,0.00018338946,0.000023728342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004891943,0.0015058173,0.0009306277,0.0017408826,0.00081381935,0.0029239808,0.001575713,0.0018466752,0.06756015],"category_scores_gemma":[0.0013621378,0.00054951507,0.00042264242,0.0033727232,0.0005992156,0.0028864695,0.0016496739,0.001807758,0.10484143],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033134434,0.000033137276,0.00013480695,0.00060340913,0.00001980692,0.00018201169,0.00017702692,0.0026854896,0.0043350677,0.095952936,0.29058945,0.60525376],"study_design_scores_gemma":[0.0000032604985,0.00002698918,0.000100751466,0.00012096738,0.0000042749984,0.00025997567,0.000020730458,0.0010851613,0.000483798,0.0070852297,0.9907962,0.000012551433],"about_ca_topic_score_codex":0.0013985458,"about_ca_topic_score_gemma":0.0015887469,"teacher_disagreement_score":0.06756015,"about_ca_system_score_codex":0.0008384299,"about_ca_system_score_gemma":0.0008515611,"threshold_uncertainty_score":0.22601122},"labels":[],"label_agreement":null},{"id":"W4240494814","doi":"10.1007/978-0-387-35973-1_629","title":"Indoor Positioning with Wireless Local Area Networks (WLAN)","year":2008,"lang":"en","type":"book-chapter","venue":"Encyclopedia of GIS","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Wi-Fi; Computer network; Local area network; Wireless lan; Computer science; Wireless network; Wireless; Telecommunications","score_opus":0.005775566836759389,"score_gpt":0.16947137266895887,"score_spread":0.16369580583219948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240494814","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028123485,0.04379375,0.6319897,0.0007928608,0.0022465577,0.00008925905,0.0004703412,0.0041676383,0.3136375],"genre_scores_gemma":[0.08261329,0.08887512,0.26844788,0.0011090065,0.002349983,0.00019441148,0.0017172755,0.00076554867,0.5539275],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959856,0.000061813174,0.000022049944,0.00009306669,0.00019328717,0.000031262185],"domain_scores_gemma":[0.9998474,0.000043822372,0.000013999525,0.00003585868,0.0000507281,0.00000821622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021501954,0.0011535903,0.00068456726,0.0008838708,0.00037697787,0.0016635727,0.0011196773,0.000925046,0.022264186],"category_scores_gemma":[0.00058305945,0.0004093319,0.00041544784,0.0029060952,0.00039914003,0.0017807939,0.0010951677,0.001001729,0.022307392],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049903734,0.000031961816,0.0004635245,0.000600438,0.000026315256,0.00020808754,0.00018611463,0.0052933213,0.008077223,0.03509817,0.061513912,0.88845104],"study_design_scores_gemma":[0.000008916251,0.00012825847,0.00096239144,0.00032215286,0.00006124565,0.0016095927,0.00014655836,0.0093671,0.00673277,0.013373874,0.96724343,0.000043638247],"about_ca_topic_score_codex":0.0010556213,"about_ca_topic_score_gemma":0.0013803192,"teacher_disagreement_score":0.022264186,"about_ca_system_score_codex":0.0003583644,"about_ca_system_score_gemma":0.0003827988,"threshold_uncertainty_score":0.07448113},"labels":[],"label_agreement":null},{"id":"W4244365807","doi":"10.1109/glocom.2014.7417730","title":"Automatic Device-Transparent RSS-Based Indoor Localization","year":2014,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Computer science; Set (abstract data type); k-nearest neighbors algorithm; Signal strength; Point (geometry); Transformation (genetics); Data mining; Artificial intelligence; Algorithm; Pattern recognition (psychology); Wireless; Mathematics; Telecommunications","score_opus":0.047377494442520415,"score_gpt":0.30046272371446264,"score_spread":0.2530852292719422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244365807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03355638,0.00028612348,0.9537447,0.00008042769,0.00006868319,0.00005791835,0.0001642472,0.009236352,0.0028051124],"genre_scores_gemma":[0.7855983,0.0002203899,0.20819674,0.00014017575,0.00009552284,0.00009882801,0.00056678534,0.0002633012,0.004819994],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989059,0.00020495725,0.00005012673,0.00022035054,0.0005427906,0.00007591233],"domain_scores_gemma":[0.9988778,0.00017730732,0.00018107393,0.00044909967,0.0002860603,0.000028614064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005046918,0.0007218028,0.00077826134,0.0010309927,0.00037243278,0.00082753645,0.0016090233,0.0006407754,0.0016910101],"category_scores_gemma":[0.0016849699,0.00036097888,0.00036865904,0.0011478618,0.00034977688,0.0010919991,0.0013295119,0.00057766377,0.0028915422],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006305676,0.00015751852,0.0070242267,0.00036499742,0.00012963942,0.0004600539,0.00038512566,0.033566464,0.17655462,0.0037727943,0.006906673,0.77004737],"study_design_scores_gemma":[0.000109158245,0.000609783,0.016778415,0.000072491355,0.00014528673,0.0027786659,0.00018347424,0.6976302,0.2498431,0.0049472544,0.026705204,0.00019699163],"about_ca_topic_score_codex":0.0007358547,"about_ca_topic_score_gemma":0.0010644337,"teacher_disagreement_score":0.0016910101,"about_ca_system_score_codex":0.00022251945,"about_ca_system_score_gemma":0.0002970681,"threshold_uncertainty_score":0.005657017},"labels":[],"label_agreement":null},{"id":"W4244443689","doi":"10.1007/978-0-387-35973-1_626","title":"Indoor Positioning","year":2008,"lang":"en","type":"book-chapter","venue":"Encyclopedia of GIS","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec; Université du Québec à Montréal; Université de Sherbrooke","funders":"","keywords":"Computer science; Environmental science","score_opus":0.007294102871443923,"score_gpt":0.18601286011942098,"score_spread":0.17871875724797706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244443689","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012579609,0.00957716,0.23288043,0.00049686525,0.0017623468,0.00008748132,0.0015768376,0.0052826786,0.7470782],"genre_scores_gemma":[0.02344908,0.012356511,0.07111285,0.0004911079,0.0005003175,0.00010879371,0.003313183,0.000768669,0.8878995],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996178,0.000034612207,0.0000145239665,0.000098774566,0.0001976849,0.000036578705],"domain_scores_gemma":[0.99981445,0.000023450684,0.000008764244,0.000053843833,0.00008516648,0.000014227765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019949597,0.0014850695,0.0007647499,0.0010878744,0.0008237161,0.0018248856,0.0015000163,0.0012003039,0.09764318],"category_scores_gemma":[0.00051088916,0.00054850196,0.00048188807,0.0022062443,0.00043876463,0.0014489347,0.0015200619,0.0009226108,0.09880573],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038625592,0.00002827785,0.00031534416,0.0004514138,0.000016718062,0.0001821113,0.000277911,0.0038406178,0.006444712,0.041468192,0.20637955,0.74055666],"study_design_scores_gemma":[0.0000029304144,0.000026350952,0.00041436008,0.00010967027,0.000010773707,0.00040998045,0.00007290317,0.0013555444,0.0019434971,0.0039324374,0.9917036,0.000017814022],"about_ca_topic_score_codex":0.003751876,"about_ca_topic_score_gemma":0.0064677815,"teacher_disagreement_score":0.09764318,"about_ca_system_score_codex":0.0005177036,"about_ca_system_score_gemma":0.0007819649,"threshold_uncertainty_score":0.326649},"labels":[],"label_agreement":null},{"id":"W4245211266","doi":"10.22215/etd/2011-07196","title":"Real-time localization in large-scale underground environments using RFID-based node maps","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Node (physics); Scale (ratio); Computer graphics (images); Cartography; Humanities; Geography; Engineering; Art","score_opus":0.010056795218552213,"score_gpt":0.21987865968824488,"score_spread":0.20982186446969267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245211266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20515361,0.0005543082,0.7797221,0.00023265548,0.00011954191,0.000034718876,0.00022246152,0.0022845934,0.01167602],"genre_scores_gemma":[0.8796397,0.00052091,0.10174683,0.000017729639,0.000030866446,0.00002999143,0.00030318418,0.00008444748,0.017626483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983656,0.000032179174,0.000005113762,0.000040579373,0.0000638724,0.000021662378],"domain_scores_gemma":[0.9997788,0.000090391404,0.000028250306,0.000034999204,0.00005413333,0.000013448174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015585554,0.00029035125,0.0001440839,0.0003009088,0.00013242554,0.00045023725,0.00032997056,0.00027625245,0.0016728075],"category_scores_gemma":[0.00062414,0.00014883223,0.00013494077,0.0003926973,0.00016567236,0.000720792,0.00050027267,0.000191128,0.00089586957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006378366,0.00014086695,0.005975597,0.00023875413,0.000060671522,0.0005593479,0.0006821304,0.21038622,0.22585297,0.008026603,0.00672206,0.54071695],"study_design_scores_gemma":[0.00007206527,0.00054603285,0.012197202,0.000053940228,0.000071087794,0.00071116746,0.00078427687,0.8200358,0.12714219,0.0054279105,0.03288199,0.00007630594],"about_ca_topic_score_codex":0.0013422384,"about_ca_topic_score_gemma":0.0023009696,"teacher_disagreement_score":0.0016728075,"about_ca_system_score_codex":0.00014061399,"about_ca_system_score_gemma":0.00016064904,"threshold_uncertainty_score":0.0055961013},"labels":[],"label_agreement":null},{"id":"W4245658313","doi":"10.22215/etd/2010-07323","title":"A landmark-bounded method for mapping of large-scale underground drift networks","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Landmark; Scale (ratio); Bounded function; Cartography; Computer science; Geography; Humanities; Mathematics; Art; Mathematical analysis","score_opus":0.007972518974820686,"score_gpt":0.2629822912035309,"score_spread":0.2550097722287102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245658313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024150317,0.00009291741,0.9964964,0.000034852736,0.000027888997,0.000015413789,0.000042837437,0.00042731207,0.0004474043],"genre_scores_gemma":[0.15238005,0.00031143817,0.84012383,0.00006997386,0.00008501565,0.00020195867,0.00052865053,0.00034320366,0.0059558298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953794,0.00013661096,0.000019069837,0.00009348205,0.00016230428,0.000050678264],"domain_scores_gemma":[0.9986753,0.0007499931,0.00007803061,0.00017602558,0.00024789965,0.00007283713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008385254,0.0008589935,0.001123167,0.0010868825,0.00051146897,0.0008717501,0.0022680094,0.0011754973,0.0031865116],"category_scores_gemma":[0.0036179535,0.00049627834,0.00075973396,0.001202941,0.00066653016,0.0011775622,0.0018965501,0.0012833446,0.0010949188],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024141652,0.00006135575,0.0005792099,0.00014413,0.00006826905,0.000084672305,0.00012279491,0.74931866,0.007561468,0.017378008,0.005911674,0.21852845],"study_design_scores_gemma":[0.0000063850725,0.000012341152,0.00004571126,0.0000044927165,0.0000032090456,0.000012638562,0.000007874284,0.99603504,0.00058687007,0.0024221037,0.00085753464,0.0000058427495],"about_ca_topic_score_codex":0.009926854,"about_ca_topic_score_gemma":0.008813562,"teacher_disagreement_score":0.009926854,"about_ca_system_score_codex":0.0006617394,"about_ca_system_score_gemma":0.0014249817,"threshold_uncertainty_score":0.019738138},"labels":[],"label_agreement":null},{"id":"W4250255039","doi":"10.22215/etd/2010-09213","title":"iCCA-MAP : a mobile node localization algorithm for wireless sensor networks","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Wireless sensor network; Computer science; Node (physics); Mobile wireless; Wireless; Computer network; Telecommunications; Engineering","score_opus":0.004521215792535449,"score_gpt":0.22218202257339215,"score_spread":0.2176608067808567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250255039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00509004,0.00051662466,0.98015195,0.00015767991,0.00017485363,0.00009374676,0.00038839484,0.010675732,0.0027510345],"genre_scores_gemma":[0.07926423,0.0006839517,0.9068795,0.00014458649,0.000107643034,0.00038708834,0.0024556003,0.0009205339,0.009156916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957305,0.0000769867,0.00002115796,0.00007082113,0.00022084705,0.00003709161],"domain_scores_gemma":[0.9994586,0.00012815616,0.000033639633,0.00011821767,0.00022581298,0.000035669193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004201082,0.00075659325,0.000671578,0.0013491457,0.0006231278,0.0008685299,0.0014539888,0.0007055578,0.0041744267],"category_scores_gemma":[0.0022953006,0.00033541006,0.00046007847,0.0013375412,0.00031577388,0.0014881805,0.0013423715,0.0011127086,0.0026516505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039785609,0.000121070836,0.001578865,0.0002543223,0.00014480157,0.00012286934,0.00015779248,0.0816351,0.01811888,0.010102934,0.05155767,0.83580774],"study_design_scores_gemma":[0.00011721129,0.00020604089,0.0010428915,0.0000523868,0.00006352037,0.00028504126,0.00006727823,0.8763247,0.021702873,0.006465773,0.09361433,0.00005791809],"about_ca_topic_score_codex":0.004588801,"about_ca_topic_score_gemma":0.006757031,"teacher_disagreement_score":0.004588801,"about_ca_system_score_codex":0.00040000243,"about_ca_system_score_gemma":0.0010293262,"threshold_uncertainty_score":0.013964891},"labels":[],"label_agreement":null},{"id":"W4250617174","doi":"10.1145/1410012","title":"Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments","year":2008,"lang":"en","type":"paratext","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Computer science; Context (archaeology); Tracking (education); Bridge (graph theory); Data science; Telecommunications; World Wide Web; Geography","score_opus":0.013157649157423963,"score_gpt":0.22702275822190693,"score_spread":0.21386510906448297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250617174","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016059376,0.054838125,0.62505686,0.030580344,0.0665813,0.00092750764,0.004141914,0.0090708975,0.19274369],"genre_scores_gemma":[0.07016409,0.046419438,0.17599872,0.004427722,0.014190069,0.0007052198,0.014413496,0.0025245717,0.67115664],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987004,0.00039769238,0.000106952546,0.00021915331,0.0004267243,0.00014909098],"domain_scores_gemma":[0.9976561,0.0006220059,0.00008443707,0.0003934801,0.00081323355,0.0004306683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018251428,0.0014085975,0.0014734435,0.0010988682,0.0007297752,0.0036492066,0.0022026824,0.0017722449,0.053414855],"category_scores_gemma":[0.0041547325,0.0004862839,0.00072893454,0.0015472582,0.00070050545,0.005143528,0.0020241346,0.0030108686,0.034216076],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031535776,0.00014760574,0.0008931136,0.0003413466,0.0000744768,0.0004881593,0.00036575634,0.0015889354,0.0033586328,0.006354972,0.6435235,0.34254813],"study_design_scores_gemma":[0.00003121698,0.000101509046,0.0008115789,0.0001489381,0.000045127566,0.00048225187,0.00022035924,0.0070230435,0.0013814594,0.0042817355,0.9854419,0.000030751267],"about_ca_topic_score_codex":0.003175966,"about_ca_topic_score_gemma":0.0058592167,"teacher_disagreement_score":0.053414855,"about_ca_system_score_codex":0.00064421864,"about_ca_system_score_gemma":0.0011380456,"threshold_uncertainty_score":0.1786905},"labels":[],"label_agreement":null},{"id":"W4250674247","doi":"10.32920/ryerson.14654106","title":"Location and sensing network for industrial automation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wireless sensor network; Latency (audio); Computer science; Efficient energy use; Computer network; Protocol (science); Automation; Throughput; Real-time computing; Metric (unit); Energy (signal processing); Wireless; Embedded system; Telecommunications; Engineering","score_opus":0.02596843939380738,"score_gpt":0.22980513504242728,"score_spread":0.2038366956486199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250674247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022794466,0.012698476,0.8669439,0.0028870674,0.0021166792,0.00020139311,0.00061553455,0.0049292357,0.08681323],"genre_scores_gemma":[0.53406435,0.011466745,0.2708005,0.0008308381,0.001137691,0.00035890666,0.0019962671,0.0002755215,0.1790691],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996544,0.00006172615,0.000016720018,0.00011034098,0.00012774364,0.000029051173],"domain_scores_gemma":[0.99977547,0.000048170717,0.000027491802,0.00006981227,0.00006497545,0.000014125462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002606244,0.000405063,0.0003340887,0.000377386,0.00043108768,0.0008946328,0.0005016642,0.0007295892,0.0143711595],"category_scores_gemma":[0.00076074636,0.00015759548,0.0002517668,0.00067626004,0.00031180205,0.0010285485,0.0008625544,0.00068051607,0.0068314984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022813641,0.00008774087,0.0010519184,0.00040818786,0.000037917685,0.0003857937,0.00013537571,0.03161364,0.023262562,0.13690731,0.04759734,0.75828403],"study_design_scores_gemma":[0.00007414405,0.00037470227,0.0033686243,0.00024359474,0.00007141409,0.0014180145,0.00015716488,0.2107454,0.023304613,0.11592518,0.64425534,0.000061778286],"about_ca_topic_score_codex":0.0011408596,"about_ca_topic_score_gemma":0.0010238669,"teacher_disagreement_score":0.0143711595,"about_ca_system_score_codex":0.00052370055,"about_ca_system_score_gemma":0.0006681615,"threshold_uncertainty_score":0.04807633},"labels":[],"label_agreement":null},{"id":"W4251268369","doi":"10.23919/fusion49465.2021.9626858","title":"Observability Analysis of Multipath Assisted Target Tracking with Unknown Reflection Surface","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reflection (computer programming); Observability; Initialization; Multipath propagation; Tracking (education); Computer science; Surface (topology); Mathematics; Telecommunications; Geometry","score_opus":0.036070243080499,"score_gpt":0.27116169071114493,"score_spread":0.23509144763064593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251268369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044013847,0.00020519213,0.9536036,0.00016282717,0.000015569509,0.000017263797,0.000058462967,0.00013624337,0.0017869368],"genre_scores_gemma":[0.97735596,0.00036500904,0.019717671,0.00005889498,0.000037225236,0.00005883766,0.00016928441,0.00003731851,0.0021996975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989801,0.00017105132,0.000042564847,0.00024516956,0.0004013863,0.00015965998],"domain_scores_gemma":[0.99564654,0.0025653103,0.00076800573,0.00022526986,0.0006909323,0.000103924154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016801363,0.0010478542,0.00082238164,0.0006772191,0.0003860916,0.0007924031,0.0010199575,0.00085510866,0.0012786643],"category_scores_gemma":[0.008457299,0.00050084165,0.0008505852,0.0006598611,0.0012059497,0.0016764945,0.0016057267,0.0013804724,0.00017545356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018541359,0.000031905623,0.003300222,0.00015391334,0.00008193179,0.00034920193,0.00024607294,0.9129577,0.010752971,0.0457604,0.00048246587,0.025697714],"study_design_scores_gemma":[0.0000043483633,0.000029630435,0.00071677496,0.0000056158246,0.000009552092,0.000042485717,0.000013048469,0.9926323,0.00069018104,0.0057454947,0.00009913862,0.000011267508],"about_ca_topic_score_codex":0.007090941,"about_ca_topic_score_gemma":0.0024904725,"teacher_disagreement_score":0.007090941,"about_ca_system_score_codex":0.001025061,"about_ca_system_score_gemma":0.001117443,"threshold_uncertainty_score":0.0140993},"labels":[],"label_agreement":null},{"id":"W4251398898","doi":"10.32920/ryerson.14656776","title":"Self-Contained Pedestrian Tracking With Mems Sensors","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gyroscope; Inertial measurement unit; Accelerometer; Computer science; Dead reckoning; Heading (navigation); Tracking (education); Tracking system; Global Positioning System; Kalman filter; Inertial navigation system; Real-time computing; Inertial frame of reference; Computer vision; Artificial intelligence; Engineering; Aerospace engineering","score_opus":0.010980871380053957,"score_gpt":0.20659069682248973,"score_spread":0.19560982544243577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251398898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32331112,0.0017421333,0.6664016,0.00013734888,0.00036874186,0.00007044082,0.0003337169,0.0020232326,0.0056116655],"genre_scores_gemma":[0.8800099,0.0008746996,0.113681145,0.000071757204,0.00009015747,0.00006352445,0.0003008455,0.000029683068,0.004878424],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997497,0.000048899023,0.000010600682,0.000062392675,0.00010518592,0.000023083883],"domain_scores_gemma":[0.99983263,0.000025591693,0.000036341924,0.000037625534,0.000058076497,0.000009713075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024526118,0.00037375322,0.0004192007,0.00052925013,0.00016023795,0.00033441916,0.00037182632,0.00036477222,0.00058801693],"category_scores_gemma":[0.00034840457,0.00024256932,0.0003872712,0.00052502856,0.000095469244,0.00044914414,0.00036828863,0.00017314103,0.00031974513],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052430166,0.00014963273,0.026224898,0.00055423786,0.000302825,0.00041458392,0.00046281022,0.11117287,0.41713285,0.0057984754,0.0051724617,0.43209007],"study_design_scores_gemma":[0.000028008266,0.00060016377,0.034989182,0.00008705926,0.0001497726,0.00041804588,0.00015718263,0.81485903,0.13222295,0.0023137063,0.014088163,0.000086709515],"about_ca_topic_score_codex":0.0008003074,"about_ca_topic_score_gemma":0.0010315139,"teacher_disagreement_score":0.0008003074,"about_ca_system_score_codex":0.00018891055,"about_ca_system_score_gemma":0.00018140771,"threshold_uncertainty_score":0.001967132},"labels":[],"label_agreement":null},{"id":"W4252698845","doi":"10.22215/etd/2019-13716","title":"Ultrasonic Localization of a Quadrotor Using a Portable Beacon","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Trilateration; Global Positioning System; Ultrasonic sensor; Position (finance); Standard deviation; Computer science; Least-squares function approximation; SIGNAL (programming language); GPS signals; Tracking (education); Real-time computing; Acoustics; Engineering; Algorithm; Simulation; Computer vision; Assisted GPS; Mathematics; Telecommunications; Physics; Statistics","score_opus":0.009010266194511011,"score_gpt":0.23133857228702884,"score_spread":0.22232830609251783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252698845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09959362,0.0019023905,0.8869469,0.00017668033,0.00016268283,0.00013017457,0.000087516666,0.00093009346,0.010069914],"genre_scores_gemma":[0.64870006,0.0022031495,0.32215267,0.0000837671,0.00008739431,0.00011958028,0.0002320792,0.0000888053,0.026332542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997625,0.000027968666,0.000007935572,0.000070548376,0.00011286152,0.000018172224],"domain_scores_gemma":[0.99987984,0.000026001162,0.000020409461,0.000017861734,0.000043062973,0.00001283011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019764356,0.00043718852,0.00034397634,0.00057362264,0.00019524977,0.0003995487,0.000495153,0.00033959336,0.0016457356],"category_scores_gemma":[0.0003795517,0.00018508204,0.00024296352,0.00051968836,0.00022306091,0.00039655878,0.00040852223,0.0002708283,0.0007950629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032723564,0.00007972488,0.0028978218,0.00034573785,0.00004045712,0.00036130846,0.00035350214,0.016803538,0.42686275,0.0060668644,0.0024737085,0.5433873],"study_design_scores_gemma":[0.00020615551,0.0038029614,0.01617869,0.00023287165,0.00021859063,0.0030874985,0.0005553447,0.35124397,0.50712615,0.0030297479,0.114166364,0.00015164711],"about_ca_topic_score_codex":0.0011232038,"about_ca_topic_score_gemma":0.0014453214,"teacher_disagreement_score":0.0016457356,"about_ca_system_score_codex":0.00027879732,"about_ca_system_score_gemma":0.0003182334,"threshold_uncertainty_score":0.005505562},"labels":[],"label_agreement":null},{"id":"W4254427895","doi":"10.1007/978-1-4939-7131-2_101244","title":"Spatiotemporal Collaborative Filtering","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Collaborative filtering; Computer science; Geography; Information retrieval; Recommender system","score_opus":0.00993224401819928,"score_gpt":0.19593530057968472,"score_spread":0.18600305656148544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254427895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008942046,0.007952682,0.9428553,0.00040872802,0.0013444048,0.000035297733,0.00029526514,0.001429881,0.04478424],"genre_scores_gemma":[0.063889004,0.029621765,0.50640094,0.00080422167,0.002931627,0.00018021812,0.0037774835,0.0013128783,0.39108187],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994018,0.000051519906,0.00003547986,0.0002242193,0.00025347283,0.000033400058],"domain_scores_gemma":[0.9994974,0.0001377567,0.000021005857,0.00015781476,0.0001657973,0.000020179801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005507767,0.0015352431,0.0011700852,0.0010870032,0.00051582395,0.0019285813,0.0011388345,0.0012171395,0.01618473],"category_scores_gemma":[0.0016558716,0.00050246145,0.0006969938,0.0020725331,0.0005038331,0.0024687133,0.0013126419,0.0013232171,0.014118902],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055954122,0.000041907304,0.00019717634,0.00028920092,0.00006155101,0.00010761295,0.00009567744,0.015835034,0.006056001,0.062407337,0.09099681,0.82385576],"study_design_scores_gemma":[0.000015654567,0.000085301945,0.00083510776,0.00020439312,0.00008761161,0.0008879689,0.0001068789,0.1770119,0.012972603,0.110476956,0.6972296,0.00008606302],"about_ca_topic_score_codex":0.0032395965,"about_ca_topic_score_gemma":0.003139539,"teacher_disagreement_score":0.01618473,"about_ca_system_score_codex":0.00062152935,"about_ca_system_score_gemma":0.00062988314,"threshold_uncertainty_score":0.05414325},"labels":[],"label_agreement":null},{"id":"W4254622648","doi":"10.1002/wcm.623","title":"Ranging error‐tolerable localization in wireless sensor networks with inaccurately positioned anchor nodes","year":2008,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Ranging; Computer science; Node (physics); Wireless sensor network; Wireless; Algorithm; Upper and lower bounds; Time of arrival; Network topology; Topology (electrical circuits); Real-time computing; Computer network; Telecommunications; Mathematics","score_opus":0.015857865546053104,"score_gpt":0.2314876716177753,"score_spread":0.21562980607172222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254622648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02975715,0.00039572912,0.9686891,0.00017570675,0.000033384727,0.00001057625,0.000013924073,0.00036700076,0.0005574176],"genre_scores_gemma":[0.8326004,0.0006698734,0.16517599,0.000105085644,0.00009593668,0.00005896873,0.000081329286,0.000073494164,0.0011388933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965191,0.0011969802,0.00017490218,0.00040870326,0.0014862404,0.00021416612],"domain_scores_gemma":[0.9945575,0.0022402918,0.0011856473,0.001189244,0.00072137616,0.00010611428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003036283,0.00067385327,0.0010417323,0.0010773686,0.0005110791,0.0011661001,0.0014197418,0.0010210221,0.00037834997],"category_scores_gemma":[0.014370524,0.00051998417,0.0004458298,0.0012438874,0.001419247,0.0018327327,0.0019353448,0.0009826893,0.0003190384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020263199,0.000028145854,0.001593214,0.00009766773,0.000047666137,0.00021061313,0.00023771802,0.8792584,0.01270201,0.012169399,0.0008677503,0.09258476],"study_design_scores_gemma":[0.000014598519,0.00009604089,0.0005015103,0.000017047878,0.00001981702,0.00020922713,0.00003660521,0.9852739,0.006846526,0.006012072,0.0009520892,0.00002050734],"about_ca_topic_score_codex":0.001865029,"about_ca_topic_score_gemma":0.00093014096,"teacher_disagreement_score":0.003036283,"about_ca_system_score_codex":0.00062175066,"about_ca_system_score_gemma":0.000698423,"threshold_uncertainty_score":0.01605755},"labels":[],"label_agreement":null},{"id":"W4255111844","doi":"10.22215/etd/2011-09198","title":"Anchor node placement for localization in wireless sensor networks","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Wireless sensor network; Node (physics); Computer science; Humanities; Telecommunications; Computer network; Art; Engineering","score_opus":0.010941505984832952,"score_gpt":0.22611010782538576,"score_spread":0.2151686018405528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255111844","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004540907,0.0014763603,0.98904383,0.00016735689,0.00029072742,0.000050426264,0.00006572022,0.0005285607,0.0038360818],"genre_scores_gemma":[0.29607165,0.010275078,0.6563808,0.00014513056,0.0006548036,0.00042921508,0.001155948,0.00033566073,0.03455174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999355,0.00022204136,0.000030073283,0.000114861694,0.00024571855,0.00003224245],"domain_scores_gemma":[0.9996517,0.00016575001,0.00002744384,0.000056234207,0.00008370733,0.00001525954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049024855,0.00069759355,0.00050047203,0.00070074317,0.0003694707,0.0006526921,0.0007800374,0.0007060004,0.0041604377],"category_scores_gemma":[0.0024304294,0.0002838712,0.00027506216,0.0012420287,0.00041603815,0.0010171575,0.00076446676,0.00067469024,0.0024935056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002672847,0.00007747719,0.0005449046,0.000426166,0.00005961522,0.00025059938,0.00016973971,0.36812943,0.028483408,0.035137326,0.0152867725,0.5511673],"study_design_scores_gemma":[0.00003945883,0.00019622779,0.00039972327,0.00008070279,0.000026256965,0.00019806504,0.000067576795,0.9317093,0.008952796,0.02474613,0.033563282,0.000020515554],"about_ca_topic_score_codex":0.0012693367,"about_ca_topic_score_gemma":0.0014239439,"teacher_disagreement_score":0.0041604377,"about_ca_system_score_codex":0.00034682977,"about_ca_system_score_gemma":0.00045434645,"threshold_uncertainty_score":0.013918042},"labels":[],"label_agreement":null},{"id":"W4255975847","doi":"10.22215/etd/2009-08679","title":"Learning based hyperbolic position bounding in wireless networks","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Bounding overwatch; Position (finance); Computer science; Telecommunications; Mathematics; Artificial intelligence; Economics","score_opus":0.003739603411895227,"score_gpt":0.2057511237344383,"score_spread":0.20201152032254308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255975847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030219758,0.00054264965,0.96431553,0.000362959,0.00008402874,0.000035846962,0.00007720955,0.00040957046,0.00395239],"genre_scores_gemma":[0.78863335,0.0012977346,0.18903828,0.0001673959,0.00023228767,0.00016375602,0.00041341054,0.00021683205,0.019836932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922717,0.00020607033,0.000048402937,0.00020141405,0.00022219957,0.000094815994],"domain_scores_gemma":[0.99586284,0.0029130057,0.0002861878,0.0003575666,0.00041315067,0.00016718311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011842145,0.0007164908,0.001083371,0.000756921,0.000434925,0.0011554664,0.0018623956,0.0007912917,0.0024760198],"category_scores_gemma":[0.0074282917,0.0006067609,0.000446665,0.0008868978,0.0011932135,0.002670847,0.0025835976,0.0013715115,0.00056411175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009332989,0.000030222522,0.00066520344,0.00004453129,0.000017730123,0.000027969556,0.000101878046,0.90975285,0.0008935167,0.026433593,0.0015761005,0.060363136],"study_design_scores_gemma":[0.0000036128217,0.000012777272,0.00006324657,0.0000041829844,0.0000020498571,0.000005665389,0.0000071593727,0.99251866,0.00026834314,0.0067849783,0.00032628357,0.0000029876087],"about_ca_topic_score_codex":0.006383976,"about_ca_topic_score_gemma":0.0033381062,"teacher_disagreement_score":0.006383976,"about_ca_system_score_codex":0.0013202091,"about_ca_system_score_gemma":0.0008935243,"threshold_uncertainty_score":0.012693644},"labels":[],"label_agreement":null},{"id":"W4256307009","doi":"10.32920/ryerson.14643726.v1","title":"Integration Of RFID With WLAN","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer network; Access control; Throughput; Wi-Fi; Interference (communication); Channel (broadcasting); Radio-frequency identification; Wireless lan; IEEE 802.11; Wireless; Media access control; Identification (biology); Wireless network; Telecommunications; Computer security","score_opus":0.010116996895363434,"score_gpt":0.20268502328122448,"score_spread":0.19256802638586104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256307009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08612003,0.0048194975,0.8495537,0.0007692418,0.0003628509,0.00015838273,0.000058103666,0.0022222481,0.05593583],"genre_scores_gemma":[0.78956294,0.0032566593,0.1807329,0.00039546288,0.00022370912,0.00009762564,0.0001884844,0.00008796012,0.02545425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987349,0.00034146642,0.0000615008,0.00022533335,0.00044616088,0.00019069304],"domain_scores_gemma":[0.99946517,0.0001304339,0.00005709582,0.0001325002,0.00018004357,0.0000348264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006898465,0.000500729,0.00040713084,0.00042654973,0.00034024913,0.0014799852,0.00079080136,0.0008093459,0.0018272279],"category_scores_gemma":[0.0011741258,0.00037518187,0.00044974097,0.0006899169,0.00025425118,0.0017120033,0.0013676306,0.0007998256,0.0014119106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038315274,0.00041024206,0.010506319,0.0006707903,0.00025014798,0.0016489996,0.0004254572,0.072400555,0.12281311,0.061167046,0.0047845524,0.7245397],"study_design_scores_gemma":[0.000068138484,0.0014510788,0.008808958,0.00026554064,0.00048776023,0.0051376075,0.00058851443,0.61551666,0.14680848,0.023122687,0.19757791,0.00016661317],"about_ca_topic_score_codex":0.00073877984,"about_ca_topic_score_gemma":0.0007115975,"teacher_disagreement_score":0.0018272279,"about_ca_system_score_codex":0.00033458084,"about_ca_system_score_gemma":0.00046526676,"threshold_uncertainty_score":0.0061127543},"labels":[],"label_agreement":null},{"id":"W4256349005","doi":"10.32920/ryerson.14656776.v1","title":"Self-Contained Pedestrian Tracking With Mems Sensors","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gyroscope; Inertial measurement unit; Accelerometer; Computer science; Dead reckoning; Heading (navigation); Tracking (education); Tracking system; Global Positioning System; Kalman filter; Inertial navigation system; Real-time computing; Inertial frame of reference; Computer vision; Engineering; Artificial intelligence; Telecommunications; Aerospace engineering","score_opus":0.010980871380053957,"score_gpt":0.20659069682248973,"score_spread":0.19560982544243577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256349005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32331112,0.0017421333,0.6664016,0.00013734888,0.00036874186,0.00007044082,0.0003337169,0.0020232326,0.0056116655],"genre_scores_gemma":[0.8800099,0.0008746996,0.113681145,0.000071757204,0.00009015747,0.00006352445,0.0003008455,0.000029683068,0.004878424],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997497,0.000048899023,0.000010600682,0.000062392675,0.00010518592,0.000023083883],"domain_scores_gemma":[0.99983263,0.000025591693,0.000036341924,0.000037625534,0.000058076497,0.000009713075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024526118,0.00037375322,0.0004192007,0.00052925013,0.00016023795,0.00033441916,0.00037182632,0.00036477222,0.00058801693],"category_scores_gemma":[0.00034840457,0.00024256932,0.0003872712,0.00052502856,0.000095469244,0.00044914414,0.00036828863,0.00017314103,0.00031974513],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052430166,0.00014963273,0.026224898,0.00055423786,0.000302825,0.00041458392,0.00046281022,0.11117287,0.41713285,0.0057984754,0.0051724617,0.43209007],"study_design_scores_gemma":[0.000028008266,0.00060016377,0.034989182,0.00008705926,0.0001497726,0.00041804588,0.00015718263,0.81485903,0.13222295,0.0023137063,0.014088163,0.000086709515],"about_ca_topic_score_codex":0.0008003074,"about_ca_topic_score_gemma":0.0010315139,"teacher_disagreement_score":0.0008003074,"about_ca_system_score_codex":0.00018891055,"about_ca_system_score_gemma":0.00018140771,"threshold_uncertainty_score":0.001967132},"labels":[],"label_agreement":null},{"id":"W4282585228","doi":"10.3390/s22124320","title":"Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trilateration; Bluetooth Low Energy; Received signal strength indication; Computer science; Real-time computing; Indoor positioning system; Artificial neural network; Wearable computer; Path loss; Artificial intelligence; Bluetooth; Computer vision; Embedded system; Wireless; Engineering; Telecommunications; Accelerometer","score_opus":0.006134418416638544,"score_gpt":0.21197757547668084,"score_spread":0.2058431570600423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282585228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049187943,0.00059981394,0.9448813,0.00012598842,0.00013967142,0.00003280779,0.00004828982,0.0022148767,0.0027693685],"genre_scores_gemma":[0.6390376,0.0005988164,0.35459527,0.00017336002,0.0000902987,0.00005823565,0.00017578906,0.0001421784,0.0051283976],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996201,0.000055514578,0.000024128776,0.00011594069,0.00013950947,0.000044881464],"domain_scores_gemma":[0.99947673,0.00012414333,0.000076076634,0.00006435216,0.0002399493,0.00001870179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005779567,0.0009868898,0.00056633266,0.0012627466,0.00022455443,0.0007673761,0.0007400196,0.0007459387,0.0011912856],"category_scores_gemma":[0.0012874937,0.00031472227,0.00047794404,0.0008811174,0.00026720727,0.0010776503,0.0005031264,0.00059068337,0.0007143561],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023454339,0.00021940134,0.0022204474,0.00014775056,0.000094387,0.00010875737,0.00008107443,0.10749083,0.06391885,0.001059591,0.0012383001,0.82318604],"study_design_scores_gemma":[0.000012733029,0.00015502953,0.0022306375,0.000016296326,0.000042662363,0.0001256193,0.0000235706,0.96608967,0.029040152,0.00064704707,0.0015854205,0.00003112058],"about_ca_topic_score_codex":0.002638568,"about_ca_topic_score_gemma":0.004537624,"teacher_disagreement_score":0.002638568,"about_ca_system_score_codex":0.00037085934,"about_ca_system_score_gemma":0.00035923903,"threshold_uncertainty_score":0.0052464604},"labels":[],"label_agreement":null},{"id":"W4283718353","doi":"10.1109/i2mtc48687.2022.9806622","title":"A WiFi-based System for Recognizing Fine-grained Multiple-Subject Human Activities","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Activity recognition; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Machine learning","score_opus":0.05670290149735533,"score_gpt":0.26336430387149073,"score_spread":0.2066614023741354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283718353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14367643,0.00083978096,0.8053496,0.00027902654,0.0004660363,0.00044702334,0.0045807,0.03554197,0.008819448],"genre_scores_gemma":[0.7585439,0.00040271203,0.22728515,0.00038249983,0.00018717573,0.0004735156,0.0051217675,0.00018636351,0.007416968],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996419,0.000044107754,0.000024871568,0.0001253963,0.000111174464,0.000052536187],"domain_scores_gemma":[0.9995497,0.0000780582,0.000057883204,0.00009824982,0.00016148482,0.00005458297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035154936,0.0006982213,0.0007545549,0.0013778198,0.0002669165,0.00038946405,0.0008289152,0.0006350944,0.0036486345],"category_scores_gemma":[0.0011299315,0.00018574146,0.0002771166,0.0010740504,0.00015942307,0.0007127645,0.00074006896,0.00045027566,0.002959899],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079043536,0.0003798711,0.029474424,0.0004800313,0.00019334554,0.00036734965,0.000154363,0.008098162,0.09439786,0.0011744814,0.01818691,0.84630275],"study_design_scores_gemma":[0.0003129132,0.0018045274,0.123544335,0.000101124264,0.00027048736,0.003547738,0.00026490024,0.69331634,0.12920119,0.0038434074,0.043527458,0.00026550062],"about_ca_topic_score_codex":0.0030812994,"about_ca_topic_score_gemma":0.006800549,"teacher_disagreement_score":0.0036486345,"about_ca_system_score_codex":0.00024623354,"about_ca_system_score_gemma":0.00036973218,"threshold_uncertainty_score":0.012205899},"labels":[],"label_agreement":null},{"id":"W4285102187","doi":"10.1109/icra46639.2022.9812340","title":"Single User WiFi Structure from Motion in the Wild","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Robotics and Automation (ICRA)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Computer science; Frame (networking); Motion (physics); Accelerometer; Real-time computing; Artificial intelligence; Computer vision; Motion sensors; Bundle adjustment; Computer network; Photogrammetry","score_opus":0.01933537974011673,"score_gpt":0.2248142764695374,"score_spread":0.20547889672942066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285102187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15298533,0.00041376695,0.84097964,0.00018921282,0.00012605492,0.00008457392,0.000595875,0.0019704883,0.0026550854],"genre_scores_gemma":[0.8339768,0.0002626867,0.16114317,0.00010627489,0.000081583,0.000083241604,0.0012923339,0.0001036939,0.0029501566],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996362,0.00005501331,0.000016358013,0.00012638727,0.00010755625,0.000058536854],"domain_scores_gemma":[0.9996599,0.000087226465,0.00005657657,0.000098741046,0.00007472011,0.000022829372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031205927,0.0009283159,0.0006654229,0.00068721885,0.0003036414,0.00046454303,0.0009459335,0.0005376347,0.00067939307],"category_scores_gemma":[0.0018435444,0.00034424372,0.00043891833,0.00073617557,0.00028336392,0.0010075782,0.00083850423,0.0004901175,0.0005408757],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040437566,0.00014084791,0.02338584,0.00020767114,0.00014730585,0.0004398038,0.0002653486,0.30253384,0.025857877,0.00421983,0.0057731094,0.6366242],"study_design_scores_gemma":[0.000018494942,0.000106597436,0.009142778,0.000014334522,0.000022222777,0.00031176157,0.00008659503,0.97782576,0.0064250045,0.0027415075,0.0032811612,0.00002379454],"about_ca_topic_score_codex":0.006851294,"about_ca_topic_score_gemma":0.013442659,"teacher_disagreement_score":0.006851294,"about_ca_system_score_codex":0.0003011169,"about_ca_system_score_gemma":0.000433782,"threshold_uncertainty_score":0.01362282},"labels":[],"label_agreement":null},{"id":"W4285167239","doi":"10.1109/jiot.2022.3151051","title":"Using Compressive Sampling to Fill Interbatch Data Gap From Low-Cost IoT Vibration Sensor","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Higher Education, Malaysia","keywords":"Computer science; Accelerometer; Sampling (signal processing); Microcontroller; Vibration; Real-time computing; Compressed sensing; Transmission (telecommunications); Data transmission; Wireless; Internet of Things; Wireless sensor network; Electronic engineering; Computer hardware; Embedded system; Telecommunications; Computer network; Artificial intelligence; Acoustics; Engineering","score_opus":0.0929431054677784,"score_gpt":0.30520770899163085,"score_spread":0.21226460352385246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285167239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16626428,0.00057325145,0.8294705,0.00031560086,0.00023397815,0.00009461418,0.00013083818,0.0008785916,0.0020384137],"genre_scores_gemma":[0.73204464,0.00033472222,0.26466662,0.00033683795,0.00013772199,0.00013683437,0.00036462361,0.00008549314,0.0018925301],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993001,0.00010211164,0.000041873376,0.00011279641,0.00040175926,0.00004123748],"domain_scores_gemma":[0.9987822,0.00044088298,0.0001653931,0.00020652341,0.00035282783,0.00005222407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056179054,0.0006443286,0.0004465222,0.00042519518,0.00037645802,0.00039158555,0.0007837815,0.00054574694,0.00082695467],"category_scores_gemma":[0.0024151811,0.00023738777,0.0002485716,0.00056083774,0.0004196917,0.0011378872,0.0008081109,0.0006198691,0.00026434948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013553278,0.00028607616,0.0058683204,0.00053185073,0.00007730795,0.00064322865,0.00070313114,0.049016483,0.45182618,0.004220143,0.0036271769,0.48184475],"study_design_scores_gemma":[0.000063424915,0.00086538313,0.004297998,0.000059322065,0.000061124534,0.0006318605,0.0002954562,0.69920355,0.28263497,0.00395495,0.007832787,0.000099141704],"about_ca_topic_score_codex":0.000901866,"about_ca_topic_score_gemma":0.0014588959,"teacher_disagreement_score":0.000901866,"about_ca_system_score_codex":0.00026596972,"about_ca_system_score_gemma":0.00046407524,"threshold_uncertainty_score":0.0029710531},"labels":[],"label_agreement":null},{"id":"W4285306100","doi":"10.1109/jsen.2022.3184188","title":"Hallway Gait Monitoring Using Novel Radar Signal Processing and Unsupervised Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Radar; Gait; Artificial intelligence; Computer vision; Radar tracker; Real-time computing; Low probability of intercept radar; Radar engineering details; Radar imaging; Telecommunications","score_opus":0.0227495914765685,"score_gpt":0.2310725034001416,"score_spread":0.2083229119235731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285306100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08000822,0.00010560506,0.9177194,0.00006300923,0.000031247295,0.000039684583,0.0000934448,0.001146471,0.000792836],"genre_scores_gemma":[0.4872995,0.0001545515,0.5102492,0.0001226459,0.00006456215,0.000088492336,0.0003001049,0.000054811528,0.0016661186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997862,0.000041073654,0.0000134771735,0.000071877126,0.00006669761,0.000020702942],"domain_scores_gemma":[0.9997032,0.000052842468,0.000078278485,0.00004440007,0.00009784037,0.00002340304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019208038,0.00038866716,0.00038020336,0.000638836,0.00013687258,0.00029071578,0.0004924128,0.0003364668,0.00046079143],"category_scores_gemma":[0.0005571574,0.00017250625,0.00023649225,0.0005048816,0.00019559095,0.0004990408,0.00032990103,0.0003042502,0.00037051053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003294217,0.00043331156,0.017919796,0.00015217093,0.00014317143,0.00019746186,0.00011215439,0.03632383,0.2002068,0.0018004338,0.0021103073,0.74027103],"study_design_scores_gemma":[0.000049459086,0.0005924411,0.023607211,0.000019064613,0.00006059074,0.00078359904,0.00005124704,0.9013199,0.06857251,0.0015948189,0.0032809663,0.000068148176],"about_ca_topic_score_codex":0.0006996872,"about_ca_topic_score_gemma":0.0017631021,"teacher_disagreement_score":0.0006996872,"about_ca_system_score_codex":0.00012612893,"about_ca_system_score_gemma":0.00023649655,"threshold_uncertainty_score":0.0015415549},"labels":[],"label_agreement":null},{"id":"W4286892208","doi":"10.48550/arxiv.2110.14789","title":"Millimeter Wave Wireless Assisted Robot Navigation with Link State Classification","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Artificial intelligence; Snapshot (computer storage); Multipath propagation; Classifier (UML); Computer vision; Mobile robot; Wireless; Real-time computing; Simultaneous localization and mapping; Robot; Channel (broadcasting); Telecommunications","score_opus":0.06388887739353893,"score_gpt":0.1729475375369551,"score_spread":0.10905866014341617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286892208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068983145,0.00048043064,0.9240875,0.00021773092,0.00015593476,0.00007040324,0.0003833261,0.0029123768,0.002709009],"genre_scores_gemma":[0.7434362,0.00034261844,0.24673052,0.00017318226,0.0001685296,0.00019748106,0.0022841808,0.00009440338,0.006572715],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961424,0.00007619341,0.000018701152,0.00011922593,0.000118243624,0.00005344072],"domain_scores_gemma":[0.9994259,0.00015032804,0.00009390258,0.00013332228,0.00017545887,0.000021133814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003809716,0.00061884255,0.0005111405,0.00074413425,0.00031752756,0.0007080572,0.00089427,0.00071172364,0.0012394694],"category_scores_gemma":[0.0012514723,0.00022590153,0.0004778826,0.0008956447,0.00025615367,0.0007840418,0.00075897627,0.00086584734,0.0008760051],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015096866,0.0002024139,0.0043484755,0.00006181617,0.00006454352,0.00008446739,0.00006413677,0.2949331,0.007145309,0.0029240204,0.0046270946,0.6853936],"study_design_scores_gemma":[0.000005490473,0.000032567335,0.00086666725,0.0000047124286,0.000007709217,0.000016460843,0.000010250826,0.99433917,0.0019074456,0.0017314024,0.0010715724,0.000006635533],"about_ca_topic_score_codex":0.0062387246,"about_ca_topic_score_gemma":0.006030383,"teacher_disagreement_score":0.0062387246,"about_ca_system_score_codex":0.00041117807,"about_ca_system_score_gemma":0.00053991814,"threshold_uncertainty_score":0.012404859},"labels":[],"label_agreement":null},{"id":"W4287906005","doi":"10.48550/arxiv.2001.02396","title":"Improving BLE Beacon Proximity Estimation Accuracy through Bayesian\\n Filtering","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Guelph","funders":"","keywords":"Beacon; Computer science; Bluetooth; Mean squared error; Kalman filter; Context (archaeology); Bayesian probability; Real-time computing; Recursive Bayesian estimation; Wireless; Filter (signal processing); Artificial intelligence; Telecommunications; Statistics; Computer vision; Mathematics; Geography","score_opus":0.055742500803119543,"score_gpt":0.1910605933939515,"score_spread":0.13531809259083197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287906005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031365864,0.0003186871,0.96513724,0.00010573,0.00007536277,0.000031825202,0.00004096707,0.0008667135,0.002057581],"genre_scores_gemma":[0.6465003,0.00073233235,0.34710556,0.00017725148,0.00008496439,0.000103524544,0.00033359067,0.00011684532,0.004845576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987884,0.00019971508,0.00007033589,0.0002712348,0.0005382612,0.00013196621],"domain_scores_gemma":[0.99818075,0.0008085367,0.0002144678,0.00020307301,0.00054867583,0.000044578388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014089879,0.00074614096,0.00081735814,0.0011017119,0.00048364687,0.0007522796,0.0011034186,0.00097442674,0.0010062659],"category_scores_gemma":[0.0066362265,0.0004513253,0.0006907834,0.0007719403,0.00035023253,0.0011396752,0.0008134154,0.00096220523,0.00073613384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007151235,0.00027166953,0.012039328,0.0002870913,0.000121435005,0.00020729314,0.00043866466,0.30037344,0.062116325,0.0050414884,0.0028967157,0.6154914],"study_design_scores_gemma":[0.000022247881,0.00014080512,0.0038583842,0.000027602831,0.000037605027,0.000108648775,0.000045564007,0.9792778,0.013166391,0.0009601984,0.0023226955,0.00003208532],"about_ca_topic_score_codex":0.008266104,"about_ca_topic_score_gemma":0.008435008,"teacher_disagreement_score":0.008266104,"about_ca_system_score_codex":0.0005458346,"about_ca_system_score_gemma":0.00084815273,"threshold_uncertainty_score":0.01643598},"labels":[],"label_agreement":null},{"id":"W4287921475","doi":"10.36680/j.itcon.2022.032","title":"An integrated RFID–UWB method for indoor localization of materials in construction","year":2022,"lang":"en","type":"article","venue":"Journal of Information Technology in Construction","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Trilateration; Radio-frequency identification; Global Positioning System; Computer science; Leverage (statistics); Identification (biology); Ultra-wideband; Radio frequency; Wideband; Tracking (education); Ranging; Real-time computing; Electronic engineering; Engineering; Telecommunications; Artificial intelligence","score_opus":0.005014984277418305,"score_gpt":0.23810668908080762,"score_spread":0.2330917048033893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287921475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051322144,0.0014670347,0.94351375,0.000083335246,0.00016100434,0.00004858288,0.000049950555,0.0007987494,0.0025554656],"genre_scores_gemma":[0.43033433,0.0011868583,0.5615872,0.00016909641,0.000067004024,0.0001151333,0.00011298118,0.0000627854,0.006364635],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992446,0.00014481161,0.000029336798,0.00019065176,0.00035011192,0.000040601008],"domain_scores_gemma":[0.99972516,0.000054815475,0.00006373009,0.00006456858,0.00008196282,0.000009693432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005342028,0.0004561802,0.00046431881,0.00086733076,0.00021149903,0.0004794087,0.0008687923,0.0008400292,0.00087906077],"category_scores_gemma":[0.0005403249,0.00031294444,0.00052469363,0.0010012143,0.0003686819,0.00080104324,0.00055806915,0.0004411009,0.000704827],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030321628,0.00012112462,0.0015737638,0.00049655314,0.00007828796,0.0002593467,0.00021212103,0.00465302,0.6652406,0.0018664231,0.0007971437,0.32439843],"study_design_scores_gemma":[0.0000635079,0.0014561625,0.007455022,0.00010044465,0.00025305373,0.0040323115,0.00023628066,0.08530536,0.8726152,0.0011115184,0.027164219,0.00020684118],"about_ca_topic_score_codex":0.00017060741,"about_ca_topic_score_gemma":0.00032742787,"teacher_disagreement_score":0.00087906077,"about_ca_system_score_codex":0.00020070866,"about_ca_system_score_gemma":0.0001807361,"threshold_uncertainty_score":0.0029407144},"labels":[],"label_agreement":null},{"id":"W4289532517","doi":"10.18280/rces.090203","title":"IoT Based Indoor and Outdoor Localization Framework with WI-FI Fingerprinting Based on Scalable Resnet Models","year":2022,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Scalability; Residual neural network; Floor plan; Classifier (UML); Architecture; Artificial intelligence; Autoencoder; Real-time computing; Pattern recognition (psychology); Deep learning; Geography","score_opus":0.01261891864544974,"score_gpt":0.22600246689275558,"score_spread":0.21338354824730585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289532517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013393446,0.00034585592,0.98293304,0.00011236734,0.00006796342,0.000021771608,0.00013259846,0.0011347379,0.0018581795],"genre_scores_gemma":[0.739414,0.00096146273,0.25278056,0.00017825984,0.00013015018,0.00013137,0.0008397651,0.00012747069,0.0054368335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998115,0.000042303476,0.000008854513,0.000053688105,0.000053357144,0.000030356789],"domain_scores_gemma":[0.999819,0.00005016179,0.000026820546,0.00002274555,0.000067020395,0.000014355511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003380656,0.0008058613,0.0006384106,0.0006502988,0.00019607642,0.0005691246,0.0010967985,0.00052327494,0.0009841186],"category_scores_gemma":[0.0005261036,0.00029960065,0.00062667,0.000601344,0.00033789597,0.001194932,0.0005613051,0.0005656403,0.00048496973],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010990832,0.000064631975,0.0012095827,0.0001074961,0.00009267317,0.00021479085,0.00007600989,0.87108564,0.009027517,0.012343291,0.0019991342,0.10366916],"study_design_scores_gemma":[0.0000029948685,0.000020134372,0.00022307441,0.0000043298783,0.000009710483,0.000029855877,0.0000090628,0.9959443,0.0009496262,0.0020222557,0.00077822054,0.0000063965454],"about_ca_topic_score_codex":0.008653369,"about_ca_topic_score_gemma":0.009103729,"teacher_disagreement_score":0.008653369,"about_ca_system_score_codex":0.0005859419,"about_ca_system_score_gemma":0.0004572148,"threshold_uncertainty_score":0.017206013},"labels":[],"label_agreement":null},{"id":"W4289824661","doi":"10.1109/lcomm.2022.3195410","title":"Spread Unsourced Random Access With an Iterative MIMO Receiver","year":2022,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preamble; Payload (computing); Computer science; Random access; Network packet; Channel (broadcasting); MIMO; Single antenna interference cancellation; Interference (communication); Algorithm; Real-time computing; Computer network","score_opus":0.024832141848698856,"score_gpt":0.2590982576589669,"score_spread":0.23426611581026802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289824661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01091465,0.00021853668,0.9839594,0.00012269111,0.00006679487,0.000057063473,0.000042789834,0.0006898102,0.003928294],"genre_scores_gemma":[0.45736247,0.00037702266,0.53079486,0.0003008108,0.00019223368,0.00017852926,0.00012146733,0.000048135713,0.010624341],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988281,0.00045296695,0.00004698059,0.00017947244,0.00040649684,0.00008594749],"domain_scores_gemma":[0.999368,0.00020178988,0.00007939825,0.00015089169,0.00017268877,0.000027255095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007313315,0.00066217687,0.0006836154,0.00034715893,0.0003552528,0.00080335524,0.001138533,0.00090082514,0.0023560477],"category_scores_gemma":[0.0014599208,0.00028218844,0.00045872244,0.0005780678,0.00043066588,0.0007739957,0.00087243743,0.00090876414,0.0019741377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089659845,0.00037431822,0.0024306849,0.0004917164,0.00030546187,0.0008317882,0.00048636275,0.27870858,0.26910925,0.10106218,0.006265513,0.33903754],"study_design_scores_gemma":[0.000049343,0.00040643525,0.00026071697,0.000024711708,0.000046053872,0.0007290928,0.000020511436,0.9504024,0.03714566,0.0035642495,0.007306642,0.0000442118],"about_ca_topic_score_codex":0.0005342644,"about_ca_topic_score_gemma":0.00093623553,"teacher_disagreement_score":0.0023560477,"about_ca_system_score_codex":0.0003783586,"about_ca_system_score_gemma":0.00064626924,"threshold_uncertainty_score":0.007881761},"labels":[],"label_agreement":null},{"id":"W4290992022","doi":"10.36227/techrxiv.20431968","title":"RIS-Aided Mobile Localization Error Bounds Under Hardware Impairments","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Cramér–Rao bound; Estimator; Transceiver; Position (finance); Computer science; Mean squared error; Interference (communication); Position error; Algorithm; Upper and lower bounds; Vulnerability (computing); Computer hardware; Mathematics; Telecommunications; Statistics; Wireless","score_opus":0.01580337346765035,"score_gpt":0.25858416967719033,"score_spread":0.24278079620954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290992022","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046300266,0.0011429733,0.94520676,0.00035498745,0.00008144289,0.000035793255,0.00016527275,0.00060110475,0.0061113443],"genre_scores_gemma":[0.91490704,0.00077024626,0.07965691,0.00011418027,0.00006449961,0.000102111575,0.00029476493,0.00012666694,0.003963495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986298,0.00041994304,0.00005396103,0.00018692584,0.00051060953,0.00019881592],"domain_scores_gemma":[0.9898087,0.0077926815,0.0006238856,0.0004403493,0.001209848,0.00012459885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002244939,0.0010300222,0.00084452087,0.0008781575,0.00033511076,0.0012905041,0.0006752605,0.0010196354,0.0026197622],"category_scores_gemma":[0.01809941,0.00035364824,0.0004262564,0.0006408799,0.0011794318,0.001209236,0.0019609483,0.0012224395,0.0006613847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011281803,0.000011793943,0.0006129828,0.00008560264,0.000016261492,0.00007662928,0.00005214243,0.97656006,0.0026458716,0.005892817,0.00045096365,0.013481951],"study_design_scores_gemma":[0.000004820429,0.000038370123,0.00023502194,0.000022524686,0.000005394234,0.000039422386,0.00002212798,0.99505746,0.002274233,0.0019953046,0.00029491374,0.00001034943],"about_ca_topic_score_codex":0.0037665442,"about_ca_topic_score_gemma":0.002824196,"teacher_disagreement_score":0.0037665442,"about_ca_system_score_codex":0.0008071335,"about_ca_system_score_gemma":0.0012864304,"threshold_uncertainty_score":0.01187247},"labels":[],"label_agreement":null},{"id":"W4290992102","doi":"10.36227/techrxiv.20431968.v1","title":"RIS-Aided Mobile Localization Error Bounds Under Hardware Impairments","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Cramér–Rao bound; Estimator; Transceiver; Position (finance); Computer science; Interference (communication); Mean squared error; Vulnerability (computing); Algorithm; Position error; SIGNAL (programming language); Computer hardware; Mathematics; Statistics; Telecommunications; Wireless","score_opus":0.01580337346765035,"score_gpt":0.25858416967719033,"score_spread":0.24278079620954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290992102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042784818,0.0011417466,0.9496223,0.00031746275,0.00007174306,0.000032014967,0.00013130793,0.0005200369,0.005378511],"genre_scores_gemma":[0.9128467,0.0007704046,0.0825926,0.00010347655,0.000058664813,0.000089206704,0.0002330035,0.000110049,0.0031959193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986534,0.00042128516,0.00005474945,0.0001818614,0.0005057187,0.00018299086],"domain_scores_gemma":[0.99113303,0.0066314307,0.00059680745,0.00044124282,0.0010841631,0.00011336766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020542606,0.0010502533,0.0007750438,0.0008056808,0.00030285647,0.0011495332,0.00067614886,0.0009776275,0.0022225387],"category_scores_gemma":[0.01686889,0.0003192103,0.0004150126,0.00061514956,0.001122578,0.0011364531,0.0018811802,0.0012138439,0.0005481999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098606804,0.000010676441,0.0005517926,0.00009312384,0.000015589872,0.000073522504,0.00005331932,0.9772867,0.0026577995,0.006286504,0.00039386997,0.012478532],"study_design_scores_gemma":[0.00000518732,0.00004075142,0.00022183491,0.000024580575,0.0000057956822,0.00004388603,0.000022755876,0.99459404,0.0025472092,0.002144138,0.0003398385,0.000009962147],"about_ca_topic_score_codex":0.0031216207,"about_ca_topic_score_gemma":0.0025882253,"teacher_disagreement_score":0.0031216207,"about_ca_system_score_codex":0.0006947857,"about_ca_system_score_gemma":0.0011396284,"threshold_uncertainty_score":0.010864079},"labels":[],"label_agreement":null},{"id":"W4290996243","doi":"10.1109/icc45855.2022.9838945","title":"Federated Learning for WiFi Fingerprinting","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer security","score_opus":0.07942833426290921,"score_gpt":0.3190124764215688,"score_spread":0.2395841421586596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290996243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038910173,0.00025045683,0.9567016,0.00017215566,0.00005756191,0.000025873649,0.00012638843,0.0026118471,0.0011439929],"genre_scores_gemma":[0.9027312,0.00012156613,0.09455731,0.00015137058,0.000032585554,0.00004648807,0.0002582258,0.000042322983,0.0020588667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999445,0.000119016615,0.000036998335,0.00015692899,0.00013339135,0.000108550805],"domain_scores_gemma":[0.9990144,0.00030968344,0.00009186005,0.00026188092,0.00027000305,0.000052236457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009427228,0.0006811961,0.00095797,0.00065085373,0.00042318265,0.0006948559,0.0015972301,0.0009844758,0.0013797692],"category_scores_gemma":[0.0031856967,0.000334533,0.00043918597,0.000831546,0.00045535254,0.0015487389,0.0013916747,0.000998634,0.00047348207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002929991,0.00020836235,0.0031288085,0.000057273643,0.000076192126,0.00012887381,0.00007241061,0.6329177,0.004595508,0.004027649,0.0023441303,0.35215014],"study_design_scores_gemma":[0.000004868799,0.000021737465,0.00019762793,0.0000030812203,0.0000046702057,0.000018953464,0.000006185861,0.99627894,0.0012940576,0.0019300047,0.00023532142,0.000004563626],"about_ca_topic_score_codex":0.008022612,"about_ca_topic_score_gemma":0.008418724,"teacher_disagreement_score":0.008022612,"about_ca_system_score_codex":0.0008116713,"about_ca_system_score_gemma":0.0009277027,"threshold_uncertainty_score":0.015951812},"labels":[],"label_agreement":null},{"id":"W4291162550","doi":"10.1016/j.inffus.2022.08.004","title":"Position estimation in indoor using networked GNSS sensors and a range-azimuth sensor","year":2022,"lang":"en","type":"article","venue":"Information Fusion","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"GNSS applications; Cramér–Rao bound; Azimuth; Computer science; Position (finance); Kalman filter; Sensor fusion; Range (aeronautics); Wireless sensor network; Dilution of precision; Node (physics); Remote sensing; Mean squared error; Satellite system; Real-time computing; Geodesy; Global Positioning System; Algorithm; Computer vision; Estimation theory; Artificial intelligence; Geography; Telecommunications; Mathematics; Engineering; Physics; Statistics; Acoustics","score_opus":0.006550275542010073,"score_gpt":0.20042562994826682,"score_spread":0.19387535440625675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291162550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1753213,0.00053160347,0.81622344,0.00012072527,0.00019041973,0.000020787815,0.00018464793,0.0014914705,0.0059156264],"genre_scores_gemma":[0.9019454,0.00032126144,0.09424179,0.000057196296,0.00006764631,0.000022281936,0.00027908536,0.000037013073,0.003028406],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996505,0.000055413544,0.000015380992,0.00009165226,0.00014535798,0.000041673607],"domain_scores_gemma":[0.9998635,0.000023947201,0.000023324455,0.000029726132,0.0000489717,0.000010412775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000229888,0.0005952757,0.00057346304,0.00061588356,0.00034345334,0.0004669037,0.00033095095,0.00047653267,0.00060779054],"category_scores_gemma":[0.0004804607,0.00025727073,0.00033977212,0.00082373613,0.000295055,0.0007391611,0.0007630457,0.00032986517,0.00050834083],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005889378,0.00019120265,0.02027817,0.0003277945,0.00020688058,0.00044478,0.00032177733,0.24158321,0.20169806,0.0046659196,0.0032481195,0.52644515],"study_design_scores_gemma":[0.000042751995,0.00034794424,0.029330568,0.000045800545,0.00017386222,0.0005614943,0.00028739325,0.86143446,0.09895603,0.0033536258,0.0053931787,0.00007288245],"about_ca_topic_score_codex":0.0034818822,"about_ca_topic_score_gemma":0.00518688,"teacher_disagreement_score":0.0034818822,"about_ca_system_score_codex":0.00022066105,"about_ca_system_score_gemma":0.0003928759,"threshold_uncertainty_score":0.0069232583},"labels":[],"label_agreement":null},{"id":"W4291326308","doi":"10.48550/arxiv.1608.03710","title":"Joint 3D Positioning and Network Synchronization in 5G Ultra-Dense\\n Networks Using UKF and EKF","year":2016,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Extended Kalman filter; Real-time computing; Synchronization (alternating current); Kalman filter; Offset (computer science); Hybrid positioning system; Wireless network; Node (physics); Positioning system; Channel (broadcasting); Wireless; Computer network; Telecommunications; Engineering; Artificial intelligence","score_opus":0.0296832276236739,"score_gpt":0.16349780096101935,"score_spread":0.13381457333734545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291326308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015996587,0.00028285268,0.9822114,0.000060743092,0.000062316256,0.000013074722,0.00003573089,0.00025919607,0.0010781299],"genre_scores_gemma":[0.6802393,0.00081976753,0.31533808,0.00008335355,0.000081929276,0.0000712716,0.00024350766,0.00004635538,0.003076435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996835,0.000073524854,0.000017680024,0.00007797936,0.00011005959,0.000037285034],"domain_scores_gemma":[0.9998092,0.000060722963,0.000042474498,0.000029805133,0.000047575897,0.000010313471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036971387,0.0004899409,0.00047083604,0.00032842517,0.0002528252,0.0005478679,0.0004647576,0.00059221475,0.00051517977],"category_scores_gemma":[0.0011759716,0.00021995128,0.00045382252,0.0005023372,0.00031527484,0.0007134305,0.00066037977,0.00048519508,0.00027898635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016301351,0.000032403823,0.003207904,0.00009973421,0.000056517314,0.00017818433,0.00021190285,0.7000262,0.013200936,0.009509828,0.0014565404,0.27185684],"study_design_scores_gemma":[0.000007448205,0.00002122788,0.0008159159,0.000007848064,0.000007543243,0.00004435897,0.000020821943,0.99518764,0.0016311659,0.00096917356,0.0012738812,0.000012967733],"about_ca_topic_score_codex":0.012399073,"about_ca_topic_score_gemma":0.00933157,"teacher_disagreement_score":0.012399073,"about_ca_system_score_codex":0.00041269974,"about_ca_system_score_gemma":0.00069998595,"threshold_uncertainty_score":0.024653792},"labels":[],"label_agreement":null},{"id":"W4293094576","doi":"10.1109/vtc2022-spring54318.2022.9860482","title":"Would Future mmWave Wireless Networks Be an Alternative Positioning Technique to GNSS-Based High Precision Positioning?","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; GNSS applications; Global Positioning System; Extended Kalman filter; Linearization; Precise Point Positioning; Real-time computing; Sensor fusion; Kalman filter; Telecommunications; Artificial intelligence; Nonlinear system","score_opus":0.008371437602070242,"score_gpt":0.2285997468384348,"score_spread":0.22022830923636455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293094576","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09506854,0.057871852,0.22948015,0.43625954,0.016568812,0.00014749097,0.0016095816,0.0012915597,0.16170245],"genre_scores_gemma":[0.70642173,0.05376208,0.10666337,0.039205994,0.0074253846,0.00016065963,0.0010473727,0.00016752578,0.085145935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995516,0.0001178321,0.00001231238,0.00008722467,0.00013808208,0.00009294461],"domain_scores_gemma":[0.99888104,0.00026109244,0.000114683346,0.000105871535,0.0005177085,0.00011955324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017463081,0.00038601097,0.00027325598,0.00024351757,0.00036026535,0.001486437,0.0008194228,0.0027316357,0.0061208922],"category_scores_gemma":[0.003193559,0.00018144076,0.00030090107,0.00048043483,0.0009838608,0.0034536102,0.0005547752,0.0019092062,0.0029302489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067652384,0.00018419135,0.0083171325,0.0009555271,0.00008871122,0.00068707427,0.00036678818,0.01277935,0.012191629,0.2707321,0.09905905,0.5939619],"study_design_scores_gemma":[0.000102912556,0.00073835126,0.008089463,0.0006170097,0.000069776506,0.0008770245,0.0014407474,0.019849438,0.0071542948,0.10753247,0.8533998,0.0001287228],"about_ca_topic_score_codex":0.0035322106,"about_ca_topic_score_gemma":0.0048689623,"teacher_disagreement_score":0.0061208922,"about_ca_system_score_codex":0.0008422924,"about_ca_system_score_gemma":0.001026432,"threshold_uncertainty_score":0.0204764},"labels":[],"label_agreement":null},{"id":"W4294982694","doi":"10.1109/icjece.2022.3187348","title":"5G-Enabled Vehicle Positioning Using EKF With Dynamic Covariance Matrix Tuning","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extended Kalman filter; Covariance matrix; Covariance; Computer science; Matrix (chemical analysis); Algorithm; Artificial intelligence; Kalman filter; Mathematics; Statistics; Chromatography; Chemistry","score_opus":0.0031691895531855358,"score_gpt":0.16370255507575884,"score_spread":0.1605333655225733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294982694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009242042,0.00017896596,0.98857504,0.00006938817,0.00006037,0.00001552365,0.000034517536,0.00060004747,0.0012241207],"genre_scores_gemma":[0.732331,0.00036850307,0.2645204,0.00010305246,0.000058447007,0.00007542701,0.0002582327,0.00007976349,0.00220532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995617,0.00008979841,0.000023191382,0.00010093033,0.00017344706,0.00005075594],"domain_scores_gemma":[0.9996685,0.00010103501,0.00005392019,0.000043429387,0.00012146804,0.000011790687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040997038,0.0007409203,0.0005143049,0.00037236462,0.00027016885,0.00044984082,0.0006282937,0.0007554722,0.00071089424],"category_scores_gemma":[0.001492506,0.00027982894,0.00044693117,0.000423224,0.0002967768,0.00061449653,0.0005191992,0.00071609794,0.00047412427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013533022,0.000033780067,0.002032494,0.00008770151,0.00007146393,0.00012911478,0.00009721143,0.77524424,0.017826874,0.0045309872,0.0016488184,0.19816193],"study_design_scores_gemma":[0.000015268826,0.00002938552,0.00057393324,0.000010893993,0.000011291676,0.00006356892,0.0000108231925,0.99290574,0.0038934099,0.00080179947,0.0016683362,0.000015637432],"about_ca_topic_score_codex":0.009045907,"about_ca_topic_score_gemma":0.008378024,"teacher_disagreement_score":0.009045907,"about_ca_system_score_codex":0.00050981063,"about_ca_system_score_gemma":0.0007360399,"threshold_uncertainty_score":0.017986476},"labels":[],"label_agreement":null},{"id":"W4296910324","doi":"10.1109/ap-s/usnc-ursi47032.2022.9886929","title":"Connectivity-Received Signal Strength Based Wireless Sensor Network Localization in a Nakagami-m Fading Channel","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Fading; Nakagami distribution; Cramér–Rao bound; RSS; Computer science; Channel (broadcasting); Algorithm; Wireless sensor network; Cumulative distribution function; Upper and lower bounds; Channel state information; Path loss; Wireless; Topology (electrical circuits); Mathematics; Estimation theory; Statistics; Telecommunications; Computer network; Probability density function; Mathematical analysis","score_opus":0.010571128641319658,"score_gpt":0.22581862747410122,"score_spread":0.21524749883278158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296910324","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3525109,0.0009594959,0.6434571,0.00022147004,0.00004946337,0.0000464048,0.00005723314,0.00045061597,0.0022473405],"genre_scores_gemma":[0.9831427,0.00041667436,0.015939003,0.00002282732,0.000016144108,0.00002287926,0.00003060865,0.000015519256,0.00039361496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992878,0.00024998575,0.000024017005,0.00009601041,0.00026977624,0.00007236872],"domain_scores_gemma":[0.9973361,0.001734355,0.00043220987,0.00013281836,0.00031993157,0.000044598855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006676271,0.00060586835,0.00053050014,0.00056362565,0.000302719,0.00045622693,0.000488978,0.00048579028,0.00028047746],"category_scores_gemma":[0.005904715,0.00019970874,0.00025719445,0.0007525331,0.00070313364,0.0012644237,0.0007303328,0.00029530717,0.0001293424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021436143,0.000027683913,0.0052480227,0.00012722825,0.000052497475,0.00028668268,0.0001006275,0.95025676,0.013992719,0.0028479928,0.0002128213,0.026632631],"study_design_scores_gemma":[0.000011185838,0.00016200512,0.0029360848,0.000008630476,0.000030813688,0.00029899922,0.000029096402,0.98959726,0.005656654,0.00094834354,0.0003058108,0.000015130642],"about_ca_topic_score_codex":0.002403548,"about_ca_topic_score_gemma":0.0025430552,"teacher_disagreement_score":0.002403548,"about_ca_system_score_codex":0.0005520357,"about_ca_system_score_gemma":0.00045638636,"threshold_uncertainty_score":0.00477916},"labels":[],"label_agreement":null},{"id":"W4297848355","doi":"10.48550/arxiv.1310.3407","title":"Joint Indoor Localization and Radio Map Construction with Limited\\n Deployment Load","year":2013,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Computer science; Bottleneck; Software deployment; Construct (python library); Set (abstract data type); Real-time computing; Radio propagation; Floor plan; Data mining; Computer network; Telecommunications; Embedded system; Geography","score_opus":0.029203409152143468,"score_gpt":0.1506836343384653,"score_spread":0.12148022518632183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297848355","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0541959,0.00007422799,0.9407473,0.000061034985,0.000021209922,0.000039432485,0.000071137525,0.003106099,0.0016836423],"genre_scores_gemma":[0.7060971,0.00010256851,0.2903814,0.000032754084,0.000026898308,0.00010054189,0.00046303743,0.00015919062,0.0026364657],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986255,0.00036089992,0.00006087243,0.00026818272,0.0005059583,0.0001786719],"domain_scores_gemma":[0.9980258,0.00036144556,0.00018012479,0.0010853407,0.00028065214,0.00006667383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080149755,0.0009171038,0.0012444906,0.0008225012,0.00043855258,0.00091835496,0.0011976047,0.0006321071,0.0021640551],"category_scores_gemma":[0.003948423,0.00044615354,0.00056596386,0.0012188404,0.0006354313,0.0019631921,0.0025387856,0.000581834,0.0017599352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005932544,0.0002777775,0.004431383,0.00020206951,0.00010436275,0.00030259232,0.00028215817,0.43642431,0.056285363,0.0065595517,0.0027021237,0.49183512],"study_design_scores_gemma":[0.000030330184,0.00023379808,0.0027135452,0.000009890892,0.000023993975,0.00026290864,0.000092398725,0.9450913,0.045698408,0.0030748453,0.0027302557,0.000038302573],"about_ca_topic_score_codex":0.002623474,"about_ca_topic_score_gemma":0.0025196332,"teacher_disagreement_score":0.002623474,"about_ca_system_score_codex":0.00039003923,"about_ca_system_score_gemma":0.0008732724,"threshold_uncertainty_score":0.0072395205},"labels":[],"label_agreement":null},{"id":"W4299299856","doi":"10.48550/arxiv.1604.03322","title":"Joint Device Positioning and Clock Synchronization in 5G Ultra-Dense\\n Networks","year":2016,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Extended Kalman filter; Synchronization (alternating current); Clock synchronization; Real-time computing; Key (lock); Telecommunications link; Kalman filter; Time of arrival; Computer network; Telecommunications; Wireless","score_opus":0.029683550918521904,"score_gpt":0.16202539533665367,"score_spread":0.13234184441813177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299299856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02861407,0.0014564818,0.96646535,0.00017250214,0.00016207936,0.00002677774,0.000040699306,0.00029230706,0.0027697603],"genre_scores_gemma":[0.9138832,0.0016463156,0.08122277,0.00011012206,0.00015678898,0.000043818156,0.00013250286,0.000015303192,0.0027892147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951017,0.00010210779,0.00002624922,0.00011576376,0.00016325292,0.0000824792],"domain_scores_gemma":[0.9996903,0.00009855279,0.0000597823,0.000055959146,0.000079446494,0.000015958183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051750994,0.00052636594,0.000515233,0.00026728294,0.00035026894,0.0006305637,0.00056720944,0.00072230963,0.0006313421],"category_scores_gemma":[0.0015045095,0.00020023214,0.00024467192,0.00051742856,0.0004233196,0.0010631542,0.0009527757,0.0005280796,0.00025070563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035984287,0.00006243287,0.0039372034,0.00030230885,0.000088026914,0.00041055153,0.00027078265,0.5650393,0.02084079,0.045217037,0.0030540605,0.36041763],"study_design_scores_gemma":[0.000012328467,0.00012005764,0.0010089938,0.00002066968,0.00002463828,0.00014544373,0.00004278053,0.98692816,0.00459259,0.0037757575,0.0033071397,0.000021479073],"about_ca_topic_score_codex":0.005339499,"about_ca_topic_score_gemma":0.004886762,"teacher_disagreement_score":0.005339499,"about_ca_system_score_codex":0.00044946454,"about_ca_system_score_gemma":0.0008130764,"threshold_uncertainty_score":0.010616839},"labels":[],"label_agreement":null},{"id":"W4299438780","doi":"10.48550/arxiv.1207.1137","title":"Background Subtraction for Online Calibration of Baseline RSS in RF\\n Sensing Networks","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"RSS; Background subtraction; Calibration; Computer science; Baseline (sea); Artificial intelligence; Similarity (geometry); Tracking (education); Pixel; Subtraction; Mathematics; Image (mathematics); Statistics","score_opus":0.06737423112131248,"score_gpt":0.198075290478023,"score_spread":0.1307010593567105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299438780","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020492934,0.00016688638,0.9770691,0.00004791221,0.000044025917,0.000017352371,0.000029742416,0.0009187965,0.001213346],"genre_scores_gemma":[0.50287783,0.0004901829,0.49290642,0.00009913262,0.00005839094,0.00007641259,0.00036879617,0.00034853976,0.0027743566],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993186,0.00013274926,0.000026057493,0.00018372567,0.00026266914,0.00007622153],"domain_scores_gemma":[0.9992329,0.00029130388,0.0000776741,0.00015160716,0.00020797792,0.000038536564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077968294,0.000739771,0.0007152154,0.0009588832,0.00054215064,0.0008265321,0.0011661318,0.0006535405,0.0013634773],"category_scores_gemma":[0.0032904686,0.0003496782,0.00047215066,0.0011841108,0.0005946531,0.0014723045,0.0010282237,0.00076412706,0.00089052063],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043193577,0.000194016,0.003704969,0.0001766364,0.000068943475,0.00033002542,0.00031686606,0.27264574,0.09971109,0.013674334,0.0029574456,0.60578805],"study_design_scores_gemma":[0.000011275977,0.00006682761,0.0021693176,0.000014897668,0.000017400238,0.00019731,0.000057325444,0.94528484,0.043127656,0.0055646678,0.0034614308,0.00002704079],"about_ca_topic_score_codex":0.0031418726,"about_ca_topic_score_gemma":0.0037318622,"teacher_disagreement_score":0.0031418726,"about_ca_system_score_codex":0.00069543096,"about_ca_system_score_gemma":0.0006979067,"threshold_uncertainty_score":0.006247163},"labels":[],"label_agreement":null},{"id":"W4300343043","doi":"10.48550/arxiv.1601.03004","title":"Novel velocity model to improve indoor localization using inertial\\n navigation with sensors on a smartphone","year":2016,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"GNSS applications; Robustness (evolution); Computer science; Inertial navigation system; Phone; Heading (navigation); Inertial measurement unit; Gaussian; Smart phone; Inertial frame of reference; Algorithm; Computer vision; Artificial intelligence; Simulation; Engineering; Global Positioning System; Physics; Telecommunications","score_opus":0.048447063564726535,"score_gpt":0.1880883996117444,"score_spread":0.13964133604701787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300343043","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019033033,0.0002458783,0.97836775,0.00009080526,0.00013334684,0.000023718125,0.000065492524,0.0009896602,0.0010502455],"genre_scores_gemma":[0.7041984,0.00080889906,0.28810656,0.00011515312,0.0001470473,0.000102397324,0.0005212994,0.00020918738,0.0057910113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977916,0.000034960314,0.000014151322,0.00006371275,0.000080109414,0.000027891303],"domain_scores_gemma":[0.9997713,0.000043568845,0.000028363609,0.000046252648,0.00009672174,0.000013800689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020086527,0.00067889085,0.00047858368,0.00043681488,0.00018329262,0.00050494395,0.0007994963,0.0005061508,0.0009490403],"category_scores_gemma":[0.0008176218,0.0002447265,0.00051423826,0.00048102182,0.00020540292,0.00086589437,0.00050495815,0.00047810146,0.0005817935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014334796,0.000084835825,0.0044841035,0.0002076745,0.000074021096,0.00022428056,0.00016900233,0.6427166,0.03498418,0.009680645,0.0044836397,0.30274776],"study_design_scores_gemma":[0.000010009665,0.00005539084,0.00067595724,0.000008412071,0.000012860695,0.000060751398,0.000011895708,0.9927591,0.0033044345,0.00056674203,0.0025206546,0.0000137836105],"about_ca_topic_score_codex":0.011937605,"about_ca_topic_score_gemma":0.010772827,"teacher_disagreement_score":0.011937605,"about_ca_system_score_codex":0.0003207279,"about_ca_system_score_gemma":0.0006924094,"threshold_uncertainty_score":0.023736238},"labels":[],"label_agreement":null},{"id":"W4300866137","doi":"10.1007/978-3-031-02478-8_9","title":"Challenges and Opportunities","year":2008,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on mobile and pervasive computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Power consumption; Perspective (graphical); Computer science; Data science; Emerging technologies; Power (physics); Artificial intelligence","score_opus":0.04801050135608637,"score_gpt":0.22127267379634175,"score_spread":0.17326217244025538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300866137","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020226196,0.07719108,0.0075139,0.4494333,0.03910891,0.00004902038,0.0002609651,0.00023972146,0.42418054],"genre_scores_gemma":[0.057814356,0.07692578,0.009770251,0.09323181,0.022776151,0.00027270592,0.0005750918,0.0003525155,0.73828125],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99779606,0.00067504105,0.000055826164,0.0002825251,0.0006886052,0.0005020063],"domain_scores_gemma":[0.9963605,0.0008716368,0.00009617791,0.00027911388,0.00088016957,0.0015123986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058832043,0.0006223595,0.000650217,0.0010490138,0.0036367397,0.011843942,0.0019403,0.004123301,0.08839399],"category_scores_gemma":[0.00692299,0.00027427785,0.00044641495,0.0010806641,0.00588838,0.016216826,0.007828312,0.005989453,0.022932688],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033689852,0.00005753519,0.00016475205,0.00018593908,0.0000040241775,0.00007270522,0.0010487395,0.000101778896,0.0002254126,0.44460562,0.44827953,0.10522025],"study_design_scores_gemma":[0.0000029797957,0.000010104167,0.00007443702,0.0002044787,0.0000013151356,0.000053910248,0.0016433845,0.00005021979,0.000045634504,0.08583363,0.9120747,0.000005200579],"about_ca_topic_score_codex":0.002223098,"about_ca_topic_score_gemma":0.0055268505,"teacher_disagreement_score":0.08839399,"about_ca_system_score_codex":0.0028654728,"about_ca_system_score_gemma":0.0077399905,"threshold_uncertainty_score":0.29570735},"labels":[],"label_agreement":null},{"id":"W4302030519","doi":"","title":"Improved Low Cost GPS Localization By Using Communicative Vehicles","year":2012,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Global Positioning System; Computer science; Real-time computing; Computer vision; Artificial intelligence; Telecommunications","score_opus":0.010964753927860879,"score_gpt":0.21415625711662006,"score_spread":0.20319150318875917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302030519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07426742,0.0008494184,0.9043467,0.00032904622,0.00048369015,0.00008191725,0.00029124535,0.0043768687,0.0149737],"genre_scores_gemma":[0.7053392,0.0007360948,0.26615393,0.00021134934,0.00029279658,0.00018912052,0.00077655626,0.00027127645,0.026029726],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926037,0.00023178321,0.000018016666,0.00012691217,0.00025803954,0.00010488508],"domain_scores_gemma":[0.9993303,0.00018638135,0.000035737165,0.00015849526,0.00025823314,0.00003083023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040286392,0.0013750461,0.0007850762,0.0010663951,0.0005476627,0.0009889676,0.0011421046,0.0014419985,0.0053960946],"category_scores_gemma":[0.0013437055,0.00045945702,0.00054532057,0.00096849806,0.0003696985,0.0011177511,0.0011301285,0.00058777316,0.0046951],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011486674,0.000104291,0.005026941,0.0006350111,0.00015728919,0.00073673116,0.0005560396,0.042274106,0.3539621,0.010468326,0.009405263,0.5755253],"study_design_scores_gemma":[0.00027247725,0.0010923597,0.012495297,0.00022011214,0.00075995305,0.0025950023,0.0005812413,0.5870848,0.3099415,0.0046231747,0.08007861,0.00025544953],"about_ca_topic_score_codex":0.0026954163,"about_ca_topic_score_gemma":0.003243542,"teacher_disagreement_score":0.0053960946,"about_ca_system_score_codex":0.00039435073,"about_ca_system_score_gemma":0.00056487473,"threshold_uncertainty_score":0.018051744},"labels":[],"label_agreement":null},{"id":"W4306167002","doi":"10.5194/isprs-annals-x-4-w3-2022-81-2022","title":"DISCOVERING CLUSTERING PATTERNS FROM E-COUNTER DATA STREAMS","year":2022,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Cisco Systems","keywords":"Data stream mining; Cluster analysis; Computer science; Data mining; Data stream; STREAMS; Affinity propagation; Data stream clustering; Machine learning; CURE data clustering algorithm; Computer network; Correlation clustering","score_opus":0.04889879753834387,"score_gpt":0.27741906059612503,"score_spread":0.22852026305778117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306167002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49616548,0.00029742526,0.49640095,0.00041901527,0.000106218264,0.00035849246,0.0029830828,0.0016030504,0.0016663175],"genre_scores_gemma":[0.8686846,0.00016944161,0.12611161,0.000047356512,0.000042051724,0.00016385283,0.003920329,0.00003918493,0.00082164875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919456,0.00013950981,0.00007492332,0.000206983,0.00029149972,0.00009255952],"domain_scores_gemma":[0.9967084,0.0013671966,0.00040239017,0.00032415226,0.0010802067,0.00011758348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001086733,0.0007904646,0.0005525305,0.003196908,0.0003622494,0.0011174674,0.00088675495,0.00066540705,0.0004558746],"category_scores_gemma":[0.0042345016,0.0001965039,0.00042538837,0.0029077232,0.0003578678,0.0009671182,0.00068165787,0.00063748733,0.00041198294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008603259,0.00087838894,0.14461622,0.00036064966,0.00022855797,0.0009903613,0.00060380896,0.46851796,0.021784682,0.0058468333,0.0047672656,0.3505449],"study_design_scores_gemma":[0.000009725371,0.000059881804,0.010089672,0.0000125927345,0.000012612071,0.00010701721,0.00018016793,0.9813141,0.0049776477,0.002190825,0.0010325072,0.000013187874],"about_ca_topic_score_codex":0.0035021636,"about_ca_topic_score_gemma":0.004414841,"teacher_disagreement_score":0.0035021636,"about_ca_system_score_codex":0.000654096,"about_ca_system_score_gemma":0.0005938244,"threshold_uncertainty_score":0.006963551},"labels":[],"label_agreement":null},{"id":"W4306252504","doi":"10.1088/1361-6501/ac9a64","title":"Position estimation and calibration for high precision human positioning and tracking using millimeter-wave radar","year":2022,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Computer science; Radar; Extremely high frequency; Position (finance); Calibration; Tracking (education); Context (archaeology); Precise Point Positioning; Positioning system; Millimeter; Remote sensing; Computer vision; Point (geometry); Global Positioning System; Telecommunications; Optics; Mathematics; Physics; Geology","score_opus":0.035332242610071125,"score_gpt":0.24262948419508898,"score_spread":0.20729724158501786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306252504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02686208,0.0005095543,0.9709122,0.00007887965,0.0000798623,0.000014310822,0.000027828557,0.00052688515,0.0009883846],"genre_scores_gemma":[0.6266048,0.0006570723,0.37072632,0.00017337348,0.00009770159,0.000053536467,0.00012930503,0.000055040706,0.0015028332],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955934,0.000097575095,0.000019202684,0.00011378803,0.00017063503,0.00003951107],"domain_scores_gemma":[0.99961877,0.00007942248,0.00007165868,0.000084844534,0.00013134965,0.000014046831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032078812,0.0004539767,0.00039751976,0.00043852898,0.00021086997,0.0003122426,0.00052953826,0.00055792555,0.0006883371],"category_scores_gemma":[0.001022908,0.00021246537,0.00023688514,0.00052952644,0.00017544019,0.0005904211,0.00053435605,0.0005249122,0.00059115846],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027678392,0.0000788455,0.006125334,0.00029960606,0.00008001691,0.00021287192,0.00022437572,0.052216507,0.28874272,0.0058090426,0.002597744,0.6433362],"study_design_scores_gemma":[0.000051777217,0.0002998099,0.012513734,0.000046389254,0.00007858471,0.00094159617,0.000105917046,0.8119812,0.1596891,0.0023085647,0.011911845,0.000071399154],"about_ca_topic_score_codex":0.0006868692,"about_ca_topic_score_gemma":0.0007870207,"teacher_disagreement_score":0.0006883371,"about_ca_system_score_codex":0.00015010554,"about_ca_system_score_gemma":0.0002667501,"threshold_uncertainty_score":0.0023027658},"labels":[],"label_agreement":null},{"id":"W4308125763","doi":"10.32920/21476622","title":"A Survey of Machine Learning for Indoor Positioning","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Scalability; Non-line-of-sight propagation; Software deployment; Adaptability; Wireless; Machine learning; Artificial intelligence; Real-time computing; Telecommunications; Database; Software engineering","score_opus":0.0225138844987278,"score_gpt":0.25248209659897175,"score_spread":0.22996821210024396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308125763","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035222846,0.26241395,0.7128476,0.0030814812,0.0014363143,0.000080221005,0.0006667428,0.0010718261,0.014879634],"genre_scores_gemma":[0.11471996,0.470154,0.38751674,0.002152704,0.006209548,0.00041643338,0.0035719702,0.00047703428,0.014781526],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99830234,0.0005183207,0.00017232112,0.0004047323,0.0005189746,0.00008330029],"domain_scores_gemma":[0.9973015,0.0016719064,0.00010739514,0.00029890763,0.0005692304,0.000051105424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018516241,0.0014724861,0.0017658493,0.002177958,0.00053185376,0.0017673394,0.002055294,0.0018609733,0.0040088804],"category_scores_gemma":[0.0058348826,0.0006645439,0.0013141287,0.0051533394,0.0006962512,0.0030903309,0.0012124649,0.0022343262,0.004027959],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049201975,0.000074441916,0.0014974849,0.0017993852,0.00012528677,0.000099163495,0.000084272935,0.033226293,0.00089580734,0.023376836,0.022182092,0.91658974],"study_design_scores_gemma":[0.000028132317,0.00032995504,0.004555226,0.0016885048,0.00015378564,0.00082409463,0.00021153636,0.43090764,0.0042766375,0.09832342,0.45854402,0.0001569253],"about_ca_topic_score_codex":0.0030805229,"about_ca_topic_score_gemma":0.0019631016,"teacher_disagreement_score":0.0040088804,"about_ca_system_score_codex":0.00086713495,"about_ca_system_score_gemma":0.0010648672,"threshold_uncertainty_score":0.013411045},"labels":[],"label_agreement":null},{"id":"W4308130458","doi":"10.32920/21476622.v1","title":"A Survey of Machine Learning for Indoor Positioning","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Scalability; Non-line-of-sight propagation; Software deployment; Adaptability; Wireless; Machine learning; Artificial intelligence; Real-time computing; Telecommunications; Software engineering; Database","score_opus":0.0225138844987278,"score_gpt":0.25248209659897175,"score_spread":0.22996821210024396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308130458","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035222846,0.26241395,0.7128476,0.0030814812,0.0014363143,0.000080221005,0.0006667428,0.0010718261,0.014879634],"genre_scores_gemma":[0.11471996,0.470154,0.38751674,0.002152704,0.006209548,0.00041643338,0.0035719702,0.00047703428,0.014781526],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99830234,0.0005183207,0.00017232112,0.0004047323,0.0005189746,0.00008330029],"domain_scores_gemma":[0.9973015,0.0016719064,0.00010739514,0.00029890763,0.0005692304,0.000051105424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018516241,0.0014724861,0.0017658493,0.002177958,0.00053185376,0.0017673394,0.002055294,0.0018609733,0.0040088804],"category_scores_gemma":[0.0058348826,0.0006645439,0.0013141287,0.0051533394,0.0006962512,0.0030903309,0.0012124649,0.0022343262,0.004027959],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049201975,0.000074441916,0.0014974849,0.0017993852,0.00012528677,0.000099163495,0.000084272935,0.033226293,0.00089580734,0.023376836,0.022182092,0.91658974],"study_design_scores_gemma":[0.000028132317,0.00032995504,0.004555226,0.0016885048,0.00015378564,0.00082409463,0.00021153636,0.43090764,0.0042766375,0.09832342,0.45854402,0.0001569253],"about_ca_topic_score_codex":0.0030805229,"about_ca_topic_score_gemma":0.0019631016,"teacher_disagreement_score":0.0040088804,"about_ca_system_score_codex":0.00086713495,"about_ca_system_score_gemma":0.0010648672,"threshold_uncertainty_score":0.013411045},"labels":[],"label_agreement":null},{"id":"W4308390366","doi":"10.1109/wisee49342.2022.9926812","title":"High