{"meta":{"query_hash":"4376f8350ec1","filters":{"venue":"International Conference on Information Fusion"},"cohort_total":68,"direct_labels_cover":0,"predictions_cover":68,"exported":68,"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/4376f8350ec1","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Conference+on+Information+Fusion"},"results":[{"id":"W1488451237","doi":"","title":"High level information fusion through a fuzzy extension to Multi-Entity Bayesian Networks in Vehicular Ad-hoc Networks","year":2013,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Vehicular Ad Hoc Networks (VANETs)","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 Waterloo","funders":"","keywords":"Computer science; Ambiguity; Bayesian network; Fuzzy logic; Artificial intelligence; Context (archaeology); Extension (predicate logic); Data mining; Bayesian probability; Machine learning","score_opus":0.02153071329612368,"score_gpt":0.23991417487595232,"score_spread":0.21838346157982863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488451237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071199164,0.00019970222,0.99157214,0.000104021245,0.000015444033,0.000025800673,0.000017788727,0.00013932357,0.0008059305],"genre_scores_gemma":[0.56869185,0.00057239796,0.4283951,0.00014557077,0.000062233106,0.0001011618,0.000120162396,0.000028666664,0.0018828309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983908,0.00053494173,0.00012410084,0.00027773753,0.00057511684,0.00009735809],"domain_scores_gemma":[0.9990237,0.0004959498,0.00008526874,0.000097979035,0.00024708695,0.00005005124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023415533,0.0005080887,0.00083565264,0.00095742114,0.0008259494,0.0016098783,0.0012836808,0.0010505035,0.00083240063],"category_scores_gemma":[0.003340744,0.00042716454,0.00080632255,0.00083602546,0.0008484352,0.003212779,0.0016861953,0.0013359688,0.00022218512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002814517,0.000108241395,0.0016251802,0.00021332064,0.00019162978,0.00046967453,0.00070524187,0.62606984,0.01763065,0.12734395,0.0015636168,0.22379716],"study_design_scores_gemma":[0.000009703017,0.000043668926,0.0002807079,0.00001937856,0.000040012488,0.00008063281,0.000033631513,0.9563512,0.0033392094,0.03732162,0.0024531034,0.000027177355],"about_ca_topic_score_codex":0.005358239,"about_ca_topic_score_gemma":0.0047994787,"teacher_disagreement_score":0.005358239,"about_ca_system_score_codex":0.0009322766,"about_ca_system_score_gemma":0.00094533304,"threshold_uncertainty_score":0.012383461},"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":"W1496756735","doi":"","title":"A cooperative game-theoretic measurement allocation algorithm for localization in unattended ground sensor networks","year":2008,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","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 British Columbia","funders":"","keywords":"Observability; Shapley value; Cooperative game theory; Game theory; Computer science; Measure (data warehouse); Node (physics); Mathematical optimization; Process (computing); Wireless sensor network; Value (mathematics); Algorithm; Mathematics; Engineering; Computer network; Data mining; Mathematical economics","score_opus":0.033884008996192615,"score_gpt":0.2547101831970356,"score_spread":0.22082617420084297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496756735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048829666,0.000039945466,0.99383754,0.00007095472,0.000015866723,0.000036578003,0.000006742874,0.00006855754,0.0010409323],"genre_scores_gemma":[0.62759817,0.00014332336,0.36882892,0.00018432012,0.000039727638,0.00031725358,0.000057638445,0.000042427924,0.0027882557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884355,0.0004885938,0.000043386677,0.00019566664,0.0002981421,0.00013070618],"domain_scores_gemma":[0.99901533,0.0005560097,0.00009096683,0.00007533407,0.00017496296,0.00008733099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019176806,0.0008211022,0.0011195148,0.00060822285,0.0007743919,0.0011001471,0.0025820264,0.0013058261,0.0012532879],"category_scores_gemma":[0.002977867,0.00036996065,0.00054418395,0.00076970214,0.0014670842,0.0018139975,0.002014063,0.001105594,0.00026783114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001360084,0.00014659052,0.00045304932,0.00008579873,0.00007748579,0.00013715973,0.00031679182,0.79774266,0.0037218377,0.107803226,0.0017581261,0.08762129],"study_design_scores_gemma":[0.00002163858,0.0000510728,0.00003829983,0.000004577566,0.00000837481,0.000028802955,0.000016436474,0.98164535,0.00042210793,0.017140282,0.000615564,0.000007499462],"about_ca_topic_score_codex":0.0019956012,"about_ca_topic_score_gemma":0.0019102904,"teacher_disagreement_score":0.0025820264,"about_ca_system_score_codex":0.0011214195,"about_ca_system_score_gemma":0.0015028092,"threshold_uncertainty_score":0.01014173},"labels":[],"label_agreement":null},{"id":"W1496835824","doi":"","title":"Data-driven diagnosis with ambiguous hypotheses in historical data: A generalized Dempter-Shafer approach","year":2013,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Fault Detection and Control Systems","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 Alberta","funders":"","keywords":"Dempster–Shafer theory; Computer science; Artificial intelligence; Data mining; Machine learning; Mathematics; Econometrics","score_opus":0.0644898697073149,"score_gpt":0.2572999865789146,"score_spread":0.1928101168715997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496835824","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":[0.0057339263,0.00039164585,0.9929548,0.00025674058,0.000025138552,0.00003446314,0.000043665863,0.00006985582,0.0004898194],"genre_scores_gemma":[0.50289154,0.0010550537,0.49354196,0.00041200477,0.00027853122,0.0001769307,0.00029064695,0.000055451852,0.00129787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958696,0.0014190489,0.00039344636,0.0008027982,0.0013147852,0.00020034442],"domain_scores_gemma":[0.9839929,0.0118946005,0.0012481004,0.0009383152,0.0016569436,0.0002691142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00847914,0.0011225839,0.002284362,0.005337134,0.00079674856,0.0024145723,0.003182086,0.0022149305,0.001215047],"category_scores_gemma":[0.028204959,0.00084789854,0.0015189202,0.0026362638,0.002479816,0.004628925,0.002359224,0.002282371,0.0002941565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017195985,0.000077553406,0.0027076907,0.0003575243,0.00028289188,0.0007137166,0.0005730831,0.67093474,0.0019144488,0.119771235,0.0013084424,0.20118675],"study_design_scores_gemma":[0.000018148266,0.000044339507,0.00031722276,0.00003853231,0.0000389973,0.00015416188,0.00005122619,0.9049023,0.00088392076,0.09261734,0.0008952361,0.00003864577],"about_ca_topic_score_codex":0.00321932,"about_ca_topic_score_gemma":0.0024834117,"teacher_disagreement_score":0.00847914,"about_ca_system_score_codex":0.0017511834,"about_ca_system_score_gemma":0.0013491786,"threshold_uncertainty_score":0.04484248},"labels":[],"label_agreement":null},{"id":"W1510049598","doi":"","title":"Fusing social network data with hard data","year":2015,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Data Management and Algorithms","field":"Computer Science","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":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Computer science; Dissemination; Event (particle physics); Fuse (electrical); Social media; Social network (sociolinguistics); Identification (biology); Class (philosophy); Process (computing); Data mining; Attack patterns; Microblogging; Sensor fusion; Computer security; Data science; Information retrieval; World Wide Web; Artificial intelligence; Intrusion detection system; Engineering","score_opus":0.17921186500788264,"score_gpt":0.3274271580848725,"score_spread":0.14821529307698986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510049598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18516615,0.0009616477,0.8076088,0.0010191089,0.0003233011,0.00022977707,0.0008909042,0.0012172689,0.002583097],"genre_scores_gemma":[0.8104374,0.0005039104,0.18457301,0.00027413285,0.00042013606,0.00012696505,0.0017254077,0.0000976467,0.0018414654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803954,0.0004706967,0.00018659135,0.00039527542,0.0007104035,0.00019750987],"domain_scores_gemma":[0.9941841,0.0027757322,0.0006854075,0.001374713,0.0007919302,0.00018820973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002226623,0.0020972237,0.0014484953,0.0040694843,0.0006202512,0.001708858,0.0012646924,0.0015019608,0.0011788292],"category_scores_gemma":[0.011283019,0.00061827304,0.0011215151,0.0034403298,0.0008186849,0.003684928,0.0027809897,0.0013857874,0.00068538846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076469564,0.00053327397,0.028690903,0.00044332634,0.0005249788,0.0008820335,0.0005038912,0.30648053,0.025160667,0.004379014,0.003616696,0.62802],"study_design_scores_gemma":[0.000012748949,0.00015684744,0.006537361,0.000025386249,0.000058356392,0.00015599266,0.00024988322,0.96584487,0.011045957,0.013344161,0.0025315476,0.000036827772],"about_ca_topic_score_codex":0.00232815,"about_ca_topic_score_gemma":0.0024765967,"teacher_disagreement_score":0.0040694843,"about_ca_system_score_codex":0.0005350245,"about_ca_system_score_gemma":0.0005140944,"threshold_uncertainty_score":0.011775613},"labels":[],"label_agreement":null},{"id":"W1511553427","doi":"","title":"Antenna allocation for MIMO radars with collocated antennas","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Radar Systems and Signal Processing","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":"Cramér–Rao bound; MIMO; Antenna (radio); Upper and lower bounds; Computer science; Radar; Mathematical optimization; Directional antenna; Convex optimization; Electronic engineering; Algorithm; Mathematics; Regular polygon; Telecommunications; Engineering; Estimation theory","score_opus":0.023211106793963494,"score_gpt":0.24422500747377546,"score_spread":0.22101390067981197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1511553427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016072564,0.0006655714,0.9810142,0.00009121541,0.00002707756,0.000016621623,0.000021837775,0.000080234815,0.0020107045],"genre_scores_gemma":[0.76259315,0.0009909726,0.23343593,0.00015644706,0.00008720671,0.0000924635,0.00006379206,0.00004689237,0.002533169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991373,0.00041022984,0.000026681046,0.00016014048,0.00018777016,0.00007792575],"domain_scores_gemma":[0.9990816,0.000516469,0.00013405894,0.000115425355,0.000120898774,0.00003147808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006131817,0.00068908173,0.00066634343,0.00028218416,0.00027274713,0.0007025278,0.0005726841,0.00075480225,0.0011418142],"category_scores_gemma":[0.0021825088,0.00040184593,0.00037202876,0.0007214061,0.0005215856,0.0008535197,0.00080647273,0.0006023363,0.0006099049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014988986,0.000050674575,0.00052767823,0.00010423581,0.000053811884,0.00013166328,0.00009778834,0.8810967,0.01901539,0.01875952,0.0012714621,0.078741156],"study_design_scores_gemma":[0.000019066412,0.00008383222,0.00028376852,0.000010270463,0.00001632978,0.00009133367,0.000030173242,0.98802364,0.0029730066,0.007115017,0.0013409364,0.000012635897],"about_ca_topic_score_codex":0.00080693554,"about_ca_topic_score_gemma":0.0014127453,"teacher_disagreement_score":0.0011418142,"about_ca_system_score_codex":0.00048700403,"about_ca_system_score_gemma":0.00037512288,"threshold_uncertainty_score":0.0038197637},"labels":[],"label_agreement":null},{"id":"W1543967334","doi":"","title":"A particle filter based on a constrained sampling method for state estimation","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Particle filter; Constraint (computer-aided design); Mathematical optimization; Importance sampling; Sampling (signal processing); Posterior probability; Computer science; Nonlinear system; Inverse problem; Constrained optimization; Algorithm; Control theory (sociology); Mathematics; Filter (signal processing); Artificial intelligence; Statistics; Bayesian probability; Physics","score_opus":0.06516672630011372,"score_gpt":0.34002452788001136,"score_spread":0.27485780157989764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543967334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059553183,0.00006113094,0.99895275,0.000020110876,0.0000271931,0.000015313592,0.000008597703,0.000074850555,0.00024461147],"genre_scores_gemma":[0.113031976,0.0006748841,0.88215965,0.00014428882,0.0001561301,0.00034955278,0.00022715777,0.000082784165,0.0031735473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992428,0.00018063808,0.000044678574,0.00016773149,0.00032559413,0.000038677554],"domain_scores_gemma":[0.9992968,0.00037306955,0.000055059812,0.000058778565,0.00018786588,0.000028465234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096019875,0.00088448747,0.0010248169,0.00083478994,0.0006413018,0.0006989007,0.0011460346,0.0011915864,0.0020291642],"category_scores_gemma":[0.0029749349,0.0004631392,0.0010322104,0.0011635056,0.000534637,0.0013418276,0.0008686781,0.0013414851,0.00052570883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021403772,0.000103751816,0.001252739,0.0003368468,0.0001589127,0.00020463465,0.00017601588,0.5244356,0.014864979,0.03985209,0.004234713,0.41416565],"study_design_scores_gemma":[0.000015121914,0.000022517388,0.00013912513,0.000008241377,0.000011331721,0.000050708448,0.0000042290035,0.99457943,0.001427871,0.0018577713,0.0018685132,0.000015167221],"about_ca_topic_score_codex":0.011280935,"about_ca_topic_score_gemma":0.006774415,"teacher_disagreement_score":0.011280935,"about_ca_system_score_codex":0.00055994146,"about_ca_system_score_gemma":0.001683728,"threshold_uncertainty_score":0.02243054},"labels":[],"label_agreement":null},{"id":"W1562854493","doi":"","title":"A context-based fusion algorithm for shape retrieval","year":2008,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","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":"Computer science; Context (archaeology); Image retrieval; Image (mathematics); Shape context; Artificial intelligence; Pattern recognition (psychology); Scheme (mathematics); Function (biology); Algorithm; Data mining; Mathematics","score_opus":0.04793185447706155,"score_gpt":0.28840488358708505,"score_spread":0.2404730291100235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1562854493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010246633,0.0011245892,0.9861514,0.0001090313,0.00013464288,0.00015103495,0.00005557813,0.0008936756,0.00113341],"genre_scores_gemma":[0.15003072,0.00072802894,0.8469926,0.00016485763,0.00012467222,0.00023561675,0.00020532522,0.00006752761,0.0014506371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986412,0.00016072145,0.00008573184,0.00031941305,0.0006828737,0.000110124805],"domain_scores_gemma":[0.9992118,0.00018221907,0.000064910586,0.000119451135,0.00038546062,0.00003608445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015139867,0.0009578437,0.0018276141,0.002550939,0.001250871,0.0011007902,0.0016127311,0.001404201,0.002019605],"category_scores_gemma":[0.00379939,0.00041767792,0.0012781644,0.0026680985,0.0005511464,0.0021368484,0.0016661008,0.00134544,0.0010386216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033887246,0.00011521608,0.0008154767,0.0001222818,0.00009354164,0.00012496262,0.00014970823,0.029156163,0.04788695,0.0104455305,0.0029153368,0.907836],"study_design_scores_gemma":[0.00007305635,0.00044974667,0.0018895213,0.00004784837,0.00016410848,0.0009489282,0.00012162486,0.92770314,0.04561655,0.010084055,0.012792324,0.00010907602],"about_ca_topic_score_codex":0.004172108,"about_ca_topic_score_gemma":0.004214189,"teacher_disagreement_score":0.004172108,"about_ca_system_score_codex":0.0009623804,"about_ca_system_score_gemma":0.0010550406,"threshold_uncertainty_score":0.008295655},"labels":[],"label_agreement":null},{"id":"W1564500635","doi":"","title":"Comparison of angle-only filtering algorithms in 3D using EKF, UKF, PF, PFF, and ensemble KF","year":2015,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"","keywords":"Extended Kalman filter; Ensemble Kalman filter; Particle filter; Kalman filter; Invariant extended Kalman filter; Cartesian coordinate system; Monte Carlo method; Algorithm; Unscented transform; Computer science; State vector; Control theory (sociology); Filter (signal processing); Mathematics; Computer vision; Artificial intelligence; Physics; Statistics; Geometry","score_opus":0.1067799483226501,"score_gpt":0.3455347602713912,"score_spread":0.2387548119487411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1564500635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09544745,0.0015651576,0.8979802,0.00018290339,0.00015609442,0.00005012446,0.0001971191,0.0017270163,0.0026939632],"genre_scores_gemma":[0.622253,0.0012002757,0.37387812,0.00009592472,0.00006417549,0.00008859934,0.00067434175,0.00019014189,0.0015554602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897397,0.00018264155,0.00010651803,0.00016785949,0.00045894302,0.0001101719],"domain_scores_gemma":[0.9957385,0.002192031,0.00024246417,0.00034361327,0.0013873847,0.00009594972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021495658,0.00097034016,0.0014194767,0.0013395803,0.00062289124,0.0011702263,0.0008397336,0.0015544291,0.0011938734],"category_scores_gemma":[0.009976003,0.00039135414,0.0010646933,0.0013488863,0.0003360576,0.0027038713,0.0006826974,0.000882497,0.00036742873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044365154,0.00010566579,0.009826314,0.00022724707,0.00024386166,0.00009297774,0.00016143393,0.60943747,0.004894728,0.0032758003,0.0014356376,0.36985525],"study_design_scores_gemma":[0.000019102035,0.000069427384,0.0028311852,0.000024545143,0.000048129343,0.00007992597,0.000047601065,0.99015677,0.004661925,0.0008777305,0.0011500425,0.000033622906],"about_ca_topic_score_codex":0.020149827,"about_ca_topic_score_gemma":0.012390464,"teacher_disagreement_score":0.020149827,"about_ca_system_score_codex":0.00058959844,"about_ca_system_score_gemma":0.0013448513,"threshold_uncertainty_score":0.04006511},"labels":[],"label_agreement":null},{"id":"W1579079628","doi":"","title":"Modified