{"meta":{"query_hash":"72d704c7571e","filters":{"topic":"Context-Aware Activity Recognition Systems"},"cohort_total":1215,"direct_labels_cover":1,"predictions_cover":1215,"exported":1215,"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/72d704c7571e","api":"https://metacan.xera.ac/api/v1/cohort?topic=Context-Aware+Activity+Recognition+Systems"},"results":[{"id":"W105673404","doi":"","title":"An XACML-based Security Pattern to achieve Socio-Technical Confidentiality in Smart Homes","year":2009,"lang":"fr","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"XACML; Confidentiality; Computer security; Computer science; Security service; Security information and event management; Computer security model; Order (exchange); Cloud computing security; Health care; Information security; Access control; Business; Cloud computing","score_opus":0.040714570028362346,"score_gpt":0.31546682330803516,"score_spread":0.2747522532796728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W105673404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20686902,0.00009817015,0.76467896,0.020072777,0.0023725252,0.0021750678,0.00016608715,0.0006674428,0.0028999317],"genre_scores_gemma":[0.98931396,0.000016059332,0.004234009,0.00513639,0.0004552307,0.000254504,0.00012995183,0.00004807366,0.0004118245],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98955566,0.003135732,0.0018038624,0.0029237515,0.0013729112,0.0012080888],"domain_scores_gemma":[0.993923,0.0007508918,0.0006062334,0.0031573558,0.00066488294,0.000897603],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0041577853,0.0010158772,0.0016947456,0.00057483476,0.00022676292,0.0016041889,0.0034610184,0.0013217311,0.00081436],"category_scores_gemma":[0.00018115259,0.0011594194,0.000650583,0.0008218211,0.00026103997,0.0010820621,0.0017325318,0.002152206,0.0010793593],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036968145,0.008434621,0.09927653,0.0010289458,0.00023750545,0.0006440237,0.009938698,0.0010209734,0.0026042708,0.013525008,0.0068870434,0.85603267],"study_design_scores_gemma":[0.005698232,0.003070206,0.7585033,0.0036019282,0.0001924908,0.00029045154,0.0010435625,0.13495187,0.0056080776,0.056187347,0.02354463,0.0073078563],"about_ca_topic_score_codex":0.026753865,"about_ca_topic_score_gemma":0.045800753,"teacher_disagreement_score":0.84872484,"about_ca_system_score_codex":0.0009808862,"about_ca_system_score_gemma":0.0011060088,"threshold_uncertainty_score":0.9999748},"labels":[],"label_agreement":null},{"id":"W113793282","doi":"10.1522/030166451","title":"Developpement d'un modele de gestion d'objets geo-localisables centralise utilisant differents moyens de communication dans une environnement OSGI /","year":2010,"lang":"fr","type":"book","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.05799471355105705,"score_gpt":0.25165928182981573,"score_spread":0.19366456827875866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W113793282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069429636,0.0006643927,0.8956473,0.006886061,0.0012145818,0.0022203082,0.00024645557,0.0004468135,0.023244463],"genre_scores_gemma":[0.88750845,0.002391452,0.04744071,0.000887842,0.00030536912,0.00052572635,0.0011001289,0.00016096835,0.059679337],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99170136,0.0017920933,0.0017283615,0.0016032783,0.0013781516,0.0017967776],"domain_scores_gemma":[0.99367076,0.0009052192,0.0010313166,0.0029806674,0.00051276654,0.00089928653],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019831317,0.001254024,0.0011479215,0.0003442325,0.0012104627,0.0007859401,0.0029180155,0.0014124837,0.00091947283],"category_scores_gemma":[0.00018259419,0.0013371371,0.00043422298,0.00038381043,0.00071107736,0.0011110051,0.0017543879,0.0018873309,0.00045884072],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019369528,0.006342902,0.018955786,0.0013556231,0.0017018742,0.00020641241,0.030278346,0.004592543,0.017663125,0.17430094,0.01885472,0.72555405],"study_design_scores_gemma":[0.0039430875,0.0003788732,0.06447717,0.0032626537,0.000683309,0.00061357376,0.0011666017,0.6938896,0.017915132,0.0182297,0.19145428,0.00398597],"about_ca_topic_score_codex":0.010967337,"about_ca_topic_score_gemma":0.015910938,"teacher_disagreement_score":0.8482066,"about_ca_system_score_codex":0.0041433168,"about_ca_system_score_gemma":0.0015677515,"threshold_uncertainty_score":0.9999938},"labels":[],"label_agreement":null},{"id":"W139854251","doi":"","title":"AVATECH: AVANET - CROWDSOURCED, REAL-TIME SNOWPACK INFORMATION","year":2014,"lang":"en","type":"article","venue":"International Snow Science Workshop 2014 Proceedings, Banff, Canada","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Snowpack; Snow; Computer science; Focus (optics); Cloud computing; Data science; World Wide Web; Meteorology; Geography","score_opus":0.006602750315591189,"score_gpt":0.2134830061411688,"score_spread":0.20688025582557762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W139854251","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29200852,0.00004860423,0.21829718,0.030197682,0.013992411,0.0018356708,0.00011796577,0.0018450987,0.44165686],"genre_scores_gemma":[0.99288243,0.000013689152,0.002915906,0.0013847189,0.00039941721,0.00005454456,0.000023266868,0.000017910597,0.0023080872],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99524766,0.000030130072,0.0007045936,0.0007857459,0.0024762994,0.0007555812],"domain_scores_gemma":[0.9964902,0.00032101324,0.0005875349,0.000469104,0.0016985476,0.00043360778],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0019196722,0.00034768315,0.0003188033,0.0006063479,0.00053662626,0.001862299,0.0036731637,0.00012344544,0.00028199118],"category_scores_gemma":[0.0014239773,0.00034995915,0.00008035324,0.0012542419,0.00029222426,0.007030631,0.00063672994,0.00033855718,0.0006407251],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007326255,0.00013718296,0.0049288627,0.00006886346,0.000092589005,0.000014087759,0.0023290154,0.0005424555,0.022770025,0.052264232,0.67983085,0.2369486],"study_design_scores_gemma":[0.0011853889,0.00011052112,0.007038754,0.000352401,0.000015944872,0.00031308553,0.0005045229,0.24352801,0.013195647,0.00183562,0.730489,0.001431106],"about_ca_topic_score_codex":0.06696207,"about_ca_topic_score_gemma":0.026616113,"teacher_disagreement_score":0.7008739,"about_ca_system_score_codex":0.0011340375,"about_ca_system_score_gemma":0.0013250002,"threshold_uncertainty_score":0.9998952},"labels":[],"label_agreement":null},{"id":"W1465357002","doi":"10.1007/978-3-319-05939-6_1","title":"A Temporal Description Logic for Resource-Bounded Rule-Based Context-Aware Agents","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Bounded function; Resource (disambiguation); Context (archaeology); Theoretical computer science; Mathematics; Geography; Computer network","score_opus":0.059697227865314076,"score_gpt":0.25834266793318433,"score_spread":0.19864544006787027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1465357002","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000120562596,0.00014278786,0.995659,0.0013615291,0.0006397365,0.0011529571,0.000109062006,0.00012718534,0.00068718713],"genre_scores_gemma":[0.16332103,0.000010352301,0.83515245,0.0008693226,0.00021496293,0.00012473771,0.00014208117,0.00002872961,0.000136308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983428,0.000029974492,0.0007741552,0.00024719766,0.00032153496,0.00028432248],"domain_scores_gemma":[0.99742615,0.00065139803,0.00079287094,0.0007330507,0.00032887788,0.000067636116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006876857,0.00034889232,0.0005185047,0.00029035777,0.0010683473,0.00047176649,0.0020979987,0.00028688274,0.0000010253468],"category_scores_gemma":[0.00009944201,0.0002966795,0.00034372226,0.00019929999,0.00036383455,0.00040744437,0.0005183447,0.0003370139,0.000001184514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026058386,0.000088047644,0.00003182354,0.001657814,0.00039955496,3.3120693e-7,0.0038840976,0.51958483,0.000029217565,0.21583946,0.0015198254,0.25693893],"study_design_scores_gemma":[0.00054634415,0.00011626347,0.000018970031,0.00038799847,0.000045958845,0.000009630711,0.0000071316017,0.87864053,0.00006609208,0.0027860429,0.116994336,0.00038069446],"about_ca_topic_score_codex":0.00004058668,"about_ca_topic_score_gemma":0.00015582638,"teacher_disagreement_score":0.3590557,"about_ca_system_score_codex":0.00013119604,"about_ca_system_score_gemma":0.00024606838,"threshold_uncertainty_score":0.99994856},"labels":[],"label_agreement":null},{"id":"W1483942847","doi":"10.4271/2005-01-1605","title":"A Comprehensive Learnability Evaluation Method for In-Car Navigation Devices","year":2005,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Context-Aware Activity Recognition Systems","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 Guelph","funders":"","keywords":"Learnability; Computer science; Artificial intelligence","score_opus":0.045513697439131286,"score_gpt":0.34171583201169636,"score_spread":0.29620213457256506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1483942847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9308572,0.001080657,0.0033671337,0.030211352,0.0009437033,0.010631871,0.00011323953,0.0050653573,0.01772949],"genre_scores_gemma":[0.93204737,0.000028773367,0.06394312,0.0019888591,0.0001946959,0.0016043294,0.000054037275,0.000058318466,0.00008050793],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99349576,0.0009765883,0.0014196004,0.0018334959,0.0014256958,0.0008488836],"domain_scores_gemma":[0.9943853,0.0024177986,0.0004676964,0.0017309743,0.0007098726,0.00028834786],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0032256122,0.0006653378,0.0010192933,0.00033336593,0.00037003128,0.00027078448,0.0015503603,0.0007101532,0.00012744528],"category_scores_gemma":[0.0013603879,0.0006300739,0.0004994204,0.0012370609,0.000372893,0.0019623842,0.0005088338,0.0009892161,0.00013295021],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016929489,0.00043428008,0.0002977598,0.00009084814,0.000034986373,0.000005719151,0.000117113515,0.00012127574,0.7885079,0.006246949,0.0005515114,0.20342237],"study_design_scores_gemma":[0.0015131845,0.00080727943,0.919493,0.00028178006,0.000058172696,0.00009818502,0.00013831844,0.000051613588,0.00038676683,0.0057096723,0.07074862,0.00071339635],"about_ca_topic_score_codex":0.00010504879,"about_ca_topic_score_gemma":0.085947424,"teacher_disagreement_score":0.91919523,"about_ca_system_score_codex":0.0009514514,"about_ca_system_score_gemma":0.0002688938,"threshold_uncertainty_score":0.9996151},"labels":[],"label_agreement":null},{"id":"W1484703625","doi":"10.1007/978-3-642-02868-7_15","title":"Interactive Calendar to Help Maintain Social Interactions for Elderly People and People with Mild Cognitive Impairments","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Elderly people; Cognition; Psychology; Older people; Gerontology; Cognitive psychology; Medicine; Psychiatry","score_opus":0.01744248053746745,"score_gpt":0.279452680447254,"score_spread":0.2620101999097866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484703625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004010414,0.00005397228,0.9896665,0.0026641267,0.00092377845,0.0019341403,0.00018277043,0.00015565858,0.0004086225],"genre_scores_gemma":[0.92817897,0.000006978122,0.06858604,0.0020290054,0.0005483537,0.00017183619,0.000023422333,0.000052462354,0.00040295377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960242,0.00008170248,0.0005203417,0.0018384256,0.0007719614,0.0007634004],"domain_scores_gemma":[0.9965363,0.0014812276,0.0004012865,0.0005150053,0.00077567814,0.00029048606],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00060149,0.0006611137,0.0008240804,0.0010567573,0.0005869554,0.0006335036,0.0012095409,0.00021900648,0.000017404629],"category_scores_gemma":[0.00012601061,0.0006219688,0.00016017884,0.000728578,0.0002604271,0.0012427161,0.00071700144,0.00075827906,0.000028191296],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014611422,0.00010104253,0.00013644242,0.00005124988,0.00006338453,0.000034442422,0.010543179,0.0001366016,0.000057315094,0.0005557124,0.000110284316,0.9880642],"study_design_scores_gemma":[0.023573147,0.03595993,0.04671833,0.021480897,0.0007937121,0.006983766,0.00061924255,0.49484414,0.010571605,0.3039751,0.034259398,0.020220732],"about_ca_topic_score_codex":0.00015310294,"about_ca_topic_score_gemma":0.0053629708,"teacher_disagreement_score":0.9678435,"about_ca_system_score_codex":0.0005692198,"about_ca_system_score_gemma":0.00053974736,"threshold_uncertainty_score":0.9996232},"labels":[],"label_agreement":null},{"id":"W1485043819","doi":"10.11575/prism/30813","title":"Where Are You and When Are You Coming Home? Foundations of Interpersonal Awareness","year":2004,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Calgary","funders":"","keywords":"Interpersonal communication; Psychology; Interpersonal relationship; Social psychology; Internet privacy; Computer science","score_opus":0.03881450387349932,"score_gpt":0.2724275626480836,"score_spread":0.23361305877458427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1485043819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30501622,0.00023904422,0.68701327,0.0055548605,0.00046952636,0.0002360041,0.00003117132,0.00018215593,0.0012577311],"genre_scores_gemma":[0.9938488,0.000011765978,0.0054318556,0.00015768764,0.000054528264,0.000020572255,0.0000041562625,0.000010593485,0.00046005272],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987697,0.000065554595,0.0002971983,0.0003967212,0.00027166263,0.00019919017],"domain_scores_gemma":[0.9988852,0.00013721256,0.00024531645,0.00038652244,0.00024430288,0.00010141922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020867461,0.00015768735,0.000295508,0.0001569367,0.00015639992,0.00022444448,0.0004365758,0.000070570735,0.00007778815],"category_scores_gemma":[0.00004286144,0.00015076935,0.000075757605,0.00022984462,0.00008350194,0.000934828,0.00027611517,0.00011650226,0.0000376175],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084723295,0.0013100165,0.61406976,0.0012896818,0.0005971687,0.00013511107,0.039459195,0.00018437716,0.0049398914,0.03644101,0.006195508,0.29529354],"study_design_scores_gemma":[0.025484407,0.0014614114,0.6099602,0.014526293,0.0003206661,0.0024565214,0.081180416,0.063705646,0.028497182,0.075411215,0.08929031,0.007705729],"about_ca_topic_score_codex":0.00060212804,"about_ca_topic_score_gemma":0.002402695,"teacher_disagreement_score":0.6888326,"about_ca_system_score_codex":0.000084630716,"about_ca_system_score_gemma":0.00011632324,"threshold_uncertainty_score":0.6148196},"labels":[],"label_agreement":null},{"id":"W1487489356","doi":"10.1007/978-3-540-39653-6_24","title":"The Design of a Context-Aware Home Media Space for Balancing Privacy and Awareness","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Space (punctuation); Context (archaeology); Multimedia; Internet privacy; Function (biology); Control (management); Human–computer interaction; Computer security; Artificial intelligence","score_opus":0.0368126949614235,"score_gpt":0.2590835686184936,"score_spread":0.22227087365707007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1487489356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000130418,0.0016179599,0.9937222,0.0011757779,0.0019920697,0.0011843159,0.000017330138,0.00008506806,0.00007487013],"genre_scores_gemma":[0.83304757,0.00016117825,0.1651499,0.00094878074,0.00034398455,0.0000967013,0.000004366296,0.00006791435,0.00017960081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99637014,0.00014138449,0.0006684909,0.001324406,0.0008491385,0.00064644375],"domain_scores_gemma":[0.9905345,0.00677287,0.00060712645,0.0012776601,0.0006369275,0.00017090418],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002237509,0.0005283214,0.0007918055,0.0004673895,0.00048396268,0.00058424537,0.0025235203,0.0002947787,0.0000027890173],"category_scores_gemma":[0.0005345917,0.00040932768,0.0001399818,0.00046849772,0.0008686558,0.0006006678,0.00083117763,0.00046701208,0.0000040181576],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002835966,0.000026654718,0.00025626537,0.00014880188,0.000041309315,0.000021724145,0.0026565383,0.0019001948,0.0002082253,0.009316153,0.00010299501,0.9852928],"study_design_scores_gemma":[0.0019407838,0.0005628383,0.00045899127,0.002216398,0.000046379366,0.0003825388,0.0000087300305,0.79073274,0.0053253006,0.18702698,0.009391271,0.001907033],"about_ca_topic_score_codex":0.000032314823,"about_ca_topic_score_gemma":0.00011712852,"teacher_disagreement_score":0.98338574,"about_ca_system_score_codex":0.00020069416,"about_ca_system_score_gemma":0.0008089225,"threshold_uncertainty_score":0.99983585},"labels":[],"label_agreement":null},{"id":"W1488341529","doi":"","title":"Presenting LibQUAL+ Results for Action","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Action (physics); Computer science; Geology; Physics","score_opus":0.11527554480442076,"score_gpt":0.35262900397558483,"score_spread":0.23735345917116407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488341529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01861038,0.0000067136484,0.94645154,0.00065504626,0.0006707127,0.00028798275,0.000002263578,0.00034476558,0.03297057],"genre_scores_gemma":[0.9736433,6.4908176e-7,0.020142607,0.00014221767,0.00023583464,0.000016238308,0.000003159628,0.0000056516405,0.0058103492],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990105,0.000028539553,0.00026101578,0.0002890911,0.00017468166,0.00023618054],"domain_scores_gemma":[0.99872833,0.0006507064,0.0001117611,0.0003050826,0.00014063534,0.00006350556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00112326,0.00007149607,0.00009046971,0.000102114755,0.00010478175,0.00014688988,0.00027819895,0.000049430524,0.0000047384283],"category_scores_gemma":[0.00021774064,0.000067439454,0.00006184105,0.00021308567,0.0000089291625,0.0007600018,0.000086532214,0.00005665066,0.000073361014],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106684296,0.00010982008,0.000554004,0.000040382693,0.00003447137,0.000006901,0.0012078913,0.0000053901476,0.008236063,0.010729181,0.023133181,0.95583606],"study_design_scores_gemma":[0.005160407,0.00029089843,0.015635928,0.00008089839,0.000016143631,0.0001286707,0.00093411084,0.077465884,0.29224986,0.0063524973,0.6007388,0.00094588153],"about_ca_topic_score_codex":0.000057085203,"about_ca_topic_score_gemma":0.00017075737,"teacher_disagreement_score":0.9550329,"about_ca_system_score_codex":0.00003242619,"about_ca_system_score_gemma":0.00002370715,"threshold_uncertainty_score":0.2750101},"labels":[],"label_agreement":null},{"id":"W1491168269","doi":"10.1109/aiccsa.2005.1387095","title":"ESPERANTO: a middleware platform to achieve interoperability in nomadic computing domains","year":2005,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Guelph","funders":"","keywords":"Middleware (distributed applications); Interoperability; Dynamism; Computer science; Context (archaeology); Ubiquitous computing; Distributed computing; Service (business); Software engineering; Service-oriented architecture; World Wide Web; Operating system; Web service; Business","score_opus":0.028761288607254354,"score_gpt":0.2754491956171574,"score_spread":0.24668790700990306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491168269","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67732906,0.000019963816,0.31417897,0.0021398477,0.00022602192,0.00040013492,0.0000025205259,0.00025538786,0.005448063],"genre_scores_gemma":[0.97680885,7.7227435e-7,0.021045139,0.0016966262,0.00012439468,0.000020285952,0.0000017187424,0.000012315474,0.00028989525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791414,0.0000974537,0.0005360269,0.00067624485,0.00030819044,0.00046792201],"domain_scores_gemma":[0.9986872,0.00023498015,0.00007272905,0.0006887538,0.000106582724,0.0002097418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077212474,0.00022157107,0.00034189466,0.0002680118,0.000106144,0.00026468685,0.00092445133,0.00008183425,0.000083510575],"category_scores_gemma":[0.000068938985,0.00020165002,0.0000976549,0.00068418786,0.000031473166,0.0010597917,0.0005544017,0.00024783023,0.00074787624],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007396629,0.00055616425,0.03169597,0.0000453149,0.000034321274,0.000039265447,0.018637676,0.00040416708,0.0019715056,0.0031114903,0.0006894949,0.9427407],"study_design_scores_gemma":[0.008944326,0.0017756821,0.22213724,0.0012305821,0.000018778272,0.0008186472,0.0041114995,0.6864078,0.014253312,0.0022058066,0.054200295,0.0038960052],"about_ca_topic_score_codex":0.00038633533,"about_ca_topic_score_gemma":0.006836641,"teacher_disagreement_score":0.9388447,"about_ca_system_score_codex":0.00036956245,"about_ca_system_score_gemma":0.00007590939,"threshold_uncertainty_score":0.96126854},"labels":[],"label_agreement":null},{"id":"W1491433259","doi":"10.15353/joci.v4i1.2963","title":"Is There A Wireless Community Informatics?","year":2008,"lang":"en","type":"article","venue":"The Journal of Community Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Informatics; Wireless; Health informatics; Computer science; Telecommunications; Medicine; Nursing; Engineering; Public health; Electrical engineering","score_opus":0.07979459749414078,"score_gpt":0.2872692568410906,"score_spread":0.20747465934694984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491433259","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8390106,0.000060411745,0.15320563,0.00062120723,0.00022960267,0.00016078126,0.000007767212,0.000060869155,0.006643139],"genre_scores_gemma":[0.99594325,0.00022432896,0.0013513506,0.002360671,0.00004045126,0.000002020773,0.0000020091165,0.000010153979,0.00006575357],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99496895,0.0022165794,0.0016699824,0.000008249298,0.0008315546,0.000304692],"domain_scores_gemma":[0.99267906,0.0024117099,0.0020102467,0.0018466016,0.00089148025,0.00016092934],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.005851086,0.00023767258,0.00050034706,0.00024516956,0.001987406,0.0002076213,0.005010315,0.000112375754,0.000023670405],"category_scores_gemma":[0.00021458414,0.00016153493,0.00020910842,0.00056875055,0.0002881468,0.0034199585,0.0011046196,0.0031580369,0.00013939805],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000308615,0.0002691807,0.00065938797,0.00015478114,0.00016901629,0.0000013213527,0.9642738,0.00008745959,0.000025498937,0.0002542622,0.0076048486,0.02646956],"study_design_scores_gemma":[0.0041964883,0.0017323841,0.017871406,0.0009819093,0.00015942944,0.015376618,0.88994116,0.0234036,0.003776355,0.0056664553,0.035797995,0.0010962257],"about_ca_topic_score_codex":0.0003003087,"about_ca_topic_score_gemma":0.000052506468,"teacher_disagreement_score":0.15693268,"about_ca_system_score_codex":0.00011744745,"about_ca_system_score_gemma":0.00028498133,"threshold_uncertainty_score":0.99931186},"labels":[],"label_agreement":null},{"id":"W1491448374","doi":"","title":"Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Health care; Pleasure; Middleware (distributed applications); Computer science; Population; Healthcare system; World Wide Web; Knowledge management; Business; Internet privacy; Medicine; Political science; Psychology","score_opus":0.049246201071637255,"score_gpt":0.2893569836335865,"score_spread":0.24011078256194923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491448374","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7119603,0.00051955384,0.26860794,0.00352228,0.0042327987,0.0033567534,0.000020805537,0.00019636723,0.0075831846],"genre_scores_gemma":[0.99796176,0.000015692342,0.00079929194,0.00017718428,0.0001432373,0.00003991693,9.541213e-7,0.000008541803,0.00085339486],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988958,0.000018348548,0.00029305636,0.00031773467,0.00028137784,0.00019364624],"domain_scores_gemma":[0.99874586,0.00070413516,0.00022808531,0.00018399689,0.00006768255,0.000070250855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008926499,0.00010710696,0.00015789457,0.00007417703,0.00012131823,0.0001594923,0.00042115198,0.00006645158,0.0000029663972],"category_scores_gemma":[0.00009960938,0.00008228815,0.000038130696,0.0001243951,0.000039343755,0.00030180824,0.000388421,0.00009049361,0.000001003449],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006971496,0.00026854684,0.24979764,0.00036963006,0.00017193613,0.0000028562795,0.0032846027,0.00000977837,0.007200063,0.0066979565,0.0019580785,0.7301692],"study_design_scores_gemma":[0.0030721654,0.00096669525,0.7903543,0.0055075577,0.00006513354,0.0005870929,0.008327894,0.06548262,0.0062930286,0.0007079726,0.1171657,0.0014698422],"about_ca_topic_score_codex":0.000032474873,"about_ca_topic_score_gemma":0.000032538876,"teacher_disagreement_score":0.7286993,"about_ca_system_score_codex":0.00006712696,"about_ca_system_score_gemma":0.00001677687,"threshold_uncertainty_score":0.33556134},"labels":[],"label_agreement":null},{"id":"W1502010353","doi":"10.1109/dcoss.2015.40","title":"Personalization Using Sensors for Preliminary Human Detection in an IoT Environment","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Personalization; Bluetooth; Computer science; Internet of Things; Wireless sensor network; Intelligent sensor; Smart environment; Embedded system; Microcontroller; Wireless; Identification (biology); Smart objects; Human–computer interaction; Activity recognition; Home automation; Computer network; Telecommunications; World Wide Web; Artificial intelligence","score_opus":0.13334514951906618,"score_gpt":0.3149402583823548,"score_spread":0.1815951088632886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502010353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4680907,0.000006640773,0.5314284,0.000033534292,0.000080568236,0.00022641667,6.389918e-7,0.000049221297,0.000083881445],"genre_scores_gemma":[0.99178207,1.9660669e-7,0.007929432,0.00004121677,0.00005991076,0.000033034656,0.0000036612716,0.000008313181,0.00014213986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912447,0.00011274709,0.00016561137,0.00028457004,0.00017643224,0.0001361459],"domain_scores_gemma":[0.9995627,0.000037393896,0.000068918875,0.0002049386,0.00004989723,0.000076178534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035690638,0.000082774495,0.00009628021,0.00012625805,0.00007430716,0.00007643095,0.00013829903,0.000052174822,0.000007987913],"category_scores_gemma":[0.000017556224,0.00008737522,0.00002851952,0.00011158731,0.0000129389755,0.00052815396,0.00005191109,0.000040403072,0.000012474179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031041313,0.0017776352,0.02527688,0.00021680478,0.00007065209,0.000039042265,0.035782162,0.027872067,0.37866902,0.0037824945,0.00033300323,0.5258698],"study_design_scores_gemma":[0.00069348206,0.00047397922,0.0031164156,0.000018615045,0.0000048608017,0.000031775944,0.0006813029,0.97868955,0.015070747,0.0005052673,0.0005187588,0.00019526418],"about_ca_topic_score_codex":0.00016572562,"about_ca_topic_score_gemma":0.00015313303,"teacher_disagreement_score":0.95081747,"about_ca_system_score_codex":0.00021701855,"about_ca_system_score_gemma":0.000024825415,"threshold_uncertainty_score":0.3563058},"labels":[],"label_agreement":null},{"id":"W1503262082","doi":"10.1002/spe.2116","title":"Playing MUSIC — building context‐aware and self‐adaptive mobile applications","year":2012,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Adaptation (eye); Computer science; Context (archaeology); Context awareness; Joint (building); Human–computer interaction; Mobile device; Mobile computing; Ubiquitous computing; Multimedia; World Wide Web; Telecommunications; Engineering; Architectural engineering; Psychology","score_opus":0.02921781814748071,"score_gpt":0.29604554579925413,"score_spread":0.2668277276517734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1503262082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11909502,0.0030826414,0.8758462,0.00016765684,0.00032090434,0.0006840437,0.0000050641784,0.0004553297,0.00034310293],"genre_scores_gemma":[0.93900764,0.00011520481,0.059126485,0.00070662535,0.00013595162,0.00086606387,0.000001230316,0.00001367149,0.000027108264],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984263,0.00014550966,0.0002436607,0.0005093006,0.00027097957,0.0004042538],"domain_scores_gemma":[0.9974617,0.0014408573,0.00022463812,0.000420813,0.00019327563,0.00025875267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004438178,0.00020017847,0.00021172086,0.00008964398,0.00060293695,0.00031417672,0.00031157673,0.00008565633,0.000012472416],"category_scores_gemma":[0.0002931491,0.00020221598,0.0000343403,0.0003426975,0.00010409784,0.0056938496,0.00038842025,0.00021351948,0.000039461138],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035026816,0.0005379852,0.015418267,0.00008816177,0.00009225166,0.000017834835,0.12955849,0.0000023135576,0.0010765392,0.010458825,0.00029272606,0.8424216],"study_design_scores_gemma":[0.0025789037,0.0008015936,0.013642882,0.0004169025,0.00021192715,0.0046783034,0.14951496,0.008320642,0.0060707894,0.0013126592,0.80908453,0.0033659171],"about_ca_topic_score_codex":0.00015645147,"about_ca_topic_score_gemma":0.000008905814,"teacher_disagreement_score":0.83905566,"about_ca_system_score_codex":0.00005609487,"about_ca_system_score_gemma":0.000056052675,"threshold_uncertainty_score":0.8246128},"labels":[],"label_agreement":null},{"id":"W1506807425","doi":"10.1046/j.1460-9592.2003.t01-1-00272.x","title":"Effect of Modern Pacing Algorithms on Generator Longevity:","year":2003,"lang":"en","type":"article","venue":"Pacing and Clinical Electrophysiology","topic":"Context-Aware Activity Recognition Systems","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":"Women's College Hospital; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Longevity; Generator (circuit theory); Algorithm; Cardiology; Internal medicine; Gerontology; Power (physics); Computer science","score_opus":0.021745840060658283,"score_gpt":0.3193552695393807,"score_spread":0.29760942947872243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506807425","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8924582,0.0002617761,0.10622146,0.00006381626,0.00036302506,0.00016502223,0.0000014398441,0.00007641894,0.00038882796],"genre_scores_gemma":[0.9989397,0.000055949982,0.0006036523,0.00022570546,0.00011795281,0.000015064889,0.0000011026574,0.000011856052,0.000029028028],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99682486,0.0015811265,0.00046463267,0.00059599866,0.00014862098,0.00038476204],"domain_scores_gemma":[0.9973027,0.0018515666,0.0002121811,0.00043653624,0.00007065023,0.00012637579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012256044,0.00019275717,0.00066022837,0.00007585744,0.000111401845,0.000032202377,0.00018592246,0.00017065583,0.0000039374218],"category_scores_gemma":[0.0006957059,0.00015383256,0.0001677799,0.00019478347,0.000104118895,0.00011418868,0.00007197515,0.00035745188,0.000024309473],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015306775,0.00020929078,0.0017708876,0.00007356577,0.00013947052,0.0000174092,0.00011246747,0.00003040871,0.11538491,0.002405472,0.000058277645,0.87964475],"study_design_scores_gemma":[0.010557733,0.058979105,0.00934923,0.0005608923,0.00028905462,0.0004288449,0.00003940168,0.13939992,0.74883574,0.027700692,0.0014900118,0.0023693915],"about_ca_topic_score_codex":0.000016419071,"about_ca_topic_score_gemma":0.00000398348,"teacher_disagreement_score":0.8772754,"about_ca_system_score_codex":0.000022979577,"about_ca_system_score_gemma":0.000057001875,"threshold_uncertainty_score":0.627311},"labels":[],"label_agreement":null},{"id":"W1507628670","doi":"","title":"An Eigenspace-Based Approach for Human Fall Detection using Integrated","year":2008,"lang":"en","type":"article","venue":"International Conference on Signal Processing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.18011144287132408,"score_gpt":0.34613571318319714,"score_spread":0.16602427031187306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1507628670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1362756,0.0000077836385,0.8610836,0.000083666855,0.00015219308,0.00028404468,0.000014987252,0.00023724511,0.0018608496],"genre_scores_gemma":[0.976442,4.932151e-7,0.022945272,0.00020107339,0.00017899083,0.00007861962,0.000051665622,0.00002154457,0.00008037097],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981043,0.000114661416,0.00035301375,0.00062786904,0.0005407234,0.0002594364],"domain_scores_gemma":[0.99812806,0.00006529087,0.0002965048,0.00023169686,0.0011648954,0.00011358162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032564634,0.00023400976,0.00021940157,0.0003616256,0.00050199183,0.00049671286,0.0008043753,0.00011313596,0.00002312468],"category_scores_gemma":[0.000031117244,0.00023192239,0.000094475516,0.00028147976,0.00008439179,0.0012368998,0.00004070407,0.00022707481,0.000008303085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035236642,0.0012439516,0.0032265482,0.0001314804,0.00011892092,0.00003778928,0.0018854559,0.00808681,0.5384996,0.010097728,0.000034964516,0.4362844],"study_design_scores_gemma":[0.00061725924,0.00021677896,0.0002526994,0.00010573347,0.000006113893,0.000059287177,0.00012271907,0.96845716,0.028853517,0.0008767203,0.00014523859,0.00028679537],"about_ca_topic_score_codex":0.00016233516,"about_ca_topic_score_gemma":0.00005356337,"teacher_disagreement_score":0.9603703,"about_ca_system_score_codex":0.00022359678,"about_ca_system_score_gemma":0.00039675648,"threshold_uncertainty_score":0.9457521},"labels":[],"label_agreement":null},{"id":"W1510237560","doi":"10.1007/11740674_4","title":"An Architecture for Developing Context-Aware Systems","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Context (archaeology); Architecture; Process (computing); Adapter (computing); Context model; Component (thermodynamics); Software engineering; Human–computer interaction; Programming language; Artificial intelligence; Operating system","score_opus":0.03102993613819677,"score_gpt":0.26603294134542577,"score_spread":0.235003005207229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510237560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006576006,0.0006047592,0.9912684,0.0008403927,0.0039744223,0.0017563192,0.00005973962,0.0004995077,0.00093071046],"genre_scores_gemma":[0.7872277,0.000010537453,0.20759468,0.0020257514,0.0020029887,0.00019407143,0.00007508644,0.00013958573,0.0007296102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940948,0.00011214515,0.0009854285,0.0025398321,0.0012129353,0.001054856],"domain_scores_gemma":[0.99504775,0.0012409139,0.0006595474,0.001968905,0.0008369377,0.0002459481],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013695603,0.00090990536,0.0010980027,0.0013195619,0.00050649274,0.0016681259,0.0043660104,0.0006217941,0.000004580375],"category_scores_gemma":[0.00008799609,0.00086913945,0.0002567948,0.00065276783,0.0005059588,0.001149701,0.00071726163,0.0008737947,0.000035530553],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013829068,0.00004275566,0.000047749912,0.00024146789,0.000032689266,0.00007164858,0.00066048396,0.025529549,0.00017716545,0.0182587,0.00022807076,0.9546959],"study_design_scores_gemma":[0.001257408,0.00058827177,0.000116541065,0.0025597846,0.000029612167,0.0006093093,0.0000022882143,0.860085,0.0018372333,0.088785514,0.041171357,0.0029576703],"about_ca_topic_score_codex":0.00020297615,"about_ca_topic_score_gemma":0.0007346777,"teacher_disagreement_score":0.95173824,"about_ca_system_score_codex":0.00070697814,"about_ca_system_score_gemma":0.0012577779,"threshold_uncertainty_score":0.99937594},"labels":[],"label_agreement":null},{"id":"W1513393364","doi":"10.1007/11880905_16","title":"Implicit Context-Sensitive Mobile Computing Using Semantic Policies","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ubiquitous computing; Provisioning; Mobile computing; Mobile device; Context (archaeology); Context awareness; Adaptability; Mobile technology; The Internet; Distributed computing; Computer network; World Wide Web; Human–computer interaction; Phone","score_opus":0.02598060272945502,"score_gpt":0.272234784432641,"score_spread":0.24625418170318597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1513393364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043980684,0.00039637185,0.98868716,0.00021788433,0.0020573963,0.0009947582,0.000021332586,0.0003930063,0.0028340055],"genre_scores_gemma":[0.94987106,0.00000645602,0.04756102,0.0013790989,0.00086477277,0.000008512189,0.0000081768385,0.000068048605,0.00023285676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941473,0.00012343108,0.0010365174,0.0021951867,0.0013180022,0.0011795479],"domain_scores_gemma":[0.99535847,0.0013120527,0.00079133525,0.0015927164,0.00071370206,0.00023170056],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00111918,0.0009132449,0.0012030747,0.0014662802,0.0005903773,0.0011992817,0.0026512446,0.00046130895,0.000009962034],"category_scores_gemma":[0.00007947258,0.00092694775,0.00030889994,0.0010114523,0.00091144786,0.0009906545,0.002112521,0.0010440039,0.00008350242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007897946,0.000077788165,0.00023536677,0.000111576825,0.000055792112,0.0003011965,0.002618011,0.059447125,0.0023654671,0.0047083804,0.00008150628,0.9299899],"study_design_scores_gemma":[0.00053144974,0.00021116245,0.00032273578,0.0014222501,0.000027711732,0.00094783877,0.0000036602476,0.9733972,0.004977671,0.0152536845,0.0012839594,0.0016206852],"about_ca_topic_score_codex":0.001143469,"about_ca_topic_score_gemma":0.0004897741,"teacher_disagreement_score":0.945473,"about_ca_system_score_codex":0.0008259201,"about_ca_system_score_gemma":0.00074915553,"threshold_uncertainty_score":0.9998376},"labels":[],"label_agreement":null},{"id":"W1514924411","doi":"10.1109/ihtc.2014.7147537","title":"Affordable erehabilitation monitoring platform","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"McGill University","funders":"","keywords":"Accelerometer; Computer science; Gyroscope; Wearable computer; Dynamic time warping; Enhanced Data Rates for GSM Evolution; Rehabilitation; Wearable technology; Real-time computing; Human–computer interaction; Artificial intelligence; Computer vision; Embedded system; Simulation; Engineering; Medicine","score_opus":0.02640942022048304,"score_gpt":0.24989635829409787,"score_spread":0.22348693807361483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514924411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13999337,0.000009069498,0.8230419,0.00030726398,0.0007099574,0.00011602819,2.2312231e-7,0.0003518487,0.035470348],"genre_scores_gemma":[0.9729215,5.8974695e-7,0.025926504,0.000051837727,0.00013028506,0.000023786602,4.674251e-7,0.0000051576862,0.0009398839],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919313,0.00003760157,0.00016075077,0.0002322677,0.00020361954,0.00017263794],"domain_scores_gemma":[0.99907386,0.00034270674,0.00005241259,0.00037448923,0.00009181725,0.00006473807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004293561,0.0000792601,0.00010604535,0.00007964797,0.00008947361,0.00016066604,0.00032147134,0.00003804881,0.000027783737],"category_scores_gemma":[0.00010198523,0.000072142575,0.000044555167,0.00019939554,0.0000117654845,0.0012029766,0.000092092654,0.00006114828,0.00041219656],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032484666,0.000057901903,0.015172691,0.000028865581,0.000012711448,6.6476235e-7,0.000897506,0.000030732925,0.0032904684,0.05019859,0.00072774536,0.9295789],"study_design_scores_gemma":[0.0037799974,0.0012370091,0.16486801,0.0003762947,0.000022620245,0.00012383772,0.0018652014,0.3597336,0.16986836,0.11459304,0.18115397,0.0023780265],"about_ca_topic_score_codex":0.0000893353,"about_ca_topic_score_gemma":0.00001863758,"teacher_disagreement_score":0.92720085,"about_ca_system_score_codex":0.000046555047,"about_ca_system_score_gemma":0.000018953007,"threshold_uncertainty_score":0.529809},"labels":[],"label_agreement":null},{"id":"W1515196472","doi":"10.5220/0003031200890094","title":"A QUALITY OF CONTEXT DRIVEN APPROACH FOR THE SELECTION OF CONTEXT SERVICES","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Athabasca University","funders":"","keywords":"Context management; Context (archaeology); Computer science; Mobile computing; Context awareness; Ubiquitous computing; Context model; Process (computing); Services computing; Adaptation (eye); Quality (philosophy); World Wide Web; Computer network; Web service; Human–computer interaction; Artificial intelligence","score_opus":0.04896230609692102,"score_gpt":0.2994241316366767,"score_spread":0.25046182553975566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515196472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16273953,0.000045541565,0.8335807,0.00032691832,0.00031947176,0.0007896175,0.00001663066,0.00008038501,0.0021012484],"genre_scores_gemma":[0.99037004,0.0000012296119,0.009006246,0.00018036753,0.000055754896,0.00009759541,0.0000030751119,0.0000067910773,0.0002789226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987069,0.00013265293,0.00045913417,0.00028296185,0.00026399447,0.00015438022],"domain_scores_gemma":[0.9974546,0.0009891781,0.0004221523,0.0004852101,0.00060581806,0.000043036503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009661767,0.00011477241,0.00031561046,0.00006602688,0.000092693714,0.000056241402,0.000724285,0.00008808355,0.000033487566],"category_scores_gemma":[0.00007368276,0.00007946144,0.00015556304,0.00026320142,0.00008033158,0.00041462405,0.00011329024,0.00013599372,0.0000054176485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001478921,0.0006811497,0.03954643,0.00070968835,0.00034998232,1.2599257e-7,0.0068454207,0.000049026537,0.120193906,0.07100364,0.00088564795,0.7595871],"study_design_scores_gemma":[0.0031104025,0.00038605975,0.054231446,0.000058519756,0.000045127294,0.00003425774,0.007049504,0.83864087,0.08601995,0.0008037319,0.009077657,0.00054247404],"about_ca_topic_score_codex":0.0019042036,"about_ca_topic_score_gemma":0.0029620063,"teacher_disagreement_score":0.8385918,"about_ca_system_score_codex":0.00001131738,"about_ca_system_score_gemma":0.000070156166,"threshold_uncertainty_score":0.32403436},"labels":[],"label_agreement":null},{"id":"W1516514233","doi":"","title":"A wearable real-time system for physical activity recognition and fall detection","year":2010,"lang":"en","type":"article","venue":"University Library - University of Saskatchewan (University of Saskatchewan)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Computer science; Artificial intelligence; Computer vision; Embedded system","score_opus":0.011076283106330923,"score_gpt":0.17125849750819333,"score_spread":0.16018221440186242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1516514233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93731624,0.0000074294962,0.059108462,0.0003733983,0.00021532936,0.00072229333,0.00046913218,0.00056860875,0.0012191357],"genre_scores_gemma":[0.97922635,0.000031382217,0.015501352,0.00000790252,0.000060745922,9.873841e-8,0.00004275274,0.000026504955,0.0051028957],"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","domain_scores_codex":[0.9976883,0.00027672877,0.00017076914,0.0009605631,0.0004215079,0.0004821218],"domain_scores_gemma":[0.9973896,0.00048161537,0.00063911395,0.0007525612,0.00034608302,0.00039100365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030528393,0.00038945107,0.00081618596,0.0006450775,0.00074184645,0.00006259689,0.0013115013,0.00041877292,0.000079056656],"category_scores_gemma":[0.000013298355,0.00058698276,0.00045341905,0.00088265305,0.00061987963,0.0052617975,0.00082446425,0.00048120684,0.00006849886],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046260213,0.0019294012,0.0019793299,0.001906666,0.0010889408,0.0003846836,0.2714339,0.000032827225,0.4563506,0.00023373886,0.00051206106,0.25952184],"study_design_scores_gemma":[0.009986575,0.0015568335,0.007823404,0.00068061653,0.00072514697,0.00013230839,0.8955165,0.02074124,0.054826282,0.0010083354,0.0048954724,0.0021072703],"about_ca_topic_score_codex":0.020204168,"about_ca_topic_score_gemma":0.009208815,"teacher_disagreement_score":0.6240826,"about_ca_system_score_codex":0.00016186145,"about_ca_system_score_gemma":0.0005992646,"threshold_uncertainty_score":0.99965817},"labels":[],"label_agreement":null},{"id":"W1519255039","doi":"10.1007/s11704-015-4404-7","title":"Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment","year":2015,"lang":"en","type":"article","venue":"Frontiers of Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Sleep (system call); Software deployment; Sleep patterns; Wireless sensor network; Artificial intelligence; Real-time computing; Human–computer interaction; Machine learning; Computer network; Medicine","score_opus":0.01869705889164716,"score_gpt":0.215603309015182,"score_spread":0.19690625012353483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519255039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19165355,0.000042733373,0.80635697,0.0002486252,0.0010826662,0.00032702865,0.000004025235,0.00005284695,0.00023153865],"genre_scores_gemma":[0.80713785,0.0000022782112,0.19260128,0.00013801242,0.00009248094,0.000008475154,0.000002290867,0.000009182738,0.000008127891],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971609,0.00011745161,0.00041018412,0.00083530036,0.0009965418,0.00047960502],"domain_scores_gemma":[0.998311,0.00006895189,0.0003574319,0.00063597783,0.00036686804,0.0002597811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009982361,0.0002371155,0.0003975133,0.0005452333,0.000109207984,0.00023512609,0.0010298453,0.000065497145,0.0000015550976],"category_scores_gemma":[0.000026945409,0.00022034488,0.000055108467,0.0009075147,0.00040375095,0.0017597051,0.00043704032,0.00019130182,0.0000393721],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000268306,0.000105567146,0.0074202013,0.000010357495,0.000020233223,0.000075667755,0.003369517,0.00077181536,0.0002490578,0.0000051361667,0.0009575558,0.98698807],"study_design_scores_gemma":[0.002412725,0.0016015864,0.03270434,0.0003065887,0.000018753748,0.00019108753,0.00070531,0.94801366,0.011879104,0.0008231014,0.00049838726,0.00084535807],"about_ca_topic_score_codex":0.00019839896,"about_ca_topic_score_gemma":0.000058161135,"teacher_disagreement_score":0.9861427,"about_ca_system_score_codex":0.00035553382,"about_ca_system_score_gemma":0.00032044863,"threshold_uncertainty_score":0.8985404},"labels":[],"label_agreement":null},{"id":"W1521259444","doi":"10.1049/cp:20081099","title":"Activity prediction for in-home activity monitoring","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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; Activities of daily living; Software; Everyday life; Baseline (sea); Period (music); Cognition; Applied