Altitude Platform Station (HAPS)-Aided GNSS for Urban Areas","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"GNSS applications; Global Positioning System; Pseudorange; Computer science; Dilution of precision; Precise Point Positioning; Satellite system; Offset (computer science); Satellite; Remote sensing; Metre; Real-time computing; Environmental science; Telecommunications; Geography; Engineering; Aerospace engineering","score_opus":0.018557641252424078,"score_gpt":0.24059440197328738,"score_spread":0.2220367607208633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308390366","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71170217,0.00036497173,0.27713925,0.00028511786,0.00008636309,0.000043026754,0.000352163,0.0010478192,0.0089791035],"genre_scores_gemma":[0.9868564,0.00010198189,0.011802915,0.000015015555,0.000006476403,0.000009741816,0.00011659877,0.000017801203,0.0010730019],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982136,0.00007252761,0.000004792656,0.000029930454,0.00004704605,0.000024389883],"domain_scores_gemma":[0.99978095,0.0000790848,0.000026158206,0.00005266018,0.000050193994,0.000011013411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024533697,0.00032272682,0.00023395506,0.0001409242,0.00019715625,0.0003011149,0.00035205128,0.00029437782,0.0012921399],"category_scores_gemma":[0.0005643008,0.00011879794,0.00016936763,0.00043086236,0.00039408787,0.00042890763,0.00034098414,0.00026825792,0.00028151934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009955002,0.000028637285,0.0073384442,0.00009201043,0.0000410344,0.00017932852,0.00009168066,0.946365,0.011709669,0.0029887145,0.0008938661,0.030172104],"study_design_scores_gemma":[0.000026225003,0.00017391896,0.0044799233,0.000009378534,0.00002269338,0.00008266143,0.00007723748,0.9827052,0.0073633287,0.0017334429,0.0033121412,0.000013966328],"about_ca_topic_score_codex":0.0073723164,"about_ca_topic_score_gemma":0.010332301,"teacher_disagreement_score":0.0073723164,"about_ca_system_score_codex":0.0002615801,"about_ca_system_score_gemma":0.0004200123,"threshold_uncertainty_score":0.014658809},"labels":[],"label_agreement":null},{"id":"W4310880224","doi":"10.1109/sensors52175.2022.9967088","title":"Ultra-wideband Automatic Anchor's Localization for Indoor Path Tracking","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Multipath propagation; Computer science; Ultra-wideband; Trajectory; Tracking (education); Process (computing); Position (finance); Real-time computing; Wireless; Simultaneous localization and mapping; Path (computing); Computer vision; Artificial intelligence; Mobile robot; Robot; Telecommunications; Computer network; Channel (broadcasting)","score_opus":0.011292421231377504,"score_gpt":0.21828503505494512,"score_spread":0.20699261382356762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310880224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030969165,0.00021016576,0.96597886,0.00003331428,0.00004810501,0.000017528893,0.000032217733,0.0015758194,0.0011347339],"genre_scores_gemma":[0.6234786,0.00023247319,0.37336662,0.00003866746,0.000025941663,0.00004167665,0.00015034323,0.0000736808,0.002591909],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967253,0.00007778398,0.000011192569,0.00005536812,0.0001537433,0.000029355939],"domain_scores_gemma":[0.9996767,0.00008243255,0.000056576555,0.00009897472,0.000075506694,0.000009768557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022471441,0.00032065666,0.00024665485,0.0004632785,0.00020966107,0.00024496837,0.00049637933,0.00041877234,0.0009909539],"category_scores_gemma":[0.0006967723,0.00014752612,0.00019103727,0.0004771661,0.00020044188,0.00045035873,0.0004760251,0.00036621912,0.00069458043],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002764802,0.000065775035,0.0012510379,0.0001492963,0.000030386253,0.0002538152,0.00030089534,0.03890608,0.490367,0.004118535,0.002187565,0.46209309],"study_design_scores_gemma":[0.000041550087,0.00042392043,0.004140739,0.000039348586,0.00004583474,0.0011360778,0.000097040196,0.614748,0.3539114,0.0019610408,0.023390412,0.000064547836],"about_ca_topic_score_codex":0.00059798534,"about_ca_topic_score_gemma":0.00082058524,"teacher_disagreement_score":0.0009909539,"about_ca_system_score_codex":0.00014507775,"about_ca_system_score_gemma":0.00019545465,"threshold_uncertainty_score":0.0033150911},"labels":[],"label_agreement":null},{"id":"W4312237231","doi":"10.1109/tii.2022.3205368","title":"A Machine Learning Assisted Method for Coverage Optimization in a Network of Mobile Sensors","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"Defence Research and Development Canada","keywords":"Computer science","score_opus":0.02542030347254687,"score_gpt":0.2507858312828514,"score_spread":0.22536552781030456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312237231","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00159108,0.00017250754,0.99741036,0.00005260054,0.000022896367,0.000017923281,0.000017216887,0.00013091842,0.0005844931],"genre_scores_gemma":[0.24174403,0.0006654776,0.7511383,0.00023605865,0.00019472046,0.0004638482,0.00026689732,0.00018675007,0.0051038708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934155,0.00019915619,0.000026691718,0.00015477289,0.00021291242,0.000064954744],"domain_scores_gemma":[0.9988487,0.00071950874,0.000104436316,0.000079231075,0.00021286668,0.00003523497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095176685,0.0011212213,0.0011832443,0.0009093506,0.00041231338,0.000796602,0.00155198,0.0013708501,0.0024732947],"category_scores_gemma":[0.003013472,0.0005323209,0.0009749366,0.0010902145,0.00061614974,0.0010861378,0.001133684,0.0012831087,0.00065814244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051591513,0.000031844196,0.00036365222,0.000112272064,0.00003756997,0.000050982864,0.00004007576,0.89115125,0.0021544273,0.0076972283,0.0015025818,0.09680661],"study_design_scores_gemma":[0.0000029339094,0.00000851842,0.000028166134,0.0000036228912,0.0000025790644,0.000009777181,0.0000023784348,0.9981937,0.00022188974,0.0011599456,0.0003644411,0.0000020707837],"about_ca_topic_score_codex":0.003924866,"about_ca_topic_score_gemma":0.003076567,"teacher_disagreement_score":0.003924866,"about_ca_system_score_codex":0.0009059828,"about_ca_system_score_gemma":0.0011916653,"threshold_uncertainty_score":0.008274019},"labels":[],"label_agreement":null},{"id":"W4312243043","doi":"10.1109/twc.2022.3218579","title":"Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"State Key Laboratory of Rail Traffic Control and Safety; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Computer science; Covariance; Computation; Embedding; Computer engineering; Real-time computing; Machine learning; Algorithm; Artificial intelligence; Mathematics","score_opus":0.01952818744623844,"score_gpt":0.23597330582153855,"score_spread":0.21644511837530012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312243043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010665059,0.00021147329,0.98663723,0.00008431874,0.000053547916,0.000024669383,0.000052522173,0.00084336946,0.0014277389],"genre_scores_gemma":[0.8237507,0.00037826755,0.17002393,0.0002580748,0.00006299095,0.00008310223,0.00027629378,0.00008764883,0.005078989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997907,0.000037066795,0.000008264973,0.00006544541,0.00006155056,0.0000369956],"domain_scores_gemma":[0.99978656,0.00006990825,0.000028619346,0.000029140872,0.000066270164,0.000019487832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040212914,0.000678399,0.00048335677,0.00038514202,0.00018456075,0.00046300702,0.001402575,0.000472712,0.0013139572],"category_scores_gemma":[0.0010477873,0.00024503353,0.00039887815,0.0004337948,0.00040574124,0.0009287847,0.0008456664,0.00081866735,0.0003444154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024918144,0.00016587136,0.001730736,0.00009419927,0.000105467596,0.00015825496,0.00007109572,0.48112932,0.020759396,0.012695878,0.004606299,0.4782344],"study_design_scores_gemma":[0.0000032591533,0.000024059938,0.00009530585,0.0000021074163,0.0000071428276,0.000018396795,0.000003077235,0.99675107,0.0013332538,0.0014433423,0.00031563774,0.0000034047143],"about_ca_topic_score_codex":0.004958924,"about_ca_topic_score_gemma":0.0063439338,"teacher_disagreement_score":0.004958924,"about_ca_system_score_codex":0.0006239848,"about_ca_system_score_gemma":0.000531264,"threshold_uncertainty_score":0.009860098},"labels":[],"label_agreement":null},{"id":"W4312557501","doi":"10.1109/ithings-greencom-cpscom-smartdata-cybermatics55523.2022.00052","title":"Reinforcement learning-based IoT sensor scheduling strategy for bridge structure health monitoring","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing &amp; Communications (GreenCom) and IEEE Cyber, Physical &amp; Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Science and Technology Commission of Shanghai Municipality","keywords":"Computer science; Wireless sensor network; Scheduling (production processes); Reinforcement learning; Distributed computing; Bridge (graph theory); Computer network; Artificial intelligence; Engineering","score_opus":0.1010809618096873,"score_gpt":0.3464359343612751,"score_spread":0.24535497255158784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312557501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10514519,0.00025259206,0.88858634,0.0003181403,0.00007258614,0.00008128813,0.000036561454,0.00040856685,0.0050988127],"genre_scores_gemma":[0.98853,0.00004305186,0.010498867,0.000049038455,0.000010127719,0.00003965465,0.000018014933,0.000007416816,0.000803848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997538,0.00004758465,0.000015501631,0.000078044286,0.00005542836,0.00004967865],"domain_scores_gemma":[0.9994863,0.00019955903,0.00010034171,0.000029181449,0.00013160342,0.000052897853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048022694,0.00051168835,0.00052005256,0.0002734577,0.00037139855,0.00031816846,0.00075144524,0.000485398,0.0012230462],"category_scores_gemma":[0.0013484264,0.00017934815,0.0003057591,0.00016918879,0.0004318591,0.0005060259,0.00056966516,0.0005331181,0.00011959972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012528994,0.00009932323,0.0016647072,0.00005648731,0.000033600314,0.00012091423,0.000107659514,0.9411587,0.0053166,0.0060618804,0.0009286196,0.04432623],"study_design_scores_gemma":[0.000008349784,0.00003487924,0.00013325998,0.0000019437516,0.000005037742,0.000010458174,0.0000061161777,0.9985245,0.0003232409,0.00083497685,0.00011440323,0.000002799568],"about_ca_topic_score_codex":0.005645858,"about_ca_topic_score_gemma":0.0045895395,"teacher_disagreement_score":0.005645858,"about_ca_system_score_codex":0.0005999146,"about_ca_system_score_gemma":0.00081325544,"threshold_uncertainty_score":0.011225998},"labels":[],"label_agreement":null},{"id":"W4312649270","doi":"10.1109/tvt.2022.3218048","title":"The Effect of Hardware Impairments on the Error Bounds of Localization and Maximum Likelihood Estimation of mm-Wave MISO-OFDM Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Estimator; Cramér–Rao bound; Telecommunications link; Orthogonal frequency-division multiplexing; Algorithm; Transmitter; Estimation theory; Channel (broadcasting); Base station; Fisher information; Process (computing); Maximum likelihood; Mathematics; Computer science; Statistics; Telecommunications","score_opus":0.006029883951133808,"score_gpt":0.20334782714351057,"score_spread":0.19731794319237678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312649270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19227146,0.0014275784,0.8001952,0.0005204806,0.000053703192,0.000027321894,0.00010109521,0.00031073525,0.0050924285],"genre_scores_gemma":[0.97137994,0.00060429575,0.027031438,0.000046303125,0.00003396071,0.000036625926,0.00007960855,0.00004599654,0.0007418125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983015,0.0005585613,0.000077830715,0.00021118304,0.0006131998,0.0002377066],"domain_scores_gemma":[0.9806291,0.016295692,0.0012192817,0.00069102505,0.0010387121,0.0001263425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022156832,0.0007714211,0.00067113707,0.0005895796,0.00046184857,0.0010298584,0.0005668949,0.00091668434,0.00092852773],"category_scores_gemma":[0.033175386,0.00032947198,0.00026526165,0.00052840885,0.0011218912,0.0020227742,0.0012979938,0.0007873991,0.0002310857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021883017,0.000023711113,0.0036003205,0.00014853844,0.000036860107,0.00020579081,0.00021492367,0.94580007,0.006129175,0.014748748,0.00024989352,0.028623195],"study_design_scores_gemma":[0.000008871997,0.00007145612,0.002650947,0.000048856706,0.000022403243,0.00014211111,0.00008055078,0.980471,0.01174314,0.004288964,0.0004427873,0.00002885987],"about_ca_topic_score_codex":0.00218738,"about_ca_topic_score_gemma":0.0012137307,"teacher_disagreement_score":0.0022156832,"about_ca_system_score_codex":0.00068509654,"about_ca_system_score_gemma":0.0008010077,"threshold_uncertainty_score":0.011717796},"labels":[],"label_agreement":null},{"id":"W4312815912","doi":"10.1109/tvt.2022.3217595","title":"Performance of Cooperative Detection in Joint Communication-Sensing Vehicular Network: A Data Analytic and Stochastic Geometry Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Non-line-of-sight propagation; Stochastic geometry; Computer science; Software deployment; Statistic; Channel (broadcasting); Interference (communication); Obstacle; Vehicular ad hoc network; Real-time computing; Joint (building); Wireless; Simulation; Wireless ad hoc network; Computer network; Engineering; Telecommunications; Geography; Mathematics; Statistics","score_opus":0.017062364468699534,"score_gpt":0.2162877319392886,"score_spread":0.19922536747058905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312815912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25065592,0.00074322365,0.7422594,0.00065553363,0.00004364624,0.00007540035,0.00013913354,0.00043597879,0.0049918047],"genre_scores_gemma":[0.9908381,0.00016415615,0.008511715,0.000028462691,0.000010027829,0.000027879292,0.000044924855,0.000015614842,0.00035910268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837154,0.0006057678,0.000052725693,0.00019569452,0.00046753604,0.0003067596],"domain_scores_gemma":[0.9928262,0.0051278644,0.00069207227,0.00030370915,0.00090826105,0.00014200514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026873706,0.0011014825,0.00089281634,0.0011348244,0.00050428836,0.0010272978,0.0010647435,0.0008197141,0.0005698294],"category_scores_gemma":[0.009334113,0.00047261623,0.0005941559,0.0009936786,0.0015094832,0.0012395297,0.0013990792,0.0006166338,0.00011316044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038640075,0.000011245901,0.000691896,0.000017254117,0.000011525585,0.000030461906,0.000029862978,0.9905984,0.00065464893,0.0050143176,0.00013578251,0.0027659324],"study_design_scores_gemma":[0.0000013559775,0.00001767512,0.00013180128,0.0000012188044,0.0000024510555,0.000011410022,0.000011558467,0.9988537,0.00024440157,0.0006870089,0.0000336894,0.0000037842613],"about_ca_topic_score_codex":0.013938851,"about_ca_topic_score_gemma":0.0049517625,"teacher_disagreement_score":0.013938851,"about_ca_system_score_codex":0.0025415753,"about_ca_system_score_gemma":0.0016984337,"threshold_uncertainty_score":0.027715445},"labels":[],"label_agreement":null},{"id":"W4312881501","doi":"10.1109/tsp.2022.3215651","title":"Location Estimates From Channel State Information via Binary Programming","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Binary number; Channel state information; Multipath propagation; Channel (broadcasting); Transmitter; Binary code; Binary data; Algorithm; State (computer science); Frame (networking); Real-time computing; Covariance; Mathematical optimization; Computer engineering; Theoretical computer science; Wireless; Telecommunications; Mathematics; Statistics","score_opus":0.0092684687929963,"score_gpt":0.2088184357551346,"score_spread":0.19954996696213828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312881501","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004987034,0.000034171007,0.9933863,0.00014049717,0.000009307748,0.00001458643,0.00003945791,0.00015910093,0.0012294702],"genre_scores_gemma":[0.47192657,0.0002667982,0.5215779,0.00020746012,0.000070278395,0.000244479,0.00045663866,0.0002289082,0.0050209565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915695,0.0003345178,0.000036358633,0.0001209291,0.00025445886,0.000096824966],"domain_scores_gemma":[0.9972229,0.0020073075,0.00023241423,0.00015936334,0.0003228245,0.00005513335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014141478,0.0007329509,0.00073223293,0.00060512766,0.00035493018,0.0016139038,0.0010584898,0.0009057835,0.0034267956],"category_scores_gemma":[0.0075062886,0.00062322704,0.00042822497,0.00083700306,0.00087707833,0.0022248619,0.0016283985,0.0018215793,0.0006713758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096881064,0.00003600473,0.00040675898,0.000061260944,0.000012358085,0.000027648517,0.00006064894,0.90907955,0.001443399,0.033834193,0.001044528,0.053896744],"study_design_scores_gemma":[0.0000047368053,0.000009085117,0.000030998657,0.0000049675814,0.0000014059789,0.000006003928,0.000006706763,0.9918268,0.00038726974,0.007466817,0.0002525588,0.0000026904313],"about_ca_topic_score_codex":0.0032563598,"about_ca_topic_score_gemma":0.0027168957,"teacher_disagreement_score":0.0034267956,"about_ca_system_score_codex":0.0007257734,"about_ca_system_score_gemma":0.001223936,"threshold_uncertainty_score":0.011463761},"labels":[],"label_agreement":null},{"id":"W4312985502","doi":"10.1109/ipin54987.2022.9918099","title":"Manhattan World Constraint for Indoor Line-based Mapping Using Ultrasonic Scans","year":2022,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ultrasonic sensor; Occupancy grid mapping; Computer science; Computer vision; Artificial intelligence; Feature (linguistics); Landmark; Grid; Mobile mapping; Point cloud; Simultaneous localization and mapping; Line (geometry); Feature extraction; Identification (biology); Mobile robot; Acoustics; Geography; Mathematics","score_opus":0.03226721245901249,"score_gpt":0.24150586095558477,"score_spread":0.20923864849657228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312985502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005519753,0.00022783923,0.9904096,0.000080469516,0.000057585577,0.0000462407,0.0003382421,0.00048075867,0.0028394873],"genre_scores_gemma":[0.30520928,0.0015022765,0.6799931,0.00022513195,0.00022149242,0.00086158304,0.0039287233,0.0005130647,0.007545317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985795,0.0002604007,0.00006868865,0.00040318287,0.00055170857,0.0001365146],"domain_scores_gemma":[0.9985897,0.00035898638,0.00017665504,0.00035247023,0.00045596383,0.00006627863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046560878,0.0015600563,0.0011146569,0.0013474996,0.00064581854,0.0017462965,0.0022256135,0.00079022284,0.0049660513],"category_scores_gemma":[0.0036253936,0.00072078826,0.0010361114,0.0030187787,0.0007314107,0.001970326,0.0018761891,0.0013738071,0.0036504152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030482587,0.00014534926,0.0052760025,0.0007224213,0.00015686399,0.0012547127,0.000437639,0.36490536,0.022116417,0.07110776,0.022666553,0.51090616],"study_design_scores_gemma":[0.00003561736,0.00012031651,0.0033655204,0.00005828667,0.000038901715,0.0009226118,0.0002200358,0.92152286,0.0104004685,0.02002319,0.04320839,0.00008380499],"about_ca_topic_score_codex":0.008542986,"about_ca_topic_score_gemma":0.011799658,"teacher_disagreement_score":0.008542986,"about_ca_system_score_codex":0.00048825186,"about_ca_system_score_gemma":0.0013331979,"threshold_uncertainty_score":0.01698649},"labels":[],"label_agreement":null},{"id":"W4312990322","doi":"10.1109/tii.2022.3217533","title":"A Non-Line-of-Sight Mitigation Method for Indoor Ultra-Wideband Localization With Multiple Walls","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Non-line-of-sight propagation; Ranging; Ultra-wideband; Computer science; Line-of-sight; Radio propagation; Multilateration; Acoustics; Electronic engineering; Algorithm; Engineering; Wireless; Telecommunications; Physics; Aerospace engineering","score_opus":0.022384288361030492,"score_gpt":0.24217266600541149,"score_spread":0.219788377644381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312990322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004987395,0.0000881531,0.9939889,0.00002215354,0.000022172182,0.000010792387,0.000004595223,0.00020583023,0.00066998677],"genre_scores_gemma":[0.26932454,0.00044685058,0.7240783,0.000077701065,0.00003436494,0.000101760736,0.00006886086,0.00010380044,0.0057637943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997806,0.00003921848,0.000012120525,0.000045512024,0.00010610218,0.000016509855],"domain_scores_gemma":[0.99981886,0.000044508564,0.00003957617,0.000032146614,0.00005628729,0.0000086987075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024106403,0.0006170683,0.00043927392,0.00046133378,0.00035710918,0.0003984225,0.0008100006,0.0005443998,0.0012934894],"category_scores_gemma":[0.00053438207,0.0002701995,0.00067024864,0.0004006777,0.00032169468,0.00071908196,0.000670393,0.0005016931,0.00053211645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011324329,0.00012533754,0.0014440957,0.00031615022,0.00009980294,0.00048659535,0.00028900345,0.2955287,0.21769556,0.026314719,0.0018869654,0.4556997],"study_design_scores_gemma":[0.000017173754,0.000134061,0.00058224425,0.00003011674,0.000045853667,0.00048319733,0.000057459274,0.92998016,0.059786376,0.0022495743,0.0065813926,0.000052440097],"about_ca_topic_score_codex":0.0008891874,"about_ca_topic_score_gemma":0.0010915207,"teacher_disagreement_score":0.0012934894,"about_ca_system_score_codex":0.00027412362,"about_ca_system_score_gemma":0.0005664493,"threshold_uncertainty_score":0.0043271184},"labels":[],"label_agreement":null},{"id":"W4313158939","doi":"10.1109/jsyst.2022.3225072","title":"In-Home Monitoring Sleep Turnover Activities and Breath Rate via WiFi Signals","year":2022,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Word error rate; Breathing; Subcarrier; Convolutional neural network; Exploit; Sleep (system call); Channel (broadcasting); Real-time computing; Artificial intelligence; Telecommunications; Computer security; Medicine","score_opus":0.009147968988112272,"score_gpt":0.21187550296472504,"score_spread":0.20272753397661278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313158939","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6997187,0.0016828307,0.28118032,0.00022842067,0.00023232268,0.00013970309,0.0013407795,0.004377418,0.011099435],"genre_scores_gemma":[0.9756216,0.00043675516,0.020915516,0.00009676395,0.000058509013,0.000056612207,0.00038587677,0.000034513898,0.0023938543],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998447,0.000025660649,0.000010475547,0.000043138156,0.00004969791,0.000026360976],"domain_scores_gemma":[0.9998217,0.000036597645,0.000035633082,0.00002530984,0.00006546043,0.000015310987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001508078,0.00046283976,0.00039379834,0.0005084397,0.00013444219,0.00025694518,0.00031082705,0.00031810356,0.0010678383],"category_scores_gemma":[0.00058066455,0.000110167384,0.00015335534,0.00029609769,0.00007292809,0.00036148855,0.0002840523,0.00018813687,0.00051774574],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010395818,0.0002789471,0.12542829,0.0005002521,0.00017514457,0.00060461287,0.00031941358,0.0063599925,0.13690656,0.0005406555,0.006504058,0.7213425],"study_design_scores_gemma":[0.00015323423,0.0011152787,0.43153465,0.00016023962,0.00053172454,0.00452702,0.0006542663,0.36676836,0.17812648,0.0018132497,0.014472643,0.00014288259],"about_ca_topic_score_codex":0.0011718855,"about_ca_topic_score_gemma":0.0027320175,"teacher_disagreement_score":0.0011718855,"about_ca_system_score_codex":0.00010786776,"about_ca_system_score_gemma":0.00011581791,"threshold_uncertainty_score":0.0035722852},"labels":[],"label_agreement":null},{"id":"W4313316246","doi":"10.1109/lra.2022.3233232","title":"Safe and Smooth: Certified Continuous-Time Range-Only Localization","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Solver; Certificate; Mathematical optimization; Range (aeronautics); Smoothness; Computer science; Mathematics; Algorithm","score_opus":0.006363341950708757,"score_gpt":0.18448280808311682,"score_spread":0.17811946613240806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313316246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040892996,0.00008382096,0.99284184,0.00016370347,0.000039555118,0.000032795007,0.00006036505,0.0009999967,0.0016886092],"genre_scores_gemma":[0.5995546,0.00029897338,0.39365944,0.00034058446,0.00012825686,0.00021066373,0.0006025222,0.0007054874,0.0044994922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971059,0.00068023003,0.00013103183,0.00044983532,0.0013092291,0.00032389362],"domain_scores_gemma":[0.9935516,0.002817168,0.00062619103,0.0015162143,0.0012150938,0.00027369725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024531365,0.000878954,0.0010470558,0.00068076776,0.00047667217,0.0017616634,0.0019075413,0.0014618023,0.0039992696],"category_scores_gemma":[0.01681665,0.0003944094,0.00060089765,0.0007390449,0.0018187205,0.0022170783,0.0033986308,0.002269853,0.0018578334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003315253,0.00007181604,0.0015076728,0.0002482186,0.000024650677,0.00033966958,0.00020481872,0.74019605,0.006845005,0.09780031,0.009206218,0.14322396],"study_design_scores_gemma":[0.00004610022,0.00008922804,0.00016217872,0.000023725292,0.000004486057,0.0001203501,0.000031334595,0.970966,0.0036593317,0.021477971,0.0034028825,0.0000164531],"about_ca_topic_score_codex":0.0023266575,"about_ca_topic_score_gemma":0.0023943605,"teacher_disagreement_score":0.0039992696,"about_ca_system_score_codex":0.00079222803,"about_ca_system_score_gemma":0.002614522,"threshold_uncertainty_score":0.013378859},"labels":[],"label_agreement":null},{"id":"W4315783554","doi":"10.1109/icnsc55942.2022.10004055","title":"Indoor PDR Method Based on Foot-Mounted Low-Cost IMMU","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Acceleration; Standard deviation; Computer science; Angular velocity; Gait; Dead reckoning; Position (finance); Inertial measurement unit; Step detection; Angular acceleration; Computer vision; Artificial intelligence; Mathematics; Statistics; Physics; Telecommunications; Global Positioning System","score_opus":0.021410720205156813,"score_gpt":0.2689973605291262,"score_spread":0.2475866403239694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315783554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010982143,0.00028784535,0.9845955,0.000044102835,0.00013426453,0.000041549083,0.000054885186,0.0017463673,0.0021132738],"genre_scores_gemma":[0.5204591,0.0004200182,0.47290972,0.00012666183,0.00012693646,0.00013254238,0.00036007832,0.0001490845,0.0053159064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928564,0.0001181207,0.00004220899,0.00017513469,0.00031534318,0.00006364725],"domain_scores_gemma":[0.9995191,0.00006802658,0.00006686624,0.0000950109,0.00022329489,0.000027775046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036079445,0.0010960542,0.00096712646,0.00086809724,0.00045052404,0.00069826766,0.0011285772,0.00050861517,0.0016947904],"category_scores_gemma":[0.0011775929,0.00038920256,0.00045808448,0.0007148048,0.00025586394,0.0009373727,0.00090088346,0.000636411,0.001359553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043474496,0.00011420708,0.0032147865,0.00044597744,0.000093631665,0.00031473796,0.00020597034,0.053715665,0.056100708,0.0041205026,0.0048584226,0.87638056],"study_design_scores_gemma":[0.00008580636,0.00057169853,0.003079825,0.000039021477,0.00009411681,0.0012286829,0.0001336142,0.90232027,0.073039554,0.0016747937,0.017629858,0.000102723214],"about_ca_topic_score_codex":0.0013508893,"about_ca_topic_score_gemma":0.00143361,"teacher_disagreement_score":0.0016947904,"about_ca_system_score_codex":0.00024378706,"about_ca_system_score_gemma":0.00063497573,"threshold_uncertainty_score":0.0056696534},"labels":[],"label_agreement":null},{"id":"W4315786477","doi":"10.1007/s40860-022-00199-w","title":"Two-stage RFID approach for localizing objects in smart homes based on gradient boosted decision trees with under- and over-sampling","year":2023,"lang":"en","type":"article","venue":"Journal of Reliable Intelligent Environments","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Fonds de Recherche du Québec-Société et Culture","keywords":"Computer science; Home automation; Object (grammar); Identification (biology); Artificial intelligence; Cluster analysis; Task (project management); Machine learning; Sampling (signal processing); Radio-frequency identification; Smart environment; Decision tree; Real-time computing; Data mining; Computer vision; Embedded system; Internet of Things; Telecommunications; Computer security","score_opus":0.02094887574497414,"score_gpt":0.24692047857774047,"score_spread":0.22597160283276632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315786477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026223162,0.00027255426,0.972002,0.00010892446,0.00005657705,0.00004677631,0.00006246528,0.0005800261,0.00064739684],"genre_scores_gemma":[0.675879,0.00020114688,0.3196594,0.00022958948,0.00009797331,0.00012944569,0.00032657926,0.00008867956,0.003388169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909854,0.00022427636,0.00006020598,0.00025013203,0.0002072734,0.00015958589],"domain_scores_gemma":[0.9989096,0.00057941576,0.00006802739,0.00008195823,0.0002815881,0.000079431404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011684096,0.00076592667,0.0019121496,0.00066965964,0.0006088066,0.00082282454,0.0020631999,0.0011624581,0.0018500023],"category_scores_gemma":[0.0018521775,0.0006175905,0.0009927767,0.00083107396,0.00038791294,0.0013190516,0.0011350304,0.0011952838,0.000502805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000797186,0.00044701862,0.0042977943,0.00023095032,0.00020013798,0.00028134277,0.00024008485,0.5591596,0.011015037,0.005159016,0.0033614596,0.4148104],"study_design_scores_gemma":[0.000009929554,0.00005284761,0.0002352312,0.0000032237156,0.000018068278,0.000025029069,0.0000139109015,0.9978782,0.00071698753,0.00087886234,0.00016242945,0.0000051945954],"about_ca_topic_score_codex":0.0060160346,"about_ca_topic_score_gemma":0.008268305,"teacher_disagreement_score":0.0060160346,"about_ca_system_score_codex":0.0005133572,"about_ca_system_score_gemma":0.0012366351,"threshold_uncertainty_score":0.011962056},"labels":[],"label_agreement":null},{"id":"W4317651090","doi":"10.2514/6.2023-2621","title":"Design and Implementation of a Low-Cost Local Beacon System for GPS-Denied Environments","year":2023,"lang":"en","type":"article","venue":"AIAA SCITECH 2023 Forum","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Beacon; GNSS applications; Global Positioning System; Computer science; Real-time computing; GNSS augmentation; Satellite system; Electric beacon; Satellite navigation; Position (finance); Radio navigation; Telecommunications; Air navigation; GPS signals; Dead reckoning; Positioning system; Assisted GPS; Engineering; Node (physics)","score_opus":0.011302277168552506,"score_gpt":0.2406127575512228,"score_spread":0.2293104803826703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317651090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019337323,0.00021553422,0.96368533,0.00028427385,0.00023061936,0.00044166713,0.00012650911,0.0054956535,0.010183185],"genre_scores_gemma":[0.4927348,0.00028196984,0.47935632,0.00027511053,0.0001182619,0.00064367894,0.0004292574,0.0005476229,0.025612999],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995041,0.00006190738,0.000026602076,0.00008952081,0.000239389,0.00007848593],"domain_scores_gemma":[0.9993967,0.000056109733,0.00007048928,0.00007718098,0.00032011291,0.000079420446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005374157,0.000457232,0.00049210404,0.0007306102,0.00050427584,0.00089360785,0.0022611183,0.00082070404,0.007534875],"category_scores_gemma":[0.00082830363,0.00037611244,0.00026829526,0.00033159112,0.00027041393,0.0005827503,0.00070096325,0.0007321063,0.0040442864],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006160609,0.00033195934,0.004973456,0.000492822,0.00007094352,0.00061129616,0.00077714963,0.01496383,0.4919456,0.013014397,0.01577737,0.45642507],"study_design_scores_gemma":[0.0004964298,0.004629658,0.0069007366,0.00018137073,0.0002245829,0.0018420443,0.00038381558,0.27313575,0.4445299,0.0023059153,0.2651724,0.0001974344],"about_ca_topic_score_codex":0.0016559095,"about_ca_topic_score_gemma":0.0012596108,"teacher_disagreement_score":0.007534875,"about_ca_system_score_codex":0.0007180619,"about_ca_system_score_gemma":0.00092437846,"threshold_uncertainty_score":0.025206625},"labels":[],"label_agreement":null},{"id":"W4318692996","doi":"10.1155/2023/4258362","title":"Development and Evaluation of BLE‐Based Room‐Level Localization to Improve Hand Hygiene Performance Estimation","year":2023,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Health Research Foundation","keywords":"Hygiene; Computer science; Beacon; Transmission (telecommunications); Health care; Binary number; Artificial intelligence; Medicine; Real-time computing; Pathology; Mathematics; Telecommunications","score_opus":0.03385854235621909,"score_gpt":0.27779613226477745,"score_spread":0.24393758990855835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318692996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.268486,0.0006014621,0.725359,0.00013015793,0.00011359692,0.0001399895,0.00030343604,0.0024484005,0.002417968],"genre_scores_gemma":[0.8219539,0.00029908514,0.17511801,0.000057923568,0.000023802932,0.000111821304,0.00047362258,0.000037037687,0.0019247886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958116,0.00013113431,0.000022350787,0.00009201531,0.00013559159,0.00003771852],"domain_scores_gemma":[0.9992036,0.00022561742,0.000056750483,0.00006494678,0.00040696893,0.0000421296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067301444,0.00044060563,0.00047380806,0.00071612763,0.00012702741,0.00032667612,0.00057737227,0.0005946389,0.0009971196],"category_scores_gemma":[0.0016713311,0.00014672494,0.0002866638,0.00037723078,0.00011829626,0.0004892486,0.00026595604,0.00021838154,0.0004885638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008022013,0.0005702209,0.019995788,0.00040121985,0.00022279397,0.00016060277,0.00011581493,0.12636378,0.163713,0.0013045965,0.002308242,0.68404174],"study_design_scores_gemma":[0.00003723457,0.0008879786,0.016436562,0.000020486312,0.00006792922,0.00016809486,0.000061173894,0.93623257,0.04400338,0.0002096864,0.0018414846,0.00003357351],"about_ca_topic_score_codex":0.0017703967,"about_ca_topic_score_gemma":0.0019249511,"teacher_disagreement_score":0.0017703967,"about_ca_system_score_codex":0.00019036848,"about_ca_system_score_gemma":0.000258253,"threshold_uncertainty_score":0.0035592914},"labels":[],"label_agreement":null},{"id":"W4319160361","doi":"10.1016/j.inffus.2023.01.025","title":"Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches","year":2023,"lang":"en","type":"article","venue":"Information Fusion","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":233,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Basic and Applied Basic Research Foundation of Guangdong Province","keywords":"Computer science; Sensor fusion; GNSS applications; Kalman filter; Artificial intelligence; Real-time computing; Global Positioning System; Analytics; Simultaneous localization and mapping; Navigation system; Data mining; Robot; Mobile robot; Telecommunications","score_opus":0.07829286667483623,"score_gpt":0.2641208792706943,"score_spread":0.18582801259585804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319160361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004632306,0.008093229,0.98309255,0.0007453975,0.00013833368,0.000048474878,0.000051124975,0.00034157003,0.0028570152],"genre_scores_gemma":[0.5110045,0.024106417,0.46004385,0.0006100587,0.00092778186,0.0001675102,0.00037328718,0.000112051,0.002654583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988483,0.00026780216,0.000087253284,0.0002495048,0.0004746128,0.000072410054],"domain_scores_gemma":[0.9991602,0.0002884352,0.00013694617,0.0001467585,0.00022252777,0.00004515824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014610926,0.0011178864,0.0010159942,0.0018445837,0.0003832222,0.0021328765,0.0012767817,0.0011696847,0.00066395797],"category_scores_gemma":[0.0018205062,0.0004247133,0.0007622176,0.002686664,0.0011531909,0.0038586464,0.0023821613,0.0014679409,0.0003432432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001388848,0.00014076523,0.004118244,0.0010186415,0.0003383781,0.00025985972,0.00056285236,0.18948525,0.014802284,0.1209374,0.0036776566,0.6645198],"study_design_scores_gemma":[0.000021555203,0.00025311852,0.0017573878,0.00034328777,0.00014156639,0.00027380203,0.00040327953,0.83265394,0.014631372,0.116499685,0.032907855,0.00011308043],"about_ca_topic_score_codex":0.0016079244,"about_ca_topic_score_gemma":0.0011145868,"teacher_disagreement_score":0.0021328765,"about_ca_system_score_codex":0.0008559471,"about_ca_system_score_gemma":0.00086624303,"threshold_uncertainty_score":0.0077270865},"labels":[],"label_agreement":null},{"id":"W4319840244","doi":"10.1177/01423312221148787","title":"Fast RSSD multi-target localization in NLOS environments","year":2023,"lang":"en","type":"article","venue":"Transactions of the Institute of Measurement and Control","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Non-line-of-sight propagation; Robustness (evolution); Computer science; Estimator; Artificial neural network; Algorithm; Artificial intelligence; Wireless; Mathematics; Statistics; Telecommunications","score_opus":0.018241476629041967,"score_gpt":0.1947639777307091,"score_spread":0.17652250110166715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319840244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027660366,0.00035579968,0.96875185,0.00010884741,0.000054600845,0.00001659787,0.000046458077,0.0009451881,0.0020603316],"genre_scores_gemma":[0.7517295,0.0003955512,0.24369359,0.000089361114,0.000037142563,0.000055398395,0.00019257433,0.000057836853,0.0037490116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997738,0.000043097516,0.000009743268,0.000069655405,0.000081902734,0.00002180411],"domain_scores_gemma":[0.9997267,0.00010176968,0.000045227174,0.00004721293,0.00006870672,0.000010319871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037055876,0.00046444655,0.00043631278,0.000442024,0.00025454126,0.0003799157,0.00048091638,0.00060664985,0.0008018379],"category_scores_gemma":[0.00092443795,0.00021400687,0.00021560502,0.0005468414,0.00029629507,0.00081414595,0.0008203287,0.0004917195,0.00046359462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003119946,0.0000633728,0.004055225,0.00019837052,0.00004058842,0.00023926453,0.00015348311,0.3190106,0.06907597,0.0055870702,0.0022857415,0.59897834],"study_design_scores_gemma":[0.000010592504,0.000053514046,0.0012086721,0.000009038302,0.00000645567,0.0001700506,0.000020502659,0.98261696,0.012665586,0.0013589532,0.001865797,0.000013833079],"about_ca_topic_score_codex":0.0016570536,"about_ca_topic_score_gemma":0.0021388624,"teacher_disagreement_score":0.0016570536,"about_ca_system_score_codex":0.00028598352,"about_ca_system_score_gemma":0.0003493803,"threshold_uncertainty_score":0.0032948256},"labels":[],"label_agreement":null},{"id":"W4319998037","doi":"10.1109/jsen.2023.3238887","title":"High-Precision and Resilient-to-Interference Ultrawideband Two-Way-Ranging Based on Clock-Less Active Reflector in 65-nm CMOS","year":2023,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"sync; Computer science; Ranging; Electronic engineering; Synchronization (alternating current); Electrical engineering; Real-time computing; Engineering; Channel (broadcasting); Telecommunications","score_opus":0.016207534729840226,"score_gpt":0.25968975726969823,"score_spread":0.24348222253985802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319998037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16458009,0.0062580653,0.7989752,0.0008443644,0.0007609629,0.00040701256,0.00036907996,0.0070840647,0.02072117],"genre_scores_gemma":[0.65482295,0.0015662812,0.33180267,0.00046568393,0.0001528415,0.00014030743,0.00023167545,0.000120513185,0.010696981],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994061,0.000054403125,0.000040560775,0.00013601994,0.00029224125,0.000070697235],"domain_scores_gemma":[0.9996451,0.000044909266,0.00011936125,0.00005387832,0.0001115293,0.000025119914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033719817,0.0005362074,0.00048736794,0.0003275562,0.00026785492,0.0007042989,0.0019880296,0.00075423246,0.0011777226],"category_scores_gemma":[0.0004976082,0.00028863645,0.00035663202,0.0003025433,0.0002426031,0.0011166082,0.00057319825,0.00053455046,0.001084714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027357938,0.0001172045,0.0010864692,0.00050300354,0.00006488785,0.0005502585,0.00022911928,0.0032363967,0.8976122,0.007745091,0.002924323,0.0856575],"study_design_scores_gemma":[0.00013257697,0.001891211,0.0018889615,0.000082134946,0.00014055138,0.003011888,0.00010707432,0.06085325,0.8746574,0.0008745659,0.056215126,0.00014518773],"about_ca_topic_score_codex":0.0008400425,"about_ca_topic_score_gemma":0.001234271,"teacher_disagreement_score":0.0019880296,"about_ca_system_score_codex":0.0005542094,"about_ca_system_score_gemma":0.00053051434,"threshold_uncertainty_score":0.0040210485},"labels":[],"label_agreement":null},{"id":"W4320029579","doi":"10.1109/globecom48099.2022.10001559","title":"JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"","keywords":"Non-line-of-sight propagation; Reinforcement learning; Computer science; Beacon; Context (archaeology); Node (physics); Jump; Internet of Things; Real-time computing; Ultra-wideband; Selection (genetic algorithm); Artificial intelligence; Wireless; Embedded system; Engineering; Telecommunications","score_opus":0.026084999481815887,"score_gpt":0.2615785019724933,"score_spread":0.23549350249067744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018796043,0.00026309074,0.97822964,0.00013963958,0.000083183884,0.000054957807,0.000025603373,0.00072852796,0.0016792296],"genre_scores_gemma":[0.9442807,0.00013141782,0.053010732,0.00018410628,0.000057342477,0.00012567823,0.000060133618,0.000050246006,0.0020997373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956375,0.00012244411,0.00001905544,0.00010757092,0.000105371764,0.00008180279],"domain_scores_gemma":[0.99881315,0.00065935455,0.00016241985,0.00007081023,0.00017427932,0.00012003119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009677763,0.00072871643,0.0008694871,0.0003076545,0.0003429294,0.00053743494,0.0018948357,0.0008052932,0.0014119388],"category_scores_gemma":[0.0029440497,0.00027686628,0.00033846294,0.00026092003,0.00090930425,0.0006230619,0.0010742489,0.0011358535,0.0002797481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017484317,0.00009175567,0.0011900179,0.00006813983,0.00004052952,0.00011469447,0.00008757801,0.9340475,0.0030074266,0.004730512,0.0011631345,0.05528392],"study_design_scores_gemma":[0.0000115229195,0.000045833804,0.00006872121,0.000003003317,0.0000051475577,0.00001378437,0.000005920086,0.99845386,0.00036935828,0.0008451681,0.00017326836,0.0000043920395],"about_ca_topic_score_codex":0.0028132016,"about_ca_topic_score_gemma":0.0032446077,"teacher_disagreement_score":0.0028132016,"about_ca_system_score_codex":0.00052973593,"about_ca_system_score_gemma":0.0009514467,"threshold_uncertainty_score":0.005593717},"labels":[],"label_agreement":null},{"id":"W4321780031","doi":"10.1109/ojsp.2023.3249121","title":"Joint Localization and Environment Sensing of Rigid Body With 5G Millimeter Wave MIMO","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); Western University","funders":"","keywords":"Rigid body; Compressed sensing; Computer science; Specular reflection; Reflection (computer programming); Computer vision; SIGNAL (programming language); Algorithm; Physics; Optics","score_opus":0.023288258738294213,"score_gpt":0.22266690962597305,"score_spread":0.19937865088767884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321780031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05468581,0.00039751048,0.9411973,0.0001976914,0.00007714643,0.000023448185,0.000063630425,0.0002872566,0.0030702013],"genre_scores_gemma":[0.8981657,0.00050452165,0.09850482,0.00015604198,0.00007785795,0.00006541769,0.00013114749,0.000014088433,0.0023805215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996209,0.00009013079,0.000015529118,0.000083582956,0.00013540793,0.000054428776],"domain_scores_gemma":[0.99978465,0.00005652366,0.000053552383,0.00004412398,0.000047213503,0.000013952266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024822017,0.0006596209,0.0004799931,0.00022393077,0.00021852461,0.0004322098,0.0004907404,0.00046089228,0.00053554005],"category_scores_gemma":[0.00077565113,0.00020414319,0.0003564208,0.0003394304,0.00034253567,0.00059865793,0.0008490292,0.00045833518,0.000253769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050425966,0.000107066764,0.0044057667,0.0003292314,0.00013853911,0.0007119012,0.00039277851,0.4703803,0.15977089,0.02186073,0.0029176397,0.33848086],"study_design_scores_gemma":[0.000017144332,0.00018024762,0.0013586245,0.000012385916,0.000022145447,0.00018733299,0.000049610284,0.98182964,0.01244414,0.0025094356,0.0013675577,0.000021693952],"about_ca_topic_score_codex":0.0014893749,"about_ca_topic_score_gemma":0.001532636,"teacher_disagreement_score":0.0014893749,"about_ca_system_score_codex":0.00016740116,"about_ca_system_score_gemma":0.0003075589,"threshold_uncertainty_score":0.0029614568},"labels":[],"label_agreement":null},{"id":"W4323519381","doi":"10.1109/jiot.2023.3253660","title":"Adaptive Path Loss Model for BLE Indoor Positioning System","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Samsung Eletrônica da Amazônia","keywords":"Computer science; Global Positioning System; Real-time computing; Path loss; Indoor positioning system; Bluetooth; Hybrid positioning system; Positioning system; Position (finance); GPS signals; SIGNAL (programming language); Simulation; Point (geometry); Assisted GPS; Wireless; Telecommunications; Accelerometer","score_opus":0.01770032503500279,"score_gpt":0.22812047063399954,"score_spread":0.21042014559899674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323519381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013222325,0.00046388857,0.9808622,0.0002946384,0.00008487212,0.000055545057,0.00027149133,0.000956389,0.0037887527],"genre_scores_gemma":[0.91439724,0.0017718351,0.06572833,0.00026244164,0.00010930215,0.00040228502,0.0010350252,0.00018042116,0.016113015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994093,0.00013740158,0.000024249492,0.00015182306,0.00019605285,0.00008102142],"domain_scores_gemma":[0.9995522,0.00014412559,0.00007457169,0.000042053718,0.00017539036,0.000011656889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047759997,0.0011092648,0.00063859957,0.0007342985,0.0003667874,0.0008310442,0.0015203528,0.0011752723,0.0023412204],"category_scores_gemma":[0.0015808089,0.00038470497,0.0007026597,0.00094499084,0.000444147,0.0013354506,0.0006273704,0.0011810885,0.0013881775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006769525,0.000019924471,0.00091179315,0.000067203546,0.000020138526,0.00011567334,0.000048737536,0.9663176,0.0034424022,0.004362495,0.0012774954,0.023348873],"study_design_scores_gemma":[0.000004660116,0.00002411388,0.0003243669,0.0000046085647,0.000008806725,0.00005371395,0.0000074675745,0.9971801,0.0003794469,0.0012249455,0.0007790409,0.0000088018405],"about_ca_topic_score_codex":0.0065764817,"about_ca_topic_score_gemma":0.004387212,"teacher_disagreement_score":0.0065764817,"about_ca_system_score_codex":0.000942885,"about_ca_system_score_gemma":0.00051551696,"threshold_uncertainty_score":0.013076425},"labels":[],"label_agreement":null},{"id":"W4323697349","doi":"10.48550/arxiv.2303.03739","title":"Path Planning Under Uncertainty to Localize mmWave Sources","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Standards and Technology; York University; National Science Foundation","keywords":"Computer science; Estimator; Solver; Motion planning; Kalman filter; SIGNAL (programming language); Mathematical optimization; Real-time computing; Wireless; Artificial intelligence; Computer vision; Robot; Telecommunications; Mathematics","score_opus":0.08406414356367173,"score_gpt":0.19384882700617448,"score_spread":0.10978468344250275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323697349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007275893,0.00012101413,0.99185234,0.00007799725,0.000012541863,0.0000119902625,0.000024365285,0.00015346233,0.00047042285],"genre_scores_gemma":[0.58930725,0.00048248834,0.40678164,0.00010240784,0.00005197637,0.00017468637,0.00024884977,0.00017533162,0.0026753931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971896,0.00008297592,0.000011955001,0.00006673098,0.00008684581,0.000032507847],"domain_scores_gemma":[0.99909675,0.0005905048,0.00010373338,0.000057964833,0.00012366008,0.00002738279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005172939,0.0006476519,0.00055122125,0.0004110221,0.0003355503,0.00043768142,0.00063048943,0.00062991166,0.001415481],"category_scores_gemma":[0.002824221,0.00045556642,0.00040259093,0.00048296596,0.00060798496,0.0011327524,0.00095362443,0.0008839618,0.00025970885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024538787,0.00000909856,0.00020908735,0.000030496758,0.000011530751,0.000029814173,0.000052976946,0.9773102,0.0010079558,0.0044806856,0.00036537537,0.01646832],"study_design_scores_gemma":[0.0000047744347,0.000013393094,0.000059076818,0.0000034520199,0.000002712707,0.0000086743485,0.000009273728,0.9945945,0.0005416427,0.0043586805,0.0004006465,0.0000031376794],"about_ca_topic_score_codex":0.0055511068,"about_ca_topic_score_gemma":0.0037948,"teacher_disagreement_score":0.0055511068,"about_ca_system_score_codex":0.0005235453,"about_ca_system_score_gemma":0.0008195654,"threshold_uncertainty_score":0.011037588},"labels":[],"label_agreement":null},{"id":"W4323697688","doi":"10.48550/arxiv.2303.04192","title":"5G Multi-BS Positioning: A Decentralized Fusion Scheme","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Extended Kalman filter; Linearization; Kalman filter; Computer science; Sensor fusion; Base station; Control theory (sociology); Filter (signal processing); Real-time computing; Range (aeronautics); Engineering; Nonlinear system; Telecommunications; Artificial intelligence; Computer vision; Control (management)","score_opus":0.09183173661907051,"score_gpt":0.19144971845128672,"score_spread":0.09961798183221621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323697688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022947846,0.00024552643,0.9731541,0.00014486448,0.00008208747,0.00004752408,0.00006147072,0.00073949783,0.0025770378],"genre_scores_gemma":[0.87197584,0.00021245677,0.124867,0.00009742798,0.00010208457,0.00007374182,0.00018700813,0.000029838178,0.0024546382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907756,0.00017859363,0.00004476971,0.00025229683,0.0003396155,0.00010713773],"domain_scores_gemma":[0.9994435,0.00009172404,0.00009053999,0.00011426056,0.00022413906,0.000035908004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008028188,0.00067351235,0.0010002762,0.0004649127,0.0006671409,0.00065587764,0.0010954608,0.0009152544,0.001053054],"category_scores_gemma":[0.00142764,0.0002898147,0.00053441414,0.0006509117,0.00038212503,0.0011301704,0.0013637792,0.0007497,0.00052534515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044009063,0.000098056364,0.0020941088,0.00012296045,0.000097456366,0.0003017018,0.00034591946,0.6230074,0.04136105,0.0131855225,0.0036437018,0.31530192],"study_design_scores_gemma":[0.000035735968,0.00012797542,0.00073306536,0.000008819581,0.00002485781,0.00011029319,0.000035715795,0.98840094,0.004826132,0.002908359,0.0027672483,0.000020889222],"about_ca_topic_score_codex":0.005548499,"about_ca_topic_score_gemma":0.0048441957,"teacher_disagreement_score":0.005548499,"about_ca_system_score_codex":0.00065053185,"about_ca_system_score_gemma":0.0010730083,"threshold_uncertainty_score":0.0110324025},"labels":[],"label_agreement":null},{"id":"W4327927743","doi":"10.1109/imas55807.2023.10066896","title":"Experimental Investigation on the Performance of Angle-of-Arrival-based Asset Localization in a Warehouse","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Angle of arrival; Scalability; Computer science; Implementation; Asset (computer security); Stack (abstract data type); Bluetooth Low Energy; Real-time computing; Tracking (education); Bluetooth; Database; Telecommunications; Computer security; Wireless; Operating system; Software engineering","score_opus":0.027216222706954587,"score_gpt":0.22546102293547926,"score_spread":0.19824480022852467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327927743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9047695,0.0006589154,0.08207075,0.00028487472,0.00038642847,0.00011267511,0.00088656275,0.0023866533,0.008443686],"genre_scores_gemma":[0.98533964,0.00020111182,0.012048316,0.00006255999,0.00001680159,0.000047504072,0.0006320152,0.000059420938,0.0015926879],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991295,0.0001565287,0.00006392001,0.00018744182,0.00027839348,0.00018425199],"domain_scores_gemma":[0.9973289,0.0008670658,0.00022026277,0.0003665989,0.0010398173,0.00017728309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086868176,0.00082426536,0.00065747724,0.000746561,0.0004828249,0.0006724111,0.0011125549,0.0010194052,0.0030219737],"category_scores_gemma":[0.0034660283,0.00020684864,0.00028805117,0.0007788846,0.0004873535,0.0008590668,0.0009245213,0.00073589286,0.0013034665],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006338122,0.0026138683,0.020324491,0.001636377,0.00027330127,0.0020362032,0.001040659,0.19671738,0.5648364,0.003901277,0.008221898,0.19206004],"study_design_scores_gemma":[0.00023715445,0.0072763064,0.021921437,0.00013745554,0.00015801359,0.0011975357,0.0011236029,0.60198575,0.35768268,0.0017001142,0.006446293,0.00013365522],"about_ca_topic_score_codex":0.0017890314,"about_ca_topic_score_gemma":0.0015078088,"teacher_disagreement_score":0.0030219737,"about_ca_system_score_codex":0.00033700938,"about_ca_system_score_gemma":0.00041469745,"threshold_uncertainty_score":0.010109544},"labels":[],"label_agreement":null},{"id":"W4361856134","doi":"10.1109/tmc.2023.3263229","title":"Pa-Count: Passenger Counting in Vehicles Using Wi-Fi Signals","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Algorithm; Artificial intelligence","score_opus":0.01924642407147712,"score_gpt":0.251195030620437,"score_spread":0.2319486065489599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361856134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13569455,0.0006439458,0.8325336,0.00031228745,0.00043521155,0.0002548218,0.0016470782,0.01662017,0.011858369],"genre_scores_gemma":[0.84279054,0.00048141385,0.14590628,0.00028262768,0.0002255609,0.00023912774,0.0020022132,0.00017914265,0.007893083],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995528,0.0000613318,0.000021314932,0.00012925922,0.00016048722,0.00007475901],"domain_scores_gemma":[0.9995565,0.00008421058,0.00008215024,0.000078783414,0.0001586783,0.00003963747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031940817,0.0014862099,0.0005522157,0.0011319525,0.000423848,0.00073487044,0.0013395988,0.000521683,0.0017702837],"category_scores_gemma":[0.0016315565,0.00022302668,0.00030216773,0.0008198567,0.00031449195,0.00091377913,0.00081207213,0.0006831484,0.0014478404],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089419534,0.0003630392,0.03248132,0.00037050183,0.00019590286,0.00066174055,0.00030712286,0.082634784,0.05363676,0.0077075046,0.019587658,0.8011596],"study_design_scores_gemma":[0.000040970688,0.0002975573,0.009524141,0.00003770065,0.00007149703,0.00064073416,0.00014961665,0.9257488,0.04679137,0.0032447672,0.013360161,0.00009269925],"about_ca_topic_score_codex":0.0056771953,"about_ca_topic_score_gemma":0.0062688864,"teacher_disagreement_score":0.0056771953,"about_ca_system_score_codex":0.0003959885,"about_ca_system_score_gemma":0.00069653534,"threshold_uncertainty_score":0.011288285},"labels":[],"label_agreement":null},{"id":"W4361986876","doi":"10.1109/jiot.2023.3263476","title":"3-D Indoor Positioning Based on Passive Radio Frequency Signal Strength Distribution","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Air Force Office of Scientific Research","keywords":"RSS; Computer science; Transmitter; Radio frequency; Mean squared error; Fading; Real-time computing; Artificial intelligence; Telecommunications; Decoding methods; Statistics; Mathematics","score_opus":0.00765465936988827,"score_gpt":0.21318529348779838,"score_spread":0.20553063411791012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361986876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033881586,0.00020725054,0.9620838,0.000068627174,0.000046249934,0.000021525413,0.00018214653,0.0014930153,0.002015792],"genre_scores_gemma":[0.80543274,0.0006142565,0.19100368,0.00010338409,0.000054429314,0.00009017269,0.00068197004,0.00010379754,0.0019155529],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994987,0.00007875108,0.000019966059,0.00012522073,0.00023635544,0.00004087216],"domain_scores_gemma":[0.99965346,0.00007311876,0.00006136635,0.0000574893,0.00013900588,0.000015578871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026709394,0.00083586684,0.00058639527,0.0010666411,0.00021713017,0.00059949275,0.0006585333,0.00046672966,0.0007588584],"category_scores_gemma":[0.0010093042,0.0003252792,0.0006557082,0.0014434067,0.00038521292,0.00074702356,0.00073114375,0.0003930709,0.00081435806],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035840806,0.00007502779,0.01770281,0.00038527537,0.00014114744,0.00053660764,0.00031991085,0.37178972,0.09662179,0.0059404816,0.003011773,0.503117],"study_design_scores_gemma":[0.000031272062,0.0001751169,0.013251158,0.000026466298,0.00007104465,0.000812119,0.000102728976,0.94681185,0.03003802,0.0025670347,0.0060306136,0.00008264336],"about_ca_topic_score_codex":0.0021210548,"about_ca_topic_score_gemma":0.001880304,"teacher_disagreement_score":0.0021210548,"about_ca_system_score_codex":0.0002837002,"about_ca_system_score_gemma":0.00035162058,"threshold_uncertainty_score":0.0042173862},"labels":[],"label_agreement":null},{"id":"W4362570904","doi":"10.1007/978-3-031-26963-9_5","title":"Spectrum Constrained Efficient Transmission for Industrial Network Systems","year":2023,"lang":"en","type":"book-chapter","venue":"Wireless networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Transmission (telecommunications); Real-time computing; Wireless sensor network; Data transmission; Field (mathematics); Quality of service; Computer network; Telecommunications","score_opus":0.021934868595654276,"score_gpt":0.20103580860896217,"score_spread":0.1791009400133079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362570904","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005885533,0.01582876,0.76356363,0.0006840379,0.00062971015,0.00004671796,0.00018169955,0.0006511077,0.21252878],"genre_scores_gemma":[0.2966801,0.03879255,0.16859403,0.0005893079,0.0012318379,0.00020519718,0.00055921887,0.00055762817,0.4927902],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998839,0.000021783333,0.00000314665,0.000017710705,0.000061044135,0.000012489543],"domain_scores_gemma":[0.9998816,0.00007038669,0.000007030742,0.000015267082,0.00002228836,0.0000033945605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015397705,0.0006322369,0.00043703977,0.00027474551,0.00027244096,0.0009036888,0.00069714425,0.0005098172,0.014101589],"category_scores_gemma":[0.00033705676,0.00025472723,0.0001403548,0.0007240101,0.00029797154,0.0007628,0.0004583511,0.0009184775,0.0028194485],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006784361,0.00008367484,0.00013976662,0.00047981433,0.000032537413,0.00014259545,0.00011619438,0.17324388,0.017295888,0.20479679,0.06937507,0.53422594],"study_design_scores_gemma":[0.00001618627,0.00013716104,0.0007805485,0.00027787357,0.000035652218,0.000512325,0.00013455827,0.54118377,0.008567833,0.16303205,0.28528205,0.000040023293],"about_ca_topic_score_codex":0.0008266128,"about_ca_topic_score_gemma":0.0015870784,"teacher_disagreement_score":0.014101589,"about_ca_system_score_codex":0.00049026,"about_ca_system_score_gemma":0.00023856218,"threshold_uncertainty_score":0.047174513},"labels":[],"label_agreement":null},{"id":"W4362600541","doi":"10.18280/i2m.220101","title":"Optimizing the Average Hop-Count and Node Distance Using an Adjusted DV-Hop Algorithm with a Distance Error Rate","year":2023,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hop (telecommunications); Algorithm; Computer science; Word error rate; Mathematics; Statistics; Telecommunications; Artificial intelligence","score_opus":0.025969798437790546,"score_gpt":0.26192005327068374,"score_spread":0.2359502548328932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362600541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028105186,0.00030272498,0.9697169,0.00007191061,0.000059108297,0.000058171987,0.000025909407,0.0005855526,0.0010745785],"genre_scores_gemma":[0.38982123,0.00027695223,0.60764235,0.000051584826,0.000035821533,0.00015852194,0.00013364707,0.0001415655,0.001738293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906963,0.00024687385,0.0000732252,0.00021130583,0.00032697112,0.000072071874],"domain_scores_gemma":[0.99877733,0.0005035667,0.00011738162,0.00011196339,0.0004428601,0.000046826477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001029412,0.000888548,0.0007496183,0.00094271346,0.00038338042,0.0007315386,0.0013478756,0.0006039997,0.00059100776],"category_scores_gemma":[0.0041863485,0.00028392422,0.0004375921,0.0009004231,0.00028130872,0.00091506087,0.00058607373,0.00054019794,0.0002580972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020584674,0.00019328401,0.0028585584,0.00015799803,0.00011627844,0.000089342306,0.00011316307,0.6748744,0.02545273,0.00470155,0.0013907204,0.2898461],"study_design_scores_gemma":[0.0000273853,0.00012876421,0.00048848265,0.0000072646635,0.000026803218,0.000079530975,0.00002240217,0.9919668,0.0053185797,0.0006867985,0.0012300719,0.000017108772],"about_ca_topic_score_codex":0.0034401417,"about_ca_topic_score_gemma":0.003283044,"teacher_disagreement_score":0.0034401417,"about_ca_system_score_codex":0.0005063793,"about_ca_system_score_gemma":0.0011116401,"threshold_uncertainty_score":0.006840229},"labels":[],"label_agreement":null},{"id":"W4362653484","doi":"10.1109/jrfid.2023.3264196","title":"A Positioning System in an Urban Vertical Heterogeneous Network (VHetNet)","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Radio Frequency Identification","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Huawei Technologies","keywords":"Pseudorange; GNSS applications; Global Positioning System; Receiver autonomous integrity monitoring; Dilution of precision; Computer science; Positioning system; Precise Point Positioning; Satellite; Real-time computing; Remote sensing; Geography; Telecommunications; Engineering; Aerospace engineering","score_opus":0.012261282984775359,"score_gpt":0.23191759327950368,"score_spread":0.21965631029472832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362653484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44602883,0.00029563467,0.53759867,0.00030165652,0.0002786649,0.00015097886,0.00054054364,0.0016732336,0.013131856],"genre_scores_gemma":[0.95135814,0.00011344772,0.04317199,0.00006861647,0.000029135115,0.000043367458,0.00047562548,0.0000132151445,0.0047265245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999869,0.000025480183,0.0000053241342,0.000035586676,0.000033987493,0.00003058841],"domain_scores_gemma":[0.99985003,0.000019197902,0.000024926925,0.000027229209,0.00005499958,0.00002367746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024996238,0.00023995392,0.00017254228,0.00024928743,0.0002981187,0.0004421148,0.00036789436,0.00026906838,0.0010518979],"category_scores_gemma":[0.00028619,0.00006793298,0.00011724669,0.00034318204,0.0001742859,0.00037988203,0.000563714,0.00015951507,0.0002606756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092622644,0.00017124572,0.029034091,0.00018550729,0.00014524464,0.0015224412,0.00020600346,0.3872831,0.16400129,0.026371963,0.010065265,0.38008767],"study_design_scores_gemma":[0.00007589182,0.00075581926,0.010341067,0.000015450416,0.00007025877,0.0004700146,0.0001469422,0.93462926,0.03302785,0.0020560566,0.018380273,0.000031122127],"about_ca_topic_score_codex":0.005821212,"about_ca_topic_score_gemma":0.009982058,"teacher_disagreement_score":0.005821212,"about_ca_system_score_codex":0.00046377184,"about_ca_system_score_gemma":0.00047896634,"threshold_uncertainty_score":0.011574626},"labels":[],"label_agreement":null},{"id":"W4365515527","doi":"10.3390/rs15082062","title":"Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Mean squared error; Constraint (computer-aided design); Computer science; Estimator; Position (finance); Algorithm; Global Positioning System; Simulation; Mathematics; Statistics; Telecommunications","score_opus":0.025714382601202943,"score_gpt":0.26060118725518805,"score_spread":0.2348868046539851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365515527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07081478,0.0005348028,0.92223996,0.00025779082,0.00015998905,0.000056002962,0.00055585324,0.0009991861,0.0043816436],"genre_scores_gemma":[0.77639866,0.00074314687,0.21751054,0.00014482973,0.00010727018,0.00009459556,0.0016307788,0.00015303837,0.0032170806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916506,0.00015106332,0.000056858426,0.00016010278,0.00036246298,0.00010451813],"domain_scores_gemma":[0.9988918,0.00038999488,0.00010513756,0.00016830987,0.0004162993,0.000028521043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073912163,0.0009345355,0.0006441449,0.00071483455,0.0003256596,0.0007236135,0.0007601601,0.00058345695,0.0019448078],"category_scores_gemma":[0.004084249,0.00025631953,0.0005510114,0.00097025756,0.00034876098,0.0012278382,0.0010507025,0.00078052847,0.00074265647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003966252,0.00010022136,0.011426178,0.0005775537,0.00010673046,0.00039502556,0.00020932904,0.5229737,0.0801708,0.008710037,0.005639431,0.3692943],"study_design_scores_gemma":[0.000019507175,0.00009765204,0.0046006325,0.000028710741,0.000023409926,0.000116771014,0.000054679444,0.973403,0.016256299,0.001161417,0.004198268,0.000039633556],"about_ca_topic_score_codex":0.013632039,"about_ca_topic_score_gemma":0.012166055,"teacher_disagreement_score":0.013632039,"about_ca_system_score_codex":0.0004657413,"about_ca_system_score_gemma":0.000884536,"threshold_uncertainty_score":0.027105391},"labels":[],"label_agreement":null},{"id":"W4366978519","doi":"10.1109/inertial56358.2023.10103968","title":"Optimization of Localization Error in Multi-Agent Systems through Cooperative Positioning: Autonomous Navigation in Partially Denied GNSS Environments","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"GNSS applications; Computer science; Real-time computing; Global Positioning System; Computer vision; Artificial intelligence; Telecommunications","score_opus":0.026834561526458422,"score_gpt":0.2579323585089236,"score_spread":0.2310977969824652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366978519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028931612,0.00024434165,0.9694016,0.00012419709,0.000025768235,0.0000133351505,0.000010024332,0.0000807143,0.0011683671],"genre_scores_gemma":[0.952538,0.00025208457,0.045961164,0.00003277792,0.000029281124,0.000043118274,0.00002115323,0.000020696407,0.0011017309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953055,0.00016751295,0.000018501587,0.00010308419,0.00013452677,0.000045921228],"domain_scores_gemma":[0.99930465,0.00030245012,0.00015646643,0.00006190814,0.0001454795,0.000029057133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006041276,0.000776168,0.0005633652,0.00032711244,0.0003614484,0.000597271,0.0007087931,0.00067666784,0.00034577693],"category_scores_gemma":[0.0020535404,0.0002658067,0.00034510726,0.0003937026,0.0007092394,0.00077681395,0.00094147027,0.000415413,0.00009791156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025903366,0.000009610881,0.0004577095,0.000029106055,0.000022553531,0.00004534171,0.00004936547,0.9801782,0.00285139,0.0038478367,0.0001433626,0.012339621],"study_design_scores_gemma":[0.000005152613,0.000046250563,0.00015191842,0.0000036050744,0.0000071862496,0.000013983592,0.00001124043,0.9979011,0.00052970805,0.0010512293,0.0002750252,0.00000369915],"about_ca_topic_score_codex":0.0039394055,"about_ca_topic_score_gemma":0.0022134727,"teacher_disagreement_score":0.0039394055,"about_ca_system_score_codex":0.00041704037,"about_ca_system_score_gemma":0.0005201108,"threshold_uncertainty_score":0.007833004},"labels":[],"label_agreement":null},{"id":"W4366992710","doi":"10.1007/s11803-023-2175-y","title":"Graph-based structural joint pose estimation in non-line-of-sight conditions","year":2023,"lang":"en","type":"article","venue":"Earthquake Engineering and Engineering Vibration","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Odometry; Landmark; Computer vision; Computer science; Pose; Robot; Extended Kalman filter; Nonlinear system; Kalman filter; Algorithm; Mobile robot","score_opus":0.007565686362933085,"score_gpt":0.20121375881174944,"score_spread":0.19364807244881635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366992710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09697812,0.00030300344,0.8990596,0.00009256262,0.000074519994,0.00003556078,0.00044299872,0.0013939594,0.0016196625],"genre_scores_gemma":[0.899216,0.00030551842,0.09551317,0.00006623601,0.00005529014,0.00003768382,0.001826921,0.00013702306,0.0028421732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996804,0.00006141139,0.000011758297,0.00010406386,0.0000863916,0.0000559253],"domain_scores_gemma":[0.99958056,0.00019321623,0.000056376146,0.000052778672,0.000085733634,0.000031394215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020414298,0.0010127602,0.0008778249,0.00078973995,0.00022492233,0.00046410595,0.00087016134,0.0007865926,0.0018474347],"category_scores_gemma":[0.0011086488,0.00034384272,0.0005411583,0.0009521618,0.00033297073,0.00073878927,0.0006302482,0.0006007986,0.0011759722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005163738,0.00018457038,0.0043748617,0.00016408908,0.00013219414,0.00023805488,0.00008234189,0.7008076,0.024544882,0.0015018823,0.002771595,0.26468152],"study_design_scores_gemma":[0.000005605672,0.000039479793,0.0016305938,0.0000039617366,0.0000100723755,0.000034099663,0.000023641307,0.995701,0.0014513311,0.0008413406,0.00025248437,0.0000064197798],"about_ca_topic_score_codex":0.012337994,"about_ca_topic_score_gemma":0.018156825,"teacher_disagreement_score":0.012337994,"about_ca_system_score_codex":0.00019901435,"about_ca_system_score_gemma":0.00052973616,"threshold_uncertainty_score":0.024532318},"labels":[],"label_agreement":null},{"id":"W4367311454","doi":"10.1016/j.comcom.2023.04.021","title":"Improved differential evolution for RSSD-based localization in Gaussian mixture noise","year":2023,"lang":"en","type":"article","venue":"Computer Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"China Scholarship Council; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Computer science; Gaussian; Gaussian noise; Algorithm; Upper and lower bounds; Chaotic; Mathematical optimization; Noise (video); Cramér–Rao bound; Artificial intelligence; Mathematics; Estimation theory; Physics","score_opus":0.016266000578998935,"score_gpt":0.24264690238940725,"score_spread":0.22638090181040832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367311454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012172818,0.0001264978,0.9867148,0.000044661938,0.00003098641,0.000006542526,0.00002101081,0.00013671636,0.0007458917],"genre_scores_gemma":[0.6320653,0.00038920413,0.36006796,0.00009820643,0.000060943807,0.000055489654,0.00026754508,0.00014185137,0.0068534724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994585,0.00012519408,0.000022987428,0.00009574991,0.00025082089,0.000046753335],"domain_scores_gemma":[0.9995141,0.00018965795,0.00003396782,0.00006782374,0.00017365578,0.000020805599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006090713,0.0004595481,0.0005913749,0.0004333098,0.0002632092,0.00054103683,0.00098619,0.0006167005,0.0011964132],"category_scores_gemma":[0.002251119,0.00029713096,0.0005076714,0.00081402296,0.000422721,0.0009346227,0.0012602072,0.00060394796,0.00049988297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002695825,0.00007290771,0.0025159998,0.00013463163,0.000083078325,0.00016213706,0.0002983554,0.6920522,0.033845406,0.025219714,0.0015837589,0.24376222],"study_design_scores_gemma":[0.0000021255821,0.000011157591,0.00015114436,0.00000229475,0.0000041271987,0.000027008002,0.0000039357938,0.9972681,0.0013941098,0.0007829713,0.00034919084,0.0000038705903],"about_ca_topic_score_codex":0.004048147,"about_ca_topic_score_gemma":0.0034733682,"teacher_disagreement_score":0.004048147,"about_ca_system_score_codex":0.00063262234,"about_ca_system_score_gemma":0.0006073479,"threshold_uncertainty_score":0.00804913},"labels":[],"label_agreement":null},{"id":"W4375868799","doi":"10.1109/icassp49357.2023.10096761","title":"A Robust Kalman Filter Based Approach for Indoor Robot Positionning with Multi-Path Contaminated UWB Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Kalman filter; Outlier; Robot; Extended Kalman filter; Mobile robot; Path (computing); Real-time computing; Filter (signal processing); Computer vision; Simultaneous localization and mapping; Artificial intelligence","score_opus":0.0741786108750947,"score_gpt":0.24790997449696753,"score_spread":0.17373136362187283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375868799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028245742,0.00008713171,0.9962929,0.00002000548,0.000028389086,0.000008383015,0.000020046638,0.00039925086,0.00031933095],"genre_scores_gemma":[0.35445789,0.000469492,0.63639367,0.00012168722,0.00010345529,0.00011860043,0.00047426013,0.0002097992,0.007651233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994611,0.000073105395,0.00003512204,0.00017765161,0.00019487391,0.0000580206],"domain_scores_gemma":[0.999511,0.00016214412,0.000058027814,0.000080209924,0.00016982028,0.000018841316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055276207,0.0007397312,0.0011866258,0.0005942783,0.00044657305,0.00083161617,0.0010865218,0.0009984043,0.0020082567],"category_scores_gemma":[0.0017586958,0.0007218241,0.00086900505,0.0008383676,0.00040860093,0.00097524637,0.00092614745,0.0011128072,0.0012964908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003201144,0.00008649369,0.0012218622,0.00021985956,0.00019785672,0.0002306351,0.00020759643,0.42881304,0.036343448,0.005031071,0.0024919473,0.524836],"study_design_scores_gemma":[0.000011499318,0.000051294588,0.0005380941,0.0000115759,0.00003313053,0.00007160845,0.00002566343,0.99049604,0.006038651,0.0013221418,0.0013781614,0.000022251636],"about_ca_topic_score_codex":0.012196368,"about_ca_topic_score_gemma":0.014104082,"teacher_disagreement_score":0.012196368,"about_ca_system_score_codex":0.00042565467,"about_ca_system_score_gemma":0.0011100586,"threshold_uncertainty_score":0.024250746},"labels":[],"label_agreement":null},{"id":"W4376480569","doi":"10.1109/wcnc55385.2023.10119036","title":"ALSensing: Human Activity Recognition using WiFi based on Active Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Simon Fraser University","funders":"HORIZON EUROPE Health","keywords":"Computer science; Baseline (sea); Artificial intelligence; Activity recognition; Deep learning; Machine learning; Training (meteorology); Training set; Active learning (machine learning)","score_opus":0.04967427940833445,"score_gpt":0.269709875892113,"score_spread":0.22003559648377852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376480569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075269476,0.001003413,0.8998956,0.00030417476,0.00032904345,0.00020270658,0.0011408854,0.015682396,0.0061722994],"genre_scores_gemma":[0.8059402,0.0006245624,0.18333241,0.0006311232,0.00020777628,0.0002546644,0.0020533712,0.00017257882,0.006783196],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954295,0.00006751258,0.000023456661,0.00014635689,0.00015839326,0.00006140329],"domain_scores_gemma":[0.9996866,0.00010201649,0.000048109145,0.000055605917,0.00007555246,0.00003207426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003828065,0.00085184723,0.0007362356,0.0010748002,0.00025182922,0.0005357976,0.0011562257,0.0005963325,0.00196979],"category_scores_gemma":[0.0011013938,0.00025900017,0.00037189687,0.00088152004,0.00028556693,0.00090862514,0.0010172966,0.0006414509,0.001137131],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005465867,0.00042683422,0.012252271,0.0002446775,0.00018129454,0.0002797171,0.0001405511,0.022110948,0.031085452,0.0013608423,0.009616647,0.9217541],"study_design_scores_gemma":[0.00009933629,0.00053477153,0.014291201,0.00004714918,0.00009296101,0.0009884253,0.00010255576,0.9242938,0.044763908,0.004668773,0.010027981,0.000089181216],"about_ca_topic_score_codex":0.002079564,"about_ca_topic_score_gemma":0.0032900681,"teacher_disagreement_score":0.002079564,"about_ca_system_score_codex":0.0002385542,"about_ca_system_score_gemma":0.00028513736,"threshold_uncertainty_score":0.0065895915},"labels":[],"label_agreement":null},{"id":"W4376607544","doi":"10.1109/jsait.2023.3276296","title":"Active Sensing for Two-Sided Beam Alignment and Reflection Design Using Ping-Pong Pilots","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Information Theory","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Huawei Technologies","keywords":"Ping pong; Reflection (computer programming); Optics; Beam (structure); Physics; Computer science; Engineering; Artificial intelligence","score_opus":0.026215546889704275,"score_gpt":0.26892948282244683,"score_spread":0.24271393593274254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376607544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08480982,0.00015174272,0.9113308,0.00012732875,0.0000665663,0.000055592824,0.000026745076,0.00054275896,0.0028887314],"genre_scores_gemma":[0.8233266,0.00013328319,0.17467238,0.00009126961,0.000043412823,0.000066181754,0.000033659955,0.000041906158,0.0015913817],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993948,0.00018188538,0.000026464126,0.00012565785,0.00020388464,0.00006735654],"domain_scores_gemma":[0.99871254,0.0004786729,0.00023075499,0.00015092306,0.00037435236,0.00005287954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005505516,0.0008691218,0.00046391867,0.00032055672,0.00033926315,0.0006417735,0.0011054239,0.00053982734,0.0013353252],"category_scores_gemma":[0.0010247403,0.00034862338,0.0003713297,0.00028617884,0.00039618558,0.00067980564,0.0005621398,0.00042547376,0.0004544487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007181148,0.0003298101,0.0014269996,0.00019891335,0.00008173998,0.00016670456,0.00024400398,0.026329735,0.82704157,0.00883825,0.00082642405,0.13379778],"study_design_scores_gemma":[0.00008444569,0.0014119252,0.0009952863,0.000024752304,0.000079494326,0.00048010758,0.00007275273,0.56258225,0.42864418,0.0019332531,0.0036498788,0.00004161939],"about_ca_topic_score_codex":0.0003781783,"about_ca_topic_score_gemma":0.0007321328,"teacher_disagreement_score":0.0013353252,"about_ca_system_score_codex":0.00032847057,"about_ca_system_score_gemma":0.00038918885,"threshold_uncertainty_score":0.0044671297},"labels":[],"label_agreement":null},{"id":"W4377970129","doi":"10.1109/ieecon56657.2023.10126633","title":"Passive Radio Localization System Using Channel Impulse Response and Deep Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Impulse response; Channel (broadcasting); Radio frequency; Common emitter; Line-of-sight; Electronic engineering; Real-time computing; Artificial intelligence; Computer vision; Wireless; Telecommunications; Engineering","score_opus":0.009513342318886807,"score_gpt":0.21184231082232627,"score_spread":0.20232896850343945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377970129","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034771755,0.0003844049,0.95698017,0.0002128968,0.00015779947,0.00004498181,0.00012958338,0.003922061,0.0033962235],"genre_scores_gemma":[0.795968,0.0003603389,0.19255294,0.00033183946,0.00009056318,0.00012817579,0.0005575997,0.000089382695,0.009921127],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998198,0.000024851095,0.000008816677,0.000056565335,0.000060987848,0.000028947836],"domain_scores_gemma":[0.99978393,0.00004228525,0.000024980605,0.000025281239,0.000108380795,0.000015122747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030545198,0.00053435785,0.0005339386,0.00044657814,0.0002617657,0.00048145346,0.0008454645,0.0005747092,0.0021721737],"category_scores_gemma":[0.0005270374,0.00024084923,0.00035429007,0.00040457462,0.00020173224,0.0008909276,0.00058509165,0.0006289147,0.00093493186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053637516,0.0004064485,0.0031172286,0.00021049423,0.00019263402,0.00020885136,0.00009576677,0.17233792,0.071554005,0.005205032,0.007545032,0.7385901],"study_design_scores_gemma":[0.000021056383,0.00014816536,0.0006223374,0.000008831573,0.000029719646,0.00007582392,0.000010797783,0.98290354,0.013350031,0.0012039896,0.0016052972,0.000020491276],"about_ca_topic_score_codex":0.0025782003,"about_ca_topic_score_gemma":0.002439321,"teacher_disagreement_score":0.0025782003,"about_ca_system_score_codex":0.00041248853,"about_ca_system_score_gemma":0.00046715388,"threshold_uncertainty_score":0.0072666407},"labels":[],"label_agreement":null},{"id":"W4378192407","doi":"10.1109/syscon53073.2023.10131059","title":"System Design and Performance Analysis of Indoor Real-time Localization using UWB Infrastructure","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Real-time locating system; Non-line-of-sight propagation; Computer science; Real-time computing; GNSS applications; Indoor positioning system; Software deployment; Global Positioning System; Position (finance); Tracing; Multilateration; Location tracking; Tracking (education); Embedded system; Wireless; Telecommunications; Engineering; Accelerometer","score_opus":0.010159868772083813,"score_gpt":0.21004035672067817,"score_spread":0.19988048794859437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378192407","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12846638,0.0005441188,0.8507873,0.00020256656,0.000078268975,0.00020021824,0.0003011861,0.005719333,0.013700584],"genre_scores_gemma":[0.96234715,0.00018411654,0.03338898,0.000045503373,0.000022390508,0.00011565313,0.0002052576,0.0000959195,0.0035949582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919766,0.00020435576,0.000034673205,0.00013602582,0.00028680597,0.0001405066],"domain_scores_gemma":[0.9993298,0.00017944378,0.00006789005,0.00009388495,0.00030094024,0.000028046077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064176676,0.0006422563,0.00049149693,0.0006170757,0.00041187106,0.00089651765,0.0008137402,0.00043452877,0.0048057823],"category_scores_gemma":[0.0011466307,0.00016904558,0.00029191727,0.0005060577,0.00023025235,0.00048402458,0.00034804223,0.00034408853,0.00150085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010045957,0.0001910294,0.008098234,0.00056232396,0.00015177066,0.00066140783,0.00034390984,0.6382875,0.09829301,0.009985541,0.0058144834,0.23660624],"study_design_scores_gemma":[0.00003302832,0.0005848355,0.0025517743,0.000022070531,0.00006143105,0.00043872284,0.00009479377,0.94588804,0.044402953,0.00092980074,0.004963647,0.000028949618],"about_ca_topic_score_codex":0.0029581306,"about_ca_topic_score_gemma":0.0021695225,"teacher_disagreement_score":0.0048057823,"about_ca_system_score_codex":0.0009600798,"about_ca_system_score_gemma":0.0006352663,"threshold_uncertainty_score":0.016076922},"labels":[],"label_agreement":null},{"id":"W4378194462","doi":"10.5194/isprs-archives-xlviii-1-w1-2023-175-2023","title":"SMARTPHONE LEVEL INDOOR/OUTDOOR UBIQUITOUS PEDESTRIAN POSITIONING 3DMA GNSS/VINS INTEGRATION USING FGO","year":2023,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Canadian Institute for Advanced Research","keywords":"GNSS applications; Computer science; Pedestrian; Real-time computing; Artificial intelligence; Global Positioning System; Engineering; Transport engineering; Telecommunications","score_opus":0.028415064288480285,"score_gpt":0.2573415215283452,"score_spread":0.22892645723986493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378194462","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.305209,0.00046463366,0.669311,0.00023451471,0.00033370443,0.00014199449,0.0006128675,0.010833849,0.012858467],"genre_scores_gemma":[0.92130864,0.000107043794,0.074609265,0.00009201643,0.000039185456,0.000042516833,0.00048519968,0.00006214013,0.003253946],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997948,0.000028022008,0.0000069669786,0.00006651353,0.00007114204,0.000032579526],"domain_scores_gemma":[0.9998702,0.000012838443,0.000020410527,0.000031621792,0.00004800513,0.000016910855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016725229,0.0006124198,0.0005366719,0.00048492398,0.0002112465,0.00041652765,0.0003597248,0.00033743645,0.0013555824],"category_scores_gemma":[0.00041404803,0.00015930226,0.00028005822,0.00036889195,0.00016644728,0.0003389536,0.0007208786,0.0002348085,0.0009448279],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087352475,0.00017943498,0.033708904,0.00029178913,0.00012751724,0.0008146341,0.00041123084,0.05957885,0.1449498,0.0024587037,0.0078090397,0.7487965],"study_design_scores_gemma":[0.0000680854,0.00068465824,0.030814175,0.00007518922,0.00011326349,0.0010484321,0.00032563842,0.87626487,0.06663371,0.002245377,0.021638755,0.000087868306],"about_ca_topic_score_codex":0.0031803832,"about_ca_topic_score_gemma":0.0063293898,"teacher_disagreement_score":0.0031803832,"about_ca_system_score_codex":0.00016457873,"about_ca_system_score_gemma":0.0002640598,"threshold_uncertainty_score":0.006323755},"labels":[],"label_agreement":null},{"id":"W4378365312","doi":"10.1109/mprv.2023.3274770","title":"Exploiting Radio Fingerprints for Simultaneous Localization and Mapping","year":2023,"lang":"en","type":"article","venue":"IEEE Pervasive Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Division of Electrical, Communications and Cyber Systems; National Science Foundation of Sri Lanka; Natural Science Foundation of Sichuan Province; University of Moratuwa; Nanyang Technological University; Agency for Science, Technology and Research; Auburn University; Imperial College London; Singapore University of Technology and Design; University of Alberta; National Science Foundation","keywords":"Simultaneous localization and mapping; Fingerprint (computing); Computer science; Wireless; Artificial intelligence; Computer vision; Fingerprint recognition; Fidelity; Lidar; Real-time computing; Remote sensing; Robot; Telecommunications; Mobile robot; Geography","score_opus":0.022785395835126433,"score_gpt":0.23886964020574558,"score_spread":0.21608424437061916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378365312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027987208,0.00048770593,0.96346396,0.00012362842,0.00010772782,0.000032669974,0.00010208223,0.0034958818,0.0041990643],"genre_scores_gemma":[0.77824134,0.0005701572,0.21771282,0.00016882256,0.00012329716,0.0000746796,0.00024763183,0.00014647988,0.0027148318],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934417,0.00012081458,0.000022111393,0.00013289718,0.00026276152,0.00011711739],"domain_scores_gemma":[0.9994122,0.000113576796,0.0000809262,0.00023945583,0.00012214575,0.000031801934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035758392,0.00068025215,0.00057128,0.0011192274,0.0004191953,0.0010499601,0.00079923356,0.00073253264,0.0014252397],"category_scores_gemma":[0.0014411089,0.00031884288,0.00033949385,0.0013137902,0.00038529534,0.0015957678,0.0018454952,0.00058251264,0.0013950911],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003631144,0.0001081464,0.0034896657,0.00022428294,0.000072742456,0.0005111905,0.0002790384,0.046728294,0.1151046,0.012482721,0.0050076423,0.8156285],"study_design_scores_gemma":[0.00009793577,0.00045420293,0.004262758,0.00007849603,0.00013505177,0.0028208946,0.00031020297,0.77435035,0.14703469,0.023398103,0.046866357,0.0001909952],"about_ca_topic_score_codex":0.0013701917,"about_ca_topic_score_gemma":0.00149139,"teacher_disagreement_score":0.0014252397,"about_ca_system_score_codex":0.0002212079,"about_ca_system_score_gemma":0.00037870163,"threshold_uncertainty_score":0.0047678947},"labels":[],"label_agreement":null},{"id":"W4378421951","doi":"10.1109/tcomm.2023.3280212","title":"Value of Service Maximization in Integrated Localization and Communication System Through Joint Resource Allocation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); Western University","funders":"","keywords":"Computer science; Resource allocation; Maximization; Particle swarm optimization; Distributed computing; Wireless; Mathematical optimization; Computer network; Bandwidth (computing); Provisioning; Telecommunications; Algorithm; Mathematics","score_opus":0.027918244147136596,"score_gpt":0.23940948211350205,"score_spread":0.21149123796636546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378421951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028374357,0.00044534483,0.96790385,0.00020980841,0.000027417058,0.000039470924,0.000019902325,0.00008998087,0.0028898637],"genre_scores_gemma":[0.9421476,0.00027349358,0.05607724,0.000065405686,0.00003081445,0.00008127986,0.000025453839,0.000025328114,0.0012734563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983834,0.0006909735,0.00005651791,0.00025597567,0.0003231487,0.0002899862],"domain_scores_gemma":[0.998896,0.00062367256,0.00015935737,0.00005798448,0.00018063051,0.0000823428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018185334,0.0009517569,0.0012790529,0.0005562455,0.00057444116,0.0016681976,0.0010422936,0.00086927915,0.0009253355],"category_scores_gemma":[0.0030128018,0.00041692055,0.000527796,0.0009696794,0.0012300594,0.0014986714,0.0014831298,0.00074815983,0.00014065034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010928606,0.00004750102,0.00055107643,0.00007873351,0.000046006382,0.00011244301,0.00009942811,0.9487662,0.003378192,0.0247805,0.0006713558,0.021359364],"study_design_scores_gemma":[0.000005387131,0.000023979306,0.00006166579,0.0000032582277,0.000005418205,0.000016560254,0.000011933319,0.99610883,0.0003037909,0.0032859019,0.00016845916,0.0000048625297],"about_ca_topic_score_codex":0.0043724985,"about_ca_topic_score_gemma":0.002543123,"teacher_disagreement_score":0.0043724985,"about_ca_system_score_codex":0.001454128,"about_ca_system_score_gemma":0.001684273,"threshold_uncertainty_score":0.010550499},"labels":[],"label_agreement":null},{"id":"W4378982855","doi":"10.1016/j.future.2023.05.031","title":"Human-to-human interaction behaviors sensing based on complex-valued neural network using Wi-Fi channel state information","year":2023,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Ministry of Electronics and Information technology; King Saud University; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Channel state information; Channel (broadcasting); Wireless; SIGNAL (programming language); Telecommunications; Human–computer interaction","score_opus":0.03790034113270989,"score_gpt":0.26604667715995967,"score_spread":0.22814633602724976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378982855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3024561,0.0004989205,0.69241154,0.00026740675,0.00017282335,0.000057133773,0.0002254794,0.00070627645,0.0032043278],"genre_scores_gemma":[0.98034084,0.0001270098,0.018240765,0.000051620824,0.000028000442,0.000026089203,0.00009016949,0.000008126902,0.0010873001],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999806,0.00003306115,0.000008839893,0.0000757147,0.00005061809,0.000025782845],"domain_scores_gemma":[0.99976534,0.00010129344,0.000027783133,0.000014359053,0.00007507043,0.000016154097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021155912,0.00038173873,0.00036480546,0.00023119476,0.00018326259,0.00031098977,0.00045344973,0.00040031827,0.0007709905],"category_scores_gemma":[0.0007262467,0.00014844559,0.00019739101,0.0003008893,0.00021922559,0.000510043,0.0003116668,0.00037025387,0.00013536077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091380003,0.000584469,0.017137114,0.00021285741,0.00022577686,0.0004154633,0.00021630904,0.41362128,0.078029245,0.00256599,0.0026667842,0.4834109],"study_design_scores_gemma":[0.0000037969678,0.000036531022,0.0026933115,0.000002385725,0.00000971855,0.000029839815,0.000008052613,0.9948078,0.0020014695,0.00032390983,0.00007686067,0.0000064271],"about_ca_topic_score_codex":0.004244762,"about_ca_topic_score_gemma":0.00522968,"teacher_disagreement_score":0.004244762,"about_ca_system_score_codex":0.00029341984,"about_ca_system_score_gemma":0.0002775979,"threshold_uncertainty_score":0.008440137},"labels":[],"label_agreement":null},{"id":"W4379230580","doi":"10.3390/s23115292","title":"An Improved Ambiguity Resolution Algorithm for Smartphone RTK Positioning","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"","keywords":"Ambiguity resolution; Ambiguity; Residual; Kinematics; GNSS applications; Computer science; Algorithm; Float (project management); Real Time Kinematic; Artificial intelligence; Engineering; Global Positioning System; Telecommunications","score_opus":0.010474768507009487,"score_gpt":0.24001367089503844,"score_spread":0.22953890238802896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379230580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025321404,0.0003642629,0.97128403,0.00007562159,0.00010978255,0.000031294203,0.00008214503,0.00094939995,0.0017821703],"genre_scores_gemma":[0.25760046,0.0002885953,0.7381917,0.00007729065,0.00009933244,0.00006429914,0.00038072446,0.00013834679,0.0031592606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994555,0.00006454815,0.00003106591,0.00010584901,0.00030022959,0.000042714368],"domain_scores_gemma":[0.99954563,0.00007785975,0.00006002179,0.000094078765,0.00020484554,0.00001750429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036222851,0.00071785564,0.0006073105,0.0009494496,0.00033278283,0.00055308535,0.000597535,0.0005359833,0.0019278794],"category_scores_gemma":[0.002013656,0.00030075942,0.00054639287,0.0008505969,0.0003267509,0.0010418672,0.0008800535,0.0007780871,0.0013884889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026680663,0.00004511758,0.001697957,0.00016502255,0.000059754777,0.00016295385,0.00018934422,0.048262615,0.0982009,0.006747196,0.0022749256,0.8419274],"study_design_scores_gemma":[0.00008822724,0.0002827783,0.005406332,0.00003516714,0.00006833256,0.0009651534,0.00013179689,0.8942967,0.078032814,0.0039940355,0.016564747,0.00013395361],"about_ca_topic_score_codex":0.0012469284,"about_ca_topic_score_gemma":0.0019614394,"teacher_disagreement_score":0.0019278794,"about_ca_system_score_codex":0.00019807728,"about_ca_system_score_gemma":0.0005640983,"threshold_uncertainty_score":0.0064494014},"labels":[],"label_agreement":null},{"id":"W4381122216","doi":"10.20944/preprints202306.1239.v1","title":"Resilient Localization and Coverage in the Internet of