value iteration algorithm and Dynamic Element Matching based MDP for Distributed data fusion and sensor management","year":2008,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Computer science; Markov decision process; Algorithm; Sensor fusion; Matching (statistics); Mathematical optimization; Markov process; Mathematics; Artificial intelligence","score_opus":0.03786654698309363,"score_gpt":0.28189110702552833,"score_spread":0.2440245600424347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579079628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028865233,0.00008642059,0.9959974,0.00009261589,0.000026405953,0.000034628432,0.00001105441,0.000059398437,0.00080553925],"genre_scores_gemma":[0.40062165,0.00025842275,0.59519434,0.00015860166,0.00006286036,0.0004414446,0.000103407605,0.000054223652,0.0031050902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985494,0.00062313996,0.000076699245,0.00026210624,0.00038058445,0.00010810367],"domain_scores_gemma":[0.9976587,0.0016466399,0.00016565005,0.00012444201,0.00033978553,0.000064683234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022275755,0.0008423056,0.0012039292,0.0006671562,0.00039032882,0.0009980905,0.0015519771,0.0015671849,0.0019370966],"category_scores_gemma":[0.0053262,0.00041079783,0.00067374797,0.0007286053,0.0010605636,0.0013945769,0.0011256218,0.0016863701,0.0002938728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007044375,0.00003856205,0.00021800978,0.000050788058,0.000031303432,0.000047281024,0.000046036595,0.9292513,0.00094026094,0.030662777,0.00046728668,0.038176082],"study_design_scores_gemma":[0.000009605779,0.00002155907,0.000020566295,0.0000028295099,0.000002825848,0.000010027897,0.0000030793465,0.99327916,0.00032830908,0.0058929296,0.00042538263,0.0000036525516],"about_ca_topic_score_codex":0.0020304827,"about_ca_topic_score_gemma":0.001329826,"teacher_disagreement_score":0.0022275755,"about_ca_system_score_codex":0.0011409068,"about_ca_system_score_gemma":0.0014806244,"threshold_uncertainty_score":0.011780679},"labels":[],"label_agreement":null},{"id":"W1591701304","doi":"","title":"An optimal sequential optimization approach in application to dynamic weapon allocation in naval warfare","year":2008,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Military Defense Systems Analysis","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":"Lockheed Martin (Canada)","funders":"","keywords":"Linear subspace; Curse of dimensionality; Computer science; Decomposition; Mathematical optimization; Optimization problem; Weapon system; Sequence (biology); Algorithm; Mathematics; Artificial intelligence","score_opus":0.014369668869576411,"score_gpt":0.24736768800609032,"score_spread":0.2329980191365139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1591701304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028067203,0.00022892353,0.994531,0.00009914814,0.000025942994,0.000024020248,0.000015743746,0.00010019219,0.0021682635],"genre_scores_gemma":[0.29054016,0.0009560901,0.7016148,0.00020222607,0.00011155116,0.00036270052,0.00013851146,0.0002196341,0.0058544273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991972,0.00031530147,0.000035983612,0.00011317829,0.00027230754,0.000066107474],"domain_scores_gemma":[0.9993699,0.00039054826,0.00004845599,0.000044607877,0.00012188007,0.000024591525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013718486,0.0012826548,0.0012198334,0.00067306927,0.00055590074,0.0007233825,0.0006048584,0.00063815725,0.0035820226],"category_scores_gemma":[0.0020800845,0.00062374724,0.0009049045,0.0010815215,0.00082591403,0.0008600561,0.0011375935,0.0009912131,0.0004107076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057790214,0.000034762983,0.00012766368,0.00008682979,0.000038178718,0.000042757198,0.000031494965,0.9296956,0.0025688556,0.019451672,0.0009416875,0.046922676],"study_design_scores_gemma":[0.000013700039,0.000052023363,0.00005068244,0.0000071382515,0.00001059662,0.000021519912,0.000006444102,0.98231286,0.0006356766,0.015610397,0.0012733078,0.000005685778],"about_ca_topic_score_codex":0.004577865,"about_ca_topic_score_gemma":0.0045405757,"teacher_disagreement_score":0.004577865,"about_ca_system_score_codex":0.0007240146,"about_ca_system_score_gemma":0.0015953045,"threshold_uncertainty_score":0.011983097},"labels":[],"label_agreement":null},{"id":"W1595836651","doi":"","title":"Extended touch mobile user interfaces through sensor fusion","year":2013,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Speech and Audio Processing","field":"Computer Science","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":"Accelerometer; Computer science; Sensor fusion; Microphone; Fusion; Classifier (UML); Mobile device; Artificial intelligence; Computer vision; Real-time computing; Speech recognition; Telecommunications","score_opus":0.02393915148136228,"score_gpt":0.281611061182464,"score_spread":0.2576719097011017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1595836651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026958203,0.0009187733,0.964533,0.00012378208,0.00010594128,0.0001041987,0.00016647777,0.003357678,0.003732006],"genre_scores_gemma":[0.73122907,0.000698289,0.25942194,0.00027576508,0.000109406865,0.00016727544,0.00055793533,0.00019503976,0.0073452583],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99880993,0.00022115711,0.0000702674,0.00021762875,0.00057484023,0.00010620691],"domain_scores_gemma":[0.99941015,0.00021672965,0.000049231752,0.00014083888,0.00015740297,0.000025691263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080367265,0.0008495439,0.00088152126,0.0006756767,0.0003054086,0.0013350785,0.00089622644,0.00091663847,0.004163679],"category_scores_gemma":[0.0022510805,0.0003143867,0.00055767404,0.0006172686,0.0003490414,0.002099809,0.0020260506,0.0006046005,0.0014759473],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007791041,0.0001382914,0.0018391833,0.0003626289,0.00016492621,0.0003744041,0.00044450906,0.043468226,0.14833228,0.008420482,0.0043072966,0.79136854],"study_design_scores_gemma":[0.000037419923,0.00061749056,0.0047513954,0.00007693542,0.00008499724,0.0010296737,0.00020611894,0.8845433,0.07558729,0.016646795,0.016310932,0.00010763467],"about_ca_topic_score_codex":0.00060945307,"about_ca_topic_score_gemma":0.000677589,"teacher_disagreement_score":0.004163679,"about_ca_system_score_codex":0.00027522637,"about_ca_system_score_gemma":0.00017287393,"threshold_uncertainty_score":0.01392889},"labels":[],"label_agreement":null},{"id":"W1600342926","doi":"","title":"Fuzzy cognitive map based situation assessment for coastal surveillance","year":2008,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Cognitive Science and Mapping","field":"Computer Science","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":"Defence Research and Development Canada; University of Calgary","funders":"","keywords":"Fuzzy cognitive map; Computer science; Fuzzy logic; Inference engine; Data mining; Sensor fusion; Artificial intelligence; Inference; Cognitive map; Adaptive neuro fuzzy inference system; Causality (physics); Fuzzy inference system; Machine learning; Fuzzy control system; Causal inference; Cognition; Mathematics","score_opus":0.0476663685618261,"score_gpt":0.3112991012531973,"score_spread":0.2636327326913712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1600342926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05701269,0.00032065646,0.93427587,0.00024484433,0.00004837141,0.00016168196,0.00012542786,0.00074269087,0.0070677046],"genre_scores_gemma":[0.8490486,0.00015875025,0.1495341,0.00003752791,0.000015198984,0.00012695818,0.000111809,0.000019743706,0.00094728085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994747,0.0001853523,0.00002576548,0.00006599474,0.0001969046,0.000051329156],"domain_scores_gemma":[0.9994387,0.00022897587,0.00005330384,0.00003239149,0.00020191388,0.000044725795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010160403,0.0006072616,0.00046444344,0.0017312107,0.0007126654,0.0014659537,0.00086373294,0.0005387597,0.0015269608],"category_scores_gemma":[0.0033260488,0.00021849689,0.000563185,0.0006642102,0.0005032931,0.0016116427,0.0012076676,0.0005758571,0.00022794808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037254367,0.00019188467,0.0040045157,0.0002101365,0.00016726728,0.00036176143,0.0010724767,0.58974504,0.005578489,0.03939676,0.002602335,0.35629687],"study_design_scores_gemma":[0.000010134193,0.00004078051,0.0007706887,0.000016423477,0.000023844977,0.00003493065,0.00019191972,0.984446,0.0015591413,0.011786264,0.0010975172,0.000022264298],"about_ca_topic_score_codex":0.01418795,"about_ca_topic_score_gemma":0.009847377,"teacher_disagreement_score":0.01418795,"about_ca_system_score_codex":0.0014116754,"about_ca_system_score_gemma":0.0011962246,"threshold_uncertainty_score":0.02821076},"labels":[],"label_agreement":null},{"id":"W1614841168","doi":"","title":"Crowd analysis with target tracking, K-means clustering and hidden Markov models","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Institut National d'Optique","funders":"","keywords":"Crowds; Hidden Markov model; Cluster analysis; Centroid; Computer science; Tracking (education); Artificial intelligence; Pattern recognition (psychology); Crowd psychology; Markov chain; k-means clustering; Data mining; Machine learning","score_opus":0.02469062103090949,"score_gpt":0.2624692680882453,"score_spread":0.23777864705733578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1614841168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006363909,0.00033380798,0.99203473,0.00009928901,0.00003500963,0.000043051987,0.000058200967,0.0004057996,0.00062624173],"genre_scores_gemma":[0.37621677,0.0010253618,0.61779416,0.00012238845,0.00023157104,0.00025406608,0.00057304004,0.00019589017,0.0035868105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984579,0.0006044456,0.000076888784,0.0003397703,0.00041353202,0.00010754315],"domain_scores_gemma":[0.99784434,0.001273181,0.0003116341,0.00018462143,0.0003051173,0.00008113947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023107359,0.0012868666,0.0014406417,0.0025298765,0.0011405791,0.0015371485,0.0014414281,0.0013162023,0.0007510168],"category_scores_gemma":[0.006055401,0.00088608515,0.0015904314,0.002149123,0.0011430422,0.0018066501,0.0018001077,0.0012333044,0.00060676323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010302285,0.00005684983,0.0021118838,0.00013215082,0.00016781516,0.000115429204,0.00032713445,0.88251,0.0016334603,0.018900665,0.0017891444,0.092152454],"study_design_scores_gemma":[0.000003433438,0.000009975398,0.00032023955,0.000009571579,0.000009972773,0.000021384136,0.00002856502,0.98513687,0.00047431426,0.013338513,0.00063108787,0.000016012955],"about_ca_topic_score_codex":0.021106351,"about_ca_topic_score_gemma":0.012672016,"teacher_disagreement_score":0.021106351,"about_ca_system_score_codex":0.0014723571,"about_ca_system_score_gemma":0.00160278,"threshold_uncertainty_score":0.041967034},"labels":[],"label_agreement":null},{"id":"W1624358614","doi":"","title":"Bias estimation for practical distributed multiradar-multitarget tracking systems","year":2013,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"McMaster University","funders":"","keywords":"Fusion center; Computer science; Tracking (education); Filter (signal processing); Sensor fusion; Transmission (telecommunications); Tracking system; Sampling (signal processing); Real-time computing; Estimation; Algorithm; Artificial intelligence; Computer vision; Telecommunications; Wireless; Engineering","score_opus":0.07902569369936314,"score_gpt":0.3190384446271419,"score_spread":0.2400127509277788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1624358614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003133872,0.00012902946,0.9962823,0.000032104617,0.000011862227,0.00000846001,0.000007086571,0.000097678676,0.0002977059],"genre_scores_gemma":[0.4269922,0.00068000087,0.56895673,0.000088253095,0.00007337219,0.00012633308,0.00013759726,0.000082802515,0.0028627769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950266,0.00009469508,0.00002889125,0.0001270969,0.00020388837,0.000042879805],"domain_scores_gemma":[0.9989503,0.000534349,0.00012966638,0.00012622951,0.0002345578,0.000024822073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010142443,0.0005385158,0.00066314026,0.0003946328,0.00045897084,0.0007058659,0.0007089356,0.00088729116,0.0011649167],"category_scores_gemma":[0.0038207504,0.00027302888,0.00041983594,0.0004945462,0.00042914445,0.0009790224,0.00093514926,0.00095801795,0.00054077955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014537478,0.00003643688,0.0015740762,0.00016423615,0.000053090327,0.0001283491,0.00014187832,0.6671059,0.018206187,0.030432543,0.0012714822,0.2807404],"study_design_scores_gemma":[0.000011638264,0.00003438265,0.00033964246,0.000009284905,0.000008886077,0.000077591714,0.000013169719,0.98635393,0.0039032686,0.007812145,0.0014272357,0.000008835881],"about_ca_topic_score_codex":0.001351471,"about_ca_topic_score_gemma":0.0016231687,"teacher_disagreement_score":0.001351471,"about_ca_system_score_codex":0.0007148818,"about_ca_system_score_gemma":0.0007083889,"threshold_uncertainty_score":0.005363941},"labels":[],"label_agreement":null},{"id":"W1671977056","doi":"","title":"Soft-Data-Constrained Multi-Model Particle Filter for agile target tracking","year":2013,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Particle filter; Computer science; Agile software development; Data mining; Process (computing); Inference; Tracking (education); Artificial intelligence; Set (abstract data type); Filter (signal processing); Soft computing; Fuzzy logic; Machine learning; Computer vision","score_opus":0.08778401810758585,"score_gpt":0.3069965736564003,"score_spread":0.21921255554881447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1671977056","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":[0.0023716572,0.000081270286,0.99716526,0.00004119296,0.000013335039,0.0000074026266,0.000011606562,0.00007010288,0.00023817334],"genre_scores_gemma":[0.4252554,0.00043469702,0.5717559,0.00016031663,0.0000628375,0.00013002154,0.00019412852,0.00006138521,0.0019453308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939203,0.00016469895,0.000030837306,0.00011530915,0.0002574759,0.00003967368],"domain_scores_gemma":[0.999126,0.0005223998,0.00008239703,0.000104208004,0.00013604992,0.000028952201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012311724,0.0005986505,0.0008578386,0.0006210012,0.00039639548,0.0006555148,0.0009647976,0.0010514737,0.0009339814],"category_scores_gemma":[0.0029747216,0.00046900535,0.000691357,0.0007261632,0.0004906639,0.001293716,0.0008639309,0.0013322567,0.00029545248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020577286,0.00006671425,0.000928274,0.00014512597,0.000112300586,0.00014366342,0.00014815411,0.7381991,0.017207252,0.02037505,0.0015503772,0.22091821],"study_design_scores_gemma":[0.0000072650887,0.000013613873,0.00016185273,0.0000040696987,0.0000056355843,0.000021486247,0.0000035144778,0.99524105,0.0013743258,0.0026685651,0.0004891814,0.000009461141],"about_ca_topic_score_codex":0.0049868007,"about_ca_topic_score_gemma":0.0043363087,"teacher_disagreement_score":0.0049868007,"about_ca_system_score_codex":0.00052396493,"about_ca_system_score_gemma":0.0009851692,"threshold_uncertainty_score":0.009915531},"labels":[],"label_agreement":null},{"id":"W1798230567","doi":"","title":"Multisensor particle filter cloud fusion for multitarget tracking","year":2008,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Defence Research and Development Canada; General Dynamics (Canada); McMaster University","funders":"","keywords":"Particle filter; Auxiliary particle filter; Tracking (education); Resampling; Fusion; Sensor fusion; Particle (ecology); Computer science; Artificial intelligence; Filter (signal processing); Computer vision; Ensemble Kalman filter; Kalman filter; Extended Kalman filter","score_opus":0.0647457781323224,"score_gpt":0.28821486069218855,"score_spread":0.22346908255986614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1798230567","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":[0.0045398325,0.0003379119,0.9942194,0.00007832354,0.000063017374,0.000018759878,0.00002861473,0.00020064712,0.0005135119],"genre_scores_gemma":[0.4486662,0.0010663037,0.546969,0.0001261667,0.00012411701,0.00013948331,0.00026625412,0.00007693361,0.0025656424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991737,0.0002153139,0.00004147951,0.00015617402,0.00034886258,0.000064487],"domain_scores_gemma":[0.99917173,0.00039166218,0.00007819846,0.00014295474,0.00018527368,0.000030207506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013240215,0.0005779336,0.0009447168,0.00087179255,0.00062053796,0.00096260494,0.0008555492,0.0012450259,0.0014931585],"category_scores_gemma":[0.002499217,0.0003941863,0.0011319832,0.0012198682,0.00047815684,0.0016459925,0.0013009426,0.0011241083,0.00049791514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031102562,0.00008092545,0.0013476504,0.00021446045,0.00016378335,0.0002453456,0.00012912441,0.73707443,0.020831188,0.025445767,0.0022170846,0.21193923],"study_design_scores_gemma":[0.0000063267184,0.000020835178,0.00019908797,0.000005670487,0.000009169285,0.00003218904,0.0000056578365,0.9918727,0.0036090233,0.0031210058,0.0011093598,0.000008911083],"about_ca_topic_score_codex":0.0048083444,"about_ca_topic_score_gemma":0.003636218,"teacher_disagreement_score":0.0048083444,"about_ca_system_score_codex":0.0009795112,"about_ca_system_score_gemma":0.0009353791,"threshold_uncertainty_score":0.009560704},"labels":[],"label_agreement":null},{"id":"W1831866948","doi":"","title":"Data