psychology; Gerontology; Psychology; Medicine","score_opus":0.06896302984715443,"score_gpt":0.28059830822301873,"score_spread":0.21163527837586432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521259444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57232225,0.0000075203966,0.42529598,0.00017476176,0.00073875865,0.00030906862,0.0000048526167,0.0002487921,0.0008980175],"genre_scores_gemma":[0.99613994,0.000009725169,0.0028863812,0.000016522155,0.00021575973,0.0001782475,6.1390585e-7,0.000010880511,0.00054190366],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986893,0.0000896404,0.0001740809,0.00047028955,0.000268534,0.00030815718],"domain_scores_gemma":[0.99894196,0.00034605077,0.00009440481,0.00042485588,0.00010415105,0.000088564804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033661525,0.00014850058,0.00023104745,0.00019626567,0.00017449608,0.00006648941,0.00034517923,0.00009651653,0.0000064809055],"category_scores_gemma":[0.00006147344,0.00015155261,0.00009570341,0.00040217963,0.000028240665,0.001946523,0.00012738151,0.00015865522,0.000032822805],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015803112,0.0008605726,0.24825579,0.000086695676,0.00006369519,0.000036137928,0.0015922388,0.000069017384,0.08053522,0.0006254141,0.00080528075,0.6669119],"study_design_scores_gemma":[0.0016420023,0.00021238717,0.82777137,0.000054062442,0.0000046327864,0.00011551412,0.00003423423,0.038053777,0.12948321,0.00047982068,0.0017368628,0.0004121537],"about_ca_topic_score_codex":0.00024765352,"about_ca_topic_score_gemma":0.00005801282,"teacher_disagreement_score":0.66649973,"about_ca_system_score_codex":0.00018564177,"about_ca_system_score_gemma":0.0000903904,"threshold_uncertainty_score":0.6180136},"labels":[],"label_agreement":null},{"id":"W1522612262","doi":"10.1007/978-3-642-13778-5_4","title":"Security, Privacy, and Dependability in Smart Homes: A Pattern Catalog Approach","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Dependability; Computer science; Computer security; Home automation; Sketch; Autonomy; Internet privacy; Ubiquitous computing; Human–computer interaction; Software engineering; Telecommunications","score_opus":0.019187038122198526,"score_gpt":0.24103005756866863,"score_spread":0.2218430194464701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1522612262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065595536,0.0002759187,0.9887635,0.00056260877,0.0012698125,0.00091118086,0.00002166412,0.00016566594,0.0014700913],"genre_scores_gemma":[0.9561402,0.000018842587,0.042903047,0.0005919311,0.00022101673,0.000035497535,0.000014585991,0.00003349055,0.000041350133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99485475,0.00011926507,0.00073341443,0.0024966071,0.0010286021,0.0007673724],"domain_scores_gemma":[0.9963496,0.0007977228,0.00034856025,0.0020118454,0.00022920025,0.0002631099],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001996575,0.00066535093,0.0008763051,0.0010580558,0.0002015537,0.00085371383,0.0033121882,0.00061994826,0.000014923592],"category_scores_gemma":[0.00018662035,0.00063391693,0.00012788421,0.0006465692,0.0009391801,0.0011204869,0.00331034,0.0019458646,0.000030350022],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060411926,0.00013410585,0.007285066,0.00021964232,0.0000130225,0.00011272612,0.0040996787,0.000098471646,0.00014236876,0.0005542696,0.000006323631,0.9873283],"study_design_scores_gemma":[0.0027227188,0.00054327154,0.020541975,0.0018065326,0.00003069924,0.0023043915,0.0000040894006,0.6003568,0.0028655562,0.35905895,0.0050397688,0.0047252234],"about_ca_topic_score_codex":0.00047477384,"about_ca_topic_score_gemma":0.0032734785,"teacher_disagreement_score":0.9826031,"about_ca_system_score_codex":0.00030382196,"about_ca_system_score_gemma":0.000575832,"threshold_uncertainty_score":0.9996112},"labels":[],"label_agreement":null},{"id":"W1523650173","doi":"10.3233/thc-2009-0548","title":"Model and algorithmic framework for detection and correction of cognitive errors","year":2009,"lang":"en","type":"article","venue":"Technology and Health Care","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Sherbrooke","keywords":"Computer science; Activity recognition; Plan (archaeology); Matching (statistics); Compensation (psychology); Cognition; Cognitive impairment; Machine learning; Artificial intelligence; Modality (human–computer interaction); Medicine","score_opus":0.031367073513241986,"score_gpt":0.32717295349916053,"score_spread":0.29580587998591856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1523650173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23018898,0.0019456168,0.7652024,0.0018691941,0.00015196373,0.0004463542,0.0000093494145,0.00016072378,0.000025441132],"genre_scores_gemma":[0.99086195,0.00011496365,0.008655957,0.000304052,0.000014589497,0.000036577534,0.0000015082869,0.0000033970716,0.0000069928656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938637,0.000019571711,0.00014874787,0.00025660323,0.00004931045,0.0001394013],"domain_scores_gemma":[0.9995024,0.00010042203,0.00012086516,0.000100605896,0.00013264925,0.00004301751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001368987,0.00007937307,0.00018713146,0.00026988957,0.00018317733,0.0000126681,0.000055244287,0.0002073601,1.946201e-7],"category_scores_gemma":[0.000056721303,0.000083666484,0.000013682154,0.00022696529,0.000075620184,0.00013687403,0.00003533761,0.00016854129,2.4343143e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014816953,0.000010433548,0.00033954487,0.000050249975,0.0000050686667,2.726379e-7,0.0016353329,0.0000016664893,0.00009350561,0.003266949,0.000009680926,0.99457246],"study_design_scores_gemma":[0.004187226,0.007949481,0.04677121,0.0018186903,0.00007208441,0.0005689139,0.018600862,0.6475751,0.011442543,0.25959373,0.000483023,0.0009371726],"about_ca_topic_score_codex":0.000023688963,"about_ca_topic_score_gemma":0.00010441022,"teacher_disagreement_score":0.9936353,"about_ca_system_score_codex":0.000029425722,"about_ca_system_score_gemma":0.000048337217,"threshold_uncertainty_score":0.34118202},"labels":[],"label_agreement":null},{"id":"W1527696412","doi":"","title":"Using social geometry to manage interruptions and co-worker attention in office environments","year":2005,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Distraction; Computer science; Orientation (vector space); Context (archaeology); Human–computer interaction; Psychology; Cognitive psychology; Geometry","score_opus":0.0658994866604885,"score_gpt":0.3347580345072497,"score_spread":0.2688585478467612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527696412","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54278153,0.000047697005,0.45600823,0.0005217158,0.00013191893,0.00015092161,0.0000051864313,0.000036120015,0.00031668748],"genre_scores_gemma":[0.9965333,0.0000098167675,0.0028296588,0.00030666447,0.000059400078,0.000013446584,0.000002104233,0.000011812159,0.00023384657],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99869543,0.00011725832,0.0002840118,0.0004220094,0.00023201117,0.00024928764],"domain_scores_gemma":[0.9994896,0.00006515667,0.0000944358,0.00025193396,0.000021605072,0.00007727259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038578158,0.0001465984,0.0001740561,0.0005339223,0.00013111634,0.00016722111,0.000348192,0.00009122559,0.000020330188],"category_scores_gemma":[0.000021153763,0.0001702278,0.000058484584,0.000589056,0.000056932087,0.0006507123,0.00031446049,0.00023424387,0.00013349143],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002552162,0.0020819944,0.100775056,0.00019656471,0.00053530204,0.00007553252,0.030294131,0.002127911,0.12857093,0.018732801,0.0035025766,0.712852],"study_design_scores_gemma":[0.0044443863,0.00041667753,0.62605906,0.0011282149,0.000092163085,0.00032088615,0.0028544746,0.19550899,0.009110608,0.0019496549,0.15523587,0.0028790508],"about_ca_topic_score_codex":0.000072383285,"about_ca_topic_score_gemma":0.00026366804,"teacher_disagreement_score":0.7099729,"about_ca_system_score_codex":0.00013484443,"about_ca_system_score_gemma":0.0000076822125,"threshold_uncertainty_score":0.6941688},"labels":[],"label_agreement":null},{"id":"W1532501984","doi":"10.1109/lascas.2015.7250435","title":"Robust smartphone-based human activity recognition using a tri-axial accelerometer","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Accelerometer; Activity recognition; Computer science; Android (operating system); Inertial measurement unit; Software portability; Wearable computer; Stairs; Embedded system; Real-time computing; Artificial intelligence; Engineering","score_opus":0.3829590942857286,"score_gpt":0.3214932454407392,"score_spread":0.0614658488449894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1532501984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48246878,0.000004898026,0.5137059,0.0001796372,0.0005349124,0.0002567599,0.000006070989,0.00033304936,0.002509971],"genre_scores_gemma":[0.97772515,2.2012333e-7,0.02145964,0.00030851839,0.00023546214,0.000040664792,0.000011838773,0.000022891665,0.00019560708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760616,0.0003870163,0.00034445082,0.00066395133,0.0005793696,0.00041907738],"domain_scores_gemma":[0.99821484,0.00018176745,0.00023520623,0.0006565739,0.00040634748,0.00030523486],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00089406414,0.0002638417,0.00035640347,0.00036885153,0.00020235885,0.0005054233,0.00058132655,0.0001540402,0.00014758289],"category_scores_gemma":[0.00011649003,0.00025924903,0.00016053957,0.0007231136,0.00005259738,0.0019468602,0.00021480661,0.00023061941,0.00035237224],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002492176,0.0016416875,0.0044389153,0.00009533078,0.00017546804,0.00008557169,0.0013582292,0.00042858184,0.09154911,0.00014007055,0.006192438,0.8936454],"study_design_scores_gemma":[0.01793759,0.0016237557,0.013205914,0.00040136513,0.00014461731,0.00040440419,0.00040423888,0.66426814,0.28819054,0.0039525125,0.005587347,0.0038795818],"about_ca_topic_score_codex":0.0009530227,"about_ca_topic_score_gemma":0.00030414428,"teacher_disagreement_score":0.8897658,"about_ca_system_score_codex":0.0002973892,"about_ca_system_score_gemma":0.00028893354,"threshold_uncertainty_score":0.999986},"labels":[],"label_agreement":null},{"id":"W1541293861","doi":"10.1007/978-3-540-30178-3_5","title":"Building Policy-Based Context Aware Applications for Mobile Environments","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Provisioning; Mobile device; Context (archaeology); Set (abstract data type); Context awareness; Ubiquitous computing; Mobile computing; Service (business); Mobile service; Human–computer interaction; World Wide Web; Computer network; Business","score_opus":0.021211352864516544,"score_gpt":0.2746714189806061,"score_spread":0.25346006611608957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1541293861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019340987,0.0002366796,0.995087,0.00077580963,0.00066323264,0.0026007162,0.00006330088,0.00019839744,0.0003554653],"genre_scores_gemma":[0.686691,0.000010681509,0.30848563,0.0027136453,0.0008260018,0.0009261113,0.000023030761,0.00007263,0.0002512557],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958675,0.000034188433,0.0006262943,0.0018556685,0.0008834738,0.00073286815],"domain_scores_gemma":[0.9965901,0.0008961226,0.00046604793,0.001622047,0.00018509114,0.00024059339],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063327816,0.0005923842,0.00062887446,0.0011030979,0.00042718832,0.00054026156,0.0030757983,0.00036169458,0.000017615355],"category_scores_gemma":[0.000057894336,0.00061642047,0.00025816303,0.00059806416,0.00055455166,0.00060436525,0.000718807,0.0005133707,0.000077122226],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051002894,0.00006318168,0.000013532504,0.000071091155,0.000015669004,0.000008521105,0.00020047344,0.0251182,0.0003861951,0.024526386,0.00000973235,0.9495819],"study_design_scores_gemma":[0.0025715923,0.0006807586,0.000056773184,0.0014211038,0.00003649584,0.00010210191,0.0000011935459,0.42713916,0.016863713,0.4373807,0.11110087,0.0026455275],"about_ca_topic_score_codex":0.0000728987,"about_ca_topic_score_gemma":0.000070512964,"teacher_disagreement_score":0.94693637,"about_ca_system_score_codex":0.0012480343,"about_ca_system_score_gemma":0.0015281567,"threshold_uncertainty_score":0.9996287},"labels":[],"label_agreement":null},{"id":"W154235046","doi":"10.1007/978-3-319-11653-2_5","title":"Streaming Model Transformations By Complex Event Processing","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Computer science; Complex event processing; Streaming data; Event (particle physics); Model transformation; Transformation (genetics); Class (philosophy); Context (archaeology); Process (computing); Stream processing; Data mining; Data stream; Theoretical computer science; Artificial intelligence; Distributed computing; Programming language","score_opus":0.03245666733986978,"score_gpt":0.26579769877965315,"score_spread":0.23334103143978335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W154235046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004182636,0.00022175373,0.99290514,0.00094668864,0.0004783,0.00052265095,0.000025753785,0.0002870051,0.004570863],"genre_scores_gemma":[0.8373996,0.00001318439,0.16067079,0.0012069648,0.00019523366,0.000025453715,0.000025254494,0.000040074716,0.00042344155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99596834,0.000052039275,0.000773481,0.0013466087,0.001189663,0.0006698695],"domain_scores_gemma":[0.9976326,0.00033583568,0.0004349631,0.0010180505,0.00036200936,0.00021652092],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00089958136,0.00056976476,0.00063673494,0.0007199051,0.00047354453,0.0009187521,0.0026560111,0.0002854109,0.00001622218],"category_scores_gemma":[0.00004172968,0.00056052895,0.00016728064,0.00051114045,0.00038609665,0.0012125359,0.0005251606,0.00070532074,0.000041197443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016433567,0.000023878278,0.000004434491,0.000058964208,0.000007034872,0.000004537955,0.0010454643,0.05398406,0.00038514702,0.0018610947,0.00010244852,0.9425213],"study_design_scores_gemma":[0.00024472384,0.000059875074,0.000008292492,0.0004914362,0.000007861131,0.000059144313,3.1454258e-7,0.97546846,0.0005175109,0.020359304,0.002167154,0.0006159266],"about_ca_topic_score_codex":0.000022866636,"about_ca_topic_score_gemma":0.00009656779,"teacher_disagreement_score":0.9419054,"about_ca_system_score_codex":0.00039905464,"about_ca_system_score_gemma":0.0006210428,"threshold_uncertainty_score":0.99968463},"labels":[],"label_agreement":null},{"id":"W1542837354","doi":"10.1109/iceis.2006.1703169","title":"Contextual Knowledge Sharing in a P2P Network","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Dalhousie University","funders":"","keywords":"Computer science; Knowledge management; Leverage (statistics); Knowledge sharing; Context (archaeology); Personalization; Data science; World Wide Web; Artificial intelligence","score_opus":0.02875151502177975,"score_gpt":0.25934336374827344,"score_spread":0.2305918487264937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542837354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07949961,0.0004649519,0.61260897,0.0004220724,0.0008442356,0.00035646182,0.0000010367228,0.0005486061,0.30525404],"genre_scores_gemma":[0.9930795,8.920183e-7,0.0017142242,0.00013133885,0.00027414254,0.000030115292,0.0000015215965,0.000007658347,0.0047606025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987662,0.0000674611,0.00029650633,0.0004015361,0.00013083483,0.00033747478],"domain_scores_gemma":[0.9992491,0.00019211278,0.000058423557,0.0003857843,0.00006524043,0.000049355494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042763315,0.00011922588,0.00019505728,0.00011143439,0.000059737507,0.00018675889,0.00058972795,0.000058606744,0.000060118877],"category_scores_gemma":[0.000019030196,0.00011645703,0.0000560003,0.0005667934,0.000019347965,0.0005535,0.0003279311,0.00012178664,0.00050431327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001891443,0.00062656275,0.14902212,0.000045023757,0.000034341316,0.000114956354,0.0015415627,0.00052848906,0.00071601156,0.43820587,0.05267503,0.35647115],"study_design_scores_gemma":[0.005474386,0.00022906598,0.21584979,0.00058257475,0.000012044532,0.00026456494,0.00032066696,0.50490826,0.0020283363,0.054862764,0.21331903,0.0021485023],"about_ca_topic_score_codex":0.00089572277,"about_ca_topic_score_gemma":0.006131199,"teacher_disagreement_score":0.9135799,"about_ca_system_score_codex":0.00007305534,"about_ca_system_score_gemma":0.00004677352,"threshold_uncertainty_score":0.6482095},"labels":[],"label_agreement":null},{"id":"W1551389946","doi":"10.1109/ihtc.2014.7147531","title":"Transmitting patient vitals over a reliable ZigBee mesh network","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"British Columbia Institute of Technology","funders":"","keywords":"Firmware; Computer science; Reliability (semiconductor); Real-time computing; Wireless sensor network; Mesh networking; Wireless; Key (lock); Embedded system; Computer network; Computer hardware; Telecommunications; Computer security","score_opus":0.010714836514939513,"score_gpt":0.21381863279030264,"score_spread":0.20310379627536312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551389946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048332546,0.0000611593,0.88966,0.00057353725,0.0008728435,0.00029305866,0.0000010650521,0.0004918139,0.059713956],"genre_scores_gemma":[0.9845334,0.0000022457157,0.012784496,0.0015614519,0.00019574433,0.000034947152,0.0000012074371,0.00001351673,0.0008729414],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982861,0.00014420989,0.00035462627,0.00045269993,0.00035205836,0.00041024789],"domain_scores_gemma":[0.9987387,0.00035045145,0.00012756206,0.0005648554,0.000092089176,0.00012633116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006039751,0.00015945312,0.0002525986,0.00005930259,0.00014932614,0.00023796562,0.0004524264,0.000071125876,0.00013764459],"category_scores_gemma":[0.000053017335,0.00014247552,0.00011451025,0.00037147544,0.000018683942,0.000710566,0.00014364335,0.00012400538,0.00030687934],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014793073,0.00018200018,0.0058638193,0.000054930162,0.000081439044,0.0000138483,0.0019133465,0.00047801595,0.0012694974,0.04314491,0.06407672,0.8829067],"study_design_scores_gemma":[0.0018626043,0.000619085,0.005783189,0.00038532898,0.000027242708,0.000108393375,0.00013819792,0.4530951,0.007825135,0.014033489,0.5146926,0.001429624],"about_ca_topic_score_codex":0.00013836264,"about_ca_topic_score_gemma":0.00004501984,"teacher_disagreement_score":0.9362009,"about_ca_system_score_codex":0.000035760957,"about_ca_system_score_gemma":0.000032153224,"threshold_uncertainty_score":0.5809983},"labels":[],"label_agreement":null},{"id":"W1552958157","doi":"10.1002/0470091703.ch2","title":"Multiple User Interfaces: Cross‐Platform Applications and Context‐Aware Interfaces","year":2003,"lang":"en","type":"other","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":111,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Human–computer interaction; Context (archaeology); User interface; Operating system; Geology","score_opus":0.0290738972356135,"score_gpt":0.2766547666127792,"score_spread":0.24758086937716572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1552958157","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019416906,0.0017412727,0.711642,0.00017831828,0.00069505937,0.0014862401,0.00011464494,0.0010996058,0.28284863],"genre_scores_gemma":[0.18892673,0.00016945352,0.0040959693,0.00065496925,0.00014897618,0.0005399995,0.000027098076,0.00028630786,0.8051505],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99779415,0.00007428405,0.0004524819,0.0010066874,0.0002975059,0.0003748724],"domain_scores_gemma":[0.99794996,0.00027569415,0.00038118576,0.0010711806,0.0001425177,0.00017945182],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00021893134,0.00048464182,0.00056628225,0.00034528074,0.00012821749,0.00078401814,0.0010190713,0.0003985289,0.0012397361],"category_scores_gemma":[0.000038855458,0.00043575573,0.00009230948,0.00028561868,0.00017964972,0.00061338686,0.00046698155,0.00031804995,0.0010984722],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011206885,0.00020767422,0.0033502409,0.0003541358,0.00035275109,0.0000087477765,0.0006615861,0.0000011618213,0.0001314396,0.0046649757,0.71558046,0.27467564],"study_design_scores_gemma":[0.00061450986,0.000041336323,0.000039075258,0.00019569718,0.000012424699,0.00006869872,0.00018340648,0.00044244502,0.0009746561,0.00011411529,0.9967516,0.00056205306],"about_ca_topic_score_codex":0.0007721889,"about_ca_topic_score_gemma":0.0037442034,"teacher_disagreement_score":0.7075461,"about_ca_system_score_codex":0.00007583067,"about_ca_system_score_gemma":0.00007943271,"threshold_uncertainty_score":0.99980944},"labels":[],"label_agreement":null},{"id":"W1554590498","doi":"10.1109/iccw.2015.7247187","title":"A ubiquitous multiple-radio patient vital sign capture platform","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"McGill University","funders":"","keywords":"Workflow; Computer science; Reliability (semiconductor); Wireless; Remote patient monitoring; Vital signs; Architecture; Health care; Telemedicine; Embedded system; Computer network; Multimedia; Telecommunications; Database","score_opus":0.0478756610535233,"score_gpt":0.23719732415559291,"score_spread":0.1893216631020696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1554590498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12241606,0.000119076794,0.84855086,0.000686571,0.001861398,0.0006369316,0.000012906674,0.0008390073,0.024877172],"genre_scores_gemma":[0.993536,5.446113e-7,0.0049996204,0.00049355533,0.00011896937,0.000046670422,0.000004834209,0.000013525106,0.0007862718],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983433,0.000060609957,0.00028374133,0.0004417421,0.0005265303,0.00034409104],"domain_scores_gemma":[0.9985404,0.00020649617,0.00011579901,0.0005706202,0.0002403273,0.00032636002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024369512,0.0001954271,0.00023294215,0.000116825475,0.00007230304,0.00024076371,0.00057022256,0.00010824674,0.000026059035],"category_scores_gemma":[0.00013893438,0.00016471307,0.00009335177,0.00028733164,0.00003916935,0.0011264507,0.00027801865,0.00016438117,0.00059233047],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111138004,0.0008705827,0.004254012,0.000047624977,0.00018483304,0.0006154408,0.035075605,0.00020677755,0.003215077,0.008011523,0.119026534,0.8283809],"study_design_scores_gemma":[0.017201042,0.0048219003,0.0034938103,0.00038577945,0.0000812962,0.006526738,0.01879701,0.41160908,0.06360696,0.010905914,0.45587164,0.0066988375],"about_ca_topic_score_codex":0.0004091886,"about_ca_topic_score_gemma":0.00028356223,"teacher_disagreement_score":0.87112,"about_ca_system_score_codex":0.00016018066,"about_ca_system_score_gemma":0.00016097703,"threshold_uncertainty_score":0.76134074},"labels":[],"label_agreement":null},{"id":"W1557514430","doi":"10.5555/963600.963632","title":"Adaptive systems for ubiquitous computing","year":2003,"lang":"en","type":"article","venue":"Proceedings of the 1st international symposium on Information and communication technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Ubiquitous computing; Computer science; Human–computer interaction","score_opus":0.01925838032723289,"score_gpt":0.2419514446752874,"score_spread":0.2226930643480545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557514430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16682833,0.0018541601,0.5380681,0.06083207,0.0042934367,0.007157577,0.00014656373,0.0045385556,0.21628116],"genre_scores_gemma":[0.9951796,0.0002083298,0.0043002553,0.00012237614,0.0000066088114,0.000118389275,0.0000029997345,0.0000041296216,0.000057345616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905056,0.000016580412,0.00042958418,0.00012339799,0.0002680671,0.00011183258],"domain_scores_gemma":[0.9980617,0.00021178924,0.0006562891,0.00027831164,0.0007760533,0.000015814769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049874827,0.00011449007,0.00013967874,0.00016250064,0.00024528804,0.0003130896,0.0014511277,0.00009194619,6.6578974e-7],"category_scores_gemma":[0.00037129188,0.00009072681,0.00005127589,0.00023738074,0.00010030194,0.0014315976,0.00039753315,0.00014629363,0.0000051053225],"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":[0.000013993936,0.00003419798,0.0003819176,0.000041900385,0.000037583995,6.353708e-9,0.0007255106,0.0000743631,0.0006376558,0.9758884,0.0008161808,0.021348337],"study_design_scores_gemma":[0.0043393164,0.0009999728,0.0019583153,0.0019376607,0.00005922508,0.00026233034,0.04003593,0.33997825,0.21253024,0.08593137,0.31050372,0.0014636752],"about_ca_topic_score_codex":0.000015444435,"about_ca_topic_score_gemma":6.636987e-7,"teacher_disagreement_score":0.88995695,"about_ca_system_score_codex":0.0000802867,"about_ca_system_score_gemma":0.00002008354,"threshold_uncertainty_score":0.36997318},"labels":[],"label_agreement":null},{"id":"W1563464907","doi":"","title":"Ontologies for effective use of context in e-learning settings","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; Simon Fraser University","funders":"","keywords":"Context (archaeology); Computer science; Electronic learning; Educational technology; Instructional design; Mathematics education; Knowledge management; Multimedia; Psychology","score_opus":0.036202276181729394,"score_gpt":0.2875322360256787,"score_spread":0.2513299598439493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563464907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20882632,0.000043184144,0.78863144,0.00020124728,0.00014859848,0.000600296,0.0000012509297,0.00013119231,0.0014164742],"genre_scores_gemma":[0.98695433,7.5125297e-7,0.012508872,0.00014141678,0.000014312264,0.000040006853,8.2509524e-7,0.0000051516186,0.00033435936],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990149,0.00009668496,0.00028082816,0.00025534394,0.0001246275,0.00022761765],"domain_scores_gemma":[0.99559045,0.0038682113,0.00015171616,0.00017888471,0.00017715475,0.0000335851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010377336,0.00009206663,0.00023579385,0.00018065878,0.000037178303,0.000057148914,0.00022640853,0.00006404414,0.000004628156],"category_scores_gemma":[0.0009608773,0.00008427826,0.00006934455,0.0002479537,0.00003286855,0.0006576175,0.00010203097,0.000109004664,0.000009092296],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050842275,0.00006025198,0.047968425,0.00004475237,0.000021283575,0.000006431683,0.0021405322,0.000022778675,0.0029708254,0.0053480063,0.00035730557,0.94100857],"study_design_scores_gemma":[0.008012028,0.0023462116,0.4438167,0.0008780577,0.000028066816,0.00012159242,0.009908556,0.112389155,0.30408347,0.002983084,0.11380098,0.0016321358],"about_ca_topic_score_codex":0.00042688986,"about_ca_topic_score_gemma":0.0008644148,"teacher_disagreement_score":0.9393764,"about_ca_system_score_codex":0.000056712484,"about_ca_system_score_gemma":0.000023553219,"threshold_uncertainty_score":0.34367678},"labels":[],"label_agreement":null},{"id":"W1565483410","doi":"10.1007/11907503_12","title":"Auditing and Inference Control for Privacy Preservation in Uncertain Environments","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"","keywords":"Computer science; Inference; Context (archaeology); Ubiquitous computing; Audit; Control (management); Point (geometry); Bayesian network; Context management; Data science; Human–computer interaction; Computer security; Internet privacy; Artificial intelligence","score_opus":0.03143484862959922,"score_gpt":0.2632148215243259,"score_spread":0.23177997289472668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1565483410","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007929839,0.00017953571,0.9961273,0.0007154533,0.0004715572,0.0011623988,0.00001706181,0.00006319105,0.0004704823],"genre_scores_gemma":[0.9225347,0.00001308617,0.07619187,0.0006263636,0.00024069888,0.00008604534,0.000012198893,0.000024972462,0.0002700394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99675626,0.0000648162,0.00062130584,0.0013654119,0.0006510709,0.00054113055],"domain_scores_gemma":[0.9967278,0.0018780599,0.00044486948,0.00074521935,0.00010750139,0.00009650388],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011183076,0.00041525488,0.0005322364,0.0006646339,0.00017166004,0.00048324518,0.0014736616,0.00026441677,0.0000042039105],"category_scores_gemma":[0.00033552127,0.00042316888,0.000072109746,0.000313223,0.00030738572,0.0013493455,0.00067852763,0.00044139003,0.0000071258996],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001568217,0.000055978377,0.0033648422,0.00011062759,0.000013825774,0.000029782927,0.00079591916,0.026243748,0.0005675354,0.0040922756,0.000029641962,0.96468014],"study_design_scores_gemma":[0.0012276801,0.00015777064,0.0038945617,0.0006358542,0.0000069263124,0.00002241287,2.414575e-7,0.89964885,0.00040812278,0.090564325,0.0027770218,0.0006562627],"about_ca_topic_score_codex":0.00012837337,"about_ca_topic_score_gemma":0.000199777,"teacher_disagreement_score":0.9640239,"about_ca_system_score_codex":0.00037527646,"about_ca_system_score_gemma":0.00026909227,"threshold_uncertainty_score":0.999822},"labels":[],"label_agreement":null},{"id":"W1565837413","doi":"10.1109/icc.2015.7248405","title":"Design of a context aware object model for smart spaces, things, and people","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"McGill University","funders":"","keywords":"Computer science; Smart objects; Context (archaeology); Object (grammar); World Wide Web; Smart environment; Space (punctuation); Ubiquitous computing; Web of Things; Inclusion (mineral); Internet of Things; Computer security; Human–computer interaction; Internet privacy; Artificial intelligence; Sociology","score_opus":0.08590736688017113,"score_gpt":0.2732825090308005,"score_spread":0.18737514215062934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1565837413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037365162,0.00016339842,0.99407274,0.0006348092,0.00014177221,0.00060430187,0.0000067900955,0.00013071163,0.00050895446],"genre_scores_gemma":[0.94372696,0.0000033148383,0.054850083,0.0002790248,0.000016253685,0.00006421303,0.0000016026164,0.000009674251,0.0010488859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895304,0.000085186744,0.00022416117,0.00031974618,0.0002263399,0.00019154912],"domain_scores_gemma":[0.9986504,0.0003950952,0.00012997973,0.00032685325,0.00036304127,0.00013466405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074118207,0.00012744658,0.00028719153,0.00009137287,0.00005345657,0.00012737943,0.0003166953,0.0000633964,0.000003155854],"category_scores_gemma":[0.00013269689,0.00011225953,0.000047896992,0.00015937958,0.000027824319,0.0008782679,0.00015256328,0.00005587529,0.000009862201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007230687,0.0010710617,0.013628144,0.00091203686,0.0005725178,0.000018121686,0.16478041,0.009173925,0.00591198,0.05702107,0.13260083,0.61358684],"study_design_scores_gemma":[0.000839209,0.00014352829,0.000041488107,0.000028875975,0.0000071407567,0.000023706756,0.000656673,0.9945616,0.0010037868,0.002132076,0.00041017012,0.00015170814],"about_ca_topic_score_codex":0.00047921864,"about_ca_topic_score_gemma":0.00029621346,"teacher_disagreement_score":0.9853877,"about_ca_system_score_codex":0.000033367465,"about_ca_system_score_gemma":0.00025375106,"threshold_uncertainty_score":0.45778105},"labels":[],"label_agreement":null},{"id":"W1566386223","doi":"10.1007/978-3-540-73289-1_21","title":"Usability Challenges in Designing Foreign Language Keypads for Handheld Devices","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Usability; Mobile device; Computer science; Human–computer interaction; Context (archaeology); Arabic; Foreign language; Point (geometry); Key (lock); Multimedia; World Wide Web; Computer security; Linguistics","score_opus":0.09294590834621078,"score_gpt":0.30733647744137704,"score_spread":0.21439056909516627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1566386223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003639541,0.0029676184,0.98788047,0.00043021957,0.00095875264,0.0011271504,0.000008773011,0.00018785146,0.00607522],"genre_scores_gemma":[0.72325194,0.000071575516,0.2751273,0.00076035794,0.0005483939,0.000069378395,0.0000058785818,0.000048121463,0.00011707827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99530077,0.00008848121,0.0007980526,0.0020051943,0.00090342714,0.0009040632],"domain_scores_gemma":[0.9947606,0.0030062716,0.00040420616,0.0013277851,0.000320873,0.00018028397],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0037715435,0.00059233303,0.0008122286,0.0012547654,0.00020431935,0.00047273716,0.00272801,0.00047743053,0.000012733565],"category_scores_gemma":[0.00032510745,0.00057294243,0.00020031293,0.0005780557,0.0004200612,0.0010214333,0.000812411,0.0007288035,0.000023181095],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013259767,0.000040185587,0.00026496252,0.00016302225,0.00001033677,0.000055077497,0.0023618687,0.00063491106,0.00013558373,0.0045380513,0.000004351314,0.9917784],"study_design_scores_gemma":[0.0034449531,0.0014788656,0.0028343564,0.0058103665,0.00004477468,0.00046872118,0.00004571735,0.6169686,0.021597037,0.3349825,0.0072705043,0.0050536105],"about_ca_topic_score_codex":0.0000815274,"about_ca_topic_score_gemma":0.0037829932,"teacher_disagreement_score":0.9867248,"about_ca_system_score_codex":0.0004943304,"about_ca_system_score_gemma":0.00044200692,"threshold_uncertainty_score":0.9996722},"labels":[],"label_agreement":null},{"id":"W1569443033","doi":"10.3233/ais-130208","title":"Detection of daily living activities using a two-stage Markov model","year":2013,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Smart Environments","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Hidden Markov model; Markov model; Artificial intelligence; Software deployment; Stage (stratigraphy); Activity recognition; Markov chain; Segmentation; Machine learning; Activities of daily living; Activity detection; Markov process; Identification (biology); Data mining; Statistics","score_opus":0.03694850915679801,"score_gpt":0.25566350067828514,"score_spread":0.21871499152148713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1569443033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51172376,0.000066327775,0.48791578,0.000016180767,0.00014562179,0.000080205704,0.0000010251254,0.0000037974248,0.000047285495],"genre_scores_gemma":[0.9936814,0.00014425768,0.0058297156,0.000053147578,0.000034906217,0.0000041931416,1.15588676e-7,0.00000890315,0.00024336313],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984738,0.00011212909,0.00057176367,0.00019734923,0.00044680006,0.00019813587],"domain_scores_gemma":[0.99881405,0.00016218045,0.000638458,0.00022690588,0.000044803688,0.00011358771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004947758,0.0001464732,0.00027658168,0.00022095925,0.0000945633,0.000111998546,0.00031288475,0.000052650616,0.000036079393],"category_scores_gemma":[0.000040436596,0.00013386126,0.00010953054,0.00011741034,0.00007960339,0.0018267403,0.00019888324,0.00018334297,0.000010877649],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032928026,0.00042888703,0.0144226,0.000066459485,0.00014959871,0.000013772928,0.004269494,0.009116569,0.659056,0.000055454424,0.000022946744,0.3123653],"study_design_scores_gemma":[0.00025262823,0.00042745823,0.008296041,0.00039138825,0.000037116795,0.00023513263,0.0016942896,0.76310664,0.22447093,0.00054669817,0.00021041282,0.00033123998],"about_ca_topic_score_codex":0.00011599385,"about_ca_topic_score_gemma":0.000008488003,"teacher_disagreement_score":0.7539901,"about_ca_system_score_codex":0.000102359605,"about_ca_system_score_gemma":0.000032213393,"threshold_uncertainty_score":0.54587036},"labels":[],"label_agreement":null},{"id":"W1570165333","doi":"","title":"Reconnaissance d'activités de base à l'aide de réseaux bayésiens dans le cadre d'un habitat intelligent en télésanté","year":2007,"lang":"fr","type":"article","venue":"Archipelago (Université du Québec à Montréal)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"De La Salle University","keywords":"Humanities; Political science; Philosophy","score_opus":0.020444918343965227,"score_gpt":0.21036403865550865,"score_spread":0.18991912031154343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1570165333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49062687,0.0046249977,0.48855627,0.012891414,0.0007452318,0.00042500862,0.00004931872,0.00024623531,0.001834685],"genre_scores_gemma":[0.9810639,0.00060749805,0.01412926,0.00093389663,0.00049633364,0.00001680453,0.000010148389,0.0001101424,0.0026320133],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9926976,0.0022647115,0.0007841485,0.0014638169,0.0007117813,0.0020779697],"domain_scores_gemma":[0.9915096,0.004771766,0.0005282694,0.0014886273,0.00035621517,0.0013454887],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.004620005,0.0008053059,0.0008447507,0.0006298257,0.001300827,0.00028652907,0.0020389925,0.0004500428,0.0003080003],"category_scores_gemma":[0.00088621146,0.001037774,0.0006504171,0.0011675676,0.0007097968,0.002296485,0.0010835801,0.0012446339,0.00058515905],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060289237,0.0010401243,0.031986978,0.00018349479,0.00047371347,0.0054981336,0.28082573,0.00053520844,0.01225226,0.01074837,0.0026943476,0.6531587],"study_design_scores_gemma":[0.0064660814,0.0015125237,0.44098902,0.001776124,0.00067154906,0.011834403,0.19489235,0.1339259,0.071020745,0.008721847,0.123262584,0.0049268864],"about_ca_topic_score_codex":0.51303846,"about_ca_topic_score_gemma":0.7718317,"teacher_disagreement_score":0.64823186,"about_ca_system_score_codex":0.0033312931,"about_ca_system_score_gemma":0.0031953838,"threshold_uncertainty_score":0.99999934},"labels":[],"label_agreement":null},{"id":"W1574009922","doi":"10.1007/978-3-540-30121-9_104","title":"A Context-Sensitive Middleware for Managing Embedded Pervasive Environments","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Profiling (computer programming); Ubiquitous computing; Middleware (distributed applications); Distributed computing; Operating system","score_opus":0.026751731254138912,"score_gpt":0.24458951667692874,"score_spread":0.21783778542278984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574009922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008792159,0.00031042658,0.9926854,0.0009691495,0.0019173091,0.0016359482,0.000045212113,0.00018435868,0.002164315],"genre_scores_gemma":[0.8565175,0.000034775614,0.13876192,0.0028776615,0.00062087283,0.00010486818,0.00002674162,0.00009295572,0.00096272095],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99479514,0.000064289685,0.0007023059,0.0023760092,0.0011342292,0.0009280496],"domain_scores_gemma":[0.99635106,0.0010798568,0.00055768964,0.0014646306,0.0002867119,0.00026007215],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00080280134,0.0007987845,0.000912674,0.0009152479,0.00042113502,0.000664398,0.0025509459,0.00040205396,0.000021537897],"category_scores_gemma":[0.00014190671,0.0008185075,0.00034181954,0.00039526835,0.0006801688,0.001065248,0.0012513913,0.00071907346,0.00012081271],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024102343,0.00006105531,0.000014737663,0.00010718264,0.00006782445,0.00021426307,0.0039213514,0.0044511533,0.00036875118,0.017710362,0.000020198442,0.97303903],"study_design_scores_gemma":[0.0042410484,0.0010800341,0.00022098412,0.0049912487,0.000092032045,0.00069078815,0.000025274325,0.36771905,0.017958578,0.5921626,0.006520615,0.004297716],"about_ca_topic_score_codex":0.000051087827,"about_ca_topic_score_gemma":0.0001776158,"teacher_disagreement_score":0.9687413,"about_ca_system_score_codex":0.0011190637,"about_ca_system_score_gemma":0.0006794264,"threshold_uncertainty_score":0.9994266},"labels":[],"label_agreement":null},{"id":"W1579420825","doi":"10.1007/11551201_15","title":"Time, Ownership and Awareness: The Value of Contextual Locations in the Home","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Knowledge management; Function (biology); Value (mathematics); Resource (disambiguation); Internet privacy; Human–computer interaction","score_opus":0.02997142164818655,"score_gpt":0.25902347540428117,"score_spread":0.2290520537560946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579420825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001654602,0.0008283184,0.99011654,0.004471155,0.0005049825,0.0006832664,0.000010549891,0.00005188636,0.0016786861],"genre_scores_gemma":[0.9909717,0.000030807525,0.0067263655,0.0017256163,0.00028066654,0.000025689742,0.0000031004745,0.000016426739,0.00021966573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970552,0.00020534862,0.00056471006,0.0008890707,0.000891295,0.00039441633],"domain_scores_gemma":[0.99528515,0.002753229,0.0003523519,0.0013142754,0.00022876711,0.000066203145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021516762,0.00035553297,0.00047496034,0.0005423835,0.00024014796,0.00039504687,0.003401414,0.00020255551,0.000011960299],"category_scores_gemma":[0.00014467849,0.0002187987,0.00009470452,0.0007226991,0.0013344588,0.0005761472,0.000718084,0.00067712663,0.00003041097],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007762423,0.00007195366,0.0004188438,0.00005066417,0.000021419975,0.000024054647,0.009357188,0.006555731,0.000092752474,0.030911915,0.0000684792,0.9524192],"study_design_scores_gemma":[0.0011829856,0.0003851568,0.0071080937,0.0015865875,0.00003821006,0.00061683246,0.000023960603,0.87389475,0.00069320586,0.1054574,0.0075206845,0.0014921323],"about_ca_topic_score_codex":0.00011672067,"about_ca_topic_score_gemma":0.0005409399,"teacher_disagreement_score":0.98931706,"about_ca_system_score_codex":0.00013034219,"about_ca_system_score_gemma":0.0005610087,"threshold_uncertainty_score":0.8922352},"labels":[],"label_agreement":null},{"id":"W1579882491","doi":"10.4230/dagrep.3.11.29","title":"Proxemics in Human-Computer Interaction (Dagstuhl Seminar 13452)","year":2014,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Context-Aware Activity Recognition Systems","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 Calgary","funders":"","keywords":"Proxemics; Interpersonal communication; Human–computer interaction; Context (archaeology); Computer science; Psychology; Social psychology; Geography","score_opus":0.018355246281505776,"score_gpt":0.27388411488478304,"score_spread":0.2555288686032773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579882491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2389086,0.000015927379,0.74984777,0.00039008594,0.0024001275,0.0017342414,0.00006604911,0.0005000288,0.006137162],"genre_scores_gemma":[0.9732401,0.0000082944825,0.024026982,0.0012752955,0.000504826,0.00030418418,0.00021351395,0.000061465114,0.00036534038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958296,0.00017374178,0.0017529937,0.0005992588,0.0006784763,0.000965928],"domain_scores_gemma":[0.996799,0.0004481693,0.0008177608,0.0012536176,0.00042623482,0.00025519758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014805795,0.0005822751,0.0007642524,0.0007409616,0.00034822043,0.0008690624,0.0016345297,0.00035253225,0.000026299589],"category_scores_gemma":[0.00012998517,0.00058167806,0.00032506665,0.0006332196,0.0000956096,0.0043750443,0.00072785,0.000771639,0.00034995296],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036420417,0.0033010703,0.0453425,0.0033718038,0.00063343084,0.00004865037,0.06711437,0.0016057448,0.0024054316,0.04450767,0.030854037,0.8004511],"study_design_scores_gemma":[0.00795144,0.0011605206,0.0039930195,0.0013001447,0.000048778358,0.00046609557,0.0014241402,0.72588474,0.004496417,0.002776011,0.24835888,0.0021398347],"about_ca_topic_score_codex":0.00006470669,"about_ca_topic_score_gemma":0.000118359065,"teacher_disagreement_score":0.79831123,"about_ca_system_score_codex":0.00038661782,"about_ca_system_score_gemma":0.00008191655,"threshold_uncertainty_score":0.9996635},"labels":[],"label_agreement":null},{"id":"W1583579902","doi":"10.15353/joci.v8i1.3071","title":"Empowering Chronically Ill Patients and Caregivers using Remote Monitoring Technology","year":2012,"lang":"en","type":"article","venue":"The Journal of Community Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Psychology","score_opus":0.04284782094521256,"score_gpt":0.30456081301666504,"score_spread":0.2617129920714525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583579902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88203686,0.00013593321,0.11684875,0.00017154322,0.00042994396,0.00006997569,7.020631e-7,0.000024620274,0.00028167377],"genre_scores_gemma":[0.9936483,0.0000541476,0.0061623384,0.00008290794,0.00004507729,1.2149904e-7,1.3969753e-7,0.000004762061,0.0000022424722],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9987119,0.00027095823,0.00051395735,0.000009099094,0.00027822115,0.00021586967],"domain_scores_gemma":[0.997791,0.00040236764,0.00069090445,0.00042976273,0.00059712934,0.000088824585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016514084,0.0001013667,0.00020073913,0.0001994813,0.00040058699,0.000081499275,0.0010059448,0.00006759974,9.280855e-7],"category_scores_gemma":[0.00030708133,0.00007524213,0.000041251253,0.00025621438,0.00009382173,0.0019122482,0.0006759043,0.0007794311,0.0000038630483],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003884501,0.00020006928,0.026935317,0.00018279492,0.00022982655,8.7608237e-7,0.44903007,0.00039809803,0.0008557568,0.0001748171,0.00016993187,0.5217836],"study_design_scores_gemma":[0.009367687,0.0027917258,0.07907412,0.0034589085,0.0004968807,0.007281836,0.8029729,0.049456067,0.017594555,0.0029765407,0.022344114,0.00218464],"about_ca_topic_score_codex":0.0000560102,"about_ca_topic_score_gemma":0.0000029864912,"teacher_disagreement_score":0.51959896,"about_ca_system_score_codex":0.00014193378,"about_ca_system_score_gemma":0.000055528384,"threshold_uncertainty_score":0.33862835},"labels":[],"label_agreement":null},{"id":"W1588792609","doi":"10.1109/ihtc.2014.7147523","title":"Context aware mobile application architecture (CAMAA) for health care systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 du Québec à Chicoutimi","funders":"","keywords":"Computer science; Context (archaeology); Interoperability; Scalability; Standardization; Abstraction; Architecture; Context model; Ubiquitous computing; Context awareness; Health care; Mobile computing; Systems architecture; Human–computer interaction; Data science; World Wide Web; Artificial intelligence; Computer network; Database; Operating system","score_opus":0.01178284013073453,"score_gpt":0.27545808166504926,"score_spread":0.2636752415343147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588792609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00075665535,0.000431483,0.9921419,0.0015489653,0.00083786674,0.00239275,0.000037694597,0.0005321144,0.0013205464],"genre_scores_gemma":[0.9937279,0.0000022723186,0.0023666644,0.0012537,0.00026952158,0.0017534094,0.000047334823,0.000020615373,0.00055853795],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99817383,0.00019667967,0.00038391905,0.0006116022,0.0002780704,0.00035587998],"domain_scores_gemma":[0.9980441,0.00037446548,0.0002457849,0.00083195174,0.00032735997,0.00017637775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045350564,0.00019502832,0.0003688988,0.00012305297,0.00022920153,0.00023222902,0.000651774,0.0000928803,0.000004418497],"category_scores_gemma":[0.000031580945,0.00017473345,0.00011802218,0.00022894591,0.000024928899,0.00024692062,0.00010634997,0.000117750365,0.00010282611],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010793005,0.000048972088,0.00020620126,0.0004124639,0.0000243139,2.2258335e-7,0.0019734204,0.00022658496,0.000155341,0.023745805,0.009409096,0.9637868],"study_design_scores_gemma":[0.0009090502,0.00045414516,0.00012593475,0.00008987614,0.0000052525415,0.00003794699,0.0013221412,0.08070959,0.00055116636,0.00031408083,0.9150943,0.0003865236],"about_ca_topic_score_codex":0.00045797,"about_ca_topic_score_gemma":0.0006376856,"teacher_disagreement_score":0.9929713,"about_ca_system_score_codex":0.00014954442,"about_ca_system_score_gemma":0.00016736775,"threshold_uncertainty_score":0.71254236},"labels":[],"label_agreement":null},{"id":"W1592318583","doi":"10.1007/978-3-540-79576-6_11","title":"Evaluation