Things","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Winnipeg","funders":"","keywords":"Internet of Things; Computer science; Software deployment; Reliability (semiconductor); Context (archaeology); Network topology; Wireless sensor network; Computer security; Computer network; Geography","score_opus":0.0592521391433369,"score_gpt":0.2905908384793912,"score_spread":0.2313386993360543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381122216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042899363,0.0018527964,0.9493758,0.00086522254,0.00012332058,0.00004501724,0.000048475722,0.0006800672,0.0041099265],"genre_scores_gemma":[0.9171214,0.0014544376,0.079092965,0.00015277827,0.000114768794,0.00006745372,0.00007991694,0.000064499356,0.0018517093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990707,0.0003211351,0.000034663473,0.00019555385,0.00026775425,0.0001101977],"domain_scores_gemma":[0.9985863,0.00079568155,0.00019538635,0.00021986847,0.00014857705,0.000054326367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008691719,0.0005539002,0.000526943,0.00094246556,0.000667097,0.00089447334,0.00067236274,0.0009203329,0.0005809322],"category_scores_gemma":[0.004886041,0.00040760534,0.00051201164,0.0008922028,0.001267734,0.0018850404,0.0018128786,0.0006088841,0.00016914234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012870818,0.000038808932,0.0027507138,0.00018486792,0.000066051856,0.0005289112,0.00032162046,0.8156465,0.012511654,0.068599164,0.0025951203,0.096627794],"study_design_scores_gemma":[0.0000094775205,0.00008251015,0.0011652259,0.00003123148,0.000028337396,0.00035851065,0.0001549472,0.93223464,0.0039225766,0.05810423,0.0038803336,0.000027980184],"about_ca_topic_score_codex":0.0019449206,"about_ca_topic_score_gemma":0.001393162,"teacher_disagreement_score":0.0019449206,"about_ca_system_score_codex":0.0007875091,"about_ca_system_score_gemma":0.0003771515,"threshold_uncertainty_score":0.0057138205},"labels":[],"label_agreement":null},{"id":"W4381158217","doi":"10.1016/j.autcon.2023.104981","title":"Two-stage clustering for improve indoor positioning accuracy","year":2023,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); University of Regina","funders":"","keywords":"Cluster analysis; Computer science; Process (computing); Matching (statistics); Data mining; Fingerprint (computing); Positioning technology; Key (lock); Artificial intelligence; Real-time computing","score_opus":0.011723178599109582,"score_gpt":0.2619654110625235,"score_spread":0.2502422324634139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381158217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025553178,0.00027832872,0.97037536,0.00007288055,0.00015727748,0.00007393504,0.00018368532,0.0018168311,0.0014885608],"genre_scores_gemma":[0.38484427,0.00028941937,0.60411495,0.00013247368,0.00013369018,0.00018082175,0.0015490869,0.00044842204,0.008306869],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998497,0.00017098573,0.00008636211,0.00039161596,0.00061651005,0.00023746083],"domain_scores_gemma":[0.9985014,0.00028551192,0.00006560607,0.0002987645,0.00079081074,0.000057919635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006396381,0.0012948655,0.0015421559,0.0013792523,0.0012097936,0.0007983693,0.0023731424,0.0012663485,0.0025491526],"category_scores_gemma":[0.0022571094,0.0006567867,0.0013084126,0.002357871,0.0003339696,0.0012695688,0.001378266,0.0009627004,0.0018124405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009521297,0.00042289137,0.0037258228,0.00029832547,0.00024926857,0.00016372302,0.00034445547,0.22898328,0.07435655,0.0040983427,0.010470916,0.6759343],"study_design_scores_gemma":[0.000028898794,0.00011434502,0.002339592,0.000008464454,0.00005426131,0.00009937955,0.000055504235,0.9739812,0.01992281,0.0010328129,0.0023222505,0.00004054411],"about_ca_topic_score_codex":0.018046252,"about_ca_topic_score_gemma":0.02350392,"teacher_disagreement_score":0.018046252,"about_ca_system_score_codex":0.00065401907,"about_ca_system_score_gemma":0.0017113497,"threshold_uncertainty_score":0.035882413},"labels":[],"label_agreement":null},{"id":"W4382119007","doi":"10.1109/tim.2023.3289547","title":"A WiFi-Based Method for Recognizing Fine-Grained Multiple-Subject Human Activities","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Support vector machine; Computer science; Random forest; Artificial intelligence; Pattern recognition (psychology); Naive Bayes classifier; Feature (linguistics); Decision tree; Linear discriminant analysis; Feature extraction; Activity recognition; F1 score; Machine learning","score_opus":0.05817660343010973,"score_gpt":0.28626730688657614,"score_spread":0.22809070345646643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382119007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08426584,0.0009923384,0.8994907,0.00011456549,0.0002185813,0.00027527296,0.0024754445,0.0077351965,0.0044320147],"genre_scores_gemma":[0.65056515,0.0007638685,0.3374602,0.00018926266,0.00017123391,0.0004056488,0.0041845688,0.00015227549,0.0061077755],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999548,0.000047709207,0.000028632388,0.00016985994,0.00014653249,0.00005919042],"domain_scores_gemma":[0.9996132,0.000082657236,0.00006299119,0.000072220035,0.0001410354,0.000027780814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028820362,0.0008896993,0.00081827666,0.0020601018,0.0002502834,0.0003318856,0.0005765777,0.0004923893,0.0018044079],"category_scores_gemma":[0.00090625655,0.00017445865,0.00052180036,0.0016640781,0.00015485504,0.0005629138,0.0005274797,0.00037936738,0.0017281339],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033724614,0.00024920673,0.023242421,0.00031254994,0.0001647734,0.00026038478,0.000093737624,0.007720776,0.045347597,0.0005751182,0.0073742624,0.9143218],"study_design_scores_gemma":[0.000112640206,0.00092805154,0.17906624,0.00010459426,0.00027661395,0.004742144,0.00027678785,0.7226906,0.06670906,0.0035119625,0.021400291,0.00018102108],"about_ca_topic_score_codex":0.0028578816,"about_ca_topic_score_gemma":0.0074580344,"teacher_disagreement_score":0.0028578816,"about_ca_system_score_codex":0.00015934251,"about_ca_system_score_gemma":0.00032411804,"threshold_uncertainty_score":0.006036341},"labels":[],"label_agreement":null},{"id":"W4382119085","doi":"10.1109/jsac.2023.3288231","title":"Semantic Communications for Wireless Sensing: RIS-Aided Encoding and Self-Supervised Decoding","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Guangdong Provincial Pearl River Talents Program; Ministry of Education - Singapore; National Science Foundation, United Arab Emirates; National Natural Science Foundation of China; Ministry of Science and ICT, South Korea; National Science Foundation","keywords":"Computer science; Decoding methods; Encoding (memory); Hash function; Wireless; Sampling (signal processing); Artificial intelligence; Algorithm; Computer vision; Telecommunications","score_opus":0.0427583445251397,"score_gpt":0.2913943387866181,"score_spread":0.24863599426147837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382119085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02453238,0.00017875237,0.97229296,0.00011334151,0.00003915999,0.000027491707,0.000030522628,0.0005741965,0.0022113058],"genre_scores_gemma":[0.58483046,0.00032598255,0.41084945,0.00020391669,0.00008693254,0.00012848213,0.00019028745,0.00012367807,0.0032607974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996191,0.00007192299,0.000026819174,0.00007726814,0.00016614469,0.000038754202],"domain_scores_gemma":[0.99947053,0.00016852918,0.00008128799,0.00014055072,0.00012014903,0.000019007653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003499867,0.0005645679,0.0003427483,0.00041853287,0.00022368642,0.0005800442,0.00062579144,0.0005007135,0.00091667037],"category_scores_gemma":[0.0013282348,0.00017873137,0.00034946026,0.00049810007,0.0006764154,0.001274991,0.0007121816,0.00059873954,0.0004203135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004072121,0.0001362063,0.0016298972,0.00026813033,0.000036655063,0.00023451423,0.00046949636,0.0821676,0.3879549,0.070357,0.0021329776,0.45420542],"study_design_scores_gemma":[0.000026745225,0.00028989572,0.0006142242,0.000024440165,0.000029338886,0.0003325554,0.00008901134,0.7210133,0.25290734,0.015292786,0.009330488,0.000049910264],"about_ca_topic_score_codex":0.0004472108,"about_ca_topic_score_gemma":0.0005459348,"teacher_disagreement_score":0.00091667037,"about_ca_system_score_codex":0.00034861188,"about_ca_system_score_gemma":0.00040830308,"threshold_uncertainty_score":0.0030665398},"labels":[],"label_agreement":null},{"id":"W4382138772","doi":"10.1109/access.2023.3289756","title":"Fuzzy Logic-Based Approach for Location Identification and Routing in the Outdoor Environment","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Zayed University","keywords":"Computer science; Fuzzy logic; Data mining; Global Positioning System; Identification (biology); The Internet; Routing (electronic design automation); Trajectory; Real-time computing; Artificial intelligence; Computer network; Telecommunications","score_opus":0.040421641765131335,"score_gpt":0.27291446564948374,"score_spread":0.2324928238843524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382138772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011656191,0.0002757163,0.9808569,0.00010820874,0.000056073957,0.00006355818,0.000073999414,0.0004011794,0.0065081897],"genre_scores_gemma":[0.7509564,0.00049534976,0.24022423,0.00015667759,0.00004689725,0.00017779686,0.00024915006,0.000031904896,0.007661602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995933,0.000060915503,0.000028208857,0.000089090165,0.00019083293,0.00003762772],"domain_scores_gemma":[0.9998153,0.000058464426,0.000022718583,0.00001267475,0.00008461353,0.0000062444597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038759378,0.00047876962,0.00041987558,0.0008000291,0.00054592773,0.0010344697,0.00082869007,0.00066375965,0.0027399592],"category_scores_gemma":[0.0007229986,0.00018771633,0.0006689825,0.00047519707,0.0002743006,0.00058402756,0.0003359711,0.00054286706,0.0006566966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019663082,0.00016058444,0.0023810158,0.000322008,0.000116237105,0.00041478855,0.00030656965,0.65669596,0.022750683,0.017156307,0.002511816,0.29698735],"study_design_scores_gemma":[0.000010950597,0.00007772223,0.00047656454,0.000026915728,0.00002864323,0.00008501395,0.000057261033,0.99081075,0.0031956043,0.003224709,0.00199179,0.00001418811],"about_ca_topic_score_codex":0.014320463,"about_ca_topic_score_gemma":0.0127936015,"teacher_disagreement_score":0.014320463,"about_ca_system_score_codex":0.0009109379,"about_ca_system_score_gemma":0.00086695934,"threshold_uncertainty_score":0.028474212},"labels":[],"label_agreement":null},{"id":"W4382340749","doi":"10.2139/ssrn.4493039","title":"Mobile Localization for Indoor Iot Services: From Proof of Concept to Real-Word Experimentation","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Word (group theory); Computer science; Proof of concept; Internet of Things; Computer security; Linguistics; Operating system","score_opus":0.01381709190340544,"score_gpt":0.27142327435349667,"score_spread":0.2576061824500912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382340749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18046865,0.0013657116,0.7824869,0.001818005,0.0010603636,0.0023921921,0.0013081301,0.007154204,0.02194581],"genre_scores_gemma":[0.7597546,0.00088214874,0.22592029,0.000581445,0.000088898785,0.0016452795,0.0012794889,0.0006705801,0.009177122],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.995994,0.0014992966,0.00019366542,0.00039921427,0.0014117354,0.0005021488],"domain_scores_gemma":[0.9944106,0.0024779628,0.00034906054,0.00088130933,0.0016083859,0.0002726612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004811122,0.0014482223,0.00090410333,0.000503172,0.0005925487,0.0018963711,0.0022588368,0.0018684223,0.014773001],"category_scores_gemma":[0.010150663,0.0004111559,0.0006079652,0.0005147691,0.0017764431,0.00295596,0.0025195768,0.0015255037,0.0031854499],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005731497,0.0043078894,0.0060879355,0.0064608427,0.0005477504,0.0019270271,0.0034293842,0.07823855,0.42826244,0.07112571,0.033221018,0.36066002],"study_design_scores_gemma":[0.0024177502,0.015688596,0.0051305974,0.001124169,0.00046902418,0.0020330655,0.0027902315,0.29639885,0.5013899,0.029945232,0.14228614,0.00032640094],"about_ca_topic_score_codex":0.0023953312,"about_ca_topic_score_gemma":0.0013567674,"teacher_disagreement_score":0.014773001,"about_ca_system_score_codex":0.001059579,"about_ca_system_score_gemma":0.0013870028,"threshold_uncertainty_score":0.049420655},"labels":[],"label_agreement":null},{"id":"W4382653022","doi":"10.1002/9781119873747.ch10","title":"DRL and Emerging Topics in Wireless Networks","year":2023,"lang":"en","type":"other","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Wireless network; Wireless; Telecommunications","score_opus":0.005776391536518043,"score_gpt":0.2040663803381949,"score_spread":0.19828998880167686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382653022","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0098685045,0.20843355,0.44681707,0.055721574,0.004809582,0.000112390524,0.00041834876,0.00048027412,0.2733387],"genre_scores_gemma":[0.34272483,0.33798417,0.16824758,0.00888493,0.01704781,0.00047378722,0.00069822825,0.00027959232,0.123659134],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99939597,0.00020742649,0.000028285282,0.000114611445,0.00019719014,0.00005657199],"domain_scores_gemma":[0.9990821,0.0005067098,0.0000893655,0.000084509724,0.00018321228,0.000054163644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010861476,0.00049922994,0.00042063955,0.0014359833,0.00066056394,0.002348804,0.00063268596,0.0011899791,0.008003411],"category_scores_gemma":[0.002258708,0.00020680623,0.0002455347,0.0020391073,0.0014815913,0.0043771635,0.0011472857,0.0022191668,0.0016855901],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009850172,0.000016087795,0.00030367286,0.0002786749,0.00000606708,0.000098615135,0.000113666945,0.003703263,0.0004901097,0.8716329,0.021439772,0.10190737],"study_design_scores_gemma":[0.000007094712,0.00002554904,0.0003801517,0.00022880106,0.0000073001584,0.0003653557,0.00025220506,0.03773633,0.00054103334,0.66848326,0.29195723,0.000015677953],"about_ca_topic_score_codex":0.0008010307,"about_ca_topic_score_gemma":0.0007357425,"teacher_disagreement_score":0.008003411,"about_ca_system_score_codex":0.0013820983,"about_ca_system_score_gemma":0.0006358611,"threshold_uncertainty_score":0.026774108},"labels":[],"label_agreement":null},{"id":"W4382699381","doi":"10.21203/rs.3.rs-3112795/v1","title":"IODnet: Indoor/Outdoor Telecommunication Signal Detection through Deep Neural Network","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Geolocation; Computer science; Metric (unit); Service provider; Service (business); Deep learning; Operator (biology); Artificial intelligence; Artificial neural network; Data mining; Machine learning; Cellular network; The Internet; Real-time computing; Telecommunications; World Wide Web","score_opus":0.0751504790076714,"score_gpt":0.348934806517024,"score_spread":0.2737843275093526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382699381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3582395,0.003125819,0.59357464,0.0015790381,0.00095423585,0.0001751743,0.0047932896,0.02103647,0.016521906],"genre_scores_gemma":[0.86665523,0.0005238865,0.109587975,0.0004159976,0.00012383303,0.000075724485,0.008716184,0.0001714325,0.01372969],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996847,0.000049950173,0.000012710556,0.00010967749,0.000069205584,0.00007373833],"domain_scores_gemma":[0.9997117,0.00007564628,0.000032064545,0.000049423557,0.00010683097,0.00002442149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044203512,0.0011581194,0.00050775707,0.0011524035,0.00031281915,0.000793745,0.0010839114,0.00097385683,0.0023283118],"category_scores_gemma":[0.00089438516,0.0002608678,0.0004760798,0.0012105752,0.00028418124,0.0008181039,0.00067548547,0.0009921986,0.0011734198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053033035,0.0005029224,0.008544988,0.00018406671,0.00020631528,0.0003581311,0.000064241896,0.337826,0.01576925,0.0021589338,0.0305064,0.6033484],"study_design_scores_gemma":[0.00000604302,0.000022381482,0.00088936504,0.000008206203,0.000010523888,0.000021179956,0.000016755828,0.9933264,0.0040278602,0.00055486674,0.0011099287,0.0000063795737],"about_ca_topic_score_codex":0.019813797,"about_ca_topic_score_gemma":0.022447268,"teacher_disagreement_score":0.019813797,"about_ca_system_score_codex":0.00089973054,"about_ca_system_score_gemma":0.0006246289,"threshold_uncertainty_score":0.03939694},"labels":[],"label_agreement":null},{"id":"W4383109007","doi":"10.1109/icra48891.2023.10160505","title":"Extrinsic calibration for highly accurate trajectories reconstruction","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Computer science; GNSS applications; Artificial intelligence; Context (archaeology); Calibration; Computer vision; Ground truth; Robotics; Total station; Position (finance); Software deployment; Real-time computing; Robot; Remote sensing; Global Positioning System; Geodesy; Geography; Mathematics; Telecommunications","score_opus":0.01950094285859293,"score_gpt":0.22996746201216742,"score_spread":0.21046651915357448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383109007","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044753156,0.000084791325,0.9936871,0.000033374614,0.000036399255,0.000012790959,0.000075031676,0.0007047495,0.00089046423],"genre_scores_gemma":[0.2765516,0.00042953534,0.7159661,0.000103852726,0.00009369436,0.00010052322,0.0014338247,0.0006613711,0.0046595726],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99880743,0.00022108339,0.000055207896,0.00031388082,0.00050580053,0.00009655508],"domain_scores_gemma":[0.99862444,0.00019847025,0.00017481047,0.00049133156,0.00046138852,0.000049610513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006789536,0.0014872157,0.00083295687,0.001173398,0.0005391722,0.0010567347,0.0010427247,0.000874002,0.00348123],"category_scores_gemma":[0.004440881,0.00056973816,0.000735487,0.0014918292,0.0006389515,0.0015939578,0.002265737,0.0016880014,0.002760921],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021921529,0.000069631766,0.0025968894,0.0002956962,0.000098365424,0.0002211024,0.0003833831,0.3216514,0.047539163,0.021813583,0.007812949,0.5972986],"study_design_scores_gemma":[0.000015773137,0.00008000427,0.0013610295,0.000038678572,0.00002232751,0.00037913915,0.00008523414,0.9536346,0.0226414,0.00905146,0.012641511,0.000048744936],"about_ca_topic_score_codex":0.0024178606,"about_ca_topic_score_gemma":0.0031460137,"teacher_disagreement_score":0.00348123,"about_ca_system_score_codex":0.00046890584,"about_ca_system_score_gemma":0.0010367018,"threshold_uncertainty_score":0.011645913},"labels":[],"label_agreement":null},{"id":"W4383720765","doi":"10.22541/au.168898342.22939562/v1","title":"MoFLeuR: Motion-based Federated Learning Gesture Recognition","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Process (computing); Accelerometer; Federated learning; Motion (physics); Machine learning; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Convergence (economics); Computation; Activity recognition; Gesture; Gesture recognition; The Internet; Inertial measurement unit; Edge computing; Data mining; World Wide Web; Algorithm","score_opus":0.032490138793467474,"score_gpt":0.23113544883657108,"score_spread":0.1986453100431036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383720765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021539556,0.00042444747,0.9704353,0.00014483469,0.00007895611,0.00005864823,0.00017412263,0.005938604,0.001205515],"genre_scores_gemma":[0.6724397,0.00041129955,0.31814966,0.0004829294,0.000055533037,0.00020569957,0.0012235674,0.00017646461,0.0068551525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938476,0.00009306356,0.00004258834,0.0002231078,0.00016420969,0.00009232823],"domain_scores_gemma":[0.9995913,0.000101975835,0.000049810227,0.00013549071,0.00009316396,0.00002830487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076376233,0.00096600107,0.0011070984,0.00053639244,0.00038340432,0.0005847151,0.0019838014,0.0009023126,0.0015884361],"category_scores_gemma":[0.0016166107,0.00023761713,0.00061147753,0.0005951486,0.0005343785,0.0016085,0.0014558155,0.0009083495,0.0004981651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041881713,0.00021345683,0.0019360599,0.00010906945,0.000084966065,0.0002917591,0.00008958556,0.28973678,0.012192326,0.0055008684,0.005975044,0.6834513],"study_design_scores_gemma":[0.000016505654,0.00006795644,0.00038430997,0.00001009054,0.000011915567,0.00009329496,0.000016857686,0.98840886,0.0064423396,0.0032072414,0.0013291173,0.000011630156],"about_ca_topic_score_codex":0.008170966,"about_ca_topic_score_gemma":0.008024582,"teacher_disagreement_score":0.008170966,"about_ca_system_score_codex":0.0006800256,"about_ca_system_score_gemma":0.00074017927,"threshold_uncertainty_score":0.016246796},"labels":[],"label_agreement":null},{"id":"W4383960525","doi":"10.1109/tvt.2023.3293189","title":"NoncovANM: Gridless DOA Estimation for LPDF System","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Computational complexity theory; Algorithm; Direction of arrival; Channel (broadcasting); Cramér–Rao bound; Saddle point; Norm (philosophy); Estimation theory; Mathematics; Telecommunications; Antenna (radio)","score_opus":0.010330204974871995,"score_gpt":0.22814501209593163,"score_spread":0.21781480712105963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383960525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028713113,0.00017895449,0.9957926,0.000059460403,0.00004277103,0.000014596503,0.00005627327,0.0002414809,0.0007425236],"genre_scores_gemma":[0.27402642,0.0011267957,0.7183341,0.00021423207,0.00017639097,0.00027659597,0.0008399981,0.00018404104,0.004821411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995185,0.000113825496,0.00002163821,0.000121677156,0.00019304769,0.0000312768],"domain_scores_gemma":[0.99956495,0.00015841216,0.00005780658,0.000095752985,0.00010551207,0.000017605782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040944666,0.0008077699,0.0005316855,0.00053125795,0.00034677918,0.00056067307,0.0009798734,0.00067193585,0.0020958062],"category_scores_gemma":[0.0025651348,0.00026917743,0.00048151086,0.0010734551,0.00047255904,0.0008383828,0.0008903214,0.0008835634,0.0010062059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020519434,0.00007210847,0.0022055295,0.0002745221,0.00006702782,0.00016018451,0.00013375012,0.4090353,0.016398558,0.020144114,0.006395757,0.544908],"study_design_scores_gemma":[0.000011193051,0.000030821237,0.00044035623,0.0000116926785,0.0000066820385,0.00010488635,0.000015940579,0.9881044,0.0025847417,0.004932285,0.0037440222,0.000013066101],"about_ca_topic_score_codex":0.0037434138,"about_ca_topic_score_gemma":0.003955105,"teacher_disagreement_score":0.0037434138,"about_ca_system_score_codex":0.0004134621,"about_ca_system_score_gemma":0.00087470224,"threshold_uncertainty_score":0.007443249},"labels":[],"label_agreement":null},{"id":"W4384343138","doi":"10.3390/s23146393","title":"A Secure ZUPT-Aided Indoor Navigation System Using Blockchain in GNSS-Denied Environments","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Inertial measurement unit; GNSS applications; Computer science; Robustness (evolution); Real-time computing; Inertial navigation system; Kalman filter; Encoder; Global Positioning System; Indoor positioning system; Navigation system; Extended Kalman filter; Artificial intelligence; Orientation (vector space); Accelerometer; Telecommunications","score_opus":0.01267645800439426,"score_gpt":0.21797814050742806,"score_spread":0.2053016825030338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384343138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13421205,0.00046023645,0.85208136,0.00028412108,0.00016081326,0.00026080076,0.00021053322,0.0055641923,0.006765866],"genre_scores_gemma":[0.9456061,0.00013911018,0.050639603,0.00006526034,0.000030755473,0.0001628728,0.00021253077,0.000051153376,0.0030924755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999213,0.00015719525,0.00006014091,0.00015027165,0.00028193998,0.00013743907],"domain_scores_gemma":[0.9990594,0.00016223014,0.00012020147,0.0002914186,0.00025895194,0.00010787755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006867013,0.00048215617,0.00070146914,0.0003836846,0.001048766,0.00070810644,0.001193497,0.0007457941,0.0027945573],"category_scores_gemma":[0.0012663829,0.00022637156,0.00020748963,0.00050242554,0.00048657673,0.001559138,0.0019240791,0.0005244701,0.0011572972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018037787,0.0003481083,0.008026292,0.00046262858,0.000117881216,0.0020106474,0.00086178497,0.34023952,0.12588732,0.026901567,0.008626809,0.48471373],"study_design_scores_gemma":[0.0001647528,0.00035316433,0.000881936,0.000029524494,0.000034700948,0.0003151031,0.00008524981,0.947847,0.03187683,0.0071663396,0.011188589,0.00005674639],"about_ca_topic_score_codex":0.0036295333,"about_ca_topic_score_gemma":0.0030588808,"teacher_disagreement_score":0.0036295333,"about_ca_system_score_codex":0.00045698296,"about_ca_system_score_gemma":0.0014324943,"threshold_uncertainty_score":0.00934875},"labels":[],"label_agreement":null},{"id":"W4384945972","doi":"10.1109/iwcmc58020.2023.10183207","title":"Dual-Hop Robust Distributed Collaborative Beamforming Over Nominally Rectangular WSNs in Slightly to Moderately Scattered Environments : (Invited Paper)","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Beamforming; Computer science; Wireless sensor network; Channel (broadcasting); Hop (telecommunications); Wireless network; Wireless; Node (physics); Channel state information; Topology (electrical circuits); Computer network; Real-time computing; Telecommunications; Mathematics; Physics; Combinatorics; Acoustics","score_opus":0.010169821323588481,"score_gpt":0.20978802017994513,"score_spread":0.19961819885635665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384945972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007737583,0.00032987766,0.99070686,0.00013151568,0.00007049739,0.0000110402325,0.000020223843,0.000108777545,0.00088368705],"genre_scores_gemma":[0.4095494,0.0013517218,0.58048004,0.0003409377,0.00030083026,0.0000917316,0.00019415969,0.0001010256,0.0075900867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997147,0.0000557668,0.000010930272,0.00010036397,0.00008540514,0.00003270423],"domain_scores_gemma":[0.999728,0.00007778844,0.00004352406,0.000043672706,0.00008279171,0.000024204583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040436254,0.00071860204,0.00047402893,0.00019132637,0.00020893507,0.000521931,0.0009106415,0.00076845253,0.00093813165],"category_scores_gemma":[0.00087934814,0.0002456258,0.0004925839,0.00035914656,0.00043021675,0.0006937876,0.0009413522,0.0005601793,0.0006365084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002790394,0.00008351195,0.0012047365,0.00037222818,0.00016281572,0.00033723062,0.00022976403,0.5154315,0.12882422,0.031269312,0.00834497,0.31346068],"study_design_scores_gemma":[0.000028329374,0.00021298196,0.00039570878,0.000016866772,0.000029679926,0.00022389903,0.000055445278,0.9671195,0.015844602,0.007793978,0.008252763,0.000026227812],"about_ca_topic_score_codex":0.00061629276,"about_ca_topic_score_gemma":0.0006367681,"teacher_disagreement_score":0.00093813165,"about_ca_system_score_codex":0.00022101543,"about_ca_system_score_gemma":0.00031092545,"threshold_uncertainty_score":0.0031383038},"labels":[],"label_agreement":null},{"id":"W4385213633","doi":"10.3390/electronics12143172","title":"Resilient Localization and Coverage in the Internet of Things","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Internet of Things; Reliability (semiconductor); Software deployment; Computer science; Context (archaeology); Network topology; Wireless sensor network; Computer network; Computer security; Geography","score_opus":0.005405656491472921,"score_gpt":0.20551752422220473,"score_spread":0.2001118677307318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385213633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0527923,0.0019402495,0.93905497,0.00073469133,0.00012972842,0.000046897403,0.000045519853,0.0007240778,0.0045315763],"genre_scores_gemma":[0.94067305,0.0011154602,0.056398865,0.00013424698,0.00007716607,0.00004996219,0.00005398225,0.00004161412,0.001455701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993311,0.00021912802,0.000024421508,0.00013820284,0.00019537105,0.00009178994],"domain_scores_gemma":[0.9990503,0.0005069653,0.00014978908,0.00014337165,0.00011098106,0.000038560476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006977365,0.00048028052,0.0004659178,0.00086551736,0.000640302,0.00072699174,0.00064326613,0.0007730246,0.00053065317],"category_scores_gemma":[0.0033177526,0.00034226617,0.00047358268,0.0007348764,0.000988854,0.0016933172,0.0014554004,0.00046039015,0.0001468047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012584277,0.00004262509,0.0035950954,0.00017913517,0.00006694597,0.0006154053,0.00030695615,0.82188994,0.015057437,0.04897342,0.002348154,0.10679905],"study_design_scores_gemma":[0.000007617093,0.000096003874,0.0013048665,0.000027166954,0.000028238414,0.0003904315,0.00015690367,0.9593269,0.003930538,0.031273738,0.0034312997,0.00002638745],"about_ca_topic_score_codex":0.0019055813,"about_ca_topic_score_gemma":0.0016142551,"teacher_disagreement_score":0.0019055813,"about_ca_system_score_codex":0.0007065675,"about_ca_system_score_gemma":0.00034567938,"threshold_uncertainty_score":0.005126536},"labels":[],"label_agreement":null},{"id":"W4385236882","doi":"10.1109/jiot.2023.3298603","title":"An Efficient and Robust Fingerprint-Based Localization Method for Multifloor Indoor Environment","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Fingerprint recognition; Fingerprint (computing); Artificial intelligence; Artificial neural network; Boosting (machine learning); Pattern recognition (psychology); Wireless; Feature extraction; Key (lock); Machine learning; Data mining; Real-time computing; Telecommunications","score_opus":0.015325290820080566,"score_gpt":0.25057371050370936,"score_spread":0.23524841968362878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385236882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01027747,0.00026717383,0.98751265,0.000064330765,0.00007339605,0.000021694384,0.00007016697,0.0010795695,0.0006334924],"genre_scores_gemma":[0.4060708,0.00069491175,0.5848283,0.0002859782,0.00013999282,0.00013148386,0.00081054086,0.00021034047,0.006827633],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996013,0.000049175138,0.000018200026,0.00013768997,0.00013959789,0.000054139204],"domain_scores_gemma":[0.99964917,0.000056354,0.00004560049,0.00005761639,0.00016902147,0.000022260705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044491846,0.00090183545,0.0009548322,0.00082106347,0.0003355791,0.0004538854,0.001214701,0.0007871707,0.0013482955],"category_scores_gemma":[0.0012461875,0.0003216183,0.0006557991,0.0010146778,0.00025345656,0.0012016543,0.0010542726,0.0010235801,0.0010585649],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002072793,0.00009934278,0.0021355017,0.00014231674,0.00006770088,0.00017361267,0.00010908111,0.08921189,0.048610587,0.0018735877,0.004131399,0.8532377],"study_design_scores_gemma":[0.000018921564,0.000087774235,0.0015524998,0.000015446629,0.000032449687,0.00025845662,0.00003501274,0.97780025,0.016184658,0.001224322,0.0027599442,0.000030227287],"about_ca_topic_score_codex":0.0027252121,"about_ca_topic_score_gemma":0.0038846368,"teacher_disagreement_score":0.0027252121,"about_ca_system_score_codex":0.00033682436,"about_ca_system_score_gemma":0.00059890107,"threshold_uncertainty_score":0.0054186583},"labels":[],"label_agreement":null},{"id":"W4385246262","doi":"10.1109/tim.2023.3298680","title":"Three-Dimensional Source Localization Based on 1-D AOA Measurements: Low-Complexity and Effective Estimator","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Estimator; Cramér–Rao bound; Algorithm; Computer science; Computational complexity theory; Mathematical optimization; Upper and lower bounds; Angle of arrival; Convergence (economics); Regularization (linguistics); Estimation theory; Mathematics; Artificial intelligence; Statistics; Telecommunications","score_opus":0.036142026281111475,"score_gpt":0.24121130414519057,"score_spread":0.20506927786407908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385246262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002559131,0.00020037926,0.9965121,0.00005720282,0.000015411471,0.000011780369,0.00001551014,0.00023154974,0.00039685125],"genre_scores_gemma":[0.15667847,0.0010976004,0.83984786,0.00014367723,0.0000851003,0.00021099999,0.00024522736,0.0000961301,0.0015949521],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950814,0.00011923704,0.000022801407,0.00009738139,0.0002274068,0.000024990391],"domain_scores_gemma":[0.99920017,0.00034673852,0.00011727552,0.00010013759,0.00020465702,0.00003103529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005226386,0.0009195971,0.00090120366,0.00079405215,0.0002858584,0.00070903735,0.0008918101,0.0007975002,0.00092926977],"category_scores_gemma":[0.0022311585,0.0005358181,0.0005682398,0.0010345875,0.00049714476,0.0013126539,0.0015776993,0.00088105025,0.0008735874],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024437555,0.00009943616,0.0026880442,0.00046294465,0.00010888447,0.00018764885,0.00025258338,0.2411322,0.090727724,0.02144017,0.0045342,0.6381217],"study_design_scores_gemma":[0.000023809856,0.000044345943,0.00057148834,0.000022928198,0.000020246147,0.00018257818,0.000028497183,0.9802942,0.011305207,0.0038482153,0.0036166916,0.000041770985],"about_ca_topic_score_codex":0.0014582388,"about_ca_topic_score_gemma":0.0014000642,"teacher_disagreement_score":0.0014582388,"about_ca_system_score_codex":0.00029244737,"about_ca_system_score_gemma":0.00079650566,"threshold_uncertainty_score":0.0031086802},"labels":[],"label_agreement":null},{"id":"W4385363722","doi":"10.21203/rs.3.rs-3197469/v1","title":"An enhanced, Robust, adaptive Kalman filter for continuous urban navigation with low-cost sensors","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National d’Etudes Spatiales; York University","keywords":"GNSS applications; Kalman filter; Computer science; Real Time Kinematic; Kinematics; Real-time computing; Adaptive filter; Key (lock); Global Positioning System; Artificial intelligence; Algorithm; Telecommunications","score_opus":0.04953890976422868,"score_gpt":0.32730317780656076,"score_spread":0.27776426804233206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385363722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054427814,0.00018350966,0.99350536,0.000033468452,0.000045730336,0.000015101823,0.00003353996,0.0003243671,0.00041614825],"genre_scores_gemma":[0.5811956,0.0005821466,0.41186473,0.000101871345,0.00013783154,0.00014123111,0.00035034597,0.000078190424,0.005548015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999318,0.000117624055,0.00004371312,0.00021604665,0.0002531583,0.000051444982],"domain_scores_gemma":[0.9994143,0.00020451259,0.00007103932,0.00006850522,0.00022752666,0.000014059972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007286845,0.0006575778,0.000773039,0.00045213028,0.000315222,0.0006449909,0.00089063053,0.0007271466,0.0016118275],"category_scores_gemma":[0.0018711097,0.0004066479,0.00056747417,0.000552088,0.00036865345,0.0009839353,0.00053943647,0.00092318415,0.0007384159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044801293,0.00009334635,0.0023578461,0.00029959786,0.00017806063,0.00013392662,0.00014070503,0.5882601,0.037216246,0.007882584,0.0028006416,0.360189],"study_design_scores_gemma":[0.00001129625,0.00003890149,0.0004204207,0.000007559039,0.000017087454,0.000018949366,0.0000048712013,0.9956115,0.0022252558,0.00041208812,0.001221483,0.000010612037],"about_ca_topic_score_codex":0.009737134,"about_ca_topic_score_gemma":0.010205355,"teacher_disagreement_score":0.009737134,"about_ca_system_score_codex":0.0005301635,"about_ca_system_score_gemma":0.0007798219,"threshold_uncertainty_score":0.0193609},"labels":[],"label_agreement":null},{"id":"W4385453422","doi":"10.1109/access.2023.3300726","title":"Deep Learning-Based Fall Detection Using WiFi Channel State Information","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Deep learning; Channel (broadcasting); State (computer science); Channel state information; Artificial intelligence; Computer network; Computer security; Telecommunications; Wireless; Algorithm","score_opus":0.01975963702663643,"score_gpt":0.2512427043802034,"score_spread":0.23148306735356697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385453422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4095485,0.000989177,0.5696473,0.00061532046,0.0002050197,0.00019964915,0.0036517186,0.010083507,0.0050598527],"genre_scores_gemma":[0.90395916,0.00041732757,0.08737399,0.00024122729,0.000053588607,0.000135538,0.0038652169,0.000060162347,0.0038939312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997336,0.00002461146,0.000017993561,0.00007931277,0.00008475408,0.00005968007],"domain_scores_gemma":[0.999716,0.000068604764,0.000050327388,0.000031035976,0.000105727166,0.000028332843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002483094,0.0010552724,0.0007587358,0.0011194043,0.0002863712,0.00040307827,0.00095195684,0.0004996279,0.0012961987],"category_scores_gemma":[0.0013264252,0.00027807205,0.00045363564,0.0008697581,0.00021192295,0.0007469648,0.00073086715,0.0007934837,0.00062333187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059443264,0.0010739119,0.041199528,0.00020162895,0.00016828712,0.00029190964,0.00011130634,0.1102268,0.016565364,0.0007294802,0.012103144,0.81673414],"study_design_scores_gemma":[0.000025220706,0.00014335483,0.012578237,0.00002910492,0.00003738065,0.0001349018,0.00003852467,0.9765162,0.008234178,0.0010374096,0.0012067809,0.00001868939],"about_ca_topic_score_codex":0.011744695,"about_ca_topic_score_gemma":0.021975812,"teacher_disagreement_score":0.011744695,"about_ca_system_score_codex":0.000453999,"about_ca_system_score_gemma":0.0007081804,"threshold_uncertainty_score":0.023352683},"labels":[],"label_agreement":null},{"id":"W4385487465","doi":"10.1109/actea58025.2023.10194203","title":"Received Signal Strength Based Localization of Wireless Sensors in a Complex Environment: a Comparative Study","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"RSS; Signal strength; Wireless; Computer science; SIGNAL (programming language); Least-squares function approximation; Wireless sensor network; Work (physics); Estimation; Real-time computing; Algorithm; Data mining; Engineering; Mathematics; Statistics; Telecommunications","score_opus":0.034178495499554915,"score_gpt":0.25023521063682475,"score_spread":0.21605671513726982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385487465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5151433,0.016294869,0.45564654,0.0003703098,0.00010270064,0.00008473849,0.0002334604,0.0005264213,0.011597744],"genre_scores_gemma":[0.9501505,0.0061583305,0.041673474,0.00004384767,0.000074286756,0.000030450758,0.00016957495,0.000043825927,0.0016556833],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99905723,0.00036427006,0.000055606375,0.00015403378,0.00032186354,0.00004694138],"domain_scores_gemma":[0.9979517,0.0011195671,0.0002340406,0.0001617245,0.00049593335,0.0000370648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001025548,0.0005400605,0.00047020835,0.0018655928,0.00018421072,0.0006852242,0.0005455844,0.000720926,0.00063299993],"category_scores_gemma":[0.0025476653,0.00014209327,0.00036848141,0.001692057,0.00049113703,0.0011401137,0.00049273507,0.0002012197,0.0003001962],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009962261,0.00023052939,0.030099303,0.0029274488,0.00047006068,0.001515928,0.001142667,0.19169003,0.082169995,0.0055325236,0.0016600111,0.6815653],"study_design_scores_gemma":[0.000077912315,0.007976829,0.122833446,0.0006065489,0.0009785502,0.008112338,0.0035576615,0.6839195,0.12548727,0.004562168,0.041594047,0.00029367593],"about_ca_topic_score_codex":0.00074348104,"about_ca_topic_score_gemma":0.0007566697,"teacher_disagreement_score":0.0018655928,"about_ca_system_score_codex":0.0002500792,"about_ca_system_score_gemma":0.00015336456,"threshold_uncertainty_score":0.005423665},"labels":[],"label_agreement":null},{"id":"W4385602300","doi":"10.1007/978-3-031-34159-5_71","title":"Real-Time Structural Inspection Using Augmented Reality","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Augmented reality; Structural health monitoring; Computer science; Engineering; Culvert; Civil infrastructure; Real-time computing; Human–computer interaction; Construction engineering; Electrical engineering","score_opus":0.015127508607863731,"score_gpt":0.22438229854574818,"score_spread":0.20925478993788443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385602300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015033271,0.00043459158,0.9763548,0.000044948792,0.00007131523,0.000039119583,0.00021543265,0.004004394,0.0038021554],"genre_scores_gemma":[0.32393727,0.0009499289,0.66393733,0.000075367796,0.00005012722,0.00007886706,0.00070777565,0.0005012295,0.009762075],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99931633,0.000105759806,0.000024844647,0.00012905966,0.0003695216,0.000054370852],"domain_scores_gemma":[0.99935764,0.00026571596,0.00005547831,0.00019278665,0.00010396371,0.000024316945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036717733,0.0012351295,0.00087321835,0.00084750605,0.00022638126,0.0014255648,0.0012067988,0.0010570417,0.007803116],"category_scores_gemma":[0.0008644888,0.0010356061,0.00075025856,0.0008435393,0.00043142747,0.0011228498,0.0014118381,0.0007157752,0.0026086448],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052998826,0.00012298464,0.00086921017,0.00044234106,0.00009087842,0.0003955236,0.00031783033,0.051583752,0.21543062,0.0024205742,0.007583497,0.7202128],"study_design_scores_gemma":[0.00006120956,0.0004886749,0.005996219,0.00012430432,0.00009959256,0.0025125842,0.00028784663,0.88130695,0.08156053,0.0045169815,0.022898525,0.00014663176],"about_ca_topic_score_codex":0.0010374374,"about_ca_topic_score_gemma":0.0020486475,"teacher_disagreement_score":0.007803116,"about_ca_system_score_codex":0.00017485801,"about_ca_system_score_gemma":0.00028319267,"threshold_uncertainty_score":0.026104033},"labels":[],"label_agreement":null},{"id":"W4385761591","doi":"10.5194/ica-abs-6-67-2023","title":"Fast high accuracy kinematic smartphone positioning for location-based services","year":2023,"lang":"en","type":"article","venue":"Abstracts of the ICA","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Kinematics; Computer science; Geodesy; Location-based service; Real-time computing; Geology; Telecommunications; Physics","score_opus":0.006354220365371523,"score_gpt":0.21201126368016485,"score_spread":0.20565704331479331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385761591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06330383,0.0026203187,0.91572714,0.00040388398,0.00069074496,0.00008896402,0.0011884663,0.0071004042,0.008876309],"genre_scores_gemma":[0.852266,0.0012458495,0.13277589,0.00009972353,0.0002800424,0.000089612244,0.0017866046,0.00016901891,0.011287244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940336,0.0000988474,0.000029066126,0.00009002305,0.00029249556,0.000086272004],"domain_scores_gemma":[0.9994605,0.00010109523,0.00005430485,0.00011258667,0.00024266478,0.00002875817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036744607,0.0008550036,0.0006940005,0.0008192831,0.00028940383,0.0008372666,0.0005326691,0.00061939756,0.004828402],"category_scores_gemma":[0.0015268917,0.0003208365,0.0002446563,0.00092821644,0.00018475128,0.0007187099,0.00089778705,0.0005200231,0.0037760197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009200396,0.00007302818,0.0074674105,0.000488834,0.00010127895,0.00069536775,0.00025952418,0.028924348,0.13143498,0.0068878974,0.02581071,0.79693645],"study_design_scores_gemma":[0.00014246684,0.0009711277,0.02376354,0.00022236722,0.00019010375,0.0026741477,0.00034652493,0.7837849,0.10943833,0.0070932065,0.07123205,0.00014123082],"about_ca_topic_score_codex":0.0028321068,"about_ca_topic_score_gemma":0.0035357333,"teacher_disagreement_score":0.004828402,"about_ca_system_score_codex":0.00026422046,"about_ca_system_score_gemma":0.0005217529,"threshold_uncertainty_score":0.01615256},"labels":[],"label_agreement":null},{"id":"W4385848828","doi":"10.1109/tvt.2023.3304856","title":"Symbol-Level Integrated Sensing and Communication Enabled Multiple Base Stations Cooperative Sensing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Demodulation; Base station; Computer science; Synchronization (alternating current); Sensor fusion; Real-time computing; Electronic engineering; Carrier-to-noise ratio; Communications system; Signal-to-noise ratio (imaging); Engineering; Telecommunications; Artificial intelligence; Channel (broadcasting)","score_opus":0.01848902317872592,"score_gpt":0.22638002439085614,"score_spread":0.20789100121213022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385848828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05302897,0.0006471109,0.94084805,0.0001839203,0.00011090952,0.000046632013,0.000042060627,0.0006453905,0.004446932],"genre_scores_gemma":[0.85520226,0.0003471558,0.14175695,0.00019168766,0.00008340889,0.00008030662,0.000092739814,0.000028092078,0.0022173391],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990324,0.00018715228,0.000036767837,0.00021474887,0.00041269368,0.000116225565],"domain_scores_gemma":[0.9993405,0.00015585266,0.00008437243,0.00013794421,0.00024232137,0.000038998256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048878737,0.0005359815,0.0005247341,0.0004334542,0.00032636392,0.0006775196,0.00093801005,0.00074302213,0.0007333873],"category_scores_gemma":[0.0010672712,0.00024894206,0.00046242238,0.0005583426,0.00047530895,0.0010771824,0.0012047493,0.0005828353,0.00040085614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043976674,0.00013690806,0.0035502496,0.00033796448,0.00015078943,0.0005070163,0.0005888064,0.19639722,0.40519732,0.024580484,0.002890467,0.36522296],"study_design_scores_gemma":[0.000042234173,0.00039555287,0.0015409332,0.000022492282,0.000067425935,0.00038276557,0.00008941221,0.9091091,0.07455818,0.006337582,0.0073880963,0.000066278946],"about_ca_topic_score_codex":0.00092100224,"about_ca_topic_score_gemma":0.0008740187,"teacher_disagreement_score":0.00093801005,"about_ca_system_score_codex":0.0003616847,"about_ca_system_score_gemma":0.0005149649,"threshold_uncertainty_score":0.0026242137},"labels":[],"label_agreement":null},{"id":"W4385859857","doi":"10.1007/978-3-031-34593-7_5","title":"The Effect of Human Body Blockage on UWB Tracking Accuracy in Construction Sites","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Non-line-of-sight propagation; Tracking (education); Computer science; Sight; Position (finance); Simulation; Real-time computing; Engineering; Telecommunications; Wireless","score_opus":0.008355980495462578,"score_gpt":0.2211935355458576,"score_spread":0.21283755505039503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385859857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93248034,0.001536141,0.059702374,0.00009623922,0.00008246172,0.000010746469,0.0002730144,0.00027926677,0.005539334],"genre_scores_gemma":[0.9956846,0.00029201535,0.002680504,0.000012516324,0.000011874121,0.0000035243092,0.00013689302,0.00004260307,0.0011355322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988838,0.00024820337,0.000039472998,0.00018757902,0.00045532713,0.00018572544],"domain_scores_gemma":[0.98571163,0.011498134,0.00093606685,0.000758727,0.0009798037,0.0001157001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011692991,0.00027230047,0.0004492221,0.00043137895,0.00032236212,0.0007047709,0.000457513,0.00089963543,0.0018162628],"category_scores_gemma":[0.010036381,0.00034028606,0.00024897055,0.0008888406,0.0005018933,0.0010058369,0.00068331056,0.0005277266,0.0006711342],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038265395,0.00018085285,0.12689812,0.00063502695,0.00022193478,0.0014032336,0.0013861101,0.43211624,0.18606229,0.0017434928,0.0014009653,0.24412513],"study_design_scores_gemma":[0.000042136126,0.0017067126,0.36221838,0.00013872332,0.00045635662,0.002848088,0.0011852239,0.44952434,0.17638178,0.0022745884,0.0031059547,0.000117773045],"about_ca_topic_score_codex":0.0029604055,"about_ca_topic_score_gemma":0.0029238279,"teacher_disagreement_score":0.0029604055,"about_ca_system_score_codex":0.00037653753,"about_ca_system_score_gemma":0.00030630117,"threshold_uncertainty_score":0.0061838627},"labels":[],"label_agreement":null},{"id":"W4386065723","doi":"10.1109/cvpr52729.2023.01672","title":"Look, Radiate, and Learn: Self-Supervised Localisation via Radio-Visual Correspondence","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Benchmark (surveying); Scalability; Key (lock); Perception; Artificial intelligence; Radio frequency; Deep learning; Telecommunications","score_opus":0.008829105122473983,"score_gpt":0.21598603474966654,"score_spread":0.20715692962719257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386065723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27942964,0.005515207,0.5905206,0.0034096197,0.0014918051,0.0006840441,0.05123888,0.04674963,0.020960493],"genre_scores_gemma":[0.6277483,0.000676913,0.24381989,0.0012920559,0.0002212917,0.00038814425,0.109300725,0.0013253163,0.015227393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897003,0.0002861069,0.00004390375,0.0004194854,0.00018231107,0.00009811374],"domain_scores_gemma":[0.99871564,0.00044190674,0.00012793625,0.00038213874,0.00023045155,0.000101823265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010523153,0.0023373754,0.0010215553,0.0011530437,0.000656901,0.0012981176,0.0038938369,0.0020353603,0.0037423135],"category_scores_gemma":[0.003840854,0.00060368265,0.0014708934,0.0011500589,0.0010484457,0.0015517444,0.0023387347,0.0024918327,0.004067761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010003551,0.00075980555,0.011059098,0.001073147,0.00041011078,0.00038873818,0.0002981095,0.48512042,0.009186957,0.0041585597,0.14146326,0.34508142],"study_design_scores_gemma":[0.00013244244,0.00022846668,0.002342124,0.0000873005,0.000041574745,0.00020571721,0.0001467781,0.96694815,0.00821863,0.007722872,0.013877711,0.000048284157],"about_ca_topic_score_codex":0.013194431,"about_ca_topic_score_gemma":0.02436751,"teacher_disagreement_score":0.013194431,"about_ca_system_score_codex":0.0011116755,"about_ca_system_score_gemma":0.00092685496,"threshold_uncertainty_score":0.026235282},"labels":[],"label_agreement":null},{"id":"W4386214967","doi":"10.1109/piers59004.2023.10221373","title":"Received Signal Strength Prediction Using Generative Adversarial Networks for Indoor Localization","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"CHIST-ERA","keywords":"RSS; Computer science; Fingerprint (computing); Generative adversarial network; Signal strength; Generative model; Fingerprint recognition; Artificial intelligence; Generative grammar; Point (geometry); Popularity; Data mining; Wireless network; Deep learning; Machine learning; Wireless; Real-time computing; Telecommunications","score_opus":0.020626696844232118,"score_gpt":0.2354095067162463,"score_spread":0.21478280987201417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386214967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021311186,0.00023271443,0.97581965,0.00020638484,0.00003573921,0.000018798213,0.00007147736,0.00055295957,0.001751107],"genre_scores_gemma":[0.9435856,0.0002997093,0.051261384,0.0001662121,0.000039482453,0.000059696715,0.00021380745,0.0000653226,0.0043087522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996929,0.00009301433,0.000011285566,0.00008069935,0.000077882876,0.000044084518],"domain_scores_gemma":[0.9989491,0.00068219466,0.00012886149,0.000089662615,0.000117075666,0.00003312114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064749573,0.0007769731,0.000604321,0.00042838542,0.00020899027,0.00051402237,0.0010678886,0.00072323566,0.0013158795],"category_scores_gemma":[0.0021872781,0.0004792986,0.0005791724,0.0004093409,0.0008169268,0.0007553289,0.0010526809,0.0014075455,0.00038655355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003096628,0.000012187598,0.0005150792,0.000012650601,0.000016406277,0.00003496846,0.000017530803,0.9827391,0.0009402672,0.003123589,0.00031346528,0.012243852],"study_design_scores_gemma":[8.0256086e-7,0.00000526896,0.000051367377,0.0000014909854,0.000002162775,0.000007251733,0.0000012911047,0.9987663,0.00023623247,0.00085415633,0.00007187862,0.0000017894656],"about_ca_topic_score_codex":0.0051622284,"about_ca_topic_score_gemma":0.0055417516,"teacher_disagreement_score":0.0051622284,"about_ca_system_score_codex":0.0008077691,"about_ca_system_score_gemma":0.00039603983,"threshold_uncertainty_score":0.010264397},"labels":[],"label_agreement":null},{"id":"W4386256423","doi":"10.32920/24050727","title":"Neural Network Based Recursive Least Square Technique for Indoor Wireless Positioning","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Trilateration; Multilateration; Mean squared error; Indoor positioning system; Computer science; Algorithm; Artificial neural network; Noise (video); Position (finance); Wireless; Recursive least squares filter; Mathematics; Artificial intelligence; Statistics; Triangulation; Telecommunications; Adaptive filter","score_opus":0.022642104580930585,"score_gpt":0.24845399343994062,"score_spread":0.22581188885901005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386256423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005900919,0.00048892235,0.9908501,0.00010495245,0.00007254365,0.000013980761,0.000029126682,0.0008772088,0.0016621955],"genre_scores_gemma":[0.43570703,0.0012084622,0.5500691,0.00018139099,0.00010586469,0.0001344149,0.0003708247,0.00020477068,0.012018121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970204,0.000078573605,0.000018703544,0.000063264466,0.0001155702,0.000021734431],"domain_scores_gemma":[0.9997111,0.00010631966,0.0000363232,0.000024430654,0.00011648412,0.000005365735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033263405,0.00058648695,0.0004874195,0.00032090436,0.00020212193,0.00034209772,0.0006430271,0.0005282374,0.0018877625],"category_scores_gemma":[0.0009264876,0.0002402568,0.0005202965,0.00059725164,0.00019483821,0.0005399684,0.00030006687,0.0007624272,0.0007721713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009272122,0.000049947175,0.0008299845,0.00013627407,0.00007288823,0.000109666405,0.00008040408,0.6460762,0.015545299,0.0056536747,0.003231482,0.32812154],"study_design_scores_gemma":[0.0000025368715,0.000016948166,0.00011146551,0.000003830083,0.0000055658124,0.000016180678,0.0000037505692,0.99673223,0.001711564,0.00041668204,0.00097464514,0.0000046167647],"about_ca_topic_score_codex":0.0062154196,"about_ca_topic_score_gemma":0.0059696534,"teacher_disagreement_score":0.0062154196,"about_ca_system_score_codex":0.0003694489,"about_ca_system_score_gemma":0.00047258608,"threshold_uncertainty_score":0.012358487},"labels":[],"label_agreement":null},{"id":"W4386256600","doi":"10.32920/24050727.v1","title":"Neural Network Based Recursive Least Square Technique for Indoor Wireless Positioning","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Trilateration; Multilateration; Mean squared error; Indoor positioning system; Computer science; Algorithm; Artificial neural network; Noise (video); Position (finance); Wireless; Mathematics; Artificial intelligence; Statistics; Triangulation; Telecommunications","score_opus":0.022642104580930585,"score_gpt":0.24845399343994062,"score_spread":0.22581188885901005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386256600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005900919,0.00048892235,0.9908501,0.00010495245,0.00007254365,0.000013980761,0.000029126682,0.0008772088,0.0016621955],"genre_scores_gemma":[0.43570703,0.0012084622,0.5500691,0.00018139099,0.00010586469,0.0001344149,0.0003708247,0.00020477068,0.012018121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970204,0.000078573605,0.000018703544,0.000063264466,0.0001155702,0.000021734431],"domain_scores_gemma":[0.9997111,0.00010631966,0.0000363232,0.000024430654,0.00011648412,0.000005365735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033263405,0.00058648695,0.0004874195,0.00032090436,0.00020212193,0.00034209772,0.0006430271,0.0005282374,0.0018877625],"category_scores_gemma":[0.0009264876,0.0002402568,0.0005202965,0.00059725164,0.00019483821,0.0005399684,0.00030006687,0.0007624272,0.0007721713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009272122,0.000049947175,0.0008299845,0.00013627407,0.00007288823,0.000109666405,0.00008040408,0.6460762,0.015545299,0.0056536747,0.003231482,0.32812154],"study_design_scores_gemma":[0.0000025368715,0.000016948166,0.00011146551,0.000003830083,0.0000055658124,0.000016180678,0.0000037505692,0.99673223,0.001711564,0.00041668204,0.00097464514,0.0000046167647],"about_ca_topic_score_codex":0.0062154196,"about_ca_topic_score_gemma":0.0059696534,"teacher_disagreement_score":0.0062154196,"about_ca_system_score_codex":0.0003694489,"about_ca_system_score_gemma":0.00047258608,"threshold_uncertainty_score":0.012358487},"labels":[],"label_agreement":null},{"id":"W4386285838","doi":"10.18280/mmep.100432","title":"Optimization of Wildfire Localization Using a Trilateration-Based Nelder-Mead Algorithm in a Wireless Sensor Network","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Trilateration; Wireless sensor network; Computer science; Optimization algorithm; Algorithm; Wireless; Real-time computing; Computer network; Mathematical optimization; Mathematics; Geography; Telecommunications; Triangulation; Cartography","score_opus":0.020611233261120167,"score_gpt":0.20574095644712306,"score_spread":0.1851297231860029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386285838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019358914,0.000104592626,0.97962564,0.00005960662,0.000013282922,0.000018243565,0.000013968177,0.00009936593,0.00070631504],"genre_scores_gemma":[0.4565441,0.00032108868,0.5406578,0.000053412652,0.00001930626,0.00018539914,0.00008566043,0.000051320076,0.0020819418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973255,0.00010196321,0.000018262823,0.000055417142,0.00006926033,0.000022540073],"domain_scores_gemma":[0.99964964,0.00017164313,0.00006942957,0.00001718286,0.000076569144,0.000015483205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064193504,0.0006710335,0.0005836461,0.0005224038,0.00038612008,0.0005633276,0.0005832568,0.0008139539,0.0006053564],"category_scores_gemma":[0.001522681,0.0003330596,0.0005360265,0.0005600314,0.0004723618,0.0007509216,0.0005585188,0.00047357546,0.00015446408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032911674,0.000015346535,0.00025683155,0.00003402671,0.000016820173,0.000034507913,0.000041088188,0.9775925,0.0024500568,0.0015337927,0.00020379903,0.017788209],"study_design_scores_gemma":[0.0000025864663,0.000013280542,0.000043181386,0.0000019732086,0.0000015592879,0.000006726337,0.000005234202,0.9990538,0.0004358045,0.00032959005,0.00010355308,0.0000028215163],"about_ca_topic_score_codex":0.0058245165,"about_ca_topic_score_gemma":0.005477178,"teacher_disagreement_score":0.0058245165,"about_ca_system_score_codex":0.0005270151,"about_ca_system_score_gemma":0.0010888501,"threshold_uncertainty_score":0.0115811825},"labels":[],"label_agreement":null},{"id":"W4386324878","doi":"10.3390/s23177551","title":"Ultra-Wideband-Based Time Occupancy Analysis for Safety Studies","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Transport Canada","keywords":"Occupancy; Ultra-wideband; Computer science; Real-time computing; Ground truth; Tracking (education); Wideband; Electronic engineering; Engineering; Telecommunications; Artificial intelligence","score_opus":0.023038194672063056,"score_gpt":0.2685842684076491,"score_spread":0.24554607373558604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386324878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14893377,0.0007746428,0.8445269,0.000042290918,0.000093623516,0.000068828806,0.00030462124,0.0006741343,0.0045812484],"genre_scores_gemma":[0.89188963,0.0005110782,0.10538348,0.000041159197,0.000045670877,0.00009517284,0.0005069784,0.000063937114,0.0014627948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951994,0.00012731661,0.000028150836,0.00008421119,0.00018501065,0.000055322565],"domain_scores_gemma":[0.9991391,0.00030321404,0.00018803841,0.00009801203,0.0002367183,0.000034956898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045381472,0.0005785217,0.0004025658,0.001688465,0.000283813,0.00065222103,0.00061272684,0.00039113782,0.0012917768],"category_scores_gemma":[0.0018479518,0.00017549908,0.00039785547,0.0012833397,0.00023543552,0.00081201945,0.000618599,0.0003258966,0.0005144162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008320229,0.0004328048,0.074996755,0.00083461526,0.00033737492,0.00062782463,0.0009267323,0.25176534,0.11654338,0.015403931,0.003381401,0.5339178],"study_design_scores_gemma":[0.000025452015,0.0007203761,0.04035192,0.00014281379,0.00013714713,0.0012018359,0.0011890956,0.88504285,0.048439544,0.0073865377,0.01524794,0.00011456011],"about_ca_topic_score_codex":0.0015561574,"about_ca_topic_score_gemma":0.0017253482,"teacher_disagreement_score":0.001688465,"about_ca_system_score_codex":0.0003294519,"about_ca_system_score_gemma":0.0003055487,"threshold_uncertainty_score":0.004321456},"labels":[],"label_agreement":null},{"id":"W4386366844","doi":"10.1016/j.comcom.2023.08.021","title":"Generation of irregular grid maps for fingerprinting-based mobile radio localization using farthest-first traversal and low-discrepancy sequences","year":2023,"lang":"en","type":"article","venue":"Computer Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Tree traversal; Grid; Context (archaeology); Radio frequency; Real-time computing; Generator (circuit theory); Global Positioning System; Algorithm; Telecommunications","score_opus":0.05268837494697695,"score_gpt":0.2659716363812879,"score_spread":0.21328326143431095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386366844","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026853548,0.00007111245,0.9704112,0.000039242786,0.00003379132,0.00004945939,0.00022370435,0.0012106209,0.0011072364],"genre_scores_gemma":[0.4626999,0.00011541652,0.53341585,0.000025411306,0.000015227218,0.00011597781,0.0014989283,0.00028238233,0.0018309555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997106,0.00005318828,0.000014975641,0.00005195816,0.00012140112,0.00004770213],"domain_scores_gemma":[0.9991242,0.00026666772,0.000069718575,0.00017961454,0.00030856358,0.000051230767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002738548,0.00067283196,0.0006740286,0.0014914207,0.00043307588,0.0006172474,0.0008222026,0.00045528205,0.0025118135],"category_scores_gemma":[0.002282258,0.0003794855,0.00048184878,0.0014560409,0.00029543613,0.00058506685,0.0009960007,0.00051231554,0.00086636236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043211202,0.00013205859,0.004094568,0.00016393678,0.000049324768,0.00033033392,0.000269568,0.5216188,0.020803582,0.013909271,0.006357532,0.43183887],"study_design_scores_gemma":[0.000012893609,0.000030607443,0.0002942615,0.000005435351,0.0000051616676,0.00007391037,0.000033146243,0.99165875,0.004055477,0.002510842,0.0013107151,0.000008783424],"about_ca_topic_score_codex":0.005763311,"about_ca_topic_score_gemma":0.006950427,"teacher_disagreement_score":0.005763311,"about_ca_system_score_codex":0.00040167684,"about_ca_system_score_gemma":0.0010211807,"threshold_uncertainty_score":0.011459529},"labels":[],"label_agreement":null},{"id":"W4386494272","doi":"10.32604/iasc.2023.040412","title":"Performance Evaluation of Three-Dimensional UWB Real-Time Locating Auto-Positioning System for Fire Rescue","year":2023,"lang":"en","type":"article","venue":"Intelligent Automation & Soft Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Multilateration; Real-time locating system; Real-time computing; Computer science; Cramér–Rao bound; Geolocation; Simulation; Algorithm; Engineering","score_opus":0.025157429258508036,"score_gpt":0.261967597942388,"score_spread":0.23681016868387997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386494272","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71047455,0.0012450814,0.27629393,0.00025882287,0.00027309798,0.00009525422,0.00013871201,0.0027420346,0.008478556],"genre_scores_gemma":[0.99004525,0.00016138822,0.0080910195,0.000052328673,0.000010114921,0.000030931413,0.000106362495,0.000020792924,0.0014817778],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995396,0.00010647005,0.000030805237,0.00008434724,0.00017838218,0.000060361945],"domain_scores_gemma":[0.99937195,0.0001387903,0.00005776544,0.00006202045,0.00032771265,0.000041778418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004932798,0.0005135116,0.0005237312,0.00050664484,0.0003368754,0.00053840596,0.0005202616,0.000591778,0.002169984],"category_scores_gemma":[0.0010701842,0.00011698067,0.00026295686,0.00023503945,0.00022855331,0.00048691587,0.0004604538,0.00024823268,0.00063051016],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004461873,0.00032983022,0.020170672,0.0010987979,0.00030219764,0.0009866266,0.0010325598,0.2840559,0.31376755,0.0033299604,0.0047111716,0.3657529],"study_design_scores_gemma":[0.00010454665,0.0026698832,0.012433161,0.000034637178,0.00013780904,0.0008565416,0.00035141772,0.8619349,0.117239386,0.0003797127,0.003772856,0.00008521155],"about_ca_topic_score_codex":0.0013298121,"about_ca_topic_score_gemma":0.00067450706,"teacher_disagreement_score":0.002169984,"about_ca_system_score_codex":0.0002748244,"about_ca_system_score_gemma":0.00036077347,"threshold_uncertainty_score":0.007259369},"labels":[],"label_agreement":null},{"id":"W4386542031","doi":"10.3390/eng4030131","title":"Comparative Analysis of Indoor Localization across Various Wireless Technologies","year":2023,"lang":"en","type":"article","venue":"Eng—Advances in Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"University of Winnipeg","keywords":"Trilateration; RSS; Computer science; Multilateration; Wireless; Node (physics); Bluetooth; Centroid; Wireless network; Noise (video); Pairwise comparison; Outlier; Standard deviation; Real-time computing; Data mining; Artificial intelligence; Statistics; Telecommunications; Mathematics; Engineering","score_opus":0.008724489539591169,"score_gpt":0.2652448093565389,"score_spread":0.25652031981694773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386542031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73140645,0.009033532,0.2375425,0.00037005366,0.00020831892,0.00021076729,0.0012616602,0.0024936185,0.017473107],"genre_scores_gemma":[0.9629636,0.0021307871,0.03261367,0.000044207205,0.000053597803,0.00007218262,0.0008535843,0.00010772692,0.0011607389],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.997331,0.00092013064,0.00019498095,0.0003849722,0.0009652004,0.00020376928],"domain_scores_gemma":[0.9908036,0.006135984,0.0006597776,0.0006418634,0.0016637463,0.000094992836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023588436,0.00059711625,0.00068983674,0.002795969,0.00041548072,0.0010844427,0.0005564462,0.0005471436,0.0010545192],"category_scores_gemma":[0.009981345,0.00016188998,0.00045525972,0.003133355,0.00040150512,0.0013767541,0.0006381147,0.00020337055,0.0004152077],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014871148,0.00023569413,0.06450108,0.002506539,0.0007597148,0.0006917546,0.00052028935,0.23798114,0.037359983,0.0035000746,0.0028697313,0.6475869],"study_design_scores_gemma":[0.0001393373,0.007036157,0.24237567,0.00048751722,0.0020314215,0.004424447,0.0033882703,0.55902636,0.14482121,0.0038182605,0.032068666,0.00038270623],"about_ca_topic_score_codex":0.0015095033,"about_ca_topic_score_gemma":0.0023012112,"teacher_disagreement_score":0.002795969,"about_ca_system_score_codex":0.00050125836,"about_ca_system_score_gemma":0.00029059077,"threshold_uncertainty_score":0.0124748945},"labels":[],"label_agreement":null},{"id":"W4386698314","doi":"10.1109/geoinformatics60313.2023.10247766","title":"Multi-user indoor cooperative localization technology with opportunity encounters","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Research and Development; Strategic Innovation Fund","keywords":"Computer science; Human–computer interaction","score_opus":0.015687483254992172,"score_gpt":0.232604780259105,"score_spread":0.21691729700411283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386698314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038890038,0.00066357007,0.95045465,0.00021753504,0.00010716731,0.00007498055,0.00003800826,0.0014982596,0.008055801],"genre_scores_gemma":[0.8621967,0.00041562706,0.12612149,0.0002018333,0.00012354447,0.00018091634,0.00009611895,0.00006310774,0.010600723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989729,0.00024680028,0.00004185607,0.0002293868,0.00033431462,0.00017491267],"domain_scores_gemma":[0.99926466,0.00017630574,0.00008430257,0.00021329008,0.00018326742,0.000078178215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005074343,0.0007334617,0.0007971803,0.0008826539,0.0009824398,0.0009832096,0.0016545053,0.0009663653,0.00270365],"category_scores_gemma":[0.001330341,0.00032388358,0.00059417065,0.00092266174,0.00048136033,0.0017519547,0.0035675194,0.00056240265,0.0012714172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008965771,0.00034121188,0.006980343,0.00046843672,0.00023057229,0.003686027,0.003418382,0.059584524,0.1303645,0.04240531,0.01185471,0.7397694],"study_design_scores_gemma":[0.00014525438,0.001467104,0.0041095903,0.000083065905,0.0002942174,0.007821327,0.0015109344,0.83214825,0.06730971,0.023758503,0.06112659,0.00022547999],"about_ca_topic_score_codex":0.000856717,"about_ca_topic_score_gemma":0.0011921682,"teacher_disagreement_score":0.00270365,"about_ca_system_score_codex":0.0003149654,"about_ca_system_score_gemma":0.00034429508,"threshold_uncertainty_score":0.009044588},"labels":[],"label_agreement":null},{"id":"W4386715971","doi":"10.18280/isi.280412","title":"Enhancing Indoor Navigation Accuracy with a Smartphone-Based Pedometer System","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedometer; Computer science; Smartphone application; Smartphone app; Real-time computing; Human–computer interaction; Multimedia; Physical medicine and rehabilitation; Physical activity; Medicine","score_opus":0.008768103869537249,"score_gpt":0.2063942957343607,"score_spread":0.19762619186482344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386715971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30561662,0.0009911519,0.6778821,0.00022831807,0.00037048105,0.00023526326,0.00059503666,0.0059771207,0.008103997],"genre_scores_gemma":[0.89625233,0.0003795566,0.09901203,0.00012349835,0.000061067694,0.000107944434,0.00039764994,0.0000507462,0.0036150462],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996182,0.00006649201,0.000031771837,0.00008215877,0.00016063415,0.000040751707],"domain_scores_gemma":[0.99952936,0.00007616704,0.000043551325,0.00007227957,0.00026078417,0.000017756745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026561823,0.00050091994,0.0004848373,0.00055947335,0.00013911338,0.00046801302,0.00065520563,0.0004126126,0.0014301839],"category_scores_gemma":[0.0010090258,0.00017370142,0.00019872689,0.00034104867,0.000095698386,0.00040449036,0.0007127407,0.0002488239,0.001039165],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009336933,0.00032358873,0.021061096,0.0009782672,0.0001262122,0.0008300853,0.0004444816,0.01620035,0.28275207,0.0013441736,0.005827231,0.6691788],"study_design_scores_gemma":[0.00025666467,0.0030514365,0.072857365,0.0003254155,0.0005058111,0.004171762,0.00042009648,0.577252,0.2902854,0.0008390251,0.04978102,0.00025406107],"about_ca_topic_score_codex":0.0012749982,"about_ca_topic_score_gemma":0.0019079136,"teacher_disagreement_score":0.0014301839,"about_ca_system_score_codex":0.00012198112,"about_ca_system_score_gemma":0.0002269832,"threshold_uncertainty_score":0.004784465},"labels":[],"label_agreement":null},{"id":"W4386952985","doi":"10.1109/ccta54093.2023.10253406","title":"An Interoperable Coverage Model for Field Sensor Networks Deployment","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Wireless sensor network; Computer science; Software deployment; Voronoi diagram; Distributed computing; Interoperability; Measure (data warehouse); Closeness; Field (mathematics); Real-time computing; Data mining; Computer network; Mathematics","score_opus":0.01642725976100998,"score_gpt":0.24334304518220354,"score_spread":0.22691578542119356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386952985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061882813,0.00014632825,0.9903674,0.00014907125,0.000015259115,0.000020468084,0.0000567854,0.00010219725,0.0029542102],"genre_scores_gemma":[0.8534163,0.0007503773,0.138499,0.0001526215,0.000083387094,0.00028400525,0.0003582997,0.00014363343,0.006312411],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873275,0.00044565432,0.000050825936,0.00023355216,0.00041862155,0.000118634205],"domain_scores_gemma":[0.9990508,0.00046488998,0.00013222615,0.0001323532,0.00016748047,0.00005234618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012738762,0.0008500743,0.0005490279,0.00096500537,0.00039981867,0.0011872402,0.0018335956,0.0012460016,0.0015500387],"category_scores_gemma":[0.0037385712,0.00032489584,0.0006476278,0.0012174051,0.0010035062,0.0024911952,0.0014644169,0.0009934566,0.00032166645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020109212,0.000014232722,0.0002646304,0.000029330306,0.000008288447,0.00006504036,0.00007636903,0.91260505,0.0012296004,0.07343953,0.00084950554,0.011398405],"study_design_scores_gemma":[0.0000016039411,0.000012172485,0.000051362473,0.000002519905,0.0000017766306,0.000021125585,0.000009213114,0.99109393,0.00012412643,0.008110624,0.00056864327,0.000002880856],"about_ca_topic_score_codex":0.0033772127,"about_ca_topic_score_gemma":0.001863777,"teacher_disagreement_score":0.0033772127,"about_ca_system_score_codex":0.0013648531,"about_ca_system_score_gemma":0.0005447652,"threshold_uncertainty_score":0.009902775},"labels":[],"label_agreement":null},{"id":"W4387087042","doi":"10.1145/3610902","title":"Environment-aware Multi-person Tracking in Indoor Environments with MmWave Radars","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Youth Innovation Promotion Association; Beijing Nova Program; HORIZON EUROPE Framework Programme; Youth Innovation Promotion Association of the Chinese Academy of Sciences; Chinese Academy of Sciences; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Multipath propagation; Shadow (psychology); Multipath interference; Radar; Reflection (computer programming); Computer vision; Tracking (education); Artificial intelligence; Remote sensing; Shadow mapping; Real-time computing; Telecommunications; Geology; Channel (broadcasting)","score_opus":0.014108010821842458,"score_gpt":0.22329007386668748,"score_spread":0.20918206304484502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387087042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07034556,0.00047495982,0.9240206,0.000082271275,0.00008089049,0.00003316799,0.00013601714,0.0029471149,0.0018794001],"genre_scores_gemma":[0.64731324,0.0006253082,0.3481706,0.00020753269,0.000055571392,0.00006773827,0.00046543137,0.00012897677,0.0029656724],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993955,0.00012931594,0.000023950925,0.00016963265,0.00020428651,0.000077262346],"domain_scores_gemma":[0.999624,0.00006671987,0.0000685596,0.00010480814,0.00010392837,0.000031962936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004581214,0.0006507037,0.0009146568,0.00060349767,0.00027997492,0.0006095753,0.00072665786,0.0006741494,0.00069589895],"category_scores_gemma":[0.00095436967,0.00033592898,0.00051690254,0.00073256623,0.00016727143,0.0008735216,0.0013090974,0.0005465486,0.0010969129],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076442224,0.000245095,0.01433404,0.00038381538,0.00023214867,0.00063621625,0.00068809296,0.10767229,0.15493038,0.0024262252,0.0044819345,0.71320534],"study_design_scores_gemma":[0.000058499023,0.00041969292,0.011814992,0.0000445865,0.00013333026,0.0012576608,0.00021370256,0.91152036,0.0649666,0.0020255588,0.007475612,0.000069414804],"about_ca_topic_score_codex":0.0014946394,"about_ca_topic_score_gemma":0.0026738883,"teacher_disagreement_score":0.0014946394,"about_ca_system_score_codex":0.00018693971,"about_ca_system_score_gemma":0.00033721072,"threshold_uncertainty_score":0.0029718876},"labels":[],"label_agreement":null},{"id":"W4387092561","doi":"10.1109/lcomm.2023.3320035","title":"Multi-Tag Localization in Cooperative AmBC","year":2023,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"Huawei Technologies","keywords":"Computer science; SIGNAL (programming language); Backscatter (email); Subspace topology; Multiple signal classification; Signal subspace; Radio frequency; Power (physics); Direction of arrival; Signal-to-noise ratio (imaging); Algorithm; Speech recognition; Telecommunications; Artificial intelligence; Wireless; Antenna (radio); Noise (video)","score_opus":0.03590839213080798,"score_gpt":0.27289293883012383,"score_spread":0.23698454669931585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387092561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13898401,0.00024954558,0.8571835,0.00020351511,0.000037773858,0.000042803185,0.000026279897,0.00037569384,0.0028969198],"genre_scores_gemma":[0.9241979,0.00008993489,0.07419785,0.0000902052,0.000021944166,0.00005581809,0.000027808579,0.000016385497,0.0013022951],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876773,0.00041665352,0.000030139998,0.00029355244,0.000323497,0.00016848426],"domain_scores_gemma":[0.9981261,0.0007965969,0.00026788196,0.0003515635,0.00033049055,0.00012731004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011847103,0.0006846188,0.0009662787,0.0005196936,0.00053029414,0.00067721197,0.0011236244,0.0012676802,0.0004619395],"category_scores_gemma":[0.0031149017,0.00037188624,0.0003420925,0.00065444334,0.0010424352,0.0014868631,0.002267963,0.0005860486,0.0003743166],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043423247,0.00019417782,0.00570861,0.00015563764,0.000078036894,0.001015876,0.0008298062,0.78877014,0.08387004,0.014015665,0.0011928219,0.10373505],"study_design_scores_gemma":[0.000016101943,0.00017830478,0.00056149793,0.000005394509,0.000011661234,0.00018888658,0.000113817034,0.9890616,0.0070986934,0.002127715,0.00061923964,0.000017071034],"about_ca_topic_score_codex":0.0028188734,"about_ca_topic_score_gemma":0.0028693352,"teacher_disagreement_score":0.0028188734,"about_ca_system_score_codex":0.00055030547,"about_ca_system_score_gemma":0.0006693288,"threshold_uncertainty_score":0.0062654614},"labels":[],"label_agreement":null},{"id":"W4387400742","doi":"10.33012/2023.19444","title":"Crowdsourcing Radar Maps with AUTO’s Integration of Multiple Imaging Radars and INS/GNSS for Autonomous Applications","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Satellite Division's International Technical Meeting (Online)/Proceedings of the Satellite Division's International Technical Meeting (CD-ROM)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"GNSS applications; Computer science; Inertial measurement unit; Radar; Sensor fusion; Inertial navigation system; Crowdsourcing; Real-time computing; Geocoding; Mobile mapping; GNSS augmentation; Global Positioning System; Remote sensing; Computer vision; Artificial intelligence; Geography; Orientation (vector space); Point cloud; Telecommunications","score_opus":0.012206255823356878,"score_gpt":0.24783642722054336,"score_spread":0.23563017139718648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387400742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24729578,0.00030154147,0.72682804,0.00022142965,0.00024813233,0.00020901395,0.0005453144,0.012077803,0.012272885],"genre_scores_gemma":[0.8351031,0.00007591989,0.16100286,0.00009350426,0.00003913892,0.000096525684,0.0004861807,0.0001974683,0.00290531],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946636,0.00009165496,0.000016679276,0.000120712335,0.00023876711,0.0000658241],"domain_scores_gemma":[0.9995484,0.000109425695,0.000034389894,0.00013698735,0.0001366447,0.000034069333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046406194,0.00056511216,0.0005223368,0.000722594,0.00032547401,0.00061702274,0.000721151,0.00034176078,0.0014286505],"category_scores_gemma":[0.00105191,0.00021415527,0.00043482197,0.00059783855,0.0003003568,0.00058381964,0.0015165898,0.00034496712,0.0005716259],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006886203,0.00025123023,0.008102808,0.00021374994,0.00026399453,0.0004611252,0.000920569,0.33363292,0.06964565,0.0044637774,0.008925763,0.57242984],"study_design_scores_gemma":[0.000047340283,0.0002112164,0.004600893,0.00001529381,0.00003909753,0.0001738128,0.0002662108,0.9609793,0.020561615,0.0031938287,0.009842674,0.00006870297],"about_ca_topic_score_codex":0.010046515,"about_ca_topic_score_gemma":0.010054466,"teacher_disagreement_score":0.010046515,"about_ca_system_score_codex":0.00037533964,"about_ca_system_score_gemma":0.00051942293,"threshold_uncertainty_score":0.01997608},"labels":[],"label_agreement":null},{"id":"W4387400811","doi":"10.33012/2023.19464","title":"Tightly Integrated Smartphone GNSS and Visual odometry for Enhanced Urban Pedestrian Positioning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Satellite Division's International Technical Meeting (Online)/Proceedings of the Satellite Division's International Technical Meeting (CD-ROM)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"GNSS applications; Computer science; Artificial intelligence; Computer vision; Non-line-of-sight propagation; Pseudorange; Odometry; Real-time computing; Global Positioning System; Remote sensing; Geography; Wireless; Mobile robot; Telecommunications","score_opus":0.012911702129730298,"score_gpt":0.2641995306939848,"score_spread":0.25128782856425447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387400811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1507178,0.00087533984,0.8368551,0.00015789112,0.00033134024,0.00010228788,0.0005937354,0.0037082024,0.0066582914],"genre_scores_gemma":[0.7996835,0.00038157255,0.19613652,0.00011333839,0.00011672144,0.0000740186,0.00075837766,0.00009430331,0.0026416823],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967,0.000039555413,0.000012596071,0.000089986715,0.00012659856,0.000061307524],"domain_scores_gemma":[0.99981827,0.000021511227,0.000021796015,0.000043908898,0.000076222335,0.000018286662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022414776,0.0009377805,0.00057641364,0.0009217304,0.00021776358,0.00044894745,0.000581521,0.0004046396,0.001254285],"category_scores_gemma":[0.0007523614,0.0002910652,0.00044730093,0.00081801036,0.00019533989,0.00061030465,0.0012638639,0.00037706987,0.00086124404],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047581168,0.00014597263,0.020171659,0.0004048911,0.00016397252,0.0005977634,0.00046645582,0.090207845,0.09072594,0.003221936,0.0055515724,0.7878662],"study_design_scores_gemma":[0.000100985686,0.00048752825,0.029694563,0.00011156797,0.00017049245,0.0009242798,0.0003288543,0.9138381,0.030912424,0.0034633449,0.019861365,0.00010636522],"about_ca_topic_score_codex":0.0052908813,"about_ca_topic_score_gemma":0.0097931065,"teacher_disagreement_score":0.0052908813,"about_ca_system_score_codex":0.00018745947,"about_ca_system_score_gemma":0.0005681563,"threshold_uncertainty_score":0.01052022},"labels":[],"label_agreement":null},{"id":"W4387449140","doi":"10.1109/jsac.2023.3322797","title":"Cooperative Localization for UAV Systems From the Perspective of Physical Clock Synchronization","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Jiangsu Province for Distinguished Young Scholars; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Ranging; Computer science; Pseudorange; Clock synchronization; Synchronization (alternating current); Clock drift; Real-time computing; Global Positioning System; Time of arrival; Control theory (sociology); GNSS applications; Artificial intelligence; Telecommunications","score_opus":0.02516528724701056,"score_gpt":0.2875835563896013,"score_spread":0.26241826914259075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387449140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023069872,0.00046250803,0.9743377,0.00010073364,0.000044271153,0.0000141966375,0.000010900761,0.00015633297,0.0018035553],"genre_scores_gemma":[0.9661529,0.0005469067,0.03184284,0.000029911296,0.00004467698,0.0000401814,0.000021033246,0.000011942545,0.0013095904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968755,0.0000852661,0.00001144255,0.0000848889,0.00009155441,0.000039374056],"domain_scores_gemma":[0.9997898,0.000071197,0.000052851574,0.000031457734,0.00004477664,0.000009990866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027629617,0.00036106922,0.00033421133,0.00026867233,0.00033632922,0.0005999849,0.0005158967,0.00044125502,0.00050165557],"category_scores_gemma":[0.0010061647,0.00015478255,0.0003246936,0.00036757658,0.0004650151,0.00076748873,0.0007834309,0.00037774304,0.00013292946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013191065,0.000037390688,0.0018723707,0.00016828242,0.000051722323,0.00035994663,0.00042243686,0.7996775,0.03651613,0.051777326,0.0010557241,0.1079293],"study_design_scores_gemma":[0.000008379318,0.00008060522,0.00030399268,0.0000064415303,0.0000135809,0.00006392405,0.000047003745,0.99237424,0.002165793,0.0035274955,0.001400586,0.00000804226],"about_ca_topic_score_codex":0.0025238467,"about_ca_topic_score_gemma":0.0016772234,"teacher_disagreement_score":0.0025238467,"about_ca_system_score_codex":0.00034625988,"about_ca_system_score_gemma":0.0004823106,"threshold_uncertainty_score":0.005018294},"labels":[],"label_agreement":null},{"id":"W4387634875","doi":"10.48550/arxiv.2310.07844","title":"Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Gyroscope; Angular velocity; Robustness (evolution); Robot; Computer science; Artificial intelligence; Robotics; Computer vision; Accelerometer; Simultaneous localization and mapping; Control theory (sociology); Mobile robot; Physics; Engineering; Aerospace engineering","score_opus":0.05990935594612713,"score_gpt":0.1965989374418948,"score_spread":0.13668958149576765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387634875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10868698,0.0014841828,0.87498647,0.00031243812,0.000305055,0.00012643449,0.0016956966,0.009107943,0.0032947904],"genre_scores_gemma":[0.81058925,0.00047960182,0.18036345,0.00020920194,0.00018677869,0.00018161624,0.005725053,0.00042423984,0.0018409324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999017,0.00013634538,0.000058338635,0.0002884549,0.00031675017,0.00018303322],"domain_scores_gemma":[0.9988675,0.00027480104,0.00013947213,0.00035875593,0.0002963435,0.00006311016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008279028,0.0014699013,0.0011579049,0.0011089145,0.0004961634,0.0008175031,0.0015810806,0.0006048794,0.0011244991],"category_scores_gemma":[0.004768406,0.0003964686,0.00060560624,0.0011597964,0.00054067804,0.0012080013,0.0019824074,0.0009960837,0.0010196981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086984877,0.00023385286,0.009910996,0.00043931988,0.00020014346,0.00030665332,0.00032003885,0.307789,0.05925079,0.0026752711,0.018117959,0.5998862],"study_design_scores_gemma":[0.000043696928,0.00010285102,0.0035408812,0.000025496434,0.000021490718,0.00011245221,0.000072702715,0.97501934,0.01334615,0.0025621774,0.0051233475,0.000029493543],"about_ca_topic_score_codex":0.007272744,"about_ca_topic_score_gemma":0.007617604,"teacher_disagreement_score":0.007272744,"about_ca_system_score_codex":0.00032801557,"about_ca_system_score_gemma":0.00092041097,"threshold_uncertainty_score":0.014460862},"labels":[],"label_agreement":null},{"id":"W4387784075","doi":"10.1049/ell2.12988","title":"Received signal strength reconstruction using pix2pix generative adversarial network","year":2023,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"CHIST-ERA; China Scholarship Council","keywords":"RSS; Fingerprint (computing); Computer science; Generative grammar; Artificial intelligence; Signal strength; Generative adversarial network; Machine learning; Wireless; Data mining; Pattern recognition (psychology); Deep learning; Telecommunications; World Wide Web","score_opus":0.009899783210038109,"score_gpt":0.20458544809453988,"score_spread":0.19468566488450176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387784075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017263064,0.00011463081,0.9794932,0.00018910278,0.000028053899,0.000027319069,0.00003762578,0.00027884456,0.0025681667],"genre_scores_gemma":[0.93917936,0.00020284645,0.054144416,0.00014059462,0.00002482168,0.00007293856,0.00010420621,0.000050995513,0.006079871],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962175,0.00012129891,0.000010488894,0.000091934315,0.0001106933,0.000043761658],"domain_scores_gemma":[0.999241,0.0004528641,0.00009660058,0.000087997025,0.00008527176,0.000036265214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080329285,0.000677848,0.0005851491,0.0003255637,0.0002720921,0.00065031584,0.0010312987,0.0007217237,0.0020638283],"category_scores_gemma":[0.0015840997,0.00037352525,0.0005530445,0.0003144065,0.0009765234,0.00070943777,0.001401158,0.0010840745,0.00033211982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005549445,0.000010531533,0.00040307536,0.000013886227,0.000016825104,0.000069960486,0.00002424965,0.98015267,0.0011200254,0.008500267,0.00038999578,0.009243057],"study_design_scores_gemma":[0.0000017317349,0.000010213471,0.000035709592,0.0000014781973,0.0000027135006,0.000015247837,0.0000022536979,0.9984517,0.00036796296,0.0009784327,0.00013019754,0.0000022967702],"about_ca_topic_score_codex":0.0022525073,"about_ca_topic_score_gemma":0.0014915052,"teacher_disagreement_score":0.0022525073,"about_ca_system_score_codex":0.0007136719,"about_ca_system_score_gemma":0.0003802599,"threshold_uncertainty_score":0.0069042444},"labels":[],"label_agreement":null},{"id":"W4387789555","doi":"10.1109/tmc.2023.3325826","title":"Combining IMU With Acoustics for Head Motion Tracking Leveraging Wireless Earphone","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Inertial measurement unit; Wireless; Tracking (education); Head (geology); Match moving; Motion (physics); Acoustics; Computer vision; Telecommunications","score_opus":0.021588765239083436,"score_gpt":0.24942493380143993,"score_spread":0.2278361685623565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387789555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0698851,0.001455485,0.9194301,0.0002816959,0.00041600797,0.00013205012,0.00027702464,0.0036552015,0.004467426],"genre_scores_gemma":[0.69321454,0.0014606498,0.29702377,0.00059657544,0.00032997847,0.0002133854,0.00042001036,0.0001371863,0.0066038626],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996025,0.00007695931,0.000026023714,0.00010186937,0.00015222156,0.00004056035],"domain_scores_gemma":[0.999655,0.00007587804,0.00005048689,0.00005615154,0.0001441987,0.000018234396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036198722,0.0008224851,0.000592947,0.00067065854,0.00022325368,0.00054021284,0.0005920068,0.00065919635,0.0013750559],"category_scores_gemma":[0.0014413203,0.0002809825,0.00028838695,0.00068349356,0.00021090155,0.0008177236,0.0009009066,0.00033329654,0.0013373692],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047654405,0.0001266102,0.0091046365,0.00042083513,0.00016142317,0.00037358867,0.00030614086,0.013848084,0.16222179,0.0016401613,0.00567724,0.80564296],"study_design_scores_gemma":[0.00012063922,0.0015002516,0.024979446,0.00016224172,0.00051345775,0.0021527517,0.00032473865,0.638331,0.2836337,0.0033801922,0.04468635,0.0002152268],"about_ca_topic_score_codex":0.001423321,"about_ca_topic_score_gemma":0.0024157257,"teacher_disagreement_score":0.001423321,"about_ca_system_score_codex":0.00016325535,"about_ca_system_score_gemma":0.00030952,"threshold_uncertainty_score":0.0045999885},"labels":[],"label_agreement":null},{"id":"W4387805796","doi":"10.1109/tsp.2023.3324727","title":"High-Accuracy Positioning Services for High-Speed Vehicles in Wideband mmWave Communications","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Simon Fraser University; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wideband; Bandwidth (computing); Wireless; Extremely high frequency; Cramér–Rao bound; Computational complexity theory; Channel state information; Electronic engineering; Channel (broadcasting); Link budget; Communications system; Telecommunications; Real-time computing; Algorithm; Engineering; Estimation theory","score_opus":0.019898374039279857,"score_gpt":0.2572199657679454,"score_spread":0.23732159172866557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387805796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06964171,0.0018798294,0.91236705,0.0014565969,0.0002597426,0.000036060846,0.00015060301,0.00052868266,0.013679678],"genre_scores_gemma":[0.85284865,0.0020445674,0.14077833,0.00021610377,0.00010389549,0.000079349855,0.00025311924,0.000055206,0.003620816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945706,0.00015302724,0.000014706996,0.00004781245,0.0002313507,0.000096064774],"domain_scores_gemma":[0.9994585,0.00025463908,0.00006835039,0.000077448996,0.000112865084,0.000028246883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076560554,0.0003871527,0.00045949707,0.00027616502,0.00045875192,0.0008041361,0.00043220402,0.00090506184,0.0016617412],"category_scores_gemma":[0.0024945033,0.0001988829,0.0003396586,0.00072766264,0.000454492,0.0010380722,0.0008426147,0.00088112074,0.0004757121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049202783,0.00002236105,0.0012312803,0.000108137836,0.000020161415,0.00012423132,0.000078378165,0.9250774,0.0068715033,0.03433255,0.0023022469,0.029782595],"study_design_scores_gemma":[0.0000075822086,0.000038441387,0.00044826363,0.0000123468035,0.0000047717936,0.000041148323,0.000051331175,0.9879353,0.0013450269,0.0072083073,0.0028977538,0.000009660915],"about_ca_topic_score_codex":0.00499394,"about_ca_topic_score_gemma":0.0061881435,"teacher_disagreement_score":0.00499394,"about_ca_system_score_codex":0.0005965542,"about_ca_system_score_gemma":0.0007726475,"threshold_uncertainty_score":0.009929776},"labels":[],"label_agreement":null},{"id":"W4387870354","doi":"10.1109/iccworkshops57953.2023.10283500","title":"Active Beamforming for Integrated Sensing and Communication","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Beamforming; Computer science; Adaptive beamformer; Transmission (telecommunications); Focus (optics); Base station; Electronic engineering; Real-time