fusion and mis-information removal in social networks","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","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":"Adjacency matrix; Computer science; Graph; Sensor fusion; Adjacency list; Key (lock); Theoretical computer science; Information flow; Data mining; Data modeling; State (computer science); Artificial intelligence; Algorithm; Computer security","score_opus":0.04071963293328879,"score_gpt":0.3179933101568979,"score_spread":0.2772736772236091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1831866948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011750193,0.00031627674,0.98656255,0.00056033616,0.000044642828,0.000030042977,0.000059051286,0.00009704986,0.00057994],"genre_scores_gemma":[0.7764134,0.0007696864,0.21967486,0.00036009165,0.00019055743,0.00020361292,0.00031906704,0.00007078862,0.001997866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929852,0.0029804357,0.00044149105,0.0014914126,0.0016287375,0.00047269094],"domain_scores_gemma":[0.9753705,0.018353378,0.001993172,0.0020987415,0.001871438,0.00031279164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008052833,0.0011876763,0.0021250886,0.002131154,0.0016134207,0.0025445882,0.0027147182,0.0029553308,0.0008913526],"category_scores_gemma":[0.035814576,0.0010748544,0.0014094589,0.0029853554,0.0025438888,0.0060938755,0.0039002337,0.0025050687,0.0003310535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040427543,0.00008520939,0.0029492558,0.0003433817,0.00029034904,0.0005781589,0.00068387925,0.727654,0.004654456,0.123644,0.0023353475,0.13637777],"study_design_scores_gemma":[0.000009682999,0.00002459102,0.00032335744,0.00001735451,0.00002624252,0.00007431867,0.000051672785,0.9403565,0.0020306334,0.05631415,0.0007501272,0.000021412718],"about_ca_topic_score_codex":0.0047701397,"about_ca_topic_score_gemma":0.0030079943,"teacher_disagreement_score":0.008052833,"about_ca_system_score_codex":0.0019527309,"about_ca_system_score_gemma":0.0014474157,"threshold_uncertainty_score":0.042587996},"labels":[],"label_agreement":null},{"id":"W1836472648","doi":"","title":"Decentralized sensor selection based on the distributed posterior Cramér-Rao lower bound","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Benchmark (surveying); Computer science; Selection (genetic algorithm); Wireless sensor network; Network topology; Sensor fusion; Upper and lower bounds; Particle filter; Distributed computing; Kalman filter; Mathematics; Artificial intelligence; Computer network","score_opus":0.026799162287250728,"score_gpt":0.2660767337507432,"score_spread":0.23927757146349246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1836472648","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":[0.003334192,0.00016353543,0.99505347,0.000101099875,0.00001421191,0.000021110414,0.000015276206,0.00012014727,0.0011769067],"genre_scores_gemma":[0.678543,0.00073606084,0.3167708,0.0001862319,0.00017162542,0.0002502597,0.00014799622,0.00010384265,0.0030901772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976762,0.00069224177,0.00007374144,0.00041440697,0.0010217732,0.00012171981],"domain_scores_gemma":[0.9965946,0.0020925156,0.0003535745,0.00029757465,0.0005729221,0.000088857814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021402256,0.000807257,0.0013072386,0.0007411467,0.000700394,0.0012752928,0.001389307,0.0009593236,0.0011275068],"category_scores_gemma":[0.008911725,0.0004918517,0.00046796037,0.0011588417,0.001232334,0.0015975472,0.0012436018,0.0011471082,0.0003635353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008513936,0.000032251155,0.000612894,0.000087610395,0.000033767734,0.000077213925,0.00007018985,0.90565765,0.0061936835,0.033395167,0.0015801719,0.05217428],"study_design_scores_gemma":[0.000014514955,0.000038684586,0.00015920782,0.0000060744383,0.00000767916,0.000039240454,0.000007711687,0.9893901,0.0012753764,0.008435015,0.0006161964,0.000010167921],"about_ca_topic_score_codex":0.0024612527,"about_ca_topic_score_gemma":0.0031263477,"teacher_disagreement_score":0.0024612527,"about_ca_system_score_codex":0.0011859739,"about_ca_system_score_gemma":0.0018794172,"threshold_uncertainty_score":0.011318743},"labels":[],"label_agreement":null},{"id":"W1848596129","doi":"","title":"A track scoring MOP for perimeter surveillance radar evaluation","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Track (disk drive); Radar tracker; Measure (data warehouse); Track-before-detect; Consistency (knowledge bases); Radar; Real-time computing; Secondary surveillance radar; Relation (database); Tracking (education); Data mining; Real world data; Simulation; Artificial intelligence; Telecommunications","score_opus":0.06809535940427182,"score_gpt":0.320704014594936,"score_spread":0.2526086551906642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1848596129","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042429656,0.00023162774,0.94413155,0.000250521,0.00009635261,0.00089573354,0.00084011274,0.0026162022,0.008508366],"genre_scores_gemma":[0.49273735,0.00013305356,0.5024635,0.00010841262,0.000080475875,0.0010998658,0.0013731216,0.00032238985,0.001681791],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9877494,0.0038012078,0.0012071765,0.0009599457,0.0058557657,0.00042653046],"domain_scores_gemma":[0.96890724,0.011011494,0.005523621,0.003656059,0.010103838,0.00079779094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012117879,0.0013251889,0.0010537218,0.0043686037,0.0011845281,0.0028663885,0.0013740191,0.0011023071,0.0024878203],"category_scores_gemma":[0.04738354,0.00035858533,0.0007213981,0.0029641124,0.0008595429,0.0023100148,0.0023786996,0.0014976263,0.00089897565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007401286,0.00054768554,0.08799836,0.00054900494,0.00033539973,0.00037812672,0.00056862645,0.16632617,0.031445734,0.039095055,0.017991964,0.6540237],"study_design_scores_gemma":[0.000092590824,0.0023558938,0.04134069,0.00018295294,0.000119129916,0.00096394576,0.0004023278,0.90135217,0.023527097,0.012739049,0.016695866,0.00022823623],"about_ca_topic_score_codex":0.0027726935,"about_ca_topic_score_gemma":0.0019695135,"teacher_disagreement_score":0.012117879,"about_ca_system_score_codex":0.0016484179,"about_ca_system_score_gemma":0.002335539,"threshold_uncertainty_score":0.0640862},"labels":[],"label_agreement":null},{"id":"W1865476398","doi":"","title":"Towards unbiased evaluation of uncertainty reasoning: The URREF ontology","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":142,"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":"Ontology; Computer science; Situation awareness; Sensor fusion; Representation (politics); Situation analysis; Knowledge representation and reasoning; Data mining; Ontology-based data integration; Data collection; Information retrieval; Data science; Artificial intelligence; Engineering; Semantic Web","score_opus":0.08811752557596993,"score_gpt":0.3427408974872192,"score_spread":0.2546233719112493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1865476398","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":[0.0027735846,0.00051415607,0.986046,0.0017593026,0.00008488921,0.00022595364,0.00019500504,0.000508268,0.007892825],"genre_scores_gemma":[0.06913987,0.0006822501,0.9267035,0.00047800987,0.00012127861,0.00046671598,0.00092651334,0.00028964577,0.0011923331],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.91779673,0.043486573,0.0075721648,0.0043420424,0.024340773,0.0024617356],"domain_scores_gemma":[0.93436253,0.022366358,0.0044848924,0.01302691,0.024311991,0.001447335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07722241,0.0015527338,0.002025025,0.009284201,0.0028991217,0.0156194065,0.0060102353,0.004026616,0.0021441774],"category_scores_gemma":[0.08800412,0.0010946946,0.003338628,0.0071413354,0.008589114,0.027034834,0.009978929,0.005461888,0.00090980896],"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":[0.00004203683,0.00007661585,0.0005799218,0.00019592905,0.00005880871,0.00011659984,0.0010663202,0.009990348,0.00065179646,0.92090327,0.0031464833,0.063171946],"study_design_scores_gemma":[0.000038947943,0.000058914615,0.00041102816,0.0006640462,0.00012492997,0.00019808524,0.0011570182,0.120462045,0.0034994828,0.8106828,0.0626002,0.00010253052],"about_ca_topic_score_codex":0.018044187,"about_ca_topic_score_gemma":0.011356745,"teacher_disagreement_score":0.07722241,"about_ca_system_score_codex":0.0065420414,"about_ca_system_score_gemma":0.011575354,"threshold_uncertainty_score":0.408396},"labels":[],"label_agreement":null},{"id":"W1911155832","doi":"","title":"Expression of uncertainty in linguistic data","year":2008,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Natural Language Processing Techniques","field":"Computer Science","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":"Defence Research and Development Canada","funders":"","keywords":"Ambiguity; Computer science; Certainty; Expression (computer science); Utterance; Natural language; Deep linguistic processing; Natural language processing; Interpretation (philosophy); Linguistics; Artificial intelligence; Sine qua non; Point (geometry); Natural (archaeology); Mathematics","score_opus":0.056087336018732926,"score_gpt":0.3233109329352256,"score_spread":0.26722359691649267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1911155832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025858615,0.0033094762,0.95501137,0.0068101576,0.00029462122,0.00015062884,0.0007159272,0.00020068404,0.007648517],"genre_scores_gemma":[0.5736148,0.0034559937,0.41710716,0.0012072448,0.00092959445,0.00061966217,0.0007981947,0.00014183269,0.002125504],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97014093,0.011474096,0.0039800704,0.002801317,0.010632191,0.00097142626],"domain_scores_gemma":[0.9123062,0.06587419,0.008983385,0.006453807,0.005757179,0.0006252715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021590408,0.00097173854,0.0014682813,0.0069729555,0.0027407787,0.011256266,0.0024850836,0.0027519593,0.0014637379],"category_scores_gemma":[0.09165405,0.0009825175,0.0015619335,0.0063616717,0.009542524,0.017478315,0.006769065,0.0036482674,0.000317212],"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":[0.000095688854,0.000026261812,0.002727542,0.00063775084,0.00014829713,0.0015807186,0.0060852696,0.021558143,0.0027652269,0.9145459,0.0012072021,0.048622027],"study_design_scores_gemma":[0.000008783767,0.000026841773,0.00064940995,0.00027319946,0.000053118234,0.00055408885,0.001311512,0.032574616,0.0016775221,0.9540008,0.008780623,0.00008957076],"about_ca_topic_score_codex":0.0025336407,"about_ca_topic_score_gemma":0.0012757235,"teacher_disagreement_score":0.021590408,"about_ca_system_score_codex":0.0037203794,"about_ca_system_score_gemma":0.0020160482,"threshold_uncertainty_score":0.11418235},"labels":[],"label_agreement":null},{"id":"W1952182896","doi":"","title":"Kalman filtering approach to multirate information fusion for soft sensor development","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Fault Detection and Control Systems","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 Alberta","funders":"","keywords":"Kalman filter; Soft sensor; Computer science; Sensor fusion; Data mining; Process (computing); Sampling (signal processing); Quality (philosophy); Filter (signal processing); Real-time computing; Artificial intelligence; Computer vision","score_opus":0.02867369135180072,"score_gpt":0.2487085551593828,"score_spread":0.22003486380758208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1952182896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009500714,0.00028318402,0.9979613,0.00004575867,0.000031069736,0.000011645653,0.000009725658,0.0000802152,0.00062703004],"genre_scores_gemma":[0.4623487,0.0023362506,0.5283262,0.00015995398,0.00022536474,0.00026970197,0.00018870053,0.000091778194,0.006053345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990075,0.0003052598,0.000092427625,0.00021067618,0.000301165,0.00008301467],"domain_scores_gemma":[0.9993498,0.0002843577,0.00007597809,0.00008310982,0.00018838792,0.000018370662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015214672,0.00084064103,0.0009255493,0.0008351325,0.00047567562,0.0011268663,0.001153904,0.00095671363,0.0020652835],"category_scores_gemma":[0.0026855003,0.00039903846,0.0010044992,0.000867194,0.00069980486,0.0019844812,0.0012048802,0.0012431192,0.0005799988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001242234,0.000062828716,0.00066418754,0.0002831769,0.00013854116,0.00015581578,0.00027075564,0.61184543,0.0085813645,0.122703224,0.0016727982,0.25349772],"study_design_scores_gemma":[0.000007486477,0.00004128399,0.0001512654,0.000015781996,0.000019599021,0.000025704438,0.000014210794,0.9833572,0.0019073656,0.012327815,0.0021114296,0.000020939135],"about_ca_topic_score_codex":0.0045551015,"about_ca_topic_score_gemma":0.0034547905,"teacher_disagreement_score":0.0045551015,"about_ca_system_score_codex":0.000955183,"about_ca_system_score_gemma":0.0009377518,"threshold_uncertainty_score":0.009057164},"labels":[],"label_agreement":null},{"id":"W1954025331","doi":"","title":"Fusion of spatial and visual information for object tracking on iPhone","year":2013,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","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":"Computer vision; Computer science; Video tracking; Artificial intelligence; Tracking (education); Object (grammar); Tracking system; Matching (statistics); Motion (physics); Eye tracking; Sensor fusion; Visualization; Match moving; Computer graphics (images); Mathematics","score_opus":0.031502281182184066,"score_gpt":0.3121970779035453,"score_spread":0.2806947967213612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1954025331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056608226,0.0019715428,0.93744534,0.000115720504,0.00019954058,0.00005273592,0.000099584024,0.0009404763,0.0025668002],"genre_scores_gemma":[0.7171776,0.00138627,0.27754715,0.00020830044,0.0001372948,0.00007186961,0.00034505178,0.000064398686,0.0030620531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995945,0.000049553866,0.000025277463,0.000101238664,0.00018161282,0.000047897753],"domain_scores_gemma":[0.99973005,0.00005407182,0.00003169193,0.00005393524,0.00011484806,0.00001539892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004885871,0.00039867192,0.00053426164,0.0010207581,0.00024507885,0.000519864,0.00042797747,0.0005524318,0.0008887969],"category_scores_gemma":[0.0010507782,0.00021545582,0.00040773785,0.0007624977,0.00018516571,0.0010771372,0.00061141665,0.0002780543,0.0004934462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004782178,0.000077091245,0.0017904937,0.00018714897,0.00008931627,0.00019481026,0.00010571044,0.015079247,0.15133847,0.001461937,0.0015483372,0.82764924],"study_design_scores_gemma":[0.000052925712,0.0006262617,0.016185846,0.00007880285,0.00034705046,0.0011132357,0.00016498523,0.79166764,0.17020139,0.0052102967,0.014260924,0.00009056125],"about_ca_topic_score_codex":0.0012971523,"about_ca_topic_score_gemma":0.0018092343,"teacher_disagreement_score":0.0012971523,"about_ca_system_score_codex":0.00024413597,"about_ca_system_score_gemma":0.0003111879,"threshold_uncertainty_score":0.002973318},"labels":[],"label_agreement":null},{"id":"W2100687146","doi":"","title":"A Bayesian inference approach for batch trajectory estimation","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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; Université de Montréal","funders":"","keywords":"Trajectory; Estimator; Spline (mechanical); Bayesian probability; Bayesian inference; Parametric equation; Computer science; Algorithm; Mathematics; Heteroscedasticity; Inference; Artificial intelligence; Mathematical optimization; Statistics; Engineering","score_opus":0.05607419125787415,"score_gpt":0.2802183908518246,"score_spread":0.22414419959395043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100687146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047230514,0.000040825358,0.99915624,0.00002089802,0.0000064300143,0.000010416003,0.000019658062,0.000120633915,0.00015269493],"genre_scores_gemma":[0.08624644,0.0003000961,0.9092535,0.00007995776,0.00010880427,0.00026749098,0.00048654043,0.00021314324,0.0030439738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857867,0.00045630767,0.0000831982,0.0003371024,0.00044051954,0.000104123304],"domain_scores_gemma":[0.99524695,0.0034178933,0.00025916536,0.0003157568,0.00066758564,0.000092748705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030053202,0.0010091686,0.0017485105,0.001468489,0.0009133749,0.0013391835,0.003043254,0.0014420582,0.0033484115],"category_scores_gemma":[0.010587848,0.0010767402,0.0012660555,0.0017843074,0.0009780728,0.0020966958,0.0013677347,0.0025187081,0.0012432697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103302824,0.000047983172,0.0006705616,0.000086591244,0.00008450263,0.00008728503,0.000098798104,0.7839627,0.0018344385,0.04048828,0.0023127866,0.17022276],"study_design_scores_gemma":[0.0000052932032,0.000010502687,0.00008218828,0.0000053394015,0.0000066266907,0.00001638724,0.0000039601864,0.9889221,0.00031522728,0.009928616,0.0006936594,0.000010099044],"about_ca_topic_score_codex":0.01785511,"about_ca_topic_score_gemma":0.01166416,"teacher_disagreement_score":0.01785511,"about_ca_system_score_codex":0.0016473517,"about_ca_system_score_gemma":0.0025374116,"threshold_uncertainty_score":0.035502374},"labels":[],"label_agreement":null},{"id":"W2103648938","doi":"","title":"Online clutter estimation using a Gaussian kernel density estimator for target