and Analysis of a Common Model for Ubiquitous Systems Interoperability","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Interoperability; Computer science; World Wide Web","score_opus":0.06617068599187466,"score_gpt":0.30554078752513125,"score_spread":0.2393701015332566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1592318583","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004154836,0.00081914413,0.9925325,0.000094364834,0.00070586265,0.0012884202,0.000047954458,0.00006851882,0.00028841486],"genre_scores_gemma":[0.9681338,0.00003230949,0.031487744,0.000120638,0.00007751844,0.00006488677,0.0000140687025,0.00001746785,0.0000515616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959359,0.00014332858,0.0008253007,0.0013818404,0.0013635213,0.00035009807],"domain_scores_gemma":[0.9956642,0.0009447844,0.0005694396,0.0012889369,0.0014149814,0.00011767132],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031829812,0.0003902717,0.0010847542,0.0014903754,0.000172312,0.00027855468,0.0014517711,0.00026732503,0.0000026956045],"category_scores_gemma":[0.00020117236,0.00036242217,0.00023844352,0.0008632891,0.00052157615,0.0005874853,0.0006692012,0.0003155328,0.0000015299985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012503891,0.00006277074,0.00021892329,0.000117796146,0.00023603629,0.0000038382873,0.0030344578,0.26129887,0.00011191103,0.00068263884,0.000011847524,0.7342084],"study_design_scores_gemma":[0.00028180773,0.00013109902,0.00021643288,0.00022056302,0.0002044104,0.000028911798,4.2497686e-7,0.99281436,0.0001335117,0.0055852393,0.00004553203,0.00033769215],"about_ca_topic_score_codex":0.00011037026,"about_ca_topic_score_gemma":0.00045171348,"teacher_disagreement_score":0.96397895,"about_ca_system_score_codex":0.00044788513,"about_ca_system_score_gemma":0.00060544314,"threshold_uncertainty_score":0.99988276},"labels":[],"label_agreement":null},{"id":"W1600140571","doi":"","title":"An energy efficient user context collection method for smartphones","year":2013,"lang":"en","type":"article","venue":"Asia-Pacific Network Operations and Management Symposium","topic":"Context-Aware Activity Recognition Systems","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 science; Compass; Android (operating system); Accelerometer; Context awareness; Microphone; Control reconfiguration; Ubiquitous computing; Data collection; Real-time computing; Energy consumption; Mobile device; Human–computer interaction; Embedded system; World Wide Web; Engineering; Phone; Operating system; Telecommunications","score_opus":0.009670911207232583,"score_gpt":0.23847546031134959,"score_spread":0.22880454910411702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1600140571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013146191,0.00010417617,0.9856295,0.0029905548,0.0011376652,0.0015524661,0.0000052310543,0.00021534269,0.007050424],"genre_scores_gemma":[0.8974501,0.00014042821,0.08940328,0.00058890454,0.000301217,0.0024367096,0.00004974823,0.000028700644,0.009600929],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813354,0.00023670528,0.00037749036,0.0006662371,0.00019945943,0.0003865615],"domain_scores_gemma":[0.99880904,0.00012858574,0.00008528919,0.00058455573,0.00024009691,0.00015240329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004851277,0.0002343908,0.0002579693,0.0001864105,0.00078827393,0.0010300159,0.0003253109,0.00007741909,0.000035608515],"category_scores_gemma":[0.000004094277,0.00022358021,0.00007879086,0.000529126,0.000033634533,0.0008292677,0.00013626157,0.00006159459,0.000035179433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004541016,0.0007288138,0.00054379797,0.00011663991,0.00047097824,0.0000070467463,0.0020345172,0.057664786,0.0029191014,0.46510994,0.16165875,0.3087002],"study_design_scores_gemma":[0.0008196967,0.00021674583,0.0005474275,0.000041930245,0.000045729532,0.000021188564,0.0010873646,0.85030496,0.00049144495,0.0006124143,0.14541055,0.000400531],"about_ca_topic_score_codex":0.00041859184,"about_ca_topic_score_gemma":0.00044279773,"teacher_disagreement_score":0.8962262,"about_ca_system_score_codex":0.0000613029,"about_ca_system_score_gemma":0.00002745097,"threshold_uncertainty_score":0.99324673},"labels":[],"label_agreement":null},{"id":"W1601453863","doi":"10.1007/978-3-642-02868-7_32","title":"A Ubiquitous Computing Environment to Support the Mobility of Users with Special Needs","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Ubiquitous computing; Point (geometry); Human–computer interaction; Context-aware pervasive systems; Mobile device; Mobile computing; World Wide Web; Multimedia; Operating system","score_opus":0.020151878985721686,"score_gpt":0.234499890427985,"score_spread":0.21434801144226331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601453863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002079686,0.000032655073,0.9912509,0.0010209668,0.00095017045,0.001051411,0.000008389062,0.00008812932,0.003517703],"genre_scores_gemma":[0.91561514,0.000006135531,0.08085534,0.0019331229,0.0012676098,0.000011516748,0.0000030625633,0.00003278475,0.00027529759],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956567,0.00008841984,0.00072708615,0.0013192187,0.0015541663,0.0006543626],"domain_scores_gemma":[0.9964842,0.0007202802,0.0005034453,0.0018699215,0.0002089247,0.00021318212],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015363368,0.00055011397,0.0007388254,0.0007322972,0.00025340787,0.0003666692,0.0034975049,0.00020637127,0.000039799124],"category_scores_gemma":[0.000050085302,0.0004024469,0.00016050172,0.0008170695,0.00074277696,0.0004159076,0.0013478427,0.0006921495,0.00005555171],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019884375,0.000072218594,0.0001353358,0.00002441236,0.000018548531,0.000041619933,0.0033838998,0.019362934,0.000101663885,0.0011071173,0.00004305057,0.9756893],"study_design_scores_gemma":[0.007005925,0.022267172,0.021220697,0.007300599,0.00030864755,0.0036937394,0.000060071692,0.64237034,0.032413084,0.12654664,0.12324724,0.013565844],"about_ca_topic_score_codex":0.00004575807,"about_ca_topic_score_gemma":0.00011509344,"teacher_disagreement_score":0.96212345,"about_ca_system_score_codex":0.00044061447,"about_ca_system_score_gemma":0.00055490556,"threshold_uncertainty_score":0.99984276},"labels":[],"label_agreement":null},{"id":"W1601631221","doi":"10.1109/wimob.2005.1512956","title":"A context-sensitive incremental learning paradigm of an ubiquitous multimodal multimedia computing system","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Ubiquitous computing; Human–computer interaction; Context (archaeology); Ambient intelligence; Task (project management); Modalities; Ubiquitous robot; Context awareness; Context-aware pervasive systems; Multimedia; Mobile device; Intelligent environment; User modeling; Artificial intelligence; User interface; World Wide Web; Embedded system; Robot learning; Operating system","score_opus":0.013377580982841001,"score_gpt":0.23911215157127078,"score_spread":0.22573457058842977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601631221","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5910352,0.000021387337,0.40420404,0.00004217012,0.00034707977,0.0004117124,0.000007223143,0.0005756444,0.003355569],"genre_scores_gemma":[0.9852331,2.2994949e-7,0.014350437,0.000051184477,0.00021334474,0.000010575358,0.000021084998,0.0000214188,0.00009856725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721247,0.0005366474,0.00068605714,0.00061949244,0.00051951216,0.0004258322],"domain_scores_gemma":[0.998208,0.00055907184,0.0004448018,0.0004162963,0.00023341748,0.00013842245],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007283424,0.00026865795,0.0005161045,0.00021573584,0.00021380206,0.00016546741,0.0005007288,0.00011401741,0.000008917323],"category_scores_gemma":[0.000046398258,0.00026768332,0.00013553842,0.0003371041,0.00009164772,0.00083681656,0.00027262504,0.00024753358,0.00008156148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020021247,0.0016587249,0.02791936,0.00047909483,0.00038742938,0.0006337513,0.0138011,0.004099478,0.15762183,0.03435107,0.00037974355,0.7584682],"study_design_scores_gemma":[0.0016291087,0.00023351316,0.008328249,0.00017689637,0.000013828021,0.00028278132,0.0030236586,0.93053734,0.05521534,0.000023951625,0.00013230111,0.00040300426],"about_ca_topic_score_codex":0.007102269,"about_ca_topic_score_gemma":0.00068524666,"teacher_disagreement_score":0.9264379,"about_ca_system_score_codex":0.0001852798,"about_ca_system_score_gemma":0.000080075166,"threshold_uncertainty_score":0.9999775},"labels":[],"label_agreement":null},{"id":"W1604352127","doi":"10.1007/978-3-642-02868-7_3","title":"Design Challenges for Mobile Assistive Technologies Applied to People with Cognitive Impairments","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Cognitive disabilities; Computer science; Assistive technology; Mobile device; Cognition; Human–computer interaction; Context (archaeology); Independence (probability theory); Multimedia; Mobile computing; Software; Mobile technology; World Wide Web; Telecommunications; Psychology","score_opus":0.03202064616972515,"score_gpt":0.260306896436448,"score_spread":0.22828625026672283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1604352127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000066859124,0.00046363924,0.9920456,0.00081514684,0.0004211791,0.0040553696,0.000019624536,0.0006477931,0.0014647958],"genre_scores_gemma":[0.62417054,0.000051044994,0.3739094,0.0006288868,0.00015128752,0.0008906833,0.0000067241267,0.000050661412,0.00014074137],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9955023,0.00004461843,0.0004578619,0.0022391027,0.0009354018,0.00082070054],"domain_scores_gemma":[0.9960287,0.0017417545,0.00038760147,0.0010454718,0.00063631526,0.00016014486],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00093470974,0.0007199925,0.0008865104,0.0011425305,0.000311288,0.0004904864,0.0024910155,0.00039251242,0.0000037247432],"category_scores_gemma":[0.00011627738,0.0006166939,0.00010979419,0.0007396237,0.00036291513,0.00052372814,0.0008445654,0.0005395222,0.000047194477],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062352796,0.00006806047,0.000007294195,0.000039692815,0.000033211374,0.000027335012,0.001750542,0.0026146716,0.00006611191,0.000638346,0.000011905086,0.99468046],"study_design_scores_gemma":[0.013193134,0.038347412,0.003619283,0.014882818,0.0003896955,0.0018237828,0.00023855777,0.36508405,0.07279669,0.46109033,0.010857603,0.017676655],"about_ca_topic_score_codex":0.00000712586,"about_ca_topic_score_gemma":0.0001325867,"teacher_disagreement_score":0.9770038,"about_ca_system_score_codex":0.00041026156,"about_ca_system_score_gemma":0.00056299084,"threshold_uncertainty_score":0.9996284},"labels":[],"label_agreement":null},{"id":"W1607904620","doi":"10.1109/pacrim.2003.1235933","title":"Context awareness in ad-hoc communication","year":2004,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Computer science; Context (archaeology); Context awareness; Context management; World Wide Web; Mobile computing; Human–computer interaction; Ubiquitous computing; Context model; Knowledge management; Artificial intelligence; Computer network","score_opus":0.03976991847049385,"score_gpt":0.2818356803042842,"score_spread":0.24206576183379033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1607904620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2679045,0.0009612534,0.71103966,0.006314674,0.00034204812,0.00044293405,0.000002195244,0.0004899419,0.012502775],"genre_scores_gemma":[0.99466723,0.000037486865,0.004379881,0.0006036102,0.000009022965,0.00004442229,0.0000031532188,0.000006384539,0.00024878306],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990158,0.00011799968,0.00025286098,0.00025848314,0.00017876508,0.000176065],"domain_scores_gemma":[0.9988328,0.00014640432,0.00007505092,0.0007893956,0.00009989979,0.000056454835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033814902,0.00009929177,0.00016307624,0.00013571557,0.00007523156,0.00013142434,0.0008162979,0.00005801814,0.000030480054],"category_scores_gemma":[0.000038561237,0.000097626726,0.000042012645,0.00040769752,0.0000322128,0.00087591587,0.00022908967,0.00012337614,0.00036841698],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001558178,0.00041213754,0.0059822355,0.000024091634,0.000020807314,0.00002297091,0.0054917806,0.00011709015,0.0009408149,0.08429201,0.0005043531,0.90217614],"study_design_scores_gemma":[0.036080327,0.0011389068,0.26247242,0.00283161,0.000035096196,0.0008574424,0.012294575,0.042192396,0.08632801,0.2137182,0.33596057,0.006090466],"about_ca_topic_score_codex":0.00089393207,"about_ca_topic_score_gemma":0.0062268404,"teacher_disagreement_score":0.8960857,"about_ca_system_score_codex":0.00014165005,"about_ca_system_score_gemma":0.00013199857,"threshold_uncertainty_score":0.47353777},"labels":[],"label_agreement":null},{"id":"W1658014787","doi":"10.3233/ais-130221","title":"A real-world deployment of the COACH prompting system","year":2013,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Smart Environments","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software deployment; Computer science; Task (project management); Dementia; Activities of daily living; Cognition; Overhead (engineering); Tracking (education); Gesture; Independent living; Human–computer interaction; Physical medicine and rehabilitation; Applied psychology; Artificial intelligence; Psychology; Medicine; Physical therapy; Gerontology","score_opus":0.028930267553445778,"score_gpt":0.24032552075316765,"score_spread":0.21139525319972188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1658014787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90056354,0.00011000441,0.09723135,0.00043745319,0.00065322145,0.00045405552,0.000001459018,0.00001193832,0.00053697167],"genre_scores_gemma":[0.9983667,0.000084058,0.0011099102,0.000071216884,0.000040611478,0.000012213638,1.356755e-7,0.0000072437815,0.000307901],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998162,0.00016063002,0.0007244301,0.00018368808,0.00058032695,0.00018895748],"domain_scores_gemma":[0.99860245,0.00011283766,0.00078652916,0.00033309357,0.000056423214,0.000108640495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005828722,0.00013024875,0.00027040363,0.00012086384,0.000100174104,0.00008484041,0.0006040031,0.000035967474,0.000018726212],"category_scores_gemma":[0.000022474114,0.000086807006,0.000121979225,0.00018459183,0.00007637066,0.0007188842,0.0002830723,0.00017328061,0.00003730936],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062814936,0.0015203608,0.5340634,0.00048669253,0.0006399797,0.00005998893,0.007178738,0.00086168747,0.083846115,0.0051627173,0.0016012312,0.3645163],"study_design_scores_gemma":[0.0011596122,0.0013811267,0.58452994,0.0033866507,0.00016887537,0.0010867016,0.005139047,0.03619786,0.35187945,0.0013546732,0.012746321,0.00096976367],"about_ca_topic_score_codex":0.00018550131,"about_ca_topic_score_gemma":0.000011378918,"teacher_disagreement_score":0.36354652,"about_ca_system_score_codex":0.00013512693,"about_ca_system_score_gemma":0.000031118878,"threshold_uncertainty_score":0.3539887},"labels":[],"label_agreement":null},{"id":"W170405163","doi":"10.1007/978-3-540-69554-7_18","title":"Rapid User-Centred Evaluation for Context-Aware Systems","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Context (archaeology); Human–computer interaction; Computer science; Ubiquitous computing; Context awareness; Mobile device; Multimedia; World Wide Web","score_opus":0.0905427230486638,"score_gpt":0.30022584590072404,"score_spread":0.20968312285206026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W170405163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000050131086,0.0027242482,0.9838019,0.00043749466,0.007453844,0.0033129833,0.000041992887,0.0003245644,0.0018528415],"genre_scores_gemma":[0.9350898,0.00006286442,0.05886664,0.0020237043,0.0020946243,0.00029553456,0.00008424012,0.00014006479,0.0013424737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99303657,0.00015378,0.0011201603,0.0023345076,0.0023144002,0.0010406043],"domain_scores_gemma":[0.9930754,0.0020059855,0.00083621853,0.0018603058,0.0019194728,0.00030266616],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003719709,0.0007956008,0.0010059893,0.0014574095,0.00041206935,0.0012125395,0.0032553468,0.0006348375,0.00004107616],"category_scores_gemma":[0.00033209592,0.0007857201,0.00031239196,0.0007480315,0.0004511871,0.0012437988,0.0007707749,0.0007111199,0.000102274],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018371002,0.00004112217,0.000024830797,0.00010576088,0.00003666345,0.000022951921,0.0006635602,0.002754614,0.000047612346,0.0059507987,0.00017424476,0.99015945],"study_design_scores_gemma":[0.0015633377,0.0003177313,0.000057593137,0.001214693,0.000044994027,0.00016851287,0.0000037967868,0.952983,0.0007998974,0.013269562,0.028300418,0.001276467],"about_ca_topic_score_codex":0.00008637755,"about_ca_topic_score_gemma":0.00029959198,"teacher_disagreement_score":0.988883,"about_ca_system_score_codex":0.0011312074,"about_ca_system_score_gemma":0.0013536373,"threshold_uncertainty_score":0.9998243},"labels":[],"label_agreement":null},{"id":"W1711687279","doi":"10.1007/978-3-540-27817-7_140","title":"Indoors Pervasive Computing and Outdoors Mobile Computing for Cognitive Assistance and Telemonitoring","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Cognitive computing; Cognition; Ubiquitous computing; Computer science; Mobile computing; Mobile device; Cognitive impairment; Human–computer interaction; Internet privacy; Computer security; Multimedia; World Wide Web; Telecommunications; Psychology; Psychiatry","score_opus":0.021626292607424696,"score_gpt":0.2767799929008376,"score_spread":0.2551537002934129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1711687279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01979697,0.0011814655,0.97485256,0.00016807822,0.0017464358,0.0015778886,0.000025355099,0.00022705989,0.0004241885],"genre_scores_gemma":[0.8800399,0.000033581502,0.118838176,0.00036907243,0.0005975813,0.000024534185,0.0000057761845,0.000051472824,0.00003992287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951456,0.00006926861,0.00078289467,0.0023176197,0.0008007746,0.0008838168],"domain_scores_gemma":[0.9947105,0.003056891,0.00067010295,0.0006198485,0.0006565184,0.00028613696],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013145353,0.0007661143,0.0009770831,0.0007976454,0.00075525045,0.001149836,0.001258041,0.00038337236,0.000003023595],"category_scores_gemma":[0.00027647073,0.00079432194,0.00014547596,0.00050399697,0.00092391315,0.00091163267,0.001613668,0.0008986941,0.0000057827947],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014388134,0.000029805216,0.0017373196,0.00022535256,0.000041093495,0.00005503403,0.0040595033,0.0022290417,0.00009339516,0.0017228398,0.0000019552012,0.98979026],"study_design_scores_gemma":[0.006104048,0.0018042141,0.010959248,0.015144968,0.00013558727,0.001233064,0.00007399857,0.86692363,0.0057226773,0.08567109,0.0007771105,0.0054503926],"about_ca_topic_score_codex":0.00006322015,"about_ca_topic_score_gemma":0.00007648072,"teacher_disagreement_score":0.9843399,"about_ca_system_score_codex":0.0004481686,"about_ca_system_score_gemma":0.0005499866,"threshold_uncertainty_score":0.99988705},"labels":[],"label_agreement":null},{"id":"W1751193738","doi":"10.3233/ais-120190","title":"A context-aware service provision system for smart environments based on the user interaction modalities","year":2013,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Smart Environments","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université TÉLUQ; Université du Québec à Montréal","funders":"","keywords":"Computer science; Modalities; Context (archaeology); Human–computer interaction; Service (business); World Wide Web; Multimedia","score_opus":0.037526087398261854,"score_gpt":0.2419542904604999,"score_spread":0.20442820306223805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1751193738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32061943,0.00006603634,0.673712,0.003159672,0.0010343349,0.0012664986,0.000013368941,0.000021991676,0.000106681844],"genre_scores_gemma":[0.99738425,0.0000400305,0.0004623908,0.0014825757,0.00008031524,0.00013787854,0.0000039766487,0.000019943784,0.00038862383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754226,0.0002646463,0.0007883767,0.00037937707,0.0007106394,0.0003146769],"domain_scores_gemma":[0.99791646,0.0006326153,0.0007244324,0.00047796706,0.00007509599,0.00017342658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007897689,0.00028130214,0.00035527736,0.0001807979,0.0002718999,0.00031779462,0.00065831,0.0001023742,0.00006536802],"category_scores_gemma":[0.00005224015,0.00019619089,0.00017781138,0.00012563771,0.00007105748,0.0013133032,0.00016042168,0.00028317815,0.0002067443],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020375643,0.0065444065,0.057890337,0.001533635,0.001763297,0.00013838989,0.020073982,0.013472565,0.054173943,0.0064398646,0.013071023,0.822861],"study_design_scores_gemma":[0.0032464562,0.005031438,0.0263493,0.003798741,0.00022729221,0.0005135773,0.023871511,0.6341214,0.1590185,0.0015708605,0.14047606,0.0017748468],"about_ca_topic_score_codex":0.00011717664,"about_ca_topic_score_gemma":0.000011290407,"teacher_disagreement_score":0.82108617,"about_ca_system_score_codex":0.00027986104,"about_ca_system_score_gemma":0.000036050646,"threshold_uncertainty_score":0.8000432},"labels":[],"label_agreement":null},{"id":"W1752035993","doi":"10.3233/nre-2011-0655","title":"The future of intelligent assistive technologies for cognition: Devices under development to support independent living and aging-with-choice","year":2011,"lang":"en","type":"article","venue":"Neurorehabilitation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Cognition; Context (archaeology); Independent living; Process (computing); Assistive technology; Human–computer interaction; Computer science; Psychology; Gerontology; Medicine; Neuroscience","score_opus":0.040129436227318974,"score_gpt":0.2757411121726763,"score_spread":0.2356116759453573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1752035993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4249154,0.00012232168,0.57138604,0.0016359874,0.00037210732,0.0011686182,0.0000075361636,0.00019973677,0.00019225224],"genre_scores_gemma":[0.9797226,0.00000490319,0.019801551,0.00011155867,0.000020066389,0.00031110863,0.000002139118,0.000008713738,0.000017324743],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99883485,0.0000901089,0.00029750174,0.0003633373,0.00024950271,0.0001647013],"domain_scores_gemma":[0.9972112,0.0018569877,0.00018126154,0.00025860805,0.00044982522,0.00004207242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004977916,0.0001273967,0.00013857367,0.00013014459,0.00019903932,0.00008410123,0.00030792903,0.000053868123,0.0000029552252],"category_scores_gemma":[0.0003137512,0.0000921994,0.000031566553,0.0002580132,0.00008182117,0.00036724674,0.00016642803,0.000087861314,0.000006512075],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043944685,0.00013868083,0.033316147,0.00014942867,0.00006005314,0.0000013502976,0.012364053,0.0000066511684,0.00052889396,0.002679345,0.00010813099,0.9506033],"study_design_scores_gemma":[0.00038958178,0.0017407696,0.9518306,0.00027425392,0.000032889384,0.000034556357,0.016989084,0.00032069584,0.014645781,0.0023440123,0.011007463,0.00039026779],"about_ca_topic_score_codex":0.000011238736,"about_ca_topic_score_gemma":0.00058514054,"teacher_disagreement_score":0.9502131,"about_ca_system_score_codex":0.000045824894,"about_ca_system_score_gemma":0.0000887474,"threshold_uncertainty_score":0.37597826},"labels":[],"label_agreement":null},{"id":"W1755277371","doi":"","title":"Context modelling for natural Human Computer Interaction applications in e-health","year":2014,"lang":"en","type":"article","venue":"Archivo Digital UPM (Universidad Politécnica de Madrid)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Interoperation; Ontology; Context (archaeology); Computer science; Context awareness; Data science; Premise; Health care; Ubiquitous computing; World Wide Web; Knowledge management; Context model; Human–computer interaction; Internet privacy; Artificial intelligence; Interoperability; Phone","score_opus":0.023090604214739657,"score_gpt":0.26912181651617956,"score_spread":0.2460312123014399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1755277371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043327488,0.000026332049,0.9489481,0.0014546699,0.00018155125,0.0007030577,0.000036650683,0.00023381013,0.005088365],"genre_scores_gemma":[0.99007505,0.0000025802763,0.008547321,0.0005337698,0.00016201276,0.00005429089,0.0000802836,0.000023578488,0.0005211187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811465,0.00013419555,0.00040306515,0.00058215717,0.00019378027,0.0005721486],"domain_scores_gemma":[0.9981271,0.00073059567,0.000257223,0.000560918,0.00009152998,0.00023265799],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029298218,0.00022609183,0.0003423772,0.00044484195,0.00028202773,0.00040355136,0.00082896586,0.00006112692,0.0000032420464],"category_scores_gemma":[0.000020039417,0.00025937238,0.00019083593,0.0003499194,0.000074862895,0.0016563988,0.00028666554,0.0002891295,0.0000438422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039142484,0.00028720728,0.0017350208,0.00013128312,0.000060911487,0.0000033522397,0.0030468423,0.0018004144,0.000112441434,0.44275132,0.00084113475,0.54919094],"study_design_scores_gemma":[0.0016482094,0.00029288308,0.0028414736,0.00019155933,0.0000075193525,0.000056579953,0.0005922766,0.85832167,0.00009393325,0.028837504,0.10654187,0.0005745113],"about_ca_topic_score_codex":0.0004920915,"about_ca_topic_score_gemma":0.0002217609,"teacher_disagreement_score":0.94674754,"about_ca_system_score_codex":0.00031670046,"about_ca_system_score_gemma":0.00011925621,"threshold_uncertainty_score":0.9999859},"labels":[],"label_agreement":null},{"id":"W1768913110","doi":"","title":"Context-aware real-time framework for local collaboration","year":2011,"lang":"en","type":"article","venue":"International Conference for Internet Technology and Secured Transactions","topic":"Context-Aware Activity Recognition Systems","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":"Lakehead University","funders":"","keywords":"Computer science; Scalability; Interactivity; Context (archaeology); Mobile device; Information exchange; Distributed computing; Mobile computing; Computer network; Human–computer interaction; World Wide Web; Operating system; Telecommunications","score_opus":0.04107346661952658,"score_gpt":0.2839712520413584,"score_spread":0.24289778542183185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1768913110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033529464,0.000029261259,0.99017465,0.0029490592,0.0009924959,0.0005849318,0.00025419812,0.00048839214,0.0011740604],"genre_scores_gemma":[0.9688459,0.00002797529,0.028983213,0.000115938434,0.000040851995,0.00079733995,0.000041282958,0.000016766784,0.0011307186],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986517,0.000033431395,0.00038084,0.00053539383,0.00014683198,0.00025181362],"domain_scores_gemma":[0.99835306,0.00025508838,0.00017881792,0.0002634726,0.00087268394,0.00007690039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016317294,0.00021185368,0.00026613864,0.0005164431,0.00015152445,0.00013896145,0.00073451723,0.00040044202,0.00025653135],"category_scores_gemma":[0.00006790008,0.00022473978,0.00011211825,0.0003120279,0.00022875812,0.0006760358,0.00003607288,0.00023782722,0.000031868545],"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":[0.00013979331,0.00012318444,0.000051126754,0.000017716373,0.00020598563,0.0000027464928,0.0019256411,0.0000020325886,0.0005223639,0.88888276,0.0002467839,0.10787987],"study_design_scores_gemma":[0.0030925707,0.0014521682,0.00021389347,0.00039758426,0.00012443664,0.00025923635,0.0050681625,0.35818228,0.036771446,0.55974245,0.033659987,0.0010357595],"about_ca_topic_score_codex":0.00007654973,"about_ca_topic_score_gemma":0.0003983671,"teacher_disagreement_score":0.96549296,"about_ca_system_score_codex":0.00007946748,"about_ca_system_score_gemma":0.00012010879,"threshold_uncertainty_score":0.9164622},"labels":[],"label_agreement":null},{"id":"W1791886866","doi":"","title":"A framework for context-aware services using service customizer","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Computer science; Service (business); Service delivery framework; Context (archaeology); Service provider; Service design; World Wide Web; Web service; Differentiated service; Internet privacy; Computer security; Knowledge management; Business; Marketing","score_opus":0.04737275798503614,"score_gpt":0.3122243932407911,"score_spread":0.26485163525575495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1791886866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12179305,0.00001823593,0.87297744,0.0018247329,0.0016634014,0.0006113935,0.000016167536,0.00046313475,0.00063243194],"genre_scores_gemma":[0.85644436,3.961739e-7,0.13839565,0.0046136617,0.00029542675,0.00008001776,0.0000040476825,0.000023871926,0.00014256107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832785,0.000062818435,0.0003376035,0.0005862322,0.00027921927,0.00040626567],"domain_scores_gemma":[0.9974623,0.00072714855,0.00019780909,0.0008555603,0.00058441993,0.00017281342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041516402,0.00023718837,0.00033041617,0.00013207512,0.00021097423,0.00046266566,0.0011416826,0.00024926715,0.00013649078],"category_scores_gemma":[0.00005604452,0.00021819948,0.00013854266,0.00047824509,0.00002593629,0.0011985964,0.00030292224,0.00032560792,0.00024406465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001840252,0.0006175277,0.004627535,0.0010274885,0.00040407135,0.00002887743,0.009911301,0.000047766513,0.06529236,0.44545028,0.0015390501,0.47086972],"study_design_scores_gemma":[0.0036306598,0.000105413375,0.0006202472,0.0004707289,0.00007728935,0.0003161238,0.002144575,0.8236064,0.030384751,0.058105867,0.07870541,0.0018325628],"about_ca_topic_score_codex":0.00072946376,"about_ca_topic_score_gemma":0.0017611597,"teacher_disagreement_score":0.8235586,"about_ca_system_score_codex":0.00003152827,"about_ca_system_score_gemma":0.00012074772,"threshold_uncertainty_score":0.8897916},"labels":[],"label_agreement":null},{"id":"W1809001740","doi":"10.1007/11853565_13","title":"Mobility Detection Using Everyday GSM Traces","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":277,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"GSM; Computer science; Mobile phone; Tower; Mobile telephony; Phone; Everyday life; Activity recognition; Real-time computing; Telecommunications; Artificial intelligence; Mobile radio; Engineering","score_opus":0.03051327408169161,"score_gpt":0.25633679779462976,"score_spread":0.22582352371293815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1809001740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033253473,0.00032515358,0.990223,0.00013377858,0.0031063226,0.00061241107,0.000008673629,0.00031888497,0.0019464091],"genre_scores_gemma":[0.9377545,0.000005716839,0.060942214,0.0003053187,0.00064639514,0.000013304243,0.0000029035632,0.00003723799,0.00029242574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99518937,0.000106838605,0.00074314943,0.0020200699,0.0012447039,0.0006958776],"domain_scores_gemma":[0.99667585,0.0006838875,0.00051244174,0.0015427279,0.0004228496,0.00016222666],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014210813,0.0006206423,0.00068455475,0.001006939,0.00039766548,0.000849982,0.0023388143,0.00046773974,0.00001897473],"category_scores_gemma":[0.00009825964,0.0006160452,0.00022959596,0.0008503401,0.0005800809,0.0015355936,0.00088459044,0.0008934756,0.000054075943],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005984849,0.00006190059,0.00012335384,0.000057646994,0.000013903307,0.00005545664,0.00032314355,0.021082355,0.0017679966,0.0002710112,0.000010321907,0.9762269],"study_design_scores_gemma":[0.00036748787,0.00016492823,0.00077148783,0.0005024136,0.000017581959,0.0003530699,2.8055595e-7,0.93859214,0.011507626,0.04443671,0.002068575,0.001217683],"about_ca_topic_score_codex":0.0003784362,"about_ca_topic_score_gemma":0.00073747284,"teacher_disagreement_score":0.97500926,"about_ca_system_score_codex":0.00084118365,"about_ca_system_score_gemma":0.0005605272,"threshold_uncertainty_score":0.9996291},"labels":[],"label_agreement":null},{"id":"W181622305","doi":"","title":"Key Player Identification in the Mashup Ecosystem","year":2011,"lang":"en","type":"article","venue":"˜The œopen source business resource","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Key (lock); Identification (biology); Mashup; Ecosystem; Computer science; Ecosystem services; World Wide Web; Ecology; Computer security; Biology; Web service; Web 2.0","score_opus":0.05887455818213795,"score_gpt":0.2528077974375591,"score_spread":0.19393323925542116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W181622305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28035814,0.00032389988,0.6400428,0.0048135924,0.00096198026,0.0042056483,0.000020539504,0.0005267871,0.06874666],"genre_scores_gemma":[0.99576473,0.0000030784151,0.00022718088,0.0009714129,0.00015409013,0.0003402224,0.000008126809,0.00003603058,0.0024951503],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964286,0.0010331164,0.0006836071,0.0007272729,0.00065900973,0.00046836367],"domain_scores_gemma":[0.9965655,0.00045571887,0.00047715814,0.0021625217,0.00026510155,0.00007398857],"candidate_categories":["scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0028673818,0.00029439243,0.00035038346,0.0002098491,0.0004702093,0.0014103067,0.0064786808,0.00012976523,0.00007286005],"category_scores_gemma":[0.00008254589,0.00019184868,0.00009485422,0.0018431473,0.000082364786,0.0010800533,0.0011288017,0.00026707322,0.00086683984],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005078654,0.0022525114,0.028141659,0.0004837817,0.00034897958,0.00032840102,0.26880655,0.0010954014,0.0031156384,0.017141579,0.04393547,0.6338422],"study_design_scores_gemma":[0.0011925319,0.00003807484,0.12513812,0.00023909168,0.000032892844,0.0004828697,0.0052213245,0.012286848,0.0003644837,0.0007186657,0.85355806,0.0007270197],"about_ca_topic_score_codex":0.0014045554,"about_ca_topic_score_gemma":0.0016461082,"teacher_disagreement_score":0.8096226,"about_ca_system_score_codex":0.00009316708,"about_ca_system_score_gemma":0.000090631984,"threshold_uncertainty_score":0.9999111},"labels":[],"label_agreement":null},{"id":"W1822891440","doi":"10.1007/978-3-540-39646-8_9","title":"Agent Support for Context-Aware Services and Personal Mobility","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artifact (error); Service discovery; Session (web analytics); Quality of service; Software architecture; Ubiquitous computing; Context (archaeology); Software agent; Context awareness; Wireless; Service (business); Software; Session Initiation Protocol; Computer network; Distributed computing; Web service; Human–computer interaction; World Wide Web; Operating system; Server; Artificial intelligence","score_opus":0.03026568681402006,"score_gpt":0.26032068364273425,"score_spread":0.2300549968287142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1822891440","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048405622,0.00047144436,0.99409956,0.0008346011,0.001926762,0.0011659223,0.00005992644,0.00015222069,0.0008055028],"genre_scores_gemma":[0.9279051,0.00004381034,0.061877105,0.00825065,0.00051944255,0.00012786855,0.000028026492,0.000070707276,0.0011773171],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956419,0.000065521774,0.0006157427,0.0021041804,0.00087338814,0.0006992908],"domain_scores_gemma":[0.9969546,0.0008632064,0.00038613938,0.0009968058,0.0005173929,0.00028185337],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013879034,0.0006252504,0.0007655487,0.0005144029,0.00036097938,0.00076461636,0.0017771188,0.00037540947,0.000042079166],"category_scores_gemma":[0.000053459124,0.0005992796,0.0002049347,0.00030603356,0.000598665,0.00090617273,0.0008184826,0.0005359008,0.000028364544],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001634953,0.000070861235,0.00034400608,0.00037479014,0.000037681006,0.000045949524,0.002415962,0.00011757201,0.00006710707,0.00246611,0.00009155349,0.99395204],"study_design_scores_gemma":[0.003049275,0.0015328308,0.0011476885,0.001879469,0.00007991076,0.001177522,0.000014378462,0.77220035,0.0025717046,0.11952446,0.09270959,0.004112841],"about_ca_topic_score_codex":0.00005165012,"about_ca_topic_score_gemma":0.0003928853,"teacher_disagreement_score":0.9898392,"about_ca_system_score_codex":0.00031901966,"about_ca_system_score_gemma":0.0004889257,"threshold_uncertainty_score":0.9996458},"labels":[],"label_agreement":null},{"id":"W1834780288","doi":"10.1155/2015/643273","title":"Method of Recognition and Assistance Combining Passive RFID and Electrical Load Analysis That Handles Cognitive Errors","year":2015,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Exploit; Activity recognition; Wireless sensor network; Wireless; Human–computer interaction; Home automation; Assisted living; Cognition; Embedded system; Field (mathematics); Cognitive impairment; Real-time computing; Computer security; Artificial intelligence; Computer network; Telecommunications","score_opus":0.05133637669692593,"score_gpt":0.3041118297796728,"score_spread":0.2527754530827469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1834780288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18589553,0.00054433785,0.8126051,0.00033987852,0.0003253228,0.000081008366,0.0001296572,0.000016835598,0.00006235583],"genre_scores_gemma":[0.9904632,0.00011463829,0.009132047,0.00008132115,0.00012891047,0.0000033160818,0.00006073278,0.000007513184,0.000008343262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767125,0.00041432178,0.0006167919,0.0002769947,0.00082957326,0.00019105968],"domain_scores_gemma":[0.9943808,0.0012026137,0.001162969,0.000105143095,0.002928656,0.00021982286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092003663,0.00017402264,0.0005150847,0.00035034335,0.000051798113,0.00021119288,0.00033231082,0.000118626136,0.0000063099274],"category_scores_gemma":[0.0005601522,0.00016132646,0.0001657778,0.0006269521,0.00009282754,0.0006765025,0.0001240567,0.00033033578,0.0000011966061],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002234143,0.0008097614,0.15916085,0.00003249825,0.013134638,0.00079410383,0.0037396303,0.005784192,0.0009970379,0.00063091645,0.0016896542,0.81099254],"study_design_scores_gemma":[0.0146823535,0.0016177204,0.21597274,0.0016762959,0.0028000227,0.0052640135,0.0049047805,0.7345644,0.008662035,0.0072406824,0.0010820301,0.001532898],"about_ca_topic_score_codex":0.00004859877,"about_ca_topic_score_gemma":0.00003554422,"teacher_disagreement_score":0.8094597,"about_ca_system_score_codex":0.00016645956,"about_ca_system_score_gemma":0.00013226399,"threshold_uncertainty_score":0.65787023},"labels":[],"label_agreement":null},{"id":"W187587118","doi":"10.1007/978-3-642-21535-3_5","title":"Activity Recognition in Smart Environments: An Information Retrieval Problem","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Activity recognition; Computer science; Activities of daily living; Smart environment; Process (computing); Artificial intelligence; Machine learning; Home automation; Human–computer interaction; Internet of Things; World Wide Web","score_opus":0.03323290163610457,"score_gpt":0.23357183024126785,"score_spread":0.20033892860516328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W187587118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021551906,0.000034441513,0.9902569,0.00018828649,0.0011037036,0.0009285872,0.000017215702,0.00015257846,0.005163098],"genre_scores_gemma":[0.9073381,0.000053787866,0.090960704,0.0010199861,0.0002921402,0.000044460947,0.000054335454,0.00004523243,0.00019129233],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961336,0.000142311,0.0007416958,0.0012608077,0.0010902233,0.0006313384],"domain_scores_gemma":[0.9974984,0.00028623737,0.0006165342,0.0012315647,0.00016311847,0.00020413102],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017179586,0.00054277555,0.0005613353,0.0014477759,0.00017519221,0.00059272593,0.0019791066,0.0004889372,0.00005222851],"category_scores_gemma":[0.000096047006,0.0005651457,0.000108529464,0.0006933097,0.0003611508,0.007317702,0.0008393489,0.0010229674,0.00031128115],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028897484,0.000076773526,0.000109935485,0.00003493739,0.000006878804,0.000019912977,0.001437953,0.0002365125,0.00024287196,0.00035006032,0.000003818134,0.9974514],"study_design_scores_gemma":[0.0040502455,0.0025597406,0.01117123,0.0035971808,0.00004959112,0.0006431527,0.0000037176917,0.3930636,0.027973348,0.5357556,0.0149296895,0.0062029334],"about_ca_topic_score_codex":0.00018819049,"about_ca_topic_score_gemma":0.00037966337,"teacher_disagreement_score":0.9912485,"about_ca_system_score_codex":0.0007425489,"about_ca_system_score_gemma":0.00041815804,"threshold_uncertainty_score":0.99968},"labels":[],"label_agreement":null},{"id":"W1876005976","doi":"10.1007/978-3-540-73035-4_19","title":"Report on the Impact of a User-Centered Approach and Usability Studies for Designing Mobile and Context-Aware Cognitive Orthosis","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Usability; Context (archaeology); Cognition; Process (computing); Computer science; Human–computer interaction; User-centered design; Psychology","score_opus":0.10362977968059685,"score_gpt":0.34377876426321274,"score_spread":0.2401489845826159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1876005976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025329502,0.00082630647,0.9709737,0.00009146576,0.00029016327,0.002202849,0.00004082036,0.00006181533,0.00018339259],"genre_scores_gemma":[0.97881824,0.000046180907,0.020624958,0.00022948786,0.00009943783,0.00011386331,0.0000051674915,0.000024418747,0.00003823326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99636,0.00012803775,0.00077107555,0.0016085686,0.0006411331,0.0004911535],"domain_scores_gemma":[0.9909507,0.006407608,0.0007310057,0.00084856024,0.00092350174,0.00013864003],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028439471,0.00054925255,0.0009569931,0.00048735464,0.0003445462,0.00029553127,0.0008649839,0.00023446415,0.0000025288812],"category_scores_gemma":[0.0007727729,0.00037581503,0.00022384088,0.0003942002,0.0015035634,0.00046845537,0.0008291241,0.0004975926,8.671272e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010993682,0.00015278303,0.0046376213,0.00025277468,0.00027660365,0.000054490723,0.005216394,0.0004897898,0.000102606864,0.00085987436,0.000022783619,0.9878243],"study_design_scores_gemma":[0.009839294,0.014706479,0.033874597,0.018299017,0.0005173471,0.005418505,0.00049097626,0.7866303,0.018155245,0.10311061,0.00074950873,0.008208175],"about_ca_topic_score_codex":0.000086840984,"about_ca_topic_score_gemma":0.00008343644,"teacher_disagreement_score":0.97961617,"about_ca_system_score_codex":0.00025606126,"about_ca_system_score_gemma":0.00034204533,"threshold_uncertainty_score":0.9998694},"labels":[],"label_agreement":null},{"id":"W1885198778","doi":"","title":"A framework for cell phone based diagnosis and management of priority tropical diseases","year":2011,"lang":"en","type":"article","venue":"2011 IST-Africa Conference Proceedings","topic":"Context-Aware Activity Recognition 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 Calgary; Mount Royal University","funders":"","keywords":"Typhoid fever; Neglected tropical diseases; Risk analysis (engineering); Analytic hierarchy process; Malaria; Global health; Business; Integrated Management of Childhood Illness; Computer science; Medicine; Health care; Operations research; Public health; Primary health care; Engineering; Economics; Economic growth","score_opus":0.07779510510720213,"score_gpt":0.2630671253175079,"score_spread":0.1852720202103058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1885198778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08611496,0.00033822862,0.9059627,0.00058664393,0.00026294152,0.0014410781,0.000059194106,0.00025179802,0.0049824505],"genre_scores_gemma":[0.88009226,0.00008027174,0.11902875,0.00005146035,0.00003080259,0.00059686316,0.0000012123784,0.000013062041,0.000105330255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99834025,0.0000199799,0.00035755246,0.0006200515,0.00030937867,0.0003527697],"domain_scores_gemma":[0.9985548,0.00021125366,0.00029036324,0.00027184462,0.0004602478,0.00021144218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013736589,0.00023335799,0.00037895198,0.00012880837,0.00010958436,0.00013923021,0.0006810741,0.00011532782,0.00010969546],"category_scores_gemma":[0.000072648734,0.00022295056,0.000109309905,0.00023148487,0.0001284692,0.00051685877,0.00027745595,0.00012903116,0.000030069104],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048770933,0.0038207595,0.2781885,0.005593895,0.00029013772,0.000023577146,0.017434519,8.054809e-8,0.0011340333,0.5373859,0.007045518,0.14859541],"study_design_scores_gemma":[0.007106812,0.0029381325,0.62732404,0.0027317037,0.00071633473,0.00002104171,0.0032452114,0.025167184,0.049663555,0.25699615,0.020955719,0.0031341182],"about_ca_topic_score_codex":0.000032020682,"about_ca_topic_score_gemma":0.0000027326294,"teacher_disagreement_score":0.7939773,"about_ca_system_score_codex":0.000033920078,"about_ca_system_score_gemma":0.000071767114,"threshold_uncertainty_score":0.909166},"labels":[],"label_agreement":null},{"id":"W1890585588","doi":"10.1007/11907503_9","title":"Toward Wide Area Interaction with Ubiquitous Computing Environments","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ubiquitous computing; Computer science; Middleware (distributed applications); Interoperability; Software deployment; Context-aware pervasive systems; Human–computer interaction; Domain (mathematical analysis); World Wide Web; Software engineering; Distributed