computing; Telecommunications; Engineering","score_opus":0.013230845730704198,"score_gpt":0.23242261804343672,"score_spread":0.2191917723127325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387870354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009030177,0.00042035696,0.99646735,0.00006829893,0.000036473157,0.000012221266,0.000009738941,0.00006183814,0.002020703],"genre_scores_gemma":[0.4318439,0.0038185485,0.5500171,0.00060206774,0.0003799849,0.0003185093,0.00015351687,0.000094463525,0.012771936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991099,0.000256602,0.00003601631,0.00018083623,0.00034692007,0.00006973952],"domain_scores_gemma":[0.9991978,0.00041298466,0.00009964199,0.000076119555,0.00018070903,0.00003269933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090017094,0.001117022,0.0005944553,0.00044677083,0.00028530785,0.0009698522,0.0010087765,0.0010417158,0.003019983],"category_scores_gemma":[0.0018622846,0.0003958383,0.00042457206,0.0007729443,0.0008535767,0.0014010473,0.001012819,0.0010262511,0.0009363212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019626127,0.00013817044,0.00062487414,0.0004952846,0.00012465278,0.00018127605,0.0002080085,0.31627494,0.061083175,0.18707119,0.004239185,0.4293629],"study_design_scores_gemma":[0.000028664668,0.00027292126,0.0003006767,0.00007910893,0.000043723863,0.00021433075,0.00005661948,0.9109241,0.011951019,0.06120683,0.014881824,0.00004015757],"about_ca_topic_score_codex":0.0007045748,"about_ca_topic_score_gemma":0.0010522412,"teacher_disagreement_score":0.003019983,"about_ca_system_score_codex":0.00050386984,"about_ca_system_score_gemma":0.00062244106,"threshold_uncertainty_score":0.010102868},"labels":[],"label_agreement":null},{"id":"W4387870408","doi":"10.1109/icc45041.2023.10279662","title":"Joint Doppler and Direction of Arrival (DoA) Based Underwater Localization with a Mobile Anchor","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Sonar; Computer science; Cramér–Rao bound; Doppler effect; Benchmark (surveying); Direction of arrival; Algorithm; Position (finance); Computational complexity theory; Underwater; Nonlinear system; Acoustics; Artificial intelligence; Geodesy; Geology; Telecommunications; Estimation theory; Antenna (radio); Physics","score_opus":0.010965609637210279,"score_gpt":0.20214923394098236,"score_spread":0.1911836243037721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387870408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065606316,0.00017477697,0.9920975,0.00004199867,0.000022744085,0.000011654759,0.000018821196,0.0002444089,0.000827537],"genre_scores_gemma":[0.39151427,0.0006164211,0.60252583,0.00006392748,0.0000664278,0.00011161821,0.00017174873,0.000044266617,0.004885532],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961287,0.000077390076,0.000022262762,0.000111543464,0.0001414134,0.000034443907],"domain_scores_gemma":[0.9997825,0.000079642574,0.000037043596,0.00003019284,0.00005836736,0.000012197381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028921932,0.00077816605,0.00071250676,0.0005428311,0.00030818288,0.00058113574,0.0006203215,0.000705695,0.0011058546],"category_scores_gemma":[0.001363589,0.00034289327,0.00040738474,0.00090679945,0.00041926623,0.0009040845,0.0013447572,0.0006723051,0.0008677693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029752587,0.000087042805,0.0024711897,0.00025013127,0.00012212672,0.00034130563,0.00032974218,0.4227709,0.05662212,0.02683334,0.002385114,0.48748943],"study_design_scores_gemma":[0.000020576503,0.00011292346,0.00039329415,0.000012305659,0.000028432183,0.00020158371,0.000030986354,0.9851804,0.008244711,0.0031268634,0.0026227676,0.000025157324],"about_ca_topic_score_codex":0.0025650093,"about_ca_topic_score_gemma":0.0025426839,"teacher_disagreement_score":0.0025650093,"about_ca_system_score_codex":0.00029460836,"about_ca_system_score_gemma":0.0005796863,"threshold_uncertainty_score":0.0051001906},"labels":[],"label_agreement":null},{"id":"W4387870917","doi":"10.23919/usnc-ursi54200.2023.10289472","title":"On the Partial RSS-Connectivity Based Localization in Wireless Sensor Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"RSS; Computer science; Wireless sensor network; Cramér–Rao bound; Upper and lower bounds; Signal strength; Algorithm; Channel (broadcasting); Cumulative distribution function; Wireless network; Wireless; Mathematics; Computer network; Statistics; Probability density function; Estimation theory; Telecommunications","score_opus":0.012424822473639345,"score_gpt":0.21196354465203568,"score_spread":0.19953872217839633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387870917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071098864,0.00393032,0.9203041,0.0002852289,0.00006093788,0.0000281032,0.00006115673,0.00032716815,0.0039042109],"genre_scores_gemma":[0.9206312,0.0052048652,0.07239179,0.00007482321,0.00012405361,0.000058327205,0.00013371349,0.00006316908,0.0013180886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935883,0.00026991847,0.000023943881,0.00008908948,0.00022602572,0.00003219268],"domain_scores_gemma":[0.9983595,0.0010461672,0.00015024583,0.00015903728,0.0002606059,0.000024433133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005528959,0.0005490954,0.0003834146,0.00069111463,0.00019236172,0.00044168154,0.0003715719,0.00037385532,0.0005136935],"category_scores_gemma":[0.003463575,0.00016612747,0.00035081833,0.0009635896,0.00057845993,0.0011097171,0.00056506426,0.00022213873,0.00014159257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015400612,0.000023915813,0.0034716378,0.00043939048,0.00011141499,0.00025439454,0.0001686607,0.78604805,0.020256914,0.017598312,0.0008474874,0.1706258],"study_design_scores_gemma":[0.0000063645407,0.00043549255,0.0025295375,0.00006451455,0.00006790448,0.0005495752,0.0000588573,0.97800297,0.0069030556,0.007558321,0.0037921395,0.000031364554],"about_ca_topic_score_codex":0.0010765387,"about_ca_topic_score_gemma":0.00098187,"teacher_disagreement_score":0.0010765387,"about_ca_system_score_codex":0.00023174717,"about_ca_system_score_gemma":0.0002194498,"threshold_uncertainty_score":0.002924025},"labels":[],"label_agreement":null},{"id":"W4387870990","doi":"10.1109/mlsp55844.2023.10285927","title":"Dual-Path Model With Fresnel Zone-Based Voting For Human Activity Recognition Using WI-FI","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Leverage (statistics); Overhead (engineering); Channel state information; Voting; Real-time computing; Feature extraction; Artificial intelligence; Wireless; Telecommunications","score_opus":0.061374445776787724,"score_gpt":0.26751869461141586,"score_spread":0.20614424883462815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387870990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02026849,0.00011798806,0.978303,0.00005166752,0.000022175158,0.000025500409,0.00003299457,0.0006083154,0.00056986837],"genre_scores_gemma":[0.74948394,0.0001455228,0.24722835,0.00011806438,0.00003763645,0.00009487943,0.00024010074,0.00008859285,0.0025628312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992085,0.00018429714,0.0000334704,0.00022746137,0.00022271476,0.0001235122],"domain_scores_gemma":[0.99956864,0.00015063568,0.000040845054,0.00009331532,0.00011496549,0.00003154546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074116257,0.0006996608,0.0013001956,0.00059897813,0.00042106182,0.0008511677,0.0019502838,0.00079102593,0.0012144262],"category_scores_gemma":[0.0015379273,0.0003727576,0.0008360393,0.0007364942,0.0005352474,0.0013946168,0.0011020178,0.0009162648,0.0006282165],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054672855,0.00019086295,0.0018625103,0.00007680609,0.000118619966,0.000101164806,0.00016958985,0.50054437,0.03289504,0.0076518226,0.001540548,0.454302],"study_design_scores_gemma":[0.0000059032322,0.000024624402,0.00012494397,0.0000016036421,0.0000058885903,0.00001801048,0.0000075466924,0.9955069,0.0024684751,0.0015905336,0.00023853626,0.0000070216297],"about_ca_topic_score_codex":0.0059260866,"about_ca_topic_score_gemma":0.007839453,"teacher_disagreement_score":0.0059260866,"about_ca_system_score_codex":0.0006422982,"about_ca_system_score_gemma":0.00080487976,"threshold_uncertainty_score":0.011783183},"labels":[],"label_agreement":null},{"id":"W4387872916","doi":"10.23919/usnc-ursi54200.2023.10289151","title":"Usage of Channel State Information for localization in a WIFI Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Channel (broadcasting); Channel state information; Computer science; Wireless; Computer network; Transmission (telecommunications); Wireless network; Information transmission; Power (physics); State (computer science); Data transmission; Power delay profile; Electronic engineering; Telecommunications; Real-time computing; Fading; Delay spread; Engineering; Algorithm","score_opus":0.00925356478284481,"score_gpt":0.20764312597646037,"score_spread":0.19838956119361556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387872916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36827376,0.00059805537,0.62698156,0.00017509847,0.000048770955,0.000036406174,0.0002684148,0.00050393865,0.0031140256],"genre_scores_gemma":[0.98721284,0.0001920891,0.012192368,0.000011641224,0.000013341625,0.000011110994,0.00008837063,0.000011637717,0.0002667216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996037,0.0001249415,0.000017927048,0.00007709485,0.000112447626,0.00006393308],"domain_scores_gemma":[0.9980276,0.001298501,0.00021514243,0.00015659111,0.00027001562,0.000032073174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000515112,0.00045030183,0.00029477887,0.00073805393,0.00032940164,0.00057552656,0.00028743804,0.00030651764,0.0005319927],"category_scores_gemma":[0.0039969,0.0001957092,0.00018108731,0.0006714243,0.0003007316,0.0011424049,0.00031482373,0.00038606746,0.00012486464],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042811775,0.000118980075,0.03628634,0.00021400487,0.00011269374,0.00026434445,0.00017949406,0.70573086,0.031882305,0.004925883,0.00065794797,0.21919902],"study_design_scores_gemma":[0.000011105866,0.00012771522,0.011974212,0.000021793496,0.00006738173,0.00017936529,0.000058732894,0.9697725,0.015808176,0.0012103418,0.00073046924,0.00003820984],"about_ca_topic_score_codex":0.0038352131,"about_ca_topic_score_gemma":0.00500993,"teacher_disagreement_score":0.0038352131,"about_ca_system_score_codex":0.00031025655,"about_ca_system_score_gemma":0.00054813403,"threshold_uncertainty_score":0.0076257586},"labels":[],"label_agreement":null},{"id":"W4387951254","doi":"10.1109/ccece58730.2023.10289018","title":"Trajectory-Based User Tracking and Beam Assignment in a Hallway using Phased Array Antenna","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Beamforming; Computer science; Trajectory; Antenna (radio); Phased array; Tracking (education); Tracking system; Interference (communication); Real-time computing; SIGNAL (programming language); Smart antenna; Directional antenna; Electronic engineering; Engineering; Telecommunications; Artificial intelligence; Kalman filter","score_opus":0.026147214450007762,"score_gpt":0.2456274554006774,"score_spread":0.21948024095066965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055978477,0.00018633631,0.93854004,0.00010018727,0.00009142386,0.000049392056,0.00008214337,0.002541123,0.0024309154],"genre_scores_gemma":[0.7072495,0.00023424021,0.28672978,0.0001673348,0.00008180735,0.000121539946,0.00022114943,0.000054028053,0.005140702],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994198,0.00017765054,0.000027416421,0.00016892258,0.00012419841,0.000082037084],"domain_scores_gemma":[0.9995515,0.00008506802,0.000079201876,0.00009579645,0.00014570303,0.00004271256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028356066,0.0006224461,0.00059432385,0.0004232434,0.00039668597,0.00051410246,0.0010054642,0.00064055866,0.0018078217],"category_scores_gemma":[0.0006457397,0.00026371132,0.00033714753,0.0007420506,0.00028686476,0.00080882973,0.0006148237,0.00040305368,0.0013764696],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015150673,0.0002574228,0.013494378,0.00024854066,0.00021971279,0.00061357586,0.0004889165,0.10329964,0.26372936,0.004564891,0.0051634084,0.6064051],"study_design_scores_gemma":[0.00017352331,0.0013644911,0.0074900216,0.000035881607,0.0001664331,0.0012825282,0.00026247482,0.8701909,0.102444075,0.002158652,0.014300603,0.00013048966],"about_ca_topic_score_codex":0.0017093826,"about_ca_topic_score_gemma":0.0020473853,"teacher_disagreement_score":0.0018078217,"about_ca_system_score_codex":0.00030787633,"about_ca_system_score_gemma":0.0004868103,"threshold_uncertainty_score":0.0060477257},"labels":[],"label_agreement":null},{"id":"W4388008106","doi":"10.1145/3616390.3618289","title":"New Machine Learning Hybrid Models to Lower Position Errors for Bluetooth-Based Indoor Localizations","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Telus (Canada); Sheridan College","funders":"","keywords":"Mean squared error; Computer science; Line (geometry); Artificial intelligence; Position (finance); Field (mathematics); Centroid; Point (geometry); Algorithm; Machine learning; Mathematics; Statistics","score_opus":0.01927212167692067,"score_gpt":0.23141976938561645,"score_spread":0.2121476477086958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388008106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035397142,0.0005593315,0.96104556,0.00014392013,0.00008605115,0.000032686698,0.00007250159,0.00083784584,0.0018249607],"genre_scores_gemma":[0.86436564,0.00058909116,0.12932326,0.00016001044,0.00009478868,0.00014583365,0.0002795256,0.00013175576,0.004910055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995876,0.00009634524,0.000027241227,0.000099909375,0.00015005871,0.00003889486],"domain_scores_gemma":[0.9991059,0.0003498527,0.0001084552,0.000091895105,0.0003247266,0.00001922185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000775145,0.0008467186,0.00078170164,0.0007740046,0.0002790159,0.0007537216,0.0014246228,0.00072459923,0.0011532963],"category_scores_gemma":[0.0025513712,0.0003207481,0.00069405476,0.00072134903,0.00036675824,0.0010043035,0.0006516833,0.00080374046,0.0006328365],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007911456,0.000060098504,0.0015091815,0.00005965584,0.00006282864,0.00005436256,0.000068739326,0.88317794,0.0031291102,0.0024255484,0.00076762296,0.10860567],"study_design_scores_gemma":[0.0000023201758,0.000018597793,0.00019370142,0.0000039711836,0.000007770455,0.000012796529,0.000004332514,0.9985409,0.0004767632,0.0004333353,0.00030097496,0.00000464312],"about_ca_topic_score_codex":0.0056903786,"about_ca_topic_score_gemma":0.0056886254,"teacher_disagreement_score":0.0056903786,"about_ca_system_score_codex":0.00041704334,"about_ca_system_score_gemma":0.000526952,"threshold_uncertainty_score":0.011314511},"labels":[],"label_agreement":null},{"id":"W4388017335","doi":"10.1109/jiot.2023.3328544","title":"Probabilistic Localization With Gateway Location Errors and Multiple Transmissions","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Time of arrival; Probability density function; Probabilistic logic; Measure (data warehouse); Context (archaeology); A priori and a posteriori; Algorithm; Radar; Radio navigation; Wireless; Telecommunications; Global Positioning System; Statistics; Mathematics; Artificial intelligence; Data mining","score_opus":0.011215910968240376,"score_gpt":0.21286863697788846,"score_spread":0.2016527260096481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388017335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10606037,0.0008040196,0.8901177,0.00034648174,0.00008389149,0.000033917477,0.00012306709,0.00032806845,0.0021024211],"genre_scores_gemma":[0.9622935,0.0005314007,0.035158265,0.00005048429,0.00008122092,0.00004117882,0.00011467035,0.0000463417,0.0016829662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716115,0.0006634183,0.00014262681,0.0007078592,0.0009963706,0.00032854965],"domain_scores_gemma":[0.98984224,0.0068814005,0.0017171236,0.00074291165,0.00068816624,0.00012813736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00372841,0.0012431471,0.0016410954,0.00087067304,0.0005702676,0.0013851897,0.0018901278,0.0017661133,0.0006267241],"category_scores_gemma":[0.016861442,0.0008969381,0.00095591997,0.0018430348,0.0019216996,0.0029164709,0.002698421,0.0014200223,0.00017681123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000727131,0.0000071968393,0.0012483415,0.000035257992,0.00002044983,0.00018647868,0.00006566188,0.98539716,0.00067063473,0.0058194255,0.00009444027,0.0063822344],"study_design_scores_gemma":[0.000013090625,0.0000593101,0.001325415,0.000011582331,0.000023917053,0.00013708061,0.000033858007,0.99124944,0.00092753605,0.0059452015,0.00024714757,0.000026440106],"about_ca_topic_score_codex":0.0062318933,"about_ca_topic_score_gemma":0.00345743,"teacher_disagreement_score":0.0062318933,"about_ca_system_score_codex":0.0012341759,"about_ca_system_score_gemma":0.0009409653,"threshold_uncertainty_score":0.019717932},"labels":[],"label_agreement":null},{"id":"W4388040557","doi":"10.1109/pimrc56721.2023.10293749","title":"A Geometric Approach for Cooperative Direct Localization","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fujitsu","keywords":"Computer science; Grid; Binary number; Relaxation (psychology); Planar; Path (computing); Range (aeronautics); SIGNAL (programming language); Algorithm; Norm (philosophy); Function (biology); Mathematical optimization; Mathematics","score_opus":0.018121361110692163,"score_gpt":0.2293428753579276,"score_spread":0.21122151424723545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388040557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063651544,0.00008851054,0.997262,0.00007189428,0.000021346304,0.000013371176,0.000010581445,0.00007243067,0.0018233525],"genre_scores_gemma":[0.19849598,0.0009917647,0.7862179,0.00033641353,0.0002217939,0.00035215027,0.00019661659,0.00016488989,0.013022442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990088,0.00024789493,0.00003072666,0.00026505665,0.00037715782,0.00007034033],"domain_scores_gemma":[0.99914384,0.0003332172,0.000088237204,0.0001856068,0.0002084894,0.000040679228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006618289,0.0010754915,0.00079147867,0.0012223322,0.0005655093,0.0010813974,0.0018569513,0.00115476,0.0036482047],"category_scores_gemma":[0.002605975,0.00053286046,0.0009368812,0.0012999307,0.0013848369,0.0016715338,0.0030651807,0.0013508126,0.0015087409],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006553527,0.000084248764,0.0004975185,0.00023833745,0.0000618363,0.00033220323,0.0003322279,0.49688298,0.016488826,0.2769555,0.007572301,0.2004885],"study_design_scores_gemma":[0.000021655354,0.00015661301,0.00023392178,0.000023435194,0.000022684359,0.00055977446,0.00007711435,0.9013331,0.003936483,0.07037535,0.023220258,0.000039620372],"about_ca_topic_score_codex":0.0012680927,"about_ca_topic_score_gemma":0.0013250243,"teacher_disagreement_score":0.0036482047,"about_ca_system_score_codex":0.0007609849,"about_ca_system_score_gemma":0.0008512853,"threshold_uncertainty_score":0.012204409},"labels":[],"label_agreement":null},{"id":"W4388264624","doi":"10.36227/techrxiv.24425536","title":"Fast Selection of Indoor Wireless Transmitter Locations with Generalizable Neural Network Propagation Models","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Computer science; Transmitter; Fidelity; Wireless; Wireless network; Software deployment; Artificial neural network; Radio propagation; Artificial intelligence; Machine learning; Telecommunications","score_opus":0.02066291195788003,"score_gpt":0.2118785364679511,"score_spread":0.19121562451007107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388264624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05671263,0.00030827321,0.938141,0.0003611211,0.00007450876,0.00002581455,0.000124185,0.0011679922,0.003084557],"genre_scores_gemma":[0.85565585,0.00023845701,0.13699088,0.00019439979,0.000068340196,0.00008030081,0.00041379963,0.00012736447,0.006230581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998153,0.00005622094,0.0000068422373,0.00005092744,0.00003615804,0.000034660807],"domain_scores_gemma":[0.9991585,0.0005459229,0.00008847224,0.000051027826,0.00012269925,0.00003332111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056653057,0.00096273015,0.0006029765,0.00036071692,0.00026338265,0.0006204986,0.0010671879,0.0011284277,0.001472165],"category_scores_gemma":[0.002740165,0.0006518457,0.00044592752,0.00042118045,0.0005240324,0.00086325494,0.00067008124,0.0013497877,0.0005027484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032446267,0.000015560541,0.00038156216,0.000009783289,0.000009613553,0.000022425671,0.000009646201,0.98263645,0.0005965805,0.0008230165,0.00048169182,0.014981229],"study_design_scores_gemma":[8.3222096e-7,0.0000014118777,0.00001995061,6.535997e-7,5.038043e-7,0.0000010494886,6.319652e-7,0.99967647,0.0000755674,0.00019879325,0.000023613733,5.804899e-7],"about_ca_topic_score_codex":0.013746225,"about_ca_topic_score_gemma":0.016867949,"teacher_disagreement_score":0.013746225,"about_ca_system_score_codex":0.0008430262,"about_ca_system_score_gemma":0.0007741255,"threshold_uncertainty_score":0.027332425},"labels":[],"label_agreement":null},{"id":"W4388267200","doi":"10.36227/techrxiv.24425536.v1","title":"Fast Selection of Indoor Wireless Transmitter Locations with Generalizable Neural Network Propagation Models","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Transmitter; Fidelity; Computer science; Wireless; Wireless network; Software deployment; Artificial neural network; Radio propagation; Artificial intelligence; Telecommunications","score_opus":0.02066291195788003,"score_gpt":0.2118785364679511,"score_spread":0.19121562451007107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388267200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062955596,0.00028322326,0.93245494,0.0003020289,0.000055906406,0.00002427293,0.00010086625,0.0008872748,0.0029359132],"genre_scores_gemma":[0.87103724,0.00021420186,0.122867584,0.00014646174,0.00005558599,0.000080359634,0.00028022283,0.00009791727,0.005220405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998179,0.000055348457,0.000006354449,0.000048248672,0.000039441693,0.000032811582],"domain_scores_gemma":[0.999252,0.00047528918,0.00008533069,0.000045434466,0.000111140114,0.00003080495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005253006,0.0008954633,0.0005393765,0.00034844095,0.0002649614,0.00056599703,0.0009857673,0.0010695991,0.0013769135],"category_scores_gemma":[0.0027165094,0.0006267704,0.00039091308,0.00041048703,0.0005158964,0.0008642042,0.0006818193,0.0011984344,0.0004514709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025505624,0.0000122506935,0.00033663187,0.000008675906,0.000007466122,0.00002051795,0.000008516421,0.98744804,0.0005662133,0.0008177128,0.00033060837,0.0104178125],"study_design_scores_gemma":[8.7547966e-7,0.0000015079876,0.000021179441,6.4685827e-7,4.6877412e-7,0.0000012663953,5.7417554e-7,0.9996722,0.00007583933,0.00020197434,0.000022981383,5.805206e-7],"about_ca_topic_score_codex":0.010577283,"about_ca_topic_score_gemma":0.013003424,"teacher_disagreement_score":0.010577283,"about_ca_system_score_codex":0.0007713895,"about_ca_system_score_gemma":0.00071445754,"threshold_uncertainty_score":0.02103144},"labels":[],"label_agreement":null},{"id":"W4388426759","doi":"10.1109/spawc53906.2023.10304519","title":"Active Sensing for Reciprocal MIMO Channels","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Precoding; Decoding methods; MIMO; Computer science; Transmitter; Channel state information; Overhead (engineering); Channel (broadcasting); Duplex (building); Algorithm; Theoretical computer science; Computer engineering; Artificial intelligence; Wireless; Computer network; Telecommunications","score_opus":0.021218229259106172,"score_gpt":0.24377689782958387,"score_spread":0.22255866857047768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388426759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010869593,0.0002945146,0.98615915,0.0000890866,0.000038492235,0.000021155925,0.000023164484,0.0001356667,0.0023692236],"genre_scores_gemma":[0.80251116,0.0008183013,0.19037147,0.0002071728,0.00013784584,0.00010138364,0.000101313475,0.000047546204,0.0057038283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993069,0.0002222813,0.000024704352,0.00014951396,0.00022323302,0.000073368086],"domain_scores_gemma":[0.99895585,0.0006462657,0.000101480306,0.00011042066,0.00015413409,0.000031914293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008699512,0.0008446577,0.00055494916,0.0003549823,0.00036187438,0.00090906466,0.00081447104,0.00081011443,0.0014769237],"category_scores_gemma":[0.002376346,0.0003592741,0.00054942473,0.00034570228,0.0010756827,0.0011970585,0.0009336873,0.0009665047,0.0003903485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030972034,0.00009751305,0.00046688478,0.00024538755,0.00008361201,0.00021245192,0.00027971884,0.70047665,0.034147173,0.100282624,0.0014713113,0.16192695],"study_design_scores_gemma":[0.000009071605,0.00005184759,0.0000787756,0.000008042731,0.0000098409555,0.00005259551,0.000013497986,0.98602027,0.0035057955,0.009292902,0.00094508956,0.000012275893],"about_ca_topic_score_codex":0.0013910605,"about_ca_topic_score_gemma":0.001698686,"teacher_disagreement_score":0.0014769237,"about_ca_system_score_codex":0.00051959255,"about_ca_system_score_gemma":0.00061792904,"threshold_uncertainty_score":0.004940748},"labels":[],"label_agreement":null},{"id":"W4388893659","doi":"10.1109/wincom59760.2023.10323029","title":"DSP: A Deep Neural Network Approach for Serving Cell Positioning in Mobile Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Deep learning; Artificial neural network; Base station; Data pre-processing; Artificial intelligence; Ranging; Raw data; Cellular network; Network topology; Hybrid positioning system; Preprocessor; Digital signal processing; Wireless; Real-time computing; Data mining; Telecommunications; Computer network; Node (physics); Positioning system; Computer hardware; Engineering","score_opus":0.007820388450491655,"score_gpt":0.2030618984743959,"score_spread":0.19524151002390425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388893659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02968222,0.0008941492,0.96490246,0.0003474392,0.00013384924,0.00004069872,0.0003086966,0.001147321,0.0025431474],"genre_scores_gemma":[0.75401413,0.0009895022,0.233419,0.00034937615,0.00012757041,0.00012256703,0.0010604557,0.00010090264,0.009816375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986553,0.00003101434,0.00000878361,0.0000324553,0.00003484746,0.000027297181],"domain_scores_gemma":[0.99980277,0.00006889935,0.000020254576,0.000013847477,0.00008025162,0.000014010962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039895266,0.00072489865,0.0003957142,0.000461687,0.0002416635,0.0004658776,0.001021692,0.0007167069,0.0013198723],"category_scores_gemma":[0.0007778984,0.000302287,0.00036034072,0.00057684456,0.000281001,0.00062762486,0.0007057139,0.0011553317,0.00039232665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009818658,0.00007036385,0.0014504417,0.00007287984,0.000062867104,0.000082461214,0.000039306575,0.74091107,0.004015067,0.0035445504,0.0031862734,0.2464665],"study_design_scores_gemma":[0.0000020237662,0.000013789316,0.00011069945,0.0000034901711,0.0000029563098,0.0000071210216,0.000004847971,0.99814177,0.0004985454,0.00090875546,0.00030382242,0.0000022726013],"about_ca_topic_score_codex":0.011267959,"about_ca_topic_score_gemma":0.014612841,"teacher_disagreement_score":0.011267959,"about_ca_system_score_codex":0.00062185345,"about_ca_system_score_gemma":0.0007232074,"threshold_uncertainty_score":0.02240473},"labels":[],"label_agreement":null},{"id":"W4389115585","doi":"10.48550/arxiv.2311.15062","title":"Simultaneous Beam Training and Target Sensing in ISAC Systems with RIS","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Government of Jiangsu Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Scheme (mathematics); Computer science; Beam (structure); Domain (mathematical analysis); Line-of-sight; Real-time computing; Line (geometry); Base station; Doppler effect; Orientation (vector space); Simulation; Electronic engineering; Acoustics; Optics; Telecommunications; Aerospace engineering; Engineering; Physics; Geometry; Mathematics","score_opus":0.04745922216901481,"score_gpt":0.16332799322611036,"score_spread":0.11586877105709555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389115585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20531358,0.00022530831,0.78980243,0.00013023773,0.000042717707,0.000028376888,0.000027717475,0.00056468823,0.0038649382],"genre_scores_gemma":[0.91280144,0.00006866079,0.08610441,0.00004879624,0.0000151916065,0.000019295485,0.000026975535,0.000012550545,0.000902614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995796,0.00008687131,0.000011236081,0.00007577469,0.0001756997,0.000070901544],"domain_scores_gemma":[0.9996215,0.00015942476,0.00006375424,0.00006472001,0.00006920814,0.00002130969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029991328,0.00041919868,0.00042316632,0.00024438795,0.00025960116,0.00041684485,0.00046047,0.00045084453,0.00046415906],"category_scores_gemma":[0.0010444009,0.00020779372,0.00025761264,0.00059719133,0.0005177478,0.00058700115,0.00064895157,0.00046320877,0.00013631149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037406824,0.000080349404,0.005664942,0.00007591335,0.00005274234,0.00027810497,0.00022142232,0.76538163,0.07089225,0.009988747,0.00062793517,0.14636187],"study_design_scores_gemma":[0.000010517475,0.00009644417,0.0008702973,0.0000029178689,0.000008360334,0.000089193985,0.000027171463,0.9890593,0.008486992,0.00086798193,0.0004711781,0.000009611174],"about_ca_topic_score_codex":0.0029855147,"about_ca_topic_score_gemma":0.0037576284,"teacher_disagreement_score":0.0029855147,"about_ca_system_score_codex":0.0003383047,"about_ca_system_score_gemma":0.0005889171,"threshold_uncertainty_score":0.005936265},"labels":[],"label_agreement":null},{"id":"W4389301022","doi":"10.33168/jsms.2022.0307","title":"Indoor Localization in Wireless Networks Using Received Signal Strength: A Model using Bregman Distance to Increase Public Utility Benefits","year":2022,"lang":"en","type":"article","venue":"Journal of System and Management Sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Signal strength; Wireless; SIGNAL (programming language); Computer science; Wireless network; Radio signal; Telecommunications; Econometrics; Mathematics; Radio frequency","score_opus":0.03105099756139901,"score_gpt":0.23481049460484205,"score_spread":0.20375949704344304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389301022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07308502,0.0022100809,0.8819025,0.0034017507,0.00017843346,0.00018423965,0.00025769806,0.00026275488,0.038517606],"genre_scores_gemma":[0.8957753,0.0029657437,0.071869,0.00039565843,0.0001018537,0.0003071212,0.00013767381,0.00011118543,0.028336389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777573,0.001068195,0.00005860632,0.00034546832,0.0004902797,0.00026171617],"domain_scores_gemma":[0.996302,0.0022681304,0.00049275224,0.00025630827,0.00055131025,0.00012938604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021917631,0.001630458,0.000943235,0.00206683,0.0008967633,0.0022305534,0.0036746713,0.0030373977,0.003621247],"category_scores_gemma":[0.008197026,0.00066647393,0.0011899134,0.002775857,0.002626094,0.0060091815,0.0029222087,0.0019871925,0.0013993444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056193247,0.00004361057,0.00072270224,0.000091724076,0.000025449564,0.00014564818,0.00019832629,0.85297304,0.0011242306,0.13432471,0.00087926985,0.009415039],"study_design_scores_gemma":[0.000025139398,0.00014530537,0.0003956701,0.000046450885,0.000028273262,0.00017393859,0.00013054362,0.9566547,0.00033867283,0.038403343,0.0036173882,0.00004057589],"about_ca_topic_score_codex":0.008928633,"about_ca_topic_score_gemma":0.006984065,"teacher_disagreement_score":0.008928633,"about_ca_system_score_codex":0.004095821,"about_ca_system_score_gemma":0.001114701,"threshold_uncertainty_score":0.029717386},"labels":[],"label_agreement":null},{"id":"W4389302254","doi":"10.1049/cit2.12274","title":"Guest Editorial: Special issue on explainable AI empowered for indoor positioning and indoor navigation","year":2023,"lang":"en","type":"editorial","venue":"CAAI Transactions on Intelligence Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Nanyang Technological University; Department of Science and Technology, Ministry of Science and Technology, India; Ministry of Education, India; Tamilnadu State Council For Science And Technology","keywords":"Computer science; Aeronautics; Architectural engineering; Positioning technology; Engineering; Real-time computing","score_opus":0.00783755641009503,"score_gpt":0.2682503812444514,"score_spread":0.26041282483435635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389302254","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000091006914,0.005837934,0.00031116244,0.037521932,0.95325893,0.000017742888,0.0000814526,0.00009058548,0.002789269],"genre_scores_gemma":[0.0006915371,0.0045794863,0.00013453516,0.012890865,0.9698713,0.000020729067,0.000059129943,0.0000694769,0.011682863],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977818,0.00030002565,0.00019813889,0.00048117156,0.00097188674,0.00026700084],"domain_scores_gemma":[0.9922655,0.002612443,0.00048504485,0.0002593877,0.0028669343,0.0015105939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027627952,0.0024877149,0.0016914724,0.0020213586,0.0016506983,0.0066951243,0.002648375,0.009082496,0.046286304],"category_scores_gemma":[0.010584931,0.00068139186,0.0021463332,0.00075799646,0.0018643518,0.0037652527,0.0019280575,0.011785071,0.01791689],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041022056,0.000013052675,0.000037371243,0.00021325877,0.000011737648,0.000106541505,0.00001727654,0.00003036372,0.00012750327,0.0006349365,0.992019,0.006748009],"study_design_scores_gemma":[0.000028139948,0.000029291992,0.00016617119,0.00021710481,0.000022616756,0.00022961694,0.000038639148,0.000116294534,0.00015254703,0.0009774562,0.9980096,0.000012393137],"about_ca_topic_score_codex":0.00081588246,"about_ca_topic_score_gemma":0.0018744477,"teacher_disagreement_score":0.046286304,"about_ca_system_score_codex":0.002046096,"about_ca_system_score_gemma":0.0015255364,"threshold_uncertainty_score":0.15484315},"labels":[],"label_agreement":null},{"id":"W4389322310","doi":"10.36227/techrxiv.24715296","title":"Freestyle Object Localization","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Object (grammar); Computer science; Artificial intelligence; Probabilistic logic; Class (philosophy); Annotation; Statistical model; Object model; Pattern recognition (psychology); Function (biology); Machine learning; Computer vision","score_opus":0.021540018325886017,"score_gpt":0.23321029008091737,"score_spread":0.21167027175503136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389322310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007845845,0.00059854577,0.9822757,0.0002116267,0.000090780246,0.00004400849,0.00045510885,0.006073951,0.0024043433],"genre_scores_gemma":[0.49419045,0.0010575702,0.4742587,0.0009082533,0.00023555805,0.00024156421,0.0057000294,0.0014834041,0.021924464],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998835,0.00014361413,0.000044969947,0.0005862274,0.00023133418,0.00015872321],"domain_scores_gemma":[0.9989579,0.00016738617,0.000101431746,0.00053355406,0.00017793661,0.00006178287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007728448,0.0014702793,0.0015117488,0.0008548301,0.00061455806,0.0017421684,0.003973181,0.0021557729,0.0053184777],"category_scores_gemma":[0.0024704412,0.00055269094,0.0012746437,0.0013763419,0.0013094728,0.0041921255,0.004446505,0.0015757836,0.0042483634],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041058796,0.00016300537,0.0033588582,0.0004412635,0.00018933907,0.0005190496,0.00033880406,0.23207225,0.01876169,0.039469417,0.043917228,0.66035855],"study_design_scores_gemma":[0.00002407302,0.00009315173,0.00079397345,0.000044787917,0.000035881316,0.00048675516,0.00009287648,0.9242481,0.01192623,0.04368687,0.018524993,0.000042246927],"about_ca_topic_score_codex":0.006136737,"about_ca_topic_score_gemma":0.006172559,"teacher_disagreement_score":0.006136737,"about_ca_system_score_codex":0.001021874,"about_ca_system_score_gemma":0.0009833988,"threshold_uncertainty_score":0.017792106},"labels":[],"label_agreement":null},{"id":"W4389370803","doi":"10.1109/ojits.2023.3336795","title":"Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Intelligent Transportation Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Channel state information; Decision tree; Feature selection; Chirp; Artificial intelligence; Feature (linguistics); Channel (broadcasting); Pattern recognition (psychology); Support vector machine; Heuristic; Short-time Fourier transform; Data mining; Principal component analysis; Tree (set theory); Task (project management); Machine learning; Fourier transform; Wireless; Engineering; Telecommunications; Mathematics","score_opus":0.02397072988502735,"score_gpt":0.24908888081529645,"score_spread":0.2251181509302691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389370803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8967705,0.00044796418,0.09937801,0.00016089871,0.000035901696,0.000064644446,0.0011034119,0.00078457844,0.0012540543],"genre_scores_gemma":[0.9818086,0.00010204948,0.01644161,0.000018527113,0.000020468138,0.000035755907,0.0012381643,0.000012257136,0.00032250764],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996717,0.00004699328,0.000024574707,0.00008618134,0.000100395926,0.00007026956],"domain_scores_gemma":[0.99943286,0.00021699806,0.00011357958,0.00004886935,0.00014721479,0.000040488434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033909467,0.0005256267,0.0005262172,0.0015665785,0.0002086693,0.000491701,0.0002680276,0.00031893194,0.00040970414],"category_scores_gemma":[0.0014797145,0.00009301067,0.00038146006,0.00074612175,0.0001346919,0.0004928845,0.00026098496,0.00028396645,0.0002377436],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006506019,0.0005586141,0.21962824,0.00025748706,0.00018199476,0.00042115565,0.00020479558,0.09466744,0.0667372,0.0006987912,0.004114182,0.6118795],"study_design_scores_gemma":[0.0000192758,0.00026803868,0.18851703,0.000027012882,0.00008127358,0.00026501785,0.00023025087,0.7849216,0.023660343,0.0005400737,0.0014330134,0.000037054757],"about_ca_topic_score_codex":0.0043845386,"about_ca_topic_score_gemma":0.0044034375,"teacher_disagreement_score":0.0043845386,"about_ca_system_score_codex":0.00021942155,"about_ca_system_score_gemma":0.00037756178,"threshold_uncertainty_score":0.008718073},"labels":[],"label_agreement":null},{"id":"W4389372428","doi":"10.1109/ipin57070.2023.10332225","title":"Proximity Estimation with BLE RSSI and UWB Range Using Machine Learning Algorithm","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"3v Geomatics (Canada)","funders":"","keywords":"Ranging; Bluetooth; Computer science; Received signal strength indication; Non-line-of-sight propagation; Context (archaeology); Real-time computing; Mobile phone; Ultra-wideband; Mobile device; Phone; Bluetooth Low Energy; Range (aeronautics); Artificial intelligence; Wireless; Algorithm; Telecommunications; Engineering","score_opus":0.011711010940368154,"score_gpt":0.2130885029225057,"score_spread":0.20137749198213753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389372428","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054652642,0.00039334694,0.94052285,0.00009129517,0.000064420055,0.000061196246,0.0001563479,0.0025349578,0.0015230206],"genre_scores_gemma":[0.63668674,0.00026311734,0.35792,0.0001327508,0.000087997,0.00024702257,0.00081603444,0.00008871661,0.0037576784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992706,0.00013366666,0.000059802238,0.00029305278,0.00016832877,0.00007451564],"domain_scores_gemma":[0.99909115,0.0003846639,0.00014228087,0.000103410006,0.00023933027,0.000039200855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006090367,0.0010402546,0.0011736003,0.002013376,0.0003804996,0.0007126015,0.0010948986,0.0011494145,0.0012890329],"category_scores_gemma":[0.002857947,0.00034714187,0.0008675292,0.0012967316,0.00030338095,0.0009131743,0.0007577459,0.00096835324,0.0016412452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028455845,0.00034291024,0.01113479,0.00014212617,0.00013860047,0.00024724423,0.00011178636,0.39876986,0.01146049,0.00138139,0.0024023396,0.5735839],"study_design_scores_gemma":[0.000008514263,0.000075577824,0.0017976868,0.000012451938,0.00001431106,0.00010811919,0.000021269596,0.9936045,0.002913261,0.00088280375,0.0005475364,0.000013953147],"about_ca_topic_score_codex":0.0021130806,"about_ca_topic_score_gemma":0.001516262,"teacher_disagreement_score":0.0021130806,"about_ca_system_score_codex":0.00035134246,"about_ca_system_score_gemma":0.0004237484,"threshold_uncertainty_score":0.004312277},"labels":[],"label_agreement":null},{"id":"W4389372448","doi":"10.1109/ipin57070.2023.10332540","title":"Demonstrating the Merits of Integrating Multipath Signals into 5G LoS-Based Positioning Systems for Navigation in Challenging Environments","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multipath propagation; Computer science; Real-time computing; Telecommunications","score_opus":0.013466851802235407,"score_gpt":0.24374112723025598,"score_spread":0.23027427542802056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389372448","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24330562,0.0025397735,0.72965354,0.0011014738,0.00030489403,0.00009336094,0.00019141154,0.0011338817,0.021675974],"genre_scores_gemma":[0.90700173,0.0012305998,0.08952548,0.0001377431,0.00011336699,0.0000278247,0.00009738133,0.000036785113,0.0018291365],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995403,0.000110220695,0.000016368196,0.00007371117,0.00019523945,0.00006417808],"domain_scores_gemma":[0.9993654,0.00022599248,0.00006585663,0.00012246172,0.00019758713,0.000022604627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004676625,0.00075791666,0.00028314302,0.00039096895,0.00029667097,0.00067267584,0.00043676034,0.0007391784,0.0015416787],"category_scores_gemma":[0.0015688363,0.00016359708,0.00020429486,0.00056927814,0.00039056383,0.0009872044,0.00071353384,0.00047025274,0.0006718468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005864701,0.00012277166,0.018312415,0.00068332785,0.00019448885,0.0007755128,0.0004587692,0.18296479,0.25209117,0.022654684,0.0024278103,0.5187278],"study_design_scores_gemma":[0.00007905922,0.0024820867,0.032214157,0.00028413357,0.00035511717,0.0025649227,0.0008173052,0.65168566,0.23879242,0.013457134,0.0570756,0.00019245727],"about_ca_topic_score_codex":0.0021345578,"about_ca_topic_score_gemma":0.0045475685,"teacher_disagreement_score":0.0021345578,"about_ca_system_score_codex":0.0003312559,"about_ca_system_score_gemma":0.0004373513,"threshold_uncertainty_score":0.005157411},"labels":[],"label_agreement":null},{"id":"W4389665680","doi":"10.1109/iros55552.2023.10342466","title":"Magnetic Navigation Using Attitude-Invariant Magnetic Field Information for Loop Closure Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Magnetometer; Robot; Computer vision; Invariant (physics); Magnetic field; Computer science; Earth's magnetic field; Global Positioning System; Artificial intelligence; Mobile robot; Magnetic survey; Remote sensing; Algorithm; Physics; Geophysics; Magnetic anomaly; Geography","score_opus":0.011697759689027892,"score_gpt":0.22781503289363184,"score_spread":0.21611727320460394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389665680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0665837,0.00024676116,0.9288922,0.000047986294,0.00007367845,0.00003499711,0.00010477543,0.0016636599,0.0023522596],"genre_scores_gemma":[0.779255,0.00018269413,0.21781752,0.00005659476,0.00006429395,0.000066536784,0.00039591757,0.000054641332,0.0021068153],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998417,0.000016320126,0.000006207171,0.000051115338,0.00006217625,0.000022428045],"domain_scores_gemma":[0.99980396,0.000033830842,0.000051758743,0.000025415866,0.000072827985,0.000012271195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000101376434,0.0005279213,0.00041978533,0.00089668937,0.00023539849,0.00031691333,0.00039619184,0.00030594107,0.00074402615],"category_scores_gemma":[0.00070848037,0.0001544794,0.00020236104,0.00060051633,0.00018677775,0.0003391155,0.0004354672,0.00027671823,0.0005832746],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025930244,0.00011788906,0.006855433,0.00010653224,0.00004499336,0.00017492818,0.00017996092,0.04293824,0.11387623,0.002061945,0.0023214952,0.8310631],"study_design_scores_gemma":[0.000047227655,0.00034133487,0.013567753,0.000035554305,0.000045969682,0.0005383539,0.00013760735,0.91528517,0.05848079,0.0032128086,0.008257189,0.000050152903],"about_ca_topic_score_codex":0.0016794293,"about_ca_topic_score_gemma":0.0031714826,"teacher_disagreement_score":0.0016794293,"about_ca_system_score_codex":0.00015886524,"about_ca_system_score_gemma":0.00034726388,"threshold_uncertainty_score":0.0033392906},"labels":[],"label_agreement":null},{"id":"W4389703979","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-619-2023","title":"LOW-COST CLOUD-BASED HD-MAP UPDATES FOR INFRASTRUCTURE MANAGEMENT AND MAINTENANCE","year":2023,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Upload; Map matching; Cloud computing; Cloud database; GNSS applications; Android (operating system); Real-time computing; Data mining; Global Map; Pipeline (software); Database; Matching (statistics); Process (computing); Artificial intelligence; Global Positioning System; Operating system; Robot","score_opus":0.010330557493555693,"score_gpt":0.2333487170201138,"score_spread":0.22301815952655812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389703979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12559262,0.0013987499,0.8072652,0.000659703,0.0006566058,0.00060645834,0.0077699255,0.037947137,0.018103553],"genre_scores_gemma":[0.8500308,0.00043020534,0.13728498,0.00016926332,0.00008979234,0.00022034327,0.0055091125,0.00032625676,0.0059392685],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99963486,0.00003115486,0.000016073835,0.00009350181,0.00016630489,0.000058010988],"domain_scores_gemma":[0.99936444,0.00006402235,0.000052773903,0.00024452215,0.00022050152,0.000053799995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024819555,0.0007804032,0.0005162632,0.0007551983,0.00049578754,0.0007379389,0.0018156447,0.00043804516,0.004646219],"category_scores_gemma":[0.0012665169,0.0002739765,0.0003552601,0.00090331037,0.00018285257,0.0015131019,0.0013941673,0.00045013145,0.0019422199],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009013014,0.00036386447,0.01536309,0.0005260818,0.00016368968,0.0010459941,0.000265633,0.0736962,0.044123717,0.005120589,0.07872855,0.77970135],"study_design_scores_gemma":[0.000068467205,0.00013157462,0.013459724,0.000050302126,0.00008021792,0.0004437185,0.00030279183,0.90077984,0.034415968,0.0057231435,0.044483118,0.000061034603],"about_ca_topic_score_codex":0.010350841,"about_ca_topic_score_gemma":0.013359667,"teacher_disagreement_score":0.010350841,"about_ca_system_score_codex":0.00049362757,"about_ca_system_score_gemma":0.0007519263,"threshold_uncertainty_score":0.020581186},"labels":[],"label_agreement":null},{"id":"W4389868623","doi":"10.51846/vol6iss2pp13-20","title":"Optimum Model for Tracking of Moving Objects","year":2023,"lang":"en","type":"article","venue":"Pakistan Journal of Engineering and Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Non-line-of-sight propagation; Multilateration; Kalman filter; Computer science; Global Positioning System; Position (finance); Tracking (education); Noise (video); Algorithm; Standard deviation; Computer vision; Artificial intelligence; Wireless; Mathematics; Statistics; Telecommunications","score_opus":0.011424368514058208,"score_gpt":0.2399071338852462,"score_spread":0.228482765371188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389868623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052655414,0.00059789955,0.99020106,0.00017186275,0.000080758284,0.000027183532,0.00012703547,0.00026797914,0.0032606248],"genre_scores_gemma":[0.7942969,0.0029380003,0.1719962,0.00022403071,0.00030324422,0.00043510488,0.0010729716,0.0002090242,0.028524427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994106,0.00013457595,0.00003185613,0.00020736564,0.00014144683,0.00007426464],"domain_scores_gemma":[0.9995901,0.00015194945,0.000075566924,0.00004072248,0.00012544722,0.000016209999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080078375,0.0010031535,0.0015790256,0.00065685716,0.0003698173,0.0013580382,0.0015043562,0.0018585101,0.004048527],"category_scores_gemma":[0.0022946158,0.00059795793,0.000945252,0.0010060712,0.00059122324,0.0015691477,0.0010357625,0.0016800713,0.001910335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007857678,0.000023764062,0.0006811268,0.00014205571,0.00003999638,0.000084522275,0.00006505527,0.9552527,0.0017171383,0.015617913,0.0013771433,0.024919894],"study_design_scores_gemma":[0.000005778518,0.000026568841,0.00018518479,0.00000918523,0.000009433788,0.000027184613,0.000007927337,0.9947761,0.00024009233,0.0032828737,0.0014221249,0.0000074419877],"about_ca_topic_score_codex":0.006867803,"about_ca_topic_score_gemma":0.0048451866,"teacher_disagreement_score":0.006867803,"about_ca_system_score_codex":0.00066270184,"about_ca_system_score_gemma":0.0008915149,"threshold_uncertainty_score":0.013655663},"labels":[],"label_agreement":null},{"id":"W4389880287","doi":"10.1109/icccmla58983.2023.10346849","title":"A Domain Adaptation Framework for Human Activity Monitoring Using Passive Wi-Fi Sensing","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Software deployment; Task (project management); Adaptation (eye); Activity recognition; Artificial intelligence; Inference; Domain adaptation; Deep learning; Domain (mathematical analysis); Human–computer interaction; Real-time computing; Systems engineering; Software engineering; Engineering","score_opus":0.05425877575741555,"score_gpt":0.30526684674424154,"score_spread":0.251008070986826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389880287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036945723,0.00024904136,0.9944823,0.00009002991,0.00004245762,0.000022364571,0.00005312044,0.00060998305,0.00075617654],"genre_scores_gemma":[0.58364636,0.0010285673,0.4042695,0.0004765138,0.00027288287,0.00041717268,0.00078323635,0.00019774138,0.008908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996979,0.00006494929,0.000013180006,0.00011370075,0.000064602726,0.000045582103],"domain_scores_gemma":[0.9998016,0.000072573224,0.000023147446,0.000023373845,0.00005781205,0.000021558822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067575305,0.0006819047,0.0007596741,0.00044410428,0.00023647511,0.0006224372,0.0016305442,0.00069883786,0.001768845],"category_scores_gemma":[0.0010828245,0.00036041372,0.00073532516,0.00052949105,0.00049616385,0.000669323,0.0011383849,0.0014524368,0.00081862556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017940826,0.00018647555,0.0013470807,0.00011309041,0.00012580991,0.00020617039,0.00015391887,0.6175335,0.009631116,0.012800231,0.0053096367,0.35241348],"study_design_scores_gemma":[0.0000032890098,0.000015131508,0.0001564396,0.000003086137,0.000005062763,0.00001790505,0.0000053625417,0.9969926,0.00039178794,0.00182098,0.0005836582,0.000004656978],"about_ca_topic_score_codex":0.008670685,"about_ca_topic_score_gemma":0.007399459,"teacher_disagreement_score":0.008670685,"about_ca_system_score_codex":0.0004454171,"about_ca_system_score_gemma":0.0007352536,"threshold_uncertainty_score":0.017240465},"labels":[],"label_agreement":null},{"id":"W4389961290","doi":"10.1109/jiot.2023.3344223","title":"Radio Map Construction via Graph Signal Processing for Indoor Localization","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Key Science and Technology Program of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Fingerprint (computing); Bottleneck; Multipath propagation; Graph; Radio propagation; Cluster analysis; Fingerprint recognition; Artificial intelligence; Sampling (signal processing); Computer vision; Real-time computing; Algorithm; Theoretical computer science; Telecommunications; Embedded system","score_opus":0.010872703318309676,"score_gpt":0.22659041928265,"score_spread":0.21571771596434033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389961290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038695855,0.00005964965,0.9950039,0.00003873933,0.000018101917,0.000016823675,0.000040234077,0.0004305335,0.00052243937],"genre_scores_gemma":[0.35997102,0.0004770188,0.6364948,0.00009363668,0.00006953114,0.00012253904,0.0005959881,0.00014776719,0.0020277002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948126,0.00013674503,0.00002170184,0.00011166042,0.00019890346,0.000049769944],"domain_scores_gemma":[0.999374,0.00020712087,0.00008616086,0.00017020815,0.00013489764,0.000027714157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003190685,0.0009394304,0.00057826313,0.0018667404,0.00039781543,0.0006815669,0.0009515161,0.0006011437,0.0016813611],"category_scores_gemma":[0.00199873,0.00028119262,0.00067486323,0.0019097042,0.00059714867,0.0011555078,0.0010032118,0.00078278536,0.00090757303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015770667,0.00006687383,0.000910994,0.00018613932,0.000057699886,0.00017613923,0.00016735964,0.50678235,0.026563978,0.03275256,0.0036420373,0.42853606],"study_design_scores_gemma":[0.000010858458,0.00006485585,0.00032322857,0.000007735614,0.000016499322,0.00010284469,0.00004046251,0.97924954,0.006634285,0.010610328,0.0029194616,0.000019829913],"about_ca_topic_score_codex":0.002976085,"about_ca_topic_score_gemma":0.003109227,"teacher_disagreement_score":0.002976085,"about_ca_system_score_codex":0.00043532797,"about_ca_system_score_gemma":0.000649575,"threshold_uncertainty_score":0.005917549},"labels":[],"label_agreement":null},{"id":"W4390031504","doi":"10.18280/mmep.100624","title":"Performance Investigation of RIS Aided Localization with TDoA in the Near-Field","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multilateration; Field (mathematics); Computer science; Geology; Acoustics; Mathematics; Physics","score_opus":0.01717752598294974,"score_gpt":0.18460055748732257,"score_spread":0.16742303150437282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390031504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6012434,0.0007740408,0.38605016,0.00018287261,0.00012130075,0.000051068375,0.00010206018,0.0015028875,0.009972248],"genre_scores_gemma":[0.9722784,0.00010773877,0.026939228,0.000016934637,0.000007407648,0.000022073536,0.000052534077,0.000014545315,0.00056111213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991959,0.0002569562,0.00003617867,0.00010739584,0.0002541113,0.00014945559],"domain_scores_gemma":[0.9970788,0.0016862086,0.00020593303,0.00021796564,0.00072514836,0.00008586686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009988276,0.0006968467,0.0006305042,0.000668897,0.0003369165,0.0007205358,0.00056491134,0.00074155116,0.00097547873],"category_scores_gemma":[0.005286269,0.00021414038,0.00026223433,0.00073267374,0.00041761485,0.0006602008,0.0007543277,0.00037354414,0.00038073235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011095528,0.0001400276,0.0083348425,0.00026939026,0.00010040195,0.00025169345,0.00031952193,0.8472217,0.02370922,0.002602621,0.0005507931,0.115390226],"study_design_scores_gemma":[0.000013122829,0.00027129604,0.0014429496,0.000011897213,0.000018136796,0.00007898877,0.00007777545,0.9899858,0.0075664483,0.00023205843,0.00028724666,0.000014296085],"about_ca_topic_score_codex":0.004880746,"about_ca_topic_score_gemma":0.0034067708,"teacher_disagreement_score":0.004880746,"about_ca_system_score_codex":0.00039613838,"about_ca_system_score_gemma":0.0009275754,"threshold_uncertainty_score":0.009704709},"labels":[],"label_agreement":null},{"id":"W4390044032","doi":"10.1109/twc.2023.3343310","title":"Cramér-Rao Lower Bound Analysis of Positioning With Planar Large Intelligent Surfaces Under Rician Channel","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Government of Canada","keywords":"Cramér–Rao bound; Terminal (telecommunication); Channel (broadcasting); Upper and lower bounds; Computer science; Mathematics; Algorithm; Planar; Rician fading; Mathematical analysis; Telecommunications","score_opus":0.02271037100553518,"score_gpt":0.2559526580968736,"score_spread":0.23324228709133843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390044032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011293616,0.0008570689,0.98374754,0.00017126402,0.000026704745,0.00001646492,0.00005387972,0.0002002545,0.0036332675],"genre_scores_gemma":[0.82835394,0.0028475926,0.16397461,0.00022950153,0.00012812443,0.00012763175,0.00028989557,0.00011802071,0.0039307293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998285,0.00038096405,0.000049264996,0.00027683922,0.0007578571,0.0002500461],"domain_scores_gemma":[0.99509615,0.0030604685,0.000428133,0.00043327018,0.0009118499,0.00007010361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018990039,0.0012680745,0.0008497722,0.0011967933,0.00050504936,0.0011948205,0.0010390591,0.0008930626,0.0015660117],"category_scores_gemma":[0.012197247,0.0004858271,0.0005518113,0.0014091516,0.002005572,0.0022749603,0.0012044362,0.0011346958,0.0006059937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048362937,0.000009798661,0.00061055634,0.000107964646,0.000027738351,0.00010450609,0.000099484176,0.92650706,0.0025207486,0.050872743,0.000867649,0.018223437],"study_design_scores_gemma":[0.0000030580438,0.000030685744,0.00030030432,0.000012704612,0.000010869877,0.00006247325,0.000026273452,0.98792446,0.0011704679,0.0097186705,0.00072164065,0.000018389659],"about_ca_topic_score_codex":0.007907038,"about_ca_topic_score_gemma":0.0047100154,"teacher_disagreement_score":0.007907038,"about_ca_system_score_codex":0.0018461752,"about_ca_system_score_gemma":0.0017715076,"threshold_uncertainty_score":0.015722036},"labels":[],"label_agreement":null},{"id":"W4390204467","doi":"10.1109/twc.2023.3344021","title":"Localization With Reconfigurable Intelligent Surface: An Active Sensing Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Huawei Technologies","keywords":"Computer science; Beamforming; Base station; Telecommunications link; Position (finance); Scalability; Artificial neural network; Frame (networking); State vector; Dimension (graph theory); Real-time computing; Artificial intelligence; Telecommunications; Mathematics","score_opus":0.033036818021243586,"score_gpt":0.25278116700224074,"score_spread":0.21974434898099715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390204467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013106885,0.00020622156,0.9847947,0.00015646314,0.000032246393,0.000010954763,0.000013839959,0.00024914503,0.0014295031],"genre_scores_gemma":[0.7993444,0.00034037232,0.1960986,0.00017483726,0.00007024147,0.000068390254,0.00006296927,0.000049936272,0.0037902298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970526,0.00007909419,0.000011169348,0.00008946074,0.00007339811,0.00004157217],"domain_scores_gemma":[0.9996164,0.00017735771,0.000052216918,0.00006171792,0.000070670845,0.000021694303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003922038,0.0007577304,0.0005762313,0.0003850152,0.0002825403,0.00062919466,0.0012520512,0.0009834025,0.0009324921],"category_scores_gemma":[0.0010453993,0.00038009445,0.000522074,0.0004303181,0.0009108177,0.0015515788,0.0009616466,0.00076095737,0.00026347584],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001838598,0.00006265383,0.0011127106,0.0001354839,0.00005970107,0.00023775626,0.00020624713,0.7788294,0.038179174,0.023074409,0.001068917,0.15684976],"study_design_scores_gemma":[0.000004887934,0.0000406097,0.0000843634,0.000003918684,0.000007792141,0.000031213684,0.000015469366,0.9927745,0.0027709787,0.0036616586,0.0005976455,0.0000069248904],"about_ca_topic_score_codex":0.002046282,"about_ca_topic_score_gemma":0.0020284855,"teacher_disagreement_score":0.002046282,"about_ca_system_score_codex":0.0004677835,"about_ca_system_score_gemma":0.00043820014,"threshold_uncertainty_score":0.0040687323},"labels":[],"label_agreement":null},{"id":"W4390479236","doi":"10.3390/rs16010181","title":"Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Impact Fund","keywords":"GNSS applications; Computer science; Non-line-of-sight propagation; Inertial navigation system; Multipath propagation; Real-time computing; Kalman filter; Satellite system; Mean squared error; Reliability (semiconductor); Global Positioning System; Artificial intelligence; Telecommunications; Inertial frame of reference; Wireless; Mathematics","score_opus":0.013258197203518679,"score_gpt":0.1965661571939487,"score_spread":0.18330795999043004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390479236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014517846,0.00007841408,0.9830755,0.000055772314,0.000028579452,0.0000185274,0.00003406547,0.00051684346,0.0016744361],"genre_scores_gemma":[0.7786288,0.00015787702,0.21778476,0.00009719882,0.00004125827,0.00008560522,0.00023650972,0.0001239042,0.002844171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998098,0.000030730454,0.000007940626,0.00006752005,0.000059531307,0.000024569994],"domain_scores_gemma":[0.99987113,0.000038404214,0.000023913744,0.000017542716,0.000040425377,0.000008581463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021746002,0.0008937777,0.00050438644,0.0002822977,0.00023829757,0.0004370337,0.0005985296,0.0005392434,0.00084383396],"category_scores_gemma":[0.0005918406,0.0003078443,0.00050968945,0.00044613075,0.00048150122,0.0005912641,0.00070398353,0.00053479936,0.00023925625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037344675,0.000018864994,0.000644882,0.000026885205,0.000031527157,0.000030301795,0.00003271703,0.94122547,0.003913695,0.0025956829,0.0006287999,0.05081376],"study_design_scores_gemma":[0.000003207579,0.000009896033,0.000109592336,0.0000010002562,0.0000035091093,0.000004226626,0.0000028553952,0.9984486,0.0004304998,0.000710278,0.00027368095,0.0000027241122],"about_ca_topic_score_codex":0.013646793,"about_ca_topic_score_gemma":0.011308299,"teacher_disagreement_score":0.013646793,"about_ca_system_score_codex":0.00044270963,"about_ca_system_score_gemma":0.000689554,"threshold_uncertainty_score":0.027134717},"labels":[],"label_agreement":null},{"id":"W4390594529","doi":"10.5383/juspn.18.01.005","title":"5G-enhanced Positioning Accuracy in Smart City","year":2023,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Bundesministerium für Bildung und Forschung","keywords":"Computer science; Implementation; Smart grid; Automation; Smart city; Wireless; Key (lock); Wireless broadband; Cover (algebra); Telecommunications; Wireless network; Systems engineering; Computer security; Engineering; Internet of Things; Software engineering","score_opus":0.01253039291681504,"score_gpt":0.23078521834991356,"score_spread":0.21825482543309851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390594529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16479677,0.013496992,0.71232975,0.0047938344,0.00086946593,0.00017226388,0.0011225884,0.0027336688,0.09968478],"genre_scores_gemma":[0.9368213,0.0050801476,0.051786013,0.00030159362,0.00020165213,0.000042928277,0.00047753984,0.000064266955,0.0052244836],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988079,0.0003119395,0.000066468216,0.00012753716,0.00043407126,0.0002520325],"domain_scores_gemma":[0.9991161,0.00023375837,0.00011754535,0.00016806055,0.0003264579,0.000038090486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010992074,0.00065022905,0.00042112905,0.001164885,0.00048382647,0.0017812083,0.0007622165,0.0012180551,0.0018063885],"category_scores_gemma":[0.0020248601,0.0002350381,0.0002822307,0.0022553178,0.0007289575,0.0023866359,0.0016809633,0.0006776262,0.0008930044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005190257,0.00007368248,0.021797977,0.0011154476,0.00010055107,0.0017769116,0.0010462055,0.19575912,0.036169183,0.2483765,0.014318828,0.47894654],"study_design_scores_gemma":[0.000053379576,0.00074960163,0.026802551,0.0006687753,0.0003103999,0.0036368694,0.0017995767,0.4830651,0.0667401,0.10161689,0.31427878,0.00027799557],"about_ca_topic_score_codex":0.0033738022,"about_ca_topic_score_gemma":0.0039195362,"teacher_disagreement_score":0.0033738022,"about_ca_system_score_codex":0.000977954,"about_ca_system_score_gemma":0.00063613965,"threshold_uncertainty_score":0.0070955157},"labels":[],"label_agreement":null},{"id":"W4390597123","doi":"10.5383/juspn.17.02.001","title":"New and Reliable Points Shifting - Based Algorithm for Indoor Location Services","year":2022,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Telus (Canada); Sheridan College","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Algorithm; Set (abstract data type); Metric (unit); Multipath propagation; Grid; Upper and lower bounds; k-nearest neighbors algorithm; Bounded function; Data mining; Artificial intelligence; Mathematics; Telecommunications","score_opus":0.006482775096025587,"score_gpt":0.20059111306842434,"score_spread":0.19410833797239874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390597123","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009450611,0.00044939163,0.98667437,0.0001156812,0.00012686255,0.00007155208,0.0000748195,0.0018460866,0.0011905944],"genre_scores_gemma":[0.23041525,0.00048527494,0.76382965,0.0001390096,0.00010827163,0.00020306157,0.000537343,0.00013585822,0.004146274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897146,0.00012569761,0.00006881614,0.00022747336,0.0005072181,0.00009934902],"domain_scores_gemma":[0.9992192,0.00014203608,0.00006215662,0.0001725615,0.00036698527,0.00003707233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005399822,0.00094243995,0.0011040936,0.0014878624,0.00074733864,0.0008108639,0.0018568541,0.0010505765,0.002647932],"category_scores_gemma":[0.002560099,0.00032041525,0.00060110615,0.0017546496,0.00047138662,0.0015112653,0.0013346481,0.0012696566,0.0022716043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005057736,0.00013512936,0.001525982,0.00013506133,0.00007052557,0.00017365404,0.00019436648,0.10227635,0.02817456,0.0088826455,0.008528603,0.8493974],"study_design_scores_gemma":[0.00006704511,0.000119912205,0.0006180754,0.000016057622,0.000024907986,0.00037243962,0.000056983587,0.9713586,0.01355584,0.0036383814,0.010133145,0.00003854706],"about_ca_topic_score_codex":0.005410727,"about_ca_topic_score_gemma":0.004811741,"teacher_disagreement_score":0.005410727,"about_ca_system_score_codex":0.0007334415,"about_ca_system_score_gemma":0.0011912794,"threshold_uncertainty_score":0.01075846},"labels":[],"label_agreement":null},{"id":"W4390702735","doi":"10.3390/s24020430","title":"Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless sensor network; Flexibility (engineering); Exploit; Artificial neural network; Internet of Things; Deep learning; Artificial intelligence; Process (computing); Machine learning; The Internet; Distributed computing; Computer network; Real-time computing; Embedded system; Computer security","score_opus":0.026781096586081177,"score_gpt":0.2913563429271612,"score_spread":0.26457524634108004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390702735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14663734,0.0015262754,0.84273756,0.0013995368,0.0002287811,0.000042599488,0.00013732097,0.0018221599,0.0054684663],"genre_scores_gemma":[0.9131079,0.0006358527,0.084337875,0.00027586037,0.00003619712,0.000044455755,0.00025685295,0.00005874846,0.0012462619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996107,0.00009231221,0.000025673444,0.00009209704,0.00013510021,0.00004417314],"domain_scores_gemma":[0.9989184,0.0006329336,0.00008037809,0.0001305148,0.00019829064,0.000039543545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095049763,0.00082991377,0.00043774588,0.000324598,0.00028121917,0.00040731387,0.00069827423,0.00077454955,0.00080768904],"category_scores_gemma":[0.003733451,0.00026267223,0.00041743615,0.00032188153,0.0006527493,0.0015580263,0.0011030644,0.0010777843,0.00021158738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028980282,0.00014022185,0.0030585367,0.00019464012,0.000064428306,0.00019512781,0.00008740065,0.72660035,0.017958682,0.007654393,0.0023664944,0.24138995],"study_design_scores_gemma":[0.000006405205,0.00004479572,0.0003247471,0.000009390642,0.00000904866,0.000031923566,0.000012900464,0.9918596,0.0052388315,0.0020104807,0.0004463612,0.000005478474],"about_ca_topic_score_codex":0.0028480634,"about_ca_topic_score_gemma":0.0031349938,"teacher_disagreement_score":0.0028480634,"about_ca_system_score_codex":0.0005576416,"about_ca_system_score_gemma":0.00079327315,"threshold_uncertainty_score":0.0056630373},"labels":[],"label_agreement":null},{"id":"W4390915787","doi":"10.1007/s11042-023-17885-3","title":"Noise signature identification using mobile phones for indoor localization","year":2024,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Beacon; Classifier (UML); Noise (video); Binary classification; Android (operating system); Segmentation; Naive Bayes classifier; Artificial intelligence; Pattern recognition (psychology); Real-time computing; Computer vision; Support vector machine","score_opus":0.015393152696943998,"score_gpt":0.25912089062885646,"score_spread":0.24372773793191246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390915787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65734595,0.0025016626,0.3226076,0.00030324946,0.00044796919,0.00021692838,0.0013385508,0.008862925,0.0063752346],"genre_scores_gemma":[0.9280468,0.0003959556,0.06855931,0.00008918913,0.000056786,0.00006140768,0.00047986145,0.000055583238,0.0022549771],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994634,0.00013102636,0.000029133396,0.00011663143,0.00021144695,0.00004833337],"domain_scores_gemma":[0.9993268,0.00017797976,0.00013017241,0.00008132608,0.00024477424,0.000038868326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025301593,0.00066533824,0.000640541,0.0011725281,0.0001619383,0.0005302075,0.00039676216,0.0005243941,0.0017877019],"category_scores_gemma":[0.0010077519,0.00012928802,0.00026177618,0.00057426404,0.000112886155,0.00035338016,0.00037345118,0.0002164165,0.002271747],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001030043,0.00018880855,0.033676486,0.0008011654,0.00016282259,0.00081193965,0.00019526083,0.017958164,0.28362212,0.0004954874,0.005328872,0.6557289],"study_design_scores_gemma":[0.0000704199,0.0021271429,0.09401092,0.00022720119,0.00023734306,0.0026096103,0.0004344993,0.60846084,0.27543956,0.001044173,0.015171601,0.0001667471],"about_ca_topic_score_codex":0.0007158878,"about_ca_topic_score_gemma":0.0011137187,"teacher_disagreement_score":0.0017877019,"about_ca_system_score_codex":0.0001476014,"about_ca_system_score_gemma":0.00015982916,"threshold_uncertainty_score":0.005980432},"labels":[],"label_agreement":null},{"id":"W4390938872","doi":"10.1109/jsen.2024.3352535","title":"A Model-Based BLE Indoor Positioning System Using Particle Swarm Optimization","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Particle swarm optimization; Computer science; Algorithm","score_opus":0.01904346657178642,"score_gpt":0.23660313621297188,"score_spread":0.21755966964118545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390938872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011140427,0.00009932985,0.9853479,0.00008050645,0.00006898926,0.00003032378,0.0000454463,0.0014519928,0.001735102],"genre_scores_gemma":[0.53783095,0.00021581046,0.4567452,0.00013488288,0.00006160125,0.00017508943,0.00040790267,0.000116214454,0.0043123397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978226,0.000046741905,0.000013250033,0.000055356722,0.000086226224,0.000016220738],"domain_scores_gemma":[0.99978894,0.000048669524,0.000030727526,0.00004016329,0.00007507907,0.000016349066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037460792,0.00067585247,0.0008800053,0.00043437455,0.0003443497,0.0006035581,0.0006438077,0.00070869917,0.0009954353],"category_scores_gemma":[0.00087138516,0.00030633507,0.00055148953,0.00046110013,0.0002241059,0.00058832637,0.0006599548,0.0006234067,0.0005835879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019368037,0.00012767036,0.0026008303,0.00013537597,0.0001432033,0.000164074,0.00010545253,0.73764765,0.018862205,0.0039064875,0.004084197,0.23202917],"study_design_scores_gemma":[0.000011299649,0.000040371702,0.0002967323,0.0000030330161,0.000008636156,0.000024795785,0.0000046407204,0.9974233,0.0011026568,0.00030581164,0.00077195006,0.0000067924575],"about_ca_topic_score_codex":0.003781949,"about_ca_topic_score_gemma":0.0031217914,"teacher_disagreement_score":0.003781949,"about_ca_system_score_codex":0.0002748603,"about_ca_system_score_gemma":0.00045234908,"threshold_uncertainty_score":0.007519841},"labels":[],"label_agreement":null},{"id":"W4390944859","doi":"10.1109/mcsoc60832.2023.00028","title":"IODnet: Indoor/Outdoor Telecommunication Signal Detection through Deep Neural Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Telecommunications; Computer science; SIGNAL (programming language); Artificial neural network; Artificial intelligence","score_opus":0.01474151941588874,"score_gpt":0.22471769418349122,"score_spread":0.20997617476760247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390944859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17423932,0.0036496825,0.7767224,0.0010980879,0.0008817971,0.00021649757,0.005157675,0.023485944,0.014548654],"genre_scores_gemma":[0.75965154,0.0010788919,0.20176838,0.0007474577,0.00015605493,0.00019319783,0.015256413,0.0003462834,0.02080175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970216,0.000037560465,0.000012780158,0.00010565777,0.000074285796,0.000067618836],"domain_scores_gemma":[0.99977237,0.00005064029,0.0000286463,0.000039607556,0.000089927125,0.000018756198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047225176,0.0015350743,0.00065536675,0.0010065621,0.00036610724,0.00080758746,0.0014481635,0.00096724625,0.0019981337],"category_scores_gemma":[0.00094285625,0.00035618147,0.0005747303,0.0011172828,0.00031739107,0.0011132583,0.0009820907,0.001316164,0.0013446196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038206112,0.0005061555,0.006993601,0.0002370293,0.00024522343,0.00033261286,0.000089755085,0.2585278,0.014556864,0.002902716,0.03813764,0.67708856],"study_design_scores_gemma":[0.000010422055,0.00003952691,0.000943154,0.0000148341305,0.000018654086,0.00004118494,0.000025959711,0.99081475,0.0044991584,0.0011183996,0.002461816,0.000012242086],"about_ca_topic_score_codex":0.020484215,"about_ca_topic_score_gemma":0.030629613,"teacher_disagreement_score":0.020484215,"about_ca_system_score_codex":0.0010169308,"about_ca_system_score_gemma":0.00082997937,"threshold_uncertainty_score":0.04072994},"labels":[],"label_agreement":null},{"id":"W4390968202","doi":"10.1109/tnet.2023.3348950","title":"GLAC: High-Precision Tracking of Mobile Objects With COTS RFID Systems","year":2024,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Algorithm; Position (finance); Inference; Artificial intelligence; Tracking (education)","score_opus":0.010852880440162378,"score_gpt":0.21899056195003105,"score_spread":0.20813768150986867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390968202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0258298,0.00063602545,0.9425706,0.00021038721,0.00022119343,0.00007760067,0.00048544037,0.025657672,0.004311365],"genre_scores_gemma":[0.36668062,0.00047138892,0.6221522,0.00033470296,0.00010998306,0.00010559048,0.001500312,0.00065554876,0.007989642],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992317,0.00009559535,0.000031031832,0.00017150133,0.00038902395,0.000081122365],"domain_scores_gemma":[0.99923754,0.00012231439,0.00010454465,0.00024710188,0.0002390763,0.000049401857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006490131,0.0008862979,0.00058213796,0.0009703665,0.0004388265,0.00092598546,0.0012183394,0.0010509879,0.0035551568],"category_scores_gemma":[0.0013740642,0.00045162134,0.00040452255,0.0010179476,0.00041892627,0.0012907677,0.0013181719,0.0009247441,0.0026507478],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059837225,0.000106339096,0.0055157086,0.00047799165,0.000121042176,0.0005845946,0.000545222,0.100640915,0.11192838,0.010896083,0.038612552,0.7299727],"study_design_scores_gemma":[0.00011691351,0.00043102482,0.0055722804,0.000080624515,0.00005374578,0.0011563234,0.00008895668,0.83845526,0.06921075,0.0043223943,0.0803481,0.00016365382],"about_ca_topic_score_codex":0.0050175968,"about_ca_topic_score_gemma":0.0040717716,"teacher_disagreement_score":0.0050175968,"about_ca_system_score_codex":0.000491526,"about_ca_system_score_gemma":0.0005641335,"threshold_uncertainty_score":0.011893153},"labels":[],"label_agreement":null},{"id":"W4391097206","doi":"10.1109/jiot.2024.3357075","title":"Direct Localization and Synchronization for High-Mobility Agents With Frequency Shifts in MIMO-OFDM Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Beijing Institute of Technology Research Fund Program for Young Scholars; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Orthogonal frequency-division multiplexing; Computer science; Cramér–Rao bound; Algorithm; Synchronization (alternating current); MIMO; Estimator; Signal-to-noise ratio (imaging); Base station; Control theory (sociology); Telecommunications; Mathematics; Estimation theory; Channel (broadcasting); Artificial intelligence; Statistics","score_opus":0.008922568421622522,"score_gpt":0.22367594870103072,"score_spread":0.21475338027940818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391097206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026586244,0.00025300815,0.97215444,0.000103473445,0.000015351743,0.0000121061275,0.000013804565,0.00004293473,0.0008185825],"genre_scores_gemma":[0.84020704,0.0005612319,0.15686223,0.0000548898,0.000050797313,0.00007289547,0.000057404883,0.000017940085,0.0021156254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966073,0.000097863835,0.00001445321,0.00009197657,0.00010009255,0.0000348889],"domain_scores_gemma":[0.9994417,0.00036376822,0.000089856316,0.00004439675,0.000045929453,0.0000144488295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004498443,0.00046072665,0.00035923335,0.0001980059,0.00023870508,0.0004117046,0.0003596329,0.00053509534,0.0005576467],"category_scores_gemma":[0.0019381968,0.00024310117,0.00030541123,0.00032579096,0.00057406595,0.00070241303,0.00084251584,0.00054698874,0.00014062163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006849107,0.000033972312,0.0020203153,0.00013509818,0.000031899275,0.00020933917,0.00020270777,0.8558031,0.016585063,0.03906546,0.0005829686,0.08526155],"study_design_scores_gemma":[0.0000054759344,0.000029757817,0.00039858144,0.000005380713,0.0000054944626,0.00005475216,0.000023334313,0.9921886,0.0021302698,0.0046584653,0.00049182394,0.000008127199],"about_ca_topic_score_codex":0.0016712983,"about_ca_topic_score_gemma":0.0017064599,"teacher_disagreement_score":0.0016712983,"about_ca_system_score_codex":0.000394488,"about_ca_system_score_gemma":0.0006011566,"threshold_uncertainty_score":0.0033231974},"labels":[],"label_agreement":null},{"id":"W4391543839","doi":"10.1177/02783649241230640","title":"UTIL: An ultra-wideband time-difference-of-arrival indoor localization dataset","year":2024,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Multilateration; Non-line-of-sight propagation; Computer science; Real-time computing; Ultra-wideband; Inertial measurement unit; Artificial intelligence; Testbed; Ground truth; Fuze; Computer vision; Simulation; Wireless; Acoustics; Telecommunications","score_opus":0.05567293960629447,"score_gpt":0.3563202104892119,"score_spread":0.30064727088291743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391543839","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012327948,0.0011227453,0.010713261,0.00030293028,0.0002871406,0.00015389505,0.9583215,0.013397363,0.003373177],"genre_scores_gemma":[0.009044008,0.00021649065,0.006565938,0.00010793006,0.000019514935,0.00019244265,0.9828692,0.00022005534,0.0007644739],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991824,0.00013721903,0.000092485956,0.00024842206,0.00022019638,0.000119327044],"domain_scores_gemma":[0.99916875,0.00015403454,0.00007360299,0.00026463805,0.00026350305,0.00007549402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005938168,0.0027329181,0.0014609506,0.001951915,0.00063388044,0.00088834687,0.0034907882,0.0019404208,0.0067380257],"category_scores_gemma":[0.0020979054,0.00043266243,0.0012691934,0.003016108,0.00038938256,0.0008646783,0.0019648296,0.0013402208,0.015816394],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067001686,0.0004110403,0.009125605,0.0034320524,0.00031574472,0.0006734346,0.00019012312,0.013604246,0.006876176,0.0011917758,0.8928995,0.070610285],"study_design_scores_gemma":[0.0007692701,0.0005917973,0.033622943,0.0009637084,0.0002735111,0.0015152307,0.0007468861,0.04692772,0.012503725,0.0046123057,0.8970938,0.00037907457],"about_ca_topic_score_codex":0.016523898,"about_ca_topic_score_gemma":0.03893687,"teacher_disagreement_score":0.016523898,"about_ca_system_score_codex":0.0007183819,"about_ca_system_score_gemma":0.0011721172,"threshold_uncertainty_score":0.03285545},"labels":[],"label_agreement":null},{"id":"W4391756826","doi":"10.1139/dsa-2022-0038","title":"AI-based landing zone detection for vertical takeoff and land LiDAR localization and mapping pipelines","year":2024,"lang":"en","type":"article","venue":"Drone Systems and Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Memorial University of Newfoundland","funders":"","keywords":"Takeoff; Lidar; Pipeline transport; Takeoff and landing; Geology; Remote sensing; Marine engineering; Environmental science; Aerospace engineering; Engineering","score_opus":0.009602221180265746,"score_gpt":0.22445665987175797,"score_spread":0.21485443869149223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391756826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13253887,0.0004280193,0.83454597,0.00019184497,0.00016022292,0.00016522904,0.0010713226,0.022124987,0.008773579],"genre_scores_gemma":[0.787883,0.00016180064,0.20064609,0.00014190076,0.000029613931,0.000129336,0.0028042747,0.00022643393,0.007977626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981886,0.000008821463,0.000006680925,0.000056808392,0.000065521206,0.000043339965],"domain_scores_gemma":[0.9998342,0.000023070801,0.000017626655,0.00002943633,0.00007740913,0.000018329101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015948799,0.00072657305,0.00031064427,0.00073460926,0.000305651,0.00046739753,0.0011060232,0.00037314196,0.0038859472],"category_scores_gemma":[0.0004985057,0.00024646445,0.00033049914,0.000460878,0.00016731785,0.0007297251,0.0008381831,0.00060856616,0.0017552092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042539797,0.00021844277,0.007547885,0.00018914221,0.000100659716,0.00018745297,0.00015686828,0.11248766,0.07157907,0.0020205998,0.010774611,0.7943121],"study_design_scores_gemma":[0.000014465606,0.00008601708,0.005072625,0.000015909585,0.000019041974,0.00007314,0.00007183953,0.96548325,0.023912396,0.0012220625,0.0040125893,0.000016679305],"about_ca_topic_score_codex":0.015362586,"about_ca_topic_score_gemma":0.022587102,"teacher_disagreement_score":0.015362586,"about_ca_system_score_codex":0.00051802245,"about_ca_system_score_gemma":0.0007744602,"threshold_uncertainty_score":0.030546367},"labels":[],"label_agreement":null},{"id":"W4391813416","doi":"10.3390/s24041210","title":"An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Wireless sensor network; Node (physics); Fading; Computer science; Multipath propagation; Context (archaeology); Log-distance path loss model; RSS; Path loss; Key distribution in wireless sensor networks; Signal strength; Wireless network; Wireless; Computer network; Real-time computing; Telecommunications; Engineering; Geography","score_opus":0.011388155615084716,"score_gpt":0.22790380674389077,"score_spread":0.21651565112880605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391813416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009585599,0.00022457511,0.98924094,0.000034431792,0.00001709156,0.000012898495,0.0000150225815,0.00020758515,0.0006619232],"genre_scores_gemma":[0.6534084,0.0010201597,0.34399968,0.00006473312,0.00007027571,0.00008300894,0.00007894239,0.000054079323,0.0012207221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947923,0.00017097546,0.000020929929,0.0000923131,0.00020908115,0.000027402235],"domain_scores_gemma":[0.99955577,0.00015500684,0.00006735781,0.00010984097,0.00009910064,0.000012866257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056197157,0.00058584084,0.00038725158,0.0005747979,0.00020897831,0.0004641792,0.00066260743,0.00052025117,0.00046586187],"category_scores_gemma":[0.0019677915,0.00026699336,0.00039508037,0.00055887614,0.0005079115,0.0010208937,0.00074032677,0.0004548168,0.00030382266],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005300019,0.000028226548,0.0010867084,0.00012568933,0.000042090574,0.00016868084,0.00009516526,0.8820037,0.032485835,0.018146716,0.0005370332,0.06522708],"study_design_scores_gemma":[0.000004333766,0.00007445738,0.00036098345,0.0000080886375,0.000015858532,0.00009793932,0.00001695392,0.9912464,0.0040362542,0.002728985,0.0013958657,0.000013933951],"about_ca_topic_score_codex":0.0013765496,"about_ca_topic_score_gemma":0.0019498104,"teacher_disagreement_score":0.0013765496,"about_ca_system_score_codex":0.00030654308,"about_ca_system_score_gemma":0.0005209518,"threshold_uncertainty_score":0.0029720068},"labels":[],"label_agreement":null},{"id":"W4391853792","doi":"10.1109/tmc.2024.3366340","title":"PPRP: Preserving Location Privacy for Range-Based Positioning in Mobile Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Hybrid positioning system; Mobile computing; Location-based service; Information privacy; Computer security; Mobile telephony; Privacy protection; Range (aeronautics); Computer network; Internet privacy; Mobile radio; Positioning system","score_opus":0.009627036629012042,"score_gpt":0.2444950658575567,"score_spread":0.23486802922854466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391853792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006065843,0.0002221927,0.9907532,0.00023243706,0.00005120047,0.00007210982,0.00005247397,0.00045156147,0.0020990807],"genre_scores_gemma":[0.6733377,0.00091772317,0.31882498,0.00045204043,0.00020448232,0.00041354788,0.00026261708,0.000119863456,0.0054670847],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964855,0.0009815833,0.00019144735,0.0005969081,0.0013870014,0.00035761108],"domain_scores_gemma":[0.99701285,0.00079809444,0.00031358862,0.0015112433,0.00028851992,0.00007572476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019879378,0.00066647393,0.0009514713,0.00058636424,0.001102683,0.0014716197,0.0024300653,0.0014033493,0.0022184965],"category_scores_gemma":[0.0050507095,0.00042923487,0.0010190998,0.0010820543,0.0019139698,0.004700237,0.0045146225,0.0022305346,0.0011706158],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045970868,0.0001319461,0.0012969332,0.0004969425,0.00014286271,0.0008379661,0.00070412667,0.15736303,0.054522622,0.5947811,0.0058154613,0.18344733],"study_design_scores_gemma":[0.00012480754,0.0005494723,0.0005289643,0.00007374405,0.000101128724,0.0016221647,0.0001861394,0.7438101,0.049197517,0.17414692,0.029537747,0.000121220066],"about_ca_topic_score_codex":0.0006039396,"about_ca_topic_score_gemma":0.00038372978,"teacher_disagreement_score":0.0024300653,"about_ca_system_score_codex":0.00065085065,"about_ca_system_score_gemma":0.0012515961,"threshold_uncertainty_score":0.010513306},"labels":[],"label_agreement":null},{"id":"W4391892421","doi":"10.1109/tim.2024.3366575","title":"CAE-MAS: Convolutional Autoencoder Interference Cancellation for Multiperson Activity Sensing With FMCW Microwave Radar","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Autoencoder; Radar; Continuous-wave radar; Interference (communication); Electronic engineering; Microwave; Computer science; Single antenna interference cancellation; Engineering; Telecommunications; Radar imaging; Artificial intelligence; Deep learning; Detector","score_opus":0.027195443662731154,"score_gpt":0.23034335137847056,"score_spread":0.2031479077157394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391892421","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031495966,0.0008310005,0.9636945,0.0001620802,0.00012984482,0.00004719147,0.00010217437,0.0018454485,0.001691892],"genre_scores_gemma":[0.57999855,0.0007111086,0.40909052,0.0003883336,0.00011224097,0.00013249039,0.00059036096,0.00012363048,0.008852756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997764,0.000036722584,0.000009651551,0.00006648421,0.00007609626,0.00003463292],"domain_scores_gemma":[0.9997154,0.00011328494,0.000032433163,0.000035926136,0.000086478045,0.000016476442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005359637,0.00080998434,0.0005034414,0.00033519734,0.00018862005,0.00030520456,0.0007564004,0.00068120845,0.0011720273],"category_scores_gemma":[0.0010075297,0.00025987704,0.00043240204,0.00028948387,0.00024366761,0.00046328784,0.00056014775,0.0008766309,0.0005444436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036656568,0.00022035289,0.001981652,0.00013821179,0.00022505324,0.00021036429,0.000085149644,0.2563206,0.066205606,0.0033067341,0.0053350227,0.66560465],"study_design_scores_gemma":[0.000005411681,0.000048237784,0.00057638146,0.000005188856,0.000012952626,0.000049375216,0.0000053720096,0.99147964,0.006236691,0.00043647716,0.0011376847,0.0000065204845],"about_ca_topic_score_codex":0.005631824,"about_ca_topic_score_gemma":0.0084600905,"teacher_disagreement_score":0.005631824,"about_ca_system_score_codex":0.00037679734,"about_ca_system_score_gemma":0.00050512684,"threshold_uncertainty_score":0.011198103},"labels":[],"label_agreement":null},{"id":"W4391974026","doi":"10.1007/978-3-031-43699-4_38","title":"Deep Adaptive Network for WiFi-Based Indoor Localization","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in geoinformation and cartography","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Computer network; Real-time computing","score_opus":0.006105855414088076,"score_gpt":0.1888435877290601,"score_spread":0.18273773231497203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391974026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0101795755,0.00091203884,0.98283273,0.00017151091,0.00016809866,0.00001322444,0.000379943,0.0015913487,0.0037515464],"genre_scores_gemma":[0.68913144,0.0019118299,0.2824915,0.0003080095,0.000283605,0.00009470532,0.0018422432,0.0002304079,0.023706304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999824,0.00002861436,0.000006521778,0.000051483206,0.000046382745,0.000043079788],"domain_scores_gemma":[0.9998078,0.00006495302,0.0000139511785,0.000038718736,0.00006338246,0.000011196581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021784144,0.00069432135,0.0005842444,0.0004452263,0.00022064192,0.00047233343,0.0010444247,0.0006067846,0.003682499],"category_scores_gemma":[0.0007360833,0.0003165021,0.00036617793,0.0009114462,0.00026244443,0.0008953213,0.0012215251,0.00094458973,0.0013933404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001779837,0.000065439526,0.00097239274,0.00010404615,0.000063131374,0.00008943288,0.0000517169,0.28889364,0.0083493395,0.009562843,0.01568811,0.67598194],"study_design_scores_gemma":[0.000006123915,0.00002674143,0.00039457722,0.000011797966,0.000016242173,0.00005280165,0.000014426387,0.9887517,0.002246659,0.0056461236,0.0028238145,0.000008935671],"about_ca_topic_score_codex":0.0074906205,"about_ca_topic_score_gemma":0.014265,"teacher_disagreement_score":0.0074906205,"about_ca_system_score_codex":0.00044212467,"about_ca_system_score_gemma":0.00042473883,"threshold_uncertainty_score":0.014894068},"labels":[],"label_agreement":null},{"id":"W4391991233","doi":"10.32920/25262809.v1","title":"Multi-sensor Integration for Land Vehicular Navigation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"GNSS applications; Inertial measurement unit; Computer science; Precise Point Positioning; Kalman filter; Real Time Kinematic; Geodetic datum; Global Positioning System; Real-time computing; Remote sensing; Geodesy; Artificial intelligence; Geography; Telecommunications","score_opus":0.020019128541648997,"score_gpt":0.2608420710150404,"score_spread":0.24082294247339142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391991233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02819853,0.002207529,0.95765054,0.0001598246,0.00043908515,0.000083422034,0.00015934047,0.00198913,0.009112544],"genre_scores_gemma":[0.7329558,0.0015570587,0.24731123,0.00020509919,0.00011696836,0.00011052966,0.0008189807,0.00011123752,0.016813109],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996855,0.000055685672,0.000014163929,0.00006834368,0.00015022262,0.000026053987],"domain_scores_gemma":[0.99988186,0.000011580908,0.000013114693,0.000024683184,0.00006221754,0.000006533909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027931904,0.00044143363,0.00030672463,0.00038009137,0.00019406008,0.0005324226,0.00052454387,0.00041400065,0.0018578405],"category_scores_gemma":[0.00042703873,0.00017834968,0.00026907556,0.00047271763,0.00013298067,0.00059550384,0.00061572355,0.00048126146,0.00091045024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019415445,0.00007897227,0.0037006359,0.00030936915,0.00012574706,0.0002867469,0.00019273722,0.13221262,0.1185633,0.012941631,0.007086378,0.72430766],"study_design_scores_gemma":[0.000021118345,0.0003285531,0.0044868523,0.000054333854,0.000056565546,0.000161735,0.00011714856,0.9046447,0.032735355,0.004685395,0.052664027,0.00004409415],"about_ca_topic_score_codex":0.0028361846,"about_ca_topic_score_gemma":0.004256258,"teacher_disagreement_score":0.0028361846,"about_ca_system_score_codex":0.00034422334,"about_ca_system_score_gemma":0.00041016878,"threshold_uncertainty_score":0.0062150955},"labels":[],"label_agreement":null},{"id":"W4391991245","doi":"10.32920/25262809","title":"Multi-sensor Integration for Land Vehicular Navigation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"GNSS applications; Inertial measurement unit; Computer science; Precise Point Positioning; Kalman filter; Real Time Kinematic; Geodetic datum; Global Positioning System; Real-time computing; Remote sensing; Geodesy; Artificial intelligence; Geography; Telecommunications","score_opus":0.020019128541648997,"score_gpt":0.2608420710150404,"score_spread":0.24082294247339142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391991245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02819853,0.002207529,0.95765054,0.0001598246,0.00043908515,0.000083422034,0.00015934047,0.00198913,0.009112544],"genre_scores_gemma":[0.7329558,0.0015570587,0.24731123,0.00020509919,0.00011696836,0.00011052966,0.0008189807,0.00011123752,0.016813109],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996855,0.000055685672,0.000014163929,0.00006834368,0.00015022262,0.000026053987],"domain_scores_gemma":[0.99988186,0.000011580908,0.000013114693,0.000024683184,0.00006221754,0.000006533909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027931904,0.00044143363,0.00030672463,0.00038009137,0.00019406008,0.0005324226,0.00052454387,0.00041400065,0.0018578405],"category_scores_gemma":[0.00042703873,0.00017834968,0.00026907556,0.00047271763,0.00013298067,0.00059550384,0.00061572355,0.00048126146,0.00091045024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019415445,0.00007897227,0.0037006359,0.00030936915,0.00012574706,0.0002867469,0.00019273722,0.13221262,0.1185633,0.012941631,0.007086378,0.72430766],"study_design_scores_gemma":[0.000021118345,0.0003285531,0.0044868523,0.000054333854,0.000056565546,0.000161735,0.00011714856,0.9046447,0.032735355,0.004685395,0.052664027,0.00004409415],"about_ca_topic_score_codex":0.0028361846,"about_ca_topic_score_gemma":0.004256258,"teacher_disagreement_score":0.0028361846,"about_ca_system_score_codex":0.00034422334,"about_ca_system_score_gemma":0.00041016878,"threshold_uncertainty_score":0.0062150955},"labels":[],"label_agreement":null},{"id":"W4392152406","doi":"10.1109/globecom54140.2023.10436815","title":"Split Learning for Sensing-Aided Single and Multi-Level Beam Selection in Multi-Vendor RAN","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Vendor; Selection (genetic algorithm); Ran; Artificial intelligence; Computer