tracking","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Clutter; Estimator; Computer science; Kernel density estimation; Variable kernel density estimation; Artificial intelligence; Kernel (algebra); Gaussian; Mathematics; Algorithm; Statistics; Pattern recognition (psychology); Kernel method; Radar; Support vector machine","score_opus":0.0812491538020137,"score_gpt":0.305231849600452,"score_spread":0.22398269579843832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103648938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003379997,0.00009186202,0.99623483,0.000015330252,0.000009371382,0.0000038963635,0.000005390202,0.00015004855,0.000109329536],"genre_scores_gemma":[0.51103234,0.0006118233,0.4859484,0.00008787616,0.00008058464,0.00006199439,0.00018035642,0.00011140892,0.0018853061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999124,0.00019591523,0.000050694394,0.00022781617,0.00032353896,0.00007802049],"domain_scores_gemma":[0.9987233,0.00057360955,0.00015137949,0.00017372485,0.00034101077,0.00003699246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011501943,0.000515057,0.0009912183,0.0007059126,0.0002533587,0.0006606338,0.001121912,0.000713077,0.0006350248],"category_scores_gemma":[0.004378473,0.00038884784,0.0006904385,0.0009037692,0.00041301438,0.0017275556,0.0008862794,0.0007925542,0.00033531274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024821656,0.00012515625,0.0030090942,0.00016848778,0.00015246593,0.00016258447,0.00014619582,0.5136687,0.023070412,0.016652413,0.0013847379,0.4412116],"study_design_scores_gemma":[0.0000060244115,0.000016910823,0.00032051053,0.0000020938744,0.000010423306,0.000038967348,0.000003922508,0.9956501,0.0022063036,0.0013631724,0.0003733819,0.000008171997],"about_ca_topic_score_codex":0.0026344429,"about_ca_topic_score_gemma":0.0015933834,"teacher_disagreement_score":0.0026344429,"about_ca_system_score_codex":0.0005665902,"about_ca_system_score_gemma":0.00086490944,"threshold_uncertainty_score":0.0060828924},"labels":[],"label_agreement":null},{"id":"W2103898652","doi":"","title":"An EM-CI based approach to fusion of IR and visual images","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Advanced Image Fusion Techniques","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":"Computer vision; Artificial intelligence; Computer science; Image fusion; Intersection (aeronautics); Infrared; Sensor fusion; Distortion (music); Image (mathematics); Optics; Physics","score_opus":0.012642092272321402,"score_gpt":0.28373798041897885,"score_spread":0.27109588814665747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103898652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00080310914,0.00007498983,0.9986186,0.00003404631,0.000017933755,0.000009479318,0.0000075682597,0.000094291376,0.00033985273],"genre_scores_gemma":[0.19677523,0.0005213637,0.7980742,0.00022267699,0.00011763248,0.0001645582,0.0002927751,0.00012789115,0.0037037074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985682,0.00039271428,0.00008847177,0.0003291824,0.00052890833,0.000092617745],"domain_scores_gemma":[0.99894196,0.000392794,0.0001165409,0.00014733766,0.0003620732,0.000039173705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025404042,0.0010517401,0.0011475316,0.0009907505,0.00043286828,0.0010115736,0.0022528288,0.0013290175,0.0013879165],"category_scores_gemma":[0.0049962974,0.00061131,0.0016901443,0.0014738651,0.00088687794,0.0017363499,0.0018697423,0.001802909,0.0006839015],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021068873,0.00010687963,0.0008430555,0.00022330036,0.00024896502,0.00017928473,0.00016777494,0.605431,0.015764376,0.039430827,0.0026296575,0.33476415],"study_design_scores_gemma":[0.000007632728,0.00005270728,0.00026408464,0.000009172316,0.000020413465,0.00011949005,0.000015033869,0.9874402,0.0054722866,0.004911369,0.0016645167,0.000023071934],"about_ca_topic_score_codex":0.002667845,"about_ca_topic_score_gemma":0.001938195,"teacher_disagreement_score":0.002667845,"about_ca_system_score_codex":0.0008936662,"about_ca_system_score_gemma":0.0011244966,"threshold_uncertainty_score":0.013435125},"labels":[],"label_agreement":null},{"id":"W2106915574","doi":"","title":"Syntactic inference for highway traffic analysis","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Traffic Prediction and Management Techniques","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; Classifier (UML); Estimator; Artificial intelligence; Markov chain; Inference; Hidden Markov model; Machine learning; Data mining","score_opus":0.01670258564401325,"score_gpt":0.2681093774615749,"score_spread":0.25140679181756165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106915574","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":[0.009863861,0.0002801151,0.98461044,0.0003821708,0.000047376114,0.000060392354,0.00075718545,0.0022029546,0.0017955111],"genre_scores_gemma":[0.39951,0.00045316157,0.59212226,0.00046725495,0.0001518104,0.00027686593,0.0038610715,0.0005761435,0.002581419],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828416,0.0006643479,0.00013644116,0.00039666527,0.0004196424,0.00009869904],"domain_scores_gemma":[0.99650216,0.0022511904,0.0002344894,0.00044280506,0.00051233417,0.000056997338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019012395,0.0006148841,0.0006815961,0.0023564794,0.0008952649,0.0013767509,0.0012765902,0.00094853766,0.0035274003],"category_scores_gemma":[0.008160667,0.00047511866,0.001685936,0.0015016971,0.0012133833,0.0022490828,0.001148889,0.0014788785,0.0010797225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121297344,0.00013474263,0.008004087,0.00036885752,0.00020101405,0.0005746785,0.0006026253,0.20616058,0.008400989,0.39676428,0.01398655,0.3646803],"study_design_scores_gemma":[0.000008948329,0.000016135358,0.00066282257,0.00003351308,0.000026042182,0.000084631974,0.00005648501,0.78138596,0.0017479094,0.21054454,0.005407961,0.000025075182],"about_ca_topic_score_codex":0.008927304,"about_ca_topic_score_gemma":0.006998876,"teacher_disagreement_score":0.008927304,"about_ca_system_score_codex":0.001593215,"about_ca_system_score_gemma":0.0020069191,"threshold_uncertainty_score":0.01775068},"labels":[],"label_agreement":null},{"id":"W2108069024","doi":"","title":"Enhanced sequential nonlinear tracking filter with denoised pseudo measurements","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"McMaster University","funders":"","keywords":"Tracking (education); Kalman filter; Filter (signal processing); Computer science; Range (aeronautics); Extended Kalman filter; Nonlinear system; Observational error; Control theory (sociology); Covariance; Position (finance); Nonlinear filter; Algorithm; Noise measurement; Monte Carlo method; Noise reduction; Mathematics; Artificial intelligence; Computer vision; Filter design; Engineering; Statistics; Physics","score_opus":0.09068314651954715,"score_gpt":0.27536144943532664,"score_spread":0.1846783029157795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108069024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007817806,0.00016928374,0.99098855,0.00005400493,0.00007425034,0.000017436672,0.000027308193,0.00020464316,0.00064670865],"genre_scores_gemma":[0.41850197,0.0006864034,0.56909007,0.00020590248,0.00017703084,0.00016685236,0.00041982217,0.000081358994,0.010670639],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999126,0.000113971066,0.00005626223,0.00022214274,0.00042144998,0.000060209302],"domain_scores_gemma":[0.9990779,0.00023157145,0.000102091086,0.00012137035,0.00043575707,0.000031265514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010033485,0.00080250326,0.0009720558,0.0005806211,0.0004963666,0.0006442435,0.00088710315,0.001109871,0.001450778],"category_scores_gemma":[0.0022445717,0.00043996033,0.00088858226,0.0007176857,0.0004405475,0.0014569265,0.0008089083,0.0009897929,0.0004918981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007995517,0.00016646212,0.0027137885,0.0003939756,0.00022397118,0.0002451132,0.00029047378,0.34481734,0.113245636,0.020061465,0.0032477116,0.5137946],"study_design_scores_gemma":[0.00003176573,0.0001192177,0.00053779647,0.000008830114,0.000032035,0.000118852164,0.000010423635,0.9817484,0.012876427,0.0015954933,0.00289322,0.000027581575],"about_ca_topic_score_codex":0.0051638247,"about_ca_topic_score_gemma":0.0047776774,"teacher_disagreement_score":0.0051638247,"about_ca_system_score_codex":0.00053393224,"about_ca_system_score_gemma":0.0010441113,"threshold_uncertainty_score":0.010267496},"labels":[],"label_agreement":null},{"id":"W2109593202","doi":"","title":"Track purity and current assignment ratio for target tracking and identification evaluation","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Defence Research and Development Canada","funders":"","keywords":"Clutter; Track (disk drive); Identification (biology); Computer science; Tracking (education); Radar tracker; Sensor fusion; Tracking system; Real-time computing; Artificial intelligence; Data mining; Radar; Kalman filter; Telecommunications","score_opus":0.10424485963219776,"score_gpt":0.3197986134460219,"score_spread":0.21555375381382413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109593202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15504545,0.00438921,0.8140087,0.0004999087,0.00025481582,0.0010466395,0.0021715167,0.0030356855,0.019548152],"genre_scores_gemma":[0.7674032,0.0010101855,0.22500034,0.00018730068,0.0001538755,0.0008103625,0.0026740409,0.00035605437,0.0024046754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9751913,0.0072764624,0.002637199,0.0013913593,0.012433681,0.0010699929],"domain_scores_gemma":[0.94209975,0.032589283,0.0065784897,0.0047894916,0.012792329,0.0011505835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016553007,0.0018387038,0.0017100542,0.006864067,0.00093166967,0.0026470087,0.0014309862,0.0017464128,0.002629277],"category_scores_gemma":[0.07053286,0.00034511072,0.00094412913,0.0048608165,0.0012898011,0.0043532806,0.0020812748,0.0011453169,0.00101806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029095428,0.0012163232,0.072289586,0.0015133093,0.00087221875,0.0004421935,0.0006070708,0.34570208,0.039757017,0.040593326,0.011215383,0.48288202],"study_design_scores_gemma":[0.00009982555,0.0029224025,0.03334081,0.00020071724,0.00033139702,0.0013094313,0.0005506215,0.87794995,0.058951825,0.011889654,0.012217605,0.00023582524],"about_ca_topic_score_codex":0.0017299576,"about_ca_topic_score_gemma":0.0012936976,"teacher_disagreement_score":0.016553007,"about_ca_system_score_codex":0.0017610757,"about_ca_system_score_gemma":0.0013183916,"threshold_uncertainty_score":0.0875417},"labels":[],"label_agreement":null},{"id":"W2110323765","doi":"","title":"Stochastic fusion of heterogeneous multisensor information for robust data-to-decision","year":2013,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Sensor fusion; Stochastic process; Computer science; Stochastic differential equation; Random variable; Mathematics; Artificial intelligence; Data mining; Mathematical optimization; Applied mathematics; Statistics","score_opus":0.06648425364391373,"score_gpt":0.2951457137150906,"score_spread":0.22866146007117683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110323765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021757656,0.00023273814,0.9969921,0.000097512435,0.000025742864,0.000014139893,0.000018093515,0.00003722308,0.0004066476],"genre_scores_gemma":[0.7722322,0.001331142,0.22406083,0.0003303465,0.00026063775,0.00019848577,0.00026600505,0.000049334503,0.0012711589],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673223,0.000918855,0.00027819525,0.0006139713,0.00122853,0.00022827763],"domain_scores_gemma":[0.9976979,0.0013092576,0.00029129582,0.00022706573,0.0003908296,0.000083707455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004125316,0.001054256,0.0019328081,0.0009668703,0.00069380476,0.0016750443,0.0013975737,0.0010497136,0.0009631632],"category_scores_gemma":[0.0071667507,0.00047547618,0.0017144238,0.0012966001,0.0013324185,0.0027164202,0.0025402205,0.0018302317,0.0002173311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015081263,0.00006807539,0.0006930425,0.00031030297,0.00021192552,0.00028394218,0.00015440982,0.6883648,0.0066542416,0.23458213,0.0010648747,0.06746145],"study_design_scores_gemma":[0.000008596114,0.000041120198,0.00013014978,0.000013933082,0.000027703669,0.000041792297,0.000015066347,0.94775254,0.0015769232,0.049629733,0.0007432905,0.000019186567],"about_ca_topic_score_codex":0.0018321396,"about_ca_topic_score_gemma":0.0012543811,"teacher_disagreement_score":0.004125316,"about_ca_system_score_codex":0.0011999924,"about_ca_system_score_gemma":0.0015509976,"threshold_uncertainty_score":0.021816969},"labels":[],"label_agreement":null},{"id":"W2111006103","doi":"","title":"Anomaly detection in maritime data based on geometrical analysis of trajectories","year":2015,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Maritime Navigation and Safety","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":"Dalhousie University","funders":"","keywords":"Anomaly detection; Abnormality; Computer science; Trajectory; Automatic Identification System; sort; Path (computing); Artificial intelligence; Anomaly (physics); Graph; Data mining; Pattern recognition (psychology); Information retrieval","score_opus":0.04880205769838712,"score_gpt":0.28135847536058173,"score_spread":0.2325564176621946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111006103","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":[0.53049403,0.0011653405,0.45199203,0.00036897874,0.00019289601,0.00021470165,0.0067729214,0.0059119277,0.0028871656],"genre_scores_gemma":[0.86997473,0.0004850774,0.11912317,0.00003160837,0.0000534281,0.00008303414,0.009095877,0.00011449655,0.0010385659],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914074,0.00010797991,0.00008990657,0.00025508765,0.0003120217,0.00009419827],"domain_scores_gemma":[0.9985331,0.00036704645,0.00031518476,0.00021820575,0.0004877382,0.00007867409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005858031,0.000739746,0.00062984234,0.0054347627,0.00036716735,0.00078425143,0.0006102197,0.00059212244,0.0007198303],"category_scores_gemma":[0.0030566496,0.00016402554,0.0006724996,0.0037042827,0.00038067612,0.001037908,0.0006635977,0.00061795645,0.0006811737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008786364,0.0002697067,0.13540778,0.00039323315,0.00030536077,0.0011687605,0.00054012303,0.18574497,0.053992238,0.0032298253,0.008022297,0.61004704],"study_design_scores_gemma":[0.000015603337,0.00015584637,0.06823065,0.000044464017,0.000054635686,0.00054567214,0.00043472613,0.9044971,0.01810625,0.0025750715,0.005286607,0.0000534115],"about_ca_topic_score_codex":0.010853459,"about_ca_topic_score_gemma":0.010578889,"teacher_disagreement_score":0.010853459,"about_ca_system_score_codex":0.0006769626,"about_ca_system_score_gemma":0.00052320363,"threshold_uncertainty_score":0.021580577},"labels":[],"label_agreement":null},{"id":"W2113343745","doi":"","title":"Optimal video camera network deployment to support security monitoring","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","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":"Institut National d'Optique","funders":"","keywords":"Backup; Software deployment; Computer science; Optimization problem; Mathematical optimization; Artificial intelligence; Real-time computing; Algorithm; Mathematics; Database","score_opus":0.03267361739714915,"score_gpt":0.27671519465114963,"score_spread":0.2440415772540005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113343745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18608713,0.0007851624,0.8057542,0.00048570393,0.000055274137,0.000062961866,0.00011450391,0.00035720813,0.006297868],"genre_scores_gemma":[0.9472868,0.00021379419,0.051565498,0.000030041023,0.00002312525,0.000027593018,0.000064272164,0.00002021143,0.00076858123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995408,0.00016111435,0.000012538858,0.000103955594,0.00008475656,0.000096882955],"domain_scores_gemma":[0.9995016,0.00020821208,0.000082553954,0.00003649369,0.00011251862,0.000058674163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054874097,0.00056573946,0.00055313285,0.00045088388,0.00026679717,0.00057756575,0.00060523726,0.00066041003,0.0010196612],"category_scores_gemma":[0.0021993313,0.00029599943,0.00015410336,0.00037254018,0.0002693276,0.0012282962,0.00053426746,0.0003261527,0.0001676892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005898205,0.00008064016,0.0026887618,0.000095824405,0.000028141038,0.00020045385,0.00007003419,0.8491274,0.04120797,0.0106038675,0.0024364335,0.09287061],"study_design_scores_gemma":[0.00001992768,0.00010609265,0.0006930994,0.000005474507,0.000008169744,0.000047690817,0.000038494265,0.99036855,0.0053279093,0.002656507,0.0007210675,0.0000069109415],"about_ca_topic_score_codex":0.0024037722,"about_ca_topic_score_gemma":0.0027534936,"teacher_disagreement_score":0.0024037722,"about_ca_system_score_codex":0.0008109033,"about_ca_system_score_gemma":0.0005157991,"threshold_uncertainty_score":0.0058835745},"labels":[],"label_agreement":null},{"id":"W2116212334","doi":"","title":"A coverage dominance approach for sensor deployment optimization","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","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":"Defence Research and Development Canada","funders":"","keywords":"Terrain; Software deployment; Wireless sensor network; Computer science; Robustness (evolution); Heuristic; Dominance (genetics); Wireless; Real-time computing; Distributed computing; Computer network; Artificial