computing","score_opus":0.027618184779971685,"score_gpt":0.23659896199626546,"score_spread":0.20898077721629377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1890585588","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00057514093,0.00012963003,0.989988,0.00052380766,0.0018958116,0.0005956396,0.0000059955332,0.00025075083,0.006035172],"genre_scores_gemma":[0.9221404,0.000008909246,0.07582959,0.0010013096,0.00040699093,0.0000118158105,0.000014904551,0.000056093144,0.0005299597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949706,0.000073405456,0.0007142314,0.0020283635,0.001431269,0.0007821151],"domain_scores_gemma":[0.99670756,0.00091771135,0.0006812009,0.0013455587,0.00015569101,0.00019229004],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000689025,0.00074578705,0.0007412083,0.00092544244,0.00029760867,0.00087882724,0.0024632462,0.0003250888,0.000021746873],"category_scores_gemma":[0.000051847786,0.0006753407,0.0001534512,0.0005221734,0.0005285168,0.0012679104,0.0012488929,0.0010519518,0.00011269963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023751436,0.00010906144,0.0005899749,0.000070494185,0.000049622962,0.0004333461,0.0012345187,0.057781197,0.00029522568,0.0005675884,0.00018108684,0.93866414],"study_design_scores_gemma":[0.0015150321,0.0008958324,0.0013891277,0.0031320693,0.00004835315,0.0015684405,0.000002915956,0.9454982,0.0062087746,0.019135488,0.017457984,0.003147726],"about_ca_topic_score_codex":0.00012189692,"about_ca_topic_score_gemma":0.00017571224,"teacher_disagreement_score":0.9355164,"about_ca_system_score_codex":0.00082527695,"about_ca_system_score_gemma":0.00033845814,"threshold_uncertainty_score":0.9995698},"labels":[],"label_agreement":null},{"id":"W1924339725","doi":"10.1007/978-3-642-13577-4_47","title":"SIMACT: A 3D Open Source Smart Home Simulator for Activity Recognition","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Java; Open source; Home automation; Field (mathematics); Set (abstract data type); Human–computer interaction; Simulation; Embedded system; Software; Operating system","score_opus":0.043301130113119264,"score_gpt":0.2823900745041111,"score_spread":0.23908894439099182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1924339725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010888801,0.000057697,0.9901326,0.00080870965,0.0035939892,0.0024505008,0.00007027939,0.00033234656,0.0014650391],"genre_scores_gemma":[0.6513196,0.000011550936,0.34346938,0.0023402409,0.0012980357,0.00021820211,0.000035612335,0.000141699,0.0011656611],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945631,0.00009551651,0.0006902445,0.0026190856,0.0010806463,0.00095141743],"domain_scores_gemma":[0.9936447,0.0024263854,0.0007524093,0.0020401867,0.0007910663,0.0003452447],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0021581226,0.0008124204,0.0010626086,0.0009805657,0.0006193581,0.0024020341,0.0058552506,0.0007595515,0.000057849775],"category_scores_gemma":[0.00038026116,0.00081531215,0.00028943454,0.000665027,0.0005779139,0.0025839212,0.003190102,0.0014650464,0.00018809862],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003495161,0.0000750789,0.000030712483,0.00006469948,0.00002307936,0.000016725064,0.00037378675,0.00067350356,0.0011529858,0.00019880828,0.000020685635,0.99733496],"study_design_scores_gemma":[0.00232514,0.0007235757,0.00023954334,0.0011349195,0.00005003077,0.00024495577,4.7448745e-7,0.79898816,0.013563501,0.13583985,0.043949407,0.0029404282],"about_ca_topic_score_codex":0.00013969583,"about_ca_topic_score_gemma":0.0006310834,"teacher_disagreement_score":0.99439454,"about_ca_system_score_codex":0.00042600426,"about_ca_system_score_gemma":0.0010456286,"threshold_uncertainty_score":0.9995235},"labels":[],"label_agreement":null},{"id":"W1929604979","doi":"10.1007/978-3-540-75696-5_15","title":"Deployment Experience Toward Core Abstractions for Context Aware Applications","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Context (archaeology); Set (abstract data type); Software deployment; Core (optical fiber); Process (computing); Iterative and incremental development; Software engineering; Context model; Data science; Human–computer interaction; Programming language; Artificial intelligence","score_opus":0.09730368469260686,"score_gpt":0.3294852479283288,"score_spread":0.23218156323572192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1929604979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004465963,0.00034862637,0.99263775,0.00082565914,0.0019621442,0.0021940556,0.000049210972,0.00038443113,0.001553432],"genre_scores_gemma":[0.8231722,0.000039698094,0.1715037,0.0024903563,0.0010317205,0.0008071872,0.000030348157,0.00008341246,0.0008413728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951815,0.000020558542,0.0008678331,0.002026182,0.0010734386,0.0008304929],"domain_scores_gemma":[0.9952692,0.0013902414,0.00056047994,0.0016710956,0.00079282967,0.000316201],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008587121,0.0006284508,0.00066359964,0.00094319973,0.0005412149,0.0006198511,0.0031170992,0.00041615806,0.000041721112],"category_scores_gemma":[0.0000777805,0.0006415539,0.00027126173,0.00060050515,0.0006653572,0.000933716,0.0007623229,0.00071928947,0.0001340199],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008740228,0.000066907836,0.000058271627,0.00006172442,0.000019820594,0.000020267273,0.0013377432,0.0007766696,0.00013262898,0.014772643,0.000079546546,0.98266506],"study_design_scores_gemma":[0.00278459,0.000965044,0.00058354996,0.001892028,0.000087829816,0.0010103129,0.00003471797,0.41201812,0.012081095,0.23728663,0.32554466,0.0057114316],"about_ca_topic_score_codex":0.00008884947,"about_ca_topic_score_gemma":0.00045657047,"teacher_disagreement_score":0.9769536,"about_ca_system_score_codex":0.0006850838,"about_ca_system_score_gemma":0.00070507126,"threshold_uncertainty_score":0.99960357},"labels":[],"label_agreement":null},{"id":"W1942601760","doi":"10.1007/s00779-015-0856-x","title":"Information and communications technologies for elderly ubiquitous healthcare in a smart home","year":2015,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":250,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Wearable computer; Health care; Life expectancy; Computer science; Population; Population ageing; Wearable technology; Internet privacy; Risk analysis (engineering); Business; Medicine; Embedded system","score_opus":0.05453878053257138,"score_gpt":0.2910366923686429,"score_spread":0.2364979118360715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1942601760","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7960549,0.00294705,0.17727089,0.021270288,0.00042379522,0.00095447473,0.000026049449,0.0007307607,0.00032177856],"genre_scores_gemma":[0.9893811,0.000056949437,0.010189169,0.00026740774,0.00003781994,0.000043034957,0.000010308502,0.0000058144838,0.00000843813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989345,0.00008114009,0.00032280118,0.00021198367,0.00017227994,0.0002772661],"domain_scores_gemma":[0.99889475,0.00036124268,0.0001514289,0.00028934574,0.00021311175,0.00009013672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063937163,0.0001392873,0.00023015354,0.00023564587,0.0002448111,0.00027232445,0.0004241151,0.0000992218,1.238966e-7],"category_scores_gemma":[0.000112862086,0.00013953258,0.000030787265,0.00028403732,0.00009024434,0.0007987658,0.0005681207,0.00019582176,0.0000043034497],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000147412175,0.000036064062,0.007257762,0.00014891953,0.0000100329835,0.0000014010039,0.01598887,0.0000035931625,0.00003805597,0.004575072,0.00021855133,0.9717069],"study_design_scores_gemma":[0.005227196,0.00171603,0.019441446,0.00083873456,0.000024295114,0.00079908536,0.046824142,0.8625552,0.00023007087,0.019356618,0.041581105,0.0014060418],"about_ca_topic_score_codex":0.00039219565,"about_ca_topic_score_gemma":0.00034589184,"teacher_disagreement_score":0.9703009,"about_ca_system_score_codex":0.00007410241,"about_ca_system_score_gemma":0.00011404471,"threshold_uncertainty_score":0.5689974},"labels":[],"label_agreement":null},{"id":"W1946904586","doi":"10.1109/ccece.2001.933563","title":"Context-based media adaptation in pervasive computing","year":2002,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Adaptation (eye); Computer science; Ubiquitous computing; Context (archaeology); The Internet; Content adaptation; Context awareness; Multimedia; Mobile device; World Wide Web; Human–computer interaction","score_opus":0.07585290601132727,"score_gpt":0.23861042174921737,"score_spread":0.1627575157378901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1946904586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050486065,0.000115193936,0.9396209,0.0020969717,0.00041147158,0.00024544136,0.0000016662351,0.0003312369,0.0066910125],"genre_scores_gemma":[0.9916199,0.0000014164864,0.007116841,0.0010674662,0.00004963462,0.000010508442,0.0000022791396,0.0000068831714,0.00012510411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987188,0.00015360538,0.00029064267,0.00033645786,0.00027549997,0.00022500141],"domain_scores_gemma":[0.99865156,0.0007684296,0.00010199964,0.000279941,0.00012481895,0.00007325613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002898822,0.00011798958,0.00018456033,0.00020066096,0.000058703565,0.00012374557,0.0003661087,0.00005618766,0.00016083349],"category_scores_gemma":[0.00015406082,0.00011758911,0.000055741042,0.0004525548,0.000024702256,0.00052362774,0.000065809014,0.00012211569,0.00041191673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031661034,0.00013993609,0.003033395,0.00001313502,0.000008089287,0.000036946356,0.0074260724,0.00041735434,0.00017739295,0.0038993645,0.0011613707,0.98368376],"study_design_scores_gemma":[0.0008281259,0.00003169535,0.0047244295,0.000054833996,0.0000014389074,0.000011727225,0.0006942282,0.99159086,0.000651283,0.0001457838,0.0010848694,0.000180737],"about_ca_topic_score_codex":0.00032057206,"about_ca_topic_score_gemma":0.0014855313,"teacher_disagreement_score":0.9911735,"about_ca_system_score_codex":0.000078719306,"about_ca_system_score_gemma":0.000037484413,"threshold_uncertainty_score":0.52944934},"labels":[],"label_agreement":null},{"id":"W1964049344","doi":"10.1007/s00779-013-0716-5","title":"Advances in context-aware mobile services","year":2013,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Mobile computing; Context (archaeology); Human–computer interaction; Telecommunications","score_opus":0.00839741745830659,"score_gpt":0.2363919345353999,"score_spread":0.2279945170770933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964049344","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9643247,0.0029850055,0.029547848,0.00045494933,0.00041649034,0.00045930437,0.0000029691475,0.00023657366,0.0015721719],"genre_scores_gemma":[0.99847883,0.00007454835,0.0006040554,0.0005658101,0.00016296485,0.0000448118,0.0000023109374,0.000009830062,0.000056847137],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852693,0.00009579565,0.00028454425,0.00048932916,0.00023568605,0.00036770254],"domain_scores_gemma":[0.9991961,0.0002885963,0.000124123,0.00015936879,0.00011484638,0.00011694447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022597227,0.00018844083,0.0002771933,0.00012824516,0.00017957177,0.0003127031,0.00037657487,0.000063669184,0.000030757867],"category_scores_gemma":[0.0000066009784,0.00017550672,0.00005341558,0.00026032884,0.000044746106,0.001281289,0.00030795773,0.0001809775,0.000098401324],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029013017,0.00005623879,0.0133313425,0.0001330091,0.000007997226,0.000010805946,0.007230589,0.000026087964,0.0002729031,0.00027986072,0.000052254098,0.97859603],"study_design_scores_gemma":[0.0014801022,0.000286843,0.034011796,0.00078337296,0.0000076083606,0.0002877453,0.011917563,0.93135643,0.00048567643,0.0016635155,0.0167477,0.00097163237],"about_ca_topic_score_codex":0.00065392547,"about_ca_topic_score_gemma":0.00036604932,"teacher_disagreement_score":0.97762436,"about_ca_system_score_codex":0.0000376194,"about_ca_system_score_gemma":0.00002971112,"threshold_uncertainty_score":0.7156957},"labels":[],"label_agreement":null},{"id":"W1964441627","doi":"10.1109/jbhi.2012.2237178","title":"Wireless Body Area Network Node Localization Using Small-Scale Spatial Information","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wireless sensor network; Computer science; Beacon; Body area network; Node (physics); Wearable computer; Accelerometer; Wireless; Sensor node; Real-time computing; Interval (graph theory); Set (abstract data type); Key distribution in wireless sensor networks; Wireless network; Computer network; Embedded system; Telecommunications; Mathematics; Acoustics; Physics","score_opus":0.040985749599108105,"score_gpt":0.27451524691653595,"score_spread":0.23352949731742784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964441627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04785475,0.00004699446,0.9486874,0.0019229908,0.0011687438,0.00022382977,0.0000035536,0.000027838962,0.00006394355],"genre_scores_gemma":[0.9635525,0.0001684288,0.030275436,0.005382939,0.0005960247,0.0000046319474,0.0000094775905,0.0000062796857,0.0000043116797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972535,0.00007355139,0.0017272256,0.00005432977,0.00056510523,0.00032631986],"domain_scores_gemma":[0.99732757,0.000086949854,0.0015378799,0.00014627283,0.00044621143,0.00045512512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012382771,0.00013312347,0.0003636048,0.00024424453,0.0002216662,0.0002904815,0.00029491782,0.00012400922,0.000014796472],"category_scores_gemma":[0.000030045205,0.000104933766,0.000057600977,0.0003561454,0.00007562278,0.0025717306,0.000076665216,0.00027071408,0.0000249717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017495886,0.00010842365,0.0032201137,0.0007044612,0.00005199359,0.0000027113226,0.007072936,0.00091558904,0.00003595626,0.0001150749,0.015845008,0.97191024],"study_design_scores_gemma":[0.00076829037,0.0002776031,0.0018060541,0.0004405826,0.0000079136735,0.000369131,0.0003313686,0.9847757,0.00003641164,0.0003525004,0.010692461,0.00014195191],"about_ca_topic_score_codex":0.0003843022,"about_ca_topic_score_gemma":0.00002625436,"teacher_disagreement_score":0.98386014,"about_ca_system_score_codex":0.0001185949,"about_ca_system_score_gemma":0.0004267073,"threshold_uncertainty_score":0.42790747},"labels":[],"label_agreement":null},{"id":"W1965524656","doi":"10.1155/2011/649563","title":"Dynamic Context-Aware and Limited Resources-Aware Service Adaptation for Pervasive Computing","year":2011,"lang":"en","type":"article","venue":"Advances in Software Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Adaptation (eye); Ubiquitous computing; Context (archaeology); Service (business); Task (project management); Constraint (computer-aided design); Context awareness; Distributed computing; Order (exchange); Human–computer interaction; Systems engineering","score_opus":0.023258321215302878,"score_gpt":0.2358858459003088,"score_spread":0.21262752468500593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965524656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05904258,0.0022288652,0.93700945,0.00004780746,0.0005819079,0.000503865,0.000014992161,0.0005508178,0.000019703952],"genre_scores_gemma":[0.9154865,0.00007367379,0.084104754,0.00014604042,0.00003734173,0.00008923714,0.000013408205,0.000035966008,0.000013043165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838257,0.000048548893,0.00040192984,0.0005761028,0.00019227399,0.0003985564],"domain_scores_gemma":[0.99817574,0.0009642611,0.0001715408,0.00034061185,0.00024468524,0.000103132545],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027576488,0.0002740019,0.00034204772,0.00025848273,0.00011530015,0.000081845734,0.000453919,0.00010519057,0.000003231208],"category_scores_gemma":[0.00029444843,0.00031104794,0.000062060884,0.0005447256,0.000023804674,0.0015507416,0.00019110282,0.00020932086,0.0000063409802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006554884,0.000099544624,0.01063744,0.001010022,0.00007040104,0.000045686276,0.031813905,0.023776397,0.00012218315,0.0007930148,0.000014503943,0.93155134],"study_design_scores_gemma":[0.0011259798,0.00012485588,0.01103824,0.0007183314,0.0000129812215,0.00006465194,0.0016771214,0.98014903,0.0002048892,0.00028419142,0.003994955,0.0006047693],"about_ca_topic_score_codex":0.000082431754,"about_ca_topic_score_gemma":0.00048671686,"teacher_disagreement_score":0.9563726,"about_ca_system_score_codex":0.000112794434,"about_ca_system_score_gemma":0.000032210406,"threshold_uncertainty_score":0.99993414},"labels":[],"label_agreement":null},{"id":"W1966089544","doi":"10.3166/jesa.40.353-368","title":"Design of a mechatronic interface for human gait","year":2006,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gait; Mechatronics; Interface (matter); Physical medicine and rehabilitation; Computer science; Engineering; Artificial intelligence; Medicine","score_opus":0.05694587879681246,"score_gpt":0.29480674455907746,"score_spread":0.237860865762265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966089544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020665731,0.015389756,0.95796156,0.00083723036,0.0027165937,0.001087068,0.000049445902,0.00021543687,0.0010772047],"genre_scores_gemma":[0.92822504,0.000095899035,0.05727938,0.00004725094,0.00085508527,0.000048697177,0.0000031859213,0.000097428194,0.01334806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99414814,0.0015673445,0.0019412389,0.0005916805,0.0007897883,0.00096180075],"domain_scores_gemma":[0.9948992,0.00095884694,0.0020160042,0.0006599804,0.0012127595,0.0002531674],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0025965725,0.00053163356,0.0010289167,0.0004842177,0.0006251012,0.0010674233,0.0013448353,0.00024531127,0.0002191995],"category_scores_gemma":[0.00024221331,0.0005434535,0.0005321639,0.0006659904,0.0003118126,0.0018884661,0.00029306134,0.00052728713,0.00018250573],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009813214,0.0010599126,0.00043134324,0.002298838,0.00073834567,0.00025213562,0.0035025754,0.009045943,0.051249903,0.046312034,0.028915398,0.85609543],"study_design_scores_gemma":[0.0069685443,0.0052438695,0.03382466,0.011547569,0.00073038874,0.018131692,0.00058157,0.62534,0.029165613,0.23509446,0.030939905,0.0024317417],"about_ca_topic_score_codex":0.00049733097,"about_ca_topic_score_gemma":0.00011250602,"teacher_disagreement_score":0.9075593,"about_ca_system_score_codex":0.0006971394,"about_ca_system_score_gemma":0.00057730515,"threshold_uncertainty_score":0.99996954},"labels":[],"label_agreement":null},{"id":"W1966260051","doi":"10.1016/j.intcom.2010.08.003","title":"Towards analytical evaluation of human machine interfaces developed in the context of smart homes","year":2010,"lang":"en","type":"article","venue":"Interacting with Computers","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Human–computer interaction; Human–machine system; Context (archaeology); Human–machine interface","score_opus":0.06116789150142283,"score_gpt":0.3426437866838642,"score_spread":0.28147589518244137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966260051","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95727205,0.0000149877,0.03949184,0.0007910686,0.0010518244,0.0003381085,0.0000025814677,0.00003532828,0.0010021862],"genre_scores_gemma":[0.9962305,3.4663552e-7,0.0036006637,0.00010124868,0.00003089606,0.0000170715,0.0000031895097,0.000008367555,0.000007734185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977925,0.0004524471,0.00056261604,0.00031476186,0.00069831073,0.0001793651],"domain_scores_gemma":[0.9969069,0.0015341006,0.0004633362,0.00048691576,0.0005709635,0.000037812442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024878832,0.00016326441,0.00034739455,0.00028065586,0.00005389793,0.00011926205,0.0009622986,0.0000508327,0.000020814841],"category_scores_gemma":[0.0004598382,0.00011300722,0.00006456053,0.00046219252,0.000101558995,0.0005896261,0.00019450777,0.0003902527,0.0000047559715],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013338626,0.00054750865,0.05003546,0.000110909335,0.00026841558,0.00002005317,0.03534114,0.00012937303,0.010890176,0.0030636373,0.00061403366,0.8988459],"study_design_scores_gemma":[0.0050720554,0.0014472967,0.13233712,0.0025391702,0.00013528767,0.00047243756,0.0077318186,0.7826737,0.0652133,0.000594208,0.0008782463,0.00090533507],"about_ca_topic_score_codex":0.00079248,"about_ca_topic_score_gemma":0.0023862256,"teacher_disagreement_score":0.8979406,"about_ca_system_score_codex":0.00004885958,"about_ca_system_score_gemma":0.00015031289,"threshold_uncertainty_score":0.46083006},"labels":[],"label_agreement":null},{"id":"W1968094879","doi":"10.1145/2413097.2413135","title":"Smart homes for people with Alzheimer's disease","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Cognition; Protocol (science); Home automation; Disease; Field (mathematics); Software; Test (biology); Human–computer interaction; Psychology; Medicine; Psychiatry; Telecommunications","score_opus":0.03367634256332355,"score_gpt":0.2607066487224735,"score_spread":0.22703030615914996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968094879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023898654,0.000488453,0.9690646,0.0016210139,0.00052375696,0.0005929778,0.000010487524,0.00034362642,0.0034564156],"genre_scores_gemma":[0.9905024,0.0000013563109,0.008255918,0.00033729465,0.00012977053,0.0001770132,0.0000048179413,0.000010167927,0.00058125216],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99915344,0.000034683966,0.00010974908,0.0002169369,0.00018232792,0.00030287734],"domain_scores_gemma":[0.99898875,0.0001988374,0.000052368247,0.00039509527,0.00009261316,0.00027236645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019491222,0.00010832382,0.00014629423,0.000058055663,0.00009097149,0.00011321832,0.0002933994,0.000021554411,0.00005081544],"category_scores_gemma":[0.000021608927,0.00008105745,0.00005990611,0.00018102507,0.00001536087,0.0011507665,0.00008480138,0.00003485827,0.0001627411],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015050916,0.0008719708,0.5911287,0.00010265187,0.00041637756,0.0000064295264,0.002988499,0.0000063474026,0.00012969978,0.056383766,0.035844084,0.31197095],"study_design_scores_gemma":[0.0034476586,0.0004531001,0.72573316,0.00011875019,0.00026409238,0.00011640631,0.00052874966,0.019352837,0.0045224787,0.0018652639,0.24178207,0.0018154405],"about_ca_topic_score_codex":0.000055494587,"about_ca_topic_score_gemma":0.00010163178,"teacher_disagreement_score":0.96660376,"about_ca_system_score_codex":0.00001425443,"about_ca_system_score_gemma":0.00006003825,"threshold_uncertainty_score":0.33054268},"labels":[],"label_agreement":null},{"id":"W1971497999","doi":"10.1007/s12243-010-0166-2","title":"User’s behavior study for smart houses occupant prediction","year":2010,"lang":"en","type":"article","venue":"Annals of Telecommunications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Toilet; Classifier (UML); Computer science; Apartment; Machine learning; Support vector machine; Artificial intelligence; Habit; Human–computer interaction; Engineering; Psychology; Civil engineering","score_opus":0.14148306294135582,"score_gpt":0.38443478720223256,"score_spread":0.24295172426087674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971497999","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9659306,0.00009675932,0.02714199,0.0027959172,0.0005717146,0.0019896582,0.00016346105,0.00037211043,0.00093778793],"genre_scores_gemma":[0.9869961,0.000029763385,0.011753931,0.00008960604,0.00004581266,0.0008773703,0.000021176054,0.000013645922,0.00017258851],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99877894,0.00014235014,0.00044261958,0.00025156754,0.00019022434,0.0001943148],"domain_scores_gemma":[0.996457,0.0005214209,0.00026145802,0.0020506103,0.0006309417,0.00007857185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069649954,0.00011929431,0.00021264532,0.0001855985,0.0002835377,0.000097345,0.0015938244,0.00006905136,0.000013940426],"category_scores_gemma":[0.00019317298,0.00012270943,0.00012846968,0.0003221173,0.000060622646,0.00065807864,0.00032636334,0.00022157781,0.000023007397],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059844842,0.014103032,0.57428646,0.00006712528,0.00047851604,0.000004637874,0.007198862,0.000013885461,0.018465074,0.013531696,0.028945446,0.34284544],"study_design_scores_gemma":[0.00177705,0.0013318743,0.8040414,0.00005683545,0.0001170051,0.00008067832,0.00088482606,0.0024288786,0.020873308,0.0010555702,0.1667562,0.00059638964],"about_ca_topic_score_codex":0.00026526873,"about_ca_topic_score_gemma":0.0013048413,"teacher_disagreement_score":0.34224904,"about_ca_system_score_codex":0.00000738748,"about_ca_system_score_gemma":0.000095053954,"threshold_uncertainty_score":0.5003945},"labels":[],"label_agreement":null},{"id":"W1971578845","doi":"10.1109/re.2012.6345832","title":"Tool support for combined rule-based and goal-based reasoning in Context-Aware systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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 Ottawa; Carleton University","funders":"","keywords":"Computer science; Context (archaeology); Rule-based system; Process (computing); Semantic reasoner; Context model; Qualitative reasoning; Notation; Reasoning system; Human–computer interaction; Rank (graph theory); Artificial intelligence; Software engineering; Programming language","score_opus":0.02522147738579365,"score_gpt":0.26124622231208827,"score_spread":0.23602474492629463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971578845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17521429,0.00016868398,0.81974125,0.0006022307,0.0010071046,0.0017136219,0.000036149297,0.00041260716,0.0011040434],"genre_scores_gemma":[0.9947957,4.9105324e-7,0.003332694,0.00076533214,0.00008742014,0.00046510092,0.00002521891,0.00002544603,0.00050258724],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978127,0.00021490929,0.0005342949,0.0004913047,0.0003188177,0.00062799244],"domain_scores_gemma":[0.9978257,0.0010040493,0.00020701441,0.00054176076,0.00020354765,0.00021794533],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014206334,0.00026896052,0.0004834626,0.00026533907,0.00013208168,0.00029773635,0.00040286905,0.0001461163,0.000031526328],"category_scores_gemma":[0.00016812155,0.00025705024,0.00010199457,0.00032469508,0.000049698676,0.0007613812,0.00009147991,0.000142736,0.00005263857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007745906,0.002328244,0.7120311,0.001973611,0.00022820075,0.00007307444,0.0030099016,0.0004642264,0.0032860318,0.07254301,0.010485672,0.19280232],"study_design_scores_gemma":[0.015424286,0.00078745984,0.039507724,0.00081349135,0.000043960845,0.00009227163,0.00096061715,0.91130185,0.007491965,0.00015696845,0.021701334,0.001718081],"about_ca_topic_score_codex":0.00035942625,"about_ca_topic_score_gemma":0.00013152353,"teacher_disagreement_score":0.9108376,"about_ca_system_score_codex":0.00013891156,"about_ca_system_score_gemma":0.00022224219,"threshold_uncertainty_score":0.9999882},"labels":[],"label_agreement":null},{"id":"W1971766608","doi":"10.1145/2030112.2030186","title":"Self-adaptive middleware for the design of context-aware software applications in public transit systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Public transport; Middleware (distributed applications); Computer science; Context (archaeology); Ubiquitous computing; Transit (satellite); Context awareness; Software; Mobile computing; Adaptation (eye); Mobile device; World Wide Web; Computer security; Human–computer interaction; Transport engineering; Telecommunications; Engineering; Distributed computing; Operating system","score_opus":0.13237390826684653,"score_gpt":0.24930102828536846,"score_spread":0.11692712001852193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971766608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000121467965,0.00051431725,0.9944747,0.0001801147,0.00019457033,0.0036900335,0.00004233057,0.00033758002,0.0004448795],"genre_scores_gemma":[0.9622875,0.000010290703,0.03514762,0.00007968696,0.000032093318,0.0022870437,0.0000043859,0.000020054655,0.00013134231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998062,0.0002641221,0.00055200455,0.000487891,0.00028250896,0.00035145332],"domain_scores_gemma":[0.9964238,0.0018361137,0.00025630224,0.0007899538,0.00059460546,0.00009924415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008058656,0.00022334613,0.00038905602,0.00021381782,0.0001757723,0.00011076221,0.0012990689,0.00012613864,0.00001638124],"category_scores_gemma":[0.00005718895,0.00016260636,0.00014299223,0.00068031234,0.00007030587,0.0007471716,0.00009551169,0.00013849913,0.000022798287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025452086,0.0031930758,0.013072525,0.0013212502,0.0013995982,0.000016418431,0.053839643,0.0005469056,0.000237048,0.46161258,0.0026272284,0.4618792],"study_design_scores_gemma":[0.0067236675,0.0014901373,0.010242189,0.0005699167,0.00024114255,0.00017153374,0.035964638,0.8948269,0.0056832572,0.0073138177,0.034268893,0.0025039064],"about_ca_topic_score_codex":0.0005009538,"about_ca_topic_score_gemma":0.0003094762,"teacher_disagreement_score":0.962166,"about_ca_system_score_codex":0.000090307476,"about_ca_system_score_gemma":0.00025188483,"threshold_uncertainty_score":0.6630895},"labels":[],"label_agreement":null},{"id":"W1971816493","doi":"10.1109/iembs.2011.6091439","title":"Wireless inertial measurement unit with GPS (WIMU-GPS) &amp;#x2014; Wearable monitoring platform for ecological assessment of lifespace and mobility in aging and disease","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Université Laval; University of Windsor; Université de Sherbrooke; Université du Québec à Montréal; Western University; Health and Social Services Centre University Institute of Geriatrics of Sherbrooke","funders":"Canadian Institutes of Health Research","keywords":"Global Positioning System; Computer science; Inertial measurement unit; Wearable computer; Assisted GPS; Precision Lightweight GPS Receiver; Real-time computing; Time to first fix; GPS signals; Embedded system; Telecommunications; Gps receiver; Artificial intelligence","score_opus":0.13263968297857504,"score_gpt":0.30878346476424523,"score_spread":0.1761437817856702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971816493","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77322495,0.000058184884,0.22561303,0.00011769371,0.00009132154,0.0005965221,0.0000029960959,0.000051469193,0.00024383448],"genre_scores_gemma":[0.98609346,0.000012487994,0.01364582,0.000022661621,0.000028297132,0.00015012278,5.687811e-7,0.0000080329755,0.000038560796],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99848807,0.000092352435,0.00030842848,0.00046474708,0.00037149386,0.00027490093],"domain_scores_gemma":[0.9988393,0.00022620197,0.0001359999,0.00032462899,0.00023371018,0.00024012326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011609526,0.00017202807,0.00031110412,0.00011626458,0.00009186668,0.00008315711,0.00021740206,0.000055611737,0.000008481942],"category_scores_gemma":[0.00008915295,0.00013474739,0.000032390155,0.00017904681,0.000076040116,0.0006505876,0.00021352038,0.00012616237,7.198397e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001974516,0.0008710581,0.9700862,0.00029281946,0.00006366852,0.000006320903,0.0013544636,0.000048833488,0.0026696245,0.0014197095,0.000011713532,0.022978123],"study_design_scores_gemma":[0.00205076,0.0003841369,0.97503114,0.0003503014,0.000028116996,0.000009880043,0.0005394654,0.018058378,0.0022963549,0.000731658,0.00014724147,0.0003725407],"about_ca_topic_score_codex":0.000490835,"about_ca_topic_score_gemma":0.00094933936,"teacher_disagreement_score":0.21286851,"about_ca_system_score_codex":0.000103372855,"about_ca_system_score_gemma":0.00020403674,"threshold_uncertainty_score":0.5494839},"labels":[],"label_agreement":null},{"id":"W1972209148","doi":"10.1145/1462027.1462034","title":"Quality-driven human-centered approach for service provisioning in ambient environment","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Ambient intelligence; Provisioning; Computer science; Quality (philosophy); Context (archaeology); Quality of service; Service (business); Service quality; Process management; Human–computer interaction; Business; Computer network; Marketing","score_opus":0.13394108036570873,"score_gpt":0.3088916474072085,"score_spread":0.17495056704149975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972209148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18936583,0.000014857794,0.8060684,0.00041049003,0.00007960059,0.00096168177,0.00000442946,0.00013181793,0.0029628975],"genre_scores_gemma":[0.95233196,0.0000024596375,0.04634034,0.00044173325,0.00003543136,0.00025087537,0.000017141392,0.000011351087,0.00056870026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981788,0.0001589802,0.00044241344,0.0005797977,0.00033565363,0.0003043685],"domain_scores_gemma":[0.999069,0.000118295735,0.00014195629,0.00053024996,0.000054144086,0.0000864082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004362629,0.00015610912,0.00026120403,0.0001308897,0.00019195917,0.00007202032,0.0005743928,0.000062998915,0.000016183565],"category_scores_gemma":[0.000016136024,0.00014928522,0.000083163664,0.00020387735,0.000023569464,0.0005017379,0.00029910353,0.00010654056,0.000056788354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038911324,0.0185775,0.24963628,0.002218599,0.00056088157,0.00018040583,0.09222958,0.010604017,0.24508062,0.08211048,0.008540903,0.2898716],"study_design_scores_gemma":[0.017245447,0.0010165677,0.22822084,0.00031529294,0.000021083917,0.00033308065,0.0029913392,0.6888419,0.021429116,0.001957514,0.034340937,0.003286922],"about_ca_topic_score_codex":0.000362123,"about_ca_topic_score_gemma":0.00009349689,"teacher_disagreement_score":0.76296616,"about_ca_system_score_codex":0.00014241034,"about_ca_system_score_gemma":0.000039084625,"threshold_uncertainty_score":0.60876745},"labels":[],"label_agreement":null},{"id":"W1972625929","doi":"10.1093/comjnl/bxv003","title":"Ambient and Context-Aware Services for the Future Web","year":2015,"lang":"en","type":"article","venue":"The Computer Journal","topic":"Context-Aware Activity Recognition Systems","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":"Acadia University","funders":"","keywords":"Computer science; World Wide Web; Mobile Web; Mobile device; Context (archaeology); Mobile technology; Mobile computing; Multimedia; Context awareness; Web service; Upload; Internet privacy; Phone; Telecommunications","score_opus":0.033632891188587206,"score_gpt":0.2554537796233071,"score_spread":0.22182088843471992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972625929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026329689,0.0031037126,0.92884666,0.03595315,0.0051468117,0.00046365667,0.000008118314,0.00009630232,0.000051907402],"genre_scores_gemma":[0.98967785,0.00008070688,0.0026924077,0.0041330685,0.0032803025,0.00002236697,8.9046665e-7,0.0000129816635,0.000099422294],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986215,0.00024085066,0.00027397316,0.0002287486,0.0003610402,0.00027387825],"domain_scores_gemma":[0.99825287,0.00046935055,0.00023338353,0.00044912897,0.00040656995,0.00018871944],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013821693,0.00016727575,0.00020078268,0.000059649472,0.00056505296,0.0010562757,0.0013720568,0.00005245326,0.0000042043152],"category_scores_gemma":[0.000006758906,0.00008657667,0.00010807374,0.00015830997,0.000061746374,0.0005978967,0.00043071975,0.0003211363,0.00002594973],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044563676,0.000054798416,0.0007766672,0.000027655264,0.000172937,0.000018859933,0.007515968,0.00010046943,0.000040480038,0.0028123292,0.045325972,0.9431093],"study_design_scores_gemma":[0.0022994447,0.00031764412,0.0030348427,0.00010956469,0.000044213415,0.003948819,0.002276455,0.4785922,0.00008618167,0.005892251,0.5030716,0.00032675645],"about_ca_topic_score_codex":0.000014324699,"about_ca_topic_score_gemma":0.000086257154,"teacher_disagreement_score":0.96334815,"about_ca_system_score_codex":0.00004634969,"about_ca_system_score_gemma":0.00012490999,"threshold_uncertainty_score":0.99998075},"labels":[],"label_agreement":null},{"id":"W1973706838","doi":"10.1109/vetecs.2012.6239914","title":"Analysis on the Accuracy of a Decision Support System for Hypertension Monitoring","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Computer science; Context (archaeology); Decision support system; Data mining; Accuracy and precision; Estimation; Decision system; Artificial intelligence; Machine learning; Statistics; Operations research; Engineering; Mathematics","score_opus":0.0795308862460354,"score_gpt":0.3044637059979103,"score_spread":0.2249328197518749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973706838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28482378,0.000026773334,0.71296483,0.00017739316,0.00071289105,0.00032374164,0.0000034480847,0.00009142851,0.0008757124],"genre_scores_gemma":[0.9951435,0.0000016813308,0.004540154,0.000049094495,0.00012371883,0.0000521803,0.0000010363043,0.000005994229,0.00008268941],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988096,0.000084128355,0.00031258608,0.00021632585,0.00035891542,0.0002184915],"domain_scores_gemma":[0.995795,0.0030112178,0.00019734727,0.00068148144,0.00024037971,0.000074586904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00105364,0.00010410395,0.00027136723,0.0002097692,0.00011227577,0.000067649875,0.00046918954,0.00004694843,0.000014048186],"category_scores_gemma":[0.00032250097,0.00006245095,0.00023010743,0.00066910975,0.000010896872,0.00053552055,0.00012568421,0.000052744697,0.000059064827],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012469066,0.00055081514,0.07730706,0.00015381865,0.0011431344,0.0000074140166,0.0020623265,0.0001282866,0.015856786,0.01795478,0.0031694463,0.88154143],"study_design_scores_gemma":[0.0033533692,0.00095989066,0.16245516,0.0010554277,0.0013930899,0.00030536397,0.0064137825,0.2806805,0.5244708,0.00043309928,0.016827023,0.0016525048],"about_ca_topic_score_codex":0.000056512014,"about_ca_topic_score_gemma":0.0000042495485,"teacher_disagreement_score":0.87988895,"about_ca_system_score_codex":0.000052465268,"about_ca_system_score_gemma":0.000024467608,"threshold_uncertainty_score":0.25466758},"labels":[],"label_agreement":null},{"id":"W1975063206","doi":"10.1145/1124772.1124828","title":"Enhancing human-machine interactions","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Human–computer interaction; Augmented reality; Wearable computer; Interface (matter); Architecture; Simple (philosophy); Virtual reality; Virtual world; User interface; Embedded system; Operating system","score_opus":0.02077168980736354,"score_gpt":0.2792165365364426,"score_spread":0.2584448467290791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975063206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015070849,0.000023522616,0.8559317,0.00063040416,0.00040741792,0.00008305913,0.0000011418349,0.0004205729,0.12743135],"genre_scores_gemma":[0.98423105,2.2354061e-7,0.005536954,0.00019621721,0.00013548975,0.000015741041,0.0000037569946,0.0000056907993,0.009874893],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919415,0.00004794476,0.00021754236,0.00024426976,0.00014071728,0.0001554095],"domain_scores_gemma":[0.9993508,0.00013163162,0.00006739275,0.00034073912,0.00007187558,0.000037589547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014680254,0.00008716581,0.000108365406,0.00011619481,0.00015546694,0.00017937529,0.00030027164,0.00002180089,0.00020346163],"category_scores_gemma":[0.000013177196,0.00008151732,0.00006032893,0.0002206846,0.00001177584,0.00075297465,0.000120053395,0.000099080105,0.00053329294],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005605869,0.0008864083,0.00846176,0.000053712763,0.000088671986,0.0000712619,0.0008836449,0.00008265505,0.5505467,0.26332656,0.042567436,0.13302554],"study_design_scores_gemma":[0.0021944125,0.00020075489,0.032975547,0.00025392126,0.000030658655,0.00064625446,0.00034072794,0.041390736,0.49092707,0.027466519,0.40159616,0.0019772535],"about_ca_topic_score_codex":0.001652527,"about_ca_topic_score_gemma":0.00299063,"teacher_disagreement_score":0.9691602,"about_ca_system_score_codex":0.000046868583,"about_ca_system_score_gemma":0.000023154716,"threshold_uncertainty_score":0.68545794},"labels":[],"label_agreement":null},{"id":"W1976567760","doi":"10.1109/icbbt.2010.5479000","title":"Implementation of a wearerable real-time system for physical activity recognition based on Naive Bayes classifier","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Naive Bayes classifier; Wearable computer; Computer science; Bayes' theorem; Artificial intelligence; Classifier (UML); Activity recognition; Bayes classifier; Machine learning; Pattern recognition (psychology); Bayesian probability; Embedded system; Support vector machine","score_opus":0.031569908510091074,"score_gpt":0.30457941531881116,"score_spread":0.2730095068087201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976567760","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7086328,2.8051164e-7,0.28243357,0.000345448,0.00044772486,0.0010215157,0.00010234131,0.00028782614,0.00672849],"genre_scores_gemma":[0.9930435,2.2494376e-7,0.006324431,0.000035777288,0.00013359549,0.00025813121,0.000021933509,0.000014977523,0.00016746148],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985802,0.0001526564,0.0002305106,0.00045557818,0.00034035122,0.00024066438],"domain_scores_gemma":[0.99802095,0.0008016513,0.00027513152,0.00045563636,0.0003617061,0.000084907195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047743603,0.00016749254,0.00030364527,0.00016418315,0.00011899161,0.000106773514,0.0002694432,0.00008754641,0.00008741314],"category_scores_gemma":[0.000045995504,0.00015403211,0.0001614134,0.00024888065,0.000029904644,0.00068801106,0.00004629265,0.0001387606,0.000116388735],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016460402,0.0005314799,0.00034789724,0.0002320077,0.00005394227,0.0000019139393,0.00044205663,0.0000070669626,0.51225215,0.0026415738,0.0012693427,0.482056],"study_design_scores_gemma":[0.0018528329,0.00071503106,0.0037349965,0.00011345131,0.000028458735,0.000007858999,0.00034108263,0.29115084,0.70095825,0.00042440512,0.00032974727,0.0003430621],"about_ca_topic_score_codex":0.00043981077,"about_ca_topic_score_gemma":0.00018113402,"teacher_disagreement_score":0.4817129,"about_ca_system_score_codex":0.00008143364,"about_ca_system_score_gemma":0.00015165942,"threshold_uncertainty_score":0.6281247},"labels":[],"label_agreement":null},{"id":"W1976819742","doi":"10.1109/icsmc.2011.6084039","title":"FRIEND: A human-aware BDI agent architecture","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of New Brunswick","funders":"","keywords":"Computer science; Architecture; Computer architecture; Human–computer interaction; Art","score_opus":0.07396340625858723,"score_gpt":0.26191676684850096,"score_spread":0.18795336058991374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976819742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024690911,0.000025561078,0.8885136,0.00028906975,0.00045285915,0.00025199234,0.0000041476733,0.0005939312,0.08517789],"genre_scores_gemma":[0.99040204,7.023197e-7,0.006275974,0.00045230403,0.00007913806,0.00004198559,0.0000020991833,0.000011411304,0.0027343594],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998633,0.00009856684,0.00023650145,0.00045123676,0.00027812592,0.00030255385],"domain_scores_gemma":[0.99889314,0.000053022737,0.00009081194,0.0007232957,0.000090023386,0.00014968752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020166117,0.00016517917,0.00018532189,0.00014155125,0.00014433695,0.00010646354,0.00084939133,0.00007038295,0.0007572165],"category_scores_gemma":[0.000015494192,0.00013975943,0.00010830954,0.00025847502,0.000034317913,0.00035872962,0.00032825264,0.00016551718,0.0006962935],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026074507,0.00089380273,0.0060314513,0.00011097009,0.00022626654,0.00036912135,0.045795545,0.0000054007564,0.0058382466,0.07049947,0.024662128,0.84554154],"study_design_scores_gemma":[0.007047665,0.0025084454,0.07691935,0.0005994328,0.00011612126,0.0021884684,0.004811272,0.014756362,0.14535546,0.12699243,0.61189395,0.0068110125],"about_ca_topic_score_codex":0.00030759722,"about_ca_topic_score_gemma":0.00029077177,"teacher_disagreement_score":0.9657111,"about_ca_system_score_codex":0.000035408048,"about_ca_system_score_gemma":0.000041356394,"threshold_uncertainty_score":0.8949677},"labels":[],"label_agreement":null},{"id":"W1977594495","doi":"10.1109/pacrim.2013.6625497","title":"Beyond user interfaces in mobile accessibility: Not just skin deep","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Victoria","funders":"","keywords":"Computer science; Schedule; Global Positioning System; Mobile device; Public transport; The Internet; Popularity; User interface; Mobile Web; Mobile computing; World Wide Web; Mobile technology; Human–computer interaction; Telecommunications; Engineering; Transport engineering; Operating system","score_opus":0.028917208033779906,"score_gpt":0.284700240811757,"score_spread":0.2557830327779771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977594495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9072843,0.00007071962,0.079844475,0.000653201,0.00049965765,0.00071980327,0.0000015403081,0.00022920789,0.010697065],"genre_scores_gemma":[0.993997,0.0000039592105,0.0029846558,0.00069764117,0.000046662768,0.00030836702,0.0000012719111,0.000010194174,0.0019502573],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982391,0.00015237732,0.00041250052,0.0005895798,0.00027992355,0.0003265239],"domain_scores_gemma":[0.998634,0.0002813775,0.00010400753,0.0007244054,0.00014928979,0.00010692116],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003819698,0.00017392433,0.0002638729,0.00019702372,0.00005233236,0.00057684444,0.0010308401,0.00009155918,0.0011308629],"category_scores_gemma":[0.00004876587,0.00014839417,0.000061478226,0.0004694873,0.000045290595,0.0027886115,0.00055475876,0.00017544178,0.0018733862],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008602834,0.00040192538,0.024275865,0.000056160752,0.00003096846,0.000011204959,0.004097405,0.0000297088,0.0075581307,0.0011406058,0.0037166432,0.95867276],"study_design_scores_gemma":[0.004664911,0.0006897757,0.47543442,0.0002990632,0.000024259318,0.0001395629,0.007711816,0.16802806,0.3014545,0.009955104,0.028451877,0.003146608],"about_ca_topic_score_codex":0.0010074466,"about_ca_topic_score_gemma":0.0018745895,"teacher_disagreement_score":0.9555262,"about_ca_system_score_codex":0.00008770363,"about_ca_system_score_gemma":0.000045758472,"threshold_uncertainty_score":0.99978226},"labels":[],"label_agreement":null},{"id":"W1979002183","doi":"10.1109/isccsp.2010.5463384","title":"Invited paper: Self-adaptive middleware for the design of context-aware software applications in public transit systems","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Public transport; Middleware (distributed applications); Computer science; Context (archaeology); Transit (satellite); Software; Context awareness; Ubiquitous computing; Mobile computing; Adaptation (eye); Open source software; Human–computer interaction; World Wide Web; Transport engineering; Telecommunications; Engineering; Distributed computing; Operating