network; Business; Marketing","score_opus":0.059564559997648266,"score_gpt":0.263018853613917,"score_spread":0.20345429361626877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392152406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019244703,0.0001824808,0.9786871,0.00010477652,0.00002245795,0.000040859573,0.000029388422,0.00043707353,0.001251077],"genre_scores_gemma":[0.79780626,0.00012632465,0.19933157,0.0002532547,0.000052771204,0.00010528008,0.00015211425,0.000059409773,0.0021129458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989022,0.00036969813,0.000049636426,0.0002354214,0.00026307124,0.00017991505],"domain_scores_gemma":[0.9980666,0.0009793139,0.00015814001,0.00028109812,0.00038190425,0.00013291513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015178971,0.00072913355,0.00087913737,0.00035944494,0.00045635703,0.0008106472,0.0013853764,0.00079219596,0.0016369029],"category_scores_gemma":[0.0041884505,0.00040384074,0.00039422052,0.00044610607,0.00083864084,0.001370073,0.0015910238,0.0010007947,0.0006182277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045579302,0.00022724758,0.0026614775,0.00011039221,0.000071760995,0.00015829613,0.0002147215,0.57372415,0.016927678,0.011329722,0.0025470813,0.39157167],"study_design_scores_gemma":[0.00001187569,0.00007107972,0.0001865322,0.0000049113573,0.000007185845,0.00004178115,0.000019793846,0.99352443,0.002712166,0.0030471592,0.00036482833,0.000008220969],"about_ca_topic_score_codex":0.0019853767,"about_ca_topic_score_gemma":0.0031317736,"teacher_disagreement_score":0.0019853767,"about_ca_system_score_codex":0.0004731128,"about_ca_system_score_gemma":0.0013740145,"threshold_uncertainty_score":0.008027494},"labels":[],"label_agreement":null},{"id":"W4392152709","doi":"10.1109/globecom54140.2023.10437537","title":"Integrated 5G mmWave Positioning in Deep Urban Environments: Advantages and Challenges","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Computer science; Remote sensing; Geology","score_opus":0.012325444932760717,"score_gpt":0.19706151273027503,"score_spread":0.1847360677975143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392152709","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40617275,0.0024968567,0.56876975,0.0028674665,0.00035280717,0.00013864899,0.0003341949,0.0011236,0.017743932],"genre_scores_gemma":[0.96935284,0.00062094,0.028793737,0.00014330394,0.000058467387,0.000027578744,0.00012796516,0.000034509056,0.0008406868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99928015,0.0002099982,0.000021260279,0.0000979783,0.0002555935,0.00013508812],"domain_scores_gemma":[0.9994486,0.00016851777,0.00005640635,0.00010819525,0.0001661791,0.00005208651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078343425,0.00079342356,0.00044455196,0.00028763266,0.00051803776,0.0013712446,0.00093113456,0.0010154934,0.0006124962],"category_scores_gemma":[0.0017742333,0.00031987074,0.00024561572,0.00059465243,0.0008482472,0.0015023736,0.0013899059,0.0009633743,0.00028409102],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033627733,0.00014023078,0.032076593,0.00048380383,0.00016851815,0.0013444623,0.00089179643,0.65100044,0.049044725,0.020765088,0.003503694,0.24024443],"study_design_scores_gemma":[0.000044455202,0.00033600343,0.012581277,0.00008490799,0.000083506595,0.00056292256,0.00089426246,0.95216143,0.012798211,0.008006231,0.012382738,0.000064025015],"about_ca_topic_score_codex":0.0123418765,"about_ca_topic_score_gemma":0.016964916,"teacher_disagreement_score":0.0123418765,"about_ca_system_score_codex":0.0005981395,"about_ca_system_score_gemma":0.0008880397,"threshold_uncertainty_score":0.024540126},"labels":[],"label_agreement":null},{"id":"W4392172926","doi":"10.1007/978-3-031-52670-1_17","title":"Passive Radio Frequency-Based 3D Indoor Positioning System via Ensemble Learning","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Real-time computing","score_opus":0.00565790231840996,"score_gpt":0.1944399280937139,"score_spread":0.18878202577530395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392172926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011956898,0.00022276792,0.98433685,0.000049770562,0.00010659958,0.000016295338,0.00014935878,0.0014726736,0.0016887112],"genre_scores_gemma":[0.4294897,0.00060438627,0.56047696,0.00028347556,0.00019807692,0.00013596714,0.001393592,0.00018311269,0.007234751],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994622,0.00007321333,0.000021596872,0.00019521058,0.00017832908,0.00006948357],"domain_scores_gemma":[0.9996543,0.00006183343,0.000030463081,0.00009867374,0.00012922946,0.000025524954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040166324,0.00094182504,0.0015457739,0.00066329836,0.00043707437,0.0006485043,0.0012690381,0.00090417877,0.0021967357],"category_scores_gemma":[0.0006870859,0.0005481889,0.0008589182,0.0015218889,0.00022962666,0.0010287625,0.0014288232,0.0010921364,0.0024735772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031196603,0.00012950464,0.002209339,0.00007725727,0.00017503022,0.000107639615,0.000093539704,0.09159031,0.03848936,0.0015967664,0.004884468,0.8603349],"study_design_scores_gemma":[0.000018443734,0.0001294715,0.0018732832,0.000012569522,0.00007502474,0.00021017905,0.000023613076,0.98369455,0.0098282425,0.0016250287,0.0024746577,0.00003492763],"about_ca_topic_score_codex":0.0028835232,"about_ca_topic_score_gemma":0.004672122,"teacher_disagreement_score":0.0028835232,"about_ca_system_score_codex":0.00026690555,"about_ca_system_score_gemma":0.00058105815,"threshold_uncertainty_score":0.007348776},"labels":[],"label_agreement":null},{"id":"W4392585594","doi":"10.5194/egusphere-egu24-4153","title":"Height-constrained uncombined PPP for enhanced pedestrian and vehicular positioning with an Android smartphone","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Android (operating system); Pedestrian; Android application; Computer science; Transport engineering; Engineering; Operating system","score_opus":0.007350322781244667,"score_gpt":0.2138994615080597,"score_spread":0.20654913872681502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392585594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04732732,0.0008732347,0.93714136,0.0002911558,0.0002973432,0.00007457673,0.0013479373,0.004173235,0.008473809],"genre_scores_gemma":[0.7127561,0.0008499693,0.27620682,0.00019407984,0.00017128124,0.0001369748,0.003293331,0.0002823398,0.006109137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996482,0.00008062959,0.000014870745,0.000069080015,0.00012694568,0.00006027496],"domain_scores_gemma":[0.9997398,0.00006484284,0.00001975412,0.0000655254,0.00008984165,0.000020128871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031131133,0.0007802388,0.00066220656,0.00036977904,0.00026420862,0.0006877889,0.000693496,0.00051181787,0.0032295715],"category_scores_gemma":[0.0014634535,0.0003239043,0.0005329941,0.0007147885,0.00020130049,0.0005398595,0.001320884,0.00065911096,0.0017482244],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059425575,0.00008428267,0.007777453,0.0004133228,0.00016799617,0.0011395172,0.00035762295,0.57485336,0.019064149,0.01999864,0.0228899,0.3526595],"study_design_scores_gemma":[0.000029129344,0.000052622025,0.0014601096,0.000017861907,0.000018160554,0.00020479472,0.00004299577,0.9883386,0.0015273999,0.0037860554,0.004494939,0.000027348702],"about_ca_topic_score_codex":0.0109998835,"about_ca_topic_score_gemma":0.015467475,"teacher_disagreement_score":0.0109998835,"about_ca_system_score_codex":0.00027105873,"about_ca_system_score_gemma":0.00078686135,"threshold_uncertainty_score":0.021871746},"labels":[],"label_agreement":null},{"id":"W4394564369","doi":"10.1109/access.2024.3386194","title":"Location-Based Unsourced Random Access","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); Dalhousie University","funders":"","keywords":"Computer science; Computer network","score_opus":0.019971593605365615,"score_gpt":0.2863507163441946,"score_spread":0.266379122738829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394564369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008268343,0.0005075356,0.9879808,0.00010068011,0.00009182909,0.000037320202,0.000045973065,0.0007948689,0.0021727027],"genre_scores_gemma":[0.7482842,0.00078547746,0.24254668,0.0004173311,0.00030571345,0.00019712628,0.00022955325,0.00012533001,0.0071086544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99708694,0.0011575959,0.00012599671,0.00044496026,0.0008827943,0.00030177168],"domain_scores_gemma":[0.9969457,0.0010445453,0.00037921735,0.00078742794,0.000740559,0.00010257374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010885592,0.0009765178,0.0010447019,0.00089670735,0.0005582833,0.0013579781,0.0024786876,0.00089693157,0.0026090506],"category_scores_gemma":[0.004269396,0.00041425254,0.0006003275,0.0009006693,0.0007271864,0.0022874214,0.0028447448,0.0009067091,0.002068975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016700041,0.0003219814,0.0023400006,0.0007687354,0.00031058048,0.0012251147,0.0006742515,0.11392898,0.15820989,0.14595787,0.008129552,0.5664631],"study_design_scores_gemma":[0.00015698586,0.0010512474,0.0008127997,0.000088367306,0.00016087566,0.0040741116,0.00014529872,0.8634269,0.07133153,0.036169957,0.022415431,0.00016653465],"about_ca_topic_score_codex":0.0002788797,"about_ca_topic_score_gemma":0.00035538134,"teacher_disagreement_score":0.0026090506,"about_ca_system_score_codex":0.00035606755,"about_ca_system_score_gemma":0.00055193284,"threshold_uncertainty_score":0.008728147},"labels":[],"label_agreement":null},{"id":"W4394951272","doi":"10.1109/twc.2024.3383807","title":"Multi-Task Learning Resource Allocation in Federated Integrated Sensing and Communication Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Task (project management); Resource allocation; Resource management (computing); Resource (disambiguation); Computer network; Distributed computing","score_opus":0.016801458143583504,"score_gpt":0.24289171785482866,"score_spread":0.22609025971124516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394951272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037854455,0.00037920038,0.9592619,0.00033354093,0.0000438142,0.000031528165,0.00004342397,0.0003613462,0.0016908586],"genre_scores_gemma":[0.939485,0.00013492735,0.058047175,0.00017487226,0.000028358068,0.0000712951,0.000057557307,0.000033685577,0.0019671032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909794,0.00028200698,0.00003634307,0.00023344578,0.00015761559,0.00019271138],"domain_scores_gemma":[0.99887556,0.0005835372,0.00013169146,0.00010998847,0.0002129191,0.000086321066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018686535,0.0008605665,0.0012681946,0.0003910638,0.00047836587,0.0010517226,0.0014763788,0.0013042078,0.0010190133],"category_scores_gemma":[0.0032836352,0.00045286832,0.00044845793,0.0006598947,0.0009308662,0.001837501,0.0013427805,0.0011080471,0.0001507743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010546212,0.000073117546,0.00040312146,0.000041544474,0.000027381242,0.00005765119,0.000038381124,0.9580539,0.0010582694,0.0049176607,0.00072273426,0.03450081],"study_design_scores_gemma":[0.0000035191,0.00001082769,0.000039371047,0.0000016825674,0.000002503403,0.0000052560604,0.0000050533445,0.9973477,0.0002023333,0.0023064434,0.00007338937,0.0000019147508],"about_ca_topic_score_codex":0.0060973885,"about_ca_topic_score_gemma":0.005735435,"teacher_disagreement_score":0.0060973885,"about_ca_system_score_codex":0.0012647695,"about_ca_system_score_gemma":0.0017353739,"threshold_uncertainty_score":0.012123764},"labels":[],"label_agreement":null},{"id":"W4395069587","doi":"10.1109/access.2024.3393127","title":"A UHF Passive RFID Tag Position Estimation Approach Exploiting Mobile Robots: Phase-Only 3D Multilateration Particle Filters With No Unwrapping","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Scuola Superiore Sant'Anna; Università di Pisa; Canadian Institute for Advanced Research","keywords":"Multilateration; Ultra high frequency; Particle filter; Computer science; Position (finance); Robot; Phase (matter); Real-time computing; Acoustics; Telecommunications; Artificial intelligence; Kalman filter; Physics","score_opus":0.015091934930787454,"score_gpt":0.2736793911985187,"score_spread":0.2585874562677312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395069587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064893537,0.00011459224,0.992618,0.0000348422,0.000036821024,0.000011605115,0.000009991144,0.00021732041,0.00046749174],"genre_scores_gemma":[0.22160125,0.00047583145,0.7729698,0.00013010201,0.00008263606,0.00007955085,0.00011183367,0.00006918851,0.004479851],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969935,0.000057326157,0.000012474026,0.00009496018,0.000111345114,0.000024563064],"domain_scores_gemma":[0.9997453,0.00006310005,0.000056268764,0.000058388294,0.0000642976,0.0000125910865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031375312,0.0007470734,0.0006150382,0.00044084972,0.00022500363,0.0006649569,0.00071006484,0.0010805427,0.00078892265],"category_scores_gemma":[0.0006412905,0.0003235879,0.0007218462,0.0003966591,0.00037386443,0.00075260515,0.00077465206,0.000610409,0.0008211167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003909121,0.00014844991,0.0015947623,0.00033152333,0.00018693808,0.00034957522,0.0003109447,0.12641035,0.22407246,0.008700935,0.0016704615,0.63583267],"study_design_scores_gemma":[0.000038229187,0.00044854943,0.0017694394,0.000025412126,0.00007961919,0.00075416494,0.000058767782,0.9068586,0.07575378,0.0020828247,0.012055028,0.00007551851],"about_ca_topic_score_codex":0.00073475,"about_ca_topic_score_gemma":0.0008058774,"teacher_disagreement_score":0.0010805427,"about_ca_system_score_codex":0.0002067124,"about_ca_system_score_gemma":0.00038700431,"threshold_uncertainty_score":0.002639234},"labels":[],"label_agreement":null},{"id":"W4395082577","doi":"10.1007/s10291-024-01651-5","title":"Enhancing smartphone precise point positioning to sub-meter accuracy in suburban environments: a new stochastic model and outlier diagnosis","year":2024,"lang":"en","type":"article","venue":"GPS Solutions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; York University","keywords":"Outlier; Computer science; Point (geometry); Metre; Anomaly detection; Smartphone application; Real-time computing; Simulation; Computer vision; Artificial intelligence; Mathematics; Multimedia","score_opus":0.013688023887563712,"score_gpt":0.22192637185781786,"score_spread":0.20823834797025415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395082577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045177147,0.00017227372,0.9533406,0.0001867684,0.000056749413,0.000018774927,0.00010522439,0.00044227752,0.0005002278],"genre_scores_gemma":[0.9237954,0.00046071093,0.07281298,0.00008751457,0.00010274493,0.000039211976,0.00032696238,0.0000908796,0.002283542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895144,0.00021300453,0.0000649471,0.0002517053,0.00038718848,0.00013180524],"domain_scores_gemma":[0.9981598,0.00061821536,0.00034734228,0.00028216062,0.00051998685,0.00007251662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010260654,0.0010589426,0.0012936784,0.0005852849,0.0003312123,0.0010605297,0.0015265514,0.0010391126,0.000734493],"category_scores_gemma":[0.0043436727,0.0005004701,0.00081700546,0.0011875107,0.0006258598,0.0017527998,0.0013844827,0.0012653111,0.00040330464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018537782,0.00009064774,0.0060885623,0.00011327662,0.00006169654,0.00017752673,0.000101135185,0.90300477,0.006750694,0.0063395617,0.0011149646,0.07597175],"study_design_scores_gemma":[0.000002997326,0.000034186247,0.00063775777,0.0000027400965,0.000007197716,0.000030935844,0.000009489477,0.99746513,0.00071141863,0.00089846016,0.00019371248,0.000005924509],"about_ca_topic_score_codex":0.009976404,"about_ca_topic_score_gemma":0.011632445,"teacher_disagreement_score":0.009976404,"about_ca_system_score_codex":0.00074427185,"about_ca_system_score_gemma":0.0012423393,"threshold_uncertainty_score":0.019836724},"labels":[],"label_agreement":null},{"id":"W4395680492","doi":"10.1109/tcomm.2024.3393977","title":"Multipath Identification, User Localization, and Environment Mapping in Radio SLAM","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Science Foundation of Beijing Municipality","keywords":"Multipath propagation; Computer science; Identification (biology); Simultaneous localization and mapping; Mobile radio; Electronic engineering; Telecommunications; Engineering; Artificial intelligence; Mobile robot; Robot","score_opus":0.017238302473879533,"score_gpt":0.2263310459018137,"score_spread":0.20909274342793416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395680492","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019255881,0.0011768201,0.9949911,0.00013926835,0.00012228865,0.00002100569,0.000026998556,0.00031057684,0.001286346],"genre_scores_gemma":[0.3145861,0.004666127,0.6750867,0.00022850868,0.000593727,0.00023046094,0.0002441951,0.00023382668,0.0041304403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99714017,0.0011702979,0.00012794817,0.00051388977,0.0007536881,0.00029406653],"domain_scores_gemma":[0.99902034,0.00037207108,0.00012579947,0.00027873577,0.00015096224,0.00005207333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020294026,0.001307347,0.0010880812,0.0014732976,0.0010248282,0.0018678833,0.0015566495,0.0016097848,0.0009333738],"category_scores_gemma":[0.0043358766,0.00086459256,0.00095384294,0.0032555598,0.002133976,0.0033737447,0.0027905537,0.0023036858,0.00077452516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014470633,0.00006503326,0.0019888263,0.0003717247,0.00012256169,0.00036634502,0.00038298356,0.4655939,0.0061097816,0.1917918,0.00496111,0.3281013],"study_design_scores_gemma":[0.000017210356,0.00011465546,0.0011049008,0.00008246526,0.000053580574,0.00031300893,0.0001450563,0.85308063,0.0038260533,0.11653475,0.024632765,0.000094867515],"about_ca_topic_score_codex":0.00763961,"about_ca_topic_score_gemma":0.006344823,"teacher_disagreement_score":0.00763961,"about_ca_system_score_codex":0.0010461779,"about_ca_system_score_gemma":0.0015496144,"threshold_uncertainty_score":0.015190303},"labels":[],"label_agreement":null},{"id":"W4396680560","doi":"10.1109/lwc.2024.3397081","title":"Near-Field ISAC: Beamforming for Multi-Target Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Beamforming; Computer science; Base station; Transmitter power output; Benchmark (surveying); Electronic engineering; Real-time computing; Telecommunications; Engineering; Transmitter","score_opus":0.027126239652734502,"score_gpt":0.27196046487934294,"score_spread":0.24483422522660844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396680560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005866793,0.00036632697,0.9875947,0.00015867752,0.000044904853,0.000021781414,0.000024080575,0.00022631847,0.0056962473],"genre_scores_gemma":[0.39794803,0.0008811992,0.59427774,0.00041777597,0.00015364724,0.00009015243,0.00013136017,0.000078951845,0.0060210475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992988,0.00017255123,0.000016630216,0.00013960502,0.000312183,0.000060062273],"domain_scores_gemma":[0.9995426,0.00016441553,0.00005653908,0.00005806674,0.00014621858,0.000032186068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005022139,0.00078100763,0.0004577643,0.00039815422,0.00030235996,0.00059766625,0.00069873856,0.0008612432,0.0018185412],"category_scores_gemma":[0.0009739269,0.00025814274,0.00028822754,0.0005336763,0.00059892307,0.00086737995,0.00062337436,0.00072737876,0.0009770758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023468058,0.00017619626,0.0014339109,0.00026035897,0.00007196064,0.00015862557,0.00015586308,0.3215841,0.17548928,0.06166137,0.0062243645,0.43254927],"study_design_scores_gemma":[0.000014743236,0.00026504678,0.0005273529,0.0000333853,0.00001481721,0.00029796944,0.0000423688,0.9551401,0.02415537,0.0085791135,0.010891192,0.000038418148],"about_ca_topic_score_codex":0.001182597,"about_ca_topic_score_gemma":0.0023074502,"teacher_disagreement_score":0.0018185412,"about_ca_system_score_codex":0.00050946284,"about_ca_system_score_gemma":0.0006333047,"threshold_uncertainty_score":0.0060836077},"labels":[],"label_agreement":null},{"id":"W4396835520","doi":"10.1016/j.pmcj.2024.101936","title":"An efficient estimator for source localization in WSNs using RSSD and TDOA measurements","year":2024,"lang":"en","type":"article","venue":"Pervasive and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Multilateration; Computer science; Estimator; Linearization; Algorithm; Reduction (mathematics); Set (abstract data type); Mathematical optimization; Mathematics; Statistics; Nonlinear system; Azimuth","score_opus":0.02435762847504868,"score_gpt":0.2843369510205622,"score_spread":0.25997932254551354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396835520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042465506,0.00025390004,0.9945089,0.000030915045,0.00006868929,0.000012825783,0.000042915966,0.0005041947,0.00033117618],"genre_scores_gemma":[0.2351961,0.00087663776,0.7589672,0.00007593226,0.00017741515,0.00011647849,0.0004962897,0.000111950154,0.0039820694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951255,0.00008440699,0.000032074364,0.00010341436,0.00023969737,0.000027817252],"domain_scores_gemma":[0.9994856,0.00018616907,0.00005727352,0.000083853854,0.00016896205,0.000018074323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005132219,0.0007597484,0.0011217458,0.00078651647,0.00026539623,0.0008083659,0.0007947633,0.00061503646,0.0010630656],"category_scores_gemma":[0.0019043981,0.0004502634,0.0005185671,0.0010762003,0.0003060087,0.0009675628,0.00086176035,0.0007453454,0.0011547711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033468087,0.00015586399,0.0030292713,0.00032553973,0.00020920657,0.0001504918,0.00008653374,0.24518028,0.105570786,0.0104138,0.0043945503,0.63014895],"study_design_scores_gemma":[0.00003780622,0.00010295052,0.001540283,0.00002292392,0.000070299946,0.0003216101,0.000024619636,0.97030514,0.01966662,0.0024341922,0.0054392647,0.000034312307],"about_ca_topic_score_codex":0.0020415462,"about_ca_topic_score_gemma":0.0034077812,"teacher_disagreement_score":0.0020415462,"about_ca_system_score_codex":0.00035712434,"about_ca_system_score_gemma":0.000803175,"threshold_uncertainty_score":0.004059255},"labels":[],"label_agreement":null},{"id":"W4398226755","doi":"10.5081/jgps.19.1.66","title":"Improving smartphone-based positioning accuracy with height constraint and application to pedestrian and vehicular positioning","year":2023,"lang":"en","type":"article","venue":"Journal of Global Positioning Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Pedestrian; Computer science; Constraint (computer-aided design); Computer vision; Artificial intelligence; Transport engineering; Simulation; Real-time computing; Engineering","score_opus":0.0038269537726519647,"score_gpt":0.20865611534486178,"score_spread":0.20482916157220982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398226755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6472763,0.0038556599,0.31989965,0.0007318896,0.00076958403,0.00021590981,0.0057380344,0.012008253,0.009504826],"genre_scores_gemma":[0.9317543,0.00067001703,0.062252514,0.000098878336,0.00008202275,0.000060425416,0.0037136546,0.000106368665,0.0012618559],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949527,0.00011131601,0.00003477431,0.00011793778,0.00016524637,0.00007541571],"domain_scores_gemma":[0.99929667,0.0001763588,0.00006570907,0.00013942536,0.00028173675,0.000040042843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000547947,0.0010114888,0.0007903599,0.0007728327,0.00024986325,0.00044652898,0.00076133746,0.00053950044,0.0016823516],"category_scores_gemma":[0.003088203,0.00021033725,0.00043924103,0.0011852342,0.00018388941,0.00045959518,0.0008219715,0.00043674096,0.0007401032],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012651104,0.00024305159,0.060996387,0.0011274011,0.0003563786,0.0011991883,0.00035577317,0.41517437,0.030821374,0.0018910355,0.014964032,0.47160596],"study_design_scores_gemma":[0.00007965781,0.0004224408,0.030740172,0.00007318929,0.00009725028,0.00052846107,0.00020594051,0.95040274,0.010271369,0.0008704944,0.0062322295,0.00007618266],"about_ca_topic_score_codex":0.021072686,"about_ca_topic_score_gemma":0.02160002,"teacher_disagreement_score":0.021072686,"about_ca_system_score_codex":0.00023326415,"about_ca_system_score_gemma":0.00058490905,"threshold_uncertainty_score":0.04190004},"labels":[],"label_agreement":null},{"id":"W4398765200","doi":"10.1109/wts60164.2024.10536688","title":"An Approach for Localizing User Terminals in 6G Mobile Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Context (archaeology); Trajectory; Terminal (telecommunication); Position (finance); Augmented reality; Terahertz radiation; Set (abstract data type); Artificial intelligence; Computer vision; Mobile robot; Robotics; Real-time computing; Telecommunications; Robot; Optics; Physics","score_opus":0.008975096257018695,"score_gpt":0.2506582357365345,"score_spread":0.2416831394795158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398765200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017287387,0.00045596235,0.97806245,0.00032887826,0.00007351915,0.0000624362,0.00003554835,0.0004920955,0.0032017217],"genre_scores_gemma":[0.47564682,0.0008512337,0.5175494,0.00020173388,0.000051883468,0.00010932346,0.00008394303,0.000029193972,0.00547646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99969316,0.00010685048,0.0000108410695,0.000047514877,0.00011416874,0.00002753758],"domain_scores_gemma":[0.9998369,0.00003870443,0.00002648086,0.00003630889,0.000047749447,0.000013790859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024054149,0.00045997082,0.00027722522,0.0005942075,0.00049742515,0.0006498759,0.00063860475,0.00074488955,0.0010562631],"category_scores_gemma":[0.00062546856,0.0001558129,0.0003656177,0.0004661168,0.0004106121,0.00074877864,0.0010794381,0.00055861595,0.0005510508],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026516762,0.00011306547,0.004676235,0.00028489385,0.000117708485,0.000823669,0.00075382914,0.39959186,0.13580845,0.071433626,0.0059309145,0.3802006],"study_design_scores_gemma":[0.000040360905,0.00032755858,0.0014737565,0.000035853976,0.00005376978,0.00096036954,0.00034035798,0.9254093,0.03274247,0.010506671,0.028057508,0.000051948336],"about_ca_topic_score_codex":0.002663147,"about_ca_topic_score_gemma":0.004495748,"teacher_disagreement_score":0.002663147,"about_ca_system_score_codex":0.00045237906,"about_ca_system_score_gemma":0.0005185058,"threshold_uncertainty_score":0.0052952766},"labels":[],"label_agreement":null},{"id":"W4399140873","doi":"10.1145/3636534.3649355","title":"WiProfile: Unlocking Diffraction Effects for Sub-Centimeter Target Profiling Using Commodity WiFi Devices","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Profiling (computer programming); Diffraction; Object (grammar); Inverse problem; Commodity; Inverse; Fresnel diffraction; Computer vision; Artificial intelligence; Optics; Mathematics; Physics","score_opus":0.016153698080887915,"score_gpt":0.2540046533726989,"score_spread":0.237850955291811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399140873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05556278,0.0002954197,0.9383386,0.0001574642,0.000060533315,0.000052067553,0.00016226986,0.002503528,0.0028673257],"genre_scores_gemma":[0.6013724,0.00040886158,0.39413217,0.0002535252,0.000050577302,0.00011159211,0.00035245952,0.00014586037,0.003172549],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961984,0.00006323756,0.000013936509,0.000070486465,0.00017193943,0.00006053986],"domain_scores_gemma":[0.99938035,0.00019480725,0.000090407964,0.00019477529,0.00010108934,0.000038481503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040477116,0.0005826245,0.0005573749,0.0005061631,0.0003024121,0.0006363328,0.0008844799,0.0006427612,0.0015569696],"category_scores_gemma":[0.002021298,0.00027011527,0.0003233215,0.00051179086,0.00043233114,0.0014362206,0.0018223486,0.0005819929,0.0007369586],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063655904,0.00015673142,0.009762444,0.00036440344,0.000098655175,0.0006954102,0.00051951065,0.08319224,0.19275168,0.012629788,0.00784673,0.69134593],"study_design_scores_gemma":[0.000055299854,0.00035914587,0.0044377046,0.00005510128,0.000044091787,0.00149004,0.00022491113,0.8788735,0.092364125,0.0066387234,0.015335178,0.00012217353],"about_ca_topic_score_codex":0.00205531,"about_ca_topic_score_gemma":0.003602696,"teacher_disagreement_score":0.00205531,"about_ca_system_score_codex":0.00031588017,"about_ca_system_score_gemma":0.0005203893,"threshold_uncertainty_score":0.005208552},"labels":[],"label_agreement":null},{"id":"W4399238370","doi":"10.5194/egusphere-2024-1546","title":"An Improved Geolocation Methodology for Spaceborne Radar and Lidar Systems","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"European Space Agency","keywords":"Geolocation; Remote sensing; Lidar; Radar; Early-warning radar; Environmental science; Computer science; Geology; Geodesy; Bistatic radar; Radar imaging; Telecommunications","score_opus":0.03478513489498428,"score_gpt":0.28972913891386465,"score_spread":0.25494400401888034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399238370","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00095489377,0.000088150126,0.998054,0.00003053312,0.000066166045,0.00001724715,0.000024239804,0.00025670318,0.00050800457],"genre_scores_gemma":[0.040176827,0.00018008387,0.9545704,0.00006908506,0.0000829587,0.00007161629,0.00015134935,0.00015701202,0.004540669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905103,0.0002767176,0.00006106219,0.00019842252,0.00036195148,0.0000508241],"domain_scores_gemma":[0.9991547,0.00011317208,0.000090514695,0.00018383801,0.000438344,0.000019409637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008921968,0.00075435714,0.0004083657,0.0015147431,0.00041148625,0.0009438489,0.0009337234,0.00074320694,0.0039609266],"category_scores_gemma":[0.0022460138,0.00040691975,0.00054765336,0.0013763877,0.00041943812,0.001319389,0.0010631608,0.00095593405,0.0032019294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061887054,0.0000430146,0.0012341273,0.00023582726,0.00006728067,0.00018628075,0.00018354619,0.059698027,0.06852753,0.080872275,0.0067836368,0.7821065],"study_design_scores_gemma":[0.000050922703,0.00014111094,0.002749198,0.0000697349,0.000060518243,0.00082138693,0.00011810044,0.77368313,0.057773467,0.037634823,0.12678437,0.00011324577],"about_ca_topic_score_codex":0.0013931991,"about_ca_topic_score_gemma":0.0021669175,"teacher_disagreement_score":0.0039609266,"about_ca_system_score_codex":0.00037903822,"about_ca_system_score_gemma":0.0006924239,"threshold_uncertainty_score":0.013250649},"labels":[],"label_agreement":null},{"id":"W4399369620","doi":"10.21428/d82e957c.6ae42b89","title":"Towards Optimal Beacon Placement for Range-Aided Localization","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Range (aeronautics); Computer science; Engineering; Aerospace engineering","score_opus":0.011825600245502017,"score_gpt":0.24124744947514246,"score_spread":0.22942184922964043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399369620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003313423,0.00012897898,0.99506843,0.00011226372,0.000021138514,0.000023653161,0.000046684454,0.00046269252,0.0008225916],"genre_scores_gemma":[0.18316697,0.0003030533,0.8130042,0.00018345736,0.00008351,0.00022810961,0.0003971835,0.0003318779,0.0023016743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985507,0.00058358395,0.000059464444,0.00027354894,0.00039645328,0.00013613871],"domain_scores_gemma":[0.9965515,0.0021332793,0.0002958657,0.0004570769,0.0004548514,0.000107393316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019647859,0.0014025839,0.0016700975,0.0017533387,0.000712791,0.0011849462,0.0022524516,0.0017727519,0.0035166028],"category_scores_gemma":[0.009861987,0.0012700434,0.0009186456,0.0019604482,0.0011943545,0.0017276059,0.0030724471,0.0018165973,0.0020420612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015475144,0.0000707422,0.0006750549,0.0001228253,0.00003187215,0.00007354342,0.00016079514,0.8497377,0.0040699295,0.019214828,0.005696591,0.11999139],"study_design_scores_gemma":[0.000022779015,0.000035960802,0.000081327926,0.00001343092,0.0000051511,0.000037083995,0.0000250433,0.9840889,0.000996513,0.013530899,0.0011543893,0.000008455127],"about_ca_topic_score_codex":0.0032502862,"about_ca_topic_score_gemma":0.0038194258,"teacher_disagreement_score":0.0035166028,"about_ca_system_score_codex":0.0011239552,"about_ca_system_score_gemma":0.0018222878,"threshold_uncertainty_score":0.011764228},"labels":[],"label_agreement":null},{"id":"W4399424610","doi":"10.36227/techrxiv.24425536.v2","title":"Fast Selection of Indoor Wireless Transmitter Locations with Generalizable Neural Network Propagation Models","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Computer science; Transmitter; Fidelity; Artificial neural network; Wireless network; Wireless; Software deployment; Radio propagation; Process (computing); Quality of service; Position (finance); Real-time computing; Artificial intelligence; Distributed computing; Machine learning; Telecommunications","score_opus":0.011318489138877985,"score_gpt":0.20234491248004421,"score_spread":0.19102642334116623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399424610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068266936,0.00023766965,0.92696935,0.00026051223,0.000051639185,0.000026923986,0.00009708144,0.0010809976,0.003008899],"genre_scores_gemma":[0.880723,0.00016743399,0.11419248,0.000113682414,0.000042516614,0.000079082965,0.0002325524,0.00009667499,0.0043525635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983406,0.000049884038,0.00000589747,0.000041084004,0.000038643022,0.0000305088],"domain_scores_gemma":[0.99923694,0.0004976707,0.00008622219,0.000040804098,0.00010893313,0.000029482335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005242085,0.0009036125,0.00049423,0.00031392818,0.0002572894,0.0005433557,0.0008773191,0.0009482273,0.0011392675],"category_scores_gemma":[0.0024753956,0.00054630486,0.00036610654,0.00037119573,0.00048094135,0.0007497654,0.0006077723,0.001152347,0.00038362516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025126154,0.000010642269,0.0002786919,0.000007678985,0.000006138602,0.000017232547,0.000008455161,0.9893693,0.00053840614,0.0007099595,0.00024059067,0.008787702],"study_design_scores_gemma":[9.650419e-7,0.0000016142125,0.000019939567,5.716909e-7,5.0327515e-7,0.0000012234212,6.261026e-7,0.99968433,0.000089811474,0.00017828368,0.000021501271,5.694344e-7],"about_ca_topic_score_codex":0.011402176,"about_ca_topic_score_gemma":0.014294955,"teacher_disagreement_score":0.011402176,"about_ca_system_score_codex":0.0007813702,"about_ca_system_score_gemma":0.0007198307,"threshold_uncertainty_score":0.02267164},"labels":[],"label_agreement":null},{"id":"W4399525909","doi":"10.1109/comst.2024.3409556","title":"A Survey of mmWave Radar-Based Sensing in Autonomous Vehicles, Smart Homes and Industry","year":2024,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; National Foundation for Science and Technology Development","keywords":"Radar; Remote sensing; Telecommunications; Computer science; Geography","score_opus":0.04455543524800443,"score_gpt":0.2821213865857927,"score_spread":0.2375659513377883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399525909","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007750396,0.84816456,0.10597514,0.0022626421,0.0014308135,0.00011064597,0.00054854626,0.00052033423,0.03323703],"genre_scores_gemma":[0.03569897,0.9073756,0.042066094,0.001362443,0.0017380452,0.00009419531,0.0011254727,0.000112508365,0.010426705],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991117,0.00018992487,0.00009276383,0.00017850983,0.00036242715,0.000064570144],"domain_scores_gemma":[0.9986278,0.0007159799,0.00011153189,0.00008407659,0.00041479373,0.00004590943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010246587,0.0010743994,0.00076833094,0.0030243506,0.00042483924,0.0014668495,0.0008809382,0.0012989789,0.0048685493],"category_scores_gemma":[0.0016803226,0.00061802764,0.00063407683,0.005618206,0.00037377744,0.0034438744,0.00084145524,0.0009795327,0.0026714748],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007992387,0.00007261664,0.0021750988,0.0051294533,0.000059622238,0.00014732825,0.00012508476,0.0054577235,0.005401454,0.013344474,0.033578414,0.934429],"study_design_scores_gemma":[0.000011263279,0.00034596815,0.005045291,0.0031944306,0.00012075986,0.0015067823,0.0003359661,0.022871064,0.007907667,0.010965509,0.9475986,0.00009677258],"about_ca_topic_score_codex":0.0016588933,"about_ca_topic_score_gemma":0.0018341944,"teacher_disagreement_score":0.0048685493,"about_ca_system_score_codex":0.00049527764,"about_ca_system_score_gemma":0.0006446209,"threshold_uncertainty_score":0.01628685},"labels":[],"label_agreement":null},{"id":"W4399526258","doi":"10.1109/tsp.2024.3411672","title":"Direct Target Localization With Low-Bit Quantization in Wireless Sensor Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University; National Natural Science Foundation of China; Queen's University Belfast; European Commission","keywords":"Quantization (signal processing); Computer science; Wireless sensor network; Wireless; Wireless network; Algorithm; Computer network; Telecommunications","score_opus":0.007010781817074752,"score_gpt":0.21108874704900726,"score_spread":0.2040779652319325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399526258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064065396,0.00066021085,0.99197584,0.00009274516,0.00002858563,0.000015407884,0.000009621432,0.00011750213,0.00069363264],"genre_scores_gemma":[0.6564418,0.0020475378,0.33918443,0.0001684433,0.00007366136,0.0001291513,0.00007494083,0.00005148987,0.0018286465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992392,0.0002234716,0.00004073249,0.00012706353,0.0003352312,0.000034331486],"domain_scores_gemma":[0.99945503,0.0003106446,0.000073689,0.00005508271,0.00009383316,0.000011679302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072707736,0.00056134735,0.00046722847,0.0002638006,0.00026701856,0.0006298033,0.0006281928,0.00056315016,0.0006238433],"category_scores_gemma":[0.0025383558,0.00028067728,0.00020633884,0.0006471614,0.00079648476,0.0013751928,0.00082630134,0.00073717034,0.0001865284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116736715,0.00004292571,0.000616901,0.00029933543,0.000023916173,0.00009375659,0.000166049,0.7552892,0.02431421,0.034706276,0.0012518715,0.18307887],"study_design_scores_gemma":[0.000010515618,0.0000498223,0.00011698425,0.000014919618,0.000005000315,0.00003481502,0.000014774584,0.9874277,0.00441491,0.006861348,0.0010390417,0.000010123944],"about_ca_topic_score_codex":0.0017661158,"about_ca_topic_score_gemma":0.0014042103,"teacher_disagreement_score":0.0017661158,"about_ca_system_score_codex":0.00043909976,"about_ca_system_score_gemma":0.0005699234,"threshold_uncertainty_score":0.0038451552},"labels":[],"label_agreement":null},{"id":"W4399666248","doi":"10.1109/jsac.2024.3414627","title":"Multiuser Association and Localization Over Doubly Dispersive Multipath Channels for Integrated Sensing and Communications","year":2024,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China; China Association for Science and Technology","keywords":"Computer science; Multipath propagation; Association (psychology); Telecommunications; Computer network; Data association; Electronic engineering; Channel (broadcasting); Artificial intelligence; Probabilistic logic","score_opus":0.02167535399593775,"score_gpt":0.27910652851539225,"score_spread":0.2574311745194545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399666248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077636256,0.00011799646,0.991037,0.000050959934,0.000012576114,0.000009998812,0.000021601512,0.00011374175,0.00087240274],"genre_scores_gemma":[0.6915444,0.00054747175,0.30447167,0.00016238239,0.000057963865,0.000080943835,0.00021234456,0.000046703062,0.0028761507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99935824,0.00018957359,0.000019234245,0.00013382302,0.00021805889,0.00008119494],"domain_scores_gemma":[0.9992805,0.0003166585,0.00008542913,0.00014131975,0.00014197138,0.00003414183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057151966,0.0004835798,0.00045044583,0.00040802502,0.00032957218,0.0005744422,0.0009341438,0.0006073357,0.00084952614],"category_scores_gemma":[0.0021825875,0.000257347,0.00043165375,0.0008518428,0.00057355623,0.00095186615,0.0013053232,0.0007433532,0.00041124783],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019137705,0.00007279062,0.0022373127,0.00014739743,0.000075379656,0.00019337812,0.00022062717,0.6486704,0.027244834,0.045243725,0.0021540315,0.2735488],"study_design_scores_gemma":[0.000004600391,0.000027242058,0.00020552063,0.000004583286,0.000007658231,0.0000669315,0.000017053093,0.9907584,0.0028167272,0.0051839026,0.00089817686,0.00000912539],"about_ca_topic_score_codex":0.0026792677,"about_ca_topic_score_gemma":0.0032260534,"teacher_disagreement_score":0.0026792677,"about_ca_system_score_codex":0.00049581093,"about_ca_system_score_gemma":0.0008419814,"threshold_uncertainty_score":0.0053274035},"labels":[],"label_agreement":null},{"id":"W4399767254","doi":"10.1109/twc.2024.3412426","title":"Optimal Measurement Geometry Directed Integrated Localization and Synchronization in Large-Scale Wireless Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; The Research Council","keywords":"Synchronization (alternating current); Computer science; Wireless; Scale (ratio); Stochastic geometry; Wireless sensor network; Wireless network; Computer network; Topology (electrical circuits); Mathematics; Telecommunications; Combinatorics; Physics; Statistics","score_opus":0.013760028286141293,"score_gpt":0.22895553067958088,"score_spread":0.2151955023934396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399767254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018981203,0.00014242347,0.9798187,0.00008297701,0.00001868387,0.000018066301,0.000018577584,0.00015892426,0.0007605],"genre_scores_gemma":[0.87353146,0.00020608008,0.12501982,0.0000656578,0.00003630103,0.00008376428,0.000072321796,0.000025678808,0.0009588138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910563,0.00027521752,0.000037743637,0.00023006735,0.00025507697,0.000096171585],"domain_scores_gemma":[0.9986156,0.0006261647,0.00031406005,0.00018104848,0.00020066174,0.00006236316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096880214,0.00076087506,0.0007361753,0.00046010784,0.00046795234,0.0006810783,0.00118314,0.0005870792,0.00043290565],"category_scores_gemma":[0.0033188676,0.00039672494,0.00041124038,0.0009321563,0.0010108824,0.0013437996,0.0016250823,0.00067409186,0.00015989119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010600093,0.000026916037,0.0007188314,0.00005804075,0.000029584722,0.000069367445,0.0001018069,0.9268592,0.0057189325,0.015527742,0.0005852811,0.05019829],"study_design_scores_gemma":[0.0000080231985,0.000032594045,0.00015458933,0.0000027059875,0.000006375074,0.0000143121615,0.000012386308,0.994464,0.0010773585,0.004021589,0.00019901313,0.0000070114043],"about_ca_topic_score_codex":0.0035098437,"about_ca_topic_score_gemma":0.0033881166,"teacher_disagreement_score":0.0035098437,"about_ca_system_score_codex":0.0007355374,"about_ca_system_score_gemma":0.000972203,"threshold_uncertainty_score":0.00697881},"labels":[],"label_agreement":null},{"id":"W4400114765","doi":"10.1109/i2mtc60896.2024.10561118","title":"Carrier Phase Based Relative Positioning Using MUSIC-Based ToA Estimation with High Resolution","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Carleton University","funders":"","keywords":"Computer science; Estimation; Phase (matter); Multiple signal classification; High resolution; Resolution (logic); Telecommunications; Remote sensing; Artificial intelligence; Engineering; Geology; Physics","score_opus":0.013537712704573093,"score_gpt":0.24257077992775297,"score_spread":0.22903306722317987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400114765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011438803,0.00024488594,0.98524624,0.00008874155,0.000095870724,0.000031589494,0.000056272806,0.0009481721,0.0018495532],"genre_scores_gemma":[0.14707176,0.000291606,0.85028523,0.00012645648,0.00012831674,0.000058602367,0.0002099266,0.0001250884,0.0017029721],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989793,0.00019658303,0.000049367616,0.00018760122,0.00052357186,0.00006346736],"domain_scores_gemma":[0.9989372,0.00030820406,0.00016868247,0.00025047373,0.0002975539,0.00003779775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006693186,0.0010418663,0.0007370724,0.0013556173,0.00050458405,0.0009931637,0.0010078744,0.00087445276,0.0017998017],"category_scores_gemma":[0.0048585837,0.0003775456,0.0005406876,0.0016008561,0.00038178166,0.0016127664,0.0011635824,0.0008531587,0.002031249],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032047657,0.00010482964,0.0024703476,0.00022898601,0.00013145799,0.00020279743,0.0002572794,0.06360102,0.13447528,0.008294665,0.0027388127,0.78717417],"study_design_scores_gemma":[0.000073168805,0.00032069604,0.00302255,0.000071948125,0.000083279454,0.0012375695,0.000120343786,0.89239895,0.08183025,0.007141342,0.013593106,0.000106739244],"about_ca_topic_score_codex":0.0010481387,"about_ca_topic_score_gemma":0.0019966927,"teacher_disagreement_score":0.0017998017,"about_ca_system_score_codex":0.00026920388,"about_ca_system_score_gemma":0.00071052276,"threshold_uncertainty_score":0.0060209036},"labels":[],"label_agreement":null},{"id":"W4400188512","doi":"10.1109/lcomm.2024.3421370","title":"Localization in Multipath Environments via Active Sensing With Reconfigurable Intelligent Surfaces","year":2024,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Multipath propagation; Computer science; Computer network; Channel (broadcasting)","score_opus":0.01654868403826395,"score_gpt":0.22838314339523955,"score_spread":0.2118344593569756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400188512","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24046998,0.0002712535,0.7565455,0.0001575426,0.00003642664,0.000012479015,0.000009496043,0.0004491097,0.0020482172],"genre_scores_gemma":[0.9705528,0.000051291354,0.028815212,0.000026342293,0.0000085951715,0.000009346321,0.0000064176993,0.000007052632,0.0005229589],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998884,0.000032235672,0.0000042272145,0.000026190151,0.00002957209,0.000019361325],"domain_scores_gemma":[0.9998332,0.000071478695,0.000036731253,0.000024957499,0.000024373408,0.000009234125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014975756,0.00033456075,0.00023055113,0.00013335247,0.00013848512,0.00027595865,0.00043723357,0.00031297846,0.000281001],"category_scores_gemma":[0.00041156274,0.0001409601,0.00019104837,0.0001545771,0.00044927583,0.000491504,0.00044629793,0.00027396213,0.00009680244],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002742002,0.00005743817,0.002646631,0.00011908381,0.00005138355,0.00044079503,0.00033880046,0.53914255,0.27674955,0.006269608,0.0005008151,0.17340921],"study_design_scores_gemma":[0.000009786992,0.00017359562,0.00039126512,0.0000049254113,0.000012543615,0.00007377166,0.000042073607,0.97302306,0.024142066,0.0013517551,0.00076574076,0.0000094360375],"about_ca_topic_score_codex":0.00057048694,"about_ca_topic_score_gemma":0.00062799617,"teacher_disagreement_score":0.00057048694,"about_ca_system_score_codex":0.00017529003,"about_ca_system_score_gemma":0.00016907202,"threshold_uncertainty_score":0.0012719035},"labels":[],"label_agreement":null},{"id":"W4400275556","doi":"10.1109/tccn.2024.3414394","title":"Cooperative NOMA Empowered Integrated Sensing and Communication: Joint Beamforming and User Pairing","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Noma; Pairing; Beamforming; Joint (building); Computer science; Telecommunications; Engineering; Physics; Telecommunications link","score_opus":0.026321900252797195,"score_gpt":0.2455912837638039,"score_spread":0.21926938351100672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400275556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033435795,0.00034203482,0.96191406,0.00015275489,0.000068911344,0.00005533951,0.000022147935,0.00010969467,0.0038992956],"genre_scores_gemma":[0.88891244,0.00026587414,0.1080805,0.0001409843,0.000075292504,0.00010855737,0.000033670454,0.000013143982,0.0023695095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981477,0.0009892618,0.000051213705,0.00025389314,0.00031662005,0.000241389],"domain_scores_gemma":[0.99901485,0.0004525218,0.00013136509,0.00016260735,0.00015194301,0.00008663745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014873622,0.0010219427,0.0010423011,0.00031548532,0.0006273899,0.0012018707,0.00090274727,0.0010833328,0.0009022128],"category_scores_gemma":[0.0021090596,0.00037397473,0.00047682892,0.0008505195,0.0010193533,0.0013514142,0.0020572422,0.00094938354,0.00038859976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004993404,0.0003035624,0.0020578755,0.00020438549,0.00011917279,0.00084967626,0.00031385483,0.7796776,0.027241917,0.10062179,0.0017983695,0.08631235],"study_design_scores_gemma":[0.000013715429,0.00013483803,0.00013898844,0.0000065381037,0.000011313001,0.00015594019,0.000051677194,0.98909175,0.0024330756,0.0068825535,0.0010625601,0.000016998625],"about_ca_topic_score_codex":0.0008851906,"about_ca_topic_score_gemma":0.0012253474,"teacher_disagreement_score":0.0014873622,"about_ca_system_score_codex":0.00042245287,"about_ca_system_score_gemma":0.0008787736,"threshold_uncertainty_score":0.007866025},"labels":[],"label_agreement":null},{"id":"W4400276277","doi":"10.1109/wcnc57260.2024.10570788","title":"PoM: RFID Positioning for Real-World Application Using the Power of Mobility","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Power (physics); Embedded system","score_opus":0.013302275866436095,"score_gpt":0.2724063913252639,"score_spread":0.2591041154588278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400276277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009592583,0.0005989284,0.97962976,0.00026798586,0.00020668074,0.000060793525,0.00008013,0.004932661,0.004630411],"genre_scores_gemma":[0.69745034,0.00096113887,0.29149792,0.0005177677,0.00015543122,0.00014771894,0.00039863694,0.00014615273,0.0087249065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996979,0.00005197841,0.000017891212,0.00007101098,0.00011410528,0.000047227284],"domain_scores_gemma":[0.99977404,0.0000439894,0.000033419095,0.000058554026,0.00006385519,0.000026167958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035129976,0.00067781506,0.00049941294,0.00046688167,0.00039908863,0.0005996524,0.0011956112,0.0007276974,0.0025958538],"category_scores_gemma":[0.0008493812,0.00022741471,0.0003678558,0.00050404563,0.0004559096,0.001103589,0.0016170394,0.0006348072,0.0013890332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092818605,0.00012887226,0.0043960214,0.0008154759,0.00013235495,0.0010527892,0.000401949,0.14575382,0.110818535,0.035292484,0.025284056,0.67499554],"study_design_scores_gemma":[0.00014478133,0.00048637096,0.0016981715,0.00006567317,0.00008140587,0.0011771644,0.00012869199,0.87485826,0.050023496,0.012171835,0.059075586,0.00008854062],"about_ca_topic_score_codex":0.001011275,"about_ca_topic_score_gemma":0.0008632956,"teacher_disagreement_score":0.0025958538,"about_ca_system_score_codex":0.0002930902,"about_ca_system_score_gemma":0.00042344758,"threshold_uncertainty_score":0.0086839795},"labels":[],"label_agreement":null},{"id":"W4400314541","doi":"10.1109/jsen.2024.3420727","title":"MLA-MFL: A Smartphone Indoor Localization Method for Fusing Multisource Sensors Under Multiple Scene Conditions","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Engineering Laboratory","keywords":"Computer science; Computer vision; Artificial intelligence; Acoustics; Real-time computing; Physics","score_opus":0.018324719701087604,"score_gpt":0.2828291637788562,"score_spread":0.2645044440777686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400314541","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013960799,0.0002844222,0.98198503,0.00008067302,0.00008268921,0.000056895176,0.0001082353,0.0021024805,0.0013387376],"genre_scores_gemma":[0.41272986,0.00035969802,0.5816777,0.0001656707,0.00007808419,0.00020383384,0.00045422156,0.00017027478,0.00416062],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959904,0.000060589195,0.00002089821,0.00011545519,0.00016633968,0.00003774975],"domain_scores_gemma":[0.9997161,0.000047624482,0.000044910656,0.000058098536,0.0001134762,0.000019764768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035553993,0.0008410733,0.0005891358,0.0010037983,0.0003883944,0.00047332974,0.0008845617,0.00062482664,0.002124579],"category_scores_gemma":[0.0009848061,0.00033126475,0.0007173995,0.00059809076,0.00026052355,0.0010450617,0.0012521477,0.00052081706,0.0014290002],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032916313,0.00010299698,0.0027659312,0.0003248626,0.000089240995,0.00045637996,0.0005611811,0.023752915,0.16127405,0.0027141501,0.005520558,0.8021086],"study_design_scores_gemma":[0.000084893116,0.00049784704,0.007891236,0.00007244692,0.00012626994,0.001597173,0.00043374338,0.8529874,0.10689248,0.0033987653,0.025862955,0.00015480272],"about_ca_topic_score_codex":0.0028621736,"about_ca_topic_score_gemma":0.004267614,"teacher_disagreement_score":0.0028621736,"about_ca_system_score_codex":0.00036512085,"about_ca_system_score_gemma":0.00044421054,"threshold_uncertainty_score":0.0071074367},"labels":[],"label_agreement":null},{"id":"W4400488979","doi":"10.1109/jsac.2024.3423629","title":"Positioning Using Wireless Networks: Applications, Recent Progress, and Future Challenges","year":2024,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless network; Wireless; Telecommunications; Computer network","score_opus":0.023285538016640434,"score_gpt":0.2765850524140299,"score_spread":0.2532995143973895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400488979","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028876478,0.9546335,0.013798049,0.015384646,0.0019367808,0.000023887827,0.000083284125,0.000089455876,0.01116278],"genre_scores_gemma":[0.02294263,0.9628462,0.006732485,0.0015040733,0.0038747375,0.000028781245,0.000108532724,0.0000227691,0.0019398313],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99853575,0.00045588572,0.0001430351,0.00022781845,0.0005220902,0.00011543493],"domain_scores_gemma":[0.99544215,0.0027103005,0.0002824393,0.00020144411,0.0012091305,0.00015452738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003429948,0.0006626106,0.0006757022,0.0016864206,0.00065650174,0.0029730857,0.0010513269,0.0019198669,0.0026877502],"category_scores_gemma":[0.004394097,0.0003840225,0.00038241458,0.0042199316,0.0016840021,0.008214583,0.001570852,0.0023511918,0.0011675874],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062849045,0.000055850294,0.001761758,0.005330152,0.00003454208,0.00014606824,0.0003906831,0.003870656,0.0012737522,0.089269295,0.02334214,0.87446225],"study_design_scores_gemma":[0.000011238804,0.00026914186,0.0030728055,0.0044578095,0.00008633233,0.0011021973,0.0018106874,0.009531125,0.0013904461,0.07289414,0.90527135,0.00010258927],"about_ca_topic_score_codex":0.0016294795,"about_ca_topic_score_gemma":0.0015588914,"teacher_disagreement_score":0.003429948,"about_ca_system_score_codex":0.0012461507,"about_ca_system_score_gemma":0.001609086,"threshold_uncertainty_score":0.018139541},"labels":[],"label_agreement":null},{"id":"W4400527587","doi":"10.1109/fg59268.2024.10581974","title":"SMCTL: Subcarrier Masking Contrastive Transfer Learning for Human Gesture Recognition with Passive Wi-Fi Sensing","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Masking (illustration); Gesture; Subcarrier; Transfer of learning; Speech recognition; Gesture recognition; Transfer (computing); Artificial intelligence; Telecommunications; Channel (broadcasting)","score_opus":0.010353634998690795,"score_gpt":0.21294212320691433,"score_spread":0.20258848820822353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400527587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06840845,0.00052775687,0.9243896,0.00023462123,0.00012280414,0.00008714265,0.00018158865,0.0034917064,0.0025563028],"genre_scores_gemma":[0.84834546,0.00024384451,0.14060967,0.00031615858,0.00006623903,0.00018603896,0.00076968624,0.00013161778,0.009331301],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998259,0.000036303816,0.000007419236,0.000056947596,0.000045223765,0.000028336039],"domain_scores_gemma":[0.9997172,0.00012076232,0.000027927474,0.00004690577,0.00006637226,0.000020828411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049362355,0.00071955455,0.00042237798,0.0003168248,0.00020829777,0.00033010647,0.0010269637,0.0005595051,0.0019838004],"category_scores_gemma":[0.0014270834,0.0001702137,0.00036362422,0.00034987694,0.00048843015,0.00069211185,0.0009360665,0.00094056106,0.0008428307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033044463,0.00032021332,0.0020347245,0.00012328505,0.00007390274,0.00017065955,0.00011368442,0.14105973,0.04021891,0.0024888595,0.006103708,0.80696183],"study_design_scores_gemma":[0.000010927282,0.000120986595,0.0006710209,0.000008520182,0.000008583944,0.000044237775,0.000017899829,0.98463917,0.011964779,0.0015690124,0.0009350351,0.000009849343],"about_ca_topic_score_codex":0.0028555414,"about_ca_topic_score_gemma":0.0043843947,"teacher_disagreement_score":0.0028555414,"about_ca_system_score_codex":0.00037905393,"about_ca_system_score_gemma":0.0005737246,"threshold_uncertainty_score":0.0066364408},"labels":[],"label_agreement":null},{"id":"W4400527698","doi":"10.1109/icmisi61517.2024.10580252","title":"Comparative Analysis of Single Antenna and Antenna Array for GNSS Jamming Resilience","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"GNSS applications; Computer science; Antenna (radio); Jamming; Resilience (materials science); Antenna array; Telecommunications; Electronic engineering; Engineering; Global Positioning System; Physics","score_opus":0.02041949585482393,"score_gpt":0.26197167351180767,"score_spread":0.24155217765698372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400527698","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8911998,0.0020119746,0.09109285,0.00025059495,0.00008575767,0.000045162633,0.00034464564,0.0008489734,0.014120278],"genre_scores_gemma":[0.9941843,0.00030373415,0.0046629594,0.000018753099,0.000011860357,0.000008802202,0.00007947771,0.000029254616,0.0007008805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945515,0.00016488129,0.00002480018,0.000073794065,0.00018062959,0.00010073216],"domain_scores_gemma":[0.99613935,0.002181052,0.0004781158,0.00042475614,0.0006678874,0.00010891416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068768894,0.0004988199,0.0005482341,0.0011308241,0.00023032086,0.0006001184,0.00035425765,0.0005258246,0.0020815579],"category_scores_gemma":[0.003384644,0.00015797341,0.00036530642,0.0009278803,0.00032009467,0.0006462501,0.00042269277,0.00027831047,0.00052533014],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00150387,0.00010099081,0.013536153,0.0004694628,0.00023339009,0.00043373287,0.00017473943,0.73994994,0.099096745,0.0041687884,0.0012677909,0.13906439],"study_design_scores_gemma":[0.000054137985,0.0045445445,0.040890217,0.00012376245,0.0005327967,0.001587476,0.0005833146,0.83070314,0.111244604,0.0025493763,0.007063228,0.0001235085],"about_ca_topic_score_codex":0.0004622785,"about_ca_topic_score_gemma":0.00049271015,"teacher_disagreement_score":0.0020815579,"about_ca_system_score_codex":0.00034199317,"about_ca_system_score_gemma":0.00017003527,"threshold_uncertainty_score":0.006963432},"labels":[],"label_agreement":null},{"id":"W4400727577","doi":"10.1109/jiot.2024.3430087","title":"Graph-Neural-Network-Based WiFi Indoor Localization System With Access Point Selection","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial neural network; Computer network; Selection (genetic algorithm); Graph; Distributed computing; Artificial intelligence; Theoretical computer science","score_opus":0.009273182228977961,"score_gpt":0.22428815468624572,"score_spread":0.21501497245726775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400727577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05483588,0.0005507058,0.93277115,0.00031175886,0.00015219642,0.00009275877,0.0006260244,0.0069165053,0.0037430248],"genre_scores_gemma":[0.8783959,0.00043504627,0.11345182,0.00022757443,0.00007727944,0.0001333902,0.0016116215,0.00008052099,0.005586875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997267,0.000041904117,0.000015199783,0.00009678774,0.00007456167,0.00004470209],"domain_scores_gemma":[0.9997981,0.000045092536,0.000027906943,0.00003074141,0.00008115465,0.000017033812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019969427,0.00074446626,0.0007094439,0.00087084115,0.0003720112,0.00043602753,0.0012534526,0.00047378612,0.0016132471],"category_scores_gemma":[0.00075117964,0.00023426734,0.0004660515,0.0013314703,0.00022609503,0.00091825397,0.00073201564,0.00050789333,0.00065977743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048808736,0.00018541137,0.0045636324,0.00016608665,0.00014237758,0.0003013139,0.000097280485,0.43743905,0.009039513,0.0028894937,0.009810949,0.53487676],"study_design_scores_gemma":[0.000019281175,0.000042497486,0.00084710936,0.0000044493863,0.000022327065,0.000083290666,0.000015059519,0.99515015,0.0019009595,0.0009462453,0.0009556433,0.0000129319615],"about_ca_topic_score_codex":0.016972719,"about_ca_topic_score_gemma":0.020245316,"teacher_disagreement_score":0.016972719,"about_ca_system_score_codex":0.000630695,"about_ca_system_score_gemma":0.00059536705,"threshold_uncertainty_score":0.03374785},"labels":[],"label_agreement":null},{"id":"W4400737280","doi":"10.1145/3678878","title":"Enabling Technologies and Techniques for Floor Identification","year":2024,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Impact","funders":"","keywords":"Computer science; Identification (biology); Data science","score_opus":0.0449923320987422,"score_gpt":0.32358863759887646,"score_spread":0.27859630550013426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400737280","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019394777,0.876143,0.065367855,0.0018604216,0.0021612525,0.00015228718,0.00041278094,0.0005606145,0.05140234],"genre_scores_gemma":[0.021684753,0.9199039,0.038914677,0.0008290701,0.0009753603,0.00016162127,0.00058373326,0.00009069781,0.0168562],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984742,0.00027517974,0.00009516626,0.00019188067,0.0008361858,0.00012741359],"domain_scores_gemma":[0.9981055,0.0008328701,0.00017015965,0.0002265158,0.00060961366,0.00005529673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001192493,0.0013544327,0.0009650674,0.0045904634,0.0005986773,0.002075893,0.0019762246,0.0014218464,0.013784957],"category_scores_gemma":[0.0035065413,0.0006439092,0.0010165621,0.004806237,0.00089827477,0.0040519875,0.001831347,0.0015085277,0.00855287],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030508127,0.000038669114,0.0004325017,0.0047663725,0.000025845333,0.00011401973,0.00010969343,0.0012820548,0.0018729778,0.025902642,0.017125934,0.9482989],"study_design_scores_gemma":[0.0000073158094,0.00010046388,0.0013794294,0.0036680724,0.00006836056,0.0010265176,0.00026745387,0.0011459048,0.0027194316,0.010206703,0.9793508,0.000059665184],"about_ca_topic_score_codex":0.002582068,"about_ca_topic_score_gemma":0.0031881705,"teacher_disagreement_score":0.013784957,"about_ca_system_score_codex":0.00089037477,"about_ca_system_score_gemma":0.0015440646,"threshold_uncertainty_score":0.04611522},"labels":[],"label_agreement":null},{"id":"W4400770599","doi":"10.1109/tiv.2024.3429489","title":"Constructing Context-Aware GNSS Stochastic Model for Code-Based Resilient Positioning in Urban Environment","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"GNSS applications; Computer science; Context (archaeology); Code (set theory); Global Positioning System; Geography; Telecommunications; Programming language","score_opus":0.019914425487551176,"score_gpt":0.23594797763381728,"score_spread":0.2160335521462661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400770599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070214935,0.00028402018,0.92744076,0.00012053291,0.000056661953,0.00002417655,0.00016282072,0.00058094703,0.0011151657],"genre_scores_gemma":[0.9379581,0.00039681015,0.05831692,0.00010668691,0.000051020063,0.00008666638,0.0006454608,0.00008278531,0.0023554335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978346,0.000026367106,0.00001106724,0.00008025574,0.00006036688,0.00003847948],"domain_scores_gemma":[0.99982685,0.00003877897,0.000033383047,0.000018455712,0.00006717571,0.000015322546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024429825,0.00078532385,0.0004690798,0.0004705213,0.00026389293,0.00044737261,0.00097555586,0.00047930863,0.00062449294],"category_scores_gemma":[0.0009936175,0.0003485504,0.0005498037,0.000523729,0.00029431048,0.0006449303,0.0006707934,0.00069465296,0.00030439964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052217518,0.000018645313,0.0027906676,0.000033561082,0.000025364321,0.000088691246,0.000045405366,0.93746716,0.004696911,0.00178356,0.00061063375,0.052387126],"study_design_scores_gemma":[0.0000012170975,0.000005703592,0.00030097755,0.0000016550458,0.0000038959456,0.000009312515,0.000003816021,0.99872094,0.00040339518,0.00040169436,0.00014441322,0.0000030396625],"about_ca_topic_score_codex":0.017501226,"about_ca_topic_score_gemma":0.016679682,"teacher_disagreement_score":0.017501226,"about_ca_system_score_codex":0.000545846,"about_ca_system_score_gemma":0.00072818814,"threshold_uncertainty_score":0.03479874},"labels":[],"label_agreement":null},{"id":"W4401626406","doi":"10.20944/preprints202408.0834.v1","title":"A Roadmap for NF ISAC in 6G: A Comprehensive Overview and Tutorial","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Systems engineering; Field (mathematics); Wireless; Telecommunications; Engineering","score_opus":0.12630923056572171,"score_gpt":0.34642384789429426,"score_spread":0.22011461732857254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401626406","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002829526,0.7441981,0.13820419,0.008448509,0.0049639205,0.00018293838,0.0002992087,0.00061180507,0.100261904],"genre_scores_gemma":[0.02748628,0.8621481,0.06541582,0.002889469,0.004694168,0.00018130286,0.00067689456,0.00017738578,0.03633059],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996215,0.00010124274,0.00002766767,0.000060086368,0.00014150662,0.00004806547],"domain_scores_gemma":[0.9994399,0.00028588137,0.000031518153,0.00003528812,0.00016603175,0.00004124388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092124264,0.0011729961,0.0005119755,0.0021561175,0.0005233754,0.0022114925,0.0009110316,0.0021245638,0.0120421],"category_scores_gemma":[0.0013181054,0.00047752212,0.00058236276,0.0023034688,0.00053444394,0.0042614364,0.0012410117,0.0022272915,0.005735805],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004867595,0.000103314145,0.00058781833,0.0036559126,0.000032666027,0.0002762147,0.0003213621,0.0066556926,0.002789143,0.15101486,0.084592566,0.7499218],"study_design_scores_gemma":[0.0000028343281,0.00010576416,0.00031597092,0.0013648036,0.000020866813,0.0005133412,0.00018462368,0.0055042286,0.0006338153,0.025620166,0.96569854,0.000034945333],"about_ca_topic_score_codex":0.0019335276,"about_ca_topic_score_gemma":0.0018915774,"teacher_disagreement_score":0.0120421,"about_ca_system_score_codex":0.0010498737,"about_ca_system_score_gemma":0.0012256766,"threshold_uncertainty_score":0.040284872},"labels":[],"label_agreement":null},{"id":"W4401633967","doi":"10.1109/tap.2024.3439826","title":"Fast Selection of Indoor Wireless Transmitter Locations With Generalizable Neural Network Propagation Models","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transmitter; Computer science; Selection (genetic algorithm); Wireless; Artificial neural network; Wireless network; Radio propagation; Radio networks; Telecommunications; Computer network; Artificial intelligence","score_opus":0.010524654576542131,"score_gpt":0.20095797100080198,"score_spread":0.19043331642425984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401633967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081402704,0.00032740834,0.91318494,0.00030335397,0.00006289211,0.000031680796,0.000108705746,0.0010379589,0.0035403816],"genre_scores_gemma":[0.9019418,0.00018085125,0.093786046,0.00013901752,0.00004207655,0.00008394954,0.00021970033,0.00007054242,0.0035359536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983263,0.00004827806,0.0000068402355,0.000041974556,0.000037380036,0.000032879663],"domain_scores_gemma":[0.99929,0.00045376972,0.00008048202,0.00003617497,0.000110934874,0.000028606113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005306097,0.0009240373,0.0005450774,0.00033472505,0.00028454995,0.00055507134,0.000996999,0.0009672287,0.0011139696],"category_scores_gemma":[0.0023312976,0.0005555604,0.00041419014,0.0003669677,0.00049021584,0.00073306693,0.0006439056,0.0012007625,0.00038793794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024204008,0.000011852486,0.0003545677,0.0000073171686,0.0000067221786,0.000017512513,0.0000082531615,0.9891171,0.00043973874,0.00062482164,0.00022254027,0.009165353],"study_design_scores_gemma":[9.3294506e-7,0.0000020609161,0.000022788594,6.24366e-7,6.082796e-7,0.0000012485517,6.376194e-7,0.9997317,0.00006990162,0.00014899479,0.000019948251,5.8106144e-7],"about_ca_topic_score_codex":0.013198674,"about_ca_topic_score_gemma":0.015917547,"teacher_disagreement_score":0.013198674,"about_ca_system_score_codex":0.00082417537,"about_ca_system_score_gemma":0.0007854773,"threshold_uncertainty_score":0.026243687},"labels":[],"label_agreement":null},{"id":"W4401766061","doi":"10.3390/rs16163068","title":"Volume-Based Occupancy Detection for In-Cabin Applications by Millimeter Wave Radar","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Occupancy; Computer science; Multipath propagation; Radar; Extremely high frequency; Real-time computing; Environmental science; Remote sensing; Telecommunications; Engineering; Geology","score_opus":0.009792478724592747,"score_gpt":0.21905567484127209,"score_spread":0.20926319611667935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401766061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24853843,0.00089007994,0.74166584,0.00013762372,0.00013558118,0.00006115647,0.00026407154,0.0036955082,0.004611651],"genre_scores_gemma":[0.8758368,0.00042219632,0.12088385,0.00008723538,0.00008011737,0.0000456941,0.00034573523,0.0000967944,0.0022016682],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970776,0.00006582957,0.000008493665,0.00004522131,0.00012925941,0.00004335011],"domain_scores_gemma":[0.99971396,0.000081999424,0.000056916793,0.000025206527,0.000102600214,0.000019183066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021184677,0.00053966686,0.00048916676,0.00087671535,0.00014966947,0.0004343586,0.00053109817,0.00033723898,0.0009352989],"category_scores_gemma":[0.0006528682,0.00016974125,0.00021459452,0.0005268626,0.00011901109,0.0003497127,0.00038440878,0.0002511456,0.00062803476],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007076278,0.00029322764,0.030518759,0.0004272559,0.00012624837,0.00026394957,0.0003464313,0.031790063,0.28582424,0.0011777551,0.0052094497,0.643315],"study_design_scores_gemma":[0.00004047299,0.0004621378,0.046512183,0.000048051454,0.000091167545,0.00086976885,0.00030134036,0.84783566,0.09508649,0.001022939,0.0076694675,0.000060384977],"about_ca_topic_score_codex":0.0015012338,"about_ca_topic_score_gemma":0.002416736,"teacher_disagreement_score":0.0015012338,"about_ca_system_score_codex":0.00017871807,"about_ca_system_score_gemma":0.00018246399,"threshold_uncertainty_score":0.0031288266},"labels":[],"label_agreement":null},{"id":"W4401870958","doi":"10.1109/jsac.2024.3423608","title":"Guest Editorial Positioning and Sensing Over Wireless Networks—Part I","year":2024,"lang":"en","type":"editorial","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Computer network; Wireless; Telecommunications; Wireless network","score_opus":0.008601224246973658,"score_gpt":0.25448842771305447,"score_spread":0.2458872034660808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401870958","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004877656,0.008770183,0.0003561946,0.013789561,0.9741002,0.000019780797,0.000033397937,0.000050879466,0.0028310174],"genre_scores_gemma":[0.00049605436,0.009060624,0.00013437179,0.0051475763,0.9754347,0.00001642195,0.000022119317,0.00002978765,0.009658344],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99795353,0.00020987546,0.0002082287,0.00030606237,0.0011762073,0.0001460967],"domain_scores_gemma":[0.9910157,0.0026681726,0.0004455132,0.00023286344,0.0042556273,0.001382156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024561035,0.0030504412,0.0023279584,0.0024856806,0.0017250725,0.0063430555,0.0023524337,0.008032803,0.014315975],"category_scores_gemma":[0.008929275,0.0007235559,0.0014235509,0.0011564185,0.0015460169,0.0036087073,0.0011069366,0.012324132,0.014645731],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004078167,0.00001880319,0.00003205144,0.000309783,0.000013205519,0.00017220216,0.000011127207,0.000074773496,0.00015668676,0.0007278666,0.9850835,0.013359139],"study_design_scores_gemma":[0.000021246993,0.000041500527,0.00014012615,0.00027067008,0.000022849434,0.0004224343,0.00002191162,0.00025099228,0.00018480503,0.00095046905,0.997658,0.000014960963],"about_ca_topic_score_codex":0.00059950474,"about_ca_topic_score_gemma":0.0010729879,"teacher_disagreement_score":0.014315975,"about_ca_system_score_codex":0.0016366988,"about_ca_system_score_gemma":0.0012832755,"threshold_uncertainty_score":0.047891676},"labels":[],"label_agreement":null},{"id":"W4402156664","doi":"10.1109/icc51166.2024.10622551","title":"Joint Parameter Estimation and Signal Detection for Integrated Sensing and Backscatter Communication","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities; Project 211","keywords":"Backscatter (email); Joint (building); Computer science; SIGNAL (programming language); Remote sensing; Signal processing; Detection theory; Estimation theory; Estimation; Telecommunications; Geology; Algorithm; Engineering; Detector; Wireless; Radar","score_opus":0.012545165611018217,"score_gpt":0.220933220421831,"score_spread":0.2083880548108128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402156664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008671172,0.00021801559,0.9904874,0.000059966296,0.000018997032,0.000017721237,0.00001579536,0.00014334223,0.00036767716],"genre_scores_gemma":[0.502569,0.00047486462,0.49385577,0.00018263698,0.00012409067,0.00011433142,0.00017258263,0.000063507825,0.0024433066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983632,0.00048891245,0.00010683989,0.00037684295,0.000514768,0.00014944357],"domain_scores_gemma":[0.9981006,0.0010247875,0.0002972772,0.00025398482,0.00026927516,0.000054179935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012609775,0.0013738857,0.0010677376,0.00060236274,0.00029088237,0.0009240941,0.0009259458,0.00100523,0.00101424],"category_scores_gemma":[0.005925564,0.0004836411,0.0005601496,0.00074911764,0.0008405841,0.0016616787,0.0015112774,0.0010443466,0.0005664828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069080666,0.0002116191,0.004239063,0.0004003563,0.00029524407,0.00041555343,0.0004490107,0.40527898,0.08428802,0.03300503,0.0012983582,0.46942794],"study_design_scores_gemma":[0.000020026371,0.00017773552,0.0006191959,0.000015075596,0.00003428905,0.0002274083,0.000027610235,0.9795049,0.014537222,0.003745714,0.0010610416,0.000029901026],"about_ca_topic_score_codex":0.0010770428,"about_ca_topic_score_gemma":0.0012180174,"teacher_disagreement_score":0.0013738857,"about_ca_system_score_codex":0.00033914042,"about_ca_system_score_gemma":0.0008524825,"threshold_uncertainty_score":0.006668806},"labels":[],"label_agreement":null},{"id":"W4402265796","doi":"10.23919/acc60939.2024.10644833","title":"Structure from WiFi (SfW): RSSI-Based Geometric Mapping of Indoor Environments","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Real-time computing","score_opus":0.006941252667701249,"score_gpt":0.1833685446366409,"score_spread":0.17642729196893966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402265796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011448273,0.0004185578,0.98108816,0.000116726165,0.00013141974,0.00003803617,0.00024509977,0.0026116131,0.0039020556],"genre_scores_gemma":[0.57065177,0.0015198668,0.41857842,0.00016872896,0.00019922829,0.00019782763,0.0019111738,0.00036823156,0.0064048306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994999,0.00006674187,0.000015215766,0.00009546866,0.0002611291,0.00006144007],"domain_scores_gemma":[0.99967897,0.00006178232,0.00005426344,0.00009764271,0.00008436793,0.00002299079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026957295,0.0010072466,0.0005807514,0.001628938,0.00034841752,0.0007463383,0.0011847302,0.00057349855,0.0016526963],"category_scores_gemma":[0.0017531568,0.00031089908,0.0006412828,0.002359086,0.00052056933,0.001178508,0.0017681213,0.00061958557,0.0019199325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016918115,0.000073744595,0.002976143,0.00030385362,0.000077208824,0.0003169626,0.00026535612,0.061698254,0.028869517,0.011481531,0.008084929,0.88568336],"study_design_scores_gemma":[0.0001283788,0.0006190638,0.010516419,0.00014306851,0.00011387797,0.0038002697,0.00038034824,0.8223017,0.05979461,0.0385134,0.063503146,0.00018574517],"about_ca_topic_score_codex":0.002469963,"about_ca_topic_score_gemma":0.0037306088,"teacher_disagreement_score":0.002469963,"about_ca_system_score_codex":0.00024994832,"about_ca_system_score_gemma":0.0005390301,"threshold_uncertainty_score":0.0055288076},"labels":[],"label_agreement":null},{"id":"W4402312193","doi":"10.1016/j.measurement.2024.115646","title":"Ultrasonic wind parameter measurement method based on cyclostationary","year":2024,"lang":"en","type":"article","venue":"Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MD Precision (Canada)","funders":"","keywords":"Cyclostationary process; Ultrasonic sensor; Acoustics; Statistics; Environmental science; Computer science; Electronic engineering; Engineering; Marine engineering; Mathematics; Telecommunications; Physics","score_opus":0.035323332365686785,"score_gpt":0.25008178454103247,"score_spread":0.21475845217534567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402312193","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07526745,0.0010319225,0.91727597,0.0001361472,0.00032592093,0.00005841396,0.00012103654,0.0007331599,0.005050035],"genre_scores_gemma":[0.8825671,0.001217583,0.11114738,0.0001230494,0.00024796053,0.000086369,0.00034763516,0.00005595451,0.0042069014],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996823,0.000042231462,0.000015976117,0.00008099055,0.00015545239,0.000023168313],"domain_scores_gemma":[0.99982613,0.00004014541,0.000023799572,0.00002343446,0.00007605396,0.000010392951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017205303,0.00045135475,0.00040177585,0.000765367,0.00025537674,0.00038260853,0.00033717245,0.00033111413,0.0009549483],"category_scores_gemma":[0.00045039714,0.00020363035,0.00020241602,0.000774106,0.00020185248,0.0006682573,0.00026838636,0.00033608658,0.00040579966],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005153758,0.00015447389,0.009849304,0.00038982378,0.000097607655,0.00024543476,0.00015777275,0.013636631,0.3513049,0.0071604857,0.00408152,0.61240673],"study_design_scores_gemma":[0.00014437869,0.00087099744,0.029828226,0.00007583767,0.00029115108,0.001834843,0.00021758307,0.6440387,0.29784217,0.0032727523,0.02139804,0.00018545787],"about_ca_topic_score_codex":0.0006773427,"about_ca_topic_score_gemma":0.00072894216,"teacher_disagreement_score":0.0009549483,"about_ca_system_score_codex":0.00015417156,"about_ca_system_score_gemma":0.00028627925,"threshold_uncertainty_score":0.0031946301},"labels":[],"label_agreement":null},{"id":"W4402430409","doi":"10.3390/e26090773","title":"A Roadmap for NF-ISAC in 6G: A Comprehensive Overview and Tutorial","year":2024,"lang":"en","type":"review","venue":"Entropy","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Systems engineering; Wireless; Field (mathematics); Resource (disambiguation); Telecommunications; Engineering; Computer network","score_opus":0.0561106320418063,"score_gpt":0.33565233703200953,"score_spread":0.27954170499020325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402430409","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006014607,0.9692534,0.008148997,0.0020069683,0.001239563,0.000044040473,0.00008747393,0.0000745521,0.018543534],"genre_scores_gemma":[0.004211155,0.9843849,0.0048753056,0.00077786815,0.00067767006,0.000035747446,0.00012974265,0.000016565438,0.0048909923],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970394,0.000061567174,0.000029196182,0.000051372663,0.0001168011,0.00003709152],"domain_scores_gemma":[0.9994024,0.00034923872,0.00004082354,0.000022001803,0.00015520713,0.000030247375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087968295,0.0010796974,0.00070043805,0.0023391952,0.00037232664,0.0013912864,0.0008370429,0.0015981566,0.008973683],"category_scores_gemma":[0.0011407015,0.0004114723,0.0006253997,0.0026886414,0.00042351658,0.003608162,0.00090289523,0.00177424,0.0037904799],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036682515,0.000074662814,0.00023653077,0.010058788,0.000037683505,0.00016933038,0.00013268161,0.0019522448,0.0018841323,0.04623525,0.043130703,0.89605135],"study_design_scores_gemma":[0.0000033417866,0.00009236957,0.00027658054,0.0026755016,0.00004210857,0.0004251049,0.0001225403,0.0008921405,0.0005145001,0.008076669,0.9868559,0.000023171546],"about_ca_topic_score_codex":0.0017519823,"about_ca_topic_score_gemma":0.0022202495,"teacher_disagreement_score":0.008973683,"about_ca_system_score_codex":0.0007991505,"about_ca_system_score_gemma":0.0014989411,"threshold_uncertainty_score":0.030019999},"labels":[],"label_agreement":null},{"id":"W4402475272","doi":"10.1109/ccece59415.2024.10667179","title":"Moving Target Localization in Distributed Sensor Networks via Nuisance Variables Elimination","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Nuisance; Computer science; Wireless sensor network; Computer network","score_opus":0.004726065430722165,"score_gpt":0.19923482139145798,"score_spread":0.19450875596073583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402475272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008597242,0.00012806512,0.99079484,0.0000346025,0.000010543469,0.0000053312087,0.000007233985,0.00006590894,0.0003563544],"genre_scores_gemma":[0.63288784,0.00067241624,0.36325702,0.00007852976,0.00005886579,0.00010648681,0.00010589095,0.00005212888,0.002780882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962854,0.00013100627,0.000012671017,0.00006501983,0.00013545119,0.000027276164],"domain_scores_gemma":[0.99951994,0.0002765759,0.00007343784,0.00003768608,0.00008074088,0.000011640739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005482242,0.00058675726,0.0006027464,0.00046683472,0.00023826507,0.0003489022,0.00064673286,0.00033989653,0.0004559335],"category_scores_gemma":[0.0019086956,0.00024617417,0.00036908066,0.000742638,0.0005961114,0.0009388081,0.00081444654,0.0006155983,0.00017074916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006098594,0.0000168727,0.0006378524,0.00009574436,0.000033725693,0.000085030355,0.00010571891,0.86614066,0.010054484,0.024383616,0.00053701294,0.09784831],"study_design_scores_gemma":[0.0000057097936,0.000023749417,0.00012588265,0.0000041097246,0.000004245996,0.00001849961,0.000007619177,0.9937582,0.001263544,0.0042008357,0.0005837469,0.000003929306],"about_ca_topic_score_codex":0.0020730589,"about_ca_topic_score_gemma":0.0023165892,"teacher_disagreement_score":0.0020730589,"about_ca_system_score_codex":0.00027284256,"about_ca_system_score_gemma":0.0005487545,"threshold_uncertainty_score":0.004121959},"labels":[],"label_agreement":null},{"id":"W4402525750","doi":"10.21203/rs.3.rs-4909328/v1","title":"Improving smartphone positioning accuracy by adapting measurement covariance with t-test on innovations","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Covariance; Test (biology); Computer science; Analysis of covariance; Statistics; Mathematics; Machine learning; Geology","score_opus":0.046549744973165545,"score_gpt":0.3133744082365039,"score_spread":0.26682466326333837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402525750","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07764809,0.000257592,0.9182775,0.00017836192,0.0002269888,0.000042103027,0.00019081226,0.0016335871,0.0015450333],"genre_scores_gemma":[0.6968422,0.00021540777,0.29939333,0.000149832,0.00018078138,0.000095138166,0.0007071791,0.00040817258,0.0020079915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99680495,0.0009347759,0.00021458334,0.00079046685,0.0009691082,0.0002860141],"domain_scores_gemma":[0.9884802,0.0059141307,0.0006653232,0.0018996599,0.0028072635,0.00023342638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024039277,0.0011712706,0.0013052741,0.000976951,0.00049130066,0.0009072471,0.0014230026,0.0015910842,0.0034145],"category_scores_gemma":[0.026340865,0.0005361992,0.0010446676,0.0015887627,0.0006288799,0.0022385418,0.0017705761,0.0015583223,0.0014284715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019370725,0.0004465263,0.019083783,0.00037775852,0.00046864979,0.00031173808,0.00019211798,0.24828443,0.04839682,0.008430123,0.0039532036,0.66811776],"study_design_scores_gemma":[0.00005756263,0.00039551075,0.0074019562,0.00002092998,0.00008379613,0.00023650043,0.000043014843,0.9694434,0.017944137,0.0029873385,0.0013362007,0.000049684026],"about_ca_topic_score_codex":0.0036251284,"about_ca_topic_score_gemma":0.004454698,"teacher_disagreement_score":0.0036251284,"about_ca_system_score_codex":0.00042377322,"about_ca_system_score_gemma":0.0013007902,"threshold_uncertainty_score":0.012713373},"labels":[],"label_agreement":null},{"id":"W4402592617","doi":"10.1109/jsac.2024.3423628","title":"Guest Editorial Positioning and Sensing Over Wireless Networks—Part II","year":2024,"lang":"en","type":"editorial","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Computer network; Telecommunications; Wireless; Wireless network","score_opus":0.008415893710896533,"score_gpt":0.2526477762908094,"score_spread":0.24423188257991288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402592617","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004741281,0.0050525246,0.00032892186,0.009571461,0.98210645,0.000012156289,0.00002920339,0.00003667183,0.0028152096],"genre_scores_gemma":[0.0006941948,0.0052176327,0.00012985372,0.0041662375,0.97608227,0.000014833841,0.000023853401,0.000035819878,0.013635323],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976908,0.0002611668,0.00021502838,0.00037236043,0.0012803121,0.00018030625],"domain_scores_gemma":[0.9913455,0.0023474745,0.0004226349,0.00026773268,0.004269139,0.0013475746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025283669,0.0023405845,0.0017957892,0.0024753697,0.0016494249,0.0054391385,0.0015025666,0.0050179563,0.017312786],"category_scores_gemma":[0.008355865,0.0005492284,0.0011826447,0.00095409615,0.0012835078,0.0028820827,0.0011322334,0.008601003,0.013416453],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037473244,0.0000122091105,0.000024793422,0.00020610698,0.000008597321,0.00009762684,0.000011704081,0.00006303269,0.00018224715,0.0010056,0.9874648,0.010885755],"study_design_scores_gemma":[0.00001306033,0.00003314241,0.000109823515,0.00017725641,0.000014263266,0.00021577955,0.000024344172,0.0001989404,0.00020759275,0.0009791119,0.99801695,0.00000966476],"about_ca_topic_score_codex":0.0004147581,"about_ca_topic_score_gemma":0.00072963233,"teacher_disagreement_score":0.017312786,"about_ca_system_score_codex":0.0014205438,"about_ca_system_score_gemma":0.0013521481,"threshold_uncertainty_score":0.05791706},"labels":[],"label_agreement":null},{"id":"W4402673224","doi":"10.1109/ojcoms.2024.3465216","title":"Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predictive coding; Computer science; Coding (social sciences); Context (archaeology); Representation (politics); Human–computer interaction; Machine learning; Multimedia; Sociology","score_opus":0.06271895711927379,"score_gpt":0.34379089953659586,"score_spread":0.2810719424173221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402673224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072470005,0.00048582608,0.9900358,0.00029621905,0.000048687987,0.00003167653,0.00019096215,0.0005621279,0.0011016416],"genre_scores_gemma":[0.6280473,0.0012936684,0.36342046,0.00056931697,0.0002900797,0.00034006248,0.0015357075,0.00019853008,0.004304898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993961,0.00019179207,0.000023687371,0.00015905655,0.00015204272,0.000077287514],"domain_scores_gemma":[0.9989268,0.0005317444,0.000105710315,0.00015737159,0.00023215929,0.000046056244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010388483,0.00083879073,0.00068163767,0.0008340585,0.00033335757,0.0009293216,0.0019933272,0.0010094956,0.0014081602],"category_scores_gemma":[0.004279366,0.00036237997,0.0006993062,0.0012078546,0.00081153837,0.0014615948,0.0013758189,0.002364263,0.0004660288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013501396,0.0001254713,0.0015078074,0.000108692446,0.00006203802,0.00011908285,0.00016994763,0.58090734,0.004651989,0.028155249,0.0057194964,0.37833783],"study_design_scores_gemma":[0.000003206127,0.000016455628,0.00011720118,0.000009503844,0.000004742019,0.00001539794,0.000007815927,0.99073446,0.00058128126,0.0078969775,0.00060654216,0.0000064639667],"about_ca_topic_score_codex":0.00848802,"about_ca_topic_score_gemma":0.008425617,"teacher_disagreement_score":0.00848802,"about_ca_system_score_codex":0.0008621061,"about_ca_system_score_gemma":0.0010399845,"threshold_uncertainty_score":0.016877234},"labels":[],"label_agreement":null},{"id":"W4402737439","doi":"10.1016/j.autcon.2024.105778","title":"Data integration using deep learning and real-time locating system (RTLS) for automated construction progress monitoring and reporting","year":2024,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Real-time locating system; Real-time computing; Computer science; Engineering; Real-time data; Systems engineering; Artificial intelligence; Embedded system; Data mining; Operating system","score_opus":0.027853642636243722,"score_gpt":0.30775286265789126,"score_spread":0.2798992200216475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402737439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05324195,0.00046126416,0.93905747,0.00026749517,0.00012939861,0.00008382839,0.0003635028,0.005012682,0.0013824406],"genre_scores_gemma":[0.6700503,0.00045318628,0.32292348,0.00029240502,0.00007315126,0.0002220145,0.0015389066,0.00014717317,0.004299433],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906915,0.00013924774,0.00009388803,0.000259508,0.00033643417,0.000101841244],"domain_scores_gemma":[0.9990283,0.00016306952,0.00012804382,0.00016584502,0.00046863378,0.000046105753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009965426,0.000864205,0.00068656227,0.0015132157,0.0002935866,0.00082466437,0.0010331942,0.00080927496,0.0014332748],"category_scores_gemma":[0.0022474,0.0003256298,0.00071084575,0.0011944746,0.00027008646,0.0015679902,0.0012040116,0.0009338978,0.0008601492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040495247,0.00036279424,0.010306056,0.00017858541,0.0001976979,0.00020892853,0.00017326762,0.08947385,0.024342421,0.0018773553,0.005015145,0.867459],"study_design_scores_gemma":[0.000016438145,0.00015285192,0.0040618135,0.000024636487,0.00005961055,0.00008749916,0.00006592877,0.9667817,0.02338911,0.0018195377,0.0035081622,0.000032741482],"about_ca_topic_score_codex":0.0054979967,"about_ca_topic_score_gemma":0.006451473,"teacher_disagreement_score":0.0054979967,"about_ca_system_score_codex":0.00078435196,"about_ca_system_score_gemma":0.0011055113,"threshold_uncertainty_score":0.010931969},"labels":[],"label_agreement":null},{"id":"W4402980614","doi":"10.1109/ap-s/inc-usnc-ursi52054.2024.10687269","title":"Predictive/Robust Microwave Sensor Using LSTM","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; University of Toronto; University of Alberta","funders":"","keywords":"Computer science; Microwave; Robustness (evolution); Artificial intelligence; Telecommunications","score_opus":0.017516947566563693,"score_gpt":0.21690022094878644,"score_spread":0.19938327338222275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402980614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038961586,0.0011162255,0.95255435,0.00042663125,0.00024886912,0.000043732638,0.0001914427,0.0022502835,0.004206838],"genre_scores_gemma":[0.8346883,0.000673128,0.15852639,0.00035481705,0.0001259414,0.000109572444,0.0002814433,0.00008958509,0.005150856],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984014,0.00002317102,0.000009460865,0.000056744248,0.00005165893,0.000018886205],"domain_scores_gemma":[0.99987674,0.000043477143,0.000021126698,0.000013299849,0.000040440293,0.0000049809605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000289328,0.0005194207,0.0002881723,0.00022149348,0.00018661327,0.00041839987,0.0006317938,0.0006849147,0.0014430522],"category_scores_gemma":[0.00066223025,0.00017352363,0.00031161722,0.000302629,0.00024326672,0.00072853034,0.00039160158,0.0007866994,0.00048617314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038835983,0.00014819203,0.0016692987,0.00032222393,0.000133077,0.00025933317,0.00012143614,0.28828847,0.22803815,0.007794411,0.0049882988,0.46784872],"study_design_scores_gemma":[0.000006656975,0.00006075201,0.00034583657,0.000013075219,0.00001538897,0.00006191727,0.000009484362,0.9663023,0.029590515,0.0015675137,0.0020131457,0.000013378589],"about_ca_topic_score_codex":0.0017045814,"about_ca_topic_score_gemma":0.0016758994,"teacher_disagreement_score":0.0017045814,"about_ca_system_score_codex":0.00036509492,"about_ca_system_score_gemma":0.00032966048,"threshold_uncertainty_score":0.0048274994},"labels":[],"label_agreement":null},{"id":"W4402991087","doi":"10.3390/s24196314","title":"Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overfitting; Computer science; Wireless sensor network; Scalability; Artificial neural network; Machine learning; Artificial intelligence; Data mining; Distributed computing; Computer network","score_opus":0.020671650928869714,"score_gpt":0.2516529711425559,"score_spread":0.2309813202136862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402991087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048918582,0.00047999024,0.9482443,0.00024281415,0.000075371754,0.000023338462,0.000080808204,0.0009741861,0.0009605377],"genre_scores_gemma":[0.7817895,0.0006351203,0.21515085,0.00021599754,0.00004660448,0.000084032115,0.0004109293,0.00008114234,0.0015858086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997695,0.00004485236,0.000016804146,0.000067040986,0.00007556161,0.00002619071],"domain_scores_gemma":[0.99950635,0.00020004567,0.00007289002,0.00007322843,0.00012909506,0.000018378041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006047324,0.00061237044,0.00044666414,0.00037847477,0.00029809654,0.00043165084,0.000966757,0.0005105574,0.0004564636],"category_scores_gemma":[0.002204152,0.00028905144,0.00042605633,0.0006655206,0.00047029334,0.0014255301,0.0010526933,0.0009098419,0.0001675313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011560798,0.00008425142,0.0019761443,0.00012114781,0.000053551317,0.00009973314,0.000098808865,0.7012361,0.019467069,0.003439232,0.0017893467,0.27151904],"study_design_scores_gemma":[0.0000029576559,0.000025269368,0.00023001431,0.000005596636,0.0000068413283,0.000017545177,0.000011920035,0.99393964,0.004093308,0.0012181619,0.00044423685,0.00000458324],"about_ca_topic_score_codex":0.003947819,"about_ca_topic_score_gemma":0.006146475,"teacher_disagreement_score":0.003947819,"about_ca_system_score_codex":0.0005877782,"about_ca_system_score_gemma":0.0005618832,"threshold_uncertainty_score":0.007849693},"labels":[],"label_agreement":null},{"id":"W4403024433","doi":"10.1109/iccims61672.2024.10690560","title":"Noise