intelligence; Telecommunications; Geography; Cartography","score_opus":0.03650896484623942,"score_gpt":0.2431458641925375,"score_spread":0.2066368993462981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116212334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047401725,0.0008835684,0.9898788,0.00022623695,0.000060274844,0.00003816358,0.00004917979,0.00006585567,0.004057819],"genre_scores_gemma":[0.624849,0.0033934077,0.35530898,0.00061319734,0.0004994619,0.00064220774,0.000371332,0.00019380273,0.014128709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991185,0.00040573222,0.000026160627,0.00007604763,0.00028209944,0.00009129354],"domain_scores_gemma":[0.9989503,0.0007020533,0.00007104563,0.000035752542,0.0001765339,0.00006432195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012810597,0.001329864,0.0010987745,0.0010867444,0.000452023,0.0007967998,0.0011079499,0.0010418921,0.0019275747],"category_scores_gemma":[0.0031247148,0.00047462186,0.0008159334,0.0011768877,0.00055444025,0.00084677665,0.0010420698,0.0008437511,0.00031573718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029241704,0.00003749213,0.00029540734,0.000108264554,0.00006275417,0.00008634336,0.00005247869,0.9407975,0.0017823416,0.021603044,0.00277116,0.03237408],"study_design_scores_gemma":[0.000008076233,0.000041029794,0.00006360361,0.000010392246,0.000008166635,0.000023751998,0.000008726663,0.99038064,0.00020134631,0.007716446,0.0015325751,0.0000051810484],"about_ca_topic_score_codex":0.0031840312,"about_ca_topic_score_gemma":0.0022801775,"teacher_disagreement_score":0.0031840312,"about_ca_system_score_codex":0.0008357172,"about_ca_system_score_gemma":0.00083872717,"threshold_uncertainty_score":0.006774962},"labels":[],"label_agreement":null},{"id":"W2117365199","doi":"","title":"Approaches to validation of information fusion systems","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Fusion; Process (computing); Sensor fusion; Adversarial system; Information fusion; Information system; Data mining; Artificial intelligence; Engineering; Programming language","score_opus":0.07865568264595083,"score_gpt":0.26174725599528675,"score_spread":0.18309157334933593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117365199","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":[0.0019816007,0.00077953294,0.9896994,0.0012795066,0.00011150518,0.00011008741,0.00004690227,0.00019044014,0.005801019],"genre_scores_gemma":[0.38869312,0.0024963748,0.59931606,0.0016281815,0.00087257894,0.0010910088,0.00046705388,0.00033260355,0.0051029404],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94447416,0.030292425,0.0042967685,0.005537871,0.013087662,0.0023110812],"domain_scores_gemma":[0.88047403,0.07245704,0.0072631375,0.024918452,0.01381021,0.0010771215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.042129207,0.0020306401,0.002191641,0.004705305,0.0033328938,0.011178859,0.0068888487,0.0075754807,0.0036266064],"category_scores_gemma":[0.097496286,0.0016061196,0.0042599174,0.0033897334,0.019558396,0.01889927,0.013718475,0.009966024,0.0010783862],"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":[0.000030283478,0.000023861237,0.0003138459,0.00014549344,0.00005118734,0.00009870665,0.00056907214,0.015366086,0.00032974465,0.96873426,0.00062798726,0.013709448],"study_design_scores_gemma":[0.000027600612,0.000048505717,0.00010745373,0.00023295204,0.000039039605,0.00012183722,0.0001553058,0.06397812,0.0016583401,0.92089576,0.012678278,0.000056771965],"about_ca_topic_score_codex":0.0032132033,"about_ca_topic_score_gemma":0.0010977868,"teacher_disagreement_score":0.042129207,"about_ca_system_score_codex":0.0055605695,"about_ca_system_score_gemma":0.0044724653,"threshold_uncertainty_score":0.22280318},"labels":[],"label_agreement":null},{"id":"W2118207253","doi":"","title":"Weight partitioned Probability Hypothesis Density filters","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Context (archaeology); Filter (signal processing); Particle filter; Computer science; Algorithm; Deconvolution; Set (abstract data type); State (computer science); Singleton; Probability density function; Mathematics; Statistics; Artificial intelligence; Pattern recognition (psychology); Computer vision","score_opus":0.07083073807537754,"score_gpt":0.24067318249866768,"score_spread":0.16984244442329013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118207253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031601568,0.00006182696,0.9961117,0.000036500845,0.0000196777,0.00002855922,0.00004682164,0.0001616386,0.00037315124],"genre_scores_gemma":[0.28475863,0.00049331936,0.70758176,0.00020502337,0.00017004817,0.00050992105,0.00089293503,0.0002211666,0.0051672384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981964,0.00041569027,0.00011756725,0.0004985577,0.0005980257,0.0001737205],"domain_scores_gemma":[0.995378,0.0024308218,0.0003288036,0.000728706,0.0010348698,0.00009880953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002609229,0.0010460792,0.0012870898,0.0011460847,0.00045776364,0.0019505343,0.0020780803,0.0017832869,0.0042601903],"category_scores_gemma":[0.016751384,0.00078052067,0.0010976477,0.0011502173,0.0010032763,0.0034853548,0.0019470007,0.0015608729,0.0012322153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083541527,0.00015315968,0.0020300776,0.0002758669,0.00024300681,0.0001964063,0.00029481604,0.4407459,0.01862336,0.08256332,0.0032754557,0.4507633],"study_design_scores_gemma":[0.00003725235,0.00008182901,0.0005743561,0.000022328984,0.000031630796,0.000071459224,0.000030502471,0.96561515,0.005661952,0.025649955,0.0021965068,0.000027008902],"about_ca_topic_score_codex":0.0021217482,"about_ca_topic_score_gemma":0.0014666085,"teacher_disagreement_score":0.0042601903,"about_ca_system_score_codex":0.0008882982,"about_ca_system_score_gemma":0.0013029614,"threshold_uncertainty_score":0.014251709},"labels":[],"label_agreement":null},{"id":"W2119845561","doi":"","title":"Wasserstein distance for the fusion of multisensor multitarget particle filter clouds","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","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":"General Dynamics (Canada); McMaster University","funders":"","keywords":"Mahalanobis distance; Particle filter; Metric (unit); Resampling; Auxiliary particle filter; Filter (signal processing); Euclidean distance; Particle (ecology); Computer science; Cloud computing; Mathematics; Algorithm; Artificial intelligence; Computer vision; Ensemble Kalman filter; Kalman filter; Engineering","score_opus":0.04521945763693226,"score_gpt":0.295675610304761,"score_spread":0.2504561526678288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119845561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007424576,0.00013469522,0.99190265,0.000057514644,0.000024163943,0.000017587776,0.000023903618,0.00010660615,0.00030824958],"genre_scores_gemma":[0.41551447,0.0003449248,0.5812603,0.00009749327,0.000065676984,0.0001435664,0.00028765947,0.00013350579,0.0021523833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775994,0.0007144421,0.00016891854,0.0004032233,0.0007917843,0.00016169467],"domain_scores_gemma":[0.9969279,0.0017326601,0.00025632602,0.00035484906,0.00062210107,0.00010614619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044501815,0.00080567476,0.0010959833,0.0011041709,0.00050514855,0.0013298906,0.0016279117,0.0014215857,0.0012053082],"category_scores_gemma":[0.015459176,0.00038783994,0.0010437956,0.001383597,0.0010113737,0.0035645035,0.0018588478,0.0013080282,0.0003424225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001407767,0.000027902684,0.00054760923,0.000060372888,0.000054411175,0.000054290613,0.00004437223,0.9098552,0.0023762505,0.037483662,0.00039777538,0.04895742],"study_design_scores_gemma":[0.0000045192824,0.00002421838,0.00016580419,0.0000041330827,0.000005298472,0.000016284934,0.000004284307,0.9897063,0.0009657093,0.008759518,0.00033269977,0.000011151238],"about_ca_topic_score_codex":0.0075859665,"about_ca_topic_score_gemma":0.004124029,"teacher_disagreement_score":0.0075859665,"about_ca_system_score_codex":0.002372686,"about_ca_system_score_gemma":0.0014329685,"threshold_uncertainty_score":0.023535132},"labels":[],"label_agreement":null},{"id":"W2121321323","doi":"","title":"Meta level tracking with multimode space-time adaptive processing of GMTI data","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Moving target indication; Computer science; Space-time adaptive processing; Radar; Clutter; Radar tracker; Computer vision; Synthetic aperture radar; Artificial intelligence; Context (archaeology); Radar imaging; Real-time computing; Radar engineering details; Continuous-wave radar; Telecommunications; Geography","score_opus":0.17007053266423475,"score_gpt":0.3095089911609061,"score_spread":0.13943845849667133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121321323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010933067,0.000045818957,0.98624784,0.00010045953,0.000022470087,0.00002255928,0.00009877668,0.0018759525,0.00065306487],"genre_scores_gemma":[0.2571207,0.000106664585,0.7399597,0.00018991984,0.000035245772,0.00010309853,0.00054897903,0.00038704774,0.0015485943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990872,0.00026320652,0.00007069719,0.00024997612,0.00026234644,0.00006649111],"domain_scores_gemma":[0.9987386,0.00064774713,0.00009044249,0.00030442362,0.00019295575,0.000025748983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010923559,0.0004889293,0.00053709815,0.0009571352,0.000508072,0.0010990907,0.00097139657,0.0008937504,0.0010450886],"category_scores_gemma":[0.0027450756,0.0002677367,0.0011597372,0.001052171,0.0009748074,0.0010644809,0.0008976455,0.00073421525,0.0006428546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003138381,0.00015213447,0.0048008664,0.00024175097,0.00015270208,0.0011114463,0.0011875716,0.30239308,0.06669502,0.16292395,0.0038634737,0.45616415],"study_design_scores_gemma":[0.000010777586,0.0000635247,0.0004979679,0.000018881405,0.000028892558,0.00016848919,0.000044111006,0.9113494,0.023544643,0.058724597,0.0055182846,0.000030394664],"about_ca_topic_score_codex":0.0028533032,"about_ca_topic_score_gemma":0.0027856976,"teacher_disagreement_score":0.0028533032,"about_ca_system_score_codex":0.0005907203,"about_ca_system_score_gemma":0.0009809097,"threshold_uncertainty_score":0.0057770014},"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":"W2121601507","doi":"","title":"A fault tolerant state estimation framework with application to UGV navigation in complex terrain","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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; AUG Signals (Canada)","funders":"","keywords":"Computer science; Sensor fusion; Kinematics; Fault detection and isolation; Terrain; Asynchronous communication; Fault tolerance; State (computer science); Artificial intelligence; Real-time computing; Algorithm; Actuator; Distributed computing","score_opus":0.032370486249366205,"score_gpt":0.2817280471768246,"score_spread":0.2493575609274584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121601507","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015495318,0.000068017594,0.99781466,0.00003627961,0.000010410453,0.0000066355115,0.00001732117,0.00019221312,0.00030477325],"genre_scores_gemma":[0.5660308,0.00056541484,0.42940843,0.00008343306,0.00011048216,0.00015300213,0.00028302558,0.0001072468,0.0032580646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968684,0.000054620457,0.00001716717,0.00008117418,0.00012968614,0.000030542324],"domain_scores_gemma":[0.9996762,0.00012830537,0.000057294,0.000037404574,0.00008417249,0.00001664951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005336031,0.0006740264,0.00071615254,0.0005225517,0.0005169311,0.0008992382,0.0008935257,0.0007153664,0.0011434756],"category_scores_gemma":[0.0017250116,0.00031014858,0.0004540103,0.00062602916,0.00059595576,0.0009984561,0.000872943,0.000907903,0.0003010345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003683384,0.0000203448,0.00032479523,0.00004097943,0.00002512717,0.00008138122,0.00006901671,0.8779267,0.002539185,0.03145227,0.00076576346,0.086717546],"study_design_scores_gemma":[0.0000029823116,0.000013915467,0.000070949834,0.0000027740098,0.0000031878062,0.000015083936,0.0000056819517,0.99314946,0.00042981489,0.0055165226,0.0007835801,0.0000061139335],"about_ca_topic_score_codex":0.009150895,"about_ca_topic_score_gemma":0.0052863588,"teacher_disagreement_score":0.009150895,"about_ca_system_score_codex":0.0005953775,"about_ca_system_score_gemma":0.00079477095,"threshold_uncertainty_score":0.018195271},"labels":[],"label_agreement":null},{"id":"W2122374289","doi":"","title":"A spline filter for multidimensional nonlinear state estimation","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Particle filter; Markov chain Monte Carlo; Monte Carlo method; Spline (mechanical); Algorithm; Gaussian; Nonlinear system; Computer science; Mathematical optimization; Mathematics; State space; Filter (signal processing); Auxiliary particle filter; Hybrid Monte Carlo; Applied mathematics; Kalman filter; Ensemble Kalman filter; Extended Kalman filter; Artificial intelligence; Statistics; Engineering","score_opus":0.05232170939670668,"score_gpt":0.2810778122672766,"score_spread":0.22875610287056994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122374289","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":[0.0013720066,0.00009878852,0.9981281,0.000028726405,0.000023670453,0.000006067369,0.000011730929,0.000079522164,0.00025133576],"genre_scores_gemma":[0.21893892,0.0009812476,0.7731549,0.0000920204,0.00011678353,0.0001522019,0.00028406043,0.00008355173,0.0061962344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995129,0.00013909255,0.00002690388,0.00008577337,0.00019642523,0.000038855695],"domain_scores_gemma":[0.99931407,0.00035734437,0.000054286847,0.00006869592,0.00017263385,0.00003291772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013263664,0.00046987215,0.0007600438,0.00062423677,0.00043542153,0.00060528197,0.0007131531,0.0011706047,0.0016909344],"category_scores_gemma":[0.003073509,0.00031920604,0.0009397997,0.0010674385,0.0004773988,0.0007640422,0.0007938766,0.0014244221,0.0006119827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017033592,0.0000621,0.0008016431,0.00016865965,0.00007979692,0.000110493944,0.00011683872,0.6499075,0.012362634,0.04821497,0.0016378616,0.28636706],"study_design_scores_gemma":[0.00000428882,0.000019075524,0.000086777414,0.00000615966,0.0000046046293,0.00002110534,0.0000034382326,0.99491477,0.0008467253,0.0029991772,0.0010850985,0.000008745272],"about_ca_topic_score_codex":0.0056245127,"about_ca_topic_score_gemma":0.0050406386,"teacher_disagreement_score":0.0056245127,"about_ca_system_score_codex":0.0004945985,"about_ca_system_score_gemma":0.0013838968,"threshold_uncertainty_score":0.0111835},"labels":[],"label_agreement":null},{"id":"W2123033014","doi":"","title":"Multiframe assignment tracker for MSTWG data","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"General Dynamics (Canada); McMaster University","funders":"","keywords":"Data association; Computer science; Tracking (education); False alarm; Artificial intelligence; Sensor fusion; Fuse (electrical); Assignment problem; Estimator; Asynchronous communication; Computer vision; Constant false alarm rate; Mathematics; Engineering; Probabilistic logic; Mathematical optimization","score_opus":0.08009435639457554,"score_gpt":0.32755670390047104,"score_spread":0.2474623475058955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123033014","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":[0.044384148,0.00010857502,0.95352244,0.000075949676,0.0000624309,0.000045659224,0.00008962759,0.00085454807,0.00085671205],"genre_scores_gemma":[0.5053432,0.00009036904,0.491078,0.000073726966,0.000042855856,0.00014867996,0.00085893046,0.00013614226,0.002228101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983398,0.00039041977,0.00007483118,0.00040559427,0.0006666514,0.00012267356],"domain_scores_gemma":[0.9980539,0.0006071975,0.00024118317,0.0004954771,0.00048746416,0.000114855124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023205855,0.0005869992,0.0011255628,0.0008842899,0.00086725724,0.0009675061,0.0010132095,0.0010755423,0.0010818121],"category_scores_gemma":[0.0067959675,0.00024500993,0.00051412184,0.0013496046,0.0004665909,0.0014536357,0.0019366589,0.0011769022,0.00068317185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007952499,0.00024876502,0.0105564995,0.000096518925,0.000109579705,0.00018778862,0.0003346984,0.50306374,0.039066512,0.012786494,0.00258952,0.43016466],"study_design_scores_gemma":[0.000016255779,0.000087204106,0.0015578723,0.0000041188823,0.000005993476,0.0000847733,0.00002427999,0.9845823,0.008879546,0.0024842056,0.0022550188,0.000018306693],"about_ca_topic_score_codex":0.002571921,"about_ca_topic_score_gemma":0.0027057158,"teacher_disagreement_score":0.002571921,"about_ca_system_score_codex":0.0007568002,"about_ca_system_score_gemma":0.0012892331,"threshold_uncertainty_score":0.012272596},"labels":[],"label_agreement":null},{"id":"W2123899469","doi":"","title":"Belief modeling for maritime surveillance","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Computer science; Anomaly detection; Ranking (information retrieval); Attack patterns; Domain (mathematical analysis); Scalability; Computer security; Point (geometry); Information overload; Order (exchange); Data mining; Data science; Information retrieval; Intrusion detection system; Database; World Wide