system; Geography","score_opus":0.05349638021716089,"score_gpt":0.24544347884189088,"score_spread":0.19194709862473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979002183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009493466,0.00024211012,0.9915618,0.0024291815,0.00035785887,0.0039151595,0.00006825284,0.00041062137,0.00006570774],"genre_scores_gemma":[0.9790072,0.000008031547,0.017814156,0.00052895537,0.00006902066,0.0024753194,0.000012571481,0.000025750027,0.0000590217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977873,0.0002543776,0.0006286566,0.0005711814,0.0003617615,0.00039670683],"domain_scores_gemma":[0.99433076,0.0035125823,0.00028289703,0.0010069206,0.00073665625,0.0001301637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010053588,0.00026538435,0.00044120243,0.00025620754,0.0002416245,0.00023863192,0.0014082383,0.00020624841,0.000014082007],"category_scores_gemma":[0.00014554114,0.00019466344,0.00015683084,0.00092614134,0.000095902265,0.00097407936,0.000109388646,0.00032579893,0.000013551751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002391429,0.003478047,0.017992817,0.0012467348,0.0015108883,0.000016735874,0.022617845,0.0016773053,0.014096986,0.18559366,0.012738364,0.73879147],"study_design_scores_gemma":[0.0062085874,0.00070411107,0.008873502,0.00032151258,0.00016280061,0.00017194371,0.012090875,0.8302979,0.0049511227,0.0029117137,0.13124087,0.0020650518],"about_ca_topic_score_codex":0.00043764838,"about_ca_topic_score_gemma":0.0013068108,"teacher_disagreement_score":0.9780578,"about_ca_system_score_codex":0.000065945234,"about_ca_system_score_gemma":0.00019225385,"threshold_uncertainty_score":0.79381454},"labels":[],"label_agreement":null},{"id":"W1979382836","doi":"10.3166/isi.9.2.107-128","title":"Modélisation de l'utilisateur et exploitation dans un système « adapté » d'aide au déplacement piéton","year":2004,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ton; Domain (mathematical analysis); Computer science; Work (physics); Software engineering; Human–computer interaction; Engineering; Mechanical engineering; Mathematics","score_opus":0.05056703447614987,"score_gpt":0.2605373729593612,"score_spread":0.2099703384832113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979382836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21973048,0.00053965393,0.7664722,0.0013878342,0.0025705565,0.0011466106,0.0000817067,0.00043748863,0.007633425],"genre_scores_gemma":[0.98820853,0.00012739882,0.009417473,0.0007443872,0.0003124255,0.0003649654,0.00027167285,0.000045771412,0.0005073701],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99476564,0.00084747793,0.0017710455,0.00052220956,0.0010583444,0.0010352946],"domain_scores_gemma":[0.99631894,0.0003202066,0.0012589341,0.00078490074,0.00094146625,0.00037556465],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0029991725,0.00061644457,0.0005723724,0.0006117516,0.00074195635,0.0015940963,0.0006972501,0.00043672632,0.000067684676],"category_scores_gemma":[0.00073810347,0.00073195994,0.00025375513,0.0011475206,0.00027804356,0.020868758,0.00026151387,0.00044380146,0.0009042499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016440217,0.0003711517,0.0020068795,0.0018357188,0.00027400182,0.000035197492,0.33796206,0.1094014,0.0011754299,0.15306945,0.00041754928,0.39328676],"study_design_scores_gemma":[0.011262205,0.0021437248,0.079214655,0.010609216,0.00043283933,0.0025410205,0.057045784,0.60100543,0.04938743,0.16614808,0.016099788,0.0041098213],"about_ca_topic_score_codex":0.008517119,"about_ca_topic_score_gemma":0.0012261746,"teacher_disagreement_score":0.76847804,"about_ca_system_score_codex":0.0076264357,"about_ca_system_score_gemma":0.0018993404,"threshold_uncertainty_score":0.99987364},"labels":[],"label_agreement":null},{"id":"W1979877133","doi":"10.1007/s12652-013-0205-8","title":"Spatial recognition of activities for cognitive assistance: realistic scenarios using clinical data from Alzheimer’s patients","year":2013,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Exploit; Ambient intelligence; Field (mathematics); Activities of daily living; Set (abstract data type); Activity recognition; Everyday life; Cognition; Artificial intelligence; Human–computer interaction; Architecture; Smart environment; Computational intelligence; Home automation; Data science; Machine learning; Psychology; Computer security; Internet of Things","score_opus":0.2847337416442691,"score_gpt":0.3880624555079232,"score_spread":0.10332871386365405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979877133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47367796,0.00015856615,0.5250468,0.000019959227,0.000689113,0.00031698946,0.000061092316,0.00001252977,0.000016984834],"genre_scores_gemma":[0.9756968,0.000048268776,0.023668898,0.000101559344,0.00041718024,0.0000029644766,0.000046901347,0.000014986769,0.0000024244648],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996977,0.00030126123,0.0015444767,0.0004514699,0.00046504938,0.0002607645],"domain_scores_gemma":[0.99333286,0.0024873794,0.002159289,0.0003555849,0.0015284696,0.00013640609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001248218,0.0002073493,0.0006529726,0.00020804469,0.00022129307,0.00030453567,0.0007787248,0.00010430414,0.000032276974],"category_scores_gemma":[0.0007530604,0.00019413534,0.0001595706,0.00015469496,0.00014641073,0.0014864234,0.0004421677,0.00030566845,0.0000066063026],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002387105,0.0006151296,0.010191945,0.00006687745,0.0005318159,0.000006324207,0.0019440961,0.00003468185,0.00050020975,0.00007598815,0.00028093986,0.98551327],"study_design_scores_gemma":[0.0046573007,0.0028038297,0.040976893,0.005040555,0.00096826226,0.000096737975,0.0045101717,0.91742414,0.009752249,0.01236895,0.00022580454,0.0011751222],"about_ca_topic_score_codex":0.0005740477,"about_ca_topic_score_gemma":0.00006207794,"teacher_disagreement_score":0.98433816,"about_ca_system_score_codex":0.000046297104,"about_ca_system_score_gemma":0.00014954679,"threshold_uncertainty_score":0.7916609},"labels":[],"label_agreement":null},{"id":"W1980066938","doi":"10.1109/i2mtc.2012.6229388","title":"Differentiating assisted and unassisted bed exits using ultrasonic sensor","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Transfer (computing); Computer science; Ultrasonic sensor; Simulation; Real-time computing; Pressure sensor; Assisted living; Acoustics; Engineering; Medicine; Mechanical engineering; Operating system; Nursing","score_opus":0.07709788447567141,"score_gpt":0.2881835271426696,"score_spread":0.2110856426669982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980066938","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71079636,0.00010863949,0.28674677,0.00011021251,0.00040156607,0.00015654507,0.0000021102053,0.00026001604,0.0014177925],"genre_scores_gemma":[0.9824585,0.0000018715323,0.0170081,0.00012514344,0.00010592792,0.000007663479,0.000002279701,0.000013125801,0.00027743063],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985036,0.00021289755,0.0002793454,0.00032253008,0.00025873407,0.00042285552],"domain_scores_gemma":[0.99882525,0.0003657077,0.0001598197,0.00035405366,0.00009431836,0.00020087161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034307624,0.00017532273,0.00024474374,0.000119754426,0.00019379098,0.00026888662,0.00021845424,0.000081684375,0.000045325385],"category_scores_gemma":[0.00011077721,0.00015689155,0.00006259155,0.00033142776,0.000031476917,0.0010951438,0.00017949773,0.00012135311,0.000038464477],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014240611,0.00052078604,0.26912373,0.00014880646,0.00026716027,0.000012829962,0.0032562215,0.0000022870818,0.35215664,0.004508893,0.00022157101,0.36976683],"study_design_scores_gemma":[0.002099401,0.00008220466,0.8550775,0.0002777956,0.00008962218,0.0011080074,0.0008263064,0.08201981,0.055892307,0.00012812758,0.001090402,0.001308531],"about_ca_topic_score_codex":0.000096030046,"about_ca_topic_score_gemma":0.000028753662,"teacher_disagreement_score":0.5859538,"about_ca_system_score_codex":0.00007049599,"about_ca_system_score_gemma":0.000031869247,"threshold_uncertainty_score":0.6397852},"labels":[],"label_agreement":null},{"id":"W1980216526","doi":"10.1504/ijwgs.2009.027574","title":"A semantic approach for accessible services delivery in a smart environment","year":2009,"lang":"en","type":"article","venue":"International Journal of Web and Grid Services","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke","funders":"","keywords":"Computer science; Adaptability; Scalability; Human–computer interaction; Smart environment; Semantic Web; Architecture; User requirements document; Formalism (music); World Wide Web; Software engineering; Database; Internet of Things","score_opus":0.013355363535880871,"score_gpt":0.24807239359185815,"score_spread":0.23471703005597727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980216526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9569538,0.0010463371,0.037798557,0.0028378777,0.0006661345,0.000194095,0.000015210552,0.000018949851,0.00046903625],"genre_scores_gemma":[0.99310696,0.00020881419,0.005430993,0.0008999343,0.00031321985,0.0000059513854,0.0000054921884,0.000003953371,0.000024692086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988502,0.000044469063,0.00040170006,0.00018996872,0.00038214642,0.00013148424],"domain_scores_gemma":[0.9992133,0.00008826308,0.00034713786,0.00010684517,0.00017722296,0.000067280096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038542456,0.00011068957,0.00020062829,0.00028666097,0.000037554273,0.00030276805,0.00084889645,0.00004495394,0.000006360264],"category_scores_gemma":[0.000003248986,0.00009503757,0.00007800862,0.00008644137,0.000010436187,0.0013881236,0.00010591294,0.00010255188,0.0000035205742],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013483727,0.0037391842,0.21788236,0.0012898368,0.0012173367,0.0004928459,0.015889335,0.0032233228,0.031732418,0.0031926709,0.00075732416,0.719235],"study_design_scores_gemma":[0.010701203,0.0012448223,0.27509853,0.0017731223,0.00009281152,0.0020072286,0.0026758641,0.6558678,0.0041883574,0.009816013,0.03544623,0.0010880268],"about_ca_topic_score_codex":0.0000640538,"about_ca_topic_score_gemma":0.00007275864,"teacher_disagreement_score":0.718147,"about_ca_system_score_codex":0.000045337994,"about_ca_system_score_gemma":0.00004092732,"threshold_uncertainty_score":0.38755196},"labels":[],"label_agreement":null},{"id":"W1980438243","doi":"10.1007/s12652-012-0156-5","title":"Activity modeling under uncertainty by trace of objects in smart homes","year":2012,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Realization (probability); Computer science; Task (project management); TRACE (psycholinguistics); Key (lock); Home automation; Property (philosophy); Cluster analysis; Point (geometry); Activity recognition; Computational intelligence; Smart environment; Artificial intelligence; Machine learning; Data science; Human–computer interaction; Internet of Things; Computer security; Engineering","score_opus":0.058625309487268754,"score_gpt":0.29584841210216545,"score_spread":0.2372231026148967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980438243","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5567432,0.0007864598,0.44197878,0.00005786287,0.00029105728,0.00007218165,5.734406e-7,0.000011263716,0.000058613223],"genre_scores_gemma":[0.997531,0.000081355276,0.002207382,0.00005945162,0.00009755878,7.935685e-7,2.8588366e-7,0.0000089364385,0.000013260143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803066,0.00020453564,0.00077161944,0.00020911836,0.00042441417,0.00035965827],"domain_scores_gemma":[0.9982535,0.00042707354,0.0006975624,0.000196799,0.00028543218,0.00013959837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017167528,0.0001756631,0.0004963313,0.000314041,0.00011056208,0.000114077724,0.00045527055,0.00007337526,0.0000077440545],"category_scores_gemma":[0.000085514286,0.00016056921,0.00012543437,0.00033985087,0.00005058009,0.0011175248,0.00018778432,0.00037230022,0.0000028676372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031755804,0.0026037942,0.02287063,0.0004020118,0.00036706994,0.000035989946,0.04885287,0.08787955,0.049833823,0.0080452375,0.00022046882,0.778571],"study_design_scores_gemma":[0.0009585751,0.00047977504,0.0031829353,0.0009990423,0.000036465986,0.0003331914,0.0046545854,0.9536806,0.032127276,0.0028396957,0.00015335648,0.00055452174],"about_ca_topic_score_codex":0.00018236393,"about_ca_topic_score_gemma":0.00003574565,"teacher_disagreement_score":0.86580104,"about_ca_system_score_codex":0.000114572365,"about_ca_system_score_gemma":0.000077021126,"threshold_uncertainty_score":0.65478224},"labels":[],"label_agreement":null},{"id":"W1981944430","doi":"10.5555/1363163.1363174","title":"Assisting elders via dynamic multi-tasks planning: a Markov decision processes based approach","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Planner; Markov decision process; Computer science; Partially observable Markov decision process; Activities of daily living; Markov process; Markov chain; Artificial intelligence; Markov model; Machine learning; Psychology","score_opus":0.060977372511647115,"score_gpt":0.2894992736525306,"score_spread":0.22852190114088347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981944430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013869895,0.000112796755,0.9802579,0.000118322954,0.00021012862,0.00035552288,0.0000026529374,0.00058938714,0.0044833664],"genre_scores_gemma":[0.7588976,0.0000015561973,0.24043258,0.00022768407,0.000021443882,0.000057660243,0.000008003638,0.000015749356,0.00033771733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978229,0.00011689924,0.00039458057,0.00073938275,0.0005381726,0.00038811067],"domain_scores_gemma":[0.9979872,0.00079225696,0.00019749782,0.00057733385,0.0002895444,0.00015614806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004109114,0.00024920015,0.0002988023,0.00026730215,0.00036010204,0.00017726267,0.0007866055,0.00011765656,0.000022750612],"category_scores_gemma":[0.0004249215,0.00022291562,0.00009298697,0.00083342864,0.00006180319,0.00079252466,0.00018418807,0.00020265306,0.000069853864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000134778,0.001969441,0.047755864,0.00061505765,0.00014733887,0.0004139442,0.0038509322,0.0045756036,0.0028932032,0.000052695377,0.004146781,0.9334444],"study_design_scores_gemma":[0.0010109785,0.000059976614,0.0071900873,0.00012664708,0.0000061906317,0.00030127686,0.00010721528,0.98943007,0.0007271037,0.00003952635,0.0005981528,0.00040280126],"about_ca_topic_score_codex":0.0000789857,"about_ca_topic_score_gemma":0.000042589174,"teacher_disagreement_score":0.98485446,"about_ca_system_score_codex":0.00009624436,"about_ca_system_score_gemma":0.00028311138,"threshold_uncertainty_score":0.9090235},"labels":[],"label_agreement":null},{"id":"W1983167057","doi":"10.1007/s00779-004-0317-4","title":"Ubiquitous service access through adapted user interfaces on multiple devices","year":2005,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Alberta","keywords":"Computer science; Human–computer interaction; Mobile device; Service (business); User interface design; User interface; Ubiquitous computing; Presentation (obstetrics); Interactor; User experience design; World Wide Web; Operating system","score_opus":0.06873798741930875,"score_gpt":0.3002947528488162,"score_spread":0.23155676542950743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983167057","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93642795,0.00042934102,0.054066073,0.005401402,0.0006592539,0.00034455053,0.000010925064,0.0005634989,0.002096997],"genre_scores_gemma":[0.9898202,0.0000152066905,0.0041082203,0.0050686924,0.00081431994,0.000013041747,0.0000054016973,0.000027334243,0.00012757945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99744457,0.00017423477,0.00043765767,0.0008741217,0.0005032971,0.00056609354],"domain_scores_gemma":[0.9979826,0.0009982125,0.00026795836,0.0003413444,0.00024701902,0.00016284865],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003497172,0.000378734,0.0004212246,0.00013803376,0.00050855934,0.0008398004,0.0010559417,0.0001244868,0.000029210429],"category_scores_gemma":[0.00006283855,0.0003490537,0.000091432135,0.00048502587,0.00005045905,0.0016339076,0.00089795416,0.00035276343,0.0001382363],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019681841,0.0008205111,0.0245395,0.00050039595,0.00037229536,0.000074289856,0.04383609,0.001857357,0.004389586,0.0020235705,0.004252958,0.9171366],"study_design_scores_gemma":[0.0023097342,0.00039685614,0.028155096,0.0009225973,0.00004201649,0.00039405836,0.0018128724,0.88526297,0.011558109,0.00020541334,0.067442894,0.0014974151],"about_ca_topic_score_codex":0.0005834159,"about_ca_topic_score_gemma":0.0012212808,"teacher_disagreement_score":0.9156392,"about_ca_system_score_codex":0.000087783825,"about_ca_system_score_gemma":0.000067545596,"threshold_uncertainty_score":0.99989617},"labels":[],"label_agreement":null},{"id":"W1985259141","doi":"10.1109/embc.2012.6346976","title":"A wireless trigger for synchronization of wearable sensors to external systems during recording of human gait","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Adidas (Canada)","funders":"","keywords":"Computer science; Wearable computer; Synchronization (alternating current); Jitter; Kinematics; Inertial measurement unit; Real-time computing; Gait; Wireless; Gait analysis; Wireless sensor network; Ground truth; Computer vision; Simulation; Embedded system; Channel (broadcasting); Telecommunications; Computer network","score_opus":0.03215082092643077,"score_gpt":0.2767944408766049,"score_spread":0.2446436199501741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985259141","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54168844,0.00005572536,0.45708218,0.000021972426,0.00033470066,0.0003872272,0.000003154465,0.000047649017,0.00037896368],"genre_scores_gemma":[0.9962536,0.0000020980285,0.0021606765,0.0000075899293,0.00014740377,0.000060582977,7.387409e-7,0.000014415857,0.0013528764],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99863493,0.00009424976,0.0004899022,0.00023046296,0.00026105138,0.00028940645],"domain_scores_gemma":[0.99880797,0.00016681658,0.00029569666,0.00033548818,0.00028629234,0.000107746564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006019838,0.00011759166,0.00033596906,0.00022429787,0.00008893738,0.000059397138,0.00029952743,0.000057933226,0.00001924225],"category_scores_gemma":[0.000046858986,0.00011465831,0.000086703534,0.0003319977,0.000014370589,0.0007558338,0.0001015271,0.000046347057,0.000014572893],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007824881,0.00044659458,0.03195216,0.0020343207,0.00015621661,0.0000022550496,0.0045034722,0.00086031214,0.90885776,0.011548007,0.0005156449,0.039045017],"study_design_scores_gemma":[0.0029088417,0.00052404334,0.012896755,0.0030876712,0.000055151195,0.00014623471,0.0011811208,0.11700259,0.86046004,0.00016325673,0.0006195944,0.0009547144],"about_ca_topic_score_codex":0.00043169814,"about_ca_topic_score_gemma":0.000014983369,"teacher_disagreement_score":0.45492148,"about_ca_system_score_codex":0.00008928267,"about_ca_system_score_gemma":0.00002932194,"threshold_uncertainty_score":0.46756303},"labels":[],"label_agreement":null},{"id":"W1985446304","doi":"10.1108/17427371011097631","title":"A framework for evaluating pervasive systems","year":2010,"lang":"en","type":"article","venue":"International Journal of Pervasive Computing and Communications","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke","funders":"","keywords":"Ubiquitous computing; Computer science; Taxonomy (biology); Data science; Context-aware pervasive systems; Process (computing); Human–computer interaction","score_opus":0.10312987519305879,"score_gpt":0.41876950865007884,"score_spread":0.31563963345702006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985446304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10620772,0.0007490063,0.8830492,0.0066143726,0.0028077092,0.00023188608,0.0000137933,0.000044976226,0.00028131827],"genre_scores_gemma":[0.7969232,0.00006450292,0.20236836,0.00020118614,0.00039609874,0.000012693718,0.0000038105577,0.000009312874,0.000020813417],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998365,0.00020764674,0.00063447637,0.00019031248,0.00044415143,0.00015842066],"domain_scores_gemma":[0.99168015,0.0034070283,0.0008732431,0.0006825699,0.0032460801,0.00011094768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013593407,0.00013257234,0.0002533394,0.0002330841,0.0003239735,0.0005464002,0.002681734,0.0000889641,0.0000041784783],"category_scores_gemma":[0.0019297272,0.00012713023,0.00016165097,0.00014886247,0.00010267185,0.00050344504,0.0006514137,0.00057847856,0.000005375014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048108657,0.00042094034,0.0057819546,0.000051855415,0.0007502892,0.000015795347,0.010530647,0.00035432077,0.009906123,0.52404946,0.0007297511,0.44736072],"study_design_scores_gemma":[0.0033986925,0.00081349065,0.011691414,0.0019617565,0.00014925559,0.0048898663,0.0055060685,0.8635653,0.0008180525,0.06967307,0.036607742,0.0009252862],"about_ca_topic_score_codex":0.00003577678,"about_ca_topic_score_gemma":0.00001943282,"teacher_disagreement_score":0.863211,"about_ca_system_score_codex":0.000053460073,"about_ca_system_score_gemma":0.00021434439,"threshold_uncertainty_score":0.526895},"labels":[],"label_agreement":null},{"id":"W1985812000","doi":"10.1109/iot.2012.6402318","title":"IoT mashups with the WoTKit","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mashup; Internet of Things; Computer science; Key (lock); World Wide Web; Web of Things; Web application; The Internet; Web development; Computer security","score_opus":0.021274911028703856,"score_gpt":0.21904874788163056,"score_spread":0.1977738368529267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985812000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05975977,0.00010973752,0.837732,0.0068245567,0.00042769362,0.00024908574,7.8368356e-7,0.00033082423,0.09456552],"genre_scores_gemma":[0.9914993,4.3335132e-7,0.002691469,0.0010514342,0.00013585671,0.000022984665,2.0310122e-7,0.000005170798,0.004593123],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992576,0.000070285634,0.0000770902,0.00013875747,0.000217907,0.00023834033],"domain_scores_gemma":[0.9992112,0.00015920984,0.000043350396,0.0004577648,0.00005098293,0.00007750109],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003273721,0.000078965255,0.000080563594,0.00003098926,0.000095817755,0.00011968234,0.00046495593,0.000023737268,0.00012645585],"category_scores_gemma":[0.000008989508,0.000041002422,0.00002887389,0.0002313264,0.000028586735,0.0004513742,0.0001214531,0.00008126099,0.0008294487],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021502647,0.00039075056,0.042719305,0.000027985145,0.00017544389,0.000012921134,0.008997546,0.000009116763,0.0018785311,0.13202085,0.09985249,0.7138936],"study_design_scores_gemma":[0.0008917098,0.00017011518,0.054276712,0.00004107979,0.000020568497,0.00048822377,0.0009916866,0.0036215663,0.012827117,0.000437522,0.9255111,0.0007225669],"about_ca_topic_score_codex":0.00005067529,"about_ca_topic_score_gemma":0.000056415403,"teacher_disagreement_score":0.93173957,"about_ca_system_score_codex":0.000018823142,"about_ca_system_score_gemma":0.00002362737,"threshold_uncertainty_score":0.9999485},"labels":[],"label_agreement":null},{"id":"W1985863808","doi":"10.1109/have.2007.4371597","title":"Dance, Dance Evolution: Accelerometer Sensor Networks as Input to Video Games","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Dance; Computer science; Video game; Mahalanobis distance; Accelerometer; Wearable computer; Multimedia; Human–computer interaction; Artificial intelligence; Embedded system","score_opus":0.020818321984755177,"score_gpt":0.26995645081944675,"score_spread":0.24913812883469158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985863808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04025726,0.00012382664,0.92740977,0.0010594181,0.0012451446,0.0003987123,0.0000015456588,0.0005013088,0.029003037],"genre_scores_gemma":[0.96443063,0.000004752539,0.024977775,0.0029756152,0.0004784084,0.000037302034,0.0000014896669,0.000018235189,0.007075806],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99755305,0.00009858525,0.00046171594,0.0007559796,0.0004903117,0.0006403813],"domain_scores_gemma":[0.99787176,0.00044024334,0.00013818027,0.0009491327,0.00028011674,0.00032058693],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008613309,0.00024699574,0.00031269345,0.0002809146,0.00016976283,0.00031909908,0.0009311296,0.00012931289,0.00015461235],"category_scores_gemma":[0.00012485184,0.0002349803,0.00011477819,0.0013076742,0.000044063214,0.0011514331,0.0004406625,0.00021935432,0.0013520303],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012704167,0.00034956966,0.018585162,0.00004948955,0.00013975996,0.00022135026,0.0016357965,0.0010871638,0.007252169,0.018147172,0.07413789,0.8782674],"study_design_scores_gemma":[0.003228648,0.0011258088,0.2398461,0.0006260705,0.000045098237,0.00137637,0.00079255045,0.085748956,0.02740978,0.0031663189,0.6326823,0.0039519924],"about_ca_topic_score_codex":0.0001695458,"about_ca_topic_score_gemma":0.0002823524,"teacher_disagreement_score":0.92417336,"about_ca_system_score_codex":0.00018884832,"about_ca_system_score_gemma":0.000070041024,"threshold_uncertainty_score":0.99942553},"labels":[],"label_agreement":null},{"id":"W1986164729","doi":"10.1155/2014/617495","title":"Usability Study of a Wireless Monitoring System among Alzheimer’s Disease Elderly Population","year":2014,"lang":"en","type":"article","venue":"International Journal of Telemedicine and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Fondazione Cassa di Risparmio di Lucca","keywords":"Usability; Wearable computer; Wearable technology; Computer science; Disease; Population; Wireless; Health care; Medicine; Human–computer interaction; Embedded system; Telecommunications; Pathology","score_opus":0.02505790679438535,"score_gpt":0.30668513143401466,"score_spread":0.2816272246396293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986164729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78901386,0.000075746764,0.20991522,0.00030055537,0.00031400143,0.0002848598,0.0000030862855,0.000022025633,0.00007066086],"genre_scores_gemma":[0.99880683,0.000007955388,0.0004901985,0.00001160244,0.0006264993,0.00004462274,0.000002172409,0.0000051424295,0.0000049557193],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99839103,0.00010378909,0.0006653862,0.00017585569,0.00058437686,0.00007955126],"domain_scores_gemma":[0.9980277,0.00022863477,0.0006635329,0.00024251346,0.00070441153,0.0001332025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006125298,0.000091199145,0.00022949543,0.00020032731,0.000062943305,0.00005476774,0.00051937776,0.00002426335,0.0000021443664],"category_scores_gemma":[0.00006129391,0.00007787349,0.00005547873,0.00016601285,0.000037166235,0.0004308043,0.00008483458,0.000113109345,0.0000012579654],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022768832,0.00038096294,0.65322423,0.000024250581,0.00015049225,0.0000050636554,0.0004475705,0.000055660843,0.0001942388,0.0019591025,0.000027266578,0.34350836],"study_design_scores_gemma":[0.001341246,0.00034324534,0.9874329,0.00025537997,0.000108988854,0.00006484296,0.0013078096,0.0077593187,0.00020532541,0.0005314357,0.00052799436,0.000121541125],"about_ca_topic_score_codex":0.00022304959,"about_ca_topic_score_gemma":0.000019656301,"teacher_disagreement_score":0.34338683,"about_ca_system_score_codex":0.000047896443,"about_ca_system_score_gemma":0.000031350235,"threshold_uncertainty_score":0.31755888},"labels":[],"label_agreement":null},{"id":"W1987913825","doi":"10.1007/s12553-013-0047-6","title":"Intelligible software delivery in smart environments supported by a macro and micro context awareness model","year":2013,"lang":"en","type":"article","venue":"Health and Technology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université TÉLUQ; Université du Québec à Montréal","funders":"","keywords":"Macro; Computer science; Context awareness; Service delivery framework; Intelligibility (philosophy); Human–computer interaction; Context (archaeology); Context model; Service (business); Knowledge management; Process management; Engineering; Artificial intelligence; Business","score_opus":0.018239450622890313,"score_gpt":0.24788252515374978,"score_spread":0.22964307453085947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987913825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8311662,0.002809538,0.16023281,0.0049426937,0.000072176175,0.00055245595,0.000016998743,0.00018942324,0.000017718325],"genre_scores_gemma":[0.99485844,0.0005219635,0.002782238,0.0014953444,0.0000037281748,0.0001420501,0.000010943074,0.00001047607,0.00017483797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986046,0.000055687568,0.00033219496,0.00048796897,0.000095118194,0.00042442075],"domain_scores_gemma":[0.9992639,0.00008176107,0.000121424,0.0003187766,0.000032273325,0.00018189267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015844229,0.0001572538,0.00031598416,0.0003265443,0.00011295121,0.000057752914,0.00025907645,0.00019450534,0.000010628016],"category_scores_gemma":[0.000036269594,0.00016113432,0.000015102923,0.00027661948,0.00013411576,0.00034804083,0.0002904447,0.00022168772,0.000045780955],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010747076,0.00014840878,0.10590935,0.00011930405,0.000016472106,0.000011113717,0.0006326185,8.6590126e-7,0.0034623751,0.00048110323,0.0047629112,0.8844447],"study_design_scores_gemma":[0.024018565,0.004877909,0.03345271,0.0020578864,0.000046664067,0.0035481965,0.008826101,0.4178086,0.10788173,0.13552576,0.25531647,0.006639419],"about_ca_topic_score_codex":0.0012769405,"about_ca_topic_score_gemma":0.00031341345,"teacher_disagreement_score":0.8778053,"about_ca_system_score_codex":0.00006901436,"about_ca_system_score_gemma":0.00014984512,"threshold_uncertainty_score":0.6570867},"labels":[],"label_agreement":null},{"id":"W1987941154","doi":"10.1145/2422518.2422520","title":"Goal models as run-time entities in context-aware systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Computer science; Workflow; Context (archaeology); Notation; Context model; Domain (mathematical analysis); Software engineering; Programming language; Human–computer interaction; Artificial intelligence; Database","score_opus":0.02995876258316474,"score_gpt":0.24583536301981765,"score_spread":0.2158766004366529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987941154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21142168,0.002011085,0.6449874,0.00075889856,0.0037670091,0.001412494,0.00002607312,0.0012536362,0.13436173],"genre_scores_gemma":[0.9872548,0.000007349353,0.000242698,0.0003123957,0.0001925079,0.00010750238,0.000005095519,0.000019248711,0.011858421],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770665,0.00027356576,0.0004849445,0.00041126958,0.0004887653,0.00063477753],"domain_scores_gemma":[0.99857336,0.00028955023,0.00014070704,0.0006204075,0.000149276,0.00022667093],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007642349,0.0002478379,0.0004235145,0.0002856302,0.00009522286,0.00036977272,0.00073664065,0.00014079345,0.00018323174],"category_scores_gemma":[0.000034181146,0.00023378254,0.000108534136,0.00037517864,0.00004818447,0.0039099096,0.00031236577,0.00018761188,0.0029893515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008663296,0.0019265012,0.027791765,0.00048108987,0.00039700992,0.00015459867,0.03328372,0.001082626,0.0032670551,0.77620643,0.03391155,0.12141101],"study_design_scores_gemma":[0.0048104604,0.00038401553,0.0042551556,0.00096537446,0.000043186203,0.0012692749,0.010556933,0.8978076,0.004967475,0.009053245,0.062557735,0.0033295467],"about_ca_topic_score_codex":0.0022901264,"about_ca_topic_score_gemma":0.00018809419,"teacher_disagreement_score":0.896725,"about_ca_system_score_codex":0.000165093,"about_ca_system_score_gemma":0.0001039055,"threshold_uncertainty_score":0.99778694},"labels":[],"label_agreement":null},{"id":"W1988567896","doi":"10.3166/ria.19.479-498","title":"Context: Representation and Reasoning. Representing and Reasoning about Context in a Mobile Environment","year":2005,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Representation (politics); Computer science; Model-based reasoning; Analytic reasoning; Qualitative reasoning; Verbal reasoning; Opportunistic reasoning; Cognitive science; Knowledge representation and reasoning; Reasoning system; Psychology; Artificial intelligence; Cognition; Geography; Political science","score_opus":0.03279709906567378,"score_gpt":0.2776604916548442,"score_spread":0.2448633925891704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988567896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6098946,0.0076209093,0.37754712,0.0009865628,0.00021362277,0.000999043,0.00000590262,0.00016350776,0.0025687579],"genre_scores_gemma":[0.9953419,0.00046636467,0.0028750743,0.0001619032,0.00009220177,0.00014837286,0.0000038814123,0.000018260494,0.0008920676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974584,0.00021469363,0.0006790915,0.0009562002,0.00024640711,0.0004451875],"domain_scores_gemma":[0.9983098,0.0004909515,0.00027614768,0.0007043599,0.00006687924,0.00015188757],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010189993,0.00022563135,0.00035807924,0.00020492048,0.00022275736,0.00030911472,0.00033750577,0.00010283727,0.00006702945],"category_scores_gemma":[0.0002639052,0.00025105305,0.00006706979,0.00035343933,0.00011780314,0.0009823545,0.0004059235,0.0002619885,0.00015739983],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001717116,0.00012368664,0.014195521,0.00003236862,0.000015613743,0.000030411633,0.010460391,0.0014945416,0.0012567608,0.0039935578,0.000100517755,0.9682795],"study_design_scores_gemma":[0.00032707953,0.00011854423,0.003421124,0.00057489163,0.0000118103035,0.0003104686,0.008761615,0.94117814,0.020923693,0.00048210993,0.02338961,0.000500892],"about_ca_topic_score_codex":0.00026988037,"about_ca_topic_score_gemma":0.00025369576,"teacher_disagreement_score":0.96777856,"about_ca_system_score_codex":0.000105872576,"about_ca_system_score_gemma":0.000027324064,"threshold_uncertainty_score":0.99999416},"labels":[],"label_agreement":null},{"id":"W1990236725","doi":"10.1109/congress.2009.20","title":"Context-Aware Data Association and Authenticity in Pervasive Healthcare","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Innovatia (Canada)","funders":"","keywords":"Computer science; Context (archaeology); Ubiquitous computing; Health care; Association (psychology); Internet privacy; Context awareness; Computer security; Personally identifiable information; Data science; World Wide Web; Human–computer interaction; Psychology","score_opus":0.060187758038827074,"score_gpt":0.3017716087084574,"score_spread":0.24158385066963028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990236725","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4573563,0.00087953825,0.38652486,0.14731388,0.0010656783,0.0016928482,0.0001580196,0.0010121212,0.0039967736],"genre_scores_gemma":[0.99652004,0.000016308379,0.00051207014,0.002406266,0.00004016555,0.0000053545286,0.000018656016,0.0000035473379,0.00047757814],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99848336,0.00020166452,0.00026883424,0.00051059126,0.00028439547,0.0002511706],"domain_scores_gemma":[0.9986988,0.00026977807,0.00014407613,0.0006505651,0.00014207621,0.00009476178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071146653,0.0001141235,0.00022454397,0.000110512265,0.00007978415,0.00019238537,0.00061750755,0.000095207404,0.000014000429],"category_scores_gemma":[0.0002134244,0.000112208894,0.000022644426,0.00027575676,0.000010516942,0.0012983653,0.00026438886,0.00016512584,0.000040848026],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000103635775,0.00018421514,0.11264692,0.000033274435,0.000026455798,0.000028350974,0.003597779,6.352949e-7,0.00013043743,0.009016998,0.0053292443,0.8689953],"study_design_scores_gemma":[0.0026246351,0.00034166404,0.8764902,0.00026211943,0.000016452177,0.00008093838,0.0018863579,0.0978072,0.000612653,0.008984138,0.010034762,0.000858916],"about_ca_topic_score_codex":0.0009579769,"about_ca_topic_score_gemma":0.00402933,"teacher_disagreement_score":0.8681364,"about_ca_system_score_codex":0.00017554912,"about_ca_system_score_gemma":0.000100357014,"threshold_uncertainty_score":0.4575746},"labels":[],"label_agreement":null},{"id":"W1990474940","doi":"10.1109/ispa.2011.53","title":"Searching in Internet of Things: Vision and Challenges","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Scalability; Field (mathematics); Locality; Internet of Things; Architecture; Key (lock); Data science; The Internet; World Wide Web; State (computer science); Computer security; Database","score_opus":0.11100992031365774,"score_gpt":0.27472115104227185,"score_spread":0.1637112307286141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990474940","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4971199,0.0011359102,0.36467382,0.00088978774,0.00021638276,0.00025549473,1.8298012e-7,0.00015247079,0.13555606],"genre_scores_gemma":[0.99238896,0.00008058662,0.0073942463,0.000042513057,0.000003964055,0.0000026869282,6.28125e-8,0.000002450464,0.00008452247],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99941444,0.00008061223,0.00013414588,0.00017793437,0.00010540577,0.00008745879],"domain_scores_gemma":[0.9996293,0.000106475905,0.00004287585,0.00016171247,0.000028926212,0.00003070365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044211952,0.000050584746,0.00010759612,0.00012750788,0.000008098293,0.000022799259,0.00023136048,0.000026088082,0.000018230532],"category_scores_gemma":[0.000025468893,0.000041278956,0.000015529798,0.00007333624,0.000020533316,0.0007348712,0.00025238097,0.00006671553,0.000011497455],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048759293,0.00005804957,0.0014977391,0.000045965433,0.0000056340577,0.000005384234,0.031216484,3.6361254e-8,0.00043173844,0.025547056,0.0000119029,0.94117516],"study_design_scores_gemma":[0.004402294,0.002395413,0.46410987,0.0028944681,0.000012753741,0.00037418175,0.012192904,0.32403004,0.10299761,0.07785774,0.00704175,0.0016909725],"about_ca_topic_score_codex":0.0007964715,"about_ca_topic_score_gemma":0.00011506688,"teacher_disagreement_score":0.9394842,"about_ca_system_score_codex":0.000008015004,"about_ca_system_score_gemma":0.000009355274,"threshold_uncertainty_score":0.16833068},"labels":[],"label_agreement":null},{"id":"W1990746512","doi":"10.1145/1101149.1101313","title":"An ambient intelligence platform for physical play","year":2005,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Simon Fraser University","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta","keywords":"Computer science; Human–computer interaction; Representation (politics); Game design; Trajectory; Motion (physics); Motion capture; Multimedia; Order (exchange); Artificial intelligence","score_opus":0.04917813635574948,"score_gpt":0.3168388710775378,"score_spread":0.2676607347217883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990746512","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077838264,0.0000063804973,0.9189098,0.00041946105,0.0001996118,0.00029856118,0.0000051568613,0.00027313977,0.00204963],"genre_scores_gemma":[0.97903126,7.4045613e-7,0.019692102,0.00045835704,0.0003126386,0.000061706705,0.000003369537,0.0000075392554,0.00043230748],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989735,0.000018038087,0.00017880226,0.00037215455,0.00020981688,0.00024770445],"domain_scores_gemma":[0.9990565,0.000202288,0.000052314823,0.00046228518,0.00009972962,0.00012686237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019864232,0.00011614186,0.00014990178,0.00006627538,0.0000902087,0.00018242755,0.0006469291,0.000037671263,0.000030052213],"category_scores_gemma":[0.000018859646,0.00010038713,0.00008419863,0.0001605601,0.000023161749,0.0014582854,0.00008538215,0.0000706833,0.000342796],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010303955,0.00029364112,0.000036887344,0.000005378199,0.000010984067,9.544111e-7,0.0016543636,0.00010641283,0.0024621151,0.04591806,0.0010730039,0.9484279],"study_design_scores_gemma":[0.00030522997,0.0005353559,0.00035306017,0.000019055586,0.0000052230835,0.00003259496,0.00018374363,0.7959006,0.16225699,0.0057144254,0.03429138,0.00040238505],"about_ca_topic_score_codex":0.000026254766,"about_ca_topic_score_gemma":0.00017397213,"teacher_disagreement_score":0.9480255,"about_ca_system_score_codex":0.000057065758,"about_ca_system_score_gemma":0.000038186223,"threshold_uncertainty_score":0.44060633},"labels":[],"label_agreement":null},{"id":"W1990956786","doi":"10.4028/www.scientific.net/amr.647.854","title":"Effective Falls Detection Method Using Two Tri-Axial Accelerometers","year":2013,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Context-Aware Activity Recognition Systems","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":"Western University","funders":"","keywords":"Accelerometer; Physical medicine and rehabilitation; Population; Simulation; Computer science; Medicine","score_opus":0.10233144761342944,"score_gpt":0.43045661995688383,"score_spread":0.3281251723434544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990956786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55392045,0.000020728172,0.4433182,0.00006318124,0.00084200705,0.0014420052,0.000006336674,0.00015406622,0.00023303287],"genre_scores_gemma":[0.95118266,0.000010981162,0.047566477,0.000042367334,0.00024250273,0.0008082182,0.0000029129753,0.00003339725,0.00011050395],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9946231,0.0024711995,0.00044242974,0.00081117934,0.00083760865,0.0008144476],"domain_scores_gemma":[0.9966136,0.0015008701,0.00017072327,0.0007359041,0.00078231026,0.00019659019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029577103,0.00023673661,0.00044930595,0.0006086068,0.00037827203,0.0009588162,0.00087576377,0.000116542156,0.00021042825],"category_scores_gemma":[0.0005808242,0.00022523933,0.00008160105,0.0010631919,0.00008676663,0.0027448302,0.0006427949,0.00030512054,0.00057922787],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000319637,0.000027947755,0.000008024213,0.000025259324,0.000019692337,0.0000047445274,0.00015005315,0.00003306936,0.6947182,0.000068905356,0.000010054913,0.30490214],"study_design_scores_gemma":[0.0010555327,0.00022737836,0.00061947486,0.00006324484,0.000004101779,0.00005149027,0.00009168608,0.0032449493,0.9907886,0.002880766,0.0007104555,0.00026233654],"about_ca_topic_score_codex":0.0015314965,"about_ca_topic_score_gemma":0.000045651588,"teacher_disagreement_score":0.3972622,"about_ca_system_score_codex":0.000380112,"about_ca_system_score_gemma":0.00008435413,"threshold_uncertainty_score":0.9245887},"labels":[],"label_agreement":null},{"id":"W1991774940","doi":"10.1007/s10586-011-0157-8","title":"A smart spaces architecture based on heterogeneous contexts, particularly social contexts","year":2011,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Ubiquitous computing; Smart environment; Architecture; Context (archaeology); Smart objects; Ambient intelligence; Wireless sensor network; Human–computer interaction; Context-aware pervasive systems; Data science; Context awareness; World Wide Web; Internet of Things; Computer network","score_opus":0.03434927812730705,"score_gpt":0.24872590352701995,"score_spread":0.21437662539971292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991774940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17159835,0.00008113209,0.82144016,0.0010253728,0.0009774216,0.0005718895,0.000006379567,0.00057269493,0.0037266116],"genre_scores_gemma":[0.9910911,1.8519555e-7,0.0052748984,0.0031754307,0.00028990034,0.000024599305,0.000004315253,0.00003512773,0.000104451465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697584,0.00057312136,0.0005001451,0.0008145406,0.0004802693,0.0006560778],"domain_scores_gemma":[0.998158,0.00046518727,0.00032585082,0.00065659074,0.00019719262,0.00019721386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000596425,0.00038232733,0.0004813748,0.00022095456,0.00045696236,0.00043253877,0.00091021357,0.0001578001,0.00003399127],"category_scores_gemma":[0.000089469584,0.00036333583,0.00028182665,0.00033608568,0.00011213082,0.0002787187,0.00039839948,0.0003846297,0.00021180653],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039486567,0.001209952,0.010015058,0.00024798157,0.00039084017,0.00066750916,0.042646345,0.0021029203,0.0010430663,0.0023700336,0.0043506734,0.9345608],"study_design_scores_gemma":[0.0078003407,0.0015238484,0.022089692,0.00090981694,0.00009788023,0.0006435511,0.000353801,0.9332564,0.0099994475,0.002339797,0.01818373,0.0028017184],"about_ca_topic_score_codex":0.00007866038,"about_ca_topic_score_gemma":0.000064267275,"teacher_disagreement_score":0.93175906,"about_ca_system_score_codex":0.000088410314,"about_ca_system_score_gemma":0.00010660071,"threshold_uncertainty_score":0.99988186},"labels":[],"label_agreement":null},{"id":"W1992534923","doi":"10.1145/1731849.1731854","title":"Using interactive objects for speech intervention","year":2010,"lang":"en","type":"article","venue":"ACM SIGACCESS Accessibility and Computing","topic":"Context-Aware Activity Recognition Systems","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":"York University","funders":"","keywords":"Intervention (counseling); Computer science; Microcontroller; Human–computer interaction; Plan (archaeology); Speech recognition; Multimedia; Embedded system; Psychology","score_opus":0.06693970753407455,"score_gpt":0.3745554076489921,"score_spread":0.30761570011491757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992534923","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5740453,0.00003438581,0.4241562,0.00009711173,0.0009820677,0.0003682513,0.0000034051152,0.00013979964,0.00017347363],"genre_scores_gemma":[0.9492637,8.538555e-7,0.050276678,0.00012756973,0.0002739815,0.000019451256,0.000004582872,0.0000150714795,0.00001810804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997779,0.0001653979,0.00056238216,0.00088237325,0.00023962103,0.00037120449],"domain_scores_gemma":[0.996765,0.0013777287,0.00045865224,0.0008671278,0.00039990802,0.00013157638],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013967142,0.0002404095,0.00035463896,0.00018477741,0.0004430185,0.0012126467,0.0015526414,0.00014077361,0.00001682327],"category_scores_gemma":[0.0012672986,0.00023811744,0.00015869818,0.00040879328,0.00008254061,0.0028165423,0.0016710609,0.00043177718,0.0000036098236],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038626953,0.00020095096,0.025001911,0.00019582508,0.000050705195,0.0000055427804,0.0014079254,0.000011160242,0.009185244,0.00059700705,0.0000203167,0.9632848],"study_design_scores_gemma":[0.0032644907,0.00026606192,0.08040683,0.00063264684,0.00007161357,0.00027715415,0.0014011037,0.77627355,0.08807573,0.047617983,0.0003564435,0.0013564107],"about_ca_topic_score_codex":0.00037743294,"about_ca_topic_score_gemma":0.00037530216,"teacher_disagreement_score":0.96192837,"about_ca_system_score_codex":0.00005304782,"about_ca_system_score_gemma":0.00007012981,"threshold_uncertainty_score":0.99982417},"labels":[],"label_agreement":null},{"id":"W1993091056","doi":"10.1109/isie.2006.296096","title":"Machine Learning-Assisted Device Selection in a Context-Sensitive Ubiquitous Multimodal Multimedia Computing System","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Ubiquitous computing; Component (thermodynamics); Adaptation (eye); Context (archaeology); Human–computer interaction; Multimedia; User modeling; Selection (genetic algorithm); User interface; Modalities; Artificial