Injection into Anchor for Improving Deep Neural Networks for Localization in WSNs for Internet of Things","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Internet of Things; Computer science; Noise (video); Artificial neural network; Wireless sensor network; The Internet; Computer network; Artificial intelligence; Embedded system; World Wide Web","score_opus":0.006755154897535732,"score_gpt":0.22544121509114318,"score_spread":0.21868606019360745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403024433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03676483,0.00034193954,0.96052414,0.00016061237,0.00008069454,0.000021606462,0.000061010498,0.0008485503,0.0011966352],"genre_scores_gemma":[0.79228663,0.00043936927,0.20373228,0.00025298438,0.000039595456,0.00007913577,0.00029018373,0.00009535414,0.0027845576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999752,0.00004932309,0.000017334321,0.00007320481,0.00007325271,0.000034895278],"domain_scores_gemma":[0.99965024,0.00014111878,0.000042091957,0.00003991694,0.00011105576,0.00001557716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065320084,0.0008448577,0.00040890524,0.00033326162,0.00022437892,0.0003737279,0.0008141335,0.0007282263,0.0007027608],"category_scores_gemma":[0.0020308383,0.00032282222,0.00044952857,0.00045598173,0.000532861,0.0010223716,0.0008717643,0.0010820295,0.00023360334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002057773,0.00010079929,0.0018361523,0.00013174972,0.00006761049,0.0001586564,0.000117171236,0.7610082,0.028201671,0.0061961664,0.0015171295,0.20045896],"study_design_scores_gemma":[0.0000045813467,0.000042789,0.0001762131,0.0000075686644,0.00001095284,0.000018078703,0.000008719541,0.9899406,0.008117262,0.0011319175,0.00053637964,0.000005051905],"about_ca_topic_score_codex":0.0038261847,"about_ca_topic_score_gemma":0.004975839,"teacher_disagreement_score":0.0038261847,"about_ca_system_score_codex":0.00057994795,"about_ca_system_score_gemma":0.00046352125,"threshold_uncertainty_score":0.0076078176},"labels":[],"label_agreement":null},{"id":"W4403024455","doi":"10.1109/pacrim61180.2024.10690227","title":"Learning-Based Ultra-Wideband Indoor Ranging","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Ranging; Computer science; Ultra-wideband; Telecommunications","score_opus":0.005094931861759772,"score_gpt":0.20071733281573262,"score_spread":0.19562240095397285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403024455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2868889,0.001219344,0.70199937,0.0002680415,0.000217951,0.000048227503,0.0004930226,0.005080101,0.0037850756],"genre_scores_gemma":[0.94621485,0.00023358078,0.050491802,0.000116977266,0.00004507596,0.000035326215,0.00082513195,0.00005269894,0.0019845688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996581,0.00006644801,0.000018287046,0.00012643104,0.00006856841,0.00006229177],"domain_scores_gemma":[0.99939585,0.0002750382,0.000069762405,0.00008704722,0.00014952382,0.000022740436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043191036,0.000698658,0.00065525406,0.0004916413,0.0001856179,0.00043532715,0.0009452182,0.00051255705,0.0008275325],"category_scores_gemma":[0.0018659453,0.00023452635,0.00040708232,0.0006904497,0.00028004753,0.0007841733,0.0006246438,0.0007654123,0.0005777268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014802432,0.00013551748,0.003350281,0.0000773423,0.000067087174,0.00011941904,0.000063641404,0.6969899,0.010699276,0.0007553885,0.0019366395,0.2856575],"study_design_scores_gemma":[0.0000059057384,0.000047285415,0.0007715746,0.0000052235537,0.0000102035,0.000038502392,0.000017171304,0.99479324,0.0033436867,0.0006171656,0.00034256888,0.0000075184316],"about_ca_topic_score_codex":0.0037292428,"about_ca_topic_score_gemma":0.0040741987,"teacher_disagreement_score":0.0037292428,"about_ca_system_score_codex":0.000282379,"about_ca_system_score_gemma":0.00042333113,"threshold_uncertainty_score":0.007415116},"labels":[],"label_agreement":null},{"id":"W4403094629","doi":"10.1109/tim.2024.3472810","title":"Novel Step Detection Algorithm for Smartphone Indoor Localization Based on CEEMDAN-HT","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Algorithm; Artificial intelligence","score_opus":0.0215250610929069,"score_gpt":0.22857471578391877,"score_spread":0.2070496546910119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403094629","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023063669,0.00020233239,0.97501117,0.000048711394,0.00004462925,0.000026306714,0.00007638176,0.0007914829,0.00073527225],"genre_scores_gemma":[0.45470423,0.00031289033,0.5412547,0.000116364274,0.000043936718,0.00011310769,0.0005154502,0.00007868752,0.0028606616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978644,0.000032672382,0.00001545785,0.00005492611,0.00008898908,0.000021499283],"domain_scores_gemma":[0.9997212,0.00007503906,0.000027589853,0.000039949522,0.0001213391,0.000014812581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025010543,0.0005353645,0.00046755376,0.00057052256,0.00017165762,0.00037066636,0.00057254353,0.00041048857,0.0011082069],"category_scores_gemma":[0.0009857819,0.00018548909,0.00035038195,0.00044205028,0.00016827269,0.00057751185,0.0004922072,0.00048239567,0.0005986779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035672984,0.000101688696,0.00601561,0.00014336302,0.0000767993,0.00019780455,0.00009110988,0.045887884,0.06730774,0.002889673,0.0036127854,0.8733189],"study_design_scores_gemma":[0.00001665964,0.0001409039,0.004800941,0.000015957407,0.000026786498,0.0004120746,0.000043843276,0.95668703,0.03391301,0.0012130103,0.002701114,0.000028631248],"about_ca_topic_score_codex":0.0009531148,"about_ca_topic_score_gemma":0.0016399972,"teacher_disagreement_score":0.0011082069,"about_ca_system_score_codex":0.00016235327,"about_ca_system_score_gemma":0.0003333973,"threshold_uncertainty_score":0.0037072897},"labels":[],"label_agreement":null},{"id":"W4403182703","doi":"10.1109/lwc.2024.3474774","title":"Novel Iterative Approach for Time-of-Arrivals-Based Localization With Maximum Likelihood Estimation","year":2024,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Engineering and Physical Sciences Research Council; Chongqing Municipal Education Commission; Natural Science Foundation of Chongqing; Chongqing Science and Technology Commission; National Natural Science Foundation of China","keywords":"Maximum likelihood; Computer science; Iterative method; Estimation; Maximum likelihood sequence estimation; Mathematical optimization; Algorithm; Estimation theory; Mathematics; Statistics","score_opus":0.014478236440935936,"score_gpt":0.23735390058550043,"score_spread":0.2228756641445645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403182703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006079771,0.00005015371,0.9988489,0.000031997544,0.000008446273,0.000007015059,0.0000052499895,0.00010515341,0.00033508413],"genre_scores_gemma":[0.1310466,0.00031617424,0.86491346,0.00013396575,0.00008990708,0.00020085475,0.00013814817,0.00015320424,0.003007657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917126,0.00027108917,0.00004334532,0.00017065142,0.00026726915,0.000076368524],"domain_scores_gemma":[0.99911314,0.0004547143,0.000100371806,0.00006868892,0.0002276799,0.00003537111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010089716,0.0010853626,0.0011109884,0.00083362736,0.0004888978,0.0008884832,0.0017964621,0.00113809,0.0016802817],"category_scores_gemma":[0.0032447667,0.00062126963,0.000915186,0.0012001678,0.000682209,0.0015066464,0.0019320429,0.0014297196,0.00088658894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012715803,0.000074835094,0.00089130335,0.00022829346,0.000107833264,0.00025583874,0.00033347416,0.7191978,0.011426806,0.031549767,0.0032345555,0.23257245],"study_design_scores_gemma":[0.000009324806,0.000020956619,0.00006422395,0.0000056050494,0.000008555128,0.000047802663,0.000011611584,0.9942526,0.0010779712,0.0035571656,0.000934817,0.0000093486515],"about_ca_topic_score_codex":0.0032461116,"about_ca_topic_score_gemma":0.0030862323,"teacher_disagreement_score":0.0032461116,"about_ca_system_score_codex":0.00064728846,"about_ca_system_score_gemma":0.0015450432,"threshold_uncertainty_score":0.006454408},"labels":[],"label_agreement":null},{"id":"W4403277747","doi":"10.1109/jiot.2024.3476973","title":"Enhancing Smartphone Relative Positioning With Partial Wide-Lane Ambiguity Resolution: Path to Real-Time, Decimeter-Level Positioning in User Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Real-time computing; Ambiguity; Path (computing); Ambiguity resolution; Global Positioning System; Computer network; Telecommunications","score_opus":0.008943192217212292,"score_gpt":0.21783851622621606,"score_spread":0.20889532400900376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403277747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16122644,0.00083500694,0.830586,0.00021945804,0.00019251477,0.00005222318,0.00018840827,0.0022328845,0.004467088],"genre_scores_gemma":[0.6818805,0.00044260765,0.31458107,0.00012399,0.000074357224,0.000043065902,0.0004042096,0.00009617998,0.0023540861],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996799,0.00006246655,0.000014698803,0.00007386193,0.00012486514,0.000044255976],"domain_scores_gemma":[0.99963176,0.00006894928,0.000044814627,0.00010281911,0.00012813362,0.00002358963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003604414,0.0008936138,0.0004752247,0.00044192286,0.00021218356,0.0005261079,0.0006956036,0.00058371865,0.00087420695],"category_scores_gemma":[0.0014540767,0.0002555465,0.0003561859,0.0006017628,0.00021322761,0.000638487,0.001110625,0.0004572438,0.0009284114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039316114,0.000106632724,0.0056204363,0.00030514112,0.00008402256,0.00050122687,0.00058340403,0.15896073,0.10173163,0.0027963712,0.0036628377,0.7252544],"study_design_scores_gemma":[0.000058499336,0.00040101525,0.006599632,0.00003942354,0.000060624378,0.00075327297,0.00022350617,0.9510489,0.03125168,0.0018488492,0.0076419027,0.00007272983],"about_ca_topic_score_codex":0.0029448029,"about_ca_topic_score_gemma":0.0039713,"teacher_disagreement_score":0.0029448029,"about_ca_system_score_codex":0.00013807109,"about_ca_system_score_gemma":0.000518282,"threshold_uncertainty_score":0.005855322},"labels":[],"label_agreement":null},{"id":"W4403722873","doi":"10.1109/tits.2024.3480525","title":"Leveraging Single-Bounce Reflections and Onboard Motion Sensors for Enhanced 5G Positioning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Tetra Tech (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Remote sensing; Motion (physics); Environmental science; Geodesy; Computer vision; Geology","score_opus":0.031254181080933315,"score_gpt":0.2606043570840277,"score_spread":0.22935017600309437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403722873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15937637,0.00076132006,0.8303359,0.00024181635,0.00025445933,0.000068862726,0.0003162661,0.001954293,0.0066906563],"genre_scores_gemma":[0.79532737,0.0004272093,0.2010867,0.00016727077,0.00007406262,0.000046815643,0.00040953074,0.00008898972,0.0023720851],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995159,0.000064927124,0.000019035728,0.00010699161,0.00019289333,0.000100248464],"domain_scores_gemma":[0.9997253,0.00006301099,0.00004544315,0.00005771674,0.00008983451,0.000018708708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003098634,0.0010629563,0.00041702169,0.00061881926,0.00018791207,0.00062311866,0.0006805741,0.00069162884,0.0009191332],"category_scores_gemma":[0.0009029262,0.00027509886,0.00041621012,0.0006897805,0.00028859542,0.000824899,0.00084315834,0.00054271,0.0007479884],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048543216,0.00016369115,0.01600696,0.0004237044,0.00017854884,0.00090801955,0.0004623755,0.19118881,0.27440238,0.0049818656,0.0028929522,0.50790524],"study_design_scores_gemma":[0.00008252296,0.0007365686,0.016451694,0.000100840196,0.0002216227,0.00087281526,0.00017834856,0.80341953,0.15619782,0.002494321,0.019086955,0.00015700213],"about_ca_topic_score_codex":0.0042239893,"about_ca_topic_score_gemma":0.009733249,"teacher_disagreement_score":0.0042239893,"about_ca_system_score_codex":0.0003196729,"about_ca_system_score_gemma":0.0005002284,"threshold_uncertainty_score":0.008398831},"labels":[],"label_agreement":null},{"id":"W4403758773","doi":"10.1109/tap.2024.3484180","title":"A Direction-Finding Model With Spatial Polarization Characteristics","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"National Natural Science Foundation of China","keywords":"Polarization (electrochemistry); Optics; Computer science; Physics","score_opus":0.009980343268983565,"score_gpt":0.20795792569662044,"score_spread":0.19797758242763688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403758773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016066373,0.000065120024,0.9960311,0.00008426828,0.000020392203,0.000010937313,0.000021502461,0.00004676622,0.0021132994],"genre_scores_gemma":[0.41317856,0.0014916306,0.57164353,0.0003080499,0.00020506627,0.0003410616,0.00036336854,0.00015327486,0.012315526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913436,0.000267071,0.00004296182,0.00023833137,0.00023353068,0.00008371047],"domain_scores_gemma":[0.99923253,0.00029119183,0.00011250475,0.00010983791,0.00021941148,0.000034611145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090884423,0.00076209847,0.0006224242,0.00062835234,0.0004828249,0.0018359195,0.0015766677,0.0011317612,0.0022404152],"category_scores_gemma":[0.002291268,0.0004187801,0.0007279786,0.0014297789,0.0010964794,0.0026088792,0.0012950229,0.0015187849,0.0012400262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068065194,0.000045293,0.0010695048,0.00012955561,0.00002991099,0.00022347894,0.00026018577,0.5016172,0.009862546,0.40168858,0.0023061496,0.08269953],"study_design_scores_gemma":[0.00001046614,0.000045340435,0.000116879055,0.000014233869,0.000013858377,0.00014518213,0.000035316654,0.94924563,0.0012585543,0.04499323,0.004103512,0.000017816717],"about_ca_topic_score_codex":0.0014852837,"about_ca_topic_score_gemma":0.0008129788,"teacher_disagreement_score":0.0022404152,"about_ca_system_score_codex":0.000603351,"about_ca_system_score_gemma":0.00091249583,"threshold_uncertainty_score":0.0074949265},"labels":[],"label_agreement":null},{"id":"W4403826654","doi":"10.1109/jsen.2024.3484585","title":"A Planar Compact Absorber for Microwave Sensing Based on Transmission-Line Metamaterials","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Canada Research Chairs; University of Toronto; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metamaterial; Planar; Microwave; Electric power transmission; Transmission line; Microwave transmission; Materials science; Optoelectronics; Transmission (telecommunications); Metamaterial absorber; Line (geometry); Split-ring resonator; Metamaterial antenna; Optics; Acoustics; Tunable metamaterials; Electrical engineering; Physics; Computer science; Telecommunications; Engineering; Microstrip antenna; Slot antenna","score_opus":0.021719151549366714,"score_gpt":0.2592777762402279,"score_spread":0.2375586246908612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403826654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38419187,0.003073639,0.597383,0.0009337696,0.000439635,0.0001487498,0.00033120593,0.0027360083,0.0107620545],"genre_scores_gemma":[0.7630146,0.0011080333,0.2293171,0.00027546418,0.00009380209,0.00009247244,0.00016025937,0.00011661741,0.0058217114],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997944,0.0000258488,0.000009293967,0.000063855456,0.00008721499,0.000019375575],"domain_scores_gemma":[0.9998092,0.000050832994,0.00007132874,0.00002710113,0.00002765424,0.000013971929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019973428,0.00038815584,0.0002897672,0.00030585093,0.0001651862,0.00040958505,0.0006166914,0.00054747355,0.0010735936],"category_scores_gemma":[0.00037345887,0.00023952944,0.00024574946,0.00022851846,0.00032510527,0.0008543342,0.00037300325,0.00052944035,0.0007027555],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031835603,0.000013029097,0.00006340741,0.000076289136,0.0000048863217,0.00006144028,0.000016424834,0.0003793257,0.9889474,0.0014715855,0.0001834901,0.0087509025],"study_design_scores_gemma":[0.0000099890485,0.00022015952,0.00030560643,0.0000084904195,0.00001512702,0.00040500177,0.000017040578,0.011596918,0.97850937,0.00036372672,0.008530444,0.000018076173],"about_ca_topic_score_codex":0.00008234225,"about_ca_topic_score_gemma":0.00014811389,"teacher_disagreement_score":0.0010735936,"about_ca_system_score_codex":0.00022473284,"about_ca_system_score_gemma":0.00018615492,"threshold_uncertainty_score":0.0035915375},"labels":[],"label_agreement":null},{"id":"W4403863445","doi":"10.1109/jiot.2024.3487822","title":"Positioning in 5G Networks: Emerging Techniques, Use Cases, and Challenges","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Telecommunications","score_opus":0.02060753959777686,"score_gpt":0.23599677810707487,"score_spread":0.215389238509298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403863445","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072815,0.8898132,0.057160143,0.014341735,0.001920033,0.00006106497,0.00015332318,0.00014582269,0.029123154],"genre_scores_gemma":[0.07471445,0.89505965,0.020810103,0.0024845165,0.0034848317,0.00005198295,0.00016451551,0.00003436148,0.0031956816],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985461,0.0005027499,0.00012963766,0.00021255252,0.00049928913,0.00010974676],"domain_scores_gemma":[0.99781346,0.0013722263,0.00015022555,0.00013134108,0.0004802104,0.000052522024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021487765,0.00077235827,0.0005503068,0.0017524961,0.00048886216,0.0025144217,0.0010407754,0.0021108536,0.0016674418],"category_scores_gemma":[0.003133297,0.00039918988,0.00050032226,0.0032843188,0.0015083151,0.004475177,0.0012652623,0.0018987374,0.0007850967],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006945046,0.000037433678,0.0033081484,0.0050547137,0.0000714485,0.00063452206,0.00073379953,0.010063797,0.0027823788,0.19881456,0.017770628,0.7606591],"study_design_scores_gemma":[0.000010860355,0.00023896336,0.0025132184,0.0039726575,0.0001100371,0.0035818315,0.0016441821,0.016795563,0.0026048466,0.09108825,0.87733096,0.00010860319],"about_ca_topic_score_codex":0.002166196,"about_ca_topic_score_gemma":0.002276135,"teacher_disagreement_score":0.0025144217,"about_ca_system_score_codex":0.0010129074,"about_ca_system_score_gemma":0.0008655405,"threshold_uncertainty_score":0.011363983},"labels":[],"label_agreement":null},{"id":"W4404386894","doi":"10.1061/9780784485842.004","title":"The Use of Wireless Geotechnical Instrumentation to Remotely Monitor Isolated Infrastructure and Manage Geohazard Risks","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MRF Geosystems (Canada)","funders":"","keywords":"Geohazard; Instrumentation (computer programming); Wireless; Computer science; Engineering; Geotechnical engineering; Telecommunications; Landslide","score_opus":0.021147599109563625,"score_gpt":0.250948653791274,"score_spread":0.2298010546817104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404386894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46856496,0.0009848463,0.5046907,0.0014692588,0.00013047142,0.00032576278,0.00019303162,0.0017368722,0.021904094],"genre_scores_gemma":[0.8943708,0.00049521093,0.10236582,0.00015524818,0.00003472479,0.00006850292,0.000071325645,0.000027696587,0.0024105993],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952185,0.0001331192,0.00001957083,0.00008586606,0.00019684123,0.000042815576],"domain_scores_gemma":[0.9988463,0.00033619205,0.00020514704,0.00020577895,0.00034144364,0.00006508225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005727665,0.00041544982,0.00010876626,0.00071833195,0.0002582183,0.00074239966,0.00066291314,0.00035103344,0.0006259854],"category_scores_gemma":[0.0014211424,0.00017683955,0.00009828105,0.00047030294,0.0004295484,0.0014184944,0.0006442309,0.00038779632,0.00029919815],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001472454,0.00025385641,0.07213334,0.00021006256,0.000050619037,0.0008733146,0.0013050378,0.030453758,0.09923317,0.007205441,0.005025521,0.7831087],"study_design_scores_gemma":[0.00012426527,0.0040384103,0.11041877,0.00045079592,0.00019307414,0.005772648,0.00543611,0.46384802,0.23313732,0.014666288,0.16163027,0.00028404026],"about_ca_topic_score_codex":0.0009728011,"about_ca_topic_score_gemma":0.0022483391,"teacher_disagreement_score":0.0009728011,"about_ca_system_score_codex":0.0003211878,"about_ca_system_score_gemma":0.00032622548,"threshold_uncertainty_score":0.0030291677},"labels":[],"label_agreement":null},{"id":"W4404511670","doi":"10.1080/08839514.2024.2429321","title":"Multi-Source Domain Adaptation Using Ambient Sensor Data","year":2024,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Computer science; Adaptation (eye); Domain adaptation; Domain (mathematical analysis); Data mining; Artificial intelligence","score_opus":0.11500089234853074,"score_gpt":0.3028469786691777,"score_spread":0.18784608632064698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404511670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06439801,0.0012620578,0.92614627,0.00028568038,0.0004818248,0.00015335355,0.0005705559,0.004292877,0.002409315],"genre_scores_gemma":[0.720072,0.00076842686,0.26903906,0.00035459962,0.00026965045,0.00026040804,0.004584484,0.00029830966,0.0043530813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886,0.00034495073,0.00006144132,0.00043944572,0.00020622833,0.000087887085],"domain_scores_gemma":[0.9977894,0.0009255196,0.00013475878,0.0004974081,0.000576564,0.000076338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017924707,0.001824249,0.0012846801,0.0011689055,0.00044867644,0.0010028229,0.0015766601,0.0009609267,0.0011644135],"category_scores_gemma":[0.004664062,0.00037186852,0.0016075302,0.0017011029,0.0006248048,0.0019798516,0.0016147093,0.0016960922,0.0009244551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004529024,0.0005734192,0.0046867393,0.00031142696,0.0005279818,0.00028326438,0.00022650477,0.44076476,0.0148560535,0.0020244485,0.007931185,0.5273614],"study_design_scores_gemma":[0.000028217391,0.000074319956,0.0016032037,0.00001335338,0.000035167956,0.00008683756,0.00007755164,0.98886883,0.0046629733,0.002254731,0.0022701323,0.000024729025],"about_ca_topic_score_codex":0.0042397305,"about_ca_topic_score_gemma":0.0035131122,"teacher_disagreement_score":0.0042397305,"about_ca_system_score_codex":0.0004406987,"about_ca_system_score_gemma":0.0007308083,"threshold_uncertainty_score":0.009479642},"labels":[],"label_agreement":null},{"id":"W4404587268","doi":"10.36227/techrxiv.173220591.12758735/v1","title":"Demonstrating the Merits of Integrating Multipath Signals into 5G LoS-Based Positioning Systems for Navigation in Challenging Environments","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Military College of Canada; Royal Canadian Navy; Queen's University","funders":"","keywords":"Multipath propagation; Computer science; Real-time computing; Systems engineering; Telecommunications; Engineering","score_opus":0.01295001119647454,"score_gpt":0.2501164404744893,"score_spread":0.23716642927801473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404587268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19230817,0.002196497,0.7827578,0.00095497817,0.0002644734,0.00007555757,0.00016187175,0.0010092863,0.020271296],"genre_scores_gemma":[0.9003351,0.001277089,0.0958243,0.00012759732,0.00011350862,0.000026309357,0.00010507687,0.00004502849,0.0021460522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949396,0.00012676218,0.000016809932,0.00008389231,0.0002117939,0.00006668511],"domain_scores_gemma":[0.9993687,0.0002327347,0.00006116462,0.00013456903,0.00018186987,0.000020961492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044889544,0.0007753064,0.0002892182,0.00033781756,0.00027657158,0.0007191131,0.0004029507,0.0007703537,0.001700211],"category_scores_gemma":[0.0016379802,0.00015606105,0.0001965558,0.0005757345,0.00041711394,0.000948813,0.00071461516,0.00048463262,0.0007331689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000562058,0.00011926732,0.014803259,0.00068370625,0.00020384736,0.0008058622,0.00046261612,0.22743323,0.23602657,0.028679395,0.002787318,0.48743284],"study_design_scores_gemma":[0.00006596036,0.0017889416,0.022765435,0.00023635397,0.0002945484,0.0021499852,0.0006808218,0.66538787,0.2356583,0.016554715,0.054268952,0.0001481057],"about_ca_topic_score_codex":0.002057255,"about_ca_topic_score_gemma":0.0036466469,"teacher_disagreement_score":0.002057255,"about_ca_system_score_codex":0.00033547866,"about_ca_system_score_gemma":0.00041550925,"threshold_uncertainty_score":0.005687833},"labels":[],"label_agreement":null},{"id":"W4404740832","doi":"10.1109/isncc62547.2024.10758991","title":"Accessible Time Interval Based Local Positioning System: Applications for Self-Driving Cars in Smart Cities","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Collège de Maisonneuve; Laboratoire Recherche Informatique Maisonneuve","funders":"","keywords":"Computer science; Self driving; Interval (graph theory); Real-time computing; Automotive engineering; Engineering","score_opus":0.005270041611122019,"score_gpt":0.21755703189113826,"score_spread":0.21228699028001624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404740832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16082375,0.00074917084,0.7961551,0.0006207327,0.00019925325,0.00018504397,0.00078204484,0.031376176,0.009108765],"genre_scores_gemma":[0.9028106,0.00029849668,0.092857376,0.000097018645,0.000037732665,0.00008031324,0.00068126654,0.00015553976,0.0029816777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980754,0.00004288806,0.000009286876,0.000038702296,0.00007319191,0.000028459091],"domain_scores_gemma":[0.99969816,0.000051753217,0.000033743872,0.000066695044,0.000101232254,0.000048566162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034890298,0.00046030545,0.0003299312,0.00051899586,0.00023759065,0.0004193502,0.00083861727,0.00051797257,0.0032276162],"category_scores_gemma":[0.0006878407,0.00013339915,0.00016894392,0.00058282417,0.00021255433,0.0006073059,0.00066627003,0.00033383368,0.0010614708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086762855,0.0002851903,0.023459157,0.0005161298,0.00012473938,0.0013681133,0.0009871796,0.16509476,0.12775388,0.010595847,0.02736189,0.6415856],"study_design_scores_gemma":[0.00010205333,0.00051957037,0.009993344,0.00005300762,0.00007005032,0.0005066179,0.00047387255,0.89129233,0.045540426,0.003109496,0.048254285,0.00008497245],"about_ca_topic_score_codex":0.0041185063,"about_ca_topic_score_gemma":0.0050811823,"teacher_disagreement_score":0.0041185063,"about_ca_system_score_codex":0.00035391384,"about_ca_system_score_gemma":0.00030194857,"threshold_uncertainty_score":0.010797441},"labels":[],"label_agreement":null},{"id":"W4404789230","doi":"10.36227/techrxiv.173273551.11908267/v1","title":"Leveraging Single-Bounce Reflections and Onboard Motion Sensors for Enhanced 5G Positioning","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion (physics); Computer science; Motion sensors; Remote sensing; Computer vision; Geology","score_opus":0.028655024852577322,"score_gpt":0.2655657274010613,"score_spread":0.236910702548484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404789230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15937637,0.00076132006,0.8303359,0.00024181635,0.00025445933,0.000068862726,0.0003162661,0.001954293,0.0066906563],"genre_scores_gemma":[0.79532737,0.0004272093,0.2010867,0.00016727077,0.00007406262,0.000046815643,0.00040953074,0.00008898972,0.0023720851],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995159,0.000064927124,0.000019035728,0.00010699161,0.00019289333,0.000100248464],"domain_scores_gemma":[0.9997253,0.00006301099,0.00004544315,0.00005771674,0.00008983451,0.000018708708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003098634,0.0010629563,0.00041702169,0.00061881926,0.00018791207,0.00062311866,0.0006805741,0.00069162884,0.0009191332],"category_scores_gemma":[0.0009029262,0.00027509886,0.00041621012,0.0006897805,0.00028859542,0.000824899,0.00084315834,0.00054271,0.0007479884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048543216,0.00016369115,0.01600696,0.0004237044,0.00017854884,0.00090801955,0.0004623755,0.19118881,0.27440238,0.0049818656,0.0028929522,0.50790524],"study_design_scores_gemma":[0.00008252296,0.0007365686,0.016451694,0.000100840196,0.0002216227,0.00087281526,0.00017834856,0.80341953,0.15619782,0.002494321,0.019086955,0.00015700213],"about_ca_topic_score_codex":0.0042239893,"about_ca_topic_score_gemma":0.009733249,"teacher_disagreement_score":0.0042239893,"about_ca_system_score_codex":0.0003196729,"about_ca_system_score_gemma":0.0005002284,"threshold_uncertainty_score":0.008398831},"labels":[],"label_agreement":null},{"id":"W4405092774","doi":"10.1109/tii.2024.3488778","title":"Tensor-Based Sparsity-Inducing Localization of AAV Swarms-Assisted Mobile Edge Computing Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Tensor (intrinsic definition); Mobile edge computing; Enhanced Data Rates for GSM Evolution; Computer vision; Mathematics; Pure mathematics","score_opus":0.03451437127891955,"score_gpt":0.24072588378082832,"score_spread":0.20621151250190878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405092774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027281458,0.00005698263,0.9716258,0.00006606874,0.000014307061,0.000009507217,0.000021348831,0.00023243426,0.00069215184],"genre_scores_gemma":[0.6075083,0.0001841169,0.3899743,0.000056576435,0.000033270866,0.000042683656,0.0002114115,0.000070753,0.0019185495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975353,0.00008313991,0.00001218367,0.000039045568,0.00008388761,0.000028205495],"domain_scores_gemma":[0.99951494,0.00015704193,0.0000959881,0.000079946,0.00011738555,0.00003472989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003438308,0.00046225314,0.00042890536,0.00034754703,0.00022442173,0.00034977624,0.0005305682,0.0002957518,0.00056266703],"category_scores_gemma":[0.0014004955,0.00022123533,0.0003562582,0.00048002606,0.00038393814,0.0006331049,0.00072254264,0.00041546507,0.00027883344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028512694,0.00005753686,0.0017174868,0.00011078818,0.000055553035,0.00013959708,0.0002998836,0.652245,0.07079051,0.013745646,0.002384258,0.2581687],"study_design_scores_gemma":[0.00000348089,0.000021755419,0.00013318067,0.0000015535156,0.0000033357828,0.000018853852,0.000010502079,0.99562705,0.002954685,0.000899166,0.00032209462,0.0000043704345],"about_ca_topic_score_codex":0.0029257052,"about_ca_topic_score_gemma":0.0030942056,"teacher_disagreement_score":0.0029257052,"about_ca_system_score_codex":0.00025587514,"about_ca_system_score_gemma":0.0005268523,"threshold_uncertainty_score":0.0058173537},"labels":[],"label_agreement":null},{"id":"W4405270522","doi":"10.1007/s10291-024-01795-4","title":"Improving smartphone positioning accuracy by adapting measurement covariance with t-test on innovations","year":2024,"lang":"en","type":"article","venue":"GPS Solutions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Outlier; Kalman filter; Covariance; Computer science; Noise (video); Multipath propagation; Filter (signal processing); Covariance matrix; Statistical hypothesis testing; Multipath mitigation; Statistics; Algorithm; Mathematics; Artificial intelligence; Computer vision; Telecommunications","score_opus":0.019637422477172938,"score_gpt":0.2088156347646654,"score_spread":0.18917821228749246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405270522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1381,0.00024737866,0.85588545,0.00019519626,0.00025114758,0.000047436628,0.00025118515,0.0022762602,0.0027459373],"genre_scores_gemma":[0.806893,0.00014151853,0.190078,0.000116548596,0.000104102306,0.000059398462,0.0005615987,0.0003073872,0.0017384846],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978357,0.0005175486,0.00013776631,0.0004923703,0.0007893299,0.00022727584],"domain_scores_gemma":[0.9940327,0.0024205572,0.00042997688,0.0010111379,0.0019817292,0.00012381414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014553866,0.0008965202,0.00087024475,0.0008404728,0.0004294319,0.00079891837,0.001045254,0.0010414376,0.0029452145],"category_scores_gemma":[0.015393689,0.0003692899,0.00077822135,0.0012253253,0.0004143647,0.0016407399,0.001257392,0.0010374705,0.0012146715],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001681207,0.00044549396,0.03084553,0.00034778722,0.00038983912,0.00032193508,0.0002134963,0.19861116,0.068003304,0.0072328052,0.004410321,0.6874971],"study_design_scores_gemma":[0.00005766502,0.00048363465,0.016674379,0.000025099225,0.00009894479,0.0003034449,0.00006415365,0.9479634,0.029737448,0.0022891692,0.0022417465,0.000060931034],"about_ca_topic_score_codex":0.004313408,"about_ca_topic_score_gemma":0.005674087,"teacher_disagreement_score":0.004313408,"about_ca_system_score_codex":0.00040998973,"about_ca_system_score_gemma":0.0010792542,"threshold_uncertainty_score":0.009852707},"labels":[],"label_agreement":null},{"id":"W4405453640","doi":"10.1007/s00607-024-01391-x","title":"Efficient exploration of indoor localization using genetic algorithm and signal propagation model","year":2024,"lang":"en","type":"article","venue":"Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Fundação de Amparo à Pesquisa do Estado do Amazonas; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Genetic algorithm; Computer science; Algorithm; Radio propagation; SIGNAL (programming language); Telecommunications; Machine learning","score_opus":0.021570172785926375,"score_gpt":0.23830924293507522,"score_spread":0.21673907014914884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405453640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034853835,0.00020430097,0.9616871,0.000084918116,0.00002425261,0.000018481105,0.000027301923,0.00042415631,0.0026756409],"genre_scores_gemma":[0.70696634,0.00025703324,0.28963232,0.00004208713,0.00002531293,0.00009985207,0.00012296783,0.00007854689,0.0027755823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998221,0.00004490257,0.0000051620345,0.000038141545,0.000058290567,0.0000314658],"domain_scores_gemma":[0.9997689,0.000116224764,0.000023518933,0.000022642218,0.00005548431,0.000013199532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023871963,0.0006435497,0.0008721884,0.0007124406,0.0005273447,0.0006554174,0.0008684253,0.00071502116,0.0010260068],"category_scores_gemma":[0.0008245383,0.0003291524,0.0006869854,0.001047177,0.00045142722,0.0007612935,0.000626958,0.0005107002,0.00022945288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040103634,0.000027938837,0.00045670837,0.000027918115,0.000022138714,0.000042094834,0.00003834972,0.9585636,0.0025231445,0.0045812177,0.00039780108,0.03327898],"study_design_scores_gemma":[0.0000033010635,0.00001038984,0.00004994686,0.0000018614778,0.0000036859687,0.000010642447,0.000005925436,0.9985759,0.00031334304,0.0008890597,0.00013347862,0.000002392308],"about_ca_topic_score_codex":0.011322921,"about_ca_topic_score_gemma":0.007823673,"teacher_disagreement_score":0.011322921,"about_ca_system_score_codex":0.00055511977,"about_ca_system_score_gemma":0.0011697152,"threshold_uncertainty_score":0.022514045},"labels":[],"label_agreement":null},{"id":"W4405573773","doi":"10.1080/14942119.2024.2438512","title":"Use of Bluetooth low energy and ultra-wideband sensor systems to detect people in forest operations danger zones","year":2024,"lang":"en","type":"article","venue":"International Journal of Forest Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture Food and Rural Development","funders":"","keywords":"Bluetooth; Bluetooth Low Energy; Energy (signal processing); Low energy; Wireless sensor network; Telecommunications; Computer science; Forestry; Embedded system; Business; Real-time computing; Engineering; Computer security; Geography; Computer network; Wireless; Physics","score_opus":0.007484470716011129,"score_gpt":0.20808037866187132,"score_spread":0.20059590794586019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405573773","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73024285,0.0041788435,0.2546318,0.00023718964,0.00023366784,0.00018108031,0.0002706531,0.00077053974,0.009253465],"genre_scores_gemma":[0.95818883,0.0007217353,0.03840408,0.00013345305,0.000023299111,0.00006965641,0.00011555382,0.000016373895,0.0023270424],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923694,0.00019193058,0.000031104944,0.00012690846,0.00035459988,0.000058559417],"domain_scores_gemma":[0.9994947,0.00014847469,0.000086387736,0.00006078821,0.00018318063,0.00002647402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060802145,0.00040211133,0.00027595315,0.00059861544,0.00019183126,0.00046150334,0.00068330724,0.0005518692,0.0006881482],"category_scores_gemma":[0.00090107066,0.00018247949,0.00023685895,0.00034378585,0.00023280489,0.00048503056,0.00042957172,0.00024926246,0.00032215414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010686085,0.0002922932,0.04882869,0.0010247128,0.0002155534,0.0006124955,0.0005835448,0.010249109,0.54291695,0.0013348355,0.0017581022,0.39111507],"study_design_scores_gemma":[0.00017699275,0.0069971923,0.1484279,0.00048779766,0.0005590447,0.005474583,0.0011335278,0.11037084,0.6878641,0.0026872316,0.035568986,0.00025175803],"about_ca_topic_score_codex":0.00047880565,"about_ca_topic_score_gemma":0.000980195,"teacher_disagreement_score":0.0006881482,"about_ca_system_score_codex":0.00014221661,"about_ca_system_score_gemma":0.00013737049,"threshold_uncertainty_score":0.003215611},"labels":[],"label_agreement":null},{"id":"W4405611863","doi":"10.2139/ssrn.5064589","title":"Task Offloading and Resource Allocation for Optimization of Simultaneous Localization and Mapping: A Rewardless Reinforcement Learning Approach","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Reinforcement learning; Computer science; Task (project management); Resource (disambiguation); Resource allocation; Artificial intelligence; Computer network; Engineering; Systems engineering","score_opus":0.00777734392151698,"score_gpt":0.2110881156121541,"score_spread":0.2033107716906371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405611863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033702444,0.00023739229,0.96155494,0.00043931417,0.00006432196,0.000078886515,0.000035552122,0.00032121624,0.0035659547],"genre_scores_gemma":[0.93828315,0.00010150039,0.057626914,0.00014550869,0.00006496408,0.00015008626,0.000036798112,0.00006134273,0.0035295812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992648,0.0002418766,0.00002709818,0.00014553835,0.00014108777,0.0001797709],"domain_scores_gemma":[0.9977476,0.0015583754,0.0001883122,0.00010676058,0.00024454316,0.0001543777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015882191,0.001114055,0.0016200814,0.0005057151,0.00043085744,0.0010795812,0.0016935065,0.0017587863,0.0032022046],"category_scores_gemma":[0.004899546,0.00058546267,0.00052233535,0.0004766892,0.0013064146,0.0011710504,0.0017279168,0.0016863581,0.00031489215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010713818,0.00008603091,0.00023695905,0.000042628533,0.000025711079,0.000038854647,0.000033747037,0.9786269,0.0009841318,0.0037825834,0.00047427145,0.0155610945],"study_design_scores_gemma":[0.000009730461,0.000016486565,0.000038054754,0.0000023057141,0.0000037617087,0.00000390419,0.0000031756708,0.99846256,0.0001103131,0.0012917588,0.000055886903,0.0000021000994],"about_ca_topic_score_codex":0.007347343,"about_ca_topic_score_gemma":0.005395624,"teacher_disagreement_score":0.007347343,"about_ca_system_score_codex":0.00090074446,"about_ca_system_score_gemma":0.001889393,"threshold_uncertainty_score":0.014609158},"labels":[],"label_agreement":null},{"id":"W4405717918","doi":"10.1109/access.2024.3521005","title":"Emerging AI and 6G-Based User Localization Technologies for Emergencies and Disasters","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer security","score_opus":0.012698294935026727,"score_gpt":0.27704276359709945,"score_spread":0.26434446866207273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405717918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019748278,0.041139837,0.8852292,0.004681078,0.0020967831,0.00014049922,0.00043286552,0.003931703,0.042599775],"genre_scores_gemma":[0.61983514,0.06975662,0.25310996,0.0052705044,0.0028915065,0.00043328755,0.0014704383,0.00029373198,0.046938777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965394,0.00008468705,0.000016782667,0.00005716512,0.00013646497,0.00005091286],"domain_scores_gemma":[0.99962854,0.00011581325,0.000051273597,0.000052174597,0.00012486255,0.000027376469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047224294,0.00075779756,0.00037525163,0.0010639769,0.00032386754,0.0008967005,0.00073510857,0.001025233,0.0039462466],"category_scores_gemma":[0.0009119062,0.00014572257,0.0003369952,0.0010056203,0.0003825134,0.0019716958,0.0010650352,0.0010636983,0.0018151901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016920884,0.00005854447,0.0022188737,0.0009986146,0.00006635477,0.00055869203,0.0004435147,0.0074935346,0.039977413,0.04818535,0.03808186,0.86174804],"study_design_scores_gemma":[0.000036447924,0.0007755406,0.0066875867,0.00080012425,0.00023340745,0.003625388,0.0010446052,0.15637231,0.03890247,0.048187144,0.74315494,0.00018010788],"about_ca_topic_score_codex":0.0009067881,"about_ca_topic_score_gemma":0.0013138377,"teacher_disagreement_score":0.0039462466,"about_ca_system_score_codex":0.0003598186,"about_ca_system_score_gemma":0.00032532137,"threshold_uncertainty_score":0.013201475},"labels":[],"label_agreement":null},{"id":"W4405819833","doi":"10.1007/s10489-024-06156-9","title":"A hybrid vision transformer and residual neural network model for fall detection using UWB radars","year":2024,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Artificial neural network; Residual; Artificial intelligence; Machine learning; Simulation","score_opus":0.0189508214807734,"score_gpt":0.25204354829857084,"score_spread":0.23309272681779744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405819833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019546822,0.00038802065,0.977495,0.000082199884,0.000070506365,0.000020074945,0.000038325346,0.0005760374,0.0017830115],"genre_scores_gemma":[0.8863942,0.0005726176,0.10447024,0.00012523809,0.000055350065,0.000067405264,0.00015724263,0.000074062824,0.0080835465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986875,0.000019947542,0.0000070848064,0.000038529586,0.000046219673,0.000019507062],"domain_scores_gemma":[0.99988604,0.00002862689,0.000012052962,0.000009848605,0.000056621757,0.000006796124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028793284,0.00042955187,0.00059598335,0.00027365462,0.00017247138,0.00039028545,0.0009897363,0.00062677334,0.0013589975],"category_scores_gemma":[0.00044026366,0.00024332493,0.00047403434,0.00036409075,0.00020255793,0.0007250378,0.00035321596,0.0006601797,0.00046991534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023011511,0.00013638876,0.0010171045,0.00013961886,0.00009457208,0.00010171398,0.000048747454,0.70575476,0.025633045,0.0045146374,0.0016158791,0.2607134],"study_design_scores_gemma":[0.0000025405952,0.000022278777,0.000088894,0.0000020143143,0.000007849885,0.0000139358135,0.0000018139372,0.9984048,0.0010488416,0.00023757525,0.00016672807,0.0000027475232],"about_ca_topic_score_codex":0.007501469,"about_ca_topic_score_gemma":0.0054990705,"teacher_disagreement_score":0.007501469,"about_ca_system_score_codex":0.00032147087,"about_ca_system_score_gemma":0.0004894725,"threshold_uncertainty_score":0.014915645},"labels":[],"label_agreement":null},{"id":"W4405878689","doi":"10.1007/s11235-024-01230-6","title":"Particle filter-based BLE and IMU fusion algorithm for indoor localization","year":2024,"lang":"en","type":"article","venue":"Telecommunication Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Computer science; Inertial measurement unit; Particle filter; Real-time computing; Global Positioning System; Multipath propagation; Sensor fusion; Software deployment; Filter (signal processing); Artificial intelligence; Computer vision; Telecommunications","score_opus":0.017443368843393498,"score_gpt":0.24564807171723022,"score_spread":0.2282047028738367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405878689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076543638,0.00042827285,0.9881887,0.000109328466,0.00016208274,0.000026912196,0.0001018214,0.0010576206,0.0022709658],"genre_scores_gemma":[0.48897713,0.0014394452,0.4891359,0.00037579008,0.00026392346,0.00019297749,0.0013546557,0.00019733445,0.018062841],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994091,0.00008920347,0.000027238753,0.0001436653,0.00025204287,0.00007867301],"domain_scores_gemma":[0.99970907,0.000044684,0.000026197493,0.00004802386,0.00015571667,0.000016222195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051568676,0.00088905357,0.0010715241,0.00097120873,0.000448478,0.0007869434,0.00084316364,0.0010452654,0.0020805744],"category_scores_gemma":[0.0012902552,0.00035390607,0.0007163324,0.001212474,0.00028161437,0.0009999402,0.0009414366,0.0010333704,0.0019788106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047021388,0.00014065043,0.0034214286,0.00020533465,0.00021130002,0.00015327829,0.00014473796,0.14711829,0.043365885,0.004964015,0.009617948,0.7901869],"study_design_scores_gemma":[0.000022828483,0.000108367356,0.0030698173,0.000024396415,0.00006301986,0.00016408254,0.00004237297,0.9681731,0.019577552,0.0018501912,0.0068657277,0.000038640606],"about_ca_topic_score_codex":0.005173922,"about_ca_topic_score_gemma":0.0064154365,"teacher_disagreement_score":0.005173922,"about_ca_system_score_codex":0.00044907845,"about_ca_system_score_gemma":0.0008051532,"threshold_uncertainty_score":0.010287583},"labels":[],"label_agreement":null},{"id":"W4406132822","doi":"10.55785/jcar.3.4.1","title":"UWB Positioning Technology Utilizing SDS-TWR and Overhearing","year":2024,"lang":"en","type":"article","venue":"Journal of Construction Automation and Robotics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Computer science","score_opus":0.005096760925348423,"score_gpt":0.21320743344647677,"score_spread":0.20811067252112836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406132822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057139874,0.0010250855,0.93604624,0.00013350275,0.00016768502,0.00004995284,0.000038248992,0.0011125765,0.004286803],"genre_scores_gemma":[0.6925162,0.001059833,0.2974864,0.00021519506,0.00018355896,0.000096554766,0.00017932102,0.00006866781,0.008194321],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993217,0.00011226955,0.000049437927,0.00013255283,0.00033680623,0.00004733118],"domain_scores_gemma":[0.99947625,0.0001069226,0.00014156663,0.00012706676,0.00012214368,0.000026054448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004073521,0.0004304116,0.00049661787,0.0007120937,0.00027083838,0.0004549918,0.0008542731,0.00048587314,0.0006304492],"category_scores_gemma":[0.0006401742,0.00031376237,0.0003280928,0.0006051625,0.00033554473,0.0010183587,0.00094843376,0.00048541278,0.00057539623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017242048,0.00004244424,0.0022756173,0.00032853105,0.000044104898,0.00041646388,0.00031600308,0.0049660816,0.60255873,0.0071806423,0.0009935948,0.38070545],"study_design_scores_gemma":[0.00011623998,0.0014799678,0.0056779194,0.00010417557,0.00025040092,0.0059417547,0.00029790768,0.1415606,0.7647059,0.0047162753,0.0749695,0.00017938405],"about_ca_topic_score_codex":0.00028181044,"about_ca_topic_score_gemma":0.00036491014,"teacher_disagreement_score":0.0008542731,"about_ca_system_score_codex":0.00022580355,"about_ca_system_score_gemma":0.00030095762,"threshold_uncertainty_score":0.0021542907},"labels":[],"label_agreement":null},{"id":"W4406266347","doi":"10.1109/vtc2024-fall63153.2024.10757499","title":"Boosting Indoor Localization and Identification Speed Using Reflection Map Construction","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Boosting (machine learning); Computer science; Artificial intelligence; Identification (biology); Reflection (computer programming); Computer vision","score_opus":0.01698532748237786,"score_gpt":0.2531196574327239,"score_spread":0.23613432995034606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406266347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040240295,0.00023136479,0.95487976,0.00006963001,0.000031213578,0.00002233254,0.000023347811,0.0017082081,0.002793889],"genre_scores_gemma":[0.7290383,0.00025166993,0.2678226,0.00005551606,0.00006055131,0.00006073082,0.00012963916,0.0001562502,0.0024247563],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991823,0.00022530879,0.000021200416,0.00019081958,0.0002577695,0.00012259024],"domain_scores_gemma":[0.9983311,0.0007913922,0.00015333242,0.00033955858,0.000329494,0.000055070323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010510305,0.00079121214,0.00093765714,0.001066611,0.00036244112,0.0006911199,0.0013177502,0.00070042507,0.0015874819],"category_scores_gemma":[0.0037254712,0.0004485195,0.00046631257,0.0008053032,0.00040696398,0.0014309264,0.0013390209,0.0005577647,0.0013668812],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033880104,0.00016414783,0.004527862,0.00013468416,0.0000824524,0.00020281423,0.00020365162,0.3604265,0.045015033,0.009531956,0.0021155253,0.57725656],"study_design_scores_gemma":[0.000028446168,0.0001867691,0.0014458292,0.000009797936,0.000042100528,0.00030387638,0.00006180574,0.97476983,0.017679371,0.0025883543,0.0028596898,0.000024183772],"about_ca_topic_score_codex":0.0010803199,"about_ca_topic_score_gemma":0.0010458162,"teacher_disagreement_score":0.0015874819,"about_ca_system_score_codex":0.0003182883,"about_ca_system_score_gemma":0.00048237084,"threshold_uncertainty_score":0.005558431},"labels":[],"label_agreement":null},{"id":"W4406499867","doi":"10.1109/cascon62161.2024.10838149","title":"An Adaptive Indoor Localization Approach Using WiFi RSSI Fingerprinting with SLAM-Enabled Robotic Platform and Deep Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Deep neural networks; Simultaneous localization and mapping; Real-time computing; Computer vision; Robot; Mobile robot","score_opus":0.014010668292435942,"score_gpt":0.21096197505722497,"score_spread":0.19695130676478903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406499867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046289798,0.0003661109,0.9450405,0.00019009713,0.0001376453,0.000071001596,0.000499947,0.004737308,0.0026675083],"genre_scores_gemma":[0.67561114,0.00034909515,0.31576812,0.00034811636,0.00010723101,0.00019437261,0.0021993911,0.0002177169,0.005204946],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99958867,0.000045655426,0.000018437595,0.00016384176,0.00009789337,0.000085403415],"domain_scores_gemma":[0.99968624,0.000046931884,0.000048844042,0.00008146293,0.000109777175,0.000026717225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033618946,0.0012169969,0.0007892498,0.0011186955,0.00042707598,0.0006290331,0.0018412785,0.0006949506,0.001028107],"category_scores_gemma":[0.0011007583,0.00047671943,0.00069545803,0.0014694709,0.00034544762,0.0010433194,0.0013172143,0.0010963504,0.0006709251],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023930099,0.0002581124,0.00506888,0.00015632632,0.00015854767,0.00025462118,0.00014355355,0.31483954,0.022354484,0.002211432,0.008065268,0.64624995],"study_design_scores_gemma":[0.000016724582,0.00006268176,0.0014553772,0.000011480767,0.000025945521,0.00008604379,0.00003905872,0.98935086,0.00582064,0.0014139576,0.0016987771,0.000018531431],"about_ca_topic_score_codex":0.012211088,"about_ca_topic_score_gemma":0.020349039,"teacher_disagreement_score":0.012211088,"about_ca_system_score_codex":0.00056444335,"about_ca_system_score_gemma":0.00092347845,"threshold_uncertainty_score":0.024280012},"labels":[],"label_agreement":null},{"id":"W4406518114","doi":"10.1016/j.eswa.2025.126547","title":"Group matching method for search-space reduction, development, proof, and comparison","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reduction (mathematics); Computer science; Matching (statistics); Space (punctuation); Proof of concept; Development (topology); Group (periodic table); Artificial intelligence; Mathematics; Statistics","score_opus":0.014457827180369226,"score_gpt":0.2934325992702911,"score_spread":0.27897477208992183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406518114","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019183601,0.00024302611,0.99412954,0.00014713957,0.00008450151,0.00008097374,0.000045770503,0.00053786836,0.0028128503],"genre_scores_gemma":[0.119559534,0.0005598559,0.8709629,0.00027483128,0.00019390883,0.00038226272,0.000366824,0.00046994217,0.007229954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99582005,0.0011660932,0.00024056785,0.00062038447,0.0018413843,0.00031146937],"domain_scores_gemma":[0.99391174,0.002948215,0.00023686343,0.0013256667,0.0014202403,0.00015732662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029584274,0.0011677931,0.0014205972,0.002419614,0.000961998,0.001503908,0.0024676768,0.0016086778,0.011691048],"category_scores_gemma":[0.011100919,0.000596751,0.0022170108,0.0018682763,0.0016807329,0.0035183125,0.0033233012,0.002595964,0.0034665158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049912464,0.00040904764,0.00079866796,0.0009599177,0.00022345537,0.00028180567,0.00032088393,0.05150452,0.014782166,0.32305995,0.018540302,0.5886202],"study_design_scores_gemma":[0.00022165722,0.0006402949,0.00077273947,0.00017209144,0.00026467894,0.0009134854,0.000159901,0.52748024,0.03220855,0.3894251,0.04764949,0.000091795846],"about_ca_topic_score_codex":0.0015138636,"about_ca_topic_score_gemma":0.0013771377,"teacher_disagreement_score":0.011691048,"about_ca_system_score_codex":0.0010072395,"about_ca_system_score_gemma":0.0027167434,"threshold_uncertainty_score":0.039110422},"labels":[],"label_agreement":null},{"id":"W4406532524","doi":"10.1016/0967-0653(95)96771-v","title":"10.1016/0967-0653(95)96771-v","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geolocation; Computer science; Algorithm; Earth (classical element); Remote sensing; Geodesy; Geology; Mathematics; World Wide Web","score_opus":0.0042035873981112705,"score_gpt":0.1530468195087225,"score_spread":0.14884323211061123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406532524","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006233009,0.00044156963,0.0008958152,0.00035642873,0.00032714056,0.00010729559,0.00065739866,0.000768607,0.9958223],"genre_scores_gemma":[0.0009892278,0.00025952756,0.00046871323,0.0002428336,0.00008202511,0.00006235876,0.00042800387,0.00016490565,0.9973023],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928856,0.00005525925,0.00006134435,0.0002606085,0.00016996895,0.00016422349],"domain_scores_gemma":[0.9977944,0.0005925092,0.000122609,0.00023724463,0.0005591122,0.0006940988],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010186969,0.002742994,0.0015135356,0.0026218824,0.0025949331,0.0036473833,0.0034021388,0.005762153,0.98490673],"category_scores_gemma":[0.0014520234,0.00085654925,0.0013050411,0.0021053306,0.0019883031,0.0047235293,0.0029494897,0.002253753,0.9897479],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043300696,0.00027969645,0.0013048616,0.00076768757,0.00004859019,0.00039301085,0.00016841468,0.00059338054,0.0033681863,0.006377419,0.36027044,0.62599534],"study_design_scores_gemma":[0.000053852316,0.0002077926,0.0010453617,0.00041795525,0.000022428634,0.000511924,0.00020997488,0.00024429001,0.0005821286,0.00090290385,0.9957723,0.000029040315],"about_ca_topic_score_codex":0.0046973196,"about_ca_topic_score_gemma":0.004088307,"teacher_disagreement_score":0.015093267,"about_ca_system_score_codex":0.0010688173,"about_ca_system_score_gemma":0.00096266647,"threshold_uncertainty_score":0.02152872},"labels":[],"label_agreement":null},{"id":"W4406542722","doi":"10.1007/s00190-024-01932-4","title":"A machine learning-based partial ambiguity resolution method for precise positioning in challenging environments","year":2025,"lang":"en","type":"article","venue":"Journal of Geodesy","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ambiguity resolution; Ambiguity; Computer science; Artificial intelligence; Resolution (logic); Computer vision; Geodesy; Geology; Global Positioning System; Telecommunications","score_opus":0.009087918938346687,"score_gpt":0.2596814249852808,"score_spread":0.2505935060469341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406542722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004439313,0.00016800463,0.9945009,0.000046753546,0.00004382495,0.000011739865,0.000026338581,0.00035784222,0.0004053341],"genre_scores_gemma":[0.19204158,0.00034667947,0.80294263,0.00017180777,0.00012333108,0.00009883541,0.00033990457,0.00015245877,0.0037828851],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947816,0.00009689555,0.000029467383,0.00012040034,0.00022538172,0.000049662493],"domain_scores_gemma":[0.9993055,0.0002537933,0.00006239053,0.000100278885,0.00025037958,0.000027609325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066741783,0.00067604386,0.0010765755,0.0007113768,0.0004916935,0.00067057187,0.0012000066,0.0010788543,0.002123534],"category_scores_gemma":[0.0022345623,0.0004414292,0.0007311364,0.0010445192,0.00041830135,0.0011058894,0.0012789164,0.0011513173,0.0010908138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012564246,0.00007216932,0.0006408954,0.000092326576,0.00007462358,0.00007498227,0.00006990662,0.2521093,0.017762573,0.004534596,0.0030701826,0.7213727],"study_design_scores_gemma":[0.0000048400852,0.000020492069,0.00019590395,0.0000048506117,0.000007434707,0.000045858997,0.0000062226095,0.99586934,0.0018311703,0.0011114938,0.0008928152,0.000009653339],"about_ca_topic_score_codex":0.0032546113,"about_ca_topic_score_gemma":0.0030590377,"teacher_disagreement_score":0.0032546113,"about_ca_system_score_codex":0.00027151912,"about_ca_system_score_gemma":0.0008839133,"threshold_uncertainty_score":0.00710392},"labels":[],"label_agreement":null},{"id":"W4406833355","doi":"10.1016/j.adhoc.2025.103765","title":"Location estimation for supporting adaptive beamforming","year":2025,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"EIT Digital; European Institute of Innovation and Technology; Engineering and Physical Sciences Research Council; UK National Ion Beam Centre; University of Glasgow; James Watt School of Engineering, University of Glasgow; University of Guelph","keywords":"Estimation; Beamforming; Computer science; Adaptive beamformer; Telecommunications; Engineering; Systems engineering","score_opus":0.006812676455384279,"score_gpt":0.24045131385390994,"score_spread":0.23363863739852567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406833355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004508544,0.00015316057,0.99193186,0.0001343792,0.00008080098,0.000015996024,0.00016573387,0.00075880165,0.0022506546],"genre_scores_gemma":[0.5148862,0.0008097488,0.4741971,0.00044328952,0.0003065372,0.0001373023,0.001408465,0.00022559478,0.0075857467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994168,0.00010887006,0.000031802418,0.0001703137,0.00020356235,0.000068610796],"domain_scores_gemma":[0.99923956,0.00020764676,0.0000783805,0.00016832753,0.00027370086,0.000032309144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000520548,0.0010068887,0.00053049135,0.0007054002,0.00040627303,0.000776169,0.0011353228,0.0008799738,0.005031724],"category_scores_gemma":[0.0035546434,0.00035481332,0.0004807899,0.0008103039,0.00042053452,0.0015267667,0.0014732336,0.0012049967,0.0033468462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025943713,0.000074470154,0.0027794032,0.00018193523,0.00006523294,0.0001659822,0.00009274423,0.33082303,0.029609669,0.016434148,0.007499077,0.6120149],"study_design_scores_gemma":[0.000013418683,0.000047696518,0.00059112156,0.000026626523,0.00001292163,0.00011367406,0.00002765464,0.977507,0.0070079863,0.00813444,0.0064987266,0.000018703251],"about_ca_topic_score_codex":0.003480436,"about_ca_topic_score_gemma":0.004483468,"teacher_disagreement_score":0.005031724,"about_ca_system_score_codex":0.00046956693,"about_ca_system_score_gemma":0.0007884308,"threshold_uncertainty_score":0.016832829},"labels":[],"label_agreement":null},{"id":"W4406983340","doi":"10.1109/tap.2025.3533739","title":"A Generalizable Physics-Guided Convolutional Neural Network for Irregular Terrain Propagation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Alberta Innovates","keywords":"Terrain; Convolutional neural network; Computer science; Artificial neural network; Radio propagation; Physics; Artificial intelligence; Telecommunications; Geography; Cartography","score_opus":0.015029564746225459,"score_gpt":0.23520010398387062,"score_spread":0.22017053923764515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406983340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051225986,0.0004565722,0.9416449,0.0006464698,0.00008306832,0.000024757615,0.00030949217,0.0009425787,0.0046661785],"genre_scores_gemma":[0.86659557,0.0005631598,0.12095844,0.00025696313,0.00006741924,0.00007972236,0.0007136837,0.00011075101,0.010654194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999273,0.000010301811,0.0000028431668,0.000026505033,0.000018225399,0.000014849257],"domain_scores_gemma":[0.99985707,0.000054416098,0.00002085305,0.000017715367,0.000037352085,0.000012610015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027669157,0.0005334032,0.00034085737,0.00030262122,0.00024387374,0.00045072613,0.001267108,0.00078842364,0.0014436143],"category_scores_gemma":[0.00083077344,0.00035433265,0.0003946326,0.00036462603,0.00048759073,0.00075847266,0.000743743,0.0009881133,0.00039859442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020330284,0.00001607517,0.00047627458,0.00001732826,0.000018060378,0.000034606303,0.000016568016,0.9700814,0.0020370323,0.0062404796,0.00077586994,0.020265996],"study_design_scores_gemma":[8.1844576e-7,0.000002594272,0.000030492223,0.0000010478898,0.0000012956999,0.0000032171376,6.5536756e-7,0.9987515,0.0001094812,0.0009926044,0.00010526566,9.892952e-7],"about_ca_topic_score_codex":0.015369916,"about_ca_topic_score_gemma":0.020490387,"teacher_disagreement_score":0.015369916,"about_ca_system_score_codex":0.0009105487,"about_ca_system_score_gemma":0.00081795186,"threshold_uncertainty_score":0.03056091},"labels":[],"label_agreement":null},{"id":"W4407449107","doi":"10.1109/tmc.2025.3541575","title":"3D Cooperative Positioning via RIS and Sidelink Communications With Zero Access Points","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer network; Telecommunications","score_opus":0.010232029976379552,"score_gpt":0.2616984447516208,"score_spread":0.25146641477524123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407449107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073117554,0.00014781173,0.9223662,0.000098750286,0.000029164567,0.000019924806,0.000029223453,0.00045721803,0.0037340971],"genre_scores_gemma":[0.8691999,0.0001183588,0.12746525,0.00007696101,0.000025665086,0.00006279416,0.00006106107,0.000019578576,0.0029704184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999196,0.00019726709,0.000030235737,0.00017087322,0.0003099129,0.00009574467],"domain_scores_gemma":[0.9992306,0.00025383866,0.00016363007,0.00021495746,0.000101889964,0.000035118996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042261241,0.000636143,0.00060282595,0.00043300717,0.00028518602,0.0007186093,0.00088204263,0.0008199715,0.00097445317],"category_scores_gemma":[0.0012529352,0.00027386838,0.00045645467,0.0005926235,0.00069213327,0.0009019305,0.0019923304,0.0005635486,0.0006493992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005624374,0.00013983848,0.009076957,0.00023866913,0.00012583537,0.00085393636,0.0007373712,0.370018,0.20908657,0.03896379,0.0016090175,0.36858752],"study_design_scores_gemma":[0.00003827323,0.00056291145,0.0020531334,0.000021943199,0.000043021246,0.000646478,0.00015593549,0.9112656,0.07157943,0.0076265964,0.005957364,0.00004929598],"about_ca_topic_score_codex":0.0004524269,"about_ca_topic_score_gemma":0.00060826313,"teacher_disagreement_score":0.00097445317,"about_ca_system_score_codex":0.00026837536,"about_ca_system_score_gemma":0.0003228453,"threshold_uncertainty_score":0.0032598376},"labels":[],"label_agreement":null},{"id":"W4407638149","doi":"10.1109/jcs64661.2025.10880643","title":"Multi-domain CSI-based Indoor Localization with Deep Attention Networks for MIMO JCAS system","year":2025,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Business Finland; HORIZON EUROPE Framework Programme; Academy of Finland","keywords":"Computer science; MIMO; Domain (mathematical analysis); Computer network; Channel (broadcasting); Mathematics","score_opus":0.005885809206238096,"score_gpt":0.20659700285327207,"score_spread":0.20071119364703396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407638149","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13148724,0.001580878,0.8515443,0.0008880083,0.00029891284,0.000050993272,0.0003495432,0.0043607936,0.009439409],"genre_scores_gemma":[0.9718394,0.00019370436,0.024622807,0.00019822853,0.000048383943,0.000022615459,0.0002859147,0.00003415956,0.0027547684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998229,0.000023571065,0.0000071565573,0.00004748186,0.000039691542,0.00005916392],"domain_scores_gemma":[0.9997776,0.000071928764,0.000023920722,0.000023488228,0.000083967185,0.00001894824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002615302,0.0007797156,0.00052573666,0.00031788074,0.00029945472,0.00045950187,0.000847502,0.0004751221,0.0015041463],"category_scores_gemma":[0.0007636794,0.00022780057,0.00030394085,0.00040817307,0.00026909533,0.0007836019,0.00088914676,0.00089384156,0.0003461838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003387413,0.00012932775,0.0028539244,0.00011873611,0.00007795196,0.00024081983,0.00009874295,0.67496693,0.014772928,0.0030636084,0.0056610215,0.29767722],"study_design_scores_gemma":[0.000006019968,0.000033253018,0.00034059238,0.0000041534627,0.000012772183,0.000028681152,0.000012687737,0.99569976,0.0025342477,0.0008575637,0.0004640103,0.000006288749],"about_ca_topic_score_codex":0.014274215,"about_ca_topic_score_gemma":0.023140948,"teacher_disagreement_score":0.014274215,"about_ca_system_score_codex":0.0006853179,"about_ca_system_score_gemma":0.0008097127,"threshold_uncertainty_score":0.028382242},"labels":[],"label_agreement":null},{"id":"W4407900438","doi":"10.1109/mnet.2025.3544694","title":"Digital Twin Empowered Wireless Positioning: Prospects, Architecture, and Challenges","year":2025,"lang":"en","type":"article","venue":"IEEE Network","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless; Architecture; Computer network; Hybrid positioning system; Computer architecture; Telecommunications; Positioning system; Engineering","score_opus":0.00807849825241659,"score_gpt":0.20302532602448253,"score_spread":0.19494682777206596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407900438","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022204246,0.0013576284,0.96630347,0.0004772746,0.00014325477,0.000026356136,0.00004443831,0.00089177105,0.008551589],"genre_scores_gemma":[0.77717674,0.0024050518,0.21245687,0.00020901681,0.00011082584,0.000031596206,0.00015767173,0.0000899398,0.007362316],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996966,0.000059904236,0.00001718847,0.00006278581,0.000120565244,0.000042926345],"domain_scores_gemma":[0.99959856,0.000068831236,0.000034919616,0.00012706703,0.0001262847,0.000044298824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000576049,0.0004600131,0.00036938852,0.0004728373,0.00027444263,0.0010787247,0.0010435788,0.0006503881,0.001663222],"category_scores_gemma":[0.0011457278,0.00022869783,0.00021031799,0.0005898269,0.0006946877,0.0025958058,0.0014804912,0.00085503573,0.0006077005],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023657668,0.000065919805,0.0039835186,0.00035376614,0.000044095355,0.00029927233,0.00031454442,0.16921806,0.049832366,0.120387554,0.002695264,0.6525692],"study_design_scores_gemma":[0.000018539406,0.0005978276,0.0010636223,0.000069862705,0.00006004173,0.0010578607,0.00016409744,0.8762305,0.032673728,0.034651376,0.053354878,0.000057798257],"about_ca_topic_score_codex":0.0014965843,"about_ca_topic_score_gemma":0.001495404,"teacher_disagreement_score":0.001663222,"about_ca_system_score_codex":0.00047199344,"about_ca_system_score_gemma":0.0004770499,"threshold_uncertainty_score":0.0055639744},"labels":[],"label_agreement":null},{"id":"W4407937675","doi":"10.1109/jiot.2025.3545816","title":"Smartphone GNSS Lane-Level Navigation With Galileo HAS Corrections and an Iterative PPP Algorithm","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"GNSS applications; Galileo (satellite navigation); Computer science; Algorithm; Satellite navigation; Global Positioning System; Iterative method; Real-time computing; GNSS augmentation; Radio navigation; Telecommunications; Remote sensing","score_opus":0.014936080461655801,"score_gpt":0.2430081260440943,"score_spread":0.2280720455824385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407937675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024427775,0.00010514267,0.97295094,0.000066377324,0.000064171865,0.000034577013,0.00006016764,0.000984252,0.0013065859],"genre_scores_gemma":[0.43040383,0.00021435428,0.564966,0.0000734771,0.000063991676,0.00013580127,0.00060704525,0.00017436528,0.0033611825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996755,0.00004823044,0.000019511217,0.00008252133,0.00013545911,0.000038739978],"domain_scores_gemma":[0.9996928,0.000059275935,0.000040695293,0.000053763913,0.00013871855,0.000014789942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030652527,0.00069844036,0.00045780203,0.00050074165,0.00027455098,0.00057283835,0.00080036913,0.0005252926,0.0011742142],"category_scores_gemma":[0.0017162953,0.0003694501,0.00061456184,0.00072357175,0.00029039028,0.0006175858,0.00081747887,0.00073824177,0.00090793765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021902157,0.000062994295,0.005447492,0.00013046051,0.000082922656,0.00019589186,0.0002725947,0.4744304,0.028141918,0.0063978047,0.003181512,0.48143697],"study_design_scores_gemma":[0.00001983244,0.00006984135,0.0014478312,0.000009534531,0.000016459886,0.00009269115,0.000036566744,0.98826885,0.005993275,0.0013409003,0.0026856884,0.000018560344],"about_ca_topic_score_codex":0.0075211595,"about_ca_topic_score_gemma":0.0062379413,"teacher_disagreement_score":0.0075211595,"about_ca_system_score_codex":0.00026307246,"about_ca_system_score_gemma":0.000978715,"threshold_uncertainty_score":0.014954746},"labels":[],"label_agreement":null},{"id":"W4407938106","doi":"10.1109/tcomm.2025.3545678","title":"Predictive Beamforming Approach for Secure Integrated Sensing and Communication With Multiple Aerial Eavesdroppers","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Memorial University of Newfoundland","funders":"","keywords":"Beamforming; Computer science; Computer network; Telecommunications","score_opus":0.011092105826457796,"score_gpt":0.22477026845793838,"score_spread":0.2136781626314806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407938106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045115054,0.00018922442,0.9934628,0.00009655711,0.000027025144,0.000010459222,0.000026662623,0.000120568686,0.0015552897],"genre_scores_gemma":[0.76386124,0.0012178507,0.2258738,0.00035882322,0.00013209392,0.00019527503,0.00024457637,0.000055056484,0.00806119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999602,0.000084396364,0.000015915663,0.00008712946,0.00014594139,0.000064572516],"domain_scores_gemma":[0.99959487,0.00021316532,0.0000459919,0.000040862335,0.00008699583,0.0000181521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045031405,0.000915421,0.000607998,0.00026589696,0.00035595184,0.000602035,0.00083394244,0.00070418045,0.0018221551],"category_scores_gemma":[0.0011764079,0.0003882952,0.00053011626,0.00053861697,0.00067652046,0.0009977168,0.001067073,0.0014351502,0.0005392637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015259183,0.000032404787,0.00066700595,0.00013825297,0.00007331631,0.0002783154,0.00017585958,0.8126452,0.020533746,0.039970763,0.0024342376,0.12289834],"study_design_scores_gemma":[0.0000050232184,0.000035922185,0.00006374544,0.000006140558,0.000010397536,0.000036365596,0.000013304259,0.99386793,0.0015795208,0.0038177243,0.0005565451,0.000007419115],"about_ca_topic_score_codex":0.0028525249,"about_ca_topic_score_gemma":0.0029374338,"teacher_disagreement_score":0.0028525249,"about_ca_system_score_codex":0.00042120452,"about_ca_system_score_gemma":0.00088153855,"threshold_uncertainty_score":0.0060957074},"labels":[],"label_agreement":null},{"id":"W4407948944","doi":"10.1109/tvt.2025.3546254","title":"Flexible Rate-Splitting Multiple Access for Near-Field Integrated Sensing and Communications","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Field (mathematics); Electronic engineering; Computer science; Engineering; Telecommunications; Electrical engineering; Mathematics","score_opus":0.01617558075976153,"score_gpt":0.27298788710618926,"score_spread":0.2568123063464277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407948944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009526705,0.00026336696,0.9883537,0.000052946358,0.000021803622,0.000019822628,0.000012312176,0.00016198235,0.0015873021],"genre_scores_gemma":[0.70524377,0.0003075775,0.2922924,0.00012490893,0.000055955818,0.00008125623,0.00004161997,0.00003337345,0.0018191361],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937373,0.00020619492,0.000017547776,0.000106147745,0.0002390683,0.000057277924],"domain_scores_gemma":[0.99950016,0.0002048038,0.000067878056,0.00009689592,0.00010163858,0.000028564145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070552964,0.0005358819,0.00043575827,0.0003109945,0.00036502094,0.00048639328,0.0014893756,0.00047986954,0.0009532083],"category_scores_gemma":[0.0010806071,0.00022914221,0.00035742574,0.00043154965,0.00069076894,0.0007424063,0.0006877715,0.00081055646,0.00027712667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033530823,0.00017699844,0.0009932453,0.00017755212,0.0001137805,0.000271787,0.00034260444,0.46740833,0.14105941,0.104226395,0.0022341895,0.28266045],"study_design_scores_gemma":[0.00001649951,0.000109990186,0.00011757168,0.0000068413196,0.000010004902,0.0001312156,0.00001768182,0.982786,0.006495093,0.008295432,0.0019952762,0.00001829558],"about_ca_topic_score_codex":0.0010681389,"about_ca_topic_score_gemma":0.001659011,"teacher_disagreement_score":0.0014893756,"about_ca_system_score_codex":0.00045647385,"about_ca_system_score_gemma":0.00048748104,"threshold_uncertainty_score":0.0037311912},"labels":[],"label_agreement":null},{"id":"W4407948986","doi":"10.1109/cdc56724.2024.10886074","title":"A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Vetenskapsrådet; Knut och Alice Wallenbergs Stiftelse; European Commission","keywords":"Computer science; Artificial intelligence; Human–computer interaction","score_opus":0.023957184189687366,"score_gpt":0.22554960104012398,"score_spread":0.2015924168504366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407948986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010110121,0.00018088553,0.97904176,0.0006273812,0.00005680498,0.000107276355,0.00009216939,0.000044714918,0.009738826],"genre_scores_gemma":[0.80671966,0.0005725358,0.1757824,0.0003958464,0.0001011168,0.0007876419,0.00012657091,0.000049243656,0.015465084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979564,0.0012391863,0.000053548236,0.00029994454,0.00027940638,0.00017142203],"domain_scores_gemma":[0.99740785,0.0019404968,0.00021355931,0.00006835387,0.00020911219,0.00016062893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002561631,0.0015926454,0.0014492997,0.0007377108,0.00073698984,0.0018020587,0.002855749,0.0023475683,0.0042864005],"category_scores_gemma":[0.005748483,0.0006490573,0.0009353515,0.0008521281,0.0020382563,0.0025770895,0.0022677558,0.0022780567,0.00046879702],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081431346,0.00005863166,0.00033774445,0.0001144081,0.000052973483,0.00031593547,0.00019578585,0.72290266,0.0011889313,0.26644212,0.0012019672,0.007107345],"study_design_scores_gemma":[0.000019919698,0.00004229808,0.00004372475,0.00001077222,0.000007754086,0.00003328514,0.000036408357,0.94850856,0.00009961634,0.050447766,0.0007370767,0.00001288005],"about_ca_topic_score_codex":0.0036455435,"about_ca_topic_score_gemma":0.0032601492,"teacher_disagreement_score":0.0042864005,"about_ca_system_score_codex":0.0021103967,"about_ca_system_score_gemma":0.00169284,"threshold_uncertainty_score":0.015312016},"labels":[],"label_agreement":null},{"id":"W4408324266","doi":"10.1109/globecom52923.2024.10901150","title":"Graph Neural Network-Based WiFi Indoor Localization System","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial neural network; Graph; Computer network; Artificial intelligence; Theoretical computer science","score_opus":0.006794321842816839,"score_gpt":0.19457325502746273,"score_spread":0.1877789331846459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408324266","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11500245,0.0015439881,0.8472482,0.0007788748,0.00047380207,0.00018592383,0.003458963,0.020991169,0.010316645],"genre_scores_gemma":[0.88727486,0.00063530996,0.09575202,0.00034584597,0.00010298988,0.00013216157,0.00542307,0.00014297555,0.010190725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973327,0.000035931575,0.000013481448,0.000101944905,0.000065184664,0.000050244715],"domain_scores_gemma":[0.9998085,0.000031531672,0.000022530692,0.000028573884,0.00009552254,0.000013446829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020318196,0.000853523,0.00076794706,0.0011018199,0.00034440227,0.00048198385,0.0014363129,0.00060998386,0.0025697115],"category_scores_gemma":[0.00069305307,0.0002173659,0.0005186427,0.0013736455,0.00018535966,0.00079306116,0.0007426642,0.0005996767,0.0014022499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050500233,0.00024358656,0.005150923,0.00021491069,0.00017801928,0.00034641963,0.00006422999,0.432193,0.006672388,0.0027450547,0.02020192,0.5314845],"study_design_scores_gemma":[0.000014641916,0.000029254872,0.0007655178,0.0000048098063,0.000021673117,0.000052617255,0.000011609815,0.9960418,0.0012481329,0.0007985936,0.0010019852,0.000009449556],"about_ca_topic_score_codex":0.028623082,"about_ca_topic_score_gemma":0.03250768,"teacher_disagreement_score":0.028623082,"about_ca_system_score_codex":0.00073230895,"about_ca_system_score_gemma":0.00065865414,"threshold_uncertainty_score":0.05691296},"labels":[],"label_agreement":null},{"id":"W4408324755","doi":"10.1109/globecom52923.2024.10901003","title":"3D Indoor Positioning Using the 2D-MUSIC Algorithm","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Multiple signal classification; Computer vision; Algorithm; Telecommunications","score_opus":0.013583489010624398,"score_gpt":0.2277622282732405,"score_spread":0.2141787392626161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408324755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030885893,0.00013818935,0.99455607,0.000047544378,0.000061037535,0.0000134207685,0.00004642247,0.0006521076,0.0013965712],"genre_scores_gemma":[0.09911185,0.0002723523,0.8982555,0.00008222809,0.00007707447,0.00006943272,0.0002247124,0.00010259943,0.0018041732],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999559,0.000086330874,0.000018834136,0.00008506087,0.00021640376,0.000034346565],"domain_scores_gemma":[0.9996524,0.00008455086,0.000041324703,0.00008298084,0.00011714133,0.000021565085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035075395,0.0008606378,0.0005578899,0.0009132752,0.00034503668,0.0008918309,0.0007498322,0.00075292005,0.0020932895],"category_scores_gemma":[0.0014240355,0.0003357143,0.0006719038,0.0010826498,0.00031831348,0.00071438163,0.0011969678,0.0006092091,0.0018681056],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002223379,0.000045503042,0.001629439,0.0001873125,0.00011672833,0.00024027945,0.00022497655,0.19808148,0.04331446,0.014541827,0.005405507,0.7359901],"study_design_scores_gemma":[0.000027874372,0.00010436619,0.0008054844,0.000025137071,0.000024102263,0.00048143842,0.00004855326,0.9653687,0.016401602,0.0046551335,0.012013106,0.000044533906],"about_ca_topic_score_codex":0.0011490324,"about_ca_topic_score_gemma":0.0017282605,"teacher_disagreement_score":0.0020932895,"about_ca_system_score_codex":0.00027848754,"about_ca_system_score_gemma":0.00053869374,"threshold_uncertainty_score":0.0070027113},"labels":[],"label_agreement":null},{"id":"W4408345891","doi":"10.1109/icassp49660.2025.10888115","title":"Cooperative ISAC for Localization and Velocity Estimation Using OFDM Waveforms in Cell-Free MIMO Systems","year":2025,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Orthogonal frequency-division multiplexing; MIMO; Computer science; Waveform; MIMO-OFDM; Estimation; Electronic engineering; Telecommunications; Engineering; Beamforming; Channel (broadcasting); Systems engineering","score_opus":0.009894882752487033,"score_gpt":0.23345472614196283,"score_spread":0.2235598433894758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408345891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027475886,0.00041455272,0.96953994,0.000119480595,0.000058880418,0.000027486012,0.000031759424,0.00028561358,0.0020463238],"genre_scores_gemma":[0.8898184,0.00031137632,0.10708484,0.00015263051,0.000078400684,0.000059599748,0.00006979289,0.00001930068,0.0024056535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948347,0.00011163608,0.00002131772,0.00012614402,0.00016287799,0.000094529234],"domain_scores_gemma":[0.99934715,0.00027756146,0.00007656575,0.000081635364,0.00018224966,0.000034768098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006336663,0.000803539,0.00065309525,0.00040359714,0.00045587352,0.0005988023,0.00095495995,0.00065592857,0.00072240253],"category_scores_gemma":[0.0016710573,0.0002552684,0.00030568158,0.0005508854,0.00058982486,0.0009944646,0.0010422643,0.00072116236,0.00025644226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032537413,0.00012335037,0.0033511743,0.0001731112,0.000098451215,0.00043825735,0.00039796234,0.6086613,0.03720864,0.013773922,0.0018718614,0.3335766],"study_design_scores_gemma":[0.000006681662,0.00006040925,0.00028160654,0.0000051704365,0.000013462169,0.00006898768,0.000026401145,0.9945775,0.0029775894,0.0013858167,0.0005866375,0.000009619391],"about_ca_topic_score_codex":0.00445351,"about_ca_topic_score_gemma":0.0056563294,"teacher_disagreement_score":0.00445351,"about_ca_system_score_codex":0.00042888435,"about_ca_system_score_gemma":0.00074540824,"threshold_uncertainty_score":0.008855164},"labels":[],"label_agreement":null},{"id":"W4408455016","doi":"10.3390/s25061806","title":"Indoor Localization Methods for Smartphones with Multi-Source Sensors Fusion: Tasks, Challenges, Strategies, and Perspectives","year":2025,"lang":"en","type":"review","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Hybrid positioning system; Multipath propagation; Bluetooth; GNSS applications; Sensor fusion; Wireless; Real-time computing; Indoor positioning system; Inertial measurement unit; Global Positioning System; Telecommunications; Positioning system; Accelerometer; Engineering; Artificial intelligence; Channel (broadcasting)","score_opus":0.05137404508897613,"score_gpt":0.329526750712493,"score_spread":0.27815270562351685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408455016","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011659432,0.9785599,0.014356159,0.0006565709,0.00041962828,0.000029125667,0.000048046353,0.00005859748,0.00470589],"genre_scores_gemma":[0.012004278,0.9742309,0.010111319,0.0003029545,0.00047248605,0.00003685999,0.000099745856,0.000014132298,0.002727398],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995158,0.00008309802,0.00005288766,0.00011075727,0.0002014311,0.00003596057],"domain_scores_gemma":[0.99912506,0.00040394347,0.00006753522,0.000038624643,0.00034185336,0.000023058834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091522606,0.000961867,0.00093279197,0.0022704492,0.00024565763,0.0011287217,0.0010623655,0.0012238619,0.0025561398],"category_scores_gemma":[0.001384016,0.000511896,0.00084703247,0.0020616495,0.00044008557,0.0022718345,0.000731814,0.0010098183,0.0015454913],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004640041,0.000054256678,0.0006541725,0.011879547,0.00009169748,0.00019057252,0.00013501184,0.0025564206,0.005190423,0.011114969,0.008710991,0.95937544],"study_design_scores_gemma":[0.000014633135,0.00044994205,0.0030820414,0.00580671,0.00034861313,0.0024185395,0.00046356072,0.011508426,0.011216144,0.009396088,0.9551473,0.00014812143],"about_ca_topic_score_codex":0.0014977611,"about_ca_topic_score_gemma":0.0016535635,"teacher_disagreement_score":0.0025561398,"about_ca_system_score_codex":0.00046178696,"about_ca_system_score_gemma":0.00078643067,"threshold_uncertainty_score":0.00855118},"labels":[],"label_agreement":null},{"id":"W4408622258","doi":"10.3390/jsan14020032","title":"Robust Distributed Collaborative Beamforming for WSANs in Dual-Hop Scattered Environments with Nominally Rectangular Layouts","year":2025,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Beamforming; Computer science; Hop (telecommunications); Dual (grammatical number); Telecommunications","score_opus":0.005422287170174569,"score_gpt":0.19188168590790217,"score_spread":0.1864593987377276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408622258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0127670085,0.000075655036,0.9843369,0.00008241778,0.000018831506,0.000014897787,0.000028726425,0.00008670271,0.0025888463],"genre_scores_gemma":[0.7283505,0.00034665337,0.2654783,0.000123394,0.00003668941,0.00013937693,0.00012858043,0.00005554362,0.005340824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996873,0.00009045738,0.000009140907,0.000059479127,0.00010344645,0.00005015499],"domain_scores_gemma":[0.99951696,0.000226275,0.00007799121,0.000043843662,0.00010568605,0.00002919476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004950046,0.0007099099,0.00043274224,0.0002471445,0.0002921559,0.000638077,0.0006611386,0.00069101906,0.0016350088],"category_scores_gemma":[0.0015291774,0.00028473558,0.00048863597,0.00043710705,0.0006421571,0.0007092911,0.0007758843,0.0005509878,0.00045092855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055967477,0.000015506417,0.00035597783,0.000039851566,0.000019943986,0.000090560294,0.000056679182,0.95845795,0.008252359,0.017845618,0.000564402,0.014245111],"study_design_scores_gemma":[0.000007460851,0.000026443018,0.00007766604,0.000004035627,0.0000034984616,0.00002257647,0.000021558197,0.9949473,0.0010498719,0.0033231738,0.0005105229,0.000005808101],"about_ca_topic_score_codex":0.0023317246,"about_ca_topic_score_gemma":0.002188619,"teacher_disagreement_score":0.0023317246,"about_ca_system_score_codex":0.0005645238,"about_ca_system_score_gemma":0.0005797667,"threshold_uncertainty_score":0.00546968},"labels":[],"label_agreement":null},{"id":"W4408715489","doi":"10.1109/itsc58415.2024.10919722","title":"Enhancing Indoor Mobility with Connected Sensor Nodes: A Real-Time, Delay-Aware Cooperative Perception Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Perception; Wireless sensor network; Real-time computing; Computer network","score_opus":0.00718205717042513,"score_gpt":0.21349494697693996,"score_spread":0.20631288980651483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408715489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037689377,0.00061112246,0.95827675,0.00016191759,0.00010751611,0.000044982917,0.00009375505,0.0014069522,0.001607593],"genre_scores_gemma":[0.7579003,0.0005524376,0.2378857,0.00011537018,0.0001045962,0.00008463369,0.0003130239,0.00013660103,0.0029073558],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995408,0.000073410876,0.000018098712,0.00015559635,0.00013732417,0.00007479056],"domain_scores_gemma":[0.99935347,0.00016067634,0.00009810873,0.00014758417,0.0001785336,0.000061531275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004837319,0.0009908259,0.0006007147,0.00058654253,0.00041352795,0.00064446306,0.002007415,0.00057727436,0.0007175711],"category_scores_gemma":[0.0014750186,0.0003904218,0.00048614648,0.0007258049,0.00033434044,0.0015786241,0.0015464879,0.0006515411,0.0004526096],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057354075,0.0003499227,0.006366856,0.00047149256,0.00023405171,0.0006424081,0.000867732,0.30373582,0.21266413,0.009754806,0.0059809247,0.45835838],"study_design_scores_gemma":[0.000036852456,0.000352343,0.0024641778,0.000022276281,0.000106270556,0.00049766863,0.0002985754,0.93850195,0.040607486,0.005104812,0.011951864,0.00005571331],"about_ca_topic_score_codex":0.0023934632,"about_ca_topic_score_gemma":0.0038902431,"teacher_disagreement_score":0.0023934632,"about_ca_system_score_codex":0.00046570535,"about_ca_system_score_gemma":0.0005843583,"threshold_uncertainty_score":0.004759133},"labels":[],"label_agreement":null},{"id":"W4408860256","doi":"10.1109/jiot.2025.3554528","title":"Parameterized TDOA: TDOA Estimation for Mobile Target Localization in a Time-Division Broadcast Positioning System","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Musculoskeletal Health and Arthritis","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Multilateration; Computer science; FDOA; Parameterized complexity; Division (mathematics); Real-time computing; Algorithm; Mathematics; Azimuth; Arithmetic","score_opus":0.004706652049339066,"score_gpt":0.23116976986245952,"score_spread":0.22646311781312045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408860256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032917315,0.00033178748,0.995581,0.00003701457,0.000038736893,0.000009362972,0.000024123818,0.00020036413,0.00048584238],"genre_scores_gemma":[0.5139435,0.0020701878,0.4803164,0.00011398212,0.0001601322,0.000139874,0.0003164914,0.00017046861,0.0027690355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956495,0.00008606587,0.00002243317,0.00011897006,0.00016916846,0.000038434475],"domain_scores_gemma":[0.9995022,0.00017162567,0.000080545266,0.00006353791,0.00016121047,0.00002085537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035811405,0.001021951,0.0007035288,0.0004949822,0.0003347496,0.000758111,0.000886097,0.0006539798,0.0009979683],"category_scores_gemma":[0.002556051,0.00034272656,0.00058709236,0.0011098234,0.0005172917,0.00097383786,0.00083664106,0.0009028195,0.0005419969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021147437,0.000033609937,0.0015729197,0.000364032,0.00007541782,0.00019826753,0.0002642692,0.5926759,0.033598915,0.01181024,0.0025002128,0.35669464],"study_design_scores_gemma":[0.000008550927,0.000047288395,0.00034086403,0.000021618556,0.000021545244,0.00014917643,0.000036624537,0.9894287,0.0038039647,0.0028493623,0.0032729176,0.00001942445],"about_ca_topic_score_codex":0.0070737284,"about_ca_topic_score_gemma":0.004753235,"teacher_disagreement_score":0.0070737284,"about_ca_system_score_codex":0.00050799013,"about_ca_system_score_gemma":0.0011197302,"threshold_uncertainty_score":0.014065087},"labels":[],"label_agreement":null},{"id":"W4409076545","doi":"10.1109/twc.2025.3554697","title":"Reflection Map Construction: Enhancing and Speeding Up Indoor Localization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reflection (computer programming); Computer science; Wireless; Computer vision; Telecommunications; Remote sensing; Artificial intelligence; Geology","score_opus":0.017063333856916888,"score_gpt":0.2615462107547615,"score_spread":0.2444828768978446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409076545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014683554,0.000106375985,0.98163074,0.00005004812,0.000025972742,0.000021126607,0.00003508259,0.0022297066,0.0012174167],"genre_scores_gemma":[0.2804357,0.0003555777,0.7164858,0.000055670636,0.00006664523,0.0000753052,0.00027289998,0.0003761176,0.0018762914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992329,0.0002026773,0.000024179495,0.00013933354,0.0003066237,0.00009436846],"domain_scores_gemma":[0.9988689,0.00041618975,0.00012141742,0.00032011844,0.00023324607,0.000040011048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005850928,0.0011732957,0.0008794768,0.0010721219,0.0003352606,0.0007046655,0.0014430413,0.0006406872,0.0020580853],"category_scores_gemma":[0.0032585678,0.00054318924,0.00076659076,0.0012479561,0.0004043433,0.0013839666,0.0018077149,0.000887175,0.0022520733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003102271,0.0001343769,0.0026918217,0.00025644628,0.00007250085,0.00039589728,0.00037930463,0.16993691,0.09835189,0.006717705,0.0034948597,0.7172581],"study_design_scores_gemma":[0.000060737635,0.00022661488,0.0017894228,0.000024351388,0.0000749482,0.0008800458,0.00013617729,0.91419894,0.07023976,0.0032067855,0.009099037,0.00006319824],"about_ca_topic_score_codex":0.0018698322,"about_ca_topic_score_gemma":0.0017833987,"teacher_disagreement_score":0.0020580853,"about_ca_system_score_codex":0.00021534493,"about_ca_system_score_gemma":0.00067250855,"threshold_uncertainty_score":0.006884992},"labels":[],"label_agreement":null},{"id":"W4409076609","doi":"10.1109/twc.2025.3554614","title":"Cooperative Direct Localization in Multipath Environments Using Binary Sparse Modeling and Cayley–Menger Determinant","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Binary number; Computer science; Multipath propagation; Theoretical computer science; Algorithm; Mathematics; Computer network; Arithmetic","score_opus":0.0260387875640603,"score_gpt":0.25917376877561565,"score_spread":0.23313498121155535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409076609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008700863,0.00013900419,0.989251,0.00007749694,0.000019333702,0.000009327072,0.0000147949795,0.000043286254,0.0017449621],"genre_scores_gemma":[0.61343235,0.000851549,0.37785378,0.00017393885,0.000108334396,0.000078379606,0.0001365754,0.00008224294,0.0072828443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995419,0.0001735568,0.000018339144,0.000092759175,0.0001378213,0.00003564897],"domain_scores_gemma":[0.9992454,0.00039172766,0.00009976835,0.00009592735,0.00012283602,0.000044384942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081575685,0.0007010992,0.000582629,0.0006380536,0.0002380405,0.00089217647,0.00075223553,0.0006509524,0.0009806887],"category_scores_gemma":[0.0023227248,0.0002642381,0.00048563056,0.0006714308,0.0009824305,0.0012690265,0.0012717866,0.0008409771,0.00037300074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007295636,0.00003912293,0.0008857083,0.00011436269,0.00004842435,0.00016603501,0.00025489833,0.49188668,0.012588241,0.4210362,0.0014826042,0.071424745],"study_design_scores_gemma":[0.000005851083,0.000044096618,0.00010807913,0.0000056961167,0.0000065642857,0.000063328946,0.000017192026,0.9606014,0.0018430407,0.035890143,0.0013975853,0.000016951413],"about_ca_topic_score_codex":0.001468284,"about_ca_topic_score_gemma":0.0014683343,"teacher_disagreement_score":0.001468284,"about_ca_system_score_codex":0.00059478113,"about_ca_system_score_gemma":0.00063913723,"threshold_uncertainty_score":0.004315436},"labels":[],"label_agreement":null},{"id":"W4409561806","doi":"10.1371/journal.pdig.0000774","title":"A BLE based turnkey indoor positioning system for mobility assessment in aging-in-place settings","year":2025,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"AGE-WELL; Natural Sciences and Engineering Research Council of Canada","keywords":"Bluetooth Low Energy; Computer science; Modular design; Bluetooth; Real-time computing; Signal strength; Calibration; Measure (data warehouse); Hybrid positioning system; SIGNAL (programming language); Embedded system; Wireless; Positioning system; Telecommunications; Engineering; Data mining; Node (physics)","score_opus":0.00773807525059642,"score_gpt":0.26215038779344724,"score_spread":0.2544123125428508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409561806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32582313,0.0014330248,0.61896324,0.0002976153,0.00043918964,0.00074726704,0.0053532706,0.02509711,0.02184624],"genre_scores_gemma":[0.8837991,0.000442608,0.09544496,0.00030265207,0.00007583028,0.0004072545,0.002897391,0.00026576003,0.016364345],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962604,0.00008944724,0.000024287503,0.00010067994,0.0001246097,0.000034895646],"domain_scores_gemma":[0.9995926,0.00006272123,0.00005058919,0.000061873376,0.00019098389,0.00004121746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027765296,0.00074579904,0.00059625093,0.00089023437,0.00019007901,0.00045769775,0.0005867707,0.00061185355,0.007142187],"category_scores_gemma":[0.0008014796,0.00017805217,0.0002735333,0.00041248125,0.00016022116,0.00048340144,0.00058251154,0.00025351797,0.0035514964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024484221,0.00036757955,0.046191785,0.0010557885,0.00028200683,0.0009526503,0.00057379395,0.0099762995,0.2989397,0.0021631268,0.017621282,0.61942756],"study_design_scores_gemma":[0.0004967764,0.0062034433,0.26012266,0.00030648534,0.0009920575,0.009718329,0.00082441553,0.24254595,0.3492306,0.002487775,0.12656598,0.0005055716],"about_ca_topic_score_codex":0.0012391306,"about_ca_topic_score_gemma":0.0031154708,"teacher_disagreement_score":0.007142187,"about_ca_system_score_codex":0.000241699,"about_ca_system_score_gemma":0.00021771251,"threshold_uncertainty_score":0.023892999},"labels":[],"label_agreement":null},{"id":"W4409682483","doi":"10.3390/s25092638","title":"Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Mitacs","keywords":"Beacon; Bluetooth; Computer science; Real-time computing; Bluetooth Low Energy; Occupancy; Embedded system; Wearable computer; Building automation; Indoor positioning system; Wireless; Home automation; Engineering; Telecommunications; Accelerometer; Operating system","score_opus":0.01347021395893806,"score_gpt":0.2481824416248508,"score_spread":0.23471222766591276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409682483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15983519,0.00093432906,0.8226266,0.00024675558,0.00019968092,0.00012457625,0.00039500947,0.009515054,0.006122769],"genre_scores_gemma":[0.9473868,0.00018330186,0.049141254,0.00011544585,0.000044092372,0.00007038151,0.0002795673,0.00005342917,0.0027257672],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996076,0.000105691004,0.00002194975,0.00009736619,0.0001159618,0.000051520834],"domain_scores_gemma":[0.9997466,0.000056901663,0.00006344771,0.000038070168,0.00006623757,0.000028735638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030129208,0.0003678609,0.0004685089,0.00073524343,0.0001846134,0.00046042394,0.00089341664,0.0005254363,0.0013055663],"category_scores_gemma":[0.0006812737,0.00019685045,0.0002807581,0.00034672118,0.00013582339,0.00047997342,0.00057975424,0.00024723972,0.0007845583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016489614,0.00042589533,0.028369233,0.00049517973,0.00018547596,0.0005979386,0.0005248348,0.031665806,0.28326628,0.0031374113,0.009423299,0.6402597],"study_design_scores_gemma":[0.00033221327,0.0023558324,0.03413066,0.0001285629,0.00028610675,0.002874825,0.0002512036,0.73792136,0.17952701,0.00225888,0.03971487,0.00021848876],"about_ca_topic_score_codex":0.00042392436,"about_ca_topic_score_gemma":0.00050869805,"teacher_disagreement_score":0.0013055663,"about_ca_system_score_codex":0.00014524344,"about_ca_system_score_gemma":0.00012490811,"threshold_uncertainty_score":0.00436759},"labels":[],"label_agreement":null},{"id":"W4409683217","doi":"10.1109/twc.2025.3560901","title":"User-Centric Multi-Static Sensing for Joint User and Target Tracking in Mobile Wireless Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless; Joint (building); Real-time computing; Computer network; Telecommunications; Engineering","score_opus":0.026098986708179988,"score_gpt":0.2729751223344422,"score_spread":0.24687613562626223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409683217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012434709,0.0009197158,0.9850353,0.000079489735,0.000050268274,0.000015747773,0.000015826681,0.0003107588,0.0011381722],"genre_scores_gemma":[0.8682112,0.00082406844,0.12887375,0.00017491792,0.00010017952,0.000048999642,0.000068768706,0.000029123667,0.0016690156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920636,0.0002941287,0.00003412608,0.00013314601,0.00025832743,0.00007400542],"domain_scores_gemma":[0.99938357,0.00020953298,0.00007612978,0.00014684003,0.00015229013,0.00003167085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008338888,0.00062061247,0.0005859274,0.00044893057,0.00035468393,0.0005583583,0.0009958085,0.00070792466,0.0005165349],"category_scores_gemma":[0.0016834257,0.00026822826,0.00036005606,0.0006145993,0.00039375926,0.0012901895,0.0010972234,0.00057720864,0.00024791883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027789373,0.00012384528,0.002743338,0.00028054134,0.00013561928,0.00026819832,0.00030357018,0.46985048,0.05046643,0.032711063,0.003255628,0.43958333],"study_design_scores_gemma":[0.0000060071166,0.00011776049,0.0005161778,0.000008498022,0.000017617784,0.00014308351,0.00002783722,0.98905236,0.004910871,0.0031804813,0.0020035652,0.000015748938],"about_ca_topic_score_codex":0.0016639631,"about_ca_topic_score_gemma":0.0023644553,"teacher_disagreement_score":0.0016639631,"about_ca_system_score_codex":0.00029936308,"about_ca_system_score_gemma":0.00060223986,"threshold_uncertainty_score":0.0044100285},"labels":[],"label_agreement":null},{"id":"W4409814381","doi":"10.1016/j.procs.2025.03.090","title":"WiFi-based Indoor Positioning using Low-cost Microcontrollers and Signal Fingerprinting","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Microcontroller; SIGNAL (programming language); Embedded system; Real-time computing","score_opus":0.004880569501374965,"score_gpt":0.2131132191013079,"score_spread":0.20823264959993293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09134415,0.00077582116,0.898733,0.00011966402,0.000115117095,0.00008793331,0.00011879147,0.004644403,0.0040610833],"genre_scores_gemma":[0.8410244,0.0003330626,0.15578401,0.00006106532,0.000036166966,0.000071558505,0.00012818782,0.000057465106,0.002504023],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995758,0.00008439938,0.00002639111,0.00010162737,0.00017440191,0.000037395643],"domain_scores_gemma":[0.9996364,0.00009388976,0.00007125522,0.00008570383,0.00009746394,0.000015296211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025465025,0.0005275083,0.00044440664,0.0006382795,0.00020761172,0.00038170052,0.0009194603,0.00045788693,0.001161796],"category_scores_gemma":[0.0010757738,0.00025768782,0.0002923771,0.00059929164,0.000174543,0.0007725739,0.00048125433,0.00024095303,0.00063549215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028212823,0.00016053213,0.013445986,0.0005136357,0.00013711255,0.0005395427,0.00017525794,0.14895615,0.1150473,0.0034123568,0.0025610954,0.7147689],"study_design_scores_gemma":[0.00004858855,0.0008868224,0.015075399,0.00008437126,0.00013380987,0.0020823898,0.00006744315,0.8621605,0.104112916,0.0018932262,0.013362044,0.000092508126],"about_ca_topic_score_codex":0.0015789932,"about_ca_topic_score_gemma":0.0017925826,"teacher_disagreement_score":0.0015789932,"about_ca_system_score_codex":0.00021350205,"about_ca_system_score_gemma":0.00018620731,"threshold_uncertainty_score":0.0038865805},"labels":[],"label_agreement":null},{"id":"W4410227453","doi":"10.1109/wcnc61545.2025.10978292","title":"Cooperative Localization and Tracking Using RISs and Sidelink Communications","year":2025,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Tracking (education); Telecommunications; Psychology","score_opus":0.02207300055628106,"score_gpt":0.276174782790617,"score_spread":0.254101782234336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410227453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064535275,0.00021495209,0.9299979,0.00010239219,0.0000635682,0.00003067983,0.000029631225,0.00064149895,0.0043840776],"genre_scores_gemma":[0.87784445,0.00019482922,0.11790528,0.000084429186,0.00004280744,0.000058123867,0.00008251146,0.000029558425,0.0037581027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990381,0.00020523593,0.000038045473,0.00022242527,0.000375845,0.00012036237],"domain_scores_gemma":[0.99915934,0.00023247013,0.00016182296,0.00022718981,0.00017514461,0.000044010256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052783126,0.00076887006,0.00077204773,0.00048764836,0.00029802608,0.0007708579,0.000983239,0.000912189,0.0011547573],"category_scores_gemma":[0.0013187382,0.0002788829,0.00065283675,0.00058307644,0.00069599843,0.001107129,0.0016964354,0.000615035,0.00078874716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058984756,0.00016597558,0.007142341,0.0002185326,0.0001435365,0.0006360641,0.00039364578,0.5181078,0.13591102,0.0144804735,0.0015455503,0.32066524],"study_design_scores_gemma":[0.00003279505,0.0005698762,0.0016210809,0.000018671537,0.00004875193,0.00035396265,0.00008199939,0.95671844,0.03246002,0.0033864372,0.004672272,0.000035570894],"about_ca_topic_score_codex":0.00077233504,"about_ca_topic_score_gemma":0.00086961634,"teacher_disagreement_score":0.0011547573,"about_ca_system_score_codex":0.00030100296,"about_ca_system_score_gemma":0.00043179758,"threshold_uncertainty_score":0.0038630366},"labels":[],"label_agreement":null},{"id":"W4410295608","doi":"10.1109/tim.2025.3568952","title":"LWiHS: A Lightweight Wi-Fi-Enabled Human Sensing Using Feature Fusion Strategy","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fusion; Sensor fusion; Feature (linguistics); Artificial intelligence","score_opus":0.027007763801474006,"score_gpt":0.25231452572085544,"score_spread":0.22530676191938143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410295608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055049054,0.0006995395,0.93287605,0.00025791055,0.00015736916,0.00012584381,0.0013083086,0.0070605213,0.0024653608],"genre_scores_gemma":[0.6965693,0.00053355604,0.29231104,0.00042017794,0.00009493481,0.00026681428,0.003911491,0.00014865288,0.005744028],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975747,0.00003245495,0.000009710122,0.00007326031,0.00007891711,0.000048315418],"domain_scores_gemma":[0.99983203,0.000046896625,0.00002013207,0.000035033056,0.00004845561,0.000017367678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000374824,0.00087079674,0.0006491303,0.0006869461,0.00025388805,0.00036664147,0.0011841897,0.00056087406,0.001543076],"category_scores_gemma":[0.0009907425,0.0002190455,0.0004746739,0.0007067605,0.0003187125,0.0010296803,0.0012590446,0.00059249956,0.0005546668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005734802,0.00027273386,0.00514031,0.00019953314,0.000168139,0.00034870105,0.00012631011,0.11228386,0.03699941,0.00365514,0.015695937,0.82453644],"study_design_scores_gemma":[0.000034337365,0.00013799753,0.0023208535,0.000015405407,0.000034759785,0.0001604896,0.000033157045,0.97437084,0.014607423,0.003923783,0.00433411,0.000026766844],"about_ca_topic_score_codex":0.006322196,"about_ca_topic_score_gemma":0.008408218,"teacher_disagreement_score":0.006322196,"about_ca_system_score_codex":0.0003858167,"about_ca_system_score_gemma":0.0006546957,"threshold_uncertainty_score":0.012570798},"labels":[],"label_agreement":null},{"id":"W4411049835","doi":"10.1016/j.geomat.2025.100058","title":"A fast and accurate maximum likelihood particle filtering for the indoor DoA-based positioning","year":2025,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Canada Excellence Research Chairs, Government of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Particle filter; Maximum likelihood; Computer science; Particle (ecology); Algorithm; Mathematics; Statistics; Artificial intelligence; Kalman filter; Geology","score_opus":0.007227377020075344,"score_gpt":0.22376024057081653,"score_spread":0.2165328635507412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411049835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063161284,0.00014756089,0.9985061,0.00005319086,0.00006129656,0.000013859927,0.00002240924,0.00016260872,0.00040132558],"genre_scores_gemma":[0.11066011,0.001137725,0.88234043,0.00016335209,0.00029500754,0.00026714796,0.0005269657,0.000108370215,0.004500882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994393,0.00008130839,0.000030244888,0.00014413349,0.0002644579,0.000040461244],"domain_scores_gemma":[0.9995633,0.00016638055,0.000049255217,0.000058886155,0.00013978546,0.00002249329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074121694,0.0009245309,0.0009595882,0.0007211892,0.0005948648,0.00071621366,0.0009753505,0.0012640192,0.0014572081],"category_scores_gemma":[0.0030123347,0.0006154789,0.0010325527,0.0012154862,0.00042538065,0.0010986908,0.0009443774,0.0018058643,0.0014280669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012769013,0.00010216,0.0014808225,0.0003005878,0.00009835429,0.00024489246,0.00020738174,0.45329294,0.01514453,0.033807565,0.0103057735,0.4848874],"study_design_scores_gemma":[0.000018536313,0.000033384942,0.0003892655,0.000022655116,0.000012330054,0.00007554494,0.000011061132,0.9876316,0.0015279844,0.003977085,0.006278067,0.00002258137],"about_ca_topic_score_codex":0.008118689,"about_ca_topic_score_gemma":0.0055955974,"teacher_disagreement_score":0.008118689,"about_ca_system_score_codex":0.00060177455,"about_ca_system_score_gemma":0.0014667321,"threshold_uncertainty_score":0.016142845},"labels":[],"label_agreement":null},{"id":"W4412028175","doi":"10.1016/j.comnet.2025.111527","title":"A comparative analysis of indoor localization technologies","year":2025,"lang":"en","type":"article","venue":"Computer Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Mitacs","keywords":"Computer science; Telecommunications","score_opus":0.007589706687746166,"score_gpt":0.23070494578989606,"score_spread":0.2231152391021499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412028175","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3651231,0.06799599,0.48708194,0.001386571,0.0010613546,0.0003085668,0.0026565124,0.0033006943,0.07108529],"genre_scores_gemma":[0.93421125,0.013822291,0.044917632,0.00015122583,0.00021378596,0.00010699573,0.0017811606,0.00012396036,0.00467161],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980013,0.00059522624,0.000108732966,0.0003141106,0.0007882395,0.0001924123],"domain_scores_gemma":[0.99570113,0.001966704,0.0004288225,0.0002875183,0.0015360971,0.00007976249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001096235,0.0006414794,0.00051729736,0.0028241207,0.0003178977,0.0011843832,0.00068933197,0.0005975637,0.0024097015],"category_scores_gemma":[0.0046981825,0.00016924914,0.000576888,0.0032455516,0.00025023066,0.0014624204,0.00072001177,0.0002531718,0.0010322005],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005982596,0.0000796404,0.027801972,0.0027973598,0.00036108878,0.00052452495,0.00041629473,0.04280369,0.011486076,0.008185085,0.009156567,0.8957894],"study_design_scores_gemma":[0.00013391512,0.0033563082,0.19644657,0.002618309,0.0019484219,0.008077394,0.005756257,0.46272203,0.08617907,0.011240097,0.22107063,0.00045106406],"about_ca_topic_score_codex":0.0020812529,"about_ca_topic_score_gemma":0.0017513076,"teacher_disagreement_score":0.0028241207,"about_ca_system_score_codex":0.00058899936,"about_ca_system_score_gemma":0.0003854669,"threshold_uncertainty_score":0.00806123},"labels":[],"label_agreement":null},{"id":"W4412107035","doi":"10.1109/jiot.2025.3586979","title":"A Stochastic-Geometry-Based Analytical Framework for Integrated Localization and Communication Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Mobile Communications Research Laboratory, Southeast University; Research Foundation for Opto-Science and Technology; National Science Foundation","keywords":"Computer science; Stochastic geometry; Stochastic process; Information geometry; Mathematical optimization; Geometry; Mathematics","score_opus":0.011630155388641763,"score_gpt":0.2604049734612377,"score_spread":0.24877481807259594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412107035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029371777,0.00028482126,0.99017805,0.00042627612,0.00005945814,0.000032032553,0.00007505397,0.00009133706,0.0059159105],"genre_scores_gemma":[0.79450154,0.0032218131,0.1890936,0.0006893965,0.00045937332,0.00043541126,0.0003433184,0.00018195287,0.0110736685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99906975,0.00034937725,0.00003122795,0.00012210238,0.00030871274,0.00011890614],"domain_scores_gemma":[0.9987961,0.0005586936,0.0002008328,0.00009072207,0.0002952127,0.000058422713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012807876,0.0012882425,0.00085021276,0.00115141,0.0006127522,0.0014278134,0.0015925613,0.0011514012,0.0024772875],"category_scores_gemma":[0.003230818,0.00059653627,0.0010925084,0.000987448,0.0014901657,0.0018063724,0.0013817594,0.0012465559,0.0006976699],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009182702,0.000015666028,0.00024082462,0.000039883846,0.000017033522,0.0000970532,0.000046389156,0.81445193,0.0010200444,0.17982057,0.0010294359,0.0032119476],"study_design_scores_gemma":[0.0000026405585,0.000014349548,0.0000667429,0.000004907227,0.0000041803955,0.000035736277,0.0000125970155,0.97969407,0.00010340103,0.019123644,0.00093014346,0.00000757989],"about_ca_topic_score_codex":0.005730195,"about_ca_topic_score_gemma":0.0030727438,"teacher_disagreement_score":0.005730195,"about_ca_system_score_codex":0.0021380393,"about_ca_system_score_gemma":0.0012925749,"threshold_uncertainty_score":0.015512586},"labels":[],"label_agreement":null},{"id":"W4412164102","doi":"10.1109/twc.2025.3584833","title":"Movable Antenna-Aided Near-Field Integrated Sensing and Communication","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Major Projects of Guangdong Education Department for Foundation Research and Applied Research; Guangzhou Municipal Science and Technology Project; National Natural Science Foundation of China","keywords":"Computer science; Antenna (radio); Telecommunications; Field (mathematics); Electronic engineering; Engineering; Mathematics","score_opus":0.013237022948432358,"score_gpt":0.24283445176962645,"score_spread":0.2295974288211941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412164102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023033928,0.00032511103,0.97366977,0.00008961263,0.000041628224,0.000027574992,0.000023151924,0.000388297,0.0024009913],"genre_scores_gemma":[0.75807637,0.00029026848,0.23842187,0.00019431845,0.000051713167,0.00009953914,0.00007510622,0.000031625346,0.0027591933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948764,0.0001458671,0.000017913466,0.00012697637,0.00015500619,0.000066668916],"domain_scores_gemma":[0.9994216,0.00022367506,0.00013239677,0.00009255089,0.00009191099,0.00003798212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044571096,0.00074670854,0.0006925358,0.00036466445,0.000343906,0.0005565221,0.0011683244,0.00078726,0.0008749553],"category_scores_gemma":[0.0011897037,0.0002907737,0.0004497356,0.00059667847,0.00053870166,0.00084380934,0.0010784899,0.000550914,0.00047884014],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002849651,0.00012405134,0.0012849126,0.00013399965,0.00009690656,0.00023375549,0.00015527327,0.67898345,0.07531063,0.012348031,0.0020625477,0.22898152],"study_design_scores_gemma":[0.000016889646,0.00015521626,0.00021567037,0.0000055877986,0.000016561042,0.00012491153,0.000018996168,0.98820436,0.008069942,0.0019228414,0.0012321732,0.000016888647],"about_ca_topic_score_codex":0.0009856974,"about_ca_topic_score_gemma":0.0015772089,"teacher_disagreement_score":0.0011683244,"about_ca_system_score_codex":0.00043814522,"about_ca_system_score_gemma":0.0006210071,"threshold_uncertainty_score":0.0031789541},"labels":[],"label_agreement":null},{"id":"W4412188406","doi":"10.1038/s44459-025-00021-y","title":"Indoor Positioning with Multi-domain CSI-based Deep Attention Networks for MIMO Wireless Systems","year":2025,"lang":"en","type":"preprint","venue":"npj Wireless Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"HORIZON EUROPE European Research Council; HORIZON EUROPE Framework Programme; Business Finland","keywords":"MIMO; Computer science; Wireless; Domain (mathematical analysis); Wireless network; Computer network; Telecommunications; Mathematics; Channel (broadcasting)","score_opus":0.008120804608865273,"score_gpt":0.22573962356914565,"score_spread":0.21761881896028037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412188406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063516974,0.0016073412,0.9257298,0.00063203735,0.0001605176,0.000026840153,0.0004975285,0.0032350358,0.0045939484],"genre_scores_gemma":[0.911838,0.00055517483,0.08022121,0.0003425933,0.0001176969,0.000042153602,0.001152298,0.00010019279,0.0056306124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975103,0.000044892182,0.000011077525,0.00007951917,0.000055003682,0.000058411184],"domain_scores_gemma":[0.999603,0.00018382791,0.00004839229,0.00005454841,0.00008975502,0.000020593608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040319833,0.0009997035,0.00060283253,0.00055225834,0.0002649091,0.0005715142,0.0012051333,0.000784473,0.0017064956],"category_scores_gemma":[0.0015356694,0.00039089535,0.00042452506,0.0008006716,0.00034149454,0.0010052741,0.0011119929,0.0011414335,0.0005257735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014658284,0.00007290282,0.0013558953,0.000089459136,0.00007275043,0.00012228185,0.00006127112,0.7187683,0.0061835684,0.0027881153,0.0042041508,0.26613474],"study_design_scores_gemma":[0.0000036761717,0.000016746133,0.00029346757,0.0000042786223,0.000008438539,0.000019432526,0.000007157531,0.99599683,0.0014866292,0.0016644957,0.00049425085,0.0000045157567],"about_ca_topic_score_codex":0.013840719,"about_ca_topic_score_gemma":0.019832093,"teacher_disagreement_score":0.013840719,"about_ca_system_score_codex":0.0009135224,"about_ca_system_score_gemma":0.0006101755,"threshold_uncertainty_score":0.027520299},"labels":[],"label_agreement":null},{"id":"W4412190138","doi":"10.24425/ijet.2025.153626","title":"BLE phase-based ranging: accuracy and capability under strong Wi-Fi interference","year":2025,"lang":"en","type":"article","venue":"International Journal of Electronics and Telecommunications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Ranging; Interference (communication); Computer science; Phase (matter); Electronic engineering; Telecommunications; Electrical engineering; Engineering; Physics; Channel (broadcasting)","score_opus":0.012522837117679146,"score_gpt":0.2978567172728747,"score_spread":0.28533388015519556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412190138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38515168,0.0036081697,0.5905286,0.00067544804,0.00033771855,0.00009283179,0.00029001647,0.0028628598,0.016452553],"genre_scores_gemma":[0.9459021,0.00080000574,0.050848573,0.00015271973,0.000056776877,0.000036704536,0.00024536232,0.000072943505,0.0018846855],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99821305,0.0003692562,0.000096724085,0.00033750047,0.0007924151,0.00019106583],"domain_scores_gemma":[0.9970655,0.0013048402,0.00031987403,0.00049860514,0.00075686316,0.00005438421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014361652,0.0006764183,0.0004887785,0.0010022551,0.00048132482,0.0009221429,0.0007519932,0.0011472234,0.0009852396],"category_scores_gemma":[0.008943536,0.00021628942,0.0003005513,0.0009006539,0.0005386601,0.0018179268,0.0010316672,0.0005774708,0.00078570785],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001177947,0.00019746862,0.030385537,0.0007753781,0.00018268054,0.00050629204,0.0005541113,0.15207057,0.20850992,0.005583926,0.002366428,0.59768975],"study_design_scores_gemma":[0.000075146156,0.0016493415,0.036864486,0.00018004359,0.00023951124,0.0043030097,0.0004883029,0.66800064,0.27179837,0.0053283405,0.010831579,0.0002412436],"about_ca_topic_score_codex":0.0011774696,"about_ca_topic_score_gemma":0.0012399683,"teacher_disagreement_score":0.0014361652,"about_ca_system_score_codex":0.00029579407,"about_ca_system_score_gemma":0.00030376008,"threshold_uncertainty_score":0.007595241},"labels":[],"label_agreement":null},{"id":"W4412190179","doi":"10.1007/978-981-96-8892-0_5","title":"Transformer-Based UWB Positioning: Learning to Correct Ranging Errors for Autonomous Agents","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Ranging; Computer science; Transformer; Artificial intelligence; Computer vision; Real-time computing; Electrical engineering; Telecommunications; Voltage; Engineering","score_opus":0.010708793780304489,"score_gpt":0.23581020882253592,"score_spread":0.22510141504223144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412190179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009448364,0.00017648305,0.9881826,0.000040764546,0.000056600828,0.000009147463,0.000016387186,0.0006916233,0.0013780957],"genre_scores_gemma":[0.59942114,0.0005074963,0.39225125,0.000102628444,0.00006869296,0.000038875016,0.00012433044,0.00016829015,0.007317229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980193,0.000034023094,0.000010145352,0.000051670308,0.00007859892,0.000023728038],"domain_scores_gemma":[0.9996686,0.00013673858,0.00003250615,0.000057625035,0.00008849927,0.000015925607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038389972,0.00043989636,0.0005211721,0.0002593733,0.00017706378,0.0005002946,0.001094712,0.00061608927,0.0017761835],"category_scores_gemma":[0.0016354278,0.00024420975,0.0002849982,0.00050375576,0.00039979233,0.0007142768,0.00072016515,0.00086139544,0.00078143086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022266536,0.000060956976,0.00064481254,0.00009159527,0.00004220917,0.0000704018,0.0001242856,0.21933503,0.029389491,0.010149939,0.0028277237,0.737041],"study_design_scores_gemma":[0.000017159104,0.00009441784,0.00026342587,0.000008389343,0.00002256126,0.00012139966,0.000024033314,0.97782564,0.012465325,0.0070897546,0.0020584888,0.000009471477],"about_ca_topic_score_codex":0.0015599917,"about_ca_topic_score_gemma":0.0016624121,"teacher_disagreement_score":0.0017761835,"about_ca_system_score_codex":0.00024197246,"about_ca_system_score_gemma":0.000443033,"threshold_uncertainty_score":0.0059419274},"labels":[],"label_agreement":null},{"id":"W4412430915","doi":"10.1016/j.eswa.2025.128912","title":"An intelligent wireless sensing algorithm for complex cross-domain scenarios based on DB-FA-YoLov6","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"State Key Laboratory of Networking and Switching Technology; Guangxi Normal University; Fundamental Research Funds for the Central Universities; Beijing University of Posts and Telecommunications; National Natural Science Foundation of China","keywords":"Computer science; Wireless; Domain (mathematical analysis); Algorithm; Data mining; Artificial intelligence; Telecommunications; Mathematics","score_opus":0.013965486768502684,"score_gpt":0.2839655763082468,"score_spread":0.27000008953974414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412430915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012449817,0.00015993872,0.9853512,0.000044835153,0.000057445643,0.000032571923,0.000032001168,0.00045058137,0.0014216185],"genre_scores_gemma":[0.25749788,0.00017869593,0.7388558,0.00007996236,0.00003607625,0.00010233215,0.0002743873,0.00008288296,0.0028919452],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997217,0.000044771452,0.000017158372,0.00008072531,0.00008902704,0.00004665081],"domain_scores_gemma":[0.9997311,0.0000749682,0.000022411303,0.000034741115,0.00011891543,0.000017803488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004219448,0.0005632817,0.00070165267,0.0007474344,0.00045786487,0.0006462744,0.000938962,0.0006269923,0.0019168777],"category_scores_gemma":[0.0012628669,0.00026091578,0.00047043565,0.00053138303,0.00030358753,0.00069502706,0.0007868647,0.00054393266,0.0007558251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038014867,0.00015066886,0.0025846742,0.00012598377,0.00009903067,0.00011911216,0.00011895358,0.21400267,0.0581838,0.011673884,0.0036263177,0.7089347],"study_design_scores_gemma":[0.0000099274475,0.000048942544,0.00040012292,0.0000070942992,0.000011523874,0.00008907257,0.000015728392,0.9885357,0.00777185,0.0009386417,0.002161018,0.000010465155],"about_ca_topic_score_codex":0.0036354288,"about_ca_topic_score_gemma":0.004102923,"teacher_disagreement_score":0.0036354288,"about_ca_system_score_codex":0.00044170808,"about_ca_system_score_gemma":0.00072549406,"threshold_uncertainty_score":0.0072285533},"labels":[],"label_agreement":null},{"id":"W4412478325","doi":"10.4108/eettti.9442","title":"Efficient Machine Learning for Wi-Fi CSI-based Human Activity Recognition Using Fast Monte Carlo based Feature Extraction","year":2025,"lang":"en","type":"article","venue":"EAI Endorsed Transactions on Tourism Technology and Intelligence","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Artificial intelligence; Monte Carlo method; Feature extraction; Pattern recognition (psychology); Feature (linguistics); Extraction (chemistry); Machine learning; Mathematics; Chemistry; Statistics; Chromatography","score_opus":0.014283371041042436,"score_gpt":0.25922190432529096,"score_spread":0.24493853328424853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412478325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019691411,0.00012460572,0.9783328,0.00006143491,0.000019099103,0.00002890545,0.000093320836,0.0012410674,0.00040747185],"genre_scores_gemma":[0.6972865,0.00020227047,0.299523,0.00011093321,0.00004989967,0.00021257687,0.0007335298,0.00009972052,0.0017815531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975985,0.000045134737,0.0000143247,0.00006127714,0.00007750959,0.00004194149],"domain_scores_gemma":[0.9995747,0.00022481164,0.000045385637,0.000044925804,0.0000905549,0.000019555504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037170528,0.0006144172,0.0007731145,0.0006064585,0.00025602768,0.00046670192,0.0008886932,0.00045092983,0.0011951455],"category_scores_gemma":[0.0018017937,0.000312679,0.0005159899,0.00064293755,0.0002551448,0.00055407366,0.0004756507,0.00066105986,0.00060003204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024913138,0.00016964869,0.0042331703,0.000084498446,0.00008922116,0.00014930808,0.000057556037,0.47323516,0.018335288,0.003538579,0.0027389263,0.4971195],"study_design_scores_gemma":[0.0000030971485,0.000009561476,0.00031041153,0.0000015854148,0.000002968678,0.00001883886,0.0000025645354,0.99727374,0.0016289797,0.00055755774,0.0001874144,0.0000033169076],"about_ca_topic_score_codex":0.0062880544,"about_ca_topic_score_gemma":0.0078192325,"teacher_disagreement_score":0.0062880544,"about_ca_system_score_codex":0.00045252673,"about_ca_system_score_gemma":0.0007864532,"threshold_uncertainty_score":0.012502909},"labels":[],"label_agreement":null},{"id":"W4412495092","doi":"10.54254/2755-2721/2025.po25263","title":"DSA-Net: A Dual-Path Spatial-Temporal Attention Network for WiFi-Based Human Activity Recognition","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Dual (grammatical number); Computer science; Path (computing); Net (polyhedron); Computer network; Real-time computing; Artificial intelligence; Mathematics; Art","score_opus":0.008029380998463242,"score_gpt":0.20688873240996278,"score_spread":0.19885935141149955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412495092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12006063,0.0024832052,0.82575864,0.0006378062,0.00065114413,0.00041522397,0.0087440265,0.03116259,0.010086625],"genre_scores_gemma":[0.7777894,0.0007812498,0.1934723,0.0006795774,0.00019674437,0.0004097157,0.015359251,0.00028293737,0.011028826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996824,0.000044572964,0.000014443918,0.00013156474,0.00007505246,0.00005200428],"domain_scores_gemma":[0.9997385,0.000078063415,0.000025679834,0.000043714084,0.00008042444,0.000033574783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044394174,0.0011053813,0.0007926014,0.0013059338,0.0003101143,0.00057378534,0.0012393509,0.0004919129,0.002923657],"category_scores_gemma":[0.0013229866,0.00027863545,0.0004925196,0.0009073456,0.00021466556,0.0009538816,0.0016979956,0.00061590143,0.0013921586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006593073,0.0005084127,0.012022095,0.00029051065,0.0002902916,0.00022012726,0.00011098027,0.035829514,0.024871841,0.00282977,0.040046096,0.8823211],"study_design_scores_gemma":[0.00007328252,0.000310363,0.010000356,0.000028318424,0.00011729504,0.00047316312,0.000112136535,0.94436103,0.020780655,0.006436718,0.017254183,0.000052500578],"about_ca_topic_score_codex":0.010735365,"about_ca_topic_score_gemma":0.023552932,"teacher_disagreement_score":0.010735365,"about_ca_system_score_codex":0.000694885,"about_ca_system_score_gemma":0.00070034456,"threshold_uncertainty_score":0.021345735},"labels":[],"label_agreement":null},{"id":"W4412631349","doi":"10.3390/info16080633","title":"Indoor Positioning and Tracking System in a Multi-Level Residential Building Using WiFi","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Indoor positioning system; Tracking (education); Tracking system; Computer science; Real-time computing; Environmental science; Architectural engineering; Engineering; Artificial intelligence; Operating system; Kalman filter; Accelerometer","score_opus":0.020691550803158244,"score_gpt":0.25554493243445925,"score_spread":0.234853381631301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412631349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63972896,0.00016008304,0.34407657,0.0002134411,0.00010661471,0.00019348024,0.00035312222,0.0065949364,0.008572768],"genre_scores_gemma":[0.9434055,0.000049234455,0.053340495,0.000048834496,0.000013394658,0.0000743655,0.000119607736,0.000022036553,0.0029264619],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970216,0.000047537196,0.000014004088,0.000070907605,0.000098170196,0.00006708603],"domain_scores_gemma":[0.9997812,0.000020515083,0.00003203468,0.000056988356,0.00007466486,0.000034660385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022580892,0.0003335782,0.00040210117,0.00038005822,0.0003727011,0.00038045098,0.0006759127,0.00042372735,0.0020298285],"category_scores_gemma":[0.00038392385,0.00012849945,0.00025545343,0.00044617502,0.00014692766,0.00041819073,0.0006552343,0.00022298373,0.0010223766],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008061125,0.000741714,0.07229269,0.0004891483,0.00016578595,0.0032326,0.0010246465,0.07277426,0.28075272,0.0033254868,0.007931409,0.55646336],"study_design_scores_gemma":[0.0001447092,0.0027276669,0.09268463,0.00008759137,0.00028926652,0.0028717807,0.0008421159,0.6812705,0.19205883,0.0010210128,0.025820276,0.00018163762],"about_ca_topic_score_codex":0.0043470254,"about_ca_topic_score_gemma":0.004672222,"teacher_disagreement_score":0.0043470254,"about_ca_system_score_codex":0.0002611144,"about_ca_system_score_gemma":0.00044033272,"threshold_uncertainty_score":0.008643448},"labels":[],"label_agreement":null},{"id":"W4412744977","doi":"10.5194/isprs-archives-xlviii-g-2025-1117-2025","title":"Attention-GANs: An Advanced GNSS Data Augmentation Method for Improved NLOS/LOS Classification","year":2025,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Non-line-of-sight propagation; Computer science; Remote sensing; Global Positioning System; Geography; Telecommunications; Wireless","score_opus":0.024608051809330494,"score_gpt":0.3009866737613843,"score_spread":0.2763786219520538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412744977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044550437,0.0009546587,0.9421444,0.00052049576,0.00036655972,0.0000956074,0.00095808535,0.006930608,0.0034791238],"genre_scores_gemma":[0.7054723,0.0005153121,0.27631578,0.0011144332,0.0002568369,0.00021362922,0.006008005,0.0006155444,0.009488229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973625,0.00006157538,0.000010185478,0.00008536049,0.00006487427,0.000041789266],"domain_scores_gemma":[0.99950755,0.00018349141,0.000033403845,0.0000856098,0.00016164177,0.000028289194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000640093,0.0011550923,0.00071610406,0.00067915936,0.00024009465,0.0004630717,0.0015554738,0.00062222016,0.002314688],"category_scores_gemma":[0.0015020773,0.00031801715,0.0007892641,0.00071739475,0.00037768742,0.00077320624,0.0009895472,0.0014793827,0.0010846267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033275137,0.00022077687,0.0032770473,0.00014654185,0.00014246326,0.00019301251,0.00014644461,0.31661013,0.020162083,0.0037742455,0.026044358,0.6289502],"study_design_scores_gemma":[0.000007695118,0.000026872925,0.00036424052,0.0000070710335,0.000010425187,0.00002574136,0.0000100848265,0.9937388,0.0031213923,0.0014303395,0.0012513463,0.0000060478183],"about_ca_topic_score_codex":0.0063531497,"about_ca_topic_score_gemma":0.010303179,"teacher_disagreement_score":0.0063531497,"about_ca_system_score_codex":0.00052183145,"about_ca_system_score_gemma":0.0005300976,"threshold_uncertainty_score":0.01263231},"labels":[],"label_agreement":null},{"id":"W4412914650","doi":"10.1007/978-3-031-96875-4_1","title":"Introduction","year":2025,"lang":"en","type":"book-chapter","venue":"Wireless networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science","score_opus":0.004254640558979504,"score_gpt":0.17397220473721492,"score_spread":0.1697175641782354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412914650","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002912396,0.0029674177,0.0052426485,0.0015911262,0.0036153374,0.000108005894,0.0010957463,0.00058740977,0.9845011],"genre_scores_gemma":[0.00060923066,0.0014995873,0.0009820717,0.00051880575,0.000440529,0.000038569793,0.00058908295,0.00013508608,0.9951871],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99957305,0.000035642224,0.000013857505,0.000086839675,0.00024549995,0.000045143348],"domain_scores_gemma":[0.99948126,0.00007340265,0.000018501416,0.00006924226,0.0002690435,0.00008867841],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00041355437,0.0009307771,0.0005217397,0.0016150798,0.0011203879,0.003336328,0.0012862299,0.0013881049,0.51855546],"category_scores_gemma":[0.0014761582,0.0003330088,0.00044150685,0.0015044207,0.00042604154,0.0027002604,0.0018700733,0.0015294557,0.43579924],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016838527,0.000039444716,0.000077379256,0.00015887144,0.0000018913396,0.000048917856,0.00010170574,0.00021834159,0.0005778534,0.029514628,0.755777,0.21346709],"study_design_scores_gemma":[9.137888e-7,0.0000049251253,0.000050932285,0.00005331624,6.4227373e-7,0.000029831472,0.00002461741,0.000030541378,0.00007184254,0.0023098078,0.9974209,0.0000018091725],"about_ca_topic_score_codex":0.0023967389,"about_ca_topic_score_gemma":0.004573434,"teacher_disagreement_score":0.48144454,"about_ca_system_score_codex":0.0011086278,"about_ca_system_score_gemma":0.0014371434,"threshold_uncertainty_score":0.68672216},"labels":[],"label_agreement":null},{"id":"W4412915062","doi":"10.1007/978-3-031-96875-4_5","title":"Location Services","year":2025,"lang":"en","type":"book-chapter","venue":"Wireless networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science","score_opus":0.004126668875025986,"score_gpt":0.17773785901579955,"score_spread":0.17361119014077356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412915062","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005964858,0.002114975,0.019388482,0.0011035609,0.00084512465,0.00007051089,0.0018413927,0.0038364432,0.970203],"genre_scores_gemma":[0.0037668026,0.0021063786,0.0037722057,0.0004775177,0.00023599918,0.000033747005,0.0015202394,0.0003494647,0.98773766],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996772,0.00003156929,0.000013523068,0.00005853138,0.00017251172,0.00004674386],"domain_scores_gemma":[0.9997458,0.00003490132,0.000013014175,0.0000715242,0.00010038116,0.00003444566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025967642,0.0009482531,0.00043061477,0.001511869,0.0009163709,0.0028964886,0.0010804917,0.0013562002,0.27653113],"category_scores_gemma":[0.00088213343,0.00033960372,0.00034069802,0.0024625058,0.00032122852,0.0031507742,0.001965115,0.0010943704,0.289506],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029153865,0.000032444368,0.00010988357,0.00013902193,0.000004131496,0.000092378614,0.00014407877,0.0003725351,0.0017432908,0.05779156,0.6029145,0.33662698],"study_design_scores_gemma":[0.000001995614,0.000004771822,0.00007184827,0.000031202308,0.0000018815261,0.000100733996,0.000032220723,0.00027708363,0.0003131181,0.0027325277,0.9964288,0.0000037939717],"about_ca_topic_score_codex":0.0035643578,"about_ca_topic_score_gemma":0.005690975,"teacher_disagreement_score":0.27653113,"about_ca_system_score_codex":0.0008577728,"about_ca_system_score_gemma":0.00075533974,"threshold_uncertainty_score":0.92508876},"labels":[],"label_agreement":null},{"id":"W4412915546","doi":"10.1007/978-3-031-96875-4_3","title":"Basic Building Blocks for Acoustic Sensing","year":2025,"lang":"en","type":"book-chapter","venue":"Wireless networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science","score_opus":0.008314288091550163,"score_gpt":0.20647242554302503,"score_spread":0.19815813745147487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412915546","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010901063,0.01259792,0.498218,0.00076030806,0.0015705354,0.00012472642,0.0003112903,0.0011412362,0.4841859],"genre_scores_gemma":[0.021677902,0.021384832,0.1816297,0.0007561824,0.00094881403,0.00034450492,0.0005123554,0.0006200796,0.77212566],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997938,0.000021940821,0.000008589445,0.000039652095,0.00011983561,0.00001623278],"domain_scores_gemma":[0.999863,0.00005783595,0.000005257856,0.000026243266,0.0000401724,0.000007475479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020556986,0.0013948805,0.0005357287,0.0007426278,0.00054059195,0.0018462788,0.0013672586,0.0012556955,0.04260762],"category_scores_gemma":[0.0004097213,0.0007147221,0.0004929352,0.0008781855,0.0009450976,0.0028111234,0.0011256444,0.0017072107,0.026448779],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027424743,0.000056451445,0.00008828989,0.0005780989,0.000015390387,0.00013710775,0.00020096582,0.0076289847,0.013697496,0.5510297,0.052791215,0.37374896],"study_design_scores_gemma":[0.000004427318,0.0000425507,0.00013959596,0.0002029657,0.000010262604,0.00032136033,0.000061954546,0.009246744,0.0045836885,0.13235857,0.8530069,0.000021052032],"about_ca_topic_score_codex":0.0007205832,"about_ca_topic_score_gemma":0.00113914,"teacher_disagreement_score":0.04260762,"about_ca_system_score_codex":0.00048242,"about_ca_system_score_gemma":0.0003929834,"threshold_uncertainty_score":0.14253676},"labels":[],"label_agreement":null},{"id":"W4413204367","doi":"10.1109/iwrfat65352.2025.11103416","title":"Security Enhancement of CSI-Based Wireless Sensing via Generative AI","year":2025,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Wireless; Generative grammar; Artificial intelligence; Telecommunications","score_opus":0.0045683261461555115,"score_gpt":0.22204535969165787,"score_spread":0.21747703354550235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413204367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037975814,0.00030324247,0.9589296,0.0001908751,0.00003991272,0.000024676998,0.000051697956,0.000669379,0.0018148583],"genre_scores_gemma":[0.9159869,0.00033453852,0.080952056,0.00029339176,0.000052524898,0.00004745279,0.00021153167,0.00006413208,0.0020575598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991697,0.00019870857,0.000035627367,0.00024326648,0.00025068288,0.000102025224],"domain_scores_gemma":[0.99840075,0.0008579187,0.00017901351,0.00029158528,0.00019845153,0.00007230172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009612528,0.0007721371,0.0007202201,0.0005641642,0.00029595825,0.0007693384,0.0011437812,0.0006465511,0.0008776396],"category_scores_gemma":[0.0034958872,0.0003618537,0.0008095125,0.0005924956,0.0009622057,0.0013532753,0.0016248935,0.0011940076,0.0003742506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047975444,0.00016323803,0.003732624,0.00018487804,0.00012527418,0.00030066978,0.00035071472,0.62864536,0.062244803,0.021546435,0.0014761531,0.2807501],"study_design_scores_gemma":[0.0000056242357,0.000053053303,0.00043280242,0.0000056915524,0.000014115451,0.00008666998,0.000013173973,0.9909292,0.00542697,0.0026181915,0.00040218755,0.000012302175],"about_ca_topic_score_codex":0.0018794646,"about_ca_topic_score_gemma":0.0019016936,"teacher_disagreement_score":0.0018794646,"about_ca_system_score_codex":0.00044115848,"about_ca_system_score_gemma":0.0005277965,"threshold_uncertainty_score":0.00508368},"labels":[],"label_agreement":null},{"id":"W4413371388","doi":"10.1109/ojcoms.2025.3600616","title":"6G WavesFM: A Foundation Model for Sensing, Communication, and Localization","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Foundation (evidence); Computer science; Geography; Archaeology","score_opus":0.03394985587141263,"score_gpt":0.30447495130843516,"score_spread":0.2705250954370225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413371388","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016371695,0.00041496634,0.978009,0.0005259595,0.00009850075,0.000034755973,0.0005101949,0.001025334,0.0030096506],"genre_scores_gemma":[0.6499469,0.001494172,0.33253857,0.00075583404,0.00024439913,0.0003605023,0.0025401458,0.00031523555,0.011804295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978715,0.0000587907,0.000008043199,0.000047493024,0.000068627836,0.000029943963],"domain_scores_gemma":[0.9997583,0.000096111966,0.000032195778,0.00003648562,0.0000553715,0.000021443577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048466632,0.0008016064,0.00052983087,0.00043695045,0.00023803303,0.00059577235,0.0012163231,0.0009568334,0.0016181275],"category_scores_gemma":[0.0012667992,0.0003462873,0.00070909003,0.0005103277,0.00043165722,0.00095914357,0.001001179,0.0014038701,0.0007633305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010752291,0.000060010676,0.0010951334,0.00006907141,0.00007165289,0.00013624757,0.0000587965,0.8463219,0.0047968407,0.02524762,0.0078019695,0.114233255],"study_design_scores_gemma":[0.0000037896282,0.000014322408,0.000099587545,0.0000044003827,0.000003465441,0.000016164431,0.0000033449855,0.9946523,0.0002807666,0.0038732032,0.0010448463,0.000003826044],"about_ca_topic_score_codex":0.0070165647,"about_ca_topic_score_gemma":0.008230838,"teacher_disagreement_score":0.0070165647,"about_ca_system_score_codex":0.000500772,"about_ca_system_score_gemma":0.0008058667,"threshold_uncertainty_score":0.01395148},"labels":[],"label_agreement":null},{"id":"W4413556905","doi":"10.1109/lsens.2025.3602011","title":"Estimating Movement Direction From Body Orientation Using Dual Ultra-Wideband Sensors","year":2025,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Movement (music); Orientation (vector space); Dual (grammatical number); Ultra-wideband; Wideband; Computer science; Acoustics; Physics; Geometry; Telecommunications; Optics; Mathematics; Art","score_opus":0.008122291107080121,"score_gpt":0.22933712993464764,"score_spread":0.2212148388275675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413556905","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81599516,0.00060521654,0.18098795,0.00006477622,0.0000940514,0.000026757014,0.0002033687,0.00024815236,0.0017745927],"genre_scores_gemma":[0.96734214,0.00032566444,0.03145198,0.00003934909,0.000023397377,0.000020964015,0.00012932459,0.000012506467,0.0006546578],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998739,0.000025983456,0.000008543483,0.000039330538,0.000038213064,0.000013977804],"domain_scores_gemma":[0.9997913,0.000061629544,0.000054063687,0.000021213056,0.000056775632,0.000014993169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013544818,0.0004045777,0.00023965852,0.00038137025,0.0000814212,0.00030351058,0.000182967,0.00028358473,0.00044125336],"category_scores_gemma":[0.0007844857,0.00015858888,0.00014152966,0.0003256686,0.000120696364,0.00030350537,0.00035296098,0.00021745464,0.0002577694],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010082126,0.00018617768,0.09468138,0.0003602147,0.0001285694,0.000326934,0.0003756591,0.009004553,0.51800215,0.000450139,0.0008112263,0.37466466],"study_design_scores_gemma":[0.00010466702,0.0019249304,0.51631486,0.00020295187,0.00033407123,0.0037455226,0.00096384477,0.24869327,0.2210006,0.001879623,0.0046804994,0.00015520502],"about_ca_topic_score_codex":0.00048483408,"about_ca_topic_score_gemma":0.0013200251,"teacher_disagreement_score":0.00048483408,"about_ca_system_score_codex":0.00005829682,"about_ca_system_score_gemma":0.00009660113,"threshold_uncertainty_score":0.0014761686},"labels":[],"label_agreement":null},{"id":"W4413982218","doi":"10.36680/j.itcon.2025.056","title":"A Deployable Solution for Indoor Tracking of Workers in Construction Sites through Bluetooth Low Energy Technology","year":2025,"lang":"en","type":"article","venue":"Journal of Information Technology in Construction","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bluetooth Low Energy; Bluetooth; Architectural engineering; Tracking (education); Engineering; Low energy; Energy (signal processing); Systems engineering; Automotive engineering; Embedded system; Computer science; Civil engineering; Telecommunications; Wireless; Physics","score_opus":0.005253542803384822,"score_gpt":0.22008201983365963,"score_spread":0.2148284770302748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413982218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055199724,0.00028517772,0.9379293,0.0001866316,0.00007487178,0.00008770285,0.00008755827,0.0023331211,0.003815876],"genre_scores_gemma":[0.70783454,0.0003197749,0.28072613,0.00016379883,0.000049565646,0.00019530463,0.00023995838,0.00006181748,0.010409132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960357,0.00007991955,0.000014451611,0.00007622625,0.00016548255,0.000060337545],"domain_scores_gemma":[0.9996829,0.0000510868,0.000065197295,0.00007653474,0.00009129669,0.000033057335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002396327,0.0005343897,0.0003589667,0.000700066,0.0003246998,0.00040783663,0.001136051,0.00074082956,0.0019864012],"category_scores_gemma":[0.0005769226,0.00021671281,0.0003055825,0.00051169057,0.00021622838,0.0005867769,0.0009370325,0.00039733638,0.0014392526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036879507,0.00027430893,0.008690216,0.00040787243,0.000065682194,0.0007846385,0.00065799744,0.029533433,0.25565207,0.0045613637,0.0071384157,0.69186527],"study_design_scores_gemma":[0.00026145656,0.0030699521,0.023496216,0.00016068059,0.00026628832,0.0032479782,0.0011802446,0.6351009,0.23083666,0.005192646,0.09696515,0.00022197192],"about_ca_topic_score_codex":0.000984724,"about_ca_topic_score_gemma":0.0015842181,"teacher_disagreement_score":0.0019864012,"about_ca_system_score_codex":0.00024794176,"about_ca_system_score_gemma":0.00041682317,"threshold_uncertainty_score":0.0066452026},"labels":[],"label_agreement":null},{"id":"W4414062405","doi":"10.3390/automation6030043","title":"Real-Time Safety Alerting System for Dynamic, Safety-Critical Environments","year":2025,"lang":"en","type":"article","venue":"Automation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Mitacs","keywords":"Software deployment; Android (operating system); Bluetooth; Reliability (semiconductor); Key (lock); System safety; Architecture; Systems architecture","score_opus":0.003446967848464855,"score_gpt":0.22308703411870737,"score_spread":0.21964006627024252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414062405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08878766,0.00051972346,0.87072146,0.0004962775,0.0005026727,0.00025079583,0.0003050839,0.03043333,0.0079829795],"genre_scores_gemma":[0.8832496,0.0002436735,0.10470083,0.0005612452,0.00012149187,0.00021967887,0.00040153807,0.00028005807,0.010221917],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957675,0.000051204923,0.000020097701,0.000083647756,0.00021250725,0.00005576102],"domain_scores_gemma":[0.9994468,0.00009830775,0.00008515513,0.00006754125,0.0002392062,0.00006294252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034485105,0.0005956088,0.0004775615,0.00066278334,0.000330301,0.0004068918,0.0009811667,0.0007195886,0.004480691],"category_scores_gemma":[0.0008261156,0.00017263048,0.0001988247,0.00018104623,0.00019110831,0.0005831848,0.0007284234,0.00044618302,0.0020456403],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082448975,0.0004920282,0.0056555,0.0005833459,0.00006846792,0.0013954958,0.00054775435,0.012508065,0.45147854,0.0037653623,0.02856737,0.49411353],"study_design_scores_gemma":[0.00038750996,0.0029001727,0.019420518,0.00018673332,0.00024430847,0.005161998,0.0003513914,0.37055352,0.4403664,0.0035647396,0.15659522,0.00026746897],"about_ca_topic_score_codex":0.00066216715,"about_ca_topic_score_gemma":0.00069629966,"teacher_disagreement_score":0.004480691,"about_ca_system_score_codex":0.0002440019,"about_ca_system_score_gemma":0.0005085267,"threshold_uncertainty_score":0.014989376},"labels":[],"label_agreement":null},{"id":"W4414153636","doi":"10.1109/ojcoms.2025.3609151","title":"Transformer-Based Multi-Modal Indoor Localization in RIS-Assisted Wireless Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Multipath propagation; Wireless; Channel state information; Wireless network; SIGNAL (programming language); Channel (broadcasting); Radio propagation","score_opus":0.031155383049537782,"score_gpt":0.29767423330020004,"score_spread":0.26651885025066224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414153636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007664804,0.00009329733,0.9903814,0.000046135123,0.000018192624,0.000009260815,0.000030519022,0.0007055924,0.0010508634],"genre_scores_gemma":[0.8524588,0.00024533123,0.14341752,0.00013781404,0.000037726277,0.000051522304,0.00020432069,0.00012670527,0.0033202497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997888,0.000049798506,0.000008233528,0.000046581743,0.000068356094,0.000038180966],"domain_scores_gemma":[0.99983144,0.000054075186,0.000021602955,0.00002653441,0.00004915434,0.000017266615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033242168,0.0004996191,0.00050986995,0.00038653513,0.00019441491,0.0004826613,0.0013992052,0.0004458491,0.0012888457],"category_scores_gemma":[0.00084484764,0.0002471313,0.00046152296,0.0004415517,0.0005161748,0.0009229502,0.0012038442,0.000613547,0.00045842846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012405889,0.000043010965,0.0010081133,0.000067953,0.00004110998,0.00012344107,0.00009660169,0.8457518,0.012041133,0.014121621,0.0016479393,0.12493308],"study_design_scores_gemma":[0.0000026556045,0.000015868707,0.00008273391,0.0000019259207,0.000004381764,0.000026245541,0.000005676446,0.99616253,0.0012762531,0.0020108349,0.0004069291,0.0000038462344],"about_ca_topic_score_codex":0.004791193,"about_ca_topic_score_gemma":0.0064244247,"teacher_disagreement_score":0.004791193,"about_ca_system_score_codex":0.00064485223,"about_ca_system_score_gemma":0.00056834886,"threshold_uncertainty_score":0.00952661},"labels":[],"label_agreement":null},{"id":"W4414405632","doi":"10.1109/iccworkshops67674.2025.11162186","title":"Computationally Efficient Indoor Positioning Using the 3D-MUSIC Algorithm","year":2025,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Snapshot (computer storage); Robustness (evolution); Smoothing; Grid; Covariance; Time synchronization; Synchronization (alternating current); Transmitter","score_opus":0.008242916637053722,"score_gpt":0.2254361194199139,"score_spread":0.21719320278286017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414405632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002042348,0.000097039745,0.9962733,0.00005260465,0.000030568764,0.0000100794805,0.00004149992,0.00050191017,0.00095072703],"genre_scores_gemma":[0.076689556,0.00019763011,0.9210116,0.000084506115,0.000057108417,0.00006486605,0.0002945,0.00008732669,0.0015128719],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996209,0.00007338204,0.000018103026,0.000070096605,0.0001807171,0.000036648933],"domain_scores_gemma":[0.9996418,0.00011689669,0.000036260422,0.00008262446,0.00010080162,0.000021620703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031758478,0.00088901544,0.0006913096,0.0006287444,0.00034186785,0.0007325236,0.000935049,0.00071276823,0.0028950104],"category_scores_gemma":[0.0015418233,0.00034226978,0.00070306234,0.00091785635,0.0002982998,0.00080227887,0.001063485,0.000814903,0.0022220502],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022426771,0.00005468357,0.001239279,0.00019140614,0.00011868572,0.00025396896,0.00013937941,0.3661486,0.026316652,0.021561088,0.006994022,0.57675797],"study_design_scores_gemma":[0.000017740784,0.0000322237,0.00024526648,0.00001027124,0.000009685492,0.00015378991,0.000024011406,0.9865552,0.0040205442,0.0050031855,0.003913112,0.000015065586],"about_ca_topic_score_codex":0.002736354,"about_ca_topic_score_gemma":0.0043716123,"teacher_disagreement_score":0.0028950104,"about_ca_system_score_codex":0.00030273144,"about_ca_system_score_gemma":0.0008626308,"threshold_uncertainty_score":0.0096847415},"labels":[],"label_agreement":null},{"id":"W4414431999","doi":"10.1109/jsac.2025.3613283","title":"Cooperative ISAC for Joint Localization and Velocity Estimation in Cell-Free MIMO Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Overhead (engineering); Multiplexing; Encoder; ENCODE; MIMO; Joint (building); Scheme (mathematics); Encoding (memory)","score_opus":0.019902590087579795,"score_gpt":0.26556317377423894,"score_spread":0.24566058368665913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414431999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013104529,0.0001905338,0.985255,0.00008013434,0.000033526467,0.000017677216,0.00002245175,0.000179384,0.0011168084],"genre_scores_gemma":[0.86286557,0.00025432277,0.13312063,0.00018297372,0.00006537663,0.00007331401,0.000112948306,0.000027831129,0.0032969886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992878,0.00016943167,0.000022885146,0.00018502714,0.00022471539,0.000110188645],"domain_scores_gemma":[0.9989692,0.0005112975,0.00010057186,0.00012346967,0.0002506859,0.000044710243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084820646,0.0007524262,0.0007585974,0.00032335264,0.0004234514,0.00063015,0.0012672276,0.000753032,0.0009445552],"category_scores_gemma":[0.0024445632,0.00033225567,0.00037971075,0.00045013725,0.0007177229,0.0010366036,0.0011481242,0.0010010746,0.00030248362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012437308,0.00006278205,0.0011519196,0.000074724725,0.000054952467,0.00014465142,0.00017876363,0.8559451,0.012378287,0.01467387,0.0013625006,0.11384806],"study_design_scores_gemma":[0.0000028568677,0.000024194678,0.000089382505,0.000002128485,0.000003792503,0.000017301514,0.000008740906,0.997389,0.0009227651,0.0012578506,0.00027782054,0.0000041962967],"about_ca_topic_score_codex":0.005982854,"about_ca_topic_score_gemma":0.006068562,"teacher_disagreement_score":0.005982854,"about_ca_system_score_codex":0.0005353742,"about_ca_system_score_gemma":0.0008252547,"threshold_uncertainty_score":0.011896014},"labels":[],"label_agreement":null},{"id":"W4415624581","doi":"10.1109/ipin66788.2025.11213105","title":"Transformer-EKF for UWB Positioning: A Benchmark Against CNN and BiLSTM Models","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Benchmark (surveying); Multipath propagation; Kalman filter; Range (aeronautics); Covariance; Artificial neural network; Noise (video); Deep learning; Focus (optics)","score_opus":0.008638025666445713,"score_gpt":0.22513756495465262,"score_spread":0.2164995392882069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415624581","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47284654,0.012906683,0.44511345,0.0022168874,0.0014345177,0.00038435034,0.006101161,0.039846048,0.019150352],"genre_scores_gemma":[0.8658327,0.0012881276,0.120720424,0.00040358928,0.00006896077,0.00014556787,0.006371166,0.0004345742,0.0047348947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994223,0.00009935425,0.00004596436,0.0002151437,0.00012301863,0.00009412424],"domain_scores_gemma":[0.99885607,0.00049111864,0.00007951181,0.00017923475,0.00033552398,0.00005845286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013271082,0.0021969436,0.0008674477,0.000767371,0.0003707031,0.0009153952,0.0023263332,0.0017754643,0.0025335636],"category_scores_gemma":[0.00549496,0.000436035,0.0005666639,0.0007311309,0.0004520363,0.0014463596,0.0009846644,0.0015282532,0.0011965288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067211676,0.00025559988,0.0040303743,0.00058553996,0.00025145532,0.00021571938,0.00007110801,0.695696,0.005221928,0.0016489247,0.008252203,0.28309894],"study_design_scores_gemma":[0.000031191426,0.00013536126,0.000541359,0.000026277537,0.00003084609,0.000044728342,0.000025835823,0.99360126,0.0035630325,0.0009166905,0.0010715481,0.000011904436],"about_ca_topic_score_codex":0.03558117,"about_ca_topic_score_gemma":0.035311956,"teacher_disagreement_score":0.03558117,"about_ca_system_score_codex":0.0012904155,"about_ca_system_score_gemma":0.0017446363,"threshold_uncertainty_score":0.07074815},"labels":[],"label_agreement":null},{"id":"W4415631237","doi":"10.36227/techrxiv.176168509.95106868/v1","title":"Scalable Fast Accurate Localization in Single Site MIMO with Small-Scale Dataset Using a Multi-Head Fourier Neural Operator","year":2025,"lang":"","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"State (computer science); Operator (biology); Scalability; Channel (broadcasting); Artificial neural network; Pattern recognition (psychology)","score_opus":0.04381966435483636,"score_gpt":0.2724753758737383,"score_spread":0.22865571151890196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415631237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068126164,0.0003597485,0.9212905,0.0003184611,0.00014994176,0.00005680757,0.000934141,0.006087163,0.0026769727],"genre_scores_gemma":[0.6991684,0.00028788755,0.29015332,0.00031898034,0.000096611635,0.00014891724,0.004189862,0.00023454036,0.0054015196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997054,0.000045676086,0.000013106076,0.000094971714,0.00008330868,0.00005756487],"domain_scores_gemma":[0.9995577,0.00012559014,0.000031069085,0.00012281278,0.00013559307,0.000027237815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047560444,0.000846769,0.0006904227,0.00042151383,0.00031944166,0.0005445098,0.0012566814,0.00061511033,0.0022194004],"category_scores_gemma":[0.002178923,0.0003276849,0.0004797246,0.00061331905,0.00034103467,0.0011231081,0.0011946873,0.0010356368,0.0011115809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005430261,0.00022702299,0.0050619533,0.0001928114,0.00013327658,0.00025466902,0.00013132498,0.44132203,0.032972444,0.0052343043,0.015173027,0.49875408],"study_design_scores_gemma":[0.000007994861,0.000020182804,0.00048743302,0.0000030512772,0.000004853454,0.000026541968,0.000016065222,0.9947903,0.0026427945,0.0012431124,0.0007509844,0.0000066435086],"about_ca_topic_score_codex":0.011313712,"about_ca_topic_score_gemma":0.019807184,"teacher_disagreement_score":0.011313712,"about_ca_system_score_codex":0.00041941355,"about_ca_system_score_gemma":0.001048322,"threshold_uncertainty_score":0.022495687},"labels":[],"label_agreement":null},{"id":"W4415883618","doi":"10.1109/tmtt.2025.3625108","title":"A Residual Neural Network Approach to Transmitter Localization","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada","keywords":"Transmitter; Residual; Artificial neural network; Overhead (engineering); SIGNAL (programming language); Scalability; Wireless; Signal processing; Point (geometry)","score_opus":0.007884101297153622,"score_gpt":0.22761794446701197,"score_spread":0.21973384316985836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415883618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058609867,0.00028400202,0.9912915,0.00014565274,0.00004861227,0.000011620452,0.00005602639,0.0005613408,0.0017403073],"genre_scores_gemma":[0.6036654,0.0008504504,0.3789041,0.00029842719,0.00022931732,0.000121166246,0.0005368365,0.00015988758,0.015234496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997216,0.00006846697,0.000012127742,0.00007836165,0.0000814036,0.000038063077],"domain_scores_gemma":[0.9997013,0.00011149367,0.000037101185,0.00003461035,0.00010206208,0.0000135264645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046604685,0.00072028115,0.00050386816,0.0005571155,0.00020154248,0.00051172497,0.0012185241,0.00085857493,0.0016481724],"category_scores_gemma":[0.0014228461,0.00028135246,0.0004005668,0.00067338656,0.00048638057,0.000855017,0.0006948509,0.0010329915,0.0007027006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011482988,0.000046148085,0.0007756508,0.00007331702,0.000059966038,0.0001006597,0.000054426786,0.81000155,0.0070272475,0.015849479,0.0024072544,0.16348943],"study_design_scores_gemma":[0.0000032381013,0.000018514032,0.00009699816,0.000003621421,0.0000057823418,0.000015875341,0.0000042768543,0.99570495,0.001189004,0.0023618867,0.0005914309,0.0000044593035],"about_ca_topic_score_codex":0.0065884455,"about_ca_topic_score_gemma":0.0053985952,"teacher_disagreement_score":0.0065884455,"about_ca_system_score_codex":0.0005373596,"about_ca_system_score_gemma":0.000622063,"threshold_uncertainty_score":0.013100207},"labels":[],"label_agreement":null},{"id":"W4415931143","doi":"10.1016/j.adhoc.2025.104089","title":"A dual-mode framework for indoor localization via temporal learning and knowledge distillation","year":2025,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Task (project management); Inference; RSS; Dynamic time warping; Selection (genetic algorithm); Multilayer perceptron; Perceptron; Pattern recognition (psychology); Task analysis","score_opus":0.005734475632000342,"score_gpt":0.25968804901972226,"score_spread":0.2539535733877219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415931143","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001747982,0.00008278372,0.9971826,0.00005011399,0.000027129023,0.000009183711,0.00003616932,0.00022476932,0.0006393374],"genre_scores_gemma":[0.4146301,0.0004552038,0.5755021,0.00022445124,0.0001765026,0.00017856475,0.0004649554,0.00015743039,0.008210763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931455,0.0001451223,0.00003272845,0.00018386725,0.00021082816,0.00011286304],"domain_scores_gemma":[0.99922717,0.00025940427,0.000060350667,0.00014884876,0.00024181881,0.00006243361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001080731,0.00079263316,0.0010520011,0.0007329736,0.0005968784,0.0012303963,0.0029147577,0.0012175327,0.003547206],"category_scores_gemma":[0.0022687756,0.0004701362,0.00079236727,0.0011107911,0.0007557548,0.002272736,0.0028650668,0.0015556901,0.0010545859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040843798,0.00025956696,0.0007558283,0.00014242725,0.00011415714,0.0001664465,0.00016098522,0.54872125,0.01082729,0.067195885,0.0049510556,0.36629668],"study_design_scores_gemma":[0.000007783608,0.000022553077,0.000046562636,0.000004313444,0.0000081074695,0.000024845485,0.000009146962,0.9899725,0.0009017605,0.00834423,0.0006502593,0.000007909622],"about_ca_topic_score_codex":0.008683084,"about_ca_topic_score_gemma":0.010990499,"teacher_disagreement_score":0.008683084,"about_ca_system_score_codex":0.00065439654,"about_ca_system_score_gemma":0.0017212026,"threshold_uncertainty_score":0.017265081},"labels":[],"label_agreement":null},{"id":"W4416420855","doi":"10.4108/eettti.10327","title":"Simultaneous Dual-Band Classification for WLAN Band Selection","year":2025,"lang":"","type":"article","venue":"EAI Endorsed Transactions on Tourism Technology and Intelligence","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Softmax function; Categorical variable; Artificial neural network; Robustness (evolution); Pattern recognition (psychology); Backpropagation; Support vector machine; Feature selection; Model selection","score_opus":0.010164207948423407,"score_gpt":0.2527409753556489,"score_spread":0.24257676740722547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416420855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3683881,0.0008543423,0.6192981,0.0005251126,0.00022846286,0.00008349036,0.0004619064,0.0033956566,0.00676494],"genre_scores_gemma":[0.9549667,0.00015012502,0.04099801,0.00011648889,0.000049255967,0.000057531823,0.00058349245,0.00006486904,0.0030134374],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995943,0.00007848044,0.00001784468,0.00009898553,0.00009165865,0.00011876905],"domain_scores_gemma":[0.9995289,0.00015631075,0.000056774425,0.0000811855,0.0001396598,0.000037316724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006347035,0.0009829464,0.0006806442,0.0009541216,0.00030619316,0.00082097313,0.0007730353,0.0007314755,0.0018055497],"category_scores_gemma":[0.0017095389,0.00020574397,0.00045531327,0.0006401446,0.00026965752,0.00092996156,0.0008741215,0.0010660173,0.0014746652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011018381,0.0005096429,0.021057796,0.000108256674,0.00009556433,0.00022286284,0.00014184586,0.26793516,0.027124465,0.0018224306,0.0059904126,0.6738897],"study_design_scores_gemma":[0.00001274712,0.000057079287,0.002909925,0.0000121423045,0.000016351274,0.00006059497,0.000048108872,0.98948646,0.005153393,0.0011502525,0.0010798251,0.000013147017],"about_ca_topic_score_codex":0.0028483071,"about_ca_topic_score_gemma":0.0034547858,"teacher_disagreement_score":0.0028483071,"about_ca_system_score_codex":0.00035744862,"about_ca_system_score_gemma":0.00052067335,"threshold_uncertainty_score":0.006040156},"labels":[],"label_agreement":null},{"id":"W4416539665","doi":"10.2139/ssrn.5786581","title":"Real-Time Delay-Compensated UWB Localization for Dynamic Agents via Deep Trajectory Prediction","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Non-line-of-sight propagation; Benchmark (surveying); Inference; Trajectory; Baseline (sea); Multipath propagation; Deep learning; Position (finance); Software deployment","score_opus":0.005874574699986233,"score_gpt":0.2316356005801495,"score_spread":0.22576102588016328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416539665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01598242,0.00011195092,0.9827604,0.00007935754,0.000034717108,0.000006648442,0.000029909275,0.0003331314,0.00066147395],"genre_scores_gemma":[0.8711372,0.0001993328,0.12516318,0.00007045444,0.000042242504,0.00003440572,0.00016123,0.00005984057,0.0031321181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980694,0.000038044735,0.000008338603,0.000056288547,0.000055351225,0.000035072022],"domain_scores_gemma":[0.99951637,0.0002187508,0.00006804457,0.0000732032,0.00009071289,0.00003299149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032326294,0.0005644966,0.0006065473,0.00023936394,0.00021962395,0.00050929387,0.00082032045,0.0006987446,0.00091342063],"category_scores_gemma":[0.0017773388,0.0003451454,0.00025711794,0.0004928389,0.0004276306,0.0009050817,0.0011173029,0.0009136921,0.00039628532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003159536,0.00004564462,0.000781432,0.00008679548,0.000034332683,0.00012897239,0.000116318006,0.8423394,0.014934743,0.0076228706,0.0013825268,0.132211],"study_design_scores_gemma":[0.0000037254215,0.000015634,0.00006467351,0.0000026804892,0.000002916105,0.00001997071,0.0000061731116,0.9967404,0.0011213018,0.0018440074,0.00017534925,0.000003171158],"about_ca_topic_score_codex":0.0034659225,"about_ca_topic_score_gemma":0.0030518074,"teacher_disagreement_score":0.0034659225,"about_ca_system_score_codex":0.00035441777,"about_ca_system_score_gemma":0.00071882294,"threshold_uncertainty_score":0.006891489},"labels":[],"label_agreement":null},{"id":"W4416965729","doi":"10.1109/jsen.2025.3637161","title":"MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach for Indoor Localization","year":2025,"lang":"","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec; Group for Research in Decision Analysis; École de Technologie Supérieure","funders":"Mitacs","keywords":"Adaptation (eye); Domain adaptation; Domain (mathematical analysis); Identification (biology); Stability (learning theory); Scheme (mathematics); Resource (disambiguation)","score_opus":0.016498630114170784,"score_gpt":0.23708900224327434,"score_spread":0.22059037212910354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416965729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005582981,0.00016335642,0.9906955,0.000109292676,0.000058687685,0.00002023393,0.00008751948,0.0025942444,0.00068825687],"genre_scores_gemma":[0.3953297,0.00035535256,0.5930533,0.0005693845,0.0002283141,0.00021937204,0.0017016084,0.0010927174,0.0074502537],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990306,0.00024666323,0.000035332992,0.00031035516,0.0002785808,0.000098513585],"domain_scores_gemma":[0.9983304,0.0005982802,0.00015042772,0.0003803517,0.00044248384,0.00009808113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011637654,0.0012747627,0.0013568521,0.0008769718,0.00052063784,0.00091434666,0.0031010115,0.0013697956,0.0020072362],"category_scores_gemma":[0.004401661,0.0006048581,0.0010750557,0.0011793352,0.00080658804,0.0020662928,0.0027781785,0.002766238,0.0016545044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003050174,0.00022917915,0.001964738,0.00014166876,0.00015919379,0.00019113603,0.00033560942,0.44674248,0.01527329,0.007848136,0.013167359,0.51364213],"study_design_scores_gemma":[0.000010830802,0.000029457882,0.00021386506,0.000005730622,0.000008529454,0.0000536306,0.00001940646,0.9920406,0.0028752096,0.0028899992,0.0018398953,0.000012786387],"about_ca_topic_score_codex":0.0052818228,"about_ca_topic_score_gemma":0.0065098708,"teacher_disagreement_score":0.0052818228,"about_ca_system_score_codex":0.00078129466,"about_ca_system_score_gemma":0.0010277432,"threshold_uncertainty_score":0.01050216},"labels":[],"label_agreement":null},{"id":"W4416997628","doi":"10.3390/ijgi14120476","title":"A Novel Smartphone PDR Framework Based on Map-Aided Adaptive Particle Filter with a Reduced State Space","year":2025,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Particle filter; Inertial measurement unit; Heading (navigation); Process (computing); Convergence (economics); Key (lock); Filter (signal processing); Dead reckoning; Trajectory; Synchronizing","score_opus":0.005360659437480644,"score_gpt":0.21722842745358803,"score_spread":0.21186776801610738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416997628","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029182194,0.00015441669,0.994693,0.000043936125,0.00005575391,0.000030123942,0.00004766198,0.0010051255,0.0010518346],"genre_scores_gemma":[0.3433836,0.0005054795,0.6483103,0.00020804036,0.00015742455,0.00026954483,0.0006813721,0.0002429271,0.006241384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960405,0.000046750334,0.00002081831,0.00011544762,0.00017067716,0.000042335494],"domain_scores_gemma":[0.99973553,0.00005954897,0.000026952453,0.000051779,0.00010452246,0.000021687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038741797,0.00094599207,0.0010762801,0.0004419437,0.0003073745,0.0007481445,0.001429354,0.00080280734,0.0023642136],"category_scores_gemma":[0.0010280228,0.00039161445,0.0008714056,0.0004158518,0.00029202027,0.000791415,0.0011077034,0.00094717444,0.00135785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027119985,0.00013623449,0.001609707,0.0003000675,0.00013605219,0.0004664187,0.00024195961,0.4971126,0.025308687,0.010664657,0.008553567,0.45519888],"study_design_scores_gemma":[0.0000160702,0.0000450193,0.00022101447,0.000005857074,0.000010389682,0.00006306173,0.000013144515,0.9946109,0.001367777,0.00090271904,0.0027313558,0.000012780283],"about_ca_topic_score_codex":0.009772149,"about_ca_topic_score_gemma":0.0072384397,"teacher_disagreement_score":0.009772149,"about_ca_system_score_codex":0.00028738048,"about_ca_system_score_gemma":0.0008793661,"threshold_uncertainty_score":0.019430518},"labels":[],"label_agreement":null},{"id":"W4417053238","doi":"10.23919/eusipco63237.2025.11226215","title":"Cross-Dual Path Attention for Concurrent CSI-Based Applications in Indoor Environments","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Task (project management); Set (abstract data type); Channel (broadcasting); Orientation (vector space); Cover (algebra); Architecture; Focus (optics); Robustness (evolution); Path (computing)","score_opus":0.010294501941560624,"score_gpt":0.2727388621017586,"score_spread":0.26244436016019795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417053238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12450928,0.0061798966,0.8410746,0.0010283211,0.0006460292,0.00021779702,0.0005272792,0.015260517,0.010556266],"genre_scores_gemma":[0.87524575,0.00088391744,0.11127173,0.000994988,0.00021783667,0.00013162909,0.0011230271,0.0002875669,0.009843542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934655,0.000092378956,0.000023986735,0.00026758027,0.000118589334,0.00015097264],"domain_scores_gemma":[0.99934906,0.00020594438,0.00004230283,0.000100552745,0.00022368059,0.00007845086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010295263,0.0016833962,0.0011864264,0.0010348049,0.0007544157,0.0009122798,0.0031250995,0.0013978233,0.0039614365],"category_scores_gemma":[0.002156051,0.00053084386,0.00066183304,0.0008979668,0.0005787092,0.002063159,0.0025893953,0.001663982,0.0016173588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006969292,0.0006043395,0.004006185,0.00022970364,0.000221215,0.00038852816,0.00029901718,0.08239607,0.028967299,0.0026248184,0.016598344,0.86296755],"study_design_scores_gemma":[0.000029347135,0.00021429591,0.0017497397,0.000021969616,0.00008028935,0.0002197958,0.00007853693,0.9792022,0.009635641,0.0048851403,0.0038521858,0.000030831594],"about_ca_topic_score_codex":0.017722871,"about_ca_topic_score_gemma":0.027444627,"teacher_disagreement_score":0.017722871,"about_ca_system_score_codex":0.0011148438,"about_ca_system_score_gemma":0.0016372341,"threshold_uncertainty_score":0.035239458},"labels":[],"label_agreement":null},{"id":"W4417131304","doi":"10.1109/jsen.2025.3637898","title":"Impact of Model Uncertainty and Sensor Deployment Geometry on the Precision of BLE RSSI-Based Indoor Positioning","year":2025,"lang":"","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec","funders":"","keywords":"Dilution of precision; Software deployment; Metering mode; Wireless sensor network; Bluetooth; Indoor positioning system; Hybrid positioning system; Received signal strength indication; Outlier; Global Positioning System","score_opus":0.016384255470066677,"score_gpt":0.2753878302757632,"score_spread":0.2590035748056965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417131304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44126412,0.0024203951,0.54950553,0.0007649169,0.00013452173,0.0000617564,0.0005921385,0.0014836201,0.0037730327],"genre_scores_gemma":[0.9806204,0.00036557982,0.018490255,0.000044491277,0.000012444402,0.000015639445,0.00024142001,0.00005189586,0.00015789404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99690706,0.0009507405,0.00021913229,0.00070199615,0.00090253266,0.00031862222],"domain_scores_gemma":[0.9815118,0.01164319,0.0019200402,0.0032556185,0.0014899197,0.00017942807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003268701,0.0012216264,0.0010902819,0.000922225,0.0005898918,0.001490869,0.0011819691,0.0009494247,0.00042221515],"category_scores_gemma":[0.025249524,0.00065329956,0.00058196904,0.0012067811,0.0012398,0.0018822226,0.0016823058,0.0008073643,0.00027352053],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018359735,0.000016217784,0.00837558,0.00009699974,0.000061820356,0.00014917096,0.000102735794,0.965888,0.003803627,0.0017284498,0.00025058768,0.019343106],"study_design_scores_gemma":[0.000028104048,0.00027017234,0.009118252,0.000048920327,0.000066409106,0.00062749593,0.00021028143,0.96854234,0.0153535195,0.00434592,0.0012988014,0.00008991254],"about_ca_topic_score_codex":0.007850703,"about_ca_topic_score_gemma":0.005540438,"teacher_disagreement_score":0.007850703,"about_ca_system_score_codex":0.0010751268,"about_ca_system_score_gemma":0.0011232471,"threshold_uncertainty_score":0.017286777},"labels":[],"label_agreement":null},{"id":"W4417131814","doi":"10.1109/ap-s/cnc-usnc-ursi55537.2025.11266807","title":"Deep Learning-Based Positioning System for Underground MIMO Communications: A CNN Approach for Complex Tunnel Environments","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Unavailability; Identification (biology); Reliability (semiconductor); Positioning system; Multipath propagation; MIMO; Convolutional neural network; SIGNAL (programming language)","score_opus":0.027327573356280516,"score_gpt":0.254884338035271,"score_spread":0.22755676467899047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417131814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045014746,0.0004198055,0.9490498,0.0002636498,0.000100276906,0.000021242806,0.00017635568,0.0010108821,0.0039432505],"genre_scores_gemma":[0.8677791,0.00046737614,0.122800864,0.00018445314,0.00007617601,0.00004815558,0.00045126316,0.000054646,0.008138027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999907,0.000010482227,0.0000044780713,0.000028364593,0.00002521089,0.000024337278],"domain_scores_gemma":[0.9998708,0.000026914746,0.000018748942,0.00001582511,0.00005711779,0.000010651339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016778035,0.0005184729,0.0003102181,0.0002769199,0.00022032323,0.00040612512,0.0007427977,0.00058703363,0.0014964752],"category_scores_gemma":[0.0005248967,0.00021883885,0.0002770181,0.00037641264,0.00020600819,0.00052990485,0.0006526697,0.000672657,0.0005533933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097812976,0.000053667674,0.0026058424,0.00006896444,0.000051572093,0.00020647496,0.00006423474,0.6636296,0.016686367,0.0049288897,0.0028958747,0.30871066],"study_design_scores_gemma":[0.0000015886519,0.000016036784,0.00028832443,0.000004017674,0.0000054019547,0.000019800218,0.000006953348,0.9969531,0.0015863182,0.00062698533,0.0004876512,0.000003874852],"about_ca_topic_score_codex":0.009750629,"about_ca_topic_score_gemma":0.012164158,"teacher_disagreement_score":0.009750629,"about_ca_system_score_codex":0.00045341605,"about_ca_system_score_gemma":0.0005914969,"threshold_uncertainty_score":0.019387782},"labels":[],"label_agreement":null},{"id":"W4417132194","doi":"10.1109/wf-iot64238.2025.11270703","title":"Enhancing LOS/NLOS Classification in UWB with Robust Feature Engineering","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mahalanobis distance; Feature extraction; Pattern recognition (psychology); Principal component analysis; Robustness (evolution); Dimensionality reduction; Outlier; Feature (linguistics); Curse of dimensionality","score_opus":0.007426909686252835,"score_gpt":0.20306949353330686,"score_spread":0.19564258384705402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417132194","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0869468,0.00052166387,0.90931666,0.00018068333,0.00009492746,0.00006598022,0.00019213402,0.0017046034,0.0009766498],"genre_scores_gemma":[0.7016557,0.0004766298,0.29423362,0.00018414723,0.00014165643,0.00014928078,0.0012540834,0.00016885185,0.0017360008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989222,0.00018617949,0.00007617988,0.0002788304,0.00041097237,0.00012567703],"domain_scores_gemma":[0.99826235,0.00072305487,0.0002439482,0.00025127485,0.00047361542,0.000045804172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001218289,0.0011663435,0.0011084393,0.0017130027,0.00044365958,0.0009835478,0.0008371115,0.0008721132,0.0006333846],"category_scores_gemma":[0.0042873104,0.00019046762,0.0008198028,0.0013242471,0.000493656,0.0012843381,0.0009336588,0.0011362014,0.0008928265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043245303,0.00037678887,0.0076145935,0.0001996469,0.000106802145,0.00032904375,0.00021832336,0.08572196,0.06576697,0.0019849555,0.0033238672,0.83392465],"study_design_scores_gemma":[0.000024250738,0.00036559184,0.008526201,0.00003412865,0.000081081096,0.00047616294,0.0001704146,0.9339893,0.04692631,0.0043624826,0.0049760453,0.00006803],"about_ca_topic_score_codex":0.0013465001,"about_ca_topic_score_gemma":0.0010993399,"teacher_disagreement_score":0.0017130027,"about_ca_system_score_codex":0.00028118136,"about_ca_system_score_gemma":0.00043288333,"threshold_uncertainty_score":0.0064430237},"labels":[],"label_agreement":null},{"id":"W4417324956","doi":"10.5194/ica-abs-10-139-2025","title":"Location-Based Services in Complex Indoor Environments via Modern Smartphone Multi-Sensor Integrated Navigation","year":2025,"lang":"en","type":"article","venue":"Abstracts of the ICA","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Key (lock); Global Positioning System; Field (mathematics); Mobile device; Bluetooth","score_opus":0.0086140880032363,"score_gpt":0.21781529371097597,"score_spread":0.20920120570773967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417324956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13706802,0.004694409,0.8366951,0.00052678847,0.00059740857,0.00009447798,0.00066171767,0.006758234,0.012903792],"genre_scores_gemma":[0.91311145,0.0012986712,0.07757028,0.00019001095,0.00016030416,0.000047575322,0.0006076704,0.00006257154,0.0069514555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964976,0.00007068831,0.00002174055,0.00007078821,0.00012342658,0.00006362733],"domain_scores_gemma":[0.9997824,0.000028107628,0.000025681748,0.00004242381,0.00010157676,0.000019947436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002119569,0.00063935044,0.0006581866,0.00048227407,0.0002589804,0.0007351706,0.00056291587,0.00055438105,0.0026094774],"category_scores_gemma":[0.00047305733,0.00018583624,0.00028477437,0.0006689162,0.00019751639,0.0008594077,0.0010353181,0.00038411937,0.0015042652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085274107,0.00017957117,0.011501958,0.0005628646,0.0001888832,0.0011444415,0.0004090076,0.028804028,0.16981997,0.008433286,0.016841123,0.76126224],"study_design_scores_gemma":[0.00009846096,0.0011474289,0.01897993,0.00019908656,0.0003521694,0.0031673599,0.0007587203,0.82981515,0.07133645,0.006140441,0.067845866,0.00015898349],"about_ca_topic_score_codex":0.0037245455,"about_ca_topic_score_gemma":0.006395471,"teacher_disagreement_score":0.0037245455,"about_ca_system_score_codex":0.00024863397,"about_ca_system_score_gemma":0.00042247606,"threshold_uncertainty_score":0.008729637},"labels":[],"label_agreement":null},{"id":"W566906973","doi":"10.1007/s12083-015-0370-y","title":"Adaptive RSSI-based localization scheme for wireless sensor networks","year":2015,"lang":"en","type":"article","venue":"Peer-to-Peer Networking and Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Wireless sensor network; Computer science; Node (physics); Received signal strength indication; Real-time computing; Channel state information; Wireless; Scheme (mathematics); Position (finance); Channel (broadcasting); Range (aeronautics); Computer network; Telecommunications; Engineering; Mathematics","score_opus":0.027853327027236887,"score_gpt":0.25590144720320956,"score_spread":0.22804812017597267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W566906973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028760904,0.0008983699,0.9648243,0.00019297522,0.00032311596,0.00007826092,0.00008727689,0.0020326788,0.0028021035],"genre_scores_gemma":[0.801389,0.0006051139,0.19087844,0.00015363563,0.00016427992,0.00013389121,0.0003055947,0.000063301246,0.0063067754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932814,0.00015807585,0.000047387315,0.00014041617,0.0002653326,0.00006074169],"domain_scores_gemma":[0.999448,0.00006610332,0.000053054966,0.00012178327,0.00028042798,0.00003062304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052468583,0.00059113366,0.00070976745,0.00064342126,0.0004912646,0.00035498908,0.0016952399,0.00053069234,0.00089595956],"category_scores_gemma":[0.00095576415,0.00020312888,0.0003225278,0.0006917749,0.0003143846,0.0007398052,0.0008103298,0.000659548,0.0007074747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091919425,0.00027154232,0.0019740982,0.00029282874,0.00013407266,0.00032570164,0.00030256936,0.10034288,0.2550057,0.009915192,0.010015292,0.6205009],"study_design_scores_gemma":[0.00012028068,0.0007121747,0.0024730565,0.000022104496,0.00012319154,0.0008533464,0.000105094616,0.91701984,0.06441523,0.003376023,0.010672876,0.00010661786],"about_ca_topic_score_codex":0.0011812769,"about_ca_topic_score_gemma":0.0017445466,"teacher_disagreement_score":0.0016952399,"about_ca_system_score_codex":0.00043356165,"about_ca_system_score_gemma":0.00046337815,"threshold_uncertainty_score":0.0031457543},"labels":[],"label_agreement":null},{"id":"W577036922","doi":"","title":"Improving facilities lifecycle management using RFID localization and BIM-based visual analytics","year":2013,"lang":"en","type":"dissertation","venue":"Espace ÉTS (ETS)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Concordia University","keywords":"Interoperability; Application lifecycle management; Building information modeling; System lifecycle; Radio-frequency identification; Process (computing); Computer science; Analytics; Interdependence; Identification (biology); Facility management; Data management; Systems engineering; Process management; Data science; Engineering; Database; World Wide Web; Software; Business; Operations management; Computer security","score_opus":0.008476915227484415,"score_gpt":0.23488349093841424,"score_spread":0.2264065757109298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W577036922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03085733,0.00069388555,0.9517005,0.0005743049,0.00005690031,0.00019314334,0.0007435035,0.0069457637,0.008234678],"genre_scores_gemma":[0.35209945,0.001266074,0.641298,0.00010736237,0.000027034135,0.00017213557,0.0021971378,0.00045792412,0.002374859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849355,0.0002697838,0.00011786127,0.00017826213,0.00081828475,0.00012224221],"domain_scores_gemma":[0.9979907,0.00037553834,0.00033794797,0.00043291156,0.0007798441,0.00008299284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017929963,0.0010284161,0.0006734847,0.0051825834,0.0005630342,0.0042395135,0.0015018951,0.00069362856,0.0028135828],"category_scores_gemma":[0.0044117444,0.0006289974,0.0012559862,0.0030498526,0.00050534814,0.005465831,0.0032403388,0.0010097149,0.0014448088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113158145,0.0002919644,0.011000839,0.000797921,0.00009097179,0.0002529478,0.0014948882,0.09277848,0.022048015,0.016073667,0.0074954843,0.8475616],"study_design_scores_gemma":[0.00004938016,0.0002872922,0.018804768,0.00068710244,0.00020464697,0.0005504394,0.0045066285,0.79134166,0.06879804,0.02368688,0.09074824,0.00033483727],"about_ca_topic_score_codex":0.0054496857,"about_ca_topic_score_gemma":0.0064315326,"teacher_disagreement_score":0.0054496857,"about_ca_system_score_codex":0.0010025826,"about_ca_system_score_gemma":0.001423189,"threshold_uncertainty_score":0.010835946},"labels":[],"label_agreement":null},{"id":"W58194215","doi":"10.1007/978-3-642-31638-8_5","title":"Uninterrupted Coverage of a Planar Region with Rotating Directional Antennae","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Planar; Orientation (vector space); Plane (geometry); Range (aeronautics); Point (geometry); Computer science; Domain (mathematical analysis); Line (geometry); Algorithm; Line segment; Physics; Geometry; Mathematics; Computer vision; Mathematical analysis; Computer graphics (images); Aerospace engineering","score_opus":0.012382884283127482,"score_gpt":0.20514382459084382,"score_spread":0.19276094030771634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W58194215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2209683,0.0013455827,0.75786656,0.00013270041,0.000097206976,0.00003500646,0.0002525482,0.000804655,0.01849749],"genre_scores_gemma":[0.9294966,0.00072907313,0.057892688,0.000048055128,0.0000425509,0.000040747353,0.00036035452,0.00007142741,0.011318521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996191,0.00007094609,0.000013570832,0.00008352248,0.00011144187,0.00010136588],"domain_scores_gemma":[0.9993869,0.00018520013,0.00009055905,0.0001993794,0.0001008261,0.000037154867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026038298,0.00062162266,0.0005571237,0.00033528268,0.00019376165,0.0004873698,0.0012268838,0.0005965335,0.0012825552],"category_scores_gemma":[0.00110414,0.00033692937,0.00040585207,0.00056405907,0.00037091857,0.0006370488,0.0010438409,0.00033109647,0.00074315805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018834291,0.00008669063,0.0023614867,0.00031590255,0.00009905046,0.0011127578,0.00022609397,0.4357176,0.24925046,0.013563119,0.0032676682,0.29211575],"study_design_scores_gemma":[0.00006783397,0.0009828255,0.004327754,0.000050179937,0.000098723736,0.002285991,0.00014628557,0.8688528,0.10086216,0.009398333,0.012861167,0.00006592494],"about_ca_topic_score_codex":0.0005383534,"about_ca_topic_score_gemma":0.00047622802,"teacher_disagreement_score":0.0012825552,"about_ca_system_score_codex":0.0002542046,"about_ca_system_score_gemma":0.00021354847,"threshold_uncertainty_score":0.004290521},"labels":[],"label_agreement":null},{"id":"W587526748","doi":"10.1017/cbo9780511978784","title":"WLAN Positioning Systems: Principles and Applications in Location-Based Services","year":2012,"lang":"en","type":"book","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto; Holland Bloorview Kids Rehabilitation Hospital","funders":"","keywords":"Computer science; Estimator; Key (lock); Hybrid positioning system; Positioning system; Parametric statistics; Estimation; Perspective (graphical); Data science; Distributed computing; Systems engineering; Engineering; Computer security; Artificial intelligence","score_opus":0.007970072952135775,"score_gpt":0.19319984721223887,"score_spread":0.1852297742601031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W587526748","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015027829,0.09751568,0.66426796,0.0035443737,0.004356749,0.0003468235,0.00094508467,0.0033303185,0.22419024],"genre_scores_gemma":[0.02693012,0.18083037,0.35432965,0.0030838149,0.0047824364,0.0006368754,0.0016879019,0.0011578752,0.426561],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99939096,0.000083884865,0.00004103878,0.000095206604,0.0003503576,0.00003862133],"domain_scores_gemma":[0.9995433,0.00019037059,0.000027602598,0.000054170563,0.0001609013,0.00002364619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054204237,0.0015163327,0.00084718346,0.001777053,0.0006468768,0.00298286,0.0014560998,0.0016420238,0.020052375],"category_scores_gemma":[0.0011490395,0.0007244984,0.0004147107,0.003462841,0.00103892,0.0035151697,0.001259747,0.0023856836,0.025388649],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003390874,0.000051000232,0.00021181972,0.0011430136,0.00002749433,0.00021002787,0.00040420005,0.00675184,0.005755361,0.19687158,0.19903183,0.5895079],"study_design_scores_gemma":[0.0000060906195,0.000050563245,0.00023706463,0.00032609477,0.000008555656,0.00053812494,0.00006339421,0.004331041,0.0008013722,0.03461242,0.95899594,0.000029287494],"about_ca_topic_score_codex":0.00088530366,"about_ca_topic_score_gemma":0.0010782906,"teacher_disagreement_score":0.020052375,"about_ca_system_score_codex":0.0006564729,"about_ca_system_score_gemma":0.0007416656,"threshold_uncertainty_score":0.06708187},"labels":[],"label_agreement":null},{"id":"W60601491","doi":"10.1007/978-3-540-72665-4_36","title":"Learning Network Topology from Simple Sensor Data","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Heuristics; Timestamp; Simple (philosophy); Set (abstract data type); Computer science; Wireless sensor network; Topology (electrical circuits); Greedy algorithm; Algorithm; Network topology; Data set; Sliding window protocol; Theoretical computer science; Window (computing); Data mining; Mathematics; Real-time computing; Artificial intelligence","score_opus":0.02662397086948446,"score_gpt":0.2547348788803867,"score_spread":0.22811090801090225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W60601491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027028508,0.00018983861,0.9702003,0.000106999,0.000033751963,0.00003845849,0.00031365966,0.0010379283,0.0010506113],"genre_scores_gemma":[0.56064874,0.00095109234,0.4307743,0.0000842376,0.00013499518,0.00018392132,0.0022938384,0.00030609252,0.0046227365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998185,0.000035852347,0.000013056284,0.00006604237,0.000053872816,0.000012637531],"domain_scores_gemma":[0.99797326,0.0012820456,0.00015010624,0.00034479605,0.00019368088,0.000056092693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046871908,0.0007958122,0.00080437213,0.0010477753,0.00022566688,0.0007299042,0.0012866216,0.0006190266,0.0019204033],"category_scores_gemma":[0.0046958867,0.0006698731,0.0005366379,0.001133723,0.00045578709,0.0024422307,0.00097631227,0.0010343485,0.0007084342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008616377,0.000039957835,0.0019638895,0.0001357602,0.000049059534,0.000078578654,0.00006875211,0.76925623,0.0029938957,0.0053015463,0.0019658739,0.21806027],"study_design_scores_gemma":[0.0000034787236,0.000015041628,0.0002630859,0.000008088799,0.0000057473,0.00003468241,0.000011224243,0.98750335,0.000761926,0.010960837,0.00042867535,0.0000039431848],"about_ca_topic_score_codex":0.0015896661,"about_ca_topic_score_gemma":0.0036270753,"teacher_disagreement_score":0.0019204033,"about_ca_system_score_codex":0.00039766944,"about_ca_system_score_gemma":0.0002796745,"threshold_uncertainty_score":0.0064243674},"labels":[],"label_agreement":null},{"id":"W626538435","doi":"","title":"Evaluation of Vehicle Positioning Accuracy by Using GPS-Enabled Smartphones","year":2014,"lang":"en","type":"article","venue":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Hybrid positioning system; Computer science; Real-time computing; Assisted GPS; Handset; Geolocation; Precision Lightweight GPS Receiver; Simulation; Positioning system; Gps receiver; Engineering; Telecommunications","score_opus":0.05755118748615521,"score_gpt":0.36246682024906135,"score_spread":0.3049156327629061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W626538435","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99169683,0.0001235027,0.0070398855,0.00002013202,0.00002839985,0.00007633588,0.00027347408,0.00014160614,0.0005997703],"genre_scores_gemma":[0.99251395,0.00009454713,0.006532325,0.000012632339,0.0000057028624,0.000042070267,0.00034375675,0.0000111001755,0.00044397466],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981926,0.00038717044,0.00016290763,0.0003088635,0.0007991234,0.00014928074],"domain_scores_gemma":[0.9947411,0.0018501137,0.0005259204,0.00048029667,0.0022638966,0.00013873802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001330882,0.0007436483,0.0005057308,0.0007978829,0.00025294526,0.00054313877,0.0007235073,0.00071561424,0.0007171244],"category_scores_gemma":[0.007032336,0.00024368225,0.0002802751,0.0006646142,0.00032603552,0.0006192579,0.00051385193,0.00025792353,0.00029438906],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0072646537,0.001687834,0.34963366,0.0016619153,0.0006821814,0.00079289475,0.0021639885,0.11319973,0.2909777,0.00063444825,0.0011122671,0.23018879],"study_design_scores_gemma":[0.00026887824,0.020835124,0.5082577,0.00013946799,0.00074151147,0.00088894775,0.0023154793,0.25687686,0.20601265,0.0002973813,0.0031274613,0.00023846272],"about_ca_topic_score_codex":0.008960377,"about_ca_topic_score_gemma":0.0114123225,"teacher_disagreement_score":0.008960377,"about_ca_system_score_codex":0.00041575267,"about_ca_system_score_gemma":0.00045498463,"threshold_uncertainty_score":0.017816424},"labels":[],"label_agreement":null},{"id":"W6887488664","doi":"10.16157/j.issn.0258-7998.245916","title":"Integrate long short-term memory networks and support vector machine for Wi-Fi indoor intrusion detection","year":2025,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Intrusion detection system; Support vector machine; Artificial neural network; Intrusion; State (computer science); SIGNAL (programming language); Anomaly-based intrusion detection system; Threshold limit value","score_opus":0.07507109205150131,"score_gpt":0.4508242444992331,"score_spread":0.3757531524477318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6887488664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05921589,0.0009351577,0.9358631,0.00022001853,0.00015910274,0.000061418476,0.0000712441,0.001820799,0.0016532154],"genre_scores_gemma":[0.807591,0.0006384565,0.18802838,0.00021231984,0.00012102489,0.0001215644,0.00027693826,0.000059667906,0.0029505505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991435,0.00016119827,0.0000667308,0.00020940475,0.00031604554,0.000103214814],"domain_scores_gemma":[0.99925596,0.00025174094,0.00007936678,0.00006607783,0.0003148539,0.000032022155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008631829,0.0011173631,0.0007320096,0.001213978,0.00034060533,0.00074548856,0.0008124867,0.0007645641,0.0007716755],"category_scores_gemma":[0.0021279885,0.00026288317,0.0006015624,0.00080157554,0.00027196138,0.0016886363,0.0006277774,0.00086335355,0.00042004453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025290286,0.0003072228,0.00960794,0.00013636374,0.00025296898,0.00015669616,0.000100599354,0.09348301,0.021273412,0.0023284745,0.0021394116,0.86996096],"study_design_scores_gemma":[0.000010307535,0.00013434673,0.0016304211,0.000009845383,0.00004554539,0.00007882547,0.000028839415,0.9860866,0.009666344,0.0013764164,0.00090953754,0.00002299169],"about_ca_topic_score_codex":0.0029342095,"about_ca_topic_score_gemma":0.0026668238,"teacher_disagreement_score":0.0029342095,"about_ca_system_score_codex":0.00043855136,"about_ca_system_score_gemma":0.00048757702,"threshold_uncertainty_score":0.0058342814},"labels":[],"label_agreement":null},{"id":"W6889051965","doi":"10.25318/3310020501-fra","title":"Importance des raisons justifiant la relocalisation des activités de services de technologies de l’information et des communications (TIC) au Canada, selon l'industrie et la taille de l’entreprise","year":2019,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Limiting; Legislation; Liberian dollar","score_opus":0.011921009741969765,"score_gpt":0.2667036516871446,"score_spread":0.25478264194517486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889051965","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77816105,0.0066880863,0.0046409513,0.0150715625,0.00013799383,0.00015960506,0.0047920346,0.00017966816,0.19016908],"genre_scores_gemma":[0.97150934,0.0020172098,0.001725426,0.0004517491,0.000037570167,0.000026650921,0.0009146263,0.000034535264,0.023282895],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947584,0.00036606172,0.00017048302,0.000562163,0.0027813874,0.0013613452],"domain_scores_gemma":[0.9848395,0.002453294,0.0025449654,0.0005105166,0.008151725,0.0015000867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025576842,0.0004274568,0.00037644638,0.003291254,0.0035132072,0.005408254,0.0009017182,0.0007089211,0.0067697736],"category_scores_gemma":[0.009746325,0.00032886412,0.00050218473,0.0058336384,0.0026653416,0.0014994207,0.0017051785,0.0017315858,0.0005757608],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038481428,0.00007990759,0.65431565,0.0010653182,0.0004061953,0.0010165003,0.04082371,0.003826575,0.0070015755,0.065465465,0.018576248,0.20703818],"study_design_scores_gemma":[0.000008605369,0.000045767825,0.8605096,0.00036469026,0.00009256126,0.00014652086,0.023350284,0.000917874,0.0019406005,0.0019515783,0.11060945,0.00006252385],"about_ca_topic_score_codex":0.94400173,"about_ca_topic_score_gemma":0.9634022,"teacher_disagreement_score":0.055998266,"about_ca_system_score_codex":0.037667066,"about_ca_system_score_gemma":0.05860861,"threshold_uncertainty_score":0.273295},"labels":[],"label_agreement":null},{"id":"W6890263353","doi":"10.34989/san-2025-9","title":"Estimating the inflation risk premium","year":2025,"lang":"en","type":"article","venue":"Bank of Canada Research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Risk premium; Inflation (cosmology); Capital asset pricing model; Asset (computer security); Real interest rate; Consumption-based capital asset pricing model; Monetary policy","score_opus":0.01354648398907924,"score_gpt":0.27794881844847247,"score_spread":0.2644023344593932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6890263353","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8617148,0.0024236292,0.1120336,0.0040434618,0.00015896147,0.000100285295,0.0040346347,0.00032807092,0.015162635],"genre_scores_gemma":[0.98819333,0.0004590065,0.007467149,0.00012769878,0.00008623235,0.000022252494,0.0012957071,0.000018740695,0.0023299763],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99914527,0.00026626672,0.000041942418,0.00017058206,0.00020287397,0.0001731291],"domain_scores_gemma":[0.99452245,0.0029650389,0.0012223789,0.00033287157,0.00069788715,0.00025924313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024444924,0.0004214915,0.0005457423,0.0010417724,0.0002459806,0.0017264878,0.00058476836,0.0011065371,0.0020619014],"category_scores_gemma":[0.015211962,0.00024707546,0.0005254819,0.001108998,0.00032592053,0.0012039159,0.0007250285,0.0013357259,0.0005909658],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003512563,0.00026778554,0.5897511,0.000101405036,0.000445671,0.0003501349,0.00034304522,0.2630517,0.0008702421,0.043752022,0.012941935,0.08777366],"study_design_scores_gemma":[0.00004242307,0.0001888476,0.13639164,0.000096140844,0.00016118305,0.00023006577,0.0004441039,0.8218401,0.0012980499,0.030576788,0.008629639,0.00010112159],"about_ca_topic_score_codex":0.048603997,"about_ca_topic_score_gemma":0.025894139,"teacher_disagreement_score":0.048603997,"about_ca_system_score_codex":0.0015062583,"about_ca_system_score_gemma":0.0013598604,"threshold_uncertainty_score":0.096642196},"labels":[],"label_agreement":null},{"id":"W6903379985","doi":"10.1109/tmc.2025.3587702","title":"A Generalizable Prompt-Based Prototypical Framework for CSI-Based Few-Shot and Cross-Domain Activity Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China; China Institute of Communications","keywords":"Activity recognition; Generalization; Representation (politics); Class (philosophy); Variety (cybernetics); Euclidean distance; Convolutional neural network; Facial recognition system; Pattern recognition (psychology)","score_opus":0.021991917191092673,"score_gpt":0.29232596668507826,"score_spread":0.2703340494939856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6903379985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055103567,0.00023403195,0.9913641,0.00007781334,0.000039785642,0.00008721232,0.00017399907,0.001809878,0.0007028831],"genre_scores_gemma":[0.39960426,0.00056442135,0.592228,0.00051036244,0.00014327496,0.00052231544,0.0015468051,0.00029530204,0.004585198],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991135,0.0001444091,0.000041785464,0.0004223705,0.00018539083,0.00009243001],"domain_scores_gemma":[0.99919814,0.00020525628,0.000088994326,0.0002042343,0.00022200673,0.00008133678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000989319,0.0011322334,0.0011327649,0.0010007767,0.0004330123,0.0008889497,0.0025965453,0.0011029746,0.002478268],"category_scores_gemma":[0.0028851384,0.00043946508,0.00087266933,0.0011182037,0.0008965471,0.0024649855,0.0019682746,0.0014327195,0.0010374577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006031757,0.00043514575,0.0031070241,0.00033628085,0.000114720984,0.0005013971,0.00040557157,0.20648193,0.039059706,0.024775084,0.0093514165,0.71482867],"study_design_scores_gemma":[0.000019537294,0.00012439232,0.0007288054,0.000011842941,0.00001630327,0.00021036205,0.000048559992,0.9781869,0.0047059967,0.012846269,0.0030755186,0.000025532445],"about_ca_topic_score_codex":0.0044034584,"about_ca_topic_score_gemma":0.0051773987,"teacher_disagreement_score":0.0044034584,"about_ca_system_score_codex":0.00075114815,"about_ca_system_score_gemma":0.0014120303,"threshold_uncertainty_score":0.008755684},"labels":[],"label_agreement":null},{"id":"W6908580677","doi":"10.26226/morressier.5c7e3e1d29d813000cb41ce6","title":"Concurrent and Divergent Validity of The Clientu2019S intervention Priorities (Cip)U00A9 Tool in a Sample of individuals With Traumatic Brain injury : Preliminary Findings On An innovative Canadian instrument","year":2017,"lang":"en","type":"other","venue":"BiblioBoard Library Catalog (Open Research Library)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Neurorehabilitation; Traumatic brain injury; Rehabilitation; Intervention (counseling); Acquired brain injury; Scale (ratio); Concurrent validity; Autonomy; Interpersonal communication; Sample (material)","score_opus":0.07296802279505239,"score_gpt":0.32617815452888643,"score_spread":0.25321013173383405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6908580677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99694675,0.00009989604,0.00030220818,0.00008112387,0.000015398518,0.00043565314,0.000350538,0.0000065878403,0.0017619361],"genre_scores_gemma":[0.99515486,0.0001939083,0.0020813625,0.000085827625,0.000009316319,0.0009447304,0.0007925274,0.000009085123,0.0007284058],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9925168,0.0019286907,0.00084529974,0.00069471146,0.0031846715,0.0008298891],"domain_scores_gemma":[0.9868774,0.0030934203,0.0020905116,0.0005304815,0.0063566393,0.00105149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012134583,0.0006790267,0.00067473494,0.0030669519,0.0029158206,0.0020126214,0.0018109332,0.00071305665,0.0014995122],"category_scores_gemma":[0.02105195,0.00070025254,0.0012196312,0.003073904,0.0019241506,0.0008496293,0.0032989383,0.0011788236,0.00023016633],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026190517,0.00049369904,0.9621228,0.00013519036,0.00013956266,0.00007800703,0.011148999,0.00017868406,0.000419709,0.00025219694,0.00065227464,0.024117066],"study_design_scores_gemma":[0.000051326053,0.000299522,0.9897878,0.0000753028,0.000042353793,0.000072211864,0.007483339,0.0006810916,0.00022651388,0.00012568272,0.0011218352,0.000032999982],"about_ca_topic_score_codex":0.4458285,"about_ca_topic_score_gemma":0.5502736,"teacher_disagreement_score":0.5541715,"about_ca_system_score_codex":0.007574097,"about_ca_system_score_gemma":0.012414399,"threshold_uncertainty_score":0.886467},"labels":[],"label_agreement":null},{"id":"W6929453562","doi":"10.48550/arxiv.2508.06053","title":"ReNiL: Event-Driven Pedestrian Bayesian Localization Using IMU for Real-World Applications","year":2025,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Robustness (evolution); Bayesian probability; Bayesian inference; Inference; Probabilistic logic; Estimator; Particle filter; Pedestrian","score_opus":0.05372568298798028,"score_gpt":0.2220143992598297,"score_spread":0.1682887162718494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929453562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014033529,0.00042419395,0.97323847,0.00019992361,0.00008388126,0.000048298443,0.0009171389,0.009269794,0.0017847331],"genre_scores_gemma":[0.5854658,0.0005290957,0.3989711,0.00038390962,0.00013570902,0.00021911717,0.006519605,0.0008382295,0.0069373897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995054,0.0001057662,0.000017465169,0.00016528695,0.00013614676,0.0000697795],"domain_scores_gemma":[0.99950397,0.000119264514,0.00006363043,0.00012098414,0.00014282891,0.000049297687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008224996,0.001115136,0.00086781144,0.0008962909,0.00030835895,0.00069877785,0.0022541005,0.0008768849,0.0027339163],"category_scores_gemma":[0.0028733243,0.00056641607,0.0005969762,0.0008990163,0.00046668257,0.0015518684,0.002162076,0.0011341133,0.0017610044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000570695,0.0002798273,0.0075162277,0.00027781908,0.00019655105,0.00024605225,0.00021328853,0.52773446,0.0077063264,0.012325134,0.03451979,0.40841386],"study_design_scores_gemma":[0.000014572208,0.000026277172,0.0005042403,0.00001135504,0.00000887244,0.00003152061,0.000013143003,0.99185866,0.0015643259,0.0035086602,0.0024477313,0.000010616249],"about_ca_topic_score_codex":0.013107687,"about_ca_topic_score_gemma":0.023774665,"teacher_disagreement_score":0.013107687,"about_ca_system_score_codex":0.00079757086,"about_ca_system_score_gemma":0.0010271261,"threshold_uncertainty_score":0.026062787},"labels":[],"label_agreement":null},{"id":"W6957766624","doi":"10.60692/4bhhr-3t646","title":"Joint injection practices in Pediatric Rheumatology - A global survey","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Rheumatology; MEDLINE; Arthritis; Juvenile; Clinical Practice","score_opus":0.04929591769685174,"score_gpt":0.22056647887206077,"score_spread":0.17127056117520903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957766624","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9928093,0.0012238098,0.00012786846,0.00064704905,0.000010878204,0.000025505886,0.002965609,0.000016362232,0.0021736303],"genre_scores_gemma":[0.99752516,0.0010903146,0.00014240632,0.00020087816,0.000011121866,0.00002702223,0.0008308115,0.000003740674,0.00016852695],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987437,0.00030526103,0.00022435734,0.00017531571,0.00037844077,0.00017294897],"domain_scores_gemma":[0.99677783,0.00044610887,0.00195514,0.00008978089,0.00036698594,0.00036419168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090265996,0.0001531953,0.00024828193,0.0010862786,0.000205847,0.0005558698,0.00027277105,0.00036753103,0.0021145479],"category_scores_gemma":[0.0023707377,0.00016862991,0.0002527124,0.002505692,0.00029477887,0.0007136117,0.0005424504,0.0004194891,0.00042889576],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009743178,0.000015097785,0.99546623,0.00005938567,0.000015475138,0.00006530157,0.0003965665,0.00002875935,0.00006711026,0.00003480051,0.00064808683,0.0031934285],"study_design_scores_gemma":[0.0000013886948,0.0000348391,0.99766845,0.00003630731,0.0000059191543,0.00031046272,0.000988255,0.000045911187,0.00003521066,0.000008075924,0.00086311105,0.0000020383372],"about_ca_topic_score_codex":0.0050318977,"about_ca_topic_score_gemma":0.0057180454,"teacher_disagreement_score":0.0050318977,"about_ca_system_score_codex":0.00052332727,"about_ca_system_score_gemma":0.0005783717,"threshold_uncertainty_score":0.010005176},"labels":[],"label_agreement":null},{"id":"W6976796860","doi":"10.60692/gev6t-z7q76","title":"Guest Editorial: Special issue on explainable AI empowered for indoor positioning and indoor navigation","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wireless sensor network; Global Positioning System; Dead reckoning; Wireless ad hoc network; Hybrid positioning system; Sensor fusion; Scheme (mathematics); Wireless network; Internet of Things","score_opus":0.01259339802170407,"score_gpt":0.21190502907258232,"score_spread":0.19931163105087826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976796860","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000089230816,0.005706744,0.00030271843,0.036403075,0.9544966,0.000017803817,0.0000787867,0.00008782941,0.0028171358],"genre_scores_gemma":[0.0006811343,0.004566424,0.0001330308,0.012635377,0.9698961,0.000020768972,0.000058065554,0.00006670372,0.011942307],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997752,0.0002957789,0.00019814639,0.00048377944,0.0009991607,0.00027110998],"domain_scores_gemma":[0.9923699,0.0025147474,0.0004755631,0.00025439257,0.0028782326,0.0015071112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027744912,0.0025279147,0.0017338408,0.0020493641,0.001687884,0.006818524,0.0027208664,0.009126289,0.045868155],"category_scores_gemma":[0.010265191,0.0007008938,0.0021601534,0.0007831738,0.0018557478,0.0037600335,0.0019131383,0.011794688,0.017711278],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004130068,0.000013473458,0.000036885514,0.00021146522,0.000011611149,0.0001022618,0.00001677962,0.000029707675,0.00012746413,0.00061744906,0.99207664,0.0067150174],"study_design_scores_gemma":[0.000028515984,0.000029921996,0.0001702021,0.00021224683,0.000022952843,0.00022147347,0.00003908106,0.00011684563,0.00015182071,0.0009371478,0.99805754,0.000012204425],"about_ca_topic_score_codex":0.0008511734,"about_ca_topic_score_gemma":0.0020010779,"teacher_disagreement_score":0.045868155,"about_ca_system_score_codex":0.0021071273,"about_ca_system_score_gemma":0.0015492071,"threshold_uncertainty_score":0.15344429},"labels":[],"label_agreement":null},{"id":"W6977205467","doi":"10.60692/a0p2b-p5e39","title":"Guest Editorial: Special issue on explainable AI empowered for indoor positioning and indoor navigation","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wireless sensor network; Global Positioning System; Dead reckoning; Wireless ad hoc network; Hybrid positioning system; Sensor fusion; Scheme (mathematics); Wireless network; Internet of Things","score_opus":0.01259339802170407,"score_gpt":0.21190502907258232,"score_spread":0.19931163105087826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977205467","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000089230816,0.005706744,0.00030271843,0.036403075,0.9544966,0.000017803817,0.0000787867,0.00008782941,0.0028171358],"genre_scores_gemma":[0.0006811343,0.004566424,0.0001330308,0.012635377,0.9698961,0.000020768972,0.000058065554,0.00006670372,0.011942307],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997752,0.0002957789,0.00019814639,0.00048377944,0.0009991607,0.00027110998],"domain_scores_gemma":[0.9923699,0.0025147474,0.0004755631,0.00025439257,0.0028782326,0.0015071112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027744912,0.0025279147,0.0017338408,0.0020493641,0.001687884,0.006818524,0.0027208664,0.009126289,0.045868155],"category_scores_gemma":[0.010265191,0.0007008938,0.0021601534,0.0007831738,0.0018557478,0.0037600335,0.0019131383,0.011794688,0.017711278],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004130068,0.000013473458,0.000036885514,0.00021146522,0.000011611149,0.0001022618,0.00001677962,0.000029707675,0.00012746413,0.00061744906,0.99207664,0.0067150174],"study_design_scores_gemma":[0.000028515984,0.000029921996,0.0001702021,0.00021224683,0.000022952843,0.00022147347,0.00003908106,0.00011684563,0.00015182071,0.0009371478,0.99805754,0.000012204425],"about_ca_topic_score_codex":0.0008511734,"about_ca_topic_score_gemma":0.0020010779,"teacher_disagreement_score":0.045868155,"about_ca_system_score_codex":0.0021071273,"about_ca_system_score_gemma":0.0015492071,"threshold_uncertainty_score":0.15344429},"labels":[],"label_agreement":null},{"id":"W6977456748","doi":"10.6084/m9.figshare.26680981","title":"Additional file 1 of The role of vaccine status homophily in the COVID-19 pandemic: a cross-sectional survey with modelling","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Homophily; File sharing; Data collection; Survey data collection","score_opus":0.047735273395073725,"score_gpt":0.25952631188893494,"score_spread":0.21179103849386122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977456748","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008971759,0.000027405003,0.0005285962,0.00013162821,0.00001924907,0.00010539108,0.996752,0.00010189926,0.0014366099],"genre_scores_gemma":[0.08008312,0.00037593275,0.009782328,0.0007349113,0.00013777505,0.0057482696,0.87606573,0.0008759147,0.026195986],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990577,0.00033116265,0.0001447409,0.00020088692,0.00015028125,0.000115264855],"domain_scores_gemma":[0.9788634,0.017473396,0.0011148887,0.00085179246,0.0014054175,0.0002911283],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019028472,0.0008205766,0.0008927601,0.0015402356,0.00067495654,0.0009429456,0.0017336368,0.0010798476,0.7680628],"category_scores_gemma":[0.034214333,0.00044410853,0.000991494,0.0035361517,0.00021231815,0.0016446414,0.00082027516,0.0011261152,0.076135665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029353905,0.000120074874,0.014920137,0.0021775498,0.000103682134,0.00009089002,0.0001776861,0.0018595664,0.000034081484,0.0018679888,0.96773547,0.010619263],"study_design_scores_gemma":[0.006839044,0.00084467465,0.16650578,0.010957748,0.0010788089,0.0012974562,0.0028750903,0.020278525,0.0006667617,0.022833785,0.76554453,0.00027785593],"about_ca_topic_score_codex":0.021310257,"about_ca_topic_score_gemma":0.019884152,"teacher_disagreement_score":0.7680628,"about_ca_system_score_codex":0.00079313107,"about_ca_system_score_gemma":0.0012953726,"threshold_uncertainty_score":0.33083028},"labels":[],"label_agreement":null},{"id":"W6987239544","doi":"","title":"Sequential Monte Carlo radio-frequency tomographic tracking","year":2011,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Tracking (education); Particle filter; Tracking system; Monte Carlo method; Wireless sensor network; Ranging; Position (finance); Radar tracker","score_opus":0.01661175668587332,"score_gpt":0.2146097807000858,"score_spread":0.1979980240142125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987239544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015616416,0.00012893646,0.98244184,0.00006544235,0.00002598761,0.00004355536,0.00002605526,0.00027598962,0.0013759185],"genre_scores_gemma":[0.608318,0.0002931108,0.38716117,0.00011819582,0.000049030794,0.00024553115,0.0002322847,0.000073843825,0.0035088547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956685,0.00010812037,0.000020940748,0.000090339585,0.00017659938,0.000037110207],"domain_scores_gemma":[0.99830675,0.0011048197,0.00014492328,0.00012137236,0.00027065913,0.000051453786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007584947,0.0005516386,0.0008632442,0.00047256335,0.00038951845,0.0006745054,0.0011212155,0.00092022767,0.0012890643],"category_scores_gemma":[0.0030054,0.00040365368,0.0006201829,0.00059052015,0.0006679886,0.00055459834,0.0005192452,0.00055707194,0.00028685873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007575785,0.000024086992,0.0009573134,0.00003756593,0.000024375873,0.000050045484,0.00002886307,0.9689173,0.0016084511,0.0033484895,0.00031131922,0.024616385],"study_design_scores_gemma":[0.0000034036905,0.0000063230227,0.000053675005,0.0000012670791,0.0000019617346,0.000010617698,0.0000013871913,0.9990528,0.00028415152,0.00044538133,0.00013729378,0.0000016738467],"about_ca_topic_score_codex":0.008915811,"about_ca_topic_score_gemma":0.0081685055,"teacher_disagreement_score":0.008915811,"about_ca_system_score_codex":0.0007238059,"about_ca_system_score_gemma":0.00095206563,"threshold_uncertainty_score":0.017727852},"labels":[],"label_agreement":null},{"id":"W7021047740","doi":"","title":"Network-based RF localization in adverse propagation environments","year":2015,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Non-line-of-sight propagation; Global Positioning System; Base station; Kalman filter; Time of arrival; Wireless; Filter (signal processing); RSS; Angle of arrival; Radio propagation","score_opus":0.011464525013131784,"score_gpt":0.2123540834197217,"score_spread":0.20088955840658992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7021047740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015421255,0.00042081074,0.98155606,0.00010057071,0.00003162094,0.000023136512,0.000035710662,0.00021024462,0.0022005925],"genre_scores_gemma":[0.7881518,0.003226439,0.20048216,0.00010715672,0.00012867234,0.00015933844,0.00022882872,0.00008607313,0.00742941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994887,0.0001324289,0.000021447078,0.00015544733,0.00015246427,0.000049453123],"domain_scores_gemma":[0.9993266,0.0003245153,0.00013961118,0.00006881769,0.0001231356,0.000017274117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062791415,0.0006864896,0.0006216615,0.0004926879,0.00031493028,0.0006743293,0.0008265363,0.0006782299,0.00073495274],"category_scores_gemma":[0.0019899814,0.0002549245,0.00046521326,0.0005743511,0.00081568764,0.0012349085,0.0013893057,0.0004912014,0.00045641052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000528699,0.000018084213,0.0014884458,0.00014972713,0.000031269636,0.00021424341,0.00014115457,0.92354345,0.011753984,0.014170527,0.00079883676,0.04763746],"study_design_scores_gemma":[0.000008588693,0.00006564159,0.00080415944,0.000016674485,0.000014807699,0.0001675437,0.000052296968,0.98640984,0.0035059652,0.0059638475,0.0029743533,0.000016195932],"about_ca_topic_score_codex":0.0028756368,"about_ca_topic_score_gemma":0.0019105516,"teacher_disagreement_score":0.0028756368,"about_ca_system_score_codex":0.0004356864,"about_ca_system_score_gemma":0.00044640453,"threshold_uncertainty_score":0.0057177544},"labels":[],"label_agreement":null},{"id":"W7022183528","doi":"","title":"Toledo, Ohio","year":2009,"lang":"en","type":"other","venue":"OhioLink ETD Center (Ohio Library and Information Network)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Christianity; Government (linguistics); George (robot)","score_opus":0.005197951904369588,"score_gpt":0.17853042970269514,"score_spread":0.17333247779832556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7022183528","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014945945,0.0008864825,0.0004939536,0.00079637126,0.0010823136,0.000063990134,0.0042705047,0.00039692063,0.9905149],"genre_scores_gemma":[0.004102287,0.00069007906,0.000417268,0.00023099767,0.00009719862,0.00004562673,0.0015157189,0.00020836844,0.9926925],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993874,0.00003875837,0.000021754628,0.00025311942,0.00020069964,0.00009818946],"domain_scores_gemma":[0.999368,0.00009337492,0.000037965776,0.00006824553,0.00021731842,0.00021502517],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00039153793,0.0016455263,0.0006406933,0.0011075484,0.0040359,0.0047448687,0.0010315998,0.0014831313,0.81021756],"category_scores_gemma":[0.0009581415,0.0005239165,0.000523733,0.0015072217,0.0007045883,0.0019256372,0.002051088,0.00225362,0.5317926],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022734399,0.00030627652,0.002083924,0.0002158386,0.000014629469,0.0005278113,0.00035205006,0.00020757804,0.0013065313,0.009196239,0.80182654,0.1837353],"study_design_scores_gemma":[0.00002892198,0.000028542288,0.0020449443,0.00010247701,0.0000047711005,0.000091313545,0.00026417337,0.00009256163,0.00018331311,0.0005698317,0.9965803,0.000008768202],"about_ca_topic_score_codex":0.012774856,"about_ca_topic_score_gemma":0.043204166,"teacher_disagreement_score":0.18978244,"about_ca_system_score_codex":0.0014293193,"about_ca_system_score_gemma":0.002167067,"threshold_uncertainty_score":0.27070165},"labels":[],"label_agreement":null},{"id":"W7036027252","doi":"","title":"Appendix : Coding of Election Studies and Commercial Polls [Book Supplement]","year":2014,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coding (social sciences); Table (database); General election; Life table","score_opus":0.007753701905759262,"score_gpt":0.18389882071240823,"score_spread":0.17614511880664896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036027252","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012138145,0.00014849678,0.003820114,0.00091136113,0.0006890056,0.003988254,0.9100155,0.0010876153,0.07812575],"genre_scores_gemma":[0.009004105,0.0007171609,0.023762792,0.0013163816,0.00046926062,0.019446645,0.79399866,0.001586729,0.14969826],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970548,0.00066041684,0.0005868085,0.0002731173,0.0011736152,0.00025123387],"domain_scores_gemma":[0.9527764,0.018428324,0.0021406217,0.002860626,0.022767795,0.0010263404],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026718446,0.0007791563,0.00072171167,0.007603091,0.0018757061,0.0015489358,0.0014362725,0.0006903885,0.5361273],"category_scores_gemma":[0.03437103,0.00069188065,0.00035397624,0.015015375,0.00046988664,0.0015314252,0.0013938494,0.001287999,0.24345104],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012992088,0.000019853882,0.0003303828,0.00015002562,9.541557e-7,0.000009135742,0.00011333755,0.000031815798,0.000036586243,0.00078227493,0.985422,0.013090718],"study_design_scores_gemma":[0.000028207993,0.000015508027,0.008596735,0.00028402498,0.000003098974,0.00005009611,0.00079890183,0.00014596655,0.00014162684,0.0018240444,0.9880889,0.000022903932],"about_ca_topic_score_codex":0.044568032,"about_ca_topic_score_gemma":0.06627442,"teacher_disagreement_score":0.5361273,"about_ca_system_score_codex":0.0030590857,"about_ca_system_score_gemma":0.0050368374,"threshold_uncertainty_score":0.66165805},"labels":[],"label_agreement":null},{"id":"W7037156570","doi":"","title":"Device-free passive indoor localization based on Wi-Fi channel state information for long-term monitoring","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"State (computer science); Channel (broadcasting); Noise (video); Key (lock); Signal processing","score_opus":0.011884391250632766,"score_gpt":0.227022522621079,"score_spread":0.21513813137044624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7037156570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19839637,0.0008804478,0.7888518,0.00016004263,0.00015070976,0.000052629493,0.0002942241,0.0021490534,0.00906469],"genre_scores_gemma":[0.936828,0.00041183783,0.055652104,0.000045551045,0.000044578515,0.00005628823,0.0003805085,0.00003704418,0.006544171],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998641,0.000016827547,0.0000040260215,0.000041058916,0.000049201626,0.000024694707],"domain_scores_gemma":[0.999864,0.000037042482,0.000019354651,0.000023839864,0.00004723968,0.000008641896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012629163,0.0003258475,0.00033493317,0.00029894552,0.00019617852,0.0003334609,0.0004193076,0.00021539278,0.0014585457],"category_scores_gemma":[0.00031894655,0.00016930788,0.0002235907,0.00039964376,0.00012786094,0.0005629446,0.00043039257,0.0002644478,0.0007320854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056820724,0.00019152531,0.008000437,0.00020148278,0.00009090959,0.00020413211,0.00020946897,0.03300549,0.30587035,0.0030664192,0.0059182243,0.64267343],"study_design_scores_gemma":[0.00006942022,0.0005856044,0.04257859,0.000047402275,0.00031218235,0.0005135372,0.00017884467,0.7463303,0.19565862,0.002363238,0.011268181,0.000094094554],"about_ca_topic_score_codex":0.0012216177,"about_ca_topic_score_gemma":0.0029259776,"teacher_disagreement_score":0.0014585457,"about_ca_system_score_codex":0.00013960438,"about_ca_system_score_gemma":0.0002783288,"threshold_uncertainty_score":0.0048793554},"labels":[],"label_agreement":null},{"id":"W7083464103","doi":"10.1109/tase.2025.3614757","title":"Securing Grid-Connected Packed E-Cell Multilevel Inverter: A LSTM-AE Approach to Hybrid Attack Mitigation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Microgrid; Resilience (materials science); Observer (physics); Software deployment; Inverter; Electric power system; Power-system protection; Power (physics); Scheme (mathematics)","score_opus":0.00910251424767103,"score_gpt":0.2162214686319165,"score_spread":0.20711895438424546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083464103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03387201,0.000252396,0.962291,0.00018624168,0.00005024386,0.000038081176,0.00001549307,0.00059582957,0.0026986813],"genre_scores_gemma":[0.90628535,0.00023307769,0.09084018,0.0001426502,0.000041557545,0.00005258905,0.000034024284,0.000015794809,0.0023546515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998585,0.00002391278,0.000011549303,0.000037245358,0.000051020445,0.000017701916],"domain_scores_gemma":[0.9998697,0.00003206866,0.000030517716,0.00001821179,0.00004249295,0.0000069550197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020202501,0.00035543353,0.0003045645,0.00018849832,0.0001750839,0.00038837423,0.000564805,0.0006079444,0.0008482049],"category_scores_gemma":[0.00041109262,0.00011754032,0.00027195516,0.00014183499,0.00023456171,0.00059600093,0.0004930663,0.0004320131,0.00022477699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025583684,0.00013372823,0.0018382812,0.00021676447,0.000110648594,0.00039132193,0.00022899125,0.31162044,0.18522663,0.014221026,0.0016288388,0.48412755],"study_design_scores_gemma":[0.0000072263915,0.00011961912,0.0002753585,0.0000094729485,0.000016648517,0.000072789335,0.000013585714,0.98243284,0.014760303,0.0012184039,0.0010675464,0.000006178102],"about_ca_topic_score_codex":0.0007293483,"about_ca_topic_score_gemma":0.0008440233,"teacher_disagreement_score":0.0008482049,"about_ca_system_score_codex":0.00019582962,"about_ca_system_score_gemma":0.0001907504,"threshold_uncertainty_score":0.0028375387},"labels":[],"label_agreement":null},{"id":"W7095522030","doi":"","title":"Author manuscript, published in &amp;quot;IEEE Workshop on Mobile Entity Localization and Tracking, Canada (2011)&amp;quot; Entity Localization and Tracking: A Sensor Fusion-based Mechanism in WSNs","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kalman filter; Wireless sensor network; Sensor fusion; Position (finance); Trajectory; Inertial measurement unit; Simple (philosophy); Scheme (mathematics); Filter (signal processing)","score_opus":0.024255328122684962,"score_gpt":0.23945695309480938,"score_spread":0.21520162497212442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095522030","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015873905,0.020954406,0.12922291,0.04920462,0.25721738,0.0011056014,0.013875122,0.0063830274,0.506163],"genre_scores_gemma":[0.024364628,0.005910254,0.018418394,0.0011576775,0.0050033475,0.00007617266,0.004583459,0.0010874824,0.93939865],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998953,0.00012364936,0.000070414055,0.00028568733,0.00039365244,0.00017356357],"domain_scores_gemma":[0.99466324,0.0005145028,0.00011065179,0.0004692444,0.0036834946,0.0005589191],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018644228,0.0009466896,0.0016139345,0.001642958,0.0020239295,0.0054870835,0.0016479746,0.0018735104,0.3273835],"category_scores_gemma":[0.005740514,0.0006467051,0.00069702003,0.002520616,0.0010440552,0.0026812444,0.0014699559,0.0011092648,0.10796157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005427787,0.000093577415,0.0018186307,0.00043305222,0.0001067551,0.0004107073,0.0001649675,0.0021774373,0.0036081218,0.007928589,0.77187294,0.21084234],"study_design_scores_gemma":[0.000049810875,0.0001041283,0.0014937883,0.00015219249,0.000042251046,0.00029448472,0.0002271276,0.0057144463,0.0037368366,0.0024449928,0.9856961,0.000043773038],"about_ca_topic_score_codex":0.026651878,"about_ca_topic_score_gemma":0.058012433,"teacher_disagreement_score":0.3273835,"about_ca_system_score_codex":0.0023620662,"about_ca_system_score_gemma":0.0034054047,"threshold_uncertainty_score":0.95940584},"labels":[],"label_agreement":null},{"id":"W7096170230","doi":"","title":"UNIVERSITY OF CALGARY Performance Evaluation of Localization Techniques for Wireless Sensor Networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Multilateration; Wireless sensor network; Beamforming; Microphone; Wearable computer; Node (physics); Network packet; Key distribution in wireless sensor networks; Sensor node","score_opus":0.01090934046055507,"score_gpt":0.2014524758417026,"score_spread":0.19054313538114753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096170230","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6069722,0.019536022,0.21605165,0.0029614726,0.0014688873,0.0008816733,0.0026087353,0.0042194603,0.14529993],"genre_scores_gemma":[0.88816357,0.005612501,0.077955306,0.00018369034,0.00017040015,0.00025169272,0.003002766,0.00019956994,0.024460487],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972844,0.0006331146,0.00014368811,0.0003135795,0.0013154232,0.00030967031],"domain_scores_gemma":[0.99158174,0.0033898216,0.00027548426,0.00058736745,0.003933477,0.00023194603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027740914,0.0006586745,0.000682618,0.0011570101,0.0006639516,0.0011280532,0.000854633,0.0005546922,0.004878808],"category_scores_gemma":[0.009392133,0.00014512659,0.00023296164,0.0023857732,0.00027467692,0.0007229914,0.0004973282,0.00045056074,0.0013439641],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001299968,0.00029988703,0.012937501,0.0007697077,0.00013159572,0.00023940223,0.00019484863,0.30899692,0.01885158,0.006553609,0.02434025,0.62538475],"study_design_scores_gemma":[0.00013228306,0.0019631262,0.024468023,0.00024220819,0.00008189746,0.0003403684,0.00038648947,0.9188314,0.025165206,0.0019275444,0.026404474,0.00005702509],"about_ca_topic_score_codex":0.028545612,"about_ca_topic_score_gemma":0.030321186,"teacher_disagreement_score":0.028545612,"about_ca_system_score_codex":0.002889264,"about_ca_system_score_gemma":0.0011907314,"threshold_uncertainty_score":0.05675894},"labels":[],"label_agreement":null},{"id":"W7099317862","doi":"","title":"TRANSGENIC SALMON FOR CULTURE AND CONSUMPTION","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transgene; Antifreeze protein; Gene; Consumption (sociology); Genetically modified organism; Genetically modified mouse; Fish <Actinopterygii>","score_opus":0.03093267385419443,"score_gpt":0.2439333518102763,"score_spread":0.21300067795608188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099317862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18711726,0.012198201,0.47673917,0.002721509,0.0041418266,0.013197446,0.0844245,0.011593256,0.20786689],"genre_scores_gemma":[0.16419646,0.0098390635,0.31882134,0.0016616867,0.00037320232,0.008950691,0.10483602,0.003973614,0.38734788],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990246,0.00009619204,0.000115387746,0.00021004865,0.00042125492,0.00013249333],"domain_scores_gemma":[0.999345,0.000058741276,0.000054692253,0.0001606955,0.0003072796,0.00007365699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009299745,0.0009079016,0.00083116535,0.0008128459,0.0009451849,0.0009463784,0.0014516646,0.0009589466,0.024355404],"category_scores_gemma":[0.0006402386,0.00048804015,0.0010124925,0.0009246177,0.0004112792,0.00062663504,0.0010923829,0.002196838,0.0327195],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001859638,0.00016050172,0.0005908601,0.00038725854,0.000024147863,0.0003427694,0.00019467848,0.00023203173,0.96024233,0.0012451641,0.011258775,0.025135469],"study_design_scores_gemma":[0.00016546178,0.0015602516,0.0062120464,0.00036787664,0.00017453611,0.0016294066,0.00028911396,0.0018721893,0.54113036,0.0014325608,0.4450342,0.00013193536],"about_ca_topic_score_codex":0.0040625986,"about_ca_topic_score_gemma":0.011432434,"teacher_disagreement_score":0.024355404,"about_ca_system_score_codex":0.00060891075,"about_ca_system_score_gemma":0.0008823898,"threshold_uncertainty_score":0.08147699},"labels":[],"label_agreement":null},{"id":"W7109972496","doi":"10.1109/ap-s/cnc-usnc-ursi55537.2025.11266851","title":"Enhanced Distance Estimation in Wireless Sensor Networks Using an Extended Path Loss Model for Underground Environments","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Wireless sensor network; Path loss; Reliability (semiconductor); Logarithm; Path (computing); Received signal strength indication; Radio propagation model; Wireless; Attenuation","score_opus":0.014645745601566191,"score_gpt":0.2571014786706519,"score_spread":0.24245573306908572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7109972496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031315707,0.00019867423,0.96737415,0.000098487166,0.000022159327,0.000014973602,0.000044485856,0.0002417809,0.0006895731],"genre_scores_gemma":[0.90014064,0.0006949776,0.096612476,0.000078684796,0.00003429498,0.00007352767,0.00018285554,0.000086480504,0.0020959922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953485,0.00014628636,0.000019910782,0.00010033453,0.00015912173,0.000039619903],"domain_scores_gemma":[0.9992818,0.00033212418,0.00011521059,0.00009971663,0.00015019246,0.000020981328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005859955,0.0007474804,0.00045137818,0.00040790232,0.0001994602,0.0005372505,0.0011264817,0.00063245115,0.00052542606],"category_scores_gemma":[0.0022977588,0.00027708255,0.00049848814,0.00060187007,0.0005082422,0.0015646092,0.00094351056,0.0008034831,0.0002895805],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029697883,0.000019298777,0.0006323413,0.000033554563,0.000011670545,0.000048395166,0.000036165955,0.9780692,0.0042179087,0.0023561246,0.00019320754,0.01435254],"study_design_scores_gemma":[0.0000017600498,0.000025358364,0.00016806195,0.0000027259664,0.0000035780624,0.00002790261,0.0000063719162,0.9980749,0.00058974023,0.0008451672,0.0002490958,0.0000053202098],"about_ca_topic_score_codex":0.0031124759,"about_ca_topic_score_gemma":0.002656807,"teacher_disagreement_score":0.0031124759,"about_ca_system_score_codex":0.0005110094,"about_ca_system_score_gemma":0.00058671756,"threshold_uncertainty_score":0.0061886907},"labels":[],"label_agreement":null},{"id":"W7115039648","doi":"","title":"A hybrid pedestrian dead reckoning and Bluetooth positioning framework for accurate indoor localization","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Dead reckoning; Pedestrian; Bluetooth; Key (lock); Global Positioning System; Hybrid positioning system","score_opus":0.01405109874005291,"score_gpt":0.24467772835535356,"score_spread":0.23062662961530064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115039648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008909427,0.00022059906,0.98767227,0.00002312854,0.00003649215,0.000035500318,0.000066981345,0.0022363067,0.0007993174],"genre_scores_gemma":[0.44494477,0.000501316,0.54949665,0.00007936556,0.00006003089,0.00017780984,0.0005777182,0.00012529375,0.0040370096],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997304,0.000033022545,0.000013901213,0.000055849323,0.00012281058,0.000044023483],"domain_scores_gemma":[0.99988353,0.000014125426,0.000015229824,0.000022576609,0.000051514366,0.000012981742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029069165,0.0005340391,0.00054469355,0.0006158239,0.0002702353,0.00046396354,0.0011886089,0.0003693575,0.0010142236],"category_scores_gemma":[0.0004326816,0.00028691234,0.0005505224,0.0005081752,0.0001640365,0.00069330574,0.00094095635,0.0004308856,0.0006291527],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028297934,0.0001641964,0.0036069716,0.00025973018,0.00014099875,0.00067488424,0.00045347845,0.1408737,0.058721982,0.012363183,0.007393759,0.7750641],"study_design_scores_gemma":[0.000040944098,0.0002869353,0.002636029,0.000033176995,0.00006775818,0.0005680196,0.00009242845,0.9641022,0.0140336435,0.0025716056,0.015494524,0.00007273344],"about_ca_topic_score_codex":0.005734038,"about_ca_topic_score_gemma":0.006812537,"teacher_disagreement_score":0.005734038,"about_ca_system_score_codex":0.00021374975,"about_ca_system_score_gemma":0.0005530695,"threshold_uncertainty_score":0.011401296},"labels":[],"label_agreement":null},{"id":"W7115584736","doi":"10.48550/arxiv.2512.11300","title":"Distributed Quantum Magnetic Sensing for Infrastructure-free Geo-localization","year":2025,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Magnetic field; Quantum; Quantum sensor; Mahalanobis distance; Quantum information; Quantum computer; Field (mathematics)","score_opus":0.024271896432780934,"score_gpt":0.17177189396460302,"score_spread":0.14749999753182208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115584736","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063268416,0.00026687302,0.9285902,0.00047742837,0.00006613234,0.000052014515,0.00011255415,0.0010727439,0.006093598],"genre_scores_gemma":[0.8973164,0.00011186647,0.100939855,0.00007290894,0.000017866267,0.000043385375,0.00008152706,0.000039330673,0.0013767937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996791,0.000097554956,0.00000971857,0.000067091205,0.00009013921,0.000056367615],"domain_scores_gemma":[0.99937916,0.00028142027,0.00005670921,0.00015495699,0.00009245548,0.00003524743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004825644,0.00032844738,0.00039069323,0.00022872728,0.000594676,0.00059533695,0.0008727388,0.0006455985,0.002594358],"category_scores_gemma":[0.0017975809,0.00017742357,0.00029174198,0.00035388753,0.00093331025,0.0012249431,0.001121357,0.00064793375,0.00036031773],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022772342,0.00009356603,0.0014001656,0.00013014102,0.00003811113,0.00014565418,0.00014211885,0.7928785,0.017417505,0.101541124,0.0042913402,0.081694044],"study_design_scores_gemma":[0.000013995033,0.000025699059,0.00013922516,0.0000033210445,0.000003909066,0.0000151878,0.000018099936,0.9802893,0.0018078554,0.016811887,0.0008656546,0.000005883187],"about_ca_topic_score_codex":0.0068030227,"about_ca_topic_score_gemma":0.0085227955,"teacher_disagreement_score":0.0068030227,"about_ca_system_score_codex":0.0009507091,"about_ca_system_score_gemma":0.0012249838,"threshold_uncertainty_score":0.013526857},"labels":[],"label_agreement":null},{"id":"W7115607707","doi":"","title":"Distributed Quantum Magnetic Sensing for Infrastructure-free Geo-localization","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Magnetic field; Quantum; Quantum sensor; Mahalanobis distance; Quantum information; Quantum computer; Field (mathematics)","score_opus":0.0106665206407124,"score_gpt":0.2289875257081937,"score_spread":0.21832100506748128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115607707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06261823,0.00026458676,0.92930275,0.00046877016,0.000062099905,0.00004830155,0.000112136346,0.0011614527,0.0059617073],"genre_scores_gemma":[0.9087128,0.00010777396,0.08969971,0.00006471487,0.000015764486,0.00003824668,0.00008105275,0.00003677979,0.0012430588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996947,0.000091112415,0.0000091425445,0.00006379163,0.00008592621,0.000055345434],"domain_scores_gemma":[0.9993911,0.00027571945,0.000056578294,0.00014842916,0.00009219332,0.00003592068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004653896,0.0003384455,0.0003808155,0.00023054145,0.00058572134,0.0005707673,0.00087887153,0.0006707017,0.0022445268],"category_scores_gemma":[0.0018101888,0.00018866286,0.0002855171,0.00035023803,0.00091304514,0.0011080701,0.0010602857,0.00062245113,0.00032481342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018019952,0.00006988131,0.0011425862,0.00010973404,0.000034117536,0.00012413312,0.00011801815,0.83115697,0.014788238,0.08329832,0.0035507123,0.06542708],"study_design_scores_gemma":[0.000013159942,0.000021605027,0.00013311557,0.000002992328,0.0000035363576,0.000014226535,0.000016114229,0.9834743,0.0015632582,0.013958111,0.000794053,0.000005542659],"about_ca_topic_score_codex":0.008957558,"about_ca_topic_score_gemma":0.010845727,"teacher_disagreement_score":0.008957558,"about_ca_system_score_codex":0.0010489059,"about_ca_system_score_gemma":0.0013068564,"threshold_uncertainty_score":0.017810881},"labels":[],"label_agreement":null},{"id":"W7117165770","doi":"10.1109/jiot.2025.3647979","title":"DBLoc: A Lightweight and Universal BFI-Enabled Deep Learning Framework for Wi-Fi Localization","year":2025,"lang":"","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Douglas College","funders":"National Key Research and Development Program of China; Guangxi Key Research and Development Program; National Natural Science Foundation of China","keywords":"Deep learning; Convolutional neural network; Residual; Inference; Transferability; Software deployment; Channel state information; Beamforming; Data transmission","score_opus":0.0062599698649278305,"score_gpt":0.23046931432389886,"score_spread":0.22420934445897103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117165770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060559902,0.00023044339,0.9886761,0.000109369044,0.00003115656,0.000020108131,0.00013377919,0.00351342,0.0012296417],"genre_scores_gemma":[0.53159046,0.000560244,0.45804092,0.0004695169,0.000063182786,0.00020235966,0.0013842655,0.00045276457,0.007236254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981993,0.000026863936,0.000008140008,0.000043686217,0.000063783256,0.00003761652],"domain_scores_gemma":[0.99978846,0.000060651328,0.00002420517,0.000042377,0.00006305832,0.000021321463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004022871,0.00079468114,0.00046080045,0.00039204513,0.00027130925,0.00052264886,0.00179582,0.00071981316,0.0020938544],"category_scores_gemma":[0.001156001,0.0003441003,0.00036174848,0.00037087259,0.00044558078,0.001255488,0.001432169,0.0014635288,0.0010012868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000204727,0.0001408903,0.0016413875,0.0001604707,0.0000973746,0.00014318162,0.000089478235,0.4997614,0.016773336,0.0175937,0.009559587,0.45383453],"study_design_scores_gemma":[0.000007530159,0.000029816858,0.00010480271,0.000008245581,0.0000069311695,0.0000308677,0.0000070779247,0.99139917,0.0028511991,0.0037250465,0.0018222666,0.0000070341453],"about_ca_topic_score_codex":0.00578638,"about_ca_topic_score_gemma":0.011222163,"teacher_disagreement_score":0.00578638,"about_ca_system_score_codex":0.0006178039,"about_ca_system_score_gemma":0.00097565114,"threshold_uncertainty_score":0.011505425},"labels":[],"label_agreement":null},{"id":"W7117298241","doi":"10.1109/twc.2025.3645480","title":"Channel Knowledge Map-Enabled 6D Movable Antenna Systems With Kinematic Constraints: A Manifold Optimization Approach","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Kinematics; Antenna (radio); Trajectory; Channel (broadcasting); Wireless; Optimization problem; Control theory (sociology); Manifold (fluid mechanics)","score_opus":0.018806048660729997,"score_gpt":0.23602720331096952,"score_spread":0.21722115465023953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117298241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011562812,0.00012437871,0.98589766,0.00013767599,0.000018422952,0.00001274498,0.00003331616,0.00017698407,0.0020360032],"genre_scores_gemma":[0.84616405,0.00031490967,0.14876895,0.0001276774,0.000042590054,0.00013172203,0.00016980611,0.00010727488,0.0041730674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978095,0.00006730325,0.0000075670173,0.00004594636,0.00006152049,0.000036859306],"domain_scores_gemma":[0.99966025,0.00014893273,0.000052138195,0.000043182306,0.00006798417,0.000027458635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038575384,0.0008321199,0.0007246628,0.0003160983,0.00032687926,0.00076644105,0.0008111491,0.0007528074,0.001593793],"category_scores_gemma":[0.0010062917,0.00039646085,0.00061979407,0.00040962154,0.0006362073,0.0010100227,0.0012017201,0.0010118643,0.00043795689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015382955,0.000009259798,0.00018843377,0.000015753822,0.000011863335,0.000025834379,0.000027990183,0.9826301,0.00094767846,0.005604033,0.00034389104,0.0101797925],"study_design_scores_gemma":[0.0000011842529,0.000009991491,0.000027436343,0.0000012754309,0.0000016095427,0.0000056695353,0.0000045379215,0.9982412,0.00014669457,0.0013334285,0.00022456954,0.0000023762736],"about_ca_topic_score_codex":0.0038624716,"about_ca_topic_score_gemma":0.00259327,"teacher_disagreement_score":0.0038624716,"about_ca_system_score_codex":0.00056298834,"about_ca_system_score_gemma":0.00074285816,"threshold_uncertainty_score":0.007679999},"labels":[],"label_agreement":null},{"id":"W7117546140","doi":"10.18280/ts.420639","title":"Efficient Recurrent Forcing Fourier Phase Distortionless Response Network for Modern Mobile Networks","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Forcing (mathematics); Phase (matter); Fourier transform; Control theory (sociology); Mobile telephony; Cellular network","score_opus":0.011006823993900305,"score_gpt":0.26393561750332045,"score_spread":0.25292879350942016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117546140","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017170385,0.00020026237,0.9796556,0.00017148063,0.0000511431,0.00001761105,0.00007405526,0.0002867217,0.0023727522],"genre_scores_gemma":[0.7570002,0.00041360117,0.22457832,0.0002285334,0.00010147058,0.00010016845,0.00038645032,0.00008876786,0.017102588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997937,0.00006886937,0.000006536649,0.00003938789,0.000065218825,0.00002633139],"domain_scores_gemma":[0.9996923,0.0001556039,0.000025164309,0.00004337314,0.000070951144,0.000012669095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004275226,0.00043571027,0.00043583472,0.00015002053,0.00019908135,0.00036605803,0.00064583047,0.0007378665,0.0026592289],"category_scores_gemma":[0.00095065706,0.00018185661,0.00024072388,0.00022231015,0.00033111288,0.00055223884,0.00050603796,0.0006171877,0.00077880186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039362983,0.000091797156,0.0005467892,0.00013650268,0.000052373172,0.00016843264,0.000092331764,0.71394145,0.040163003,0.05489292,0.005913867,0.1836069],"study_design_scores_gemma":[0.0000040815453,0.0000228673,0.000059197646,0.0000027794463,0.0000036604326,0.000026692853,0.0000035541727,0.994926,0.0015415944,0.0027100306,0.0006961391,0.0000033697954],"about_ca_topic_score_codex":0.001609125,"about_ca_topic_score_gemma":0.0031996258,"teacher_disagreement_score":0.0026592289,"about_ca_system_score_codex":0.00040353925,"about_ca_system_score_gemma":0.00038266196,"threshold_uncertainty_score":0.008895993},"labels":[],"label_agreement":null},{"id":"W7117560966","doi":"10.1109/mswim67937.2025.11309178","title":"Low-Error Indoor Positioning via Synthetic RSSI Augmentation and Zx–WKNN Hybrid Model","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Custom Security Industries (Canada); Sheridan College","funders":"Research and Development; Natural Sciences and Engineering Research Council of Canada","keywords":"Mean squared error; Scalability; Multipath interference; Received signal strength indication; Feature (linguistics); Multipath propagation; Key (lock); Pattern recognition (psychology); Interference (communication); Point (geometry)","score_opus":0.006657282563554543,"score_gpt":0.23026119198084063,"score_spread":0.22360390941728608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117560966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07568565,0.00016546788,0.9207595,0.00014867651,0.00004885408,0.000018644772,0.000093260496,0.0011945231,0.0018853651],"genre_scores_gemma":[0.8879152,0.00014809941,0.108265355,0.00008427738,0.000026488975,0.00005048603,0.0003289123,0.0000792961,0.0031019105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997477,0.000060129496,0.000012768339,0.0000870122,0.000064355794,0.000028044935],"domain_scores_gemma":[0.999587,0.000174788,0.00004899851,0.00007054662,0.00010477128,0.000013976859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052432064,0.00063969544,0.00047743297,0.000276268,0.00018165956,0.0004923682,0.0010087427,0.00061667,0.0006243822],"category_scores_gemma":[0.0013688334,0.0004004614,0.00053329393,0.0003476443,0.0004110313,0.00067538954,0.000624517,0.0007223568,0.0003762695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052570285,0.000019322271,0.0007312596,0.000018442997,0.000019559695,0.000029243089,0.000032756456,0.96083784,0.0031845788,0.0007918796,0.00027184727,0.03401076],"study_design_scores_gemma":[0.0000011467863,0.0000065816316,0.00013148083,9.811873e-7,0.0000021374544,0.0000053144818,0.0000018701186,0.99914,0.0004852519,0.00014068311,0.00008228424,0.0000022149557],"about_ca_topic_score_codex":0.009905543,"about_ca_topic_score_gemma":0.008000288,"teacher_disagreement_score":0.009905543,"about_ca_system_score_codex":0.00044075056,"about_ca_system_score_gemma":0.00041525878,"threshold_uncertainty_score":0.019695759},"labels":[],"label_agreement":null},{"id":"W7117578025","doi":"10.1109/mswim67937.2025.11308743","title":"Privacy-Preserving Device Counting Using Wi-Fi Channel State Information and Deep Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Discriminative model; Network packet; Deep learning; Channel (broadcasting); Population; State (computer science); Tracking (education); Mobile device; Fingerprint (computing)","score_opus":0.010011588973866788,"score_gpt":0.231683448190965,"score_spread":0.22167185921709823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117578025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056922764,0.0002097079,0.9377288,0.00039799113,0.00007844728,0.000054517768,0.00022623422,0.0019972688,0.0023841625],"genre_scores_gemma":[0.9060523,0.0001688306,0.08811062,0.00031234632,0.00007823987,0.00010369746,0.00057151105,0.00007729502,0.0045251762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999433,0.00009621044,0.00002813486,0.00019641551,0.00014272821,0.000103530656],"domain_scores_gemma":[0.9988783,0.00043611065,0.00017298646,0.00025932337,0.00018950025,0.00006374629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075322285,0.0009768974,0.0009409891,0.0005335815,0.0004518416,0.0008986369,0.0019755878,0.0009004356,0.00093641755],"category_scores_gemma":[0.0033450334,0.0004514944,0.0005793753,0.00060700264,0.000818486,0.002040675,0.0014536233,0.0017089881,0.00045167582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024392323,0.00028577054,0.005675351,0.00007211003,0.00007891706,0.00018446107,0.00013255073,0.68876296,0.007775012,0.009012765,0.00410851,0.28366765],"study_design_scores_gemma":[0.0000033125596,0.000015215817,0.00023656714,0.0000039816314,0.0000051286506,0.000017627763,0.00000547922,0.9957794,0.0013630225,0.0023035884,0.0002618257,0.00000493146],"about_ca_topic_score_codex":0.0052605676,"about_ca_topic_score_gemma":0.007922524,"teacher_disagreement_score":0.0052605676,"about_ca_system_score_codex":0.000831529,"about_ca_system_score_gemma":0.0011845327,"threshold_uncertainty_score":0.0104599},"labels":[],"label_agreement":null},{"id":"W7117584770","doi":"10.1109/jsen.2025.3646617","title":"Sensing Line-of-Sight Perturbations in 6-GHz Wi-Fi Using Channel Model-Based Features","year":2025,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Engineering and Physical Sciences Research Council","keywords":"Feature extraction; Channel (broadcasting); Random forest; Set (abstract data type); Feature selection; Decision tree; Pattern recognition (psychology); Feature (linguistics)","score_opus":0.017511214403187318,"score_gpt":0.2546834134696422,"score_spread":0.23717219906645487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117584770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2595406,0.00028770923,0.7373281,0.00009294753,0.000043271302,0.000035953486,0.000215984,0.0009754199,0.0014799519],"genre_scores_gemma":[0.92294234,0.00016338579,0.07571403,0.00003019924,0.000017781718,0.000030090345,0.00036166672,0.000024300558,0.0007161941],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984646,0.000024651636,0.000005461967,0.000030428399,0.00005778383,0.000035107678],"domain_scores_gemma":[0.9997749,0.00009175646,0.000042175245,0.00002730879,0.000051683448,0.000012199569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018700512,0.0005281936,0.00037259987,0.00053665554,0.00020739133,0.00030538067,0.0002780177,0.00028291877,0.00038488637],"category_scores_gemma":[0.0008691492,0.00010447283,0.00026650174,0.00049791706,0.00015641846,0.00047535202,0.00029364985,0.00035339952,0.00024107093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047915167,0.00022975987,0.01998594,0.00011567118,0.000076446035,0.00029482512,0.00014417357,0.26490825,0.10110452,0.0018253182,0.0026653553,0.6081706],"study_design_scores_gemma":[0.000007766704,0.00012496916,0.013235016,0.0000104450155,0.00002417996,0.00022642742,0.00005565888,0.9643885,0.019518517,0.0012009551,0.0011848719,0.000022645583],"about_ca_topic_score_codex":0.0022247827,"about_ca_topic_score_gemma":0.003775087,"teacher_disagreement_score":0.0022247827,"about_ca_system_score_codex":0.00015571043,"about_ca_system_score_gemma":0.00023755597,"threshold_uncertainty_score":0.004423678},"labels":[],"label_agreement":null},{"id":"W7124149287","doi":"10.1109/cyberscitech68397.2025.00075","title":"Clustering and optimization of kindergarten's playing area using time series","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Education and Early Childhood Development","funders":"Ministry of Education","keywords":"Cluster analysis; Process (computing); Resource (disambiguation); Artifact (error); Feature (linguistics); Resource allocation; Core (optical fiber); Key (lock)","score_opus":0.010036904555180014,"score_gpt":0.21411798075092017,"score_spread":0.20408107619574015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124149287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46368444,0.00048590035,0.53118676,0.00025550852,0.00006307227,0.00012622272,0.00067507505,0.0012505163,0.0022724248],"genre_scores_gemma":[0.88119733,0.00026508735,0.1150188,0.000031142972,0.000022366501,0.00010917129,0.0013983492,0.00010764493,0.0018500568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999608,0.000074301264,0.000029216637,0.00014673414,0.00007283649,0.000069020345],"domain_scores_gemma":[0.99955505,0.00016336133,0.00006770735,0.000031082902,0.00014474032,0.00003804121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062536757,0.00089743466,0.0007599142,0.0021660135,0.00043702475,0.00089882273,0.0008057142,0.000513049,0.0012035358],"category_scores_gemma":[0.0018614341,0.00036637593,0.0008238382,0.0015033789,0.00030022283,0.00061618997,0.00051792763,0.00044501346,0.00042976957],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003847756,0.0003650581,0.03845454,0.0001593141,0.00018885992,0.00013681887,0.00044326356,0.63027,0.010484527,0.0025377132,0.0026538176,0.31392127],"study_design_scores_gemma":[0.0000074357504,0.00004325514,0.012479462,0.000009090171,0.000023714658,0.000023854747,0.00024162368,0.9838627,0.0019188862,0.00080802036,0.00056556286,0.000016345997],"about_ca_topic_score_codex":0.030528786,"about_ca_topic_score_gemma":0.025474094,"teacher_disagreement_score":0.030528786,"about_ca_system_score_codex":0.0008406614,"about_ca_system_score_gemma":0.0011338093,"threshold_uncertainty_score":0.060702145},"labels":[],"label_agreement":null},{"id":"W7130563242","doi":"10.1109/ictc66702.2025.11388967","title":"Enhancing Wi-Fi RSSI-Based Indoor Positioning with a Covariance-Weighted Distance Metric","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Metric (unit); Covariance; Euclidean distance; Global Positioning System; Point (geometry); Software deployment; Ranging","score_opus":0.004254897672711625,"score_gpt":0.20999115357617681,"score_spread":0.2057362559034652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130563242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00843794,0.0002809054,0.98931783,0.00007180704,0.000053533928,0.000022312322,0.000065486376,0.00046289718,0.0012873244],"genre_scores_gemma":[0.49998593,0.0009256883,0.49567905,0.00014036083,0.00015875006,0.00012535382,0.0006237745,0.00023882739,0.0021222553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728847,0.0005930345,0.00017246688,0.00036659784,0.0014645458,0.00011481717],"domain_scores_gemma":[0.99735236,0.00067330967,0.00034017715,0.0004806779,0.0010871617,0.00006635072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011044119,0.001286015,0.0009005913,0.0015160071,0.00042076106,0.0010065443,0.0016498319,0.0006167346,0.0007998717],"category_scores_gemma":[0.0073161367,0.00036692663,0.0006158964,0.0024903102,0.00042919107,0.002195278,0.0020608655,0.0007557267,0.00087494444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023138849,0.00014580968,0.0053303824,0.00041132042,0.0002050172,0.0002940225,0.00024223233,0.31656298,0.070243314,0.026731407,0.0038485646,0.57575357],"study_design_scores_gemma":[0.000019560022,0.00021951161,0.0033358075,0.000028183815,0.000061744264,0.000657484,0.000048519352,0.9531528,0.026545992,0.004891757,0.010941359,0.00009737889],"about_ca_topic_score_codex":0.0026376534,"about_ca_topic_score_gemma":0.0029157554,"teacher_disagreement_score":0.0026376534,"about_ca_system_score_codex":0.0005995624,"about_ca_system_score_gemma":0.000917706,"threshold_uncertainty_score":0.0058407784},"labels":[],"label_agreement":null},{"id":"W7132957019","doi":"","title":"Capsule Networks and Lightweight Dual-Path Model With Fresnel Zone-Based Voting for Human Activity Recognition Using Wi-Fi Channel State Information","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Voting; Activity recognition; Overhead (engineering); Channel (broadcasting); State (computer science); Adaptability; Feature extraction; Channel state information","score_opus":0.02992118171449939,"score_gpt":0.2809511741180995,"score_spread":0.25102999240360013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132957019","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023061486,0.00012487799,0.9751532,0.000078610225,0.000028857374,0.000026336225,0.00005950256,0.0003771562,0.001090064],"genre_scores_gemma":[0.8162326,0.00021575691,0.17821273,0.00012709267,0.000047349604,0.000108198015,0.00042542283,0.000084762956,0.0045459825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932563,0.00018271456,0.000031000913,0.00019196574,0.00016624424,0.00010255068],"domain_scores_gemma":[0.9993598,0.00026494628,0.00007224429,0.00014137421,0.00012726356,0.000034520082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074742624,0.0006426628,0.0009527376,0.0005148101,0.00039478726,0.0010175912,0.0017686379,0.00071320107,0.0015850246],"category_scores_gemma":[0.0019481722,0.00028069413,0.0007358368,0.00073660817,0.0005998011,0.0018228092,0.0012259078,0.0010381809,0.0005252981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035589127,0.00011715517,0.0024146775,0.000059512276,0.00008136521,0.00008284989,0.00013398335,0.71206105,0.011770421,0.01969675,0.0018570861,0.25136927],"study_design_scores_gemma":[0.0000029927382,0.000019818945,0.00013152717,0.0000018363933,0.000005151285,0.0000122528245,0.000007580431,0.995871,0.0014872354,0.002125185,0.00033113232,0.000004390666],"about_ca_topic_score_codex":0.005065828,"about_ca_topic_score_gemma":0.005504498,"teacher_disagreement_score":0.005065828,"about_ca_system_score_codex":0.00084621087,"about_ca_system_score_gemma":0.00072187063,"threshold_uncertainty_score":0.010072708},"labels":[],"label_agreement":null},{"id":"W7137988208","doi":"10.1109/icest65883.2025.11428460","title":"Robust Distributed Collaborative Beamforming to Polychromatic Ray Estimation Errors in WSANs with Nodes Scattered Over Nominally Rectangular Grids","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Beamforming; Robustness (evolution); Signal processing; Feature (linguistics); Noise (video)","score_opus":0.0065234711925658325,"score_gpt":0.22451764033128163,"score_spread":0.2179941691387158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7137988208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070221466,0.00007125033,0.9919442,0.000043815213,0.000016926908,0.000011141239,0.000015918868,0.00009317722,0.0007814367],"genre_scores_gemma":[0.75346017,0.00045776978,0.24236831,0.00013102559,0.00005752667,0.000111295776,0.00010575488,0.000083488274,0.0032247298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993223,0.00021289289,0.000024264291,0.00013677585,0.00023304483,0.000070785405],"domain_scores_gemma":[0.9989255,0.0005590421,0.00017724004,0.00012695472,0.0001760554,0.000035311085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091681455,0.0008715658,0.0005414501,0.00029276876,0.0003303426,0.0007093077,0.0008616688,0.0006513434,0.0009968118],"category_scores_gemma":[0.003519157,0.00041114318,0.00043322868,0.00058563426,0.0009341225,0.00094139937,0.0012272175,0.00079356163,0.00030582323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009989212,0.000013038748,0.00048687315,0.00006421264,0.00002649413,0.000105575855,0.00009148765,0.9424554,0.007204864,0.014365123,0.000530991,0.034556095],"study_design_scores_gemma":[0.0000069774637,0.000037164817,0.00015498682,0.0000073051324,0.000007381311,0.000050394454,0.000026955358,0.99305254,0.0021067252,0.003910367,0.00062782085,0.000011286111],"about_ca_topic_score_codex":0.0031858964,"about_ca_topic_score_gemma":0.0031368257,"teacher_disagreement_score":0.0031858964,"about_ca_system_score_codex":0.0006193326,"about_ca_system_score_gemma":0.00087534805,"threshold_uncertainty_score":0.006334722},"labels":[],"label_agreement":null},{"id":"W7138939958","doi":"10.1109/globecom59602.2025.11431648","title":"Inception-LSTM: A Two Stage Approach for Indoor Position Estimation Using Channel Impulse Response Measurements","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Ericsson (Canada); Carleton University","funders":"","keywords":"Stage (stratigraphy); Impulse response; Channel (broadcasting); Position (finance); Control theory (sociology); Impulse (physics)","score_opus":0.042510859410264175,"score_gpt":0.3110357980521218,"score_spread":0.2685249386418576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7138939958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0104774395,0.00052963657,0.98024344,0.0001381326,0.0002933731,0.000049749462,0.00040435363,0.0064115557,0.0014523895],"genre_scores_gemma":[0.24428302,0.000581463,0.73732734,0.0005666549,0.0002087561,0.00020794345,0.0023788654,0.00067020924,0.013775802],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995578,0.00006709624,0.000022744985,0.00015538308,0.00011527638,0.000081675316],"domain_scores_gemma":[0.99953544,0.00014395201,0.000031149208,0.00008752707,0.00017278377,0.000029255501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006089647,0.0014761545,0.0010766842,0.0006878292,0.0004230173,0.0007527916,0.0024008125,0.0018059759,0.0039509838],"category_scores_gemma":[0.0013961757,0.0006957842,0.00078020024,0.0010904439,0.00031510237,0.0011909645,0.0015108199,0.0023085931,0.003759553],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030539444,0.00022610636,0.00085623405,0.00015838948,0.00015239854,0.00014083974,0.00010544324,0.063497886,0.032124948,0.0016560676,0.010208943,0.8905673],"study_design_scores_gemma":[0.000012240237,0.00006855888,0.00050083327,0.000016667404,0.000030676314,0.00006345396,0.000023523622,0.98499197,0.010437101,0.0014188489,0.0024148026,0.000021383623],"about_ca_topic_score_codex":0.010512517,"about_ca_topic_score_gemma":0.019229861,"teacher_disagreement_score":0.010512517,"about_ca_system_score_codex":0.00044498927,"about_ca_system_score_gemma":0.0012032286,"threshold_uncertainty_score":0.020902693},"labels":[],"label_agreement":null},{"id":"W7139966084","doi":"10.23919/cnc-usnc-ursi64444.2025.11419996","title":"Multi-Band Wireless Sensing Fusion and Communication Through Hybrid Multiplexing","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Wireless; Multiplexing; Sensor fusion; Key (lock); Fusion; Signal processing","score_opus":0.015931975190097206,"score_gpt":0.2539934526618707,"score_spread":0.23806147747177347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7139966084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14698285,0.0016630384,0.8347834,0.0003398814,0.00013164282,0.00006535445,0.00006435152,0.00064230897,0.015327204],"genre_scores_gemma":[0.83783555,0.00083944906,0.15557925,0.0001711497,0.00009135133,0.00007523412,0.000054537864,0.000028701006,0.0053246995],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995435,0.00008622397,0.000016669765,0.00010364979,0.0001765722,0.00007336978],"domain_scores_gemma":[0.9998104,0.00006857723,0.000037235044,0.000032805263,0.000034978486,0.000016124872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004902839,0.00047538732,0.00025029364,0.00051403634,0.00040238854,0.00071027054,0.0005550309,0.00038620734,0.0009008921],"category_scores_gemma":[0.00039201786,0.00021279418,0.000357203,0.0005251449,0.00037410366,0.001373499,0.0009755111,0.00038304683,0.000355623],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043432254,0.00014223224,0.0012810294,0.00013956676,0.000098766446,0.00025827563,0.00026777163,0.06592351,0.556989,0.05171916,0.0011568071,0.3215896],"study_design_scores_gemma":[0.000034964076,0.00041592424,0.0013057062,0.000032022723,0.000058868758,0.0006238084,0.00010863336,0.6632373,0.29377,0.023753243,0.016546063,0.000113469374],"about_ca_topic_score_codex":0.0005783003,"about_ca_topic_score_gemma":0.0008408277,"teacher_disagreement_score":0.0009008921,"about_ca_system_score_codex":0.00050984806,"about_ca_system_score_gemma":0.00029697918,"threshold_uncertainty_score":0.0036991835},"labels":[],"label_agreement":null},{"id":"W7152726401","doi":"10.1109/icecet63943.2025.11472283","title":"Transfer Learning from Wi-Fi Access Point Data to Air Quality Monitoring: A Spatial-Temporal Graph-Based Approach","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Quality (philosophy); Point (geometry); Key (lock); Data collection; Transfer of learning; Transfer (computing)","score_opus":0.08590538685933301,"score_gpt":0.3250113487824909,"score_spread":0.23910596192315786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7152726401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06376468,0.00056381745,0.9294233,0.00047178037,0.00010587877,0.00009068927,0.00028950683,0.0031355876,0.002154858],"genre_scores_gemma":[0.8921463,0.00032023015,0.1021025,0.00028826634,0.00009316865,0.00013835248,0.0009520769,0.00016414493,0.00379496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999534,0.000116500814,0.000019119128,0.00019756638,0.000074835065,0.000058093214],"domain_scores_gemma":[0.99914,0.0003642827,0.000081684324,0.0001949782,0.000170009,0.000048970298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095341675,0.0012106553,0.0006996261,0.0008664403,0.00037776123,0.0006248933,0.0021330512,0.0011459849,0.0014767036],"category_scores_gemma":[0.0031126936,0.0004116473,0.00082350307,0.0010825972,0.0007037414,0.001720344,0.0016301909,0.0017134455,0.0005827947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013356909,0.00023847444,0.0030080492,0.000075426826,0.00012175868,0.00014740726,0.00009660238,0.7801837,0.004670023,0.0028027233,0.0027941247,0.20572819],"study_design_scores_gemma":[0.000004290201,0.000021754367,0.00034143086,0.0000031489606,0.000007748307,0.00001210769,0.000013074878,0.9950322,0.0009677842,0.0032242066,0.00036718886,0.0000050858616],"about_ca_topic_score_codex":0.012739189,"about_ca_topic_score_gemma":0.010742848,"teacher_disagreement_score":0.012739189,"about_ca_system_score_codex":0.00094978523,"about_ca_system_score_gemma":0.0008878104,"threshold_uncertainty_score":0.025330067},"labels":[],"label_agreement":null},{"id":"W7154521179","doi":"10.1109/upinlbs68186.2025.11468451","title":"Distributed UWB-Aided Cooperative Positioning with Height Constraints for Multi-AGV Systems in Gnss Challenged Conditions","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Science Foundation of Beijing Municipality; Leading Edge Endowment Fund","keywords":"GNSS applications; Global Positioning System; Satellite system; Precise Point Positioning; Satellite navigation; Key (lock)","score_opus":0.020400307917756173,"score_gpt":0.27291781609067556,"score_spread":0.2525175081729194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7154521179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028322984,0.00010329784,0.97025853,0.000051402516,0.000025129735,0.000011873752,0.000014384324,0.00022132555,0.0009910575],"genre_scores_gemma":[0.93178624,0.000075063865,0.06652139,0.000033797907,0.000029388257,0.00003902132,0.000048446524,0.000019209943,0.0014475018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954766,0.00007673611,0.000014648891,0.00014759088,0.0001548986,0.000058419075],"domain_scores_gemma":[0.99959236,0.000110189125,0.00007673171,0.000084810505,0.000105058,0.000030832667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039407884,0.0005624399,0.0005930303,0.0002716584,0.0004369365,0.0005249675,0.0014702518,0.00063397683,0.0004979209],"category_scores_gemma":[0.0009807551,0.00025284183,0.0003901835,0.00034724682,0.0005246016,0.000862206,0.0012434692,0.0005183178,0.00021455954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012984355,0.00004792712,0.0022374901,0.00009366061,0.00005535977,0.00027301357,0.00035378677,0.8047929,0.031688213,0.008817147,0.0008486868,0.15066211],"study_design_scores_gemma":[0.000017782264,0.00012203094,0.0006433702,0.0000042643383,0.000017713766,0.00008338923,0.00006727694,0.9914786,0.004011599,0.0023623186,0.0011785314,0.000013152522],"about_ca_topic_score_codex":0.0044411863,"about_ca_topic_score_gemma":0.004853238,"teacher_disagreement_score":0.0044411863,"about_ca_system_score_codex":0.0004269331,"about_ca_system_score_gemma":0.00078880467,"threshold_uncertainty_score":0.008830667},"labels":[],"label_agreement":null},{"id":"W7457788","doi":"10.1007/978-3-642-38227-7_23","title":"Mechanisms to Locate Non-cooperative Transmitters in Wireless Networks","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Transmitter; Computer science; Set (abstract data type); Wireless; Wireless sensor network; Measure (data warehouse); Residual; Wireless network; Algorithm; Computer network; Telecommunications; Data mining","score_opus":0.008336327617876024,"score_gpt":0.19648036459975332,"score_spread":0.1881440369818773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7457788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014200256,0.0035714114,0.9679538,0.0004161392,0.00037284303,0.00012801283,0.000042274423,0.0008408143,0.012474507],"genre_scores_gemma":[0.64878553,0.006208846,0.2984863,0.00050504936,0.0005418234,0.00072547625,0.0001751363,0.00017849043,0.044393323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993124,0.00018670446,0.00006117464,0.000110034685,0.00023419369,0.0000954848],"domain_scores_gemma":[0.9975643,0.0011789514,0.00026119125,0.0006159495,0.00030798928,0.00007163359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016661262,0.0009336571,0.0007923945,0.0010401694,0.0006946139,0.0017476735,0.003644697,0.0017216369,0.0036772685],"category_scores_gemma":[0.0045103896,0.000662199,0.0005069898,0.0010993012,0.001125268,0.0029673525,0.0026195939,0.001265484,0.0013544972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028090985,0.00017181512,0.0006576762,0.0010027132,0.00014062232,0.00041928157,0.00054871646,0.11356589,0.030935869,0.4479075,0.009452924,0.3949161],"study_design_scores_gemma":[0.00026271914,0.00073731196,0.0009944348,0.00034936517,0.00030779897,0.002592047,0.00039024526,0.5034946,0.0368248,0.38040423,0.073474854,0.00016754109],"about_ca_topic_score_codex":0.00024657304,"about_ca_topic_score_gemma":0.00037291792,"teacher_disagreement_score":0.0036772685,"about_ca_system_score_codex":0.00042496502,"about_ca_system_score_gemma":0.0003833188,"threshold_uncertainty_score":0.012301743},"labels":[],"label_agreement":null},{"id":"W75520331","doi":"10.1007/978-3-642-23181-0_6","title":"Localization of Industrial Wireless Sensor Networks: An Artificial Neural Network Approach","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wireless sensor network; Computer science; Factory (object-oriented programming); Artificial neural network; Noise (video); Wireless; Wireless network; Key distribution in wireless sensor networks; Network topology; Node (physics); Sensitivity (control systems); Real-time computing; Artificial intelligence; Computer network; Telecommunications; Electronic engineering; Engineering","score_opus":0.03175446056078629,"score_gpt":0.21590154701825104,"score_spread":0.18414708645746475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W75520331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059875096,0.004606545,0.98364717,0.00033614362,0.00014411303,0.000012828998,0.0000421666,0.00021834049,0.005005188],"genre_scores_gemma":[0.6801318,0.018255906,0.27696377,0.000336957,0.00085276144,0.00013137102,0.0002635178,0.00017046797,0.022893503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981636,0.000058584767,0.00001250696,0.00004612065,0.000047150384,0.00001926765],"domain_scores_gemma":[0.99976736,0.0001282696,0.000030343524,0.000015698895,0.00005273726,0.000005595927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038482182,0.0006620418,0.00065926893,0.00061749393,0.00023602461,0.0008511677,0.0012116203,0.0012037795,0.0012459015],"category_scores_gemma":[0.0011402908,0.00041472667,0.0004967544,0.001388202,0.0005558724,0.0012322635,0.0005266934,0.0008196028,0.00032015998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023995703,0.000017321789,0.00031739054,0.00012596698,0.000037525628,0.00006260833,0.00003355928,0.9030815,0.0017287051,0.014282943,0.0012993332,0.07898925],"study_design_scores_gemma":[0.0000012514378,0.000006567034,0.000099326346,0.000008498795,0.0000074198388,0.000017116523,0.0000062484705,0.99292845,0.00029988468,0.0058525335,0.000768242,0.0000044668536],"about_ca_topic_score_codex":0.004925361,"about_ca_topic_score_gemma":0.00497706,"teacher_disagreement_score":0.004925361,"about_ca_system_score_codex":0.00061769114,"about_ca_system_score_gemma":0.00028553454,"threshold_uncertainty_score":0.009793401},"labels":[],"label_agreement":null},{"id":"W760567250","doi":"10.1007/s11276-015-0982-4","title":"LIP: an efficient lightweight iterative positioning algorithm for wireless sensor networks","year":2015,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless sensor network; Scalability; Algorithm; Flooding (psychology); Node (physics); Real-time computing; Position (finance); Wireless; Key distribution in wireless sensor networks; Wireless network; Computer network; Telecommunications","score_opus":0.011694078488254045,"score_gpt":0.22598076930038433,"score_spread":0.21428669081213028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W760567250","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002003526,0.00010346148,0.9954626,0.00004422189,0.000044456443,0.00002529812,0.000040277817,0.0015793476,0.0006967637],"genre_scores_gemma":[0.12142024,0.0002754256,0.871283,0.00015609096,0.00007018616,0.0003012252,0.0004118283,0.00043854452,0.005643512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938,0.00012805396,0.000030377098,0.000082656086,0.0003134103,0.000065439745],"domain_scores_gemma":[0.999345,0.00024539948,0.000049489325,0.00012353991,0.00019469393,0.000041934883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000755271,0.0009351462,0.0008542438,0.0008860092,0.0005271341,0.00094843376,0.0018379776,0.00088132476,0.0036234153],"category_scores_gemma":[0.0031206254,0.0004677463,0.00047894192,0.0009547304,0.00046178894,0.0018402806,0.0026794577,0.0012435254,0.0026550677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047154314,0.00009866884,0.0008040741,0.00027479566,0.00008347373,0.00015008372,0.00026704025,0.17098215,0.029424645,0.017585216,0.014308608,0.7655497],"study_design_scores_gemma":[0.00005602842,0.00011081383,0.00019042895,0.000023439463,0.000019310439,0.000109951055,0.000058390666,0.97142965,0.012729483,0.0074678888,0.007776684,0.00002786937],"about_ca_topic_score_codex":0.0020940865,"about_ca_topic_score_gemma":0.0029230635,"teacher_disagreement_score":0.0036234153,"about_ca_system_score_codex":0.0005378431,"about_ca_system_score_gemma":0.0011588924,"threshold_uncertainty_score":0.012121558},"labels":[],"label_agreement":null},{"id":"W76653589","doi":"10.1007/978-3-642-23822-2_1","title":"Secure Localization Using Dynamic Verifiers","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Collusion; Computer science; Protocol (science); Node (physics); Adversary; Position (finance); Computer security; Set (abstract data type); Security analysis; Computer network; Theoretical computer science","score_opus":0.012790920832332506,"score_gpt":0.2162632176952496,"score_spread":0.20347229686291707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W76653589","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011488669,0.000998317,0.97005534,0.0005295536,0.00023234866,0.000058140315,0.00010926549,0.0015941495,0.014934118],"genre_scores_gemma":[0.76801103,0.0031448544,0.18137448,0.0003346652,0.00038152107,0.00026089803,0.00048808058,0.00046064772,0.045543864],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747366,0.0004022,0.0001330319,0.00047279286,0.0011175184,0.00040072444],"domain_scores_gemma":[0.99492764,0.0019798726,0.00033954537,0.0022895534,0.00038717434,0.00007626191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014001975,0.001306689,0.00096807734,0.0012312586,0.0010436529,0.0028130785,0.0019044336,0.0020668595,0.008154425],"category_scores_gemma":[0.0050832387,0.0011345721,0.000733633,0.0011403353,0.002485409,0.007648577,0.0048939553,0.0038469522,0.0036959753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048233962,0.00007812456,0.00044812428,0.00034090652,0.000062568644,0.00034073065,0.0003373808,0.05990193,0.031132508,0.6284003,0.0061532124,0.27232194],"study_design_scores_gemma":[0.00016868443,0.00036721028,0.00035191927,0.00026814445,0.00013237246,0.001278103,0.00015099261,0.36815402,0.124401666,0.44459775,0.05999847,0.0001306197],"about_ca_topic_score_codex":0.00039366938,"about_ca_topic_score_gemma":0.00035337376,"teacher_disagreement_score":0.008154425,"about_ca_system_score_codex":0.0013090008,"about_ca_system_score_gemma":0.0009954604,"threshold_uncertainty_score":0.027279258},"labels":[],"label_agreement":null},{"id":"W86099913","doi":"10.1007/978-3-642-29187-6_66","title":"An Integrated MEMS IMU/Camera System for Pedestrian Indoor Navigation Using Smartphones","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Pedestrian; Computer science; Computer vision; Real-time computing; Artificial intelligence; Engineering; Transport engineering","score_opus":0.010878858198346702,"score_gpt":0.21850231818191185,"score_spread":0.20762345998356516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W86099913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36330888,0.007460922,0.5596896,0.00057744817,0.0021080603,0.00053249125,0.0018859901,0.019912258,0.04452441],"genre_scores_gemma":[0.7941308,0.0015309874,0.15804027,0.0006626265,0.00026432046,0.00019742877,0.0008296125,0.00021027173,0.04413369],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997737,0.000024257106,0.000010398592,0.000055468387,0.00010266615,0.000033632754],"domain_scores_gemma":[0.99984026,0.000014696437,0.000010635941,0.000016478656,0.00010255076,0.000015403011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012909264,0.00074810674,0.00074962963,0.0003951453,0.00031316082,0.0003784901,0.0008373963,0.0007074593,0.0059354394],"category_scores_gemma":[0.00020152902,0.0003211269,0.00034106002,0.00034959172,0.00008482973,0.00043301852,0.00039420644,0.0003219137,0.002575161],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007393285,0.00017606757,0.0054973806,0.0005357489,0.00014605313,0.0010289748,0.00025194453,0.0014086057,0.5302322,0.0011494532,0.023140214,0.435694],"study_design_scores_gemma":[0.00022674206,0.0036634982,0.046892192,0.00025864525,0.0009046705,0.0095388675,0.00047798705,0.12241,0.6879343,0.0008441725,0.1265533,0.0002956665],"about_ca_topic_score_codex":0.0025455942,"about_ca_topic_score_gemma":0.0056854836,"teacher_disagreement_score":0.0059354394,"about_ca_system_score_codex":0.00021098464,"about_ca_system_score_gemma":0.00032789208,"threshold_uncertainty_score":0.019855976},"labels":[],"label_agreement":null},{"id":"W865478343","doi":"","title":"Models For Locating RFID Nodes","year":2006,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wireless sensor network; Computer science; Scalability; Distributed computing; Flexibility (engineering); Triangulation; Key distribution in wireless sensor networks; Range (aeronautics); Wireless; Wireless ad hoc network; Real-time computing; Field (mathematics); Computer network; Wireless network; Engineering; Telecommunications; Geography","score_opus":0.009047023237069901,"score_gpt":0.1886198079561913,"score_spread":0.1795727847191214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W865478343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007750788,0.0010300019,0.97073394,0.00074775965,0.00018797054,0.00005872903,0.0004835378,0.00039277843,0.018614635],"genre_scores_gemma":[0.6930172,0.0061641713,0.20475388,0.0005777426,0.00057287765,0.0010303039,0.0019039441,0.000413392,0.09156656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913675,0.00026943546,0.000053823143,0.00019903165,0.00022574479,0.000115261995],"domain_scores_gemma":[0.99828917,0.0008943418,0.0002439857,0.0002376445,0.00028091608,0.000053902928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012171367,0.0012366553,0.001062609,0.0013911666,0.0007268134,0.002305541,0.0043422044,0.003757987,0.007368019],"category_scores_gemma":[0.005470873,0.00081485027,0.0013730832,0.0022596505,0.0012608448,0.0035269712,0.0016408797,0.0013517406,0.003950676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036135407,0.000015380134,0.00042810876,0.00007052551,0.00001822059,0.00015593614,0.000121069104,0.8321896,0.00048415267,0.15500335,0.0019860957,0.009491471],"study_design_scores_gemma":[0.000012456649,0.000022250213,0.00010260734,0.000016388922,0.000012549607,0.00009489003,0.000042243184,0.946993,0.00017384009,0.047602218,0.0049099363,0.000017691673],"about_ca_topic_score_codex":0.005567445,"about_ca_topic_score_gemma":0.0031944714,"teacher_disagreement_score":0.007368019,"about_ca_system_score_codex":0.0012299183,"about_ca_system_score_gemma":0.00063782616,"threshold_uncertainty_score":0.024648488},"labels":[],"label_agreement":null},{"id":"W996535676","doi":"10.11575/prism/24651","title":"Accuracy Assessment of UWB for Locating Resources on Construction Sites","year":2013,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Computer science; Engineering; Construction engineering","score_opus":0.008754380592122992,"score_gpt":0.2209957514395055,"score_spread":0.2122413708473825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W996535676","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6954913,0.0022326147,0.29241523,0.00016972805,0.0001548556,0.00008942684,0.00037554052,0.0008768734,0.008194389],"genre_scores_gemma":[0.9581953,0.00079515955,0.039698005,0.00003018795,0.000018352886,0.000036738573,0.00020195023,0.000032501237,0.0009916676],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99826247,0.00030032254,0.00009906401,0.0002767165,0.0009320783,0.00012953287],"domain_scores_gemma":[0.99741745,0.00088100205,0.00033085834,0.00032464621,0.0009994282,0.000046455923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018131714,0.0004936165,0.00047230686,0.0016146778,0.0003079306,0.0010586688,0.0008566219,0.0008465346,0.0005716801],"category_scores_gemma":[0.0061273705,0.00023882047,0.0004207107,0.0010183952,0.00032091478,0.0008146787,0.0007939334,0.00040182064,0.0004593414],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011621837,0.00012482498,0.13471362,0.0012783577,0.0001806824,0.00060119934,0.0013651416,0.18846498,0.18205479,0.0034034038,0.0015322119,0.48511857],"study_design_scores_gemma":[0.00004350882,0.0020211178,0.124823794,0.00039605956,0.00041356345,0.001173497,0.0024882883,0.634089,0.22271505,0.0026694473,0.008979067,0.00018763402],"about_ca_topic_score_codex":0.0017400094,"about_ca_topic_score_gemma":0.00164551,"teacher_disagreement_score":0.0018131714,"about_ca_system_score_codex":0.00036386715,"about_ca_system_score_gemma":0.0003908387,"threshold_uncertainty_score":0.009589076},"labels":[],"label_agreement":null}]}