Web","score_opus":0.03120990749814043,"score_gpt":0.2773515951467125,"score_spread":0.24614168764857206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123899469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006937733,0.0014289294,0.983941,0.001296769,0.00007571612,0.00006016098,0.00030469164,0.0003135133,0.005641402],"genre_scores_gemma":[0.6493989,0.0024456375,0.3407813,0.00051239197,0.00034376103,0.0004293519,0.000973703,0.00008497305,0.0050299587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977168,0.0011486171,0.00013174595,0.00031252878,0.0005143983,0.00017586333],"domain_scores_gemma":[0.9940632,0.0045660795,0.00043765374,0.0002907909,0.00048739012,0.00015497315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003294764,0.0009374008,0.001135767,0.0014398277,0.0007291933,0.0028231295,0.002140308,0.0018621436,0.0032216052],"category_scores_gemma":[0.010460504,0.0006132331,0.0014328198,0.001606934,0.0015450338,0.0034217022,0.0016271716,0.0027597593,0.00059152284],"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":[0.00013975972,0.00008632463,0.0013285056,0.00024396619,0.00016047784,0.00020527738,0.00046178405,0.5065046,0.0006252567,0.44837117,0.002871509,0.039001383],"study_design_scores_gemma":[0.000022459719,0.00001816195,0.0001370879,0.00003219029,0.000027683498,0.000022362046,0.00004821885,0.7671112,0.00017942305,0.23000497,0.0023788163,0.000017339627],"about_ca_topic_score_codex":0.015930025,"about_ca_topic_score_gemma":0.013874638,"teacher_disagreement_score":0.015930025,"about_ca_system_score_codex":0.003034149,"about_ca_system_score_gemma":0.001513661,"threshold_uncertainty_score":0.031674623},"labels":[],"label_agreement":null},{"id":"W2128470788","doi":"","title":"Combined particle and smooth variable structure filtering for nonlinear estimation problems","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Nonlinear system; Particle filter; Covariance; Control theory (sociology); Variable (mathematics); State variable; Filter (signal processing); Computer science; Mathematical optimization; Mathematics; Algorithm; Statistics; Physics; Artificial intelligence","score_opus":0.03766503110743105,"score_gpt":0.2514763947341293,"score_spread":0.21381136362669825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128470788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019551427,0.00026073444,0.9972927,0.000037352915,0.000033893688,0.0000072083,0.0000072222683,0.00007650971,0.00032909037],"genre_scores_gemma":[0.33047748,0.0015321977,0.6619657,0.00011392137,0.00028673612,0.00013747702,0.00015802741,0.000076104116,0.0052522784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999395,0.00013090175,0.000032037824,0.00009737445,0.00030465738,0.000040032348],"domain_scores_gemma":[0.99912053,0.00054074015,0.00006303458,0.00008264866,0.00016826112,0.000024817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009965223,0.0006376481,0.0009668345,0.00071414444,0.0003067167,0.0007051072,0.00063380133,0.0010708602,0.0009885742],"category_scores_gemma":[0.0024045238,0.00042935702,0.00069110433,0.0007828533,0.0005778196,0.0012830365,0.0008196153,0.0009801255,0.00030263045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001807651,0.00006847545,0.0010989809,0.00029314304,0.0002086466,0.00016684576,0.000135685,0.6040207,0.0095552085,0.04093621,0.0016867496,0.34164855],"study_design_scores_gemma":[0.00001093824,0.000037903686,0.00019746684,0.000005417941,0.000014007604,0.000023914494,0.000004178021,0.992904,0.0009387307,0.004235141,0.0016195683,0.000008820644],"about_ca_topic_score_codex":0.0032844313,"about_ca_topic_score_gemma":0.003103691,"teacher_disagreement_score":0.0032844313,"about_ca_system_score_codex":0.00032003364,"about_ca_system_score_gemma":0.0007342943,"threshold_uncertainty_score":0.0065306425},"labels":[],"label_agreement":null},{"id":"W2131894460","doi":"","title":"An assessment of hierarchical data fusion using SEABAR'07 data","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Defence Research and Development Canada; General Dynamics (Canada)","funders":"","keywords":"Sonar; Sensor fusion; Computer science; Tracking (education); Artificial intelligence; Fusion; Multistatic radar; Sonar signal processing; Data mining; Radar; Bistatic radar; Signal processing; Radar imaging; Telecommunications","score_opus":0.11771778764492083,"score_gpt":0.40144781666913054,"score_spread":0.28373002902420974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131894460","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":[0.8878429,0.0010296578,0.092601046,0.0006696786,0.00017899614,0.0005307968,0.006002697,0.0048492784,0.0062950184],"genre_scores_gemma":[0.9266747,0.00015037597,0.05728435,0.00013431904,0.000025711626,0.00013451221,0.014445245,0.00018409731,0.0009667195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99438393,0.0018231551,0.0003728544,0.00093224103,0.0021058624,0.00038201982],"domain_scores_gemma":[0.99127334,0.003028873,0.0005379181,0.0018022666,0.0030638063,0.00029380005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01331801,0.0011846054,0.00090386876,0.0015928737,0.00074172183,0.001396562,0.0012904266,0.0008485285,0.0010538263],"category_scores_gemma":[0.019913897,0.00030613187,0.00089585246,0.0016294577,0.0006316253,0.0019994606,0.0022189538,0.00080964866,0.0005671973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004597084,0.00070581277,0.090171166,0.0006009366,0.0014869921,0.00032811385,0.0006224949,0.47021714,0.03507215,0.0026470541,0.011482138,0.38206887],"study_design_scores_gemma":[0.0003536697,0.0018360658,0.115112,0.00008404663,0.00031579423,0.00018171791,0.0006693567,0.8412347,0.03061621,0.0020277067,0.0074209105,0.00014779791],"about_ca_topic_score_codex":0.03286114,"about_ca_topic_score_gemma":0.042066,"teacher_disagreement_score":0.03286114,"about_ca_system_score_codex":0.0012294117,"about_ca_system_score_gemma":0.0013973735,"threshold_uncertainty_score":0.07043326},"labels":[],"label_agreement":null},{"id":"W2132961188","doi":"","title":"A knowledge-based system for multiple hypothesis sensemaking support","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"AI-based Problem Solving and Planning","field":"Computer Science","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":"Defence Research and Development Canada","funders":"","keywords":"Sensemaking; Computer science; Notional amount; Knowledge management; Data science; Knowledge representation and reasoning; Intelligence analysis; Decision support system; Process management; Artificial intelligence; Engineering","score_opus":0.09244771287266797,"score_gpt":0.27269889539720804,"score_spread":0.1802511825245401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132961188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015370088,0.00036235424,0.90618646,0.0014862885,0.00022996553,0.0005467914,0.0010427053,0.060421813,0.014353615],"genre_scores_gemma":[0.23701745,0.00038726075,0.7451657,0.0012451363,0.0003009359,0.000771573,0.0033227329,0.0016718796,0.010117372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972704,0.0005002331,0.00037846874,0.0006976188,0.00096206367,0.00019114718],"domain_scores_gemma":[0.994241,0.002613264,0.00040742182,0.0012388328,0.00094489136,0.0005546752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043551847,0.0008300308,0.0012189599,0.0033340347,0.001781288,0.00419633,0.004783347,0.0028251612,0.015161982],"category_scores_gemma":[0.012949378,0.00064561446,0.000881865,0.0020522508,0.001121279,0.0062391064,0.0045571327,0.0022389765,0.008381544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001962583,0.0012778665,0.005040049,0.00084468425,0.00035069452,0.003553433,0.0036514155,0.025646016,0.037309922,0.0818848,0.06679297,0.7716856],"study_design_scores_gemma":[0.00053674367,0.00046101565,0.002490134,0.00039375978,0.00038548996,0.0017987478,0.00067667593,0.5215747,0.04836993,0.1527055,0.27016443,0.00044285707],"about_ca_topic_score_codex":0.002995243,"about_ca_topic_score_gemma":0.0022884803,"teacher_disagreement_score":0.015161982,"about_ca_system_score_codex":0.0011272567,"about_ca_system_score_gemma":0.002398841,"threshold_uncertainty_score":0.050721884},"labels":[],"label_agreement":null},{"id":"W2135698068","doi":"","title":"User information fusion decision making analysis with the C-OODA model","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":57,"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":"Computer science; Context (archaeology); Sensor fusion; Context model; Artificial intelligence; Information processing; Data mining; Human–computer interaction","score_opus":0.05318706537131615,"score_gpt":0.3487195512593194,"score_spread":0.2955324858880033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135698068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03716225,0.000070995375,0.9434611,0.0007080169,0.000038506347,0.00029488033,0.00015040717,0.00028856995,0.017825313],"genre_scores_gemma":[0.6399917,0.00008378134,0.35568437,0.00012827283,0.0000270486,0.00060423097,0.0002200074,0.00006623506,0.003194209],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98838556,0.005749917,0.00068328273,0.0015237022,0.002976577,0.0006809279],"domain_scores_gemma":[0.9800325,0.012357871,0.0013802145,0.0018220153,0.0037593872,0.0006479796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00998226,0.001158489,0.00080629677,0.0022414685,0.0012934302,0.0051672678,0.0018681572,0.0012005977,0.0056561367],"category_scores_gemma":[0.023858704,0.0005654829,0.0020890983,0.0015485155,0.002559826,0.004535227,0.002357965,0.0021851459,0.00062953687],"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":[0.00045133763,0.00034385925,0.007087943,0.00020380996,0.00022932501,0.00021996125,0.0035890536,0.21806808,0.0025849997,0.68555254,0.0019492588,0.079719774],"study_design_scores_gemma":[0.00004285835,0.00014212696,0.0009645387,0.000033445067,0.000069935224,0.000049510927,0.0005285781,0.848853,0.0015307405,0.14267254,0.0050492766,0.000063399224],"about_ca_topic_score_codex":0.022329824,"about_ca_topic_score_gemma":0.01035863,"teacher_disagreement_score":0.022329824,"about_ca_system_score_codex":0.0045945807,"about_ca_system_score_gemma":0.0050920215,"threshold_uncertainty_score":0.052791834},"labels":[],"label_agreement":null},{"id":"W2136739364","doi":"","title":"Performance measures for multiple target tracking problems","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"McMaster University","funders":"","keywords":"Computer science; Tracking (education); Artificial intelligence; Machine learning; Data mining; Statistical classification; Pattern recognition (psychology)","score_opus":0.08252286723461763,"score_gpt":0.2607177616495735,"score_spread":0.17819489441495587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136739364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006219654,0.009466725,0.97913647,0.00030793867,0.00024023904,0.00014649666,0.00026021304,0.00040608537,0.003816222],"genre_scores_gemma":[0.49998522,0.014199092,0.47475076,0.0005009161,0.0015942004,0.00138151,0.0022306938,0.0006331676,0.004724431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98494154,0.0063673127,0.001514852,0.0014951018,0.0051218937,0.00055932964],"domain_scores_gemma":[0.9606207,0.026785014,0.0039366493,0.0026777294,0.005308553,0.00067127834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012127828,0.0035247195,0.0020591982,0.004946704,0.00082386134,0.0034124579,0.0024258199,0.0024475348,0.0022925145],"category_scores_gemma":[0.047596473,0.0003669546,0.001442824,0.0046161753,0.0015689909,0.0052961544,0.0022924817,0.002774176,0.0012925547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047274394,0.00035593007,0.006948098,0.0028148722,0.0005962873,0.00025451285,0.0002803077,0.49308178,0.010293429,0.1299256,0.008910556,0.34606594],"study_design_scores_gemma":[0.000027345535,0.0010587631,0.0048510823,0.0003404874,0.00017605927,0.00070567755,0.00017753273,0.8929292,0.0063776616,0.079350114,0.013858435,0.00014771795],"about_ca_topic_score_codex":0.0008563191,"about_ca_topic_score_gemma":0.00035821646,"teacher_disagreement_score":0.012127828,"about_ca_system_score_codex":0.0017164177,"about_ca_system_score_gemma":0.001095716,"threshold_uncertainty_score":0.06413889},"labels":[],"label_agreement":null},{"id":"W2149729344","doi":"","title":"Multisensor joint tracking and identification using particle filter and Dempster-Shafer fusion","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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 Laval; Defence Research and Development Canada; University of Calgary","funders":"","keywords":"Particle filter; Sensor fusion; Clutter; Computer science; Radar tracker; Artificial intelligence; Identification (biology); Tracking (education); Dempster–Shafer theory; Computer vision; Radar; Flexibility (engineering); Filter (signal processing); Fusion; Data association; Data mining; Mathematics","score_opus":0.08753641320238842,"score_gpt":0.2982129205675012,"score_spread":0.2106765073651128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149729344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022107891,0.00013863445,0.9970613,0.00003973008,0.00002486188,0.000012835801,0.0000121424855,0.00013576489,0.00036386366],"genre_scores_gemma":[0.35420108,0.00088635786,0.64082646,0.00012269047,0.00009493424,0.0001891161,0.00018213084,0.000064164095,0.0034331107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888295,0.0002766246,0.000081556755,0.00022325006,0.00046117668,0.00007444646],"domain_scores_gemma":[0.9990571,0.00040419848,0.00014913954,0.000148393,0.00020437593,0.00003678165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019365915,0.0011244885,0.0015723229,0.0014818367,0.0006523078,0.0012892159,0.0011662262,0.0017584797,0.000998694],"category_scores_gemma":[0.004035777,0.0005942233,0.0014706819,0.001792893,0.0007674394,0.0022988205,0.0016826695,0.0014684064,0.0004772277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017518725,0.000077095,0.0009944437,0.00015320888,0.00014367873,0.00015133731,0.00016501323,0.7601538,0.0062791673,0.025205487,0.0013237523,0.20517784],"study_design_scores_gemma":[0.0000067216574,0.000020834414,0.00017216084,0.0000059635604,0.0000086983055,0.000036801466,0.0000069486573,0.99412894,0.0015473774,0.0034906855,0.0005581193,0.000016781314],"about_ca_topic_score_codex":0.0050351145,"about_ca_topic_score_gemma":0.0032631722,"teacher_disagreement_score":0.0050351145,"about_ca_system_score_codex":0.0009514473,"about_ca_system_score_gemma":0.0011214857,"threshold_uncertainty_score":0.010241747},"labels":[],"label_agreement":null},{"id":"W2153841986","doi":"","title":"Upper bounds for the sensor subset selection problem","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","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 British Columbia","funders":"","keywords":"Lagrangian relaxation; Upper and lower bounds; Mathematics; Mathematical optimization; Metric (unit); Selection (genetic algorithm); Optimization problem; Relaxation (psychology); Combinatorial optimization; Combinatorics; Convex optimization; Fisher information; Regular polygon; Computer science; Artificial intelligence; Statistics; Mathematical analysis","score_opus":0.01965920555752451,"score_gpt":0.26446036641799736,"score_spread":0.24480116086047285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153841986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010044166,0.0018304377,0.9728631,0.0020325417,0.00011695706,0.000103515194,0.00029311786,0.0002763421,0.012439968],"genre_scores_gemma":[0.48202655,0.0058168205,0.49255434,0.001774668,0.0011772795,0.0013290172,0.0019212494,0.0008534278,0.01254673],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9911404,0.003595579,0.00030906315,0.001124521,0.0028922784,0.0009381265],"domain_scores_gemma":[0.96013933,0.032653604,0.0016276471,0.0019775855,0.0027171078,0.0008846436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0085554095,0.002383779,0.0021710342,0.001808053,0.0013912016,0.0041396297,0.0031380276,0.002167005,0.0093384],"category_scores_gemma":[0.039685816,0.00089946185,0.0014727035,0.002537209,0.0029201102,0.00791439,0.0044020144,0.0056606573,0.0016744217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026426066,0.00021875274,0.0012778244,0.0006673169,0.00010588188,0.00022678639,0.0003599934,0.3440273,0.003952674,0.56248045,0.01261123,0.073807545],"study_design_scores_gemma":[0.000042098694,0.0001236006,0.00037061816,0.00012305583,0.000031035146,0.00016542019,0.00008320737,0.7293349,0.0016348955,0.2624021,0.005658584,0.000030473037],"about_ca_topic_score_codex":0.0013002502,"about_ca_topic_score_gemma":0.0010917255,"teacher_disagreement_score":0.0093384,"about_ca_system_score_codex":0.0034682292,"about_ca_system_score_gemma":0.0024301948,"threshold_uncertainty_score":0.045245886},"labels":[],"label_agreement":null},{"id":"W2154321420","doi":"","title":"Flexible ID association-based tracking algorithm","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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; AUG Signals (Canada)","funders":"","keywords":"Kinematics; Computer science; Association (psychology); Ambiguity; Data association; Tracking (education); Association rule learning; Mechanism (biology); Data mining; Algorithm; Artificial intelligence","score_opus":0.02363506767342393,"score_gpt":0.27313097295012134,"score_spread":0.2494959052766974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154321420","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":[0.0018869627,0.00018163737,0.994935,0.00006985223,0.00009146558,0.00005771479,0.00008617056,0.0010668144,0.0016242871],"genre_scores_gemma":[0.17075728,0.0004005199,0.8193599,0.00028120872,0.000105168554,0.000319709,0.0009152425,0.0001359082,0.0077251135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988029,0.00013380888,0.00007794418,0.00042382246,0.00042099436,0.00014049355],"domain_scores_gemma":[0.9989736,0.0002067938,0.00010157167,0.00031556477,0.0003436784,0.00005878478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001268002,0.0008169977,0.0012524805,0.0015353946,0.0011801741,0.0016360461,0.0029643448,0.0012261402,0.0039287717],"category_scores_gemma":[0.0029467454,0.0004549235,0.0006728803,0.002572402,0.0006881839,0.0015492331,0.0027631603,0.0014308079,0.0031624911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029881462,0.00010365879,0.001976771,0.00014360655,0.00007983233,0.00017942776,0.00015500656,0.12215419,0.009383254,0.031256843,0.013167387,0.8211012],"study_design_scores_gemma":[0.000068714646,0.00009359686,0.0005110812,0.000020947415,0.00003486466,0.00039863063,0.0000310891,0.9568597,0.007632159,0.015758324,0.018543603,0.00004736274],"about_ca_topic_score_codex":0.0027997019,"about_ca_topic_score_gemma":0.0021633923,"teacher_disagreement_score":0.0039287717,"about_ca_system_score_codex":0.0007128405,"about_ca_system_score_gemma":0.0020358562,"threshold_uncertainty_score":0.013143063},"labels":[],"label_agreement":null},{"id":"W2154444545","doi":"","title":"A modular architecture for optimal video analytics deployment","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Image and Video Quality Assessment","field":"Computer Science","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":"Institut National d'Optique","funders":"","keywords":"Software deployment; Modular design; Computer science; Architecture; Analytics; Virtual reality; Real-time computing; Artificial intelligence; Human–computer interaction; Software engineering; Data science; Operating system","score_opus":0.07419457831728217,"score_gpt":0.3163266007387412,"score_spread":0.24213202242145904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154444545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019572949,0.00009232138,0.97554547,0.00015926106,0.000026249834,0.00008278288,0.000034868564,0.0016485564,0.0028375925],"genre_scores_gemma":[0.48599043,0.00016762076,0.50942355,0.00011149318,0.00006323555,0.00023208893,0.00019315712,0.00014871266,0.003669719],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954116,0.00008489533,0.000029948076,0.00012179823,0.00012479865,0.0000975616],"domain_scores_gemma":[0.9994535,0.000098766795,0.00006589259,0.00015207074,0.00016605697,0.0000637042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005625954,0.00065344025,0.0003641977,0.00053714734,0.00036444672,0.0009036926,0.0014263822,0.00066552474,0.0034330133],"category_scores_gemma":[0.0011407031,0.00040588438,0.00035613275,0.00040982623,0.00048126213,0.0014154325,0.001103353,0.00069612946,0.001315094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006301556,0.0002993446,0.0022564998,0.00035859982,0.00011164035,0.00047911558,0.00027688176,0.33935723,0.2032651,0.062991336,0.008806169,0.38116792],"study_design_scores_gemma":[0.0000692317,0.00038695554,0.0007785674,0.000027893524,0.00005731453,0.00030137677,0.00005669446,0.9371849,0.031067632,0.02124762,0.008788596,0.0000332196],"about_ca_topic_score_codex":0.00083624525,"about_ca_topic_score_gemma":0.0011176106,"teacher_disagreement_score":0.0034330133,"about_ca_system_score_codex":0.0005109006,"about_ca_system_score_gemma":0.0008385473,"threshold_uncertainty_score":0.011484563},"labels":[],"label_agreement":null},{"id":"W2156346395","doi":"","title":"Average-consensus with switched Markovian network links","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","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 British Columbia","funders":"","keywords":"Weighting; Node (physics); Mathematics; Markov chain; Markov process; Bounded function; Mathematical optimization; Ergodic theory; Stationary distribution; Topology (electrical circuits); Computer science; Algorithm; Statistics; Combinatorics","score_opus":0.01641189852416032,"score_gpt":0.23857952040280717,"score_spread":0.22216762187864686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156346395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13474295,0.000083998486,0.86179376,0.00012598943,0.000020550526,0.000023284443,0.000042583546,0.00022204114,0.0029448066],"genre_scores_gemma":[0.97777766,0.00004628094,0.020703854,0.000021509628,0.000012071861,0.000038586437,0.00004508596,0.000012281861,0.0013425702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994661,0.00013983375,0.0000226408,0.00013678704,0.00017950576,0.000055102235],"domain_scores_gemma":[0.99822754,0.0008955125,0.00035640635,0.0001922008,0.00026585677,0.00006246724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010643207,0.0003606555,0.00063176226,0.00042955723,0.00041393022,0.0006260101,0.0011116348,0.0006503339,0.00079724065],"category_scores_gemma":[0.0040920377,0.0002049764,0.00028835755,0.0005832854,0.0007468402,0.0013046601,0.00082348974,0.0005083975,0.00014440987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036362893,0.00001062107,0.00026456904,0.000011735259,0.000009663699,0.000037568843,0.000029904853,0.9787022,0.00187715,0.011483887,0.00011125889,0.0074252286],"study_design_scores_gemma":[0.0000030386507,0.000009421506,0.000060952258,6.515058e-7,0.0000016629549,0.000005674019,0.0000029155638,0.99508345,0.00030558638,0.0044590435,0.0000650423,0.0000026549396],"about_ca_topic_score_codex":0.004009657,"about_ca_topic_score_gemma":0.0029371756,"teacher_disagreement_score":0.004009657,"about_ca_system_score_codex":0.0008173921,"about_ca_system_score_gemma":0.0006711499,"threshold_uncertainty_score":0.007972658},"labels":[],"label_agreement":null},{"id":"W2156627171","doi":"","title":"Improved MeMBer filter with modeling of spurious targets","year":2013,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"McMaster University","funders":"","keywords":"Spurious relationship; Filter (signal processing); Cardinality (data modeling); Bernoulli's principle; Computer science; Algorithm; Bernoulli distribution; Mathematics; Data mining; Engineering; Random variable; Statistics; Machine learning; Computer vision","score_opus":0.0245602753669001,"score_gpt":0.24417140017281194,"score_spread":0.21961112480591183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156627171","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":[0.0055465344,0.00007255379,0.9938677,0.00004045666,0.000021602395,0.0000065557792,0.000016425241,0.00014933226,0.00027873597],"genre_scores_gemma":[0.31705096,0.00036542027,0.67694736,0.0002393536,0.00016278349,0.00010967618,0.0003321573,0.00013739207,0.004654865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985207,0.00038011975,0.000072626455,0.00031076054,0.0005752871,0.00014054267],"domain_scores_gemma":[0.99734735,0.0011943401,0.00023995563,0.00039836727,0.0007279174,0.00009207044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028434368,0.0007287279,0.0011513832,0.00067912735,0.00056973164,0.0009956971,0.0016390396,0.0014532192,0.0010964224],"category_scores_gemma":[0.006725773,0.00050208706,0.00093094155,0.0007907972,0.0005886635,0.0022458802,0.0013597057,0.0013540953,0.0006508416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006236735,0.00012536174,0.0044238474,0.00015056184,0.00016700184,0.00017766102,0.00032410948,0.55333865,0.031171229,0.034747604,0.0026766919,0.3720736],"study_design_scores_gemma":[0.000008939757,0.00004266307,0.0003777731,0.0000054999878,0.000016170927,0.00007038574,0.000006182263,0.9929959,0.0036533598,0.0018483815,0.0009603348,0.000014373535],"about_ca_topic_score_codex":0.00309593,"about_ca_topic_score_gemma":0.0026758232,"teacher_disagreement_score":0.00309593,"about_ca_system_score_codex":0.0006006257,"about_ca_system_score_gemma":0.0012695054,"threshold_uncertainty_score":0.015037715},"labels":[],"label_agreement":null},{"id":"W2163800811","doi":"","title":"Application of search theory for large volume surveillance planning","year":2008,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","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":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Focus (optics); General partnership; Control (management); Sensor fusion; Distributed computing; Operations research; Systems engineering; Artificial intelligence; Engineering","score_opus":0.036164169937719134,"score_gpt":0.3265113665868683,"score_spread":0.29034719664914915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163800811","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":[0.0109048365,0.000501439,0.9816507,0.00039406266,0.000027303471,0.000038055372,0.00003674613,0.000097689604,0.0063491957],"genre_scores_gemma":[0.77357686,0.0011116179,0.22030014,0.0001925044,0.00012662548,0.00024598336,0.00014396396,0.000119677665,0.0041826516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986172,0.0007211017,0.000052139607,0.00010100819,0.00041401,0.00009458322],"domain_scores_gemma":[0.99458474,0.0045912373,0.0002475083,0.00014511502,0.00031502265,0.000116399984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028200466,0.0006032231,0.0008753747,0.0017444321,0.00060088106,0.0015650815,0.00087379926,0.0006647368,0.002214425],"category_scores_gemma":[0.009348843,0.00048108233,0.0010600206,0.0016059673,0.001929258,0.0021176976,0.0013397672,0.0011095802,0.00018749307],"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":[0.000023730132,0.000014104345,0.00029931558,0.0000410192,0.000033457512,0.000029567542,0.000042755848,0.8119705,0.00023916649,0.17354764,0.00036464902,0.013394132],"study_design_scores_gemma":[0.000006325701,0.0000137838315,0.00005387666,0.000008103205,0.0000043413966,0.000008189641,0.000011751981,0.9353888,0.00014966795,0.06390183,0.000448951,0.000004346699],"about_ca_topic_score_codex":0.0064672786,"about_ca_topic_score_gemma":0.0037572212,"teacher_disagreement_score":0.0064672786,"about_ca_system_score_codex":0.002737404,"about_ca_system_score_gemma":0.0017841682,"threshold_uncertainty_score":0.01986134},"labels":[],"label_agreement":null},{"id":"W2164393601","doi":"","title":"A novel measure for data stream anomaly detection in a bio-surveillance system","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Defence Research and Development Canada","funders":"","keywords":"Anomaly detection; Measure (data warehouse); Computer science; Notice; Constant false alarm rate; Data mining; False alarm; Anomaly (physics); Interval (graph theory); Artificial intelligence; Mathematics","score_opus":0.08155373264300304,"score_gpt":0.28181412324190797,"score_spread":0.20026039059890494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164393601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054696415,0.0019220834,0.94016814,0.00032738174,0.00033313766,0.00013788493,0.0002676928,0.0008834419,0.001263888],"genre_scores_gemma":[0.5962158,0.00064574456,0.40096,0.00023068833,0.0004469051,0.0002879471,0.00045680426,0.00007063544,0.0006856078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953548,0.0007204516,0.00062709744,0.0006528458,0.002479371,0.00016536904],"domain_scores_gemma":[0.9907657,0.0043744254,0.0016272039,0.0005522619,0.0022590896,0.00042142178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029183102,0.0013379782,0.0014434624,0.0043701907,0.0005195559,0.0020089,0.00150441,0.0014619653,0.0006134611],"category_scores_gemma":[0.015314563,0.00026813138,0.0007279852,0.0023377123,0.0010684091,0.003634593,0.0015881145,0.0012120312,0.00032831967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011956133,0.00079395203,0.037805386,0.0011983123,0.0006836734,0.00051716,0.0005703204,0.07717689,0.08587746,0.025619356,0.0053242887,0.76323754],"study_design_scores_gemma":[0.00008198108,0.0021751102,0.025915843,0.00014322088,0.00031454655,0.0022297597,0.00025924627,0.900759,0.043765713,0.01441282,0.0096794395,0.00026335943],"about_ca_topic_score_codex":0.0004769608,"about_ca_topic_score_gemma":0.0003819644,"teacher_disagreement_score":0.0043701907,"about_ca_system_score_codex":0.0009784866,"about_ca_system_score_gemma":0.0007735146,"threshold_uncertainty_score":0.015433669},"labels":[],"label_agreement":null},{"id":"W2165315096","doi":"","title":"Ship movement anomaly detection using specialized distance measures","year":2015,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Dalhousie University","funders":"","keywords":"Anomaly detection; Trajectory; Cluster analysis; Computer science; Anomaly (physics); Set (abstract data type); Point (geometry); Division (mathematics); Movement (music); Domain (mathematical analysis); Work (physics); Longitude; Shore; Latitude; Geodesy; Real-time computing; Data mining; Artificial intelligence; Geography; Geology; Engineering; Mathematics; Geometry","score_opus":0.07999069881559094,"score_gpt":0.3042374426532906,"score_spread":0.22424674383769966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165315096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061997373,0.0002399404,0.9353173,0.000048643244,0.00005570384,0.0000661461,0.00019545862,0.0009752843,0.0011041875],"genre_scores_gemma":[0.5670256,0.0002927305,0.42983076,0.000029723116,0.000054613574,0.00009144902,0.0008429062,0.000105180705,0.0017270508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988876,0.00012838033,0.000107436244,0.00035702484,0.00042559628,0.00009399532],"domain_scores_gemma":[0.9989951,0.00021963798,0.00018012097,0.0001787544,0.00037561244,0.000050767194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048759187,0.0008748156,0.0009113147,0.0032180073,0.00040362717,0.00093640946,0.0011499856,0.00055515126,0.0007224741],"category_scores_gemma":[0.002162956,0.00023237997,0.0007536967,0.002590835,0.00033879772,0.0012126629,0.0009753202,0.00065465044,0.00050239346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033502342,0.00029032305,0.025223266,0.00035177017,0.00032942998,0.00037378058,0.00037775186,0.10638611,0.0590333,0.011828586,0.0030026268,0.79246795],"study_design_scores_gemma":[0.000015384421,0.0001391865,0.011989641,0.00001555842,0.000050970524,0.0003975853,0.00013702441,0.95400566,0.023304846,0.0048822677,0.005013364,0.000048446615],"about_ca_topic_score_codex":0.003002267,"about_ca_topic_score_gemma":0.0025188818,"teacher_disagreement_score":0.0032180073,"about_ca_system_score_codex":0.0005317296,"about_ca_system_score_gemma":0.0005418636,"threshold_uncertainty_score":0.005969584},"labels":[],"label_agreement":null},{"id":"W2166389316","doi":"","title":"Accurate Murty's algorithm for multitarget top hypothesis extraction","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Data association; Algorithm; Tree (set theory); Association (psychology); Tracking (education); Computer science; Set (abstract data type); Mathematics; Artificial intelligence","score_opus":0.07435404959345929,"score_gpt":0.2909683800675793,"score_spread":0.21661433047412001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166389316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015896895,0.00013211808,0.9969223,0.000048291706,0.00003701894,0.000055790897,0.000057178688,0.0006863128,0.00047138313],"genre_scores_gemma":[0.037062217,0.00009676506,0.9604942,0.00009812519,0.000049499457,0.00021179926,0.0004574853,0.00014332763,0.0013864718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970715,0.0006178296,0.0002671341,0.00071732333,0.001076413,0.0002498365],"domain_scores_gemma":[0.99622995,0.0016256097,0.00026310014,0.00077483343,0.0009883107,0.000118171585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036197982,0.0014273233,0.0018797374,0.0029335204,0.00124472,0.0023730577,0.0033940016,0.0025642456,0.007912186],"category_scores_gemma":[0.012665611,0.000797483,0.0017052678,0.0020547945,0.0009785444,0.0030481333,0.003905398,0.0024474221,0.0036658973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044576867,0.00007110328,0.0012365329,0.00023801687,0.00011634527,0.00019432635,0.00017931419,0.08705792,0.009822627,0.017412402,0.0068677342,0.87635803],"study_design_scores_gemma":[0.00008637531,0.00011041956,0.00050648756,0.000033472075,0.000045404,0.00027666736,0.000057121364,0.96042585,0.007894051,0.022200905,0.008317948,0.000045269706],"about_ca_topic_score_codex":0.002133319,"about_ca_topic_score_gemma":0.0028184834,"teacher_disagreement_score":0.007912186,"about_ca_system_score_codex":0.00080827647,"about_ca_system_score_gemma":0.0022469854,"threshold_uncertainty_score":0.026468933},"labels":[],"label_agreement":null},{"id":"W2169947777","doi":"","title":"Dynamic coalition formation for efficient sleep time allocation in wireless sensor networks using cooperative game theory","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","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":"Wireless sensor network; Core (optical fiber); Computer science; Game theory; Mathematical optimization; Upper and lower bounds; Cramér–Rao bound; Wireless; Time allocation; Regular polygon; Convex optimization; Sleep (system call); Cooperative game theory; Algorithm; Computer network; Mathematics; Telecommunications; Estimation theory; Mathematical economics","score_opus":0.017823038864073226,"score_gpt":0.27142724450923417,"score_spread":0.25360420564516095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169947777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006924807,0.00007433152,0.99216664,0.00005469177,0.000012913187,0.0000277567,0.0000050281483,0.000040097777,0.0006936843],"genre_scores_gemma":[0.68939966,0.00028440106,0.30802804,0.000086566775,0.00003676222,0.00031252956,0.000054183678,0.000045832203,0.0017521676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992508,0.0003317875,0.000037726557,0.00011716464,0.00018598746,0.00007644879],"domain_scores_gemma":[0.9989428,0.00063291675,0.0001169351,0.00008096939,0.00015360264,0.000072806215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014551801,0.00069266546,0.00096755684,0.00051826244,0.0006335664,0.0006704205,0.0013992864,0.000669383,0.0005104048],"category_scores_gemma":[0.0032899305,0.00035727312,0.0006718814,0.00056746276,0.000979717,0.0013989526,0.0015984093,0.0009132152,0.0001349081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007900007,0.000059730275,0.0004453056,0.00006837617,0.00006221851,0.000106335116,0.0002840881,0.8785002,0.0045646606,0.066775665,0.00088341784,0.048170958],"study_design_scores_gemma":[0.0000120645345,0.000029394805,0.00004249135,0.000003665557,0.0000067297087,0.000021576387,0.000017932682,0.9875816,0.00047753242,0.011320335,0.00048062485,0.0000059890212],"about_ca_topic_score_codex":0.0026842644,"about_ca_topic_score_gemma":0.0026148031,"teacher_disagreement_score":0.0026842644,"about_ca_system_score_codex":0.0007315633,"about_ca_system_score_gemma":0.0012805125,"threshold_uncertainty_score":0.007695794},"labels":[],"label_agreement":null},{"id":"W2205395973","doi":"","title":"Spatio-temporal trajectory models for target tracking","year":2014,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Data Management and Algorithms","field":"Computer Science","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; Trajectory; Hidden Markov model; BitTorrent tracker; Tracking (education); Context model; Markov chain; Artificial intelligence; Context (archaeology); Scale (ratio); Machine learning; Eye tracking","score_opus":0.04446074168811947,"score_gpt":0.2722947473471788,"score_spread":0.2278340056590593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2205395973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009529824,0.00049547484,0.98646253,0.00032644268,0.000073882184,0.000028476978,0.00074502855,0.00043839446,0.0018997992],"genre_scores_gemma":[0.7447996,0.0024908409,0.23302048,0.00029421944,0.0002216914,0.00039670622,0.0036856118,0.0002891172,0.014801704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994529,0.00015501735,0.000039709445,0.00014691941,0.00014556866,0.000059918086],"domain_scores_gemma":[0.9986405,0.0007021325,0.00020703439,0.00016936682,0.0002272859,0.000053728425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096517784,0.0008848529,0.0006985136,0.00094874605,0.00044184644,0.0010708445,0.0017998257,0.0012473078,0.002469015],"category_scores_gemma":[0.0039288276,0.00043349943,0.0011331658,0.0017228855,0.0006879767,0.0019835446,0.0009066456,0.0015181184,0.0009190741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023189408,0.000012140263,0.0006839046,0.000034331228,0.000032282285,0.00006168251,0.0000671942,0.92215914,0.00046514513,0.06537342,0.00091546663,0.010172085],"study_design_scores_gemma":[0.000002302231,0.000004765259,0.000084964064,0.0000044460917,0.0000061512787,0.0000114508475,0.000006571011,0.9746328,0.00007279062,0.024188649,0.0009810284,0.0000040534624],"about_ca_topic_score_codex":0.026326643,"about_ca_topic_score_gemma":0.02030832,"teacher_disagreement_score":0.026326643,"about_ca_system_score_codex":0.0013724001,"about_ca_system_score_gemma":0.0013472089,"threshold_uncertainty_score":0.052346826},"labels":[],"label_agreement":null},{"id":"W2223319633","doi":"","title":"POMDP sensor scheduling with adaptive sampling","year":2014,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","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 British Columbia","funders":"","keywords":"Partially observable Markov decision process; Computer science; Markov decision process; Scheduling (production processes); Markov process; Mathematical optimization; False alarm; Sampling (signal processing); Markov chain; Real-time computing; Artificial intelligence; Markov model; Mathematics; Machine learning; Statistics; Filter (signal processing)","score_opus":0.030085465294377098,"score_gpt":0.2556907742646632,"score_spread":0.22560530897028608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2223319633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02793166,0.00031114495,0.9661656,0.00044430405,0.000096433905,0.00015023375,0.00022861773,0.00024160136,0.0044304193],"genre_scores_gemma":[0.89710116,0.0003182192,0.098132044,0.00013110755,0.00009068278,0.0002788148,0.00019772949,0.00004207652,0.0037082043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988011,0.00039303728,0.000063813866,0.00030075837,0.00023860205,0.00020260614],"domain_scores_gemma":[0.9975332,0.0017292215,0.0002527585,0.00013362935,0.00019091641,0.00016025371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001572904,0.0010208363,0.0014210436,0.00035080427,0.0005096047,0.0010434128,0.0013728274,0.0010266276,0.0025211351],"category_scores_gemma":[0.0045287265,0.0005779198,0.0007522975,0.0006351686,0.001001699,0.0010759968,0.0010684676,0.0014975914,0.00024719868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010834802,0.000031882555,0.00029820198,0.000055188124,0.00001936901,0.00006989979,0.000031755833,0.97402126,0.0004692767,0.018064631,0.0004388773,0.006391327],"study_design_scores_gemma":[0.000023178533,0.000024102965,0.000052138173,0.000002865213,0.000004680306,0.000007500794,0.000007998721,0.9904587,0.00014441174,0.009010267,0.00026049901,0.000003630878],"about_ca_topic_score_codex":0.0068085184,"about_ca_topic_score_gemma":0.0044102473,"teacher_disagreement_score":0.0068085184,"about_ca_system_score_codex":0.001408063,"about_ca_system_score_gemma":0.0016109409,"threshold_uncertainty_score":0.013537765},"labels":[],"label_agreement":null},{"id":"W222671543","doi":"","title":"Uncertainty representations for a Vehicle-Borne IED surveillance problem","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","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":"Defence Research and Development Canada","funders":"","keywords":"Computer science; A priori and a posteriori; Statement (logic); Certainty; Representation (politics); Problem statement; Risk analysis (engineering); Uncertainty quantification; Sensor fusion; Data mining; Operations research; Artificial intelligence; Machine learning; Management science; Engineering","score_opus":0.047697919637159046,"score_gpt":0.31465794482938947,"score_spread":0.26696002519223044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W222671543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012386912,0.0005779293,0.9811601,0.001210499,0.00003539179,0.000030412022,0.00022699346,0.000050770086,0.0043209493],"genre_scores_gemma":[0.7774383,0.0016762376,0.21417035,0.00031854358,0.00024832698,0.00030004012,0.00084459066,0.00004658767,0.004957066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975708,0.0012490813,0.00015127432,0.00035543434,0.00047227286,0.00020110587],"domain_scores_gemma":[0.9953929,0.0034517944,0.000526941,0.00016154045,0.00033500735,0.00013179117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040680547,0.0008214301,0.0010094503,0.0012546106,0.00056840904,0.0029319962,0.0015410234,0.0020774049,0.0019194164],"category_scores_gemma":[0.008447029,0.0004480471,0.001344619,0.0014492217,0.0015452964,0.0031983517,0.0028275508,0.0024386193,0.00019324523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005091863,0.000023554358,0.00038765027,0.000115779294,0.00003763039,0.00015174308,0.00018836526,0.62851006,0.00038862822,0.35608843,0.0010986368,0.012958623],"study_design_scores_gemma":[0.000008669184,0.000020207499,0.00012437654,0.000029545203,0.000010774312,0.000036098736,0.00005086487,0.8566378,0.00014327986,0.14168146,0.0012423892,0.000014619856],"about_ca_topic_score_codex":0.0049155178,"about_ca_topic_score_gemma":0.0020431518,"teacher_disagreement_score":0.0049155178,"about_ca_system_score_codex":0.0019404736,"about_ca_system_score_gemma":0.001088652,"threshold_uncertainty_score":0.021514237},"labels":[],"label_agreement":null},{"id":"W2269111190","doi":"","title":"Behavioral learning of vessel types with fuzzy-rough decision trees","year":2014,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Maritime Navigation and Safety","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":true,"ca_institutions":"Larus Technologies (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Identification (biology); Synthetic aperture radar; Dependency (UML); Fuzzy logic; Decision tree; Fuzzy set; Machine learning; Rough set; Data mining; Operations research; Engineering","score_opus":0.014656558232237436,"score_gpt":0.2564166160944178,"score_spread":0.24176005786218036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2269111190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13938901,0.00020122467,0.8588805,0.00026496002,0.000030572817,0.0000999177,0.00022246047,0.00027110838,0.0006401274],"genre_scores_gemma":[0.85954285,0.00012455804,0.13910496,0.000075950215,0.00003812168,0.00014579805,0.0005035228,0.000017048682,0.00044719843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989145,0.00042877567,0.00010288507,0.00023514942,0.00020239371,0.0001162553],"domain_scores_gemma":[0.9947566,0.0038853264,0.0004813088,0.0002038221,0.00051949057,0.00015341255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027580317,0.0008165502,0.0014329836,0.001637454,0.00057698064,0.001049363,0.0013111307,0.0009809261,0.00062445475],"category_scores_gemma":[0.007434443,0.00057984877,0.0016732089,0.00090731145,0.0006261661,0.0013523548,0.0006471842,0.0014928338,0.00017282878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093011215,0.00008391011,0.0037469366,0.000039366903,0.00005205727,0.000060085662,0.000098049044,0.9479779,0.00052339747,0.0026710853,0.00031696833,0.044337224],"study_design_scores_gemma":[0.0000034710588,0.000014443794,0.00020787887,0.0000037929053,0.0000058606106,0.000003879957,0.0000076242827,0.9970402,0.00013236716,0.002539795,0.00003672203,0.0000039386073],"about_ca_topic_score_codex":0.012845473,"about_ca_topic_score_gemma":0.010968125,"teacher_disagreement_score":0.012845473,"about_ca_system_score_codex":0.0013224523,"about_ca_system_score_gemma":0.0011856937,"threshold_uncertainty_score":0.025541425},"labels":[],"label_agreement":null},{"id":"W2510909510","doi":"","title":"Distributed consensus in noisy wireless sensor networks","year":2016,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","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":"Wireless sensor network; Computer science; Noise (video); Noise measurement; Quantization (signal processing); Algorithm; Wireless; Probability density function; Wireless network; Key distribution in wireless sensor networks; Network topology; Mean squared error; Imperfect; Topology (electrical circuits); Mathematics; Computer network; Telecommunications; Artificial intelligence; Statistics; Noise reduction","score_opus":0.02118830602183096,"score_gpt":0.2528816401560713,"score_spread":0.23169333413424031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510909510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017211063,0.000818825,0.9803657,0.00018411748,0.00007219461,0.000019086934,0.000019067049,0.00013646184,0.0011734199],"genre_scores_gemma":[0.903708,0.0013161071,0.09236376,0.00014518622,0.00016533259,0.00016004348,0.00010340292,0.000060300354,0.0019778495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795,0.0007065367,0.00012594185,0.000535327,0.000573278,0.00010883922],"domain_scores_gemma":[0.99764663,0.0013795904,0.00035298266,0.00017462081,0.00038106696,0.000065076405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002150321,0.0007134995,0.0013129095,0.0007562728,0.00064230984,0.0009754499,0.0012675276,0.0011778908,0.000459955],"category_scores_gemma":[0.006254241,0.00037756914,0.00047960068,0.0011421154,0.0011815593,0.001798357,0.0012768285,0.0008409691,0.00012916356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006257094,0.000015314348,0.0004919151,0.00014292586,0.0000589847,0.00013915729,0.00012098005,0.94364333,0.002252845,0.028120054,0.00047350253,0.024478419],"study_design_scores_gemma":[0.000011714442,0.000027416845,0.0001079059,0.0000073718775,0.000009184272,0.000026779053,0.000022875975,0.97558254,0.00060121773,0.02296526,0.0006293033,0.000008449128],"about_ca_topic_score_codex":0.0016589803,"about_ca_topic_score_gemma":0.00075121725,"teacher_disagreement_score":0.002150321,"about_ca_system_score_codex":0.000747817,"about_ca_system_score_gemma":0.0005845126,"threshold_uncertainty_score":0.011372149},"labels":[],"label_agreement":null},{"id":"W2512718628","doi":"","title":"Hidden Markov models with discrete infinite logistic normal distribution priors","year":2016,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","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":"Hidden Markov model; Prior probability; Pattern recognition (psychology); Bayesian probability; Computer science; Posterior probability; Artificial intelligence; Algorithm; Markov model; Mathematics; Stochastic matrix; Markov chain; Applied mathematics; Machine learning","score_opus":0.03193451441721599,"score_gpt":0.2764315793795729,"score_spread":0.24449706496235693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512718628","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":[0.008773983,0.00030147514,0.98930407,0.00023990902,0.00004114604,0.00003404571,0.00019902247,0.0002944005,0.0008118808],"genre_scores_gemma":[0.6510482,0.0011633574,0.3353797,0.00033630084,0.00022378999,0.000483238,0.0019854074,0.00024863955,0.009131385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963894,0.0015120796,0.00017013606,0.0010500333,0.0005911143,0.00028730248],"domain_scores_gemma":[0.99303377,0.005144161,0.00061072316,0.00044979624,0.00061614974,0.00014543333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00434218,0.0011149049,0.0016434838,0.0013787204,0.0007856917,0.0019715803,0.0036719912,0.0017608117,0.0032851354],"category_scores_gemma":[0.016391234,0.001154131,0.001516402,0.001500192,0.0018970219,0.004252527,0.0020462195,0.003907612,0.0013180069],"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":[0.00022047813,0.00007519871,0.0034929137,0.00013433865,0.00010795639,0.00032967524,0.00035611494,0.75303215,0.0012223435,0.18004388,0.0017884269,0.059196543],"study_design_scores_gemma":[0.000009654874,0.000009507647,0.00020757214,0.000011644434,0.000008706122,0.000036571982,0.000012634491,0.95650214,0.00020955398,0.042392194,0.00058115594,0.0000187158],"about_ca_topic_score_codex":0.01196271,"about_ca_topic_score_gemma":0.008445028,"teacher_disagreement_score":0.01196271,"about_ca_system_score_codex":0.0016644194,"about_ca_system_score_gemma":0.0014315117,"threshold_uncertainty_score":0.023786187},"labels":[],"label_agreement":null},{"id":"W2515805800","doi":"","title":"An improved Multitarget Multi-Bernoulli filter with cardinality corrected","year":2016,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Cardinality (data modeling); Filter (signal processing); Clutter; Bernoulli's principle; Algorithm; Gaussian; Mathematics; Computer science; Filter design; Radar; Telecommunications; Data mining; Engineering; Computer vision","score_opus":0.0287262078221894,"score_gpt":0.27278438986868414,"score_spread":0.24405818204649474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515805800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002803265,0.00013655216,0.99641764,0.00005444952,0.00003898426,0.0000121922285,0.000018008914,0.00016166759,0.00035720566],"genre_scores_gemma":[0.13270041,0.0005262497,0.8622729,0.0002795312,0.00014558021,0.000110459405,0.00024051663,0.00009988507,0.0036245026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815553,0.00040689894,0.00008879622,0.00038412304,0.00080936414,0.00015535908],"domain_scores_gemma":[0.9972658,0.0011594639,0.0002569769,0.0004138083,0.0008015456,0.0001024053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030320396,0.0008682494,0.0015741993,0.0009422433,0.0005908568,0.0011141856,0.0019492259,0.0017853387,0.0014189685],"category_scores_gemma":[0.008594095,0.00060812954,0.0010386894,0.0011913866,0.00074652716,0.0028316032,0.0015126935,0.0020112598,0.000769216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008048824,0.00010950831,0.0038450423,0.00028352684,0.00023173449,0.00022195799,0.00040044312,0.3297975,0.041545786,0.047097653,0.0041502565,0.5715116],"study_design_scores_gemma":[0.00003655247,0.00008959921,0.0007155394,0.000019118663,0.000044776396,0.00026182944,0.000012336158,0.9798513,0.010068283,0.004699983,0.0041507217,0.000049938717],"about_ca_topic_score_codex":0.0037751351,"about_ca_topic_score_gemma":0.0035063063,"teacher_disagreement_score":0.0037751351,"about_ca_system_score_codex":0.0010131501,"about_ca_system_score_gemma":0.0016780156,"threshold_uncertainty_score":0.0160352},"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":"W43427029","doi":"","title":"A sequential tracking filter without requirement of measurement decorrelation","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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":"Decorrelation; Covariance; Sequential estimation; Computer science; Filter (signal processing); Covariance matrix; Algorithm; Measurement uncertainty; Tracking (education); Nonlinear system; Observational error; Correlation coefficient; Mathematics; Statistics; Computer vision; Machine learning","score_opus":0.09508718448114455,"score_gpt":0.30259765299171837,"score_spread":0.2075104685105738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W43427029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029029956,0.00009660592,0.9959312,0.00003783976,0.0000824165,0.000023752187,0.00003112617,0.00029036627,0.0006037862],"genre_scores_gemma":[0.13392977,0.00036415347,0.8591283,0.00020375737,0.0001961907,0.00020191466,0.00025265175,0.00007182461,0.0056513753],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990294,0.00008862852,0.00005630606,0.0002978264,0.0004648947,0.00006290291],"domain_scores_gemma":[0.99942195,0.00013675804,0.000066896224,0.00012177301,0.00022628155,0.000026441761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009067363,0.0007937957,0.0008795275,0.0006207009,0.00060767384,0.000641894,0.00078998867,0.0012744981,0.0023572058],"category_scores_gemma":[0.0016665093,0.00045640746,0.00076480024,0.0009504058,0.0003516261,0.0011601344,0.0008016011,0.0009498753,0.0012929862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056374073,0.00016589888,0.0010904382,0.00030855555,0.00017812445,0.00020339248,0.00013475769,0.054030877,0.17258064,0.016150549,0.004970234,0.7496228],"study_design_scores_gemma":[0.00010829321,0.0004550466,0.0018535587,0.00003072011,0.00014840711,0.0008335332,0.000019973417,0.91211265,0.060111098,0.0046832482,0.019559821,0.00008366511],"about_ca_topic_score_codex":0.00288922,"about_ca_topic_score_gemma":0.0035014616,"teacher_disagreement_score":0.00288922,"about_ca_system_score_codex":0.00037550338,"about_ca_system_score_gemma":0.0012657179,"threshold_uncertainty_score":0.0078856945},"labels":[],"label_agreement":null}]}