intelligence; Operating system","score_opus":0.016011072388505156,"score_gpt":0.24247223425407755,"score_spread":0.22646116186557239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993091056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31643036,0.000077506294,0.6743836,0.0002023225,0.0005407523,0.0008034847,0.0000061446626,0.0015663964,0.0059894696],"genre_scores_gemma":[0.98929083,6.409294e-7,0.0098385075,0.00011707622,0.00019921608,0.000026810649,0.000018343682,0.000027841053,0.0004807276],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967121,0.00074900023,0.00072697614,0.0008038354,0.0004549839,0.0005531448],"domain_scores_gemma":[0.99805593,0.00083876465,0.00035936074,0.00023660828,0.00039396068,0.000115370145],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00082260143,0.000335197,0.0005328674,0.00043056844,0.00025319748,0.0002731515,0.00038517066,0.00017885162,0.000009795014],"category_scores_gemma":[0.00011925209,0.00033764256,0.00012604459,0.0010579763,0.000043725588,0.0006261914,0.0002143615,0.00049704674,0.00021771718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019037756,0.0012429728,0.10423283,0.00039848578,0.00019603757,0.00064118707,0.0057919887,0.0060251714,0.054829374,0.0062506706,0.00043762737,0.8197633],"study_design_scores_gemma":[0.0016522397,0.000087742585,0.045688197,0.00018192355,0.0000087765575,0.00041630236,0.0006197953,0.9453232,0.0051293243,0.0000070689666,0.0004910417,0.00039436322],"about_ca_topic_score_codex":0.019856805,"about_ca_topic_score_gemma":0.016640851,"teacher_disagreement_score":0.93929803,"about_ca_system_score_codex":0.00053636596,"about_ca_system_score_gemma":0.00010675557,"threshold_uncertainty_score":0.99990755},"labels":[],"label_agreement":null},{"id":"W1993191639","doi":"10.1109/wimob.2010.5645032","title":"Anytime and anywhere monitoring for the elderly","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Guelph","funders":"","keywords":"Computer science; Middleware (distributed applications); Protocol stack; Flexibility (engineering); Architecture; Embedded system; Mobile phone; Layer (electronics); Systems architecture; Node (physics); Computer network; Protocol (science); Stack (abstract data type); Operating system; Engineering","score_opus":0.02558516464697836,"score_gpt":0.2673134656825697,"score_spread":0.24172830103559134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993191639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18800929,0.00010509783,0.8031153,0.0031600096,0.0018097373,0.0004585347,0.0000027564922,0.00023286277,0.0031064227],"genre_scores_gemma":[0.9867226,0.0000031670697,0.011664614,0.000048685284,0.00023209277,0.000048580467,1.2451746e-7,0.00000487096,0.0012752732],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994777,0.000014144387,0.0000895285,0.00019028204,0.00009533526,0.00013302361],"domain_scores_gemma":[0.99906784,0.000452454,0.000034338125,0.0003229804,0.00007455424,0.000047856516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025441105,0.00006644636,0.0000735103,0.000024653236,0.0001566892,0.00026619973,0.00033436262,0.000039696108,0.000016210073],"category_scores_gemma":[0.00004473272,0.000043828015,0.000031434167,0.00008036523,0.000024257624,0.00039959612,0.00009168443,0.00010357076,0.00002360628],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017461391,0.000011564145,0.0024108007,0.0000063754483,0.000013103021,6.471837e-7,0.0002579837,1.590752e-7,0.012150648,0.0029050815,0.0009003655,0.98134154],"study_design_scores_gemma":[0.0038170838,0.0010195824,0.09054301,0.00011195131,0.00006033339,0.0005392571,0.0014594453,0.098703034,0.18114974,0.014562227,0.60638255,0.0016517824],"about_ca_topic_score_codex":0.00008237292,"about_ca_topic_score_gemma":0.0000687896,"teacher_disagreement_score":0.9796897,"about_ca_system_score_codex":0.0000043434047,"about_ca_system_score_gemma":0.000020298792,"threshold_uncertainty_score":0.25669703},"labels":[],"label_agreement":null},{"id":"W1993197252","doi":"10.5383/juspn.01.01.001","title":"Ambient Things on the Web","year":2010,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"World Wide Web; Computer science","score_opus":0.01652226279357138,"score_gpt":0.22656795924155548,"score_spread":0.2100456964479841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993197252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8717399,0.00212369,0.09432039,0.008697192,0.018876212,0.00077952293,0.000005187086,0.000093128445,0.0033647653],"genre_scores_gemma":[0.996596,0.00013328558,0.00012332268,0.00091144774,0.0020007286,0.000010738605,2.3368261e-7,0.000013426306,0.000210831],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981162,0.00027772595,0.00058239035,0.00022788493,0.00051263376,0.00028319823],"domain_scores_gemma":[0.997174,0.00093521905,0.00075778284,0.00043510192,0.000514167,0.00018369616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018578464,0.0001918638,0.00038635536,0.0001227553,0.00027803928,0.0006158457,0.0007614825,0.00015479558,0.000013492925],"category_scores_gemma":[0.00012809274,0.00011467605,0.00016848928,0.00021867499,0.00006677889,0.00060194684,0.00013533227,0.00095597655,0.000013746395],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049792964,0.001374183,0.016928539,0.0005114808,0.0019192181,0.0017872086,0.027368786,0.0074157603,0.06503183,0.2109147,0.33876243,0.32748795],"study_design_scores_gemma":[0.0034864054,0.0031287284,0.008283186,0.002690288,0.00015144213,0.01934295,0.0039049694,0.44393247,0.0012773017,0.0014958475,0.5107303,0.001576075],"about_ca_topic_score_codex":0.000069820795,"about_ca_topic_score_gemma":0.000054299417,"teacher_disagreement_score":0.4365167,"about_ca_system_score_codex":0.000034064316,"about_ca_system_score_gemma":0.00011271054,"threshold_uncertainty_score":0.59386146},"labels":[],"label_agreement":null},{"id":"W1995005751","doi":"10.1109/wimob.2011.6085408","title":"A context-aware and location prediction framework for dynamic environments","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Ottawa","funders":"","keywords":"Computer science; Context (archaeology); Context model; Ontology; Context awareness; Adaptation (eye); Context management; Architecture; Dissemination; Negotiation; Contextual design; Flooding (psychology); World Wide Web; Ubiquitous computing; Human–computer interaction; Knowledge management; Artificial intelligence","score_opus":0.03829552185529459,"score_gpt":0.2458249073277568,"score_spread":0.20752938547246222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995005751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007581465,0.000050774735,0.99076015,0.00018715275,0.00039635826,0.0004999116,0.000008038693,0.00014867338,0.00036750585],"genre_scores_gemma":[0.97706443,0.0000098463215,0.021996852,0.00022658252,0.000024702756,0.00015105607,0.00000673324,0.000009108428,0.00051069487],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913114,0.00004122013,0.00018736787,0.00035301148,0.00013131408,0.00015594783],"domain_scores_gemma":[0.9993307,0.0001392706,0.000091590875,0.00030841219,0.000056156307,0.00007386478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015213332,0.00010827059,0.000120543285,0.00006993342,0.00010053561,0.0000602599,0.00020412292,0.00009772908,0.000035747915],"category_scores_gemma":[0.00005331611,0.00010521731,0.000028707695,0.00013674847,0.000036052934,0.00064979144,0.00008161465,0.00007198212,0.00005182785],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054568587,0.00030770313,0.012970935,0.00009014522,0.00011276079,0.0000028868362,0.005318844,0.0000028867394,0.0006166768,0.063872494,0.0007606818,0.91588944],"study_design_scores_gemma":[0.003902859,0.0014621529,0.25120106,0.00053378235,0.00008618877,0.00019412929,0.0025302013,0.5627548,0.009402162,0.14062296,0.02582684,0.0014828248],"about_ca_topic_score_codex":0.000045187528,"about_ca_topic_score_gemma":0.000038917908,"teacher_disagreement_score":0.96948296,"about_ca_system_score_codex":0.0000525016,"about_ca_system_score_gemma":0.000022969822,"threshold_uncertainty_score":0.42906374},"labels":[],"label_agreement":null},{"id":"W1996749058","doi":"10.1007/s11042-012-1008-1","title":"Adaptive interaction support in ambient-aware environments based on quality of context information","year":2012,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Distrust; Human–computer interaction; Context (archaeology); Quality (philosophy); Action (physics); Order (exchange); Automation","score_opus":0.06097861735669529,"score_gpt":0.3000286873237823,"score_spread":0.239050069967087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996749058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046175454,0.000017986826,0.9507359,0.0002733402,0.00015453256,0.0011127645,0.00016055776,0.00004911305,0.0013202997],"genre_scores_gemma":[0.9975702,0.0000046329437,0.0015842314,0.00022182726,0.000031456115,0.00047626678,0.000088692395,0.000003666866,0.00001899238],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99900615,0.000087282606,0.00039139576,0.0001547051,0.0002068697,0.00015359998],"domain_scores_gemma":[0.998943,0.0003896913,0.00025434888,0.00028665617,0.000038753114,0.00008757068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035817086,0.00010070893,0.00016330638,0.00012974758,0.000052493375,0.000043341715,0.0001519238,0.000058546015,0.000027226257],"category_scores_gemma":[0.000041705676,0.00010118514,0.000038171675,0.0001684902,0.000041128827,0.0016257856,0.00005483755,0.000106692554,0.00013210591],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031522308,0.0004603142,0.029841606,0.00002577424,0.000010940033,9.82553e-8,0.0019687766,0.00006945858,0.0007830386,0.0013728187,0.00011018173,0.9653255],"study_design_scores_gemma":[0.0027631244,0.00019600682,0.7472533,0.00009350823,0.000013613653,0.0000046570626,0.0022826456,0.18925692,0.0079938,0.00010594807,0.04951728,0.00051920686],"about_ca_topic_score_codex":0.00011550863,"about_ca_topic_score_gemma":0.000022776368,"teacher_disagreement_score":0.96480626,"about_ca_system_score_codex":0.00007095969,"about_ca_system_score_gemma":0.000023824952,"threshold_uncertainty_score":0.41262102},"labels":[],"label_agreement":null},{"id":"W1997334841","doi":"10.1016/j.compeleceng.2014.10.006","title":"Discreet verification of user identity in pervasive computing environments using a non-intrusive technique","year":2014,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Assertion; Identity (music); Computer science; Reliability (semiconductor); Intrusion detection system; Intrusion; Human–computer interaction; Ubiquitous computing; Computer security; Tracking (education); Distributed computing; Programming language","score_opus":0.009579262916890199,"score_gpt":0.23474425934737816,"score_spread":0.22516499643048796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997334841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13304813,0.000024645225,0.86627924,0.000029753153,0.0001884851,0.00034124343,7.477194e-7,0.00006715215,0.000020604299],"genre_scores_gemma":[0.93881977,0.0000030444455,0.06103602,0.000035622495,0.00006684947,0.000016208358,0.0000025681925,0.000017372138,0.0000025630384],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983255,0.00010188682,0.00045346032,0.0004491787,0.00031508485,0.00035488524],"domain_scores_gemma":[0.9989491,0.00031276816,0.00019112963,0.00041112403,0.000043752996,0.000092116155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004483271,0.00020319798,0.00038486166,0.00037719563,0.000051150666,0.000079514684,0.00066056114,0.000100825964,0.0000011593713],"category_scores_gemma":[0.0001091121,0.00023358178,0.000094263865,0.00093291287,0.000022801287,0.0007141038,0.00028293065,0.0002688799,0.0000062297127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021231946,0.0004074904,0.0056095817,0.00020198795,0.0001114765,0.00003272437,0.0016227206,0.19366314,0.70537645,0.008844523,0.000042655047,0.08406605],"study_design_scores_gemma":[0.00033297317,0.00006383238,0.0151125975,0.00015009432,0.000005862373,0.000027980112,0.0000048488473,0.96187234,0.02198684,0.00008481001,0.00012580595,0.0002320197],"about_ca_topic_score_codex":0.00008193472,"about_ca_topic_score_gemma":0.0000039720953,"teacher_disagreement_score":0.80577165,"about_ca_system_score_codex":0.00026690107,"about_ca_system_score_gemma":0.000036171063,"threshold_uncertainty_score":0.9525189},"labels":[],"label_agreement":null},{"id":"W1997889343","doi":"10.1145/1988051.1988061","title":"The smart condo","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Home automation; Computer science; Variety (cybernetics); Set (abstract data type); Intelligent sensor; Component (thermodynamics); Human–computer interaction; Wireless sensor network; Computer security; Telecommunications; Artificial intelligence; Computer network","score_opus":0.0680893773614304,"score_gpt":0.2297733125368905,"score_spread":0.1616839351754601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997889343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059906,0.0000596453,0.531,0.00088450825,0.000887908,0.00015867942,4.4769047e-7,0.00032016606,0.46069804],"genre_scores_gemma":[0.99199104,0.0000027859437,0.0018022257,0.00037837846,0.000022720835,0.000016844248,1.0067971e-7,0.0000028509783,0.0057830606],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994287,0.000059496495,0.00010813764,0.00014586611,0.00011903906,0.00013875059],"domain_scores_gemma":[0.9992457,0.00017267308,0.000037545386,0.00043262696,0.00006634963,0.00004509217],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002769625,0.000052520925,0.000057852973,0.00002005612,0.00013611016,0.00011221816,0.0005875483,0.000019843512,0.000110324414],"category_scores_gemma":[0.000028959692,0.000031969306,0.00003729503,0.00011623173,0.000032123964,0.00031652575,0.00014046181,0.000050956136,0.0010763997],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066561897,0.0000649394,0.004372934,0.0000034344778,0.000041383966,0.000013577419,0.0019592051,3.218011e-8,0.00041522513,0.3121966,0.021601388,0.6593246],"study_design_scores_gemma":[0.0009280282,0.00019699507,0.03653758,0.000023925957,0.000008507597,0.00022031045,0.0005844831,0.0066051697,0.034105018,0.032684293,0.8874768,0.0006289095],"about_ca_topic_score_codex":0.00016408564,"about_ca_topic_score_gemma":0.0001630389,"teacher_disagreement_score":0.9860004,"about_ca_system_score_codex":0.000009505136,"about_ca_system_score_gemma":0.000027004153,"threshold_uncertainty_score":0.9997014},"labels":[],"label_agreement":null},{"id":"W1998579474","doi":"10.1109/icacci.2014.6968205","title":"An intelligent medical monitoring system based on sensors and wireless sensor network","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Wireless sensor network; Spare part; Android (operating system); Wireless; Vital signs; Health care; Remote patient monitoring; Embedded system; Computer network; Telecommunications; Engineering; Medicine; Operating system; Operations management","score_opus":0.020523856113811974,"score_gpt":0.2590592973962875,"score_spread":0.23853544128247556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998579474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2948654,0.000008093215,0.70086974,0.0003873487,0.0010744685,0.0001697343,5.801577e-7,0.00049823185,0.0021263969],"genre_scores_gemma":[0.99557865,0.0000021107085,0.0034160388,0.00021539422,0.0006890606,0.000016604577,8.3646967e-7,0.000014652907,0.000066664135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977092,0.0004959407,0.00029979664,0.00052091153,0.00065245456,0.00032165644],"domain_scores_gemma":[0.99807876,0.0007338967,0.00009086977,0.00061615073,0.00009783264,0.000382485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001190873,0.0001812596,0.00027648054,0.000092048285,0.00014818068,0.0002334186,0.0004341003,0.00012449469,0.000015125256],"category_scores_gemma":[0.00006390759,0.00015433677,0.00004897174,0.00022035104,0.00003731113,0.00024300396,0.00009385305,0.00018294495,0.000093621224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050107465,0.00025140136,0.030564692,0.00024052225,0.000058883412,0.00012011981,0.0006708021,0.0041889474,0.00055810215,0.023096992,0.00048232885,0.9397171],"study_design_scores_gemma":[0.00025410217,0.00015093786,0.001943414,0.0003199725,0.0000040613954,0.0000558277,0.00016444345,0.99416876,0.0017669335,0.000021462143,0.0009357348,0.0002143731],"about_ca_topic_score_codex":0.000083761704,"about_ca_topic_score_gemma":0.000017193131,"teacher_disagreement_score":0.9899798,"about_ca_system_score_codex":0.00006807249,"about_ca_system_score_gemma":0.000044572786,"threshold_uncertainty_score":0.62936705},"labels":[],"label_agreement":null},{"id":"W1999113448","doi":"10.1145/1839294.1839310","title":"Pervasive technology to facilitate wellness","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Economic and Social Research Council; AGE-WELL","keywords":"Usability; Computer science; Telehealth; Accelerometer; Human–computer interaction; Population; Ubiquitous computing; State (computer science); Healthcare system; Health care; Telemedicine; Medicine","score_opus":0.033567437708660254,"score_gpt":0.26467306393215273,"score_spread":0.23110562622349248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999113448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27507722,0.0000054865113,0.69033474,0.011786392,0.0011127689,0.00028114946,0.0000037619154,0.00070201146,0.020696443],"genre_scores_gemma":[0.985163,3.1957524e-7,0.009753919,0.00049876515,0.000036860543,0.000059834874,4.3112314e-7,0.000005261326,0.004481607],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990238,0.000027308437,0.00015367895,0.0003927813,0.000161703,0.00024072496],"domain_scores_gemma":[0.9989006,0.00010709928,0.000033928904,0.00069371954,0.00015040129,0.00011425034],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020166794,0.00010648183,0.00014620431,0.0002609827,0.00008506535,0.00010291271,0.0008553407,0.000096189266,0.00017444222],"category_scores_gemma":[0.00012912399,0.00009564779,0.00004277226,0.00061867357,0.000039539613,0.00036714168,0.00035465386,0.00020526344,0.0033787717],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003385824,0.0000644428,0.0017647032,0.000009300895,0.00001785077,0.000023920997,0.0013829622,0.0000017807184,0.06435376,0.063960224,0.004402043,0.86401564],"study_design_scores_gemma":[0.00069942645,0.00024261861,0.0037773412,0.00002669519,0.0000054042252,0.00038871646,0.0008755174,0.002756346,0.20428042,0.016177345,0.76987386,0.00089633936],"about_ca_topic_score_codex":0.00010511093,"about_ca_topic_score_gemma":0.0005944233,"teacher_disagreement_score":0.8631193,"about_ca_system_score_codex":0.0000150019905,"about_ca_system_score_gemma":0.00004924724,"threshold_uncertainty_score":0.9973972},"labels":[],"label_agreement":null},{"id":"W1999976008","doi":"10.1108/14636690510628300","title":"The dark side of ambient intelligence","year":2005,"lang":"en","type":"article","venue":"Info","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Swami; Context (archaeology); Originality; Quarter (Canadian coin); Value (mathematics); Identity (music); Business; Internet privacy; Computer science; Sociology; Social science; Geography; Qualitative research","score_opus":0.02605067775023021,"score_gpt":0.26723018053986275,"score_spread":0.24117950278963254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999976008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08482627,0.0003736187,0.8464568,0.0048059803,0.0009833538,0.0003394961,0.000004188944,0.00019165185,0.062018607],"genre_scores_gemma":[0.99773026,0.000011771804,0.0014620781,0.00022311334,0.000056702662,0.000010925969,1.8122297e-7,0.000002644147,0.0005023371],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99920607,0.000047365964,0.00024469767,0.00013025472,0.00023395213,0.00013763813],"domain_scores_gemma":[0.998923,0.0003482399,0.000112439186,0.00046737347,0.00010935356,0.000039616487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003430085,0.000061103194,0.00008590564,0.000036037196,0.00007723311,0.000086478496,0.0006776386,0.000023417504,0.00000990781],"category_scores_gemma":[0.00009961298,0.000044385364,0.000048546466,0.00019974817,0.0000534467,0.00034637557,0.00017709573,0.000071104616,0.0002812749],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024102155,0.000021233802,0.00024583557,0.0000034724899,0.0000091233105,0.000001166973,0.0009862737,0.00005161742,0.00081347936,0.007913227,0.0010936125,0.9888585],"study_design_scores_gemma":[0.00025822935,0.00015474754,0.006358026,0.000112736074,0.000007333661,0.000086417174,0.00042133842,0.024341943,0.2638752,0.0076004057,0.6963811,0.00040255944],"about_ca_topic_score_codex":0.00009270072,"about_ca_topic_score_gemma":0.00021746746,"teacher_disagreement_score":0.988456,"about_ca_system_score_codex":0.000047299283,"about_ca_system_score_gemma":0.000047367907,"threshold_uncertainty_score":0.36153138},"labels":[],"label_agreement":null},{"id":"W2000658861","doi":"10.1109/icis.2013.6607856","title":"Intelligent temporal data driven world actuation in ambient environments: Case study: Anomaly recognition and assistance provision in smart home","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Home automation; Assisted living; Context (archaeology); Computer science; Smart environment; Activities of daily living; Human–computer interaction; Intelligent sensor; Activity recognition; Ubiquitous computing; Ambient intelligence; Anomaly detection; Intelligent environment; Work (physics); Computer security; Artificial intelligence; Embedded system; Internet of Things; Wireless sensor network; Engineering; Medicine; Telecommunications; Computer network","score_opus":0.08482887185541116,"score_gpt":0.2831961402791544,"score_spread":0.19836726842374325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000658861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92614526,0.000026858466,0.07149878,0.00030232096,0.00014644307,0.0017043287,0.000008478198,0.00005167534,0.000115856994],"genre_scores_gemma":[0.9970381,0.000009779201,0.0023271912,0.00009230526,0.00001852708,0.00020888062,0.00005433874,0.000010852674,0.00024000516],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9977047,0.00029296966,0.00056551554,0.0008561329,0.00033091023,0.0002498082],"domain_scores_gemma":[0.99867487,0.00017433302,0.00018443575,0.0008293226,0.000037700447,0.00009935584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007815267,0.0001891172,0.00025198428,0.0004672116,0.0000630867,0.0003195423,0.00044526465,0.000051378032,0.000065624736],"category_scores_gemma":[0.000048350274,0.00018114923,0.000019212683,0.0005473973,0.000033259086,0.0030896033,0.0006405807,0.00018049602,0.00021102343],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016203136,0.0011336642,0.4493337,0.000020536878,0.000017111108,0.0004161927,0.002556498,0.000006911566,0.00050968543,0.0000071261006,0.00016510894,0.54581726],"study_design_scores_gemma":[0.0029813296,0.00055614195,0.83109236,0.0003150052,0.000016965707,0.00042865396,0.00704389,0.15390414,0.0005980784,0.0007312189,0.0013876159,0.0009445972],"about_ca_topic_score_codex":0.008710984,"about_ca_topic_score_gemma":0.05359821,"teacher_disagreement_score":0.54487264,"about_ca_system_score_codex":0.00022838742,"about_ca_system_score_gemma":0.00003879401,"threshold_uncertainty_score":0.9978901},"labels":[],"label_agreement":null},{"id":"W2002966538","doi":"10.1145/2769493.2769530","title":"Sensors activation time predictions in smart home","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Activity recognition; Computer science; Intervention (counseling); Assisted living; Home automation; Artificial intelligence; Activities of daily living; Order (exchange); Machine learning; Human–computer interaction; Gerontology; Psychology; Telecommunications; Business; Medicine","score_opus":0.04097356010161979,"score_gpt":0.248059792535276,"score_spread":0.2070862324336562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002966538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38802108,0.000008376947,0.54666764,0.0030738607,0.00065447827,0.0004003573,0.0000038078158,0.0007566484,0.060413748],"genre_scores_gemma":[0.99419177,4.4742032e-7,0.0016570599,0.0001347768,0.00005490826,0.000024514382,0.0000037413204,0.0000056044364,0.003927204],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990555,0.00011038667,0.00018380335,0.00024641393,0.0002467485,0.00015714054],"domain_scores_gemma":[0.99928916,0.000119107586,0.000053110212,0.00030844632,0.00012475792,0.00010540931],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035906513,0.00008164526,0.00011858481,0.00021175662,0.00003341411,0.00009683062,0.00023021217,0.000054830514,0.000039480154],"category_scores_gemma":[0.000085706,0.000079368154,0.00002937687,0.0005187426,0.000014599999,0.0011141093,0.000091792695,0.00009313221,0.0012894367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020072928,0.0032036928,0.15195085,0.000103441016,0.00033268085,0.00016095118,0.03228314,0.0049407375,0.032773573,0.04069679,0.29069507,0.44265833],"study_design_scores_gemma":[0.0040877536,0.00040782167,0.10266647,0.00016472004,0.000010780236,0.0002197861,0.0008631817,0.80399746,0.012221335,0.007815173,0.06637186,0.0011736831],"about_ca_topic_score_codex":0.00023729104,"about_ca_topic_score_gemma":0.000059944,"teacher_disagreement_score":0.7990567,"about_ca_system_score_codex":0.00012653615,"about_ca_system_score_gemma":0.00009438634,"threshold_uncertainty_score":0.9994882},"labels":[],"label_agreement":null},{"id":"W2003423520","doi":"10.1007/s10845-008-0211-4","title":"U-HMS: hybrid system for secure intelligent multimedia data services in Ubi-Home","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Interoperability; Multimedia; Authentication (law); IP Multimedia Subsystem; Context (archaeology); Service (business); Authorization; World Wide Web; Computer network; Quality of service; Computer security","score_opus":0.04087663204871159,"score_gpt":0.28474522574541317,"score_spread":0.24386859369670158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003423520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3325226,0.0011243639,0.66171163,0.00081577845,0.0027771348,0.0007318554,0.000040180286,0.00012252745,0.00015394356],"genre_scores_gemma":[0.98971045,0.000096455995,0.009372964,0.00019070937,0.0005428657,0.000007687912,0.000015052992,0.000021816564,0.000042024156],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965942,0.00015421747,0.0015020914,0.0005642931,0.0006711569,0.000514025],"domain_scores_gemma":[0.9968629,0.00050404196,0.0010506973,0.0010338405,0.00029292895,0.00025561804],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001737452,0.00034816327,0.0007026324,0.0007035564,0.00009877025,0.0004046841,0.0031238103,0.000105959385,0.000015976277],"category_scores_gemma":[0.000075566226,0.00030177904,0.0002454472,0.00020288143,0.000021373904,0.002002355,0.00041529906,0.0004969014,0.000055511708],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002630674,0.00057091826,0.000655546,0.0009166022,0.00027883024,0.00047948663,0.004543844,0.0028153472,0.001423777,0.00024066931,0.0010472717,0.98676467],"study_design_scores_gemma":[0.002109001,0.00087017607,0.0035342078,0.0045031044,0.00010293266,0.002300025,0.0039117876,0.1898692,0.753621,0.0017423205,0.036140084,0.0012961703],"about_ca_topic_score_codex":0.000046740683,"about_ca_topic_score_gemma":0.000066996276,"teacher_disagreement_score":0.98546845,"about_ca_system_score_codex":0.000414896,"about_ca_system_score_gemma":0.00011612761,"threshold_uncertainty_score":0.99994344},"labels":[],"label_agreement":null},{"id":"W2003639862","doi":"10.2316/journal.208.2005.4.208-0861","title":"A Conceptual Framework of Computer-Supported Ubiquitous Learning Environment","year":2005,"lang":"en","type":"article","venue":"Advanced Technology for Learning","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Human–computer interaction; Conceptual framework; Multimedia; World Wide Web; Ubiquitous computing; Sociology","score_opus":0.012036777385416381,"score_gpt":0.25228421533928685,"score_spread":0.24024743795387046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003639862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15315405,0.00029820733,0.8441592,0.0008155044,0.00017773513,0.00040017997,0.0000019277302,0.00087555445,0.000117640855],"genre_scores_gemma":[0.73206043,0.000023535078,0.26745927,0.00005834721,0.00007156946,0.00010595556,0.0000060785383,0.000022663618,0.00019213639],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980556,0.000094485775,0.00049557316,0.0006299732,0.00023280205,0.00049159385],"domain_scores_gemma":[0.99822706,0.0005843758,0.00051933754,0.0004889028,0.0001110055,0.00006933542],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030860442,0.00023679528,0.0004562733,0.0003490679,0.00024989826,0.00002870861,0.0007230013,0.000325485,0.00003333847],"category_scores_gemma":[0.0003325888,0.00026660462,0.00013348176,0.00044462192,0.00026525892,0.00046731232,0.00037667705,0.0008213085,0.00007875622],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018867826,0.00008686394,0.0024702528,0.00001661932,0.00006207608,0.000006173512,0.00087602227,0.019235583,0.010588876,0.022776715,0.000020117526,0.9438418],"study_design_scores_gemma":[0.003993104,0.00356747,0.00078952033,0.0004675641,0.000059190894,0.0002548281,0.0025490997,0.1853872,0.115406774,0.011098116,0.674993,0.001434162],"about_ca_topic_score_codex":0.0000022841623,"about_ca_topic_score_gemma":0.0000019850584,"teacher_disagreement_score":0.94240767,"about_ca_system_score_codex":0.00010424602,"about_ca_system_score_gemma":0.00004376407,"threshold_uncertainty_score":0.9999786},"labels":[],"label_agreement":null},{"id":"W2005199022","doi":"10.4018/jkss.2011010102","title":"A Task Context Aware Physical Distribution Knowledge Service System","year":2011,"lang":"en","type":"article","venue":"International Journal of Knowledge and Systems Science","topic":"Context-Aware Activity Recognition Systems","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":"Task (project management); Context (archaeology); Computer science; Service (business); Resource (disambiguation); Context awareness; Knowledge management; Service system; Product (mathematics); Distribution (mathematics); Process management; Human–computer interaction; Systems engineering; Business; Engineering; Marketing","score_opus":0.045516105721710075,"score_gpt":0.29031912688062933,"score_spread":0.24480302115891925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005199022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51524216,0.003067923,0.45120797,0.00042158208,0.01556338,0.0005510387,0.0001081006,0.000204413,0.0136334365],"genre_scores_gemma":[0.9990798,0.000011382615,0.00009122615,0.000030640647,0.0006569457,0.000012493508,0.000002469654,0.000008707231,0.00010628864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974479,0.00022359575,0.0007290755,0.00042135798,0.00086663314,0.00031141072],"domain_scores_gemma":[0.99345577,0.00023360172,0.0006948415,0.00032742674,0.0049675144,0.0003208299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017962707,0.00021480651,0.00042352252,0.00040117328,0.00023093309,0.00048397464,0.0022974834,0.0000675383,0.000002787967],"category_scores_gemma":[0.00015671352,0.0001762946,0.00013097454,0.00083639746,0.00021345446,0.0024355638,0.0004749437,0.00023470141,0.00012229654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036347428,0.0037415095,0.016842058,0.0011230755,0.0010133352,0.00087757496,0.13439307,0.000029237519,0.031098843,0.3757776,0.009443638,0.42529657],"study_design_scores_gemma":[0.009328112,0.002123325,0.054643795,0.010039112,0.00024805978,0.03139115,0.025431663,0.7250301,0.022750076,0.0018989948,0.11402412,0.0030914636],"about_ca_topic_score_codex":0.00020869148,"about_ca_topic_score_gemma":0.00007009756,"teacher_disagreement_score":0.7250009,"about_ca_system_score_codex":0.00044416098,"about_ca_system_score_gemma":0.00067462935,"threshold_uncertainty_score":0.7189085},"labels":[],"label_agreement":null},{"id":"W2005383722","doi":"10.1109/icsmc.2012.6378297","title":"Toward an augmented shoe for preventing falls related to physical conditions of the soil","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Falling (accident); Actuator; Software; Computer science; Set (abstract data type); Clothing; Embedded system; Human–computer interaction; Simulation; Engineering; Artificial intelligence; Operating system","score_opus":0.052985067928049225,"score_gpt":0.32087210822128004,"score_spread":0.2678870402932308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005383722","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7886573,0.0000067977794,0.20739263,0.0007973969,0.0004476003,0.000679649,0.000019956879,0.00012834043,0.0018702956],"genre_scores_gemma":[0.99729,9.6324875e-8,0.0013958705,0.00015684938,0.000059224236,0.000093110764,0.0000031989732,0.000007627824,0.000993978],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990318,0.00012034774,0.00022143807,0.00019330041,0.0001946187,0.00023850349],"domain_scores_gemma":[0.99903905,0.00020444217,0.00011112912,0.00040392188,0.00013341222,0.00010803209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003305772,0.000090446454,0.00015572028,0.000050514533,0.00010312827,0.000045918892,0.00047075722,0.00003630578,0.000023383735],"category_scores_gemma":[0.0000797713,0.000066638066,0.00012635243,0.00030449982,0.000024618574,0.0007531505,0.00019099962,0.000065638866,0.000040735245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006401303,0.0059182127,0.021521948,0.00032368302,0.00070860906,0.0000016413866,0.07364924,0.0003624309,0.36186343,0.29484183,0.013699245,0.22704571],"study_design_scores_gemma":[0.0045712697,0.0013180063,0.07575497,0.0006218519,0.00021244845,0.00012343113,0.0044586645,0.1913035,0.6838461,0.019210378,0.01675962,0.0018197556],"about_ca_topic_score_codex":0.000033655848,"about_ca_topic_score_gemma":0.000027486492,"teacher_disagreement_score":0.32198268,"about_ca_system_score_codex":0.000030145538,"about_ca_system_score_gemma":0.000037686084,"threshold_uncertainty_score":0.27174217},"labels":[],"label_agreement":null},{"id":"W2006733141","doi":"10.1109/ntms.2014.6813999","title":"A New Business Model and Architecture for Context-Aware Applications Provisioning in the Cloud","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Provisioning; Cloud computing; Context (archaeology); Scalability; Context awareness; Context management; Reuse; Resource (disambiguation); Context model; Ubiquitous computing; World Wide Web; Database; Human–computer interaction; Operating system; Computer network; Engineering; Artificial intelligence; Phone","score_opus":0.022673025400667315,"score_gpt":0.2552404377374731,"score_spread":0.2325674123368058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006733141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015844894,0.00004092812,0.9899158,0.0063507427,0.000052143532,0.0010716657,0.0000031659015,0.00009402023,0.00088703353],"genre_scores_gemma":[0.9632418,0.0000013112627,0.034563366,0.0012752044,0.00012791877,0.00040688185,0.0000033046438,0.000008194542,0.0003719789],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906904,0.00007355335,0.00018550415,0.00034601422,0.00014891407,0.00017698648],"domain_scores_gemma":[0.9987626,0.0005758544,0.00007001323,0.0004207939,0.000113607326,0.00005711273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044111678,0.0001151139,0.0001535699,0.000086738284,0.00014111208,0.00023417409,0.00054052274,0.00005183088,0.0000018353555],"category_scores_gemma":[0.0000662535,0.000078202094,0.0000338465,0.00034172015,0.000022483082,0.00024398051,0.00011340547,0.00010660168,0.000006531654],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059124895,0.000026254087,0.000117663505,0.0000309389,0.0000044745616,1.8560516e-7,0.0015090606,0.00053152075,0.00010091102,0.049840223,0.0011451712,0.9466877],"study_design_scores_gemma":[0.0014599793,0.000064090986,0.0012762211,0.00008991962,0.000009700135,0.00006840317,0.00037868577,0.8401941,0.00011683452,0.059890863,0.096084505,0.00036667273],"about_ca_topic_score_codex":0.00017077742,"about_ca_topic_score_gemma":0.0007753866,"teacher_disagreement_score":0.96165735,"about_ca_system_score_codex":0.000013764061,"about_ca_system_score_gemma":0.00009138006,"threshold_uncertainty_score":0.31889886},"labels":[],"label_agreement":null},{"id":"W2006833924","doi":"10.1109/smc.2013.590","title":"Towards ROS Based Multi-robot Architecture for Ambient Assisted Living","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Interreg; AGE-WELL","keywords":"Robot; Assisted living; Architecture; Bridge (graph theory); Computer science; Ageing society; Independent living; Human–computer interaction; Artificial intelligence; Gerontology; Medicine","score_opus":0.05184614439289677,"score_gpt":0.2769765238339819,"score_spread":0.22513037944108516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006833924","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058535594,0.000016489184,0.98868746,0.00244715,0.00048388273,0.0010745515,0.000006379171,0.00049218175,0.0009383294],"genre_scores_gemma":[0.7901844,1.8559948e-7,0.20701669,0.0011032673,0.000061855695,0.00042181654,0.0000032865094,0.000015172474,0.0011933206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983891,0.00012798021,0.00029439025,0.000518443,0.00028967138,0.00038040636],"domain_scores_gemma":[0.99828494,0.00053506007,0.000120396755,0.00059328304,0.00029806912,0.00016822618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032571921,0.00019942039,0.00024821342,0.00015962968,0.00014271468,0.00045344853,0.0006599952,0.00009089773,0.00019837447],"category_scores_gemma":[0.00023641573,0.00016678798,0.00017918456,0.00027783582,0.000027369413,0.00047867885,0.00019177656,0.00012896358,0.00022653046],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053820922,0.00045498437,0.0008237952,0.00009055795,0.00005269876,0.0000032436865,0.0007120295,0.00022572404,0.029174006,0.00034479942,0.00484621,0.96326655],"study_design_scores_gemma":[0.001701734,0.00026703044,0.09708054,0.00023897015,0.000016185577,0.000047335576,0.000119440956,0.8661384,0.021473028,0.00032925606,0.011744577,0.0008434715],"about_ca_topic_score_codex":0.00062253955,"about_ca_topic_score_gemma":0.00031753344,"teacher_disagreement_score":0.9624231,"about_ca_system_score_codex":0.0000752612,"about_ca_system_score_gemma":0.000115171126,"threshold_uncertainty_score":0.6801416},"labels":[],"label_agreement":null},{"id":"W2006858408","doi":"10.1109/icacci.2013.6637340","title":"An ontology based system for social networking for health application support","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Montréal","funders":"","keywords":"Ontology; Computer science; Context (archaeology); Domain (mathematical analysis); Ontology-based data integration; Upper ontology; Social network (sociolinguistics); World Wide Web; Process ontology; Data science; Information retrieval; Knowledge management; Semantic Web; Social media","score_opus":0.050545131647818015,"score_gpt":0.3312673013564901,"score_spread":0.28072216970867203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006858408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00065875554,0.000006638275,0.99069315,0.0047215526,0.0006359525,0.0023532414,0.000008860196,0.00040466306,0.000517214],"genre_scores_gemma":[0.9711686,1.00895456e-7,0.02376894,0.0017848695,0.0006695356,0.0024691569,0.000044964945,0.000012552647,0.000081289065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870086,0.000102956656,0.00032743145,0.00040539604,0.00011673424,0.00034663154],"domain_scores_gemma":[0.9988618,0.00025159045,0.00023253921,0.00031096302,0.00023905643,0.000104089966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063916063,0.00010800839,0.00025591961,0.000068731,0.0002847498,0.0001556958,0.0003980391,0.00008133847,0.0000071088875],"category_scores_gemma":[0.00000661793,0.00010632318,0.000086782944,0.00012392776,0.000014295251,0.00045482803,0.000024951376,0.000040688603,0.00004139544],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018859728,0.000121046585,0.000623308,0.0003023577,0.00002350067,1.7492134e-7,0.0005228084,0.000016043465,0.0006224373,0.023593448,0.02579852,0.9483575],"study_design_scores_gemma":[0.0013143315,0.00048557972,0.0012412021,0.000022036402,0.000006575541,0.000013988379,0.00024947376,0.92828035,0.0004735714,0.0009530534,0.06665933,0.00030052365],"about_ca_topic_score_codex":0.00037718148,"about_ca_topic_score_gemma":0.00038707105,"teacher_disagreement_score":0.9705098,"about_ca_system_score_codex":0.00015351776,"about_ca_system_score_gemma":0.00019171102,"threshold_uncertainty_score":0.43357337},"labels":[],"label_agreement":null},{"id":"W2007882431","doi":"10.1109/ccece.2010.5575129","title":"A wearable real-time fall detector based on Naive Bayes classifier","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Saskatchewan","funders":"","keywords":"Naive Bayes classifier; Wearable computer; Computer science; Bayes' theorem; Artificial intelligence; Classifier (UML); Detector; Real-time computing; Sensitivity (control systems); Acceleration; Computer vision; Pattern recognition (psychology); Machine learning; Bayesian probability; Embedded system; Engineering; Support vector machine","score_opus":0.017100191754060766,"score_gpt":0.23884325868985273,"score_spread":0.22174306693579196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007882431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2637752,0.0000073561428,0.37494364,0.0031549102,0.002044554,0.0008559133,0.000017329101,0.0017927137,0.3534084],"genre_scores_gemma":[0.979239,0.0000010120922,0.011065693,0.000633634,0.0001236145,0.000051624782,0.0000014777593,0.000017089096,0.008866868],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983822,0.00012082563,0.00022556561,0.00054404617,0.00039356042,0.00033377545],"domain_scores_gemma":[0.99811697,0.0005701197,0.0000980916,0.00087849743,0.00015952624,0.00017680955],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039752133,0.00018794477,0.00022672125,0.00017774264,0.00012875043,0.00028697727,0.00066973193,0.00013717076,0.0008024956],"category_scores_gemma":[0.00012625787,0.00016090137,0.000112212605,0.00031732002,0.000043265027,0.0005043482,0.00011035635,0.0002903134,0.0033897397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011470439,0.00080398127,0.0046822303,0.00005919616,0.00009389607,0.00010172623,0.000746401,0.0000379075,0.60063064,0.0067783133,0.033131108,0.3528199],"study_design_scores_gemma":[0.0022823901,0.0007021865,0.012621425,0.00017027547,0.000017411096,0.000066327026,0.000057899848,0.8176005,0.10682475,0.001326244,0.057033177,0.0012974106],"about_ca_topic_score_codex":0.0004706622,"about_ca_topic_score_gemma":0.0005046155,"teacher_disagreement_score":0.8175626,"about_ca_system_score_codex":0.00004113675,"about_ca_system_score_gemma":0.0001495463,"threshold_uncertainty_score":0.9973862},"labels":[],"label_agreement":null},{"id":"W2008308316","doi":"10.1007/s13177-014-0108-x","title":"VANET IR-CAS for Safety ACN: Information Retrieval Context Aware System for VANET Automatic Crash Notification Safety Application","year":2014,"lang":"en","type":"article","venue":"International Journal of Intelligent Transportation Systems Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Crash; Context (archaeology); Computer science; Vehicular ad hoc network; Relevance (law); Computer security; Wireless ad hoc network; Operating system","score_opus":0.05357738745743404,"score_gpt":0.3496565375630198,"score_spread":0.2960791501055857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008308316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064839367,0.000118170414,0.98479676,0.0012885894,0.0035026053,0.0029570558,0.0005167881,0.00014231828,0.00019378227],"genre_scores_gemma":[0.9952018,0.00006358198,0.002624751,0.00007004426,0.0007366118,0.0003054075,0.0008201851,0.000034801204,0.00014281081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926599,0.00059943844,0.0031352562,0.0004714581,0.0026529122,0.000480982],"domain_scores_gemma":[0.9827332,0.0028090086,0.0019645074,0.0005507432,0.011676536,0.0002659872],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0083500445,0.00031458863,0.00065049774,0.0012737262,0.00035797473,0.0007738404,0.0019049312,0.00026590403,0.000019328209],"category_scores_gemma":[0.0007980578,0.00030200268,0.0003794034,0.00064201304,0.000086663094,0.0026680736,0.000034099015,0.00042950507,0.00013035281],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0041814833,0.0007323613,0.0015801,0.0038400076,0.001875007,0.000017615168,0.013008744,0.023979511,0.004549674,0.147359,0.00838188,0.7904946],"study_design_scores_gemma":[0.004373055,0.00096700917,0.0033291788,0.0016017907,0.00009559811,0.0002454934,0.005614016,0.6266951,0.01122347,0.00087243033,0.3443031,0.00067976076],"about_ca_topic_score_codex":0.00018194591,"about_ca_topic_score_gemma":0.00009599285,"teacher_disagreement_score":0.98871785,"about_ca_system_score_codex":0.0012479045,"about_ca_system_score_gemma":0.00046408133,"threshold_uncertainty_score":0.9999432},"labels":[],"label_agreement":null},{"id":"W2008587891","doi":"10.1109/uic-atc.2013.44","title":"uOS: A Resource Rerouting Middleware for Ubiquitous Games","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Western University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Ubiquitous computing; Middleware (distributed applications); Software deployment; Smart environment; Context-aware pervasive systems; Mobile device; Architecture; Resource (disambiguation); Adaptation (eye); Distributed computing; Service-oriented architecture; Mobile computing; Context (archaeology); Service (business); Smart objects; Human–computer interaction; Web service; World Wide Web; Software engineering; Operating system; Internet of Things; Computer network","score_opus":0.02798227686237303,"score_gpt":0.24064061957731633,"score_spread":0.2126583427149433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008587891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038355805,0.00009854455,0.9342556,0.003497355,0.00036640614,0.0010962745,0.0000042625206,0.0008413596,0.021484403],"genre_scores_gemma":[0.968886,7.999824e-7,0.02257707,0.0012206209,0.00019595643,0.0003128616,0.000003039619,0.00001683054,0.006786791],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998574,0.00007930996,0.00029103455,0.0004618353,0.00022230418,0.0003715217],"domain_scores_gemma":[0.99839216,0.0005473887,0.00012419722,0.0005870865,0.00023231976,0.000116860494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032974666,0.00014885119,0.00020984738,0.00010218893,0.00015168119,0.00043900067,0.00067315646,0.000075148964,0.00009225647],"category_scores_gemma":[0.00019788631,0.00013156433,0.000114486684,0.00023789286,0.000024216408,0.0008018657,0.00023708043,0.000088834546,0.00046726695],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064377728,0.00008959037,0.0008656421,0.00008546625,0.000048963848,0.000004607655,0.0025752904,0.000006315029,0.0031057983,0.0051566823,0.07102394,0.9170313],"study_design_scores_gemma":[0.0034593525,0.00074684824,0.006343426,0.0005901595,0.000034930876,0.00035452662,0.00592194,0.18513191,0.028948514,0.014596059,0.75159734,0.0022749733],"about_ca_topic_score_codex":0.00024099545,"about_ca_topic_score_gemma":0.00004667485,"teacher_disagreement_score":0.93053025,"about_ca_system_score_codex":0.00004730987,"about_ca_system_score_gemma":0.000043028154,"threshold_uncertainty_score":0.60059273},"labels":[],"label_agreement":null},{"id":"W2009767985","doi":"10.4018/jaci.2009062204","title":"Intelligent Recognition of Activities of Daily Living for Assisting Memory and/or Cognitively Impaired Elders in Smart Homes","year":2009,"lang":"en","type":"article","venue":"International Journal of Ambient Computing and Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton; Université de Sherbrooke; Université de Montréal","funders":"Université de Sherbrooke","keywords":"Activities of daily living; Computer science; Architecture; Activity recognition; Modular design; Home automation; Assisted living; Human–computer interaction; Embedded system; Artificial intelligence; Psychology; Gerontology; Operating system; Medicine","score_opus":0.050251850831984204,"score_gpt":0.31098866652366586,"score_spread":0.26073681569168167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009767985","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5350911,0.00020561284,0.46388805,0.00020264876,0.00041580098,0.000118983786,0.0000066326243,0.000010257698,0.00006091743],"genre_scores_gemma":[0.98951775,0.00017314356,0.010090737,0.0000912084,0.00010021525,0.0000017305596,0.0000013945229,0.000006121495,0.000017714225],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99801064,0.00013761531,0.0010078955,0.00024108368,0.0004259691,0.00017677993],"domain_scores_gemma":[0.9958702,0.0019477506,0.0011012142,0.000091589034,0.0009179101,0.00007133373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013504678,0.0001544393,0.0003994041,0.00058234786,0.000052060626,0.00013054212,0.00042816886,0.00005729157,0.0000044621747],"category_scores_gemma":[0.00087299105,0.00014114688,0.00011962026,0.00021255505,0.000101558195,0.00081298646,0.00013390009,0.00017715072,3.1371434e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001980912,0.00024822183,0.0032514185,0.000066323526,0.00009735013,0.000013868773,0.0053633978,0.0001481977,0.0041206838,0.00019305464,0.000020855647,0.98627853],"study_design_scores_gemma":[0.0029917455,0.008077893,0.11736665,0.02485491,0.00014399436,0.0025195163,0.03486531,0.24383605,0.55250615,0.011140759,0.00021358227,0.0014834293],"about_ca_topic_score_codex":0.00006004931,"about_ca_topic_score_gemma":0.000016436214,"teacher_disagreement_score":0.9847951,"about_ca_system_score_codex":0.00007081213,"about_ca_system_score_gemma":0.00011411707,"threshold_uncertainty_score":0.5755803},"labels":[],"label_agreement":null},{"id":"W2011611076","doi":"10.1007/s12652-011-0081-z","title":"Pervasive Social Computing: augmenting five facets of human intelligence","year":2011,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Ubiquitous computing; Computer science; Social computing; Computational intelligence; Ambient intelligence; Context-aware pervasive systems; Social intelligence; Social consciousness; Human–computer interaction; Collective intelligence; Architecture; End-user computing; Data science; World Wide Web; Social media; Artificial intelligence; Utility computing; Cloud computing; Psychology","score_opus":0.11774421858370064,"score_gpt":0.3136067386627274,"score_spread":0.19586252007902677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011611076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49497193,0.0001942767,0.5035039,0.00003898241,0.00046157118,0.00015597299,0.0000010635035,0.000039015413,0.00063327816],"genre_scores_gemma":[0.9876616,0.000024088326,0.011909681,0.00009551198,0.00025147584,7.751696e-7,7.6433787e-7,0.000017809178,0.00003831648],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965238,0.00025908012,0.0016873532,0.00043236185,0.000644616,0.00045277242],"domain_scores_gemma":[0.9958162,0.0003636468,0.0022570165,0.0002793882,0.0011253046,0.00015848],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016661793,0.0003054422,0.00076803914,0.00047013964,0.00058245,0.00021566267,0.0012679856,0.00011182513,0.00004873657],"category_scores_gemma":[0.00013283585,0.00029482442,0.00030924033,0.00044703775,0.00022079403,0.0007388261,0.000694004,0.00052622845,0.000012775532],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012221314,0.0011250038,0.0056916666,0.0003926642,0.00064923434,0.00019195354,0.26520887,0.00026326498,0.017481517,0.06849794,0.00023926252,0.6401364],"study_design_scores_gemma":[0.0031706942,0.008052086,0.033962402,0.006420248,0.00052928703,0.0031880147,0.0904697,0.15603131,0.627613,0.06585344,0.0007620405,0.003947746],"about_ca_topic_score_codex":0.00012302799,"about_ca_topic_score_gemma":0.000014560672,"teacher_disagreement_score":0.6361887,"about_ca_system_score_codex":0.00008996999,"about_ca_system_score_gemma":0.00010178189,"threshold_uncertainty_score":0.9999504},"labels":[],"label_agreement":null},{"id":"W2012281465","doi":"10.3390/s140405742","title":"Context-Aware Personal Navigation Using Embedded Sensor Fusion in Smartphones","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Context (archaeology); Sensor fusion; Computer science; Embedded system; Human–computer interaction; Real-time computing; Artificial intelligence; Geography","score_opus":0.02909765489912279,"score_gpt":0.2634323839016649,"score_spread":0.2343347290025421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012281465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91429925,0.000021840156,0.083772175,0.00024425844,0.0005895593,0.0002514393,0.0000055848814,0.00018616233,0.0006297309],"genre_scores_gemma":[0.99645257,0.0000015423776,0.002911013,0.00021701597,0.00016409965,0.000008633961,0.000011058381,0.00001913373,0.00021493417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978693,0.0004502649,0.00036539073,0.0005514053,0.00040990388,0.00035372973],"domain_scores_gemma":[0.9988736,0.00027772653,0.00018249961,0.00037582213,0.00017707229,0.00011330339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005525431,0.00021724576,0.0003190198,0.00023735741,0.00015277957,0.00014958261,0.0002855186,0.0001332723,0.000022141387],"category_scores_gemma":[0.000099895246,0.00022087786,0.000105172694,0.0004943185,0.00005543326,0.00048649166,0.000117819116,0.00021698022,0.0001509397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024658578,0.0009795886,0.05339985,0.0005370409,0.00016196514,0.00030452572,0.06898853,0.004988301,0.1423412,0.0034891008,0.0010073982,0.7235559],"study_design_scores_gemma":[0.0010711614,0.000057371963,0.006376221,0.00022792851,0.0000075621215,0.00023506771,0.0019166231,0.98332477,0.0047786846,0.0002104456,0.0013489071,0.00044525106],"about_ca_topic_score_codex":0.00047135248,"about_ca_topic_score_gemma":0.000121214995,"teacher_disagreement_score":0.97833645,"about_ca_system_score_codex":0.00013213747,"about_ca_system_score_gemma":0.00005816061,"threshold_uncertainty_score":0.9007138},"labels":[],"label_agreement":null},{"id":"W2013079384","doi":"10.3166/isi.9.2.87-106","title":"Mobility Aware Adaptation of Space Based Coordination Patterns","year":2004,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adaptation (eye); Computer science; Space (punctuation); Psychology; Operating system; Neuroscience","score_opus":0.03538507248143703,"score_gpt":0.25021944078811664,"score_spread":0.21483436830667962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013079384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19129331,0.00023052967,0.8035451,0.001147779,0.0015880859,0.00082701846,0.00019672765,0.00017625936,0.0009951481],"genre_scores_gemma":[0.99463516,0.000014056308,0.004649418,0.00015077626,0.000099721256,0.000102064136,0.00020342023,0.000016706075,0.00012869277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99688536,0.00032151613,0.0013066652,0.00031434966,0.0007366976,0.0004354249],"domain_scores_gemma":[0.9957683,0.00025964555,0.00129713,0.0006279388,0.0018919731,0.00015500643],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012091751,0.00034003024,0.00045478466,0.0004823177,0.00027550786,0.00048714288,0.00044909056,0.00030401186,0.00008524638],"category_scores_gemma":[0.00052674435,0.0004034001,0.00020476301,0.0010377258,0.00024086256,0.011742139,0.00012122585,0.00024272116,0.00022171754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009564406,0.0005615296,0.007510677,0.0052142562,0.000118686046,0.000009474883,0.04045427,0.07541577,0.0002798356,0.030880075,0.00021795473,0.8392418],"study_design_scores_gemma":[0.004758532,0.0009796256,0.057997774,0.005396093,0.00011656664,0.0001660441,0.00905023,0.8829062,0.0172446,0.011832606,0.008291141,0.0012605947],"about_ca_topic_score_codex":0.0058302744,"about_ca_topic_score_gemma":0.0006526074,"teacher_disagreement_score":0.8379812,"about_ca_system_score_codex":0.0012693092,"about_ca_system_score_gemma":0.000802299,"threshold_uncertainty_score":0.9998418},"labels":[],"label_agreement":null},{"id":"W2013328030","doi":"10.1007/s10207-009-0088-z","title":"Uncertain inference control in privacy protection","year":2009,"lang":"en","type":"article","venue":"International Journal of Information Security","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; York University","funders":"","keywords":"Context (archaeology); Computer science; Inference; Enabling; Key (lock); Control (management); Computer security; Cryptography; Context awareness; Information privacy; Context management; Risk analysis (engineering); Data science; Ubiquitous computing; Human–computer interaction; Artificial intelligence; Business","score_opus":0.018990640509313414,"score_gpt":0.28939872644700015,"score_spread":0.2704080859376867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013328030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09777985,0.000023724768,0.8941976,0.005606966,0.0008905594,0.000215492,0.0000060334955,0.000037727532,0.0012420461],"genre_scores_gemma":[0.9974726,0.000012907525,0.0012696818,0.0011180497,0.00011426102,0.0000050159974,0.0000024340313,0.0000013915097,0.0000036564481],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807817,0.00010652319,0.000911978,0.00006593974,0.0007142821,0.0001230805],"domain_scores_gemma":[0.9975546,0.00010274045,0.0008954324,0.00013905832,0.0012426939,0.00006546515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009202496,0.00009883487,0.00017516804,0.0006192521,0.000029556544,0.00033047743,0.0008675478,0.00006435435,0.000018867187],"category_scores_gemma":[0.0004971459,0.00009174366,0.00008699314,0.0002833645,0.000013981465,0.00692951,0.00004581133,0.00029352095,0.00004376897],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004291916,0.00048316194,0.0047812415,0.000025277579,0.00012851029,0.00006876702,0.021043755,0.0019367662,0.0015609651,0.03916974,0.0012706002,0.929102],"study_design_scores_gemma":[0.020928483,0.0016082716,0.21169989,0.0013092639,0.00002504037,0.0031866133,0.0015701456,0.49926654,0.0076575824,0.107799985,0.14355348,0.0013946983],"about_ca_topic_score_codex":0.000040100633,"about_ca_topic_score_gemma":0.000009072113,"teacher_disagreement_score":0.9277073,"about_ca_system_score_codex":0.00024969032,"about_ca_system_score_gemma":0.00018100887,"threshold_uncertainty_score":0.50237274},"labels":[],"label_agreement":null},{"id":"W2013535883","doi":"10.3166/isi.11.5.9-34","title":"Modéliser et faire évoluer le contexte dans des environnements intelligents. Représenter et apprendre des modèles de comportement humain dans des environnements intelligents","year":2006,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.03906959694904007,"score_gpt":0.26698914767196696,"score_spread":0.2279195507229269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013535883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4010398,0.0009862032,0.58403337,0.00011929859,0.00077137596,0.0011786345,0.000251712,0.00023790359,0.011381683],"genre_scores_gemma":[0.9815131,0.0004669619,0.012503305,0.0005282748,0.00016100111,0.00043966592,0.00081184605,0.0000999323,0.0034759126],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9917708,0.0014471145,0.0028812513,0.000982485,0.0012674457,0.0016508936],"domain_scores_gemma":[0.995822,0.00029632566,0.0015626239,0.0012759637,0.0005839482,0.0004591543],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0028244606,0.0011140924,0.00095447054,0.0006497407,0.0019576806,0.0019728867,0.0014213575,0.00047633206,0.00025426733],"category_scores_gemma":[0.00027156668,0.0012634704,0.00049331196,0.0007524466,0.0024700416,0.014105594,0.0010453009,0.00061406085,0.00063005934],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002689107,0.003210979,0.1792108,0.0038098516,0.0013610618,0.000114127535,0.2725517,0.026879262,0.006259943,0.028983645,0.003413466,0.4739363],"study_design_scores_gemma":[0.008534755,0.0015729096,0.37941298,0.01311531,0.00060329546,0.0014235196,0.09478868,0.27059925,0.06283787,0.10851987,0.052343592,0.0062479647],"about_ca_topic_score_codex":0.013128768,"about_ca_topic_score_gemma":0.0057164887,"teacher_disagreement_score":0.5804733,"about_ca_system_score_codex":0.0030740923,"about_ca_system_score_gemma":0.00038594365,"threshold_uncertainty_score":0.9996836},"labels":[],"label_agreement":null},{"id":"W2014365169","doi":"10.2196/jmir.3465","title":"A Web-Based Non-Intrusive Ambient System to Measure and Classify Activities of Daily Living","year":2014,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Bern","keywords":"Measure (data warehouse); Activities of daily living; Computer science; Assisted living; World Wide Web; Data mining; Gerontology; Psychology; Medicine","score_opus":0.05669072575926528,"score_gpt":0.3500652823558448,"score_spread":0.2933745565965795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014365169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6222651,0.000106432744,0.3707528,0.00484893,0.00063302397,0.00023074505,0.0000020158618,0.0000232869,0.0011376602],"genre_scores_gemma":[0.99855435,0.000007916929,0.00077964796,0.00016047127,0.0003214844,0.000013464851,1.1229715e-7,0.000011709904,0.00015086959],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929961,0.0014610451,0.0007172708,0.0002949993,0.004133329,0.0003972408],"domain_scores_gemma":[0.9939797,0.0034304655,0.0003537769,0.0003670028,0.001168844,0.0007002272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013386839,0.00014099586,0.00053718954,0.00076759513,0.00006267127,0.0002675474,0.0015349421,0.00017971097,0.00006445419],"category_scores_gemma":[0.0039360803,0.000108297565,0.00013110098,0.0004645206,0.00021208433,0.0003676473,0.00069630105,0.0010590616,0.000024368115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082262396,0.0013526562,0.014855929,0.0026722718,0.0007327814,0.00088100065,0.020687688,0.00004230695,0.04352422,0.006984368,0.0746107,0.83283347],"study_design_scores_gemma":[0.0064971284,0.008254947,0.018309738,0.0380272,0.000056432982,0.0025624416,0.010190479,0.83962446,0.046118166,0.00018397956,0.029308844,0.0008662029],"about_ca_topic_score_codex":0.00022567333,"about_ca_topic_score_gemma":0.0001460774,"teacher_disagreement_score":0.83958215,"about_ca_system_score_codex":0.00024279905,"about_ca_system_score_gemma":0.0007472141,"threshold_uncertainty_score":0.471214},"labels":[],"label_agreement":null},{"id":"W2016265610","doi":"10.4108/icst.mobiquitous2008.3473","title":"A Quantitative Approach to Non-Intrusive Computing","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Ubiquitous computing; Focus (optics); Context-aware pervasive systems; Software; Human–computer interaction; Data science; Computer security; World Wide Web; Programming language","score_opus":0.06431418989130926,"score_gpt":0.28293848844589903,"score_spread":0.21862429855458976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016265610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0671138,0.000009018403,0.8553831,0.00030858544,0.00021804226,0.0003260003,0.0000012422971,0.00023082177,0.076409385],"genre_scores_gemma":[0.8023868,5.5663304e-7,0.19622956,0.00072170916,0.00004853513,0.000015912512,0.0000010632211,0.00000711878,0.0005886934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986325,0.00008628181,0.00023114742,0.00048180434,0.00029016286,0.00027809618],"domain_scores_gemma":[0.9988424,0.00030700918,0.000080446596,0.00039838767,0.00021951103,0.0001522357],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023301101,0.00014148625,0.00023626679,0.00016525017,0.00021262748,0.00009245483,0.00059649517,0.000041749372,0.000010037928],"category_scores_gemma":[0.00009762631,0.00013030917,0.00007162863,0.00065240584,0.00003588241,0.00046335536,0.0003403943,0.00010993105,0.0008292757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013344277,0.0024184568,0.008585393,0.00017976004,0.0005456523,0.00037034825,0.22635747,0.0025693781,0.01470767,0.31586266,0.10655469,0.3217151],"study_design_scores_gemma":[0.001464553,0.0006364437,0.016364511,0.000097440534,0.000008877626,0.0010720713,0.0035071343,0.96028256,0.0077338507,0.0007339497,0.0067898426,0.0013087621],"about_ca_topic_score_codex":0.00017948356,"about_ca_topic_score_gemma":0.000011931302,"teacher_disagreement_score":0.9577132,"about_ca_system_score_codex":0.00005212482,"about_ca_system_score_gemma":0.00008078141,"threshold_uncertainty_score":0.9999487},"labels":[],"label_agreement":null},{"id":"W2016535693","doi":"10.1007/s12652-012-0129-8","title":"Smart home technology for telemedicine and emergency management","year":2012,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Telemedicine; Computational intelligence; Computer science; Computer security; Internet of Things; Medical emergency; Telecommunications; Artificial intelligence; Medicine; Health care; Political science","score_opus":0.04124311490575766,"score_gpt":0.2979043251600431,"score_spread":0.25666121025428545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016535693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2512482,0.0017781925,0.7450634,0.00044442463,0.0010816627,0.00021864459,4.627003e-7,0.00003417943,0.00013082386],"genre_scores_gemma":[0.973502,0.00033909257,0.025695344,0.000083265484,0.0003007379,0.000003897868,3.2616873e-7,0.000008386225,0.000066978435],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99852633,0.000039895556,0.00067706086,0.00019464496,0.00023374698,0.00032831324],"domain_scores_gemma":[0.9987819,0.00013406714,0.0004970012,0.00017412822,0.00027945483,0.00013345403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011472923,0.00014520898,0.0003272773,0.00046674456,0.00021105241,0.00007047585,0.00037691536,0.00005399486,0.000011069702],"category_scores_gemma":[0.000047393216,0.00012522668,0.000071433824,0.0002849978,0.000055701163,0.00048326363,0.00028460854,0.0001752572,0.0000051684306],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033425375,0.00019643884,0.015066315,0.0001855841,0.00018333989,0.000016950453,0.0033765617,0.000008961151,0.00082660024,0.055627324,0.0004668058,0.9240117],"study_design_scores_gemma":[0.015663557,0.014984134,0.116467305,0.0075974423,0.0014869241,0.017586606,0.046295945,0.1683624,0.05240858,0.2651169,0.2872305,0.0067997305],"about_ca_topic_score_codex":0.0000034815212,"about_ca_topic_score_gemma":0.0000018461616,"teacher_disagreement_score":0.91721195,"about_ca_system_score_codex":0.000034815854,"about_ca_system_score_gemma":0.000012803635,"threshold_uncertainty_score":0.5106596},"labels":[],"label_agreement":null},{"id":"W2016639467","doi":"10.1109/iccasm.2010.5623201","title":"A probabilistic-approach based resource allocation algorithm in pervasive computing systems","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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 Waterloo","funders":"","keywords":"Computer science; Ubiquitous computing; Idle; Pipeline (software); Probabilistic logic; Node (physics); Distributed computing; Resource allocation; Resource (disambiguation); Algorithm; Router; Task (project management); Construct (python library); Computer network; Artificial intelligence; Operating system; Engineering","score_opus":0.020341159657031,"score_gpt":0.23958197362806286,"score_spread":0.21924081397103184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016639467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019271959,0.000016956681,0.9691562,0.0007276759,0.0004774481,0.0008729916,0.000002990446,0.00041579935,0.009057969],"genre_scores_gemma":[0.89804196,1.1347776e-7,0.10118483,0.00029933295,0.0001349146,0.000083847146,0.000012735619,0.000013896875,0.00022838028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978608,0.00027788858,0.0004646213,0.0006469372,0.00041266077,0.00033708784],"domain_scores_gemma":[0.99820906,0.0004420839,0.00017755889,0.0007144404,0.0003388277,0.00011805773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013838563,0.00018998903,0.00028010816,0.00024323449,0.00011244446,0.00038841757,0.00072837877,0.00013502524,0.000008121447],"category_scores_gemma":[0.00026313151,0.0001804518,0.000063662104,0.0006544808,0.00004876712,0.0004471738,0.00016632,0.00038000284,0.000055112323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018071225,0.0014966355,0.0033057833,0.0004816306,0.000059083304,0.000058744088,0.0056251246,0.008592514,0.005357991,0.04923342,0.003120489,0.9226505],"study_design_scores_gemma":[0.00048894505,0.00003190092,0.0014814156,0.00005049664,0.000002838996,0.00006452956,0.0002957151,0.9937881,0.00024049605,0.000077705015,0.003242581,0.00023528226],"about_ca_topic_score_codex":0.00059635594,"about_ca_topic_score_gemma":0.00015620732,"teacher_disagreement_score":0.9851956,"about_ca_system_score_codex":0.00010426199,"about_ca_system_score_gemma":0.0002024681,"threshold_uncertainty_score":0.73586106},"labels":[],"label_agreement":null},{"id":"W2017182448","doi":"10.1145/2815347.2830325","title":"HCI in VANET IR-CAS","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Context (archaeology); Usability; Interface (matter); Eye tracking; Relevance (law); Selection (genetic algorithm); Crash; User interface; Human–computer interaction; Visualization; Computer vision; Artificial intelligence; Operating system","score_opus":0.08210273600636658,"score_gpt":0.2845980853635263,"score_spread":0.20249534935715974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017182448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085265376,0.00012902828,0.71518123,0.00291677,0.0012273235,0.00031403822,0.0000019197635,0.00049342145,0.1944709],"genre_scores_gemma":[0.994307,0.0000011168015,0.003274546,0.00039657412,0.000047003497,0.000011836068,9.235915e-7,0.000003956406,0.0019570459],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915254,0.00007873242,0.00015637328,0.00023175856,0.00021293042,0.00016763924],"domain_scores_gemma":[0.9993591,0.00008299605,0.00003585283,0.000340059,0.00007048608,0.00011155044],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003788603,0.000072325085,0.00011848884,0.00010635629,0.000013882467,0.00010185055,0.00039951148,0.00003915909,0.000041053907],"category_scores_gemma":[0.000057232566,0.00006565729,0.000022839706,0.00030376203,0.000011587673,0.00059604534,0.00015665585,0.0000747846,0.0011378174],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002299249,0.00050940836,0.035424434,0.000027206475,0.000043480424,0.00029834092,0.008634905,0.00012852841,0.00058963103,0.034813277,0.13465229,0.7848555],"study_design_scores_gemma":[0.005163664,0.00047591716,0.029710388,0.0001115028,0.000008306946,0.00049596897,0.0012838648,0.14571938,0.0074211326,0.024496403,0.7836005,0.0015129895],"about_ca_topic_score_codex":0.0006263566,"about_ca_topic_score_gemma":0.0006072445,"teacher_disagreement_score":0.90904164,"about_ca_system_score_codex":0.000063937165,"about_ca_system_score_gemma":0.000077408586,"threshold_uncertainty_score":0.9996399},"labels":[],"label_agreement":null},{"id":"W2018464370","doi":"10.1145/2504335.2504342","title":"Activity recognition in smart homes based on electrical devices identification","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Software deployment; Computer science; Home automation; Key (lock); Activity recognition; Process (computing); Identification (biology); Energy consumption; Embedded system; Smart grid; Feature extraction; Real-time computing; Distributed computing; Artificial intelligence; Computer security; Engineering; Telecommunications; Software engineering; Operating system","score_opus":0.031624837928193775,"score_gpt":0.2558711096481629,"score_spread":0.22424627171996914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018464370","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6416177,0.0000105473855,0.3476673,0.0025567424,0.0003080865,0.00072114385,0.0000022830554,0.00034878944,0.0067673908],"genre_scores_gemma":[0.997923,0.0000017919851,0.0010538592,0.0005243264,0.000033142525,0.0002145588,0.000007835042,0.000007856614,0.00023361368],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99838424,0.00027787208,0.00026711004,0.0004799626,0.00034021246,0.0002506103],"domain_scores_gemma":[0.998714,0.00049336225,0.00013487619,0.00041674857,0.00016119011,0.00007979478],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045325558,0.0001389058,0.00017786441,0.0003943947,0.00006880785,0.0003720128,0.00035730834,0.00008845081,0.00018365326],"category_scores_gemma":[0.00012993996,0.0001304155,0.000061549224,0.0007756366,0.000016757218,0.0016315719,0.00004361675,0.00016863666,0.0019990315],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001117422,0.00035737007,0.012867674,0.000015446498,0.0000066086336,0.0000026969703,0.000059923757,0.0000075388457,0.0054499726,0.00014464076,0.0008958424,0.9801811],"study_design_scores_gemma":[0.0005722857,0.0001195788,0.5765531,0.000053374155,0.0000033711349,0.000006360091,0.000018522911,0.39856666,0.021983774,0.0014580748,0.0003458734,0.00031902007],"about_ca_topic_score_codex":0.00072876527,"about_ca_topic_score_gemma":0.00038406384,"teacher_disagreement_score":0.9798621,"about_ca_system_score_codex":0.00013196669,"about_ca_system_score_gemma":0.00006584291,"threshold_uncertainty_score":0.99877805},"labels":[],"label_agreement":null},{"id":"W2019151722","doi":"10.4018/ijrat.2013010103","title":"Ambient Activity Recognition in Smart Environments for Cognitive Assistance","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics Applications and Technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Université de Sherbrooke; Université du Québec à Chicoutimi","funders":"","keywords":"Ambient intelligence; Activity recognition; Context (archaeology); Computer science; Field (mathematics); Cognition; Data science; Key (lock); Human–computer interaction; Artificial intelligence; Psychology; Computer security","score_opus":0.026173182816385317,"score_gpt":0.26962792582388434,"score_spread":0.24345474300749903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019151722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064513676,0.0001466164,0.93045104,0.004145388,0.0001384248,0.0004674813,0.000015755522,0.00004366387,0.000077959485],"genre_scores_gemma":[0.96955824,0.0002321077,0.029818619,0.000043374832,0.000024282568,0.00029117896,0.000003454543,0.0000050360436,0.000023726616],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991665,0.00002068263,0.00030159313,0.00018338267,0.00021593229,0.000111927475],"domain_scores_gemma":[0.9988501,0.0002815935,0.00037700208,0.0001279614,0.00033685422,0.000026502692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019437114,0.00009099255,0.00014897258,0.00029918403,0.000047732654,0.00014944738,0.00046068185,0.00007634515,0.0000027712533],"category_scores_gemma":[0.00011840718,0.0000862317,0.000050890878,0.00014844816,0.000077085555,0.0006551105,0.00013709675,0.00015043371,0.0000120673],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014440135,0.00024651655,0.0030020121,0.000007649857,0.00006730057,0.000002672506,0.0000533974,0.000030183366,0.004068218,0.0032275196,0.00008435454,0.98919576],"study_design_scores_gemma":[0.009623462,0.0012130246,0.26021475,0.0012575424,0.00012974572,0.0009343706,0.0040956927,0.046699025,0.1265455,0.5180343,0.029446801,0.0018058424],"about_ca_topic_score_codex":0.000014753714,"about_ca_topic_score_gemma":0.000013029623,"teacher_disagreement_score":0.9873899,"about_ca_system_score_codex":0.0000962943,"about_ca_system_score_gemma":0.000026937805,"threshold_uncertainty_score":0.3516427},"labels":[],"label_agreement":null},{"id":"W202039301","doi":"10.1007/978-3-642-18472-7_6","title":"Dynamic Routing Using Health Information Policy with Apache Camel","year":2011,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Routing (electronic design automation); Computer science; Policy-based routing; Adaptive routing; Architecture; Distributed computing; Static routing; Computer network; Routing protocol; World Wide Web; Geography","score_opus":0.07015197582868347,"score_gpt":0.3172567996501585,"score_spread":0.24710482382147503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W202039301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000114376744,0.0001316747,0.91818345,0.00103989,0.00015224976,0.0006137134,0.000022078117,0.00015538055,0.07958716],"genre_scores_gemma":[0.7199023,0.0011650709,0.27446413,0.0038014231,0.00004961873,0.000040631625,0.0001168966,0.000023536186,0.00043643656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997666,0.00007165968,0.0010274398,0.00027704405,0.0005862902,0.00037156107],"domain_scores_gemma":[0.9962308,0.00014434659,0.00096300547,0.0018226228,0.00066249137,0.00017673417],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001585842,0.00030147214,0.0003815711,0.0020964246,0.0007700151,0.0010165367,0.0026955616,0.00013233995,0.0000038414128],"category_scores_gemma":[0.00004194743,0.00029129296,0.000048316302,0.00087330997,0.0006441665,0.02170396,0.0019305688,0.0005277614,0.00006761829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003290904,0.000014378027,0.00004030392,0.000058487298,0.000010864812,2.0705583e-7,0.01134798,0.00027396757,0.0000013292142,0.3198966,0.000026956777,0.6683256],"study_design_scores_gemma":[0.00054931134,0.00013302002,0.0012276503,0.0008589178,0.0000065520144,0.00021995272,0.00016122598,0.95972323,0.0000056709955,0.002446729,0.034047537,0.00062021444],"about_ca_topic_score_codex":0.00052644423,"about_ca_topic_score_gemma":0.00009060809,"teacher_disagreement_score":0.95944923,"about_ca_system_score_codex":0.0007180832,"about_ca_system_score_gemma":0.0017724615,"threshold_uncertainty_score":0.9999539},"labels":[],"label_agreement":null},{"id":"W2020599945","doi":"10.1109/memea.2012.6226657","title":"Stride time estimation: Realtime peak detection implemented on an 8-bit, portable microcontroller","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"STRIDE; Bluetooth; Microcontroller; Computer science; Accelerometer; Gait; Population; Real-time computing; Simulation; Wireless; Physical medicine and rehabilitation; Embedded system; Computer security; Medicine; Telecommunications","score_opus":0.018214206094249816,"score_gpt":0.2718981229402892,"score_spread":0.25368391684603936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020599945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1121525,0.000037742593,0.86504704,0.000657648,0.0006582636,0.00094631116,0.000015582136,0.0009190418,0.019565882],"genre_scores_gemma":[0.99051005,8.856352e-7,0.005579315,0.00045832037,0.00022375258,0.000074363394,0.000025420588,0.000017067627,0.0031108556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816203,0.00019718432,0.0004040166,0.00039569792,0.00035070602,0.0004903935],"domain_scores_gemma":[0.9985726,0.00016768098,0.00020601922,0.00065260293,0.00014988087,0.00025120095],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00084821245,0.00020307586,0.00025569514,0.00020417145,0.00021587324,0.00021412202,0.00036856625,0.0000795487,0.0006732113],"category_scores_gemma":[0.000046273824,0.00018779228,0.00008234027,0.00034569012,0.000014384485,0.0024005128,0.00009374613,0.00013545422,0.001964936],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011318627,0.00086495554,0.0011814511,0.000025412352,0.00014830973,0.000008203117,0.00105333,0.00011619471,0.08371636,0.0020721604,0.012129351,0.8985711],"study_design_scores_gemma":[0.007284272,0.0016072413,0.031346634,0.00011427231,0.000088602916,0.0005151404,0.00025518672,0.6349666,0.22723529,0.0014188148,0.09310718,0.0020607668],"about_ca_topic_score_codex":0.00039858662,"about_ca_topic_score_gemma":0.00011520864,"teacher_disagreement_score":0.8965103,"about_ca_system_score_codex":0.00013789927,"about_ca_system_score_gemma":0.000056729234,"threshold_uncertainty_score":0.99881214},"labels":[],"label_agreement":null},{"id":"W2020884679","doi":"10.1007/s11227-013-1056-y","title":"Integrating the enriched feature with machine learning algorithms for human movement and fall detection","year":2013,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wearable computer; Artificial intelligence; Accelerometer; Wireless sensor network; Feature (linguistics); Wireless; Machine learning; Algorithm; Artificial neural network; Computer vision; Embedded system; Telecommunications","score_opus":0.020646474950314,"score_gpt":0.24443531803512167,"score_spread":0.22378884308480768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020884679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4610828,0.00018332386,0.53725535,0.0011070526,0.000097230586,0.00022338009,2.0540453e-7,0.000021874199,0.000028780498],"genre_scores_gemma":[0.98935914,0.00000613046,0.010097034,0.00021909471,0.00024887096,0.0000062068902,3.762853e-7,0.0000116106785,0.000051542058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988263,0.00028089024,0.00030225897,0.0001281068,0.0002652072,0.00019722283],"domain_scores_gemma":[0.9983997,0.0007361885,0.00033385388,0.00015675672,0.0003216288,0.00005187888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017627224,0.00013696431,0.00019714532,0.00007871517,0.00067776756,0.0003306183,0.00045037648,0.000037433085,0.000002045262],"category_scores_gemma":[0.00008731128,0.00006609423,0.00006223834,0.00018170358,0.0000409202,0.0005741409,0.00014041313,0.0005444789,0.0000013637562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002291344,0.00004749536,0.0046212617,0.000040255818,0.00018379223,0.000003923327,0.017135713,0.0007177784,0.085891545,0.0002780858,0.000084517065,0.89097273],"study_design_scores_gemma":[0.0021225282,0.0016697213,0.014171846,0.0004179057,0.00009700338,0.0016572542,0.013531992,0.9541346,0.008294295,0.0026427167,0.000823711,0.00043644168],"about_ca_topic_score_codex":0.00048731556,"about_ca_topic_score_gemma":0.0001854787,"teacher_disagreement_score":0.9534168,"about_ca_system_score_codex":0.000051047908,"about_ca_system_score_gemma":0.000022232469,"threshold_uncertainty_score":0.5212909},"labels":[],"label_agreement":null},{"id":"W2022271401","doi":"10.1109/syscon.2012.6189538","title":"Network of wireless medical devices to assess activity using non-traditional sensors","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Wearable computer; Software deployment; Computer science; Scalability; Biotelemetry; Wireless; Body area network; Wireless sensor network; Mobile device; Wearable technology; Embedded system; Remote patient monitoring; Architecture; Telecommunications; Computer network; Telemetry; World Wide Web","score_opus":0.15460699803594358,"score_gpt":0.32541722453430694,"score_spread":0.17081022649836336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022271401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5174841,0.000006761327,0.47989473,0.00036540272,0.0005979497,0.000109283465,0.0000028151378,0.00004482883,0.0014940923],"genre_scores_gemma":[0.9884425,5.714666e-7,0.010430486,0.000450233,0.000611923,0.000011027665,0.0000012739454,0.000009390274,0.00004262863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979897,0.00021021657,0.00027385034,0.00027569436,0.0008381924,0.00041233213],"domain_scores_gemma":[0.99826354,0.0006979047,0.00015118705,0.00033306988,0.00017546487,0.0003788639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010428318,0.00014738907,0.0003041554,0.00007574273,0.00010176113,0.00006631515,0.00051714684,0.000113088754,0.00016497476],"category_scores_gemma":[0.00007226085,0.0001364515,0.00007937703,0.00052697904,0.00004366525,0.0012233935,0.0002523163,0.0001537133,0.00006911864],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014250561,0.003862699,0.4093826,0.00056266383,0.00071505527,0.000087740664,0.0044740275,0.0023665659,0.056413013,0.07193891,0.017660372,0.43239382],"study_design_scores_gemma":[0.0013762858,0.00023834575,0.45565748,0.0009007721,0.00005797037,0.0007558165,0.00029208217,0.4773471,0.05601505,0.0008263157,0.0048474427,0.0016853326],"about_ca_topic_score_codex":0.00019501807,"about_ca_topic_score_gemma":0.00008700561,"teacher_disagreement_score":0.47498056,"about_ca_system_score_codex":0.00007664843,"about_ca_system_score_gemma":0.00019692112,"threshold_uncertainty_score":0.55643314},"labels":[],"label_agreement":null},{"id":"W2022586732","doi":"10.1108/17427370580000113","title":"Towards a smart world and ubiquitous intelligence: A walkthrough from smart things to smart hyperspaces and UbicKids","year":2005,"lang":"en","type":"article","venue":"International Journal of Pervasive Computing and Communications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":147,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Hyperspace; Smart environment; Computer science; Smart objects; Ubiquitous computing; Internet of Things; Smart device; Ambient intelligence; Variety (cybernetics); Human–computer interaction; World Wide Web; Artificial intelligence","score_opus":0.039675071149730706,"score_gpt":0.32469667112129064,"score_spread":0.28502159997155996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022586732","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5861087,0.005064069,0.3126713,0.09323946,0.00084513606,0.00024728235,0.000022502767,0.00008934755,0.0017122352],"genre_scores_gemma":[0.927773,0.00067522016,0.06916872,0.0020384404,0.00023617376,0.000004900564,0.000003797582,0.000010503462,0.000089273],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99830234,0.00022267066,0.0005729569,0.00028889868,0.0004330072,0.00018011776],"domain_scores_gemma":[0.996922,0.0010185058,0.00040176406,0.00048794,0.00096174935,0.00020803258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067977654,0.00018649966,0.00031324365,0.00037402744,0.00029754068,0.00062436145,0.0015675355,0.000056000063,0.000010217034],"category_scores_gemma":[0.00030093032,0.00017933607,0.00008378171,0.00025219904,0.00016693865,0.00095076795,0.0015091434,0.000398663,0.0000099184035],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003457244,0.00019311525,0.013663053,0.000009805701,0.00034474532,0.000017250544,0.022984797,0.000041972216,0.0007343585,0.005746102,0.00090402854,0.9553262],"study_design_scores_gemma":[0.004882213,0.001398017,0.18547519,0.005161891,0.00037050084,0.0063889804,0.022572637,0.28195384,0.0054196394,0.026741639,0.45679262,0.0028428382],"about_ca_topic_score_codex":0.0008367698,"about_ca_topic_score_gemma":0.0007224699,"teacher_disagreement_score":0.95248336,"about_ca_system_score_codex":0.00010710177,"about_ca_system_score_gemma":0.00012189224,"threshold_uncertainty_score":0.73131126},"labels":[],"label_agreement":null},{"id":"W2024400101","doi":"10.1145/1551722.1551724","title":"Next generation of transportation &amp; information technologies","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Becton Dickinson (Canada)","funders":"","keywords":"The Internet; Cyberspace; Emerging technologies; Telecommunications; Intelligent transportation system; Computer science; Information technology; Engineering management; World Wide Web; Engineering; Transport engineering","score_opus":0.07174669158726678,"score_gpt":0.26136071559388707,"score_spread":0.1896140240066203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024400101","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09399609,0.000023208571,0.9025721,0.0011413126,0.00009879005,0.00013641495,0.0000026302964,0.00042043842,0.0016090054],"genre_scores_gemma":[0.9813168,0.000008904941,0.018461136,0.00012502799,0.000010328281,0.0000065490117,0.00003084115,9.2037817e-7,0.00003951212],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993956,0.000015021042,0.00025784798,0.00009063435,0.00016620848,0.00007468674],"domain_scores_gemma":[0.9994568,0.000019801515,0.00012808458,0.00023050231,0.00015243543,0.000012328846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011738332,0.000058556456,0.000088646324,0.00015045596,0.00003303227,0.00008085732,0.00020366335,0.000057810186,0.000009720605],"category_scores_gemma":[0.00002545789,0.000053837462,0.000032772885,0.0002928081,0.000011091267,0.0031423904,0.0000058126548,0.000041294315,0.00005636757],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020068358,0.000026470463,0.00013555639,0.0000075004314,0.0000041932185,1.6167085e-7,0.0008375818,0.000038621256,0.030084943,0.022137992,0.0011879031,0.9455371],"study_design_scores_gemma":[0.0021205633,0.000725188,0.046792112,0.00012829452,0.000029936626,0.000042339238,0.0016551538,0.119967,0.7039703,0.018275747,0.105089314,0.0012040692],"about_ca_topic_score_codex":0.000025888261,"about_ca_topic_score_gemma":0.00007301233,"teacher_disagreement_score":0.944333,"about_ca_system_score_codex":0.000016364542,"about_ca_system_score_gemma":0.000022517204,"threshold_uncertainty_score":0.22781572},"labels":[],"label_agreement":null},{"id":"W2024434863","doi":"10.1145/1387663.1387665","title":"Activity recognition via user-trace segmentation","year":2008,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Research Grants Council, University Grants Committee","keywords":"Computer science; Activity recognition; Wireless sensor network; Probabilistic logic; Segmentation; TRACE (psycholinguistics); Data mining; Wireless; Artificial intelligence; Real-time computing; Machine learning; Pattern recognition (psychology); Computer network; Telecommunications","score_opus":0.045612878750203964,"score_gpt":0.25737554445987426,"score_spread":0.21176266570967028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024434863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10708833,0.000033609092,0.8896257,0.0006926109,0.001090225,0.0004951551,0.00001528547,0.0006023423,0.00035672437],"genre_scores_gemma":[0.96790487,0.0001427415,0.030593317,0.00033649127,0.00018382898,0.000119908,0.000012913927,0.00003474628,0.00067120575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976653,0.000351829,0.000364041,0.000709753,0.00046524688,0.00044386127],"domain_scores_gemma":[0.9976618,0.00065525854,0.0002095486,0.0010733987,0.00021226027,0.00018770597],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002589508,0.00031046226,0.00030985495,0.00025510768,0.000658965,0.00012581226,0.00057192595,0.00022890767,0.00013440846],"category_scores_gemma":[0.00002806558,0.0003404325,0.00021547855,0.0008148844,0.000077361554,0.0014998318,0.000018077075,0.00055652607,0.0005074558],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081610655,0.00040678098,0.00014532925,0.000010411252,0.000090708636,0.000045920802,0.0003986206,0.006251909,0.0031660746,0.0000053278445,0.00038157016,0.98901576],"study_design_scores_gemma":[0.010708492,0.0023804633,0.034289364,0.0005951602,0.00038598714,0.00619697,0.0008029256,0.7114968,0.20569591,0.0020202743,0.01948541,0.005942301],"about_ca_topic_score_codex":0.00014071466,"about_ca_topic_score_gemma":0.00008602491,"teacher_disagreement_score":0.9830734,"about_ca_system_score_codex":0.00017308092,"about_ca_system_score_gemma":0.00005153367,"threshold_uncertainty_score":0.99990475},"labels":[],"label_agreement":null},{"id":"W2025469679","doi":"10.1145/2769493.2769554","title":"Maintaining good relationships in clinical setting","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Residence; Living lab; Quality (philosophy); Simple (philosophy); Computer science; Face (sociological concept); Psychology; Engineering management; Engineering; World Wide Web; Sociology","score_opus":0.19451131213925732,"score_gpt":0.3591807188624565,"score_spread":0.16466940672319919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025469679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10485949,0.000030488029,0.76636386,0.003525979,0.00073487835,0.00021065693,6.303276e-7,0.00036767285,0.12390632],"genre_scores_gemma":[0.9770894,4.7276404e-7,0.02205426,0.00028494376,0.00011130253,0.000010521751,0.0000011002962,0.0000052842206,0.00044271277],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816996,0.00063037314,0.00048126816,0.00030401358,0.00021737456,0.00019702334],"domain_scores_gemma":[0.99828273,0.0010207413,0.000108554654,0.0003441298,0.00009638861,0.00014745015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047864197,0.000076118835,0.00015401306,0.0001211732,0.000051788487,0.00011987116,0.0003541045,0.000081539336,0.000010661861],"category_scores_gemma":[0.0012236023,0.00007420634,0.00004793848,0.00036223364,0.000019027148,0.0007260404,0.00020707614,0.0003415151,0.0004311504],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006172369,0.000111244684,0.70154333,0.0000038987123,0.000013031001,0.000043593827,0.002007673,0.00010156835,0.0000059382924,0.084519416,0.0021209517,0.20952316],"study_design_scores_gemma":[0.0048505603,0.0003346639,0.37742633,0.000244861,0.000010093662,0.00028880613,0.006157126,0.50993985,0.00013427294,0.064339355,0.035192993,0.001081106],"about_ca_topic_score_codex":0.00007221187,"about_ca_topic_score_gemma":0.00027013363,"teacher_disagreement_score":0.87222993,"about_ca_system_score_codex":0.000072141156,"about_ca_system_score_gemma":0.00015680295,"threshold_uncertainty_score":0.55417097},"labels":[],"label_agreement":null},{"id":"W2025517477","doi":"10.1109/ipin.2011.6071911","title":"Detection of quasi-static instants from handheld MEMS devices","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Mobile device; Computer science; Detector; Accelerometer; Mobile phone; Classifier (UML); Decision tree; Phone; Real-time computing; Artificial intelligence; Telecommunications; Operating system","score_opus":0.06899486670745852,"score_gpt":0.25056963881811006,"score_spread":0.18157477211065154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025517477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35848022,0.000040442086,0.634752,0.000025368663,0.00036105706,0.00011367541,0.0000049330197,0.00012243005,0.006099893],"genre_scores_gemma":[0.99533665,0.0000033409513,0.004401113,0.00010187549,0.000023178402,0.00001283797,8.9129173e-7,0.0000059046897,0.000114209775],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900854,0.000076826946,0.00028548788,0.00026551768,0.00022537967,0.00013822292],"domain_scores_gemma":[0.9990913,0.00017161452,0.00017655944,0.00037582082,0.00012311476,0.00006156091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019207869,0.000100551064,0.00019304384,0.00011045076,0.000046718284,0.000047415364,0.00039583835,0.000053130167,0.00014443255],"category_scores_gemma":[0.000039570914,0.00008862086,0.000053374755,0.00026837602,0.000026976551,0.00079199683,0.0000969452,0.00006421581,0.00016409234],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037963684,0.00040070075,0.010608644,0.000053480573,0.00012947017,0.000012024226,0.008621562,4.5564275e-7,0.059411842,0.0012430585,0.000093402625,0.9193874],"study_design_scores_gemma":[0.0010816739,0.00043333427,0.05213747,0.00016211516,0.000028611481,0.000021326778,0.0012319537,0.015702896,0.92051023,0.006580346,0.0016099727,0.0005000761],"about_ca_topic_score_codex":0.0042717117,"about_ca_topic_score_gemma":0.003509633,"teacher_disagreement_score":0.9188873,"about_ca_system_score_codex":0.000022575217,"about_ca_system_score_gemma":0.000036029356,"threshold_uncertainty_score":0.64575785},"labels":[],"label_agreement":null},{"id":"W2025543936","doi":"10.1145/1920778.1920800","title":"Activate your GAIM","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Queen's University","funders":"","keywords":"Computer science; Accelerometer; Code (set theory); Human–computer interaction; Programming language; Operating system","score_opus":0.028096094874281594,"score_gpt":0.26944222746602026,"score_spread":0.24134613259173865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025543936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28044933,0.0000045569354,0.5790354,0.0023669186,0.001971865,0.00017242403,0.0000014406542,0.00070169667,0.13529634],"genre_scores_gemma":[0.98561704,3.6491227e-7,0.0083433995,0.00041698015,0.00012330216,0.00001209101,4.5883425e-7,0.0000057474263,0.0054805954],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923384,0.000026635855,0.00011969811,0.00026146366,0.00017813833,0.0001802284],"domain_scores_gemma":[0.9991743,0.00007793806,0.000049568323,0.000525515,0.00007896566,0.000093707946],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019361293,0.0000850576,0.00010077396,0.00006841387,0.00006785396,0.00018238844,0.0005493139,0.00005539239,0.00026520877],"category_scores_gemma":[0.000047121357,0.000072969626,0.000050360984,0.00019042204,0.000017654025,0.00076122105,0.00016504181,0.00019193269,0.0012031236],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037698735,0.00011468088,0.0023501692,0.0000077174955,0.000025600191,0.000017851915,0.0007515839,5.336761e-7,0.1942885,0.041454062,0.005848463,0.7551371],"study_design_scores_gemma":[0.0010246963,0.000094915624,0.01911389,0.000021167092,0.000006237589,0.0003503042,0.00009722235,0.012583819,0.26078343,0.008117503,0.69695294,0.0008538512],"about_ca_topic_score_codex":0.00011003951,"about_ca_topic_score_gemma":0.00011816569,"teacher_disagreement_score":0.7542832,"about_ca_system_score_codex":0.000015959962,"about_ca_system_score_gemma":0.0000873803,"threshold_uncertainty_score":0.99957454},"labels":[],"label_agreement":null},{"id":"W2025831891","doi":"10.1109/icosc.2015.7050790","title":"&amp;#x201C;CANthings&amp;#x201D;(Context Aware Network for the Design of Connected Things) service modeling based on Timed CPN","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Context (archaeology); Service (business); Computer network; Business","score_opus":0.19784419481727009,"score_gpt":0.297851897329779,"score_spread":0.10000770251250893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025831891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001700411,0.00017625678,0.9898302,0.0047237747,0.00095434353,0.0016238905,0.000017075388,0.0004877586,0.0004862656],"genre_scores_gemma":[0.8404033,0.000003772245,0.15097319,0.007482699,0.00028301848,0.0002409318,0.000028351904,0.000056327313,0.0005283971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99657285,0.00055087317,0.0007233193,0.0007480176,0.00077864423,0.00062628085],"domain_scores_gemma":[0.9919836,0.004279882,0.00042311093,0.0014577522,0.001593931,0.00026173645],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031077163,0.00039950607,0.0005883406,0.00016165085,0.000336448,0.00029598377,0.0017000114,0.00020534893,0.000056575278],"category_scores_gemma":[0.00073123374,0.0002942774,0.00019354411,0.00084980053,0.000050320356,0.00092896476,0.0002386388,0.00030932136,0.00020511122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008129456,0.00040391996,0.00017244644,0.00024242394,0.00034002116,0.000001983987,0.008358709,0.9148447,0.0014980988,0.0069632814,0.032576125,0.033785332],"study_design_scores_gemma":[0.0013460937,0.0001338113,0.000009615919,0.0002448392,0.00003746926,0.0000111156905,0.00029629385,0.9813019,0.00022286798,0.0014397966,0.014570498,0.00038570917],"about_ca_topic_score_codex":0.0016961105,"about_ca_topic_score_gemma":0.0010476152,"teacher_disagreement_score":0.83885705,"about_ca_system_score_codex":0.00014167519,"about_ca_system_score_gemma":0.0005364446,"threshold_uncertainty_score":0.99995095},"labels":[],"label_agreement":null},{"id":"W2026490487","doi":"10.1587/transcom.e95.b.3067","title":"Application of a Telemedical Tool in an Isolated Island and a Disaster Area of the Great East Japan Earthquake","year":2012,"lang":"en","type":"article","venue":"IEICE Transactions on Communications","topic":"Context-Aware Activity Recognition Systems","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":"Institute of Aging","funders":"Omron Healthcare","keywords":"Software portability; Operability; Computer science; Medical emergency; Telecommunications; Computer security; Medicine","score_opus":0.035561585453955824,"score_gpt":0.2703782153976063,"score_spread":0.2348166299436505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026490487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35219428,0.00007429631,0.6455982,0.0013045988,0.00005568895,0.00041897068,0.00003999614,0.00003477401,0.00027920544],"genre_scores_gemma":[0.9984173,0.000021778784,0.0013128946,0.00006199077,0.0000077189825,0.00013320564,0.0000055058786,0.000007680145,0.000031912914],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99876004,0.00033804393,0.0003876091,0.00016390823,0.00019783231,0.0001525442],"domain_scores_gemma":[0.997455,0.0004116387,0.00016106179,0.0018038102,0.00010886993,0.000059597325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034584405,0.00010580316,0.0001821456,0.00014569143,0.00015682065,0.000027766931,0.00086360855,0.00007640281,0.000017362961],"category_scores_gemma":[0.000021540933,0.00008805684,0.000061275205,0.00060206826,0.00019875121,0.0006413042,0.000044336583,0.0002547478,0.0000061319543],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008031925,0.003758375,0.07607114,0.000088142624,0.00011634578,1.673737e-7,0.034940723,0.00019639434,0.006923707,0.005034083,0.000029525308,0.8727611],"study_design_scores_gemma":[0.0037800262,0.00059119286,0.5767257,0.00063435617,0.00018419648,0.00018730354,0.004857585,0.40057877,0.0024170813,0.001079287,0.008028552,0.00093589886],"about_ca_topic_score_codex":0.00023381527,"about_ca_topic_score_gemma":0.0042978725,"teacher_disagreement_score":0.87182516,"about_ca_system_score_codex":0.00002284886,"about_ca_system_score_gemma":0.00004290703,"threshold_uncertainty_score":0.35908538},"labels":[],"label_agreement":null},{"id":"W2027111352","doi":"10.1108/17427371211262644","title":"Novel model for inhabitants prediction in smart houses","year":2012,"lang":"en","type":"article","venue":"International Journal of Pervasive Computing and Communications","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke","funders":"","keywords":"Computer science; Support vector machine; Feature selection; Classifier (UML); Artificial intelligence; Machine learning; Data mining; Feature (linguistics); Pattern recognition (psychology)","score_opus":0.10095738267091194,"score_gpt":0.346245863919207,"score_spread":0.24528848124829505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027111352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14387797,0.00054595486,0.852518,0.0021633187,0.000558365,0.00010703783,0.000020931111,0.000023077273,0.00018529639],"genre_scores_gemma":[0.9363723,0.000069875765,0.06317712,0.00019463323,0.00014415685,0.000007074804,0.0000042029164,0.000006447821,0.000024234745],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989917,0.0000756614,0.00046454318,0.00009564346,0.00023380067,0.00013862872],"domain_scores_gemma":[0.9978009,0.0006615161,0.00036825676,0.00029948086,0.0007962763,0.00007360151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007753511,0.00008428842,0.00015397806,0.0002654354,0.00011095716,0.0001115904,0.0011341294,0.00004179061,8.9539725e-7],"category_scores_gemma":[0.00029433938,0.000083797946,0.00007785414,0.000121328245,0.000046101453,0.00087268,0.00039333128,0.00018402848,0.0000019786396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011397485,0.003218612,0.4629427,0.000058061174,0.00068491686,0.000005084818,0.039585847,0.0059672478,0.012403229,0.048889633,0.0020145592,0.42411616],"study_design_scores_gemma":[0.0012029966,0.000059157213,0.076756105,0.0002653946,0.00001432545,0.00043865986,0.00041443753,0.9180721,0.00015778768,0.0012806483,0.001201344,0.00013701721],"about_ca_topic_score_codex":0.000023848914,"about_ca_topic_score_gemma":0.000041539675,"teacher_disagreement_score":0.9121049,"about_ca_system_score_codex":0.000100180565,"about_ca_system_score_gemma":0.00009274257,"threshold_uncertainty_score":0.3417181},"labels":[],"label_agreement":null},{"id":"W2028156639","doi":"10.1109/cis.2014.30","title":"A Wireless Sensor System for the Training of Hammer Throwers","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Lethbridge","funders":"","keywords":"Hammer; Wireless; Computer science; Simulation; Tracing; Training (meteorology); Engineering; Mechanical engineering; Telecommunications","score_opus":0.05363994307655993,"score_gpt":0.25770413518310553,"score_spread":0.2040641921065456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028156639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012808027,0.000015659618,0.97881037,0.0008368259,0.00056769256,0.00036405204,0.0000031016152,0.00015034454,0.0064439336],"genre_scores_gemma":[0.995333,4.25184e-7,0.003940778,0.0001526926,0.00009034315,0.0000591635,3.6034527e-7,0.000008084295,0.0004151186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990605,0.000096657364,0.0002355802,0.00022625797,0.00019599339,0.00018497863],"domain_scores_gemma":[0.99794483,0.0012753333,0.00013824605,0.0004524059,0.0001475595,0.000041614883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079365866,0.00009228108,0.00021429318,0.000045966353,0.00010547658,0.0000744103,0.00048808058,0.000039914365,0.0000060371403],"category_scores_gemma":[0.000056302328,0.00005992513,0.000109331355,0.00015546223,0.000041242936,0.0002058807,0.00006494524,0.00004971229,0.00002041012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001749138,0.000040382292,0.00024229819,0.00022314388,0.00013940004,0.0000010901567,0.0070795123,0.0000480525,0.006474869,0.106315285,0.0015619285,0.87785655],"study_design_scores_gemma":[0.0017753224,0.00023672727,0.00060677226,0.00028278906,0.00004389522,0.00011868279,0.011985303,0.9183939,0.030413978,0.00033178728,0.035335273,0.00047557135],"about_ca_topic_score_codex":0.00009341264,"about_ca_topic_score_gemma":0.000039678078,"teacher_disagreement_score":0.982525,"about_ca_system_score_codex":0.000022124601,"about_ca_system_score_gemma":0.000039640378,"threshold_uncertainty_score":0.24436758},"labels":[],"label_agreement":null},{"id":"W2029470807","doi":"10.1109/memea.2013.6549764","title":"Toward a context awareness framework for healthcare applications","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Health care; Computer science; Context (archaeology); Context awareness; Knowledge management; Political science","score_opus":0.08851238108694713,"score_gpt":0.3291975592031693,"score_spread":0.24068517811622214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029470807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007453165,0.0001437806,0.9780871,0.017453477,0.00031854957,0.002027607,0.000013325425,0.00045263986,0.0007581832],"genre_scores_gemma":[0.9157462,0.0000041474127,0.07610055,0.0027425599,0.00016137623,0.0047746496,0.0000071655595,0.00001312071,0.0004502097],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986889,0.00005903461,0.00028700905,0.00046471646,0.00018884311,0.0003115151],"domain_scores_gemma":[0.9978655,0.000650868,0.00011457141,0.0006831004,0.0005087616,0.00017719509],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016297006,0.00014266773,0.00022915217,0.00008651606,0.00018704153,0.00030896315,0.00072913815,0.00011859983,0.00009910912],"category_scores_gemma":[0.00006597097,0.00013100245,0.00010617866,0.00032857602,0.000028465185,0.0006616273,0.00013728504,0.000118252654,0.0008317772],"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":[0.0000016673421,0.00005443495,0.0007830258,0.0000693316,0.00001724566,2.8548718e-7,0.0005219691,5.492873e-7,0.00004209237,0.4074266,0.0032609354,0.5878219],"study_design_scores_gemma":[0.0014403993,0.0003193076,0.005303457,0.0002427941,0.000017081416,0.00008478635,0.00274477,0.026724925,0.00424695,0.6891678,0.2683552,0.0013525262],"about_ca_topic_score_codex":0.0009924823,"about_ca_topic_score_gemma":0.00013004617,"teacher_disagreement_score":0.9150009,"about_ca_system_score_codex":0.000060067272,"about_ca_system_score_gemma":0.00014720937,"threshold_uncertainty_score":0.9999462},"labels":[],"label_agreement":null},{"id":"W2030864203","doi":"10.1145/1953005.1953006","title":"Homogeneous accelerometer-based sensor networks for game interaction","year":2011,"lang":"en","type":"article","venue":"Computers in entertainment","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Wearable computer; Context (archaeology); Human–computer interaction; Accelerometer; Set (abstract data type); Embedded system","score_opus":0.06615402132517188,"score_gpt":0.26562355480345434,"score_spread":0.19946953347828245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030864203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08081265,0.000035079396,0.91539574,0.0001437909,0.0024085683,0.0007493001,0.000003606113,0.00017173954,0.00027951653],"genre_scores_gemma":[0.93549377,0.0000045861425,0.06338726,0.0007166182,0.00010562762,0.00023249889,0.000010150202,0.000020735539,0.000028751154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809617,0.0001602885,0.00046659322,0.00061723095,0.00021784114,0.00044185287],"domain_scores_gemma":[0.9986448,0.0003488092,0.00020330178,0.00060365873,0.00007989328,0.000119511096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032274757,0.00025041722,0.00030814993,0.0003150319,0.000060140155,0.00014366486,0.0007784187,0.000088163106,0.000021782129],"category_scores_gemma":[0.000017355827,0.000260929,0.00017891462,0.00025918023,0.00003167447,0.00037106083,0.00022190502,0.00015581452,0.00002463891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051451824,0.0027633864,0.015003124,0.00019907442,0.0002990118,0.00029053245,0.00724181,0.012154192,0.0010418097,0.0010285502,0.0022753987,0.9571886],"study_design_scores_gemma":[0.0021811228,0.0006127087,0.0038817774,0.00025405156,0.000012868855,0.0000691034,0.000121291916,0.984918,0.0032801097,0.00032368707,0.0038251332,0.0005201727],"about_ca_topic_score_codex":0.000050401075,"about_ca_topic_score_gemma":0.000030267885,"teacher_disagreement_score":0.9727638,"about_ca_system_score_codex":0.00030311837,"about_ca_system_score_gemma":0.000030815117,"threshold_uncertainty_score":0.99998426},"labels":[],"label_agreement":null},{"id":"W2031500487","doi":"10.1109/mis.2004.70","title":"Negotiating Context Information in Context-Aware Systems","year":2004,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Middleware (distributed applications); Provisioning; Ubiquitous computing; Context (archaeology); Ontology; Context awareness; Negotiation; Human–computer interaction; Distributed computing; Protocol (science); Set (abstract data type); Context model; World Wide Web; Computer network; Artificial intelligence","score_opus":0.03226189978936599,"score_gpt":0.2547535974250463,"score_spread":0.22249169763568027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031500487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07674176,0.0018143778,0.9025345,0.00028730792,0.013906939,0.0024888471,0.00004555411,0.00067187636,0.0015088647],"genre_scores_gemma":[0.998466,0.000038033922,0.000064371605,0.00026662857,0.0003732578,0.00046139903,0.000023076158,0.0000355653,0.00027167084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948848,0.00047160275,0.0020862862,0.0006971976,0.0010259155,0.00083419686],"domain_scores_gemma":[0.9965386,0.0004774381,0.000954995,0.001061985,0.00067064964,0.0002963798],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014555977,0.00053685595,0.0008983128,0.00079227285,0.00022335636,0.0016762667,0.0013286879,0.0003341961,0.000008269201],"category_scores_gemma":[0.0001846042,0.00053750107,0.00020035889,0.00096409075,0.00007442963,0.0039731124,0.0001650934,0.0005200017,0.0020423932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001970724,0.0013065956,0.0118422955,0.003186888,0.0007670093,0.0005094473,0.07478404,0.51232946,0.0015769816,0.16223125,0.008040827,0.22322813],"study_design_scores_gemma":[0.007176204,0.00084349886,0.0008521464,0.00869363,0.000062564686,0.0020613128,0.07121491,0.73038167,0.00871775,0.0006011747,0.16530152,0.004093641],"about_ca_topic_score_codex":0.0143562155,"about_ca_topic_score_gemma":0.0006266032,"teacher_disagreement_score":0.92172426,"about_ca_system_score_codex":0.0010809336,"about_ca_system_score_gemma":0.0003461305,"threshold_uncertainty_score":0.99970764},"labels":[],"label_agreement":null},{"id":"W2031723277","doi":"10.1016/j.procs.2011.07.041","title":"A Data Fusion Approach to Context-Aware Service Delivery in Heterogeneous Network Environments","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Context awareness; Context (archaeology); Ubiquitous computing; Adaptation (eye); Service (business); Key (lock); Context model; Human–computer interaction; World Wide Web; Data science; Computer security; Artificial intelligence; Object (grammar)","score_opus":0.09207404540182018,"score_gpt":0.24496586047412114,"score_spread":0.15289181507230096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031723277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07133725,0.00009313914,0.9259348,0.00017648908,0.00095204136,0.0008245845,0.000017038756,0.00019468386,0.00046999246],"genre_scores_gemma":[0.9014211,0.0000073174488,0.09576443,0.0025031592,0.0001842849,0.000077834775,0.000011441355,0.000016634582,0.000013783077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99561113,0.00012367923,0.0005102013,0.0019589192,0.000897807,0.0008982845],"domain_scores_gemma":[0.99702847,0.00010091104,0.00018954663,0.0020952206,0.00016038002,0.00042548197],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0014222366,0.00032614425,0.00035995524,0.00035226575,0.00029067154,0.0003611189,0.0068954593,0.000089887384,0.000008055649],"category_scores_gemma":[0.000034919136,0.00032579142,0.000042251555,0.0025015809,0.00013906963,0.0028196564,0.0063039092,0.00023522033,0.00030484408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010163795,0.0017838376,0.020683482,0.00018196323,0.000054496802,0.00016062097,0.021417879,0.0041811285,0.0013833799,0.0012134716,0.0026161005,0.946222],"study_design_scores_gemma":[0.0006066003,0.00016277011,0.01207275,0.0001554433,0.000006691705,0.00025165148,0.000065633794,0.9808806,0.00094794907,0.00032333654,0.0038119855,0.0007145907],"about_ca_topic_score_codex":0.00021453045,"about_ca_topic_score_gemma":0.00026792585,"teacher_disagreement_score":0.9766995,"about_ca_system_score_codex":0.0001596365,"about_ca_system_score_gemma":0.00029593267,"threshold_uncertainty_score":0.9999194},"labels":[],"label_agreement":null},{"id":"W2033484258","doi":"10.4236/jgis.2013.52015","title":"A Web Service for Managing Spatial Context Dedicated to Serious Games on and for Smartphones","year":2013,"lang":"en","type":"article","venue":"Journal of Geographic Information System","topic":"Context-Aware Activity Recognition Systems","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":"Université Laval; Centre de Géomatique du Québec","funders":"","keywords":"Computer science; Spatial contextual awareness; Context (archaeology); Augmented reality; Service (business); Geomatics; Human–computer interaction; Multimedia; Mobile device; Architecture; World Wide Web; Ubiquitous computing; Context awareness; Web service; Geography; Artificial intelligence; Cartography","score_opus":0.013472865999582452,"score_gpt":0.22706276777011983,"score_spread":0.21358990177053738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033484258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34419695,0.000041239826,0.64809924,0.004102322,0.0013699995,0.0018628505,0.00002400151,0.0000947412,0.00020863314],"genre_scores_gemma":[0.9957336,0.0000043306413,0.002209803,0.0016677937,0.00014476938,0.00021657629,0.000003962259,0.000008103147,0.0000110206465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826497,0.000065773784,0.00093034504,0.00012621001,0.000375973,0.00023671557],"domain_scores_gemma":[0.99665344,0.00025806963,0.00095126784,0.00021591804,0.0017316225,0.0001896586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009398632,0.00016402685,0.0003763719,0.00089621026,0.00017729908,0.0005092212,0.00038864528,0.0000805139,0.0000031165023],"category_scores_gemma":[0.000086689804,0.0001377062,0.0001437356,0.00041871544,0.000014330058,0.0025437458,0.000062432795,0.00010047231,0.000046449797],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048017802,0.000072629904,0.0060956725,0.0023934974,0.00044914,0.0000032875173,0.009852263,0.00016311412,0.00055695657,0.003622513,0.007601511,0.96870923],"study_design_scores_gemma":[0.022353025,0.0049160942,0.07822384,0.0060044113,0.00021836207,0.0032441749,0.025782002,0.6832889,0.0022805196,0.0010737716,0.17067009,0.0019448459],"about_ca_topic_score_codex":0.00017045876,"about_ca_topic_score_gemma":0.000066929155,"teacher_disagreement_score":0.9667644,"about_ca_system_score_codex":0.00005753307,"about_ca_system_score_gemma":0.00008295095,"threshold_uncertainty_score":0.5615496},"labels":[],"label_agreement":null},{"id":"W2033708485","doi":"10.1007/s11277-011-0387-3","title":"A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments","year":2011,"lang":"en","type":"article","venue":"Wireless Personal Communications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Provisioning; Context (archaeology); Ubiquitous computing; Task (project management); Service (business); Relevance (law); Reputation; World Wide Web; Context awareness; Human–computer interaction; Service provider; Telecommunications","score_opus":0.09774780789140862,"score_gpt":0.2818962769179205,"score_spread":0.18414846902651189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033708485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083328664,0.0009067442,0.9026552,0.0025011837,0.00013394991,0.0025689527,0.00007821055,0.00028187176,0.007545245],"genre_scores_gemma":[0.9317282,0.00006448751,0.06632492,0.00037476595,0.000020579546,0.00094496703,0.000071459595,0.000023982797,0.00044664356],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810725,0.00033856323,0.00037263436,0.0005009343,0.00031395038,0.0003666423],"domain_scores_gemma":[0.9977933,0.00040120052,0.00021451336,0.0013391625,0.00013078109,0.00012105192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049672136,0.00021467786,0.00029684248,0.00022992527,0.0004100563,0.00010548806,0.002380296,0.00010713361,0.000023396542],"category_scores_gemma":[0.00005012219,0.00022946073,0.0001358441,0.000673352,0.00011260692,0.00076536543,0.00074535084,0.00030211685,0.000048442755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047662228,0.011935173,0.056768358,0.0007759477,0.0007332666,0.000025169966,0.3613368,0.000051383002,0.011524002,0.24948217,0.0011403632,0.30575076],"study_design_scores_gemma":[0.0063101593,0.00024549288,0.035201423,0.0003525748,0.00007901199,0.00013589334,0.01801954,0.9044732,0.0007665482,0.0008750222,0.032023966,0.001517191],"about_ca_topic_score_codex":0.00045251515,"about_ca_topic_score_gemma":0.00022251258,"teacher_disagreement_score":0.9044218,"about_ca_system_score_codex":0.00019642693,"about_ca_system_score_gemma":0.00016568185,"threshold_uncertainty_score":0.93571365},"labels":[],"label_agreement":null},{"id":"W2034159722","doi":"10.1504/ijwgs.2010.035091","title":"A standard ontology for smart spaces","year":2010,"lang":"en","type":"article","venue":"International Journal of Web and Grid Services","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Ontology; Computer science; Ontology-based data integration; Process ontology; Middleware (distributed applications); Upper ontology; OWL-S; Suggested Upper Merged Ontology; Ontology Inference Layer; World Wide Web; Semantic Web; Database","score_opus":0.009728341160729395,"score_gpt":0.27457832287273737,"score_spread":0.26484998171200796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034159722","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9473083,0.00021721232,0.031126747,0.012803024,0.007757239,0.000095210824,0.00004451657,0.000023303572,0.00062446285],"genre_scores_gemma":[0.9928801,0.000030719424,0.0055997847,0.00052711525,0.0008987825,0.0000034936634,0.0000016371362,0.000003850012,0.00005450885],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99920744,0.000026356316,0.0002628095,0.000119138465,0.00028894164,0.00009529058],"domain_scores_gemma":[0.99850017,0.00022050092,0.00032075722,0.00008891139,0.00079756184,0.000072102455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040294425,0.00007485524,0.00016190481,0.00015942195,0.000043964916,0.00028983722,0.0006515668,0.00004809382,0.000020220234],"category_scores_gemma":[0.00003507214,0.000060979357,0.000072951705,0.000044532808,0.000024898212,0.0007528659,0.000088860834,0.00014051217,0.000004185077],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014253122,0.00050387654,0.15631668,0.00028755405,0.0017256197,0.0003355815,0.008425654,0.000019228712,0.09400301,0.056710742,0.009866308,0.6703804],"study_design_scores_gemma":[0.004118775,0.0007110059,0.019309519,0.00022790095,0.000037405196,0.0031935396,0.0005843922,0.0085997395,0.0066805454,0.015865266,0.9403023,0.00036958218],"about_ca_topic_score_codex":0.000041870022,"about_ca_topic_score_gemma":0.0012474806,"teacher_disagreement_score":0.930436,"about_ca_system_score_codex":0.0000136037725,"about_ca_system_score_gemma":0.00009123628,"threshold_uncertainty_score":0.2794907},"labels":[],"label_agreement":null},{"id":"W2034621254","doi":"10.1109/bibm.2014.6999236","title":"Assistance in smart homes: Combining passive RFID localization and load signatures of electrical devices","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Exploit; Cognition; Human–computer interaction; Independent living; Population; Order (exchange); Computer security; Business; Medicine","score_opus":0.011442626645521743,"score_gpt":0.23681792835085005,"score_spread":0.22537530170532832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034621254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112740785,0.00029811737,0.88365114,0.0002569774,0.00011224171,0.00015087724,7.377577e-7,0.00007730591,0.0027118048],"genre_scores_gemma":[0.99834114,0.0000073442247,0.0012935319,0.000278484,0.00001401143,0.000010399484,0.0000012343822,0.000004662763,0.000049160335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988365,0.00018091103,0.00026054034,0.00029109645,0.00027382522,0.00015712019],"domain_scores_gemma":[0.99893636,0.00049647805,0.00015083585,0.0001956254,0.00017070098,0.000050032555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034866112,0.00009969584,0.00023054681,0.00013221164,0.000041377545,0.00009888579,0.00023315146,0.00007482804,0.000006693682],"category_scores_gemma":[0.00018115476,0.00008937656,0.00002458214,0.0005077232,0.000036916124,0.00043914767,0.00007813622,0.00010414165,0.000004965866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006489404,0.00040201703,0.3384159,0.0002599817,0.00007435434,0.0000154141,0.003074509,0.0002567138,0.005744415,0.088611,0.0019375925,0.5611432],"study_design_scores_gemma":[0.0021801963,0.0004243999,0.18599568,0.00041136888,0.000012847498,0.000024228873,0.0002457584,0.78518766,0.012072561,0.0048693824,0.007946104,0.0006297982],"about_ca_topic_score_codex":0.00017729068,"about_ca_topic_score_gemma":0.00078718975,"teacher_disagreement_score":0.8856004,"about_ca_system_score_codex":0.000049487862,"about_ca_system_score_gemma":0.000061017832,"threshold_uncertainty_score":0.36446705},"labels":[],"label_agreement":null},{"id":"W2035675395","doi":"10.1109/embc.2014.6943521","title":"The effect of window size and lead time on pre-impact fall detection accuracy using support vector machine analysis of waist mounted inertial sensor data","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Accelerometer; Wearable computer; Gyroscope; Support vector machine; Inertial measurement unit; Computer science; Waist; Real-time computing; Artificial intelligence; Sensitivity (control systems); Event (particle physics); Simulation; Engineering; Medicine; Embedded system","score_opus":0.020674556368905903,"score_gpt":0.3100693474094995,"score_spread":0.28939479104059357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035675395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90302664,0.000009157024,0.09626125,0.000057725923,0.00009463029,0.00030398837,0.00006559774,0.00005747535,0.00012354525],"genre_scores_gemma":[0.99960434,0.000001350903,0.00020825109,0.00001931344,0.00004164449,0.0000035033218,0.000021258662,0.000009894835,0.000090446076],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794453,0.00063767313,0.0004180412,0.0004324606,0.00037218616,0.00019511936],"domain_scores_gemma":[0.9941244,0.0040972633,0.00039325628,0.0011937756,0.0001238883,0.00006742319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014233954,0.00019136381,0.0005088154,0.00020886322,0.00011660964,0.00013083716,0.0006173976,0.00006510634,0.00002628127],"category_scores_gemma":[0.0014563621,0.00011866347,0.00014158482,0.0007253155,0.00007181462,0.00057784276,0.00031998084,0.00011962312,0.0000085154215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007242856,0.000110538596,0.034537997,0.0000767213,0.001798989,0.000002656749,0.0003399804,0.00096131803,0.7826739,0.000008737633,0.000060568716,0.1787043],"study_design_scores_gemma":[0.00062225014,0.00068075233,0.07840648,0.000020113466,0.00028742093,0.000015627267,0.0000042063457,0.8544083,0.06536695,0.0000014989336,0.00005535265,0.00013101216],"about_ca_topic_score_codex":0.00359774,"about_ca_topic_score_gemma":0.001207516,"teacher_disagreement_score":0.853447,"about_ca_system_score_codex":0.00005766845,"about_ca_system_score_gemma":0.000034161665,"threshold_uncertainty_score":0.5438731},"labels":[],"label_agreement":null},{"id":"W2036006374","doi":"10.1145/2579700","title":"Automatic Task Assistance for People with Cognitive Disabilities in Brushing Teeth - A User Study with the TEBRA System","year":2014,"lang":"en","type":"article","venue":"ACM Transactions on Accessible Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Waterloo","funders":"Deutsche Forschungsgemeinschaft","keywords":"Cognition; Computer science; Activities of daily living; Task (project management); Dementia; Human–computer interaction; Independent living; Context (archaeology); Psychology; Applied psychology; Cognitive psychology; Medicine; Engineering; Gerontology","score_opus":0.022455848452372727,"score_gpt":0.27679017879686596,"score_spread":0.2543343303444932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036006374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42718762,0.000009817831,0.570632,0.0003682194,0.00011956277,0.0012362372,0.000006279355,0.00030691616,0.00013334994],"genre_scores_gemma":[0.9898355,3.021717e-7,0.009289285,0.0001482686,0.000054642456,0.000576857,0.000002073717,0.0000397526,0.00005332893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970777,0.00058591523,0.0005003736,0.0008048997,0.0005416181,0.0004894766],"domain_scores_gemma":[0.9925526,0.0057722228,0.00033431873,0.000988978,0.00026561818,0.00008630007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013324938,0.00034509343,0.00054136326,0.00026713646,0.0008863647,0.0009158417,0.0013321275,0.000063972744,0.000005543944],"category_scores_gemma":[0.00016504026,0.00023048448,0.00008742932,0.0013250895,0.0000894391,0.0012858693,0.000055382956,0.0003988468,0.000010830648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060925604,0.0031954583,0.1498268,0.0019798812,0.0008518017,0.000023222507,0.073042706,0.019906592,0.00007502854,0.0015399433,0.00007751362,0.7488718],"study_design_scores_gemma":[0.006735009,0.0020508145,0.12374983,0.0041012964,0.00020316137,0.00011704048,0.07628335,0.7848865,0.00044172662,0.00016965106,0.00010487043,0.0011567611],"about_ca_topic_score_codex":0.00040239314,"about_ca_topic_score_gemma":0.005563659,"teacher_disagreement_score":0.7649799,"about_ca_system_score_codex":0.0001999075,"about_ca_system_score_gemma":0.00013600053,"threshold_uncertainty_score":0.9398885},"labels":[],"label_agreement":null},{"id":"W2036495852","doi":"10.1109/iembs.2011.6091294","title":"A new safety support system for wandering elderly persons","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Wearable computer; Microphone; Presumption; Computer science; Phone; Mobile phone; The Internet; Sound (geography); Software; Computer security; Human–computer interaction; Internet privacy; Multimedia; World Wide Web; Telecommunications; Embedded system; Operating system","score_opus":0.06544796307141162,"score_gpt":0.24651325518564843,"score_spread":0.1810652921142368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036495852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000772826,0.000011605263,0.9283283,0.00019225267,0.000655179,0.00040363122,0.00000574002,0.0005553268,0.06907519],"genre_scores_gemma":[0.936186,4.5094365e-7,0.057334773,0.00008223358,0.00010117345,0.00004520382,0.0000017333483,0.000012708234,0.0062357243],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988554,0.00003594687,0.00025928224,0.00037864092,0.00017461846,0.00029610313],"domain_scores_gemma":[0.99909276,0.00010698639,0.00008876108,0.00044442422,0.0000884706,0.00017858479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031489923,0.00013587759,0.00021486368,0.000091147456,0.00013091283,0.00011607877,0.0005259254,0.00006285178,0.00015885751],"category_scores_gemma":[0.000016981865,0.0001238217,0.0001334948,0.00016202111,0.000009756664,0.0006475008,0.00011199768,0.00006305613,0.0002041546],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077232544,0.00009604668,0.001036636,0.00026566273,0.0001730837,0.00004041945,0.013859064,0.0000010169558,0.001381964,0.07160868,0.026477987,0.8849822],"study_design_scores_gemma":[0.023532772,0.007214997,0.013205531,0.0015604179,0.00034405483,0.005447013,0.02903952,0.08732981,0.1463729,0.006181405,0.6720293,0.007742254],"about_ca_topic_score_codex":0.00044334464,"about_ca_topic_score_gemma":0.00017013586,"teacher_disagreement_score":0.9354132,"about_ca_system_score_codex":0.00007948963,"about_ca_system_score_gemma":0.00017822531,"threshold_uncertainty_score":0.5049302},"labels":[],"label_agreement":null},{"id":"W2036845658","doi":"10.1109/iembs.2011.6091299","title":"Change-of-state determination to recognize mobility activities using a BlackBerry smartphone","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"","keywords":"Accelerometer; Timer; Computer science; Context (archaeology); Global Positioning System; Wearable computer; Elevator; Identification (biology); Real-time computing; Embedded system; Human–computer interaction; Microcontroller; Engineering","score_opus":0.17574512769410988,"score_gpt":0.2980822269059269,"score_spread":0.12233709921181701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036845658","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5770118,0.000013587214,0.42029575,0.000038901886,0.00034842658,0.00038519147,0.000005718912,0.00012489123,0.0017757013],"genre_scores_gemma":[0.95009637,0.0000036698268,0.049398486,0.00016570165,0.000042188625,0.000074446965,7.1026255e-7,0.000011638756,0.0002067608],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99851626,0.00016554554,0.0003460881,0.00043940623,0.0002664706,0.00026624353],"domain_scores_gemma":[0.99875623,0.0001705165,0.00018696031,0.0005461617,0.00021922922,0.00012087338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000566757,0.00016431524,0.0002774884,0.00025929228,0.0000671233,0.00005997398,0.00041762064,0.00005974111,0.00009616975],"category_scores_gemma":[0.00005317775,0.00016293222,0.000087797256,0.0005020279,0.000047934816,0.0015022923,0.00027899185,0.000082883234,0.000069537666],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053187778,0.00032308316,0.0043511665,0.000068345595,0.00002878886,0.000010426535,0.02000507,0.0000010262572,0.023684064,0.00013839648,0.000069302834,0.9512671],"study_design_scores_gemma":[0.00133146,0.0007399906,0.08821056,0.00037610432,0.00003106548,0.0001307026,0.0013082401,0.03856971,0.8619723,0.0045512756,0.0013464456,0.0014321917],"about_ca_topic_score_codex":0.0010940324,"about_ca_topic_score_gemma":0.00035644998,"teacher_disagreement_score":0.94983494,"about_ca_system_score_codex":0.00008601544,"about_ca_system_score_gemma":0.000052930383,"threshold_uncertainty_score":0.66441834},"labels":[],"label_agreement":null},{"id":"W2037690338","doi":"10.1145/1292331.1292346","title":"Context handling in a pervasive computing system framework","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National Research Council Canada","keywords":"Ubiquitous computing; Context-aware pervasive systems; Computer science; End-user computing; Context (archaeology); Architecture; Utility computing; Context awareness; Data science; World Wide Web; Human–computer interaction; Cloud computing; Operating system","score_opus":0.014335425138734247,"score_gpt":0.23209159746109914,"score_spread":0.21775617232236488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037690338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122239344,0.00017212899,0.86532813,0.00037547786,0.0005290133,0.00027153624,0.0000011484569,0.00053341803,0.01054981],"genre_scores_gemma":[0.9853523,4.379898e-7,0.013974492,0.00024699315,0.00018591224,0.00001447958,0.0000013061294,0.000010857014,0.00021323742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982831,0.00016179132,0.0004527452,0.00046993434,0.00028092536,0.00035149098],"domain_scores_gemma":[0.9985089,0.00072937174,0.00014977969,0.00041670472,0.00013591153,0.000059344256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004963284,0.00016608958,0.00032073059,0.00019700198,0.000121485704,0.0003150718,0.0005200439,0.000112687725,0.000011368449],"category_scores_gemma":[0.00006646686,0.00016111709,0.00008675325,0.0005554063,0.000024238654,0.00046595375,0.0002037796,0.00022888179,0.00022387333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019015215,0.00032437,0.073976524,0.00030656756,0.000056403576,0.00039223643,0.0052572098,0.0008604191,0.0009788853,0.6158202,0.0015802609,0.30042785],"study_design_scores_gemma":[0.0031490154,0.0001679066,0.043059908,0.004098566,0.000016882232,0.00069464615,0.008206962,0.91999125,0.006343624,0.006915423,0.0055779656,0.001777874],"about_ca_topic_score_codex":0.002023578,"about_ca_topic_score_gemma":0.00081720285,"teacher_disagreement_score":0.9191308,"about_ca_system_score_codex":0.00018162825,"about_ca_system_score_gemma":0.00007504366,"threshold_uncertainty_score":0.6570164},"labels":[],"label_agreement":null},{"id":"W2037957726","doi":"10.1109/comswa.2007.382486","title":"Mobile Agent Based Messaging Mechanism for Emergency Medical Data Transmission Over Cellular Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of New Brunswick","funders":"","keywords":"Computer science; Computer network; Cellular network; Middleware (distributed applications); Data transmission; Transmission (telecommunications); Data sharing; Mobile device; Mobile telephony; Mobile radio; Telecommunications; Distributed computing; World Wide Web; Medicine","score_opus":0.04589095341794447,"score_gpt":0.3089579749134307,"score_spread":0.2630670214954862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037957726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00070096535,0.00020816129,0.9949236,0.00033502645,0.0009202537,0.0006453523,0.000007591997,0.00028012166,0.001978962],"genre_scores_gemma":[0.95976007,0.00004608013,0.038462006,0.00073121296,0.00023409593,0.00006424013,0.000075272554,0.00002491745,0.00060210907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743867,0.00009299867,0.00052140595,0.00072905305,0.0007436328,0.00047423353],"domain_scores_gemma":[0.9980436,0.00028045606,0.00011706994,0.0010830536,0.00012481536,0.0003510162],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024935044,0.00019203722,0.00023669109,0.00011662929,0.000169364,0.00007152426,0.0016131792,0.00016930026,0.0010015866],"category_scores_gemma":[0.00004444274,0.00016888851,0.00011890325,0.00031814724,0.000015617088,0.00059877004,0.00031473508,0.00017112996,0.000021519249],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045711884,0.00049951696,0.00020718527,0.0001255328,0.00008779254,0.00009946323,0.0003636569,0.00008531126,0.005696018,0.03262086,0.021718368,0.9384506],"study_design_scores_gemma":[0.00069819903,0.00007556591,0.000031596683,0.000056375084,0.000013484324,0.00000715399,0.000032952263,0.93782634,0.006104599,0.0011899371,0.053699616,0.00026415603],"about_ca_topic_score_codex":0.00007137191,"about_ca_topic_score_gemma":0.00006435339,"teacher_disagreement_score":0.9590591,"about_ca_system_score_codex":0.000043104257,"about_ca_system_score_gemma":0.00011044435,"threshold_uncertainty_score":0.9999116},"labels":[],"label_agreement":null},{"id":"W2038676369","doi":"10.4108/icst.intetain.2015.259265","title":"Wearable Sensor Networks for Motion Capture","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Wearable computer; Computer science; Motion capture; Match moving; Inertial measurement unit; Tracking (education); Motion (physics); Character (mathematics); Wearable technology; Wireless sensor network; Motion sensors; Computer vision; Artificial intelligence; Embedded system; Computer network","score_opus":0.055272624177120366,"score_gpt":0.2629465239616663,"score_spread":0.20767389978454592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038676369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012141868,0.00009538952,0.9868187,0.0011700881,0.0008404236,0.00031871191,0.0000016457653,0.00027579124,0.009265065],"genre_scores_gemma":[0.96970946,0.0000014782559,0.022230124,0.0004973308,0.00022155841,0.0000488103,0.0000032527548,0.000008599342,0.0072794086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991866,0.000056074146,0.00013544131,0.00025941356,0.00015791022,0.00020459581],"domain_scores_gemma":[0.99914914,0.000108268425,0.000056943285,0.0003087474,0.00025042985,0.00012646105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039512102,0.00008740535,0.00012512768,0.000047398386,0.000054831704,0.0001581961,0.0002508442,0.00007651351,0.000011761035],"category_scores_gemma":[0.00006222129,0.00007692722,0.000055208206,0.00017536382,0.000010281046,0.00053762505,0.000065085726,0.00006873367,0.00011768965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006432368,0.00030298333,0.0022756553,0.0000567101,0.000103627724,0.000020066756,0.002591414,0.011645216,0.0004487748,0.021474564,0.23345518,0.7275615],"study_design_scores_gemma":[0.00084877276,0.00006378149,0.00019573193,0.000021158385,0.000005459108,0.000056351026,0.00023524374,0.9522096,0.00047249245,0.001469336,0.04418772,0.0002343655],"about_ca_topic_score_codex":0.00011648723,"about_ca_topic_score_gemma":0.000056999434,"teacher_disagreement_score":0.96849525,"about_ca_system_score_codex":0.00004826891,"about_ca_system_score_gemma":0.000043243803,"threshold_uncertainty_score":0.3137001},"labels":[],"label_agreement":null},{"id":"W2039336208","doi":"10.1109/pimrcw.2010.5670415","title":"Methodology and software development platform for wearable computers","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Windsor","funders":"Baden-Württemberg Stiftung","keywords":"Wearable computer; Computer science; Software; Embedded system; Wearable technology; Human–computer interaction; Software engineering; Operating system","score_opus":0.09919491032023951,"score_gpt":0.30444001805989246,"score_spread":0.20524510773965293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039336208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039614007,0.00002008499,0.9580696,0.0003663718,0.00096887466,0.00028647442,0.0000011702152,0.00023306112,0.00044036],"genre_scores_gemma":[0.16629018,0.0000010202948,0.8326625,0.00040555748,0.000044320124,0.00005469372,0.0000015345902,0.0000064757646,0.000533711],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990961,0.000031835683,0.00019377541,0.0003377542,0.00010717626,0.00023333529],"domain_scores_gemma":[0.998316,0.0011608393,0.00006537321,0.0002536569,0.00010402448,0.00010008811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074171793,0.00010999006,0.00019155853,0.00009295856,0.00015096749,0.00013419964,0.00034077495,0.00008494873,0.000021604945],"category_scores_gemma":[0.00013748408,0.00009779997,0.000033739125,0.00009668842,0.000033092525,0.00045304626,0.00021431717,0.000118022974,0.00003477935],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057154903,0.00002266842,0.0006696664,0.00002457679,0.000025826415,0.00000146161,0.00071795785,9.1381975e-7,0.0018263655,0.006804188,0.0010612913,0.9888394],"study_design_scores_gemma":[0.004790905,0.0004429342,0.02071741,0.00012308295,0.000024509456,0.0009414024,0.00039117658,0.053832892,0.114336014,0.03068759,0.7717877,0.0019244074],"about_ca_topic_score_codex":0.000033653014,"about_ca_topic_score_gemma":0.00026063094,"teacher_disagreement_score":0.98691493,"about_ca_system_score_codex":0.00001573992,"about_ca_system_score_gemma":0.000095335185,"threshold_uncertainty_score":0.39881673},"labels":[],"label_agreement":null},{"id":"W2039365526","doi":"10.1109/compsacw.2014.117","title":"Towards an Ontology-Based Context-Aware Meta-model for the Software Domain","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University; Concordia University","funders":"","keywords":"Computer science; Ontology; Semantic Web; World Wide Web; Domain (mathematical analysis); Context (archaeology); Context model; Context awareness; Software engineering; Data science; Information retrieval; Artificial intelligence","score_opus":0.09275381626720917,"score_gpt":0.30090703659513557,"score_spread":0.20815322032792638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039365526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006560864,0.00009755158,0.9896558,0.0070003117,0.0004238026,0.0008552922,0.000036221973,0.0006324818,0.00064244756],"genre_scores_gemma":[0.90122247,6.3147337e-7,0.08972355,0.0075825886,0.00010345232,0.0006538123,0.000011887756,0.000024533503,0.0006771044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768746,0.0003448524,0.00040119293,0.0007163998,0.00038390272,0.00046618728],"domain_scores_gemma":[0.9963002,0.0015584865,0.00019307472,0.0013709184,0.00039777838,0.0001795204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016073121,0.00030846943,0.00056471775,0.00010892786,0.00039546727,0.00030389108,0.0016207853,0.00015321733,0.00008207406],"category_scores_gemma":[0.00024304402,0.00019476419,0.00043267326,0.00021022589,0.000112022244,0.00073054683,0.00016155971,0.00017061665,0.00006186383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010091252,0.0004102328,0.00037255854,0.000093254894,0.0011266532,0.0000048314387,0.002200635,0.0021819782,0.00021330183,0.14125834,0.012281357,0.83975595],"study_design_scores_gemma":[0.0011156398,0.00019959846,0.00016367709,0.000008828523,0.00016269604,0.000017014128,0.00012780701,0.9557118,0.0005781666,0.016957738,0.024593845,0.00036319328],"about_ca_topic_score_codex":0.00027577032,"about_ca_topic_score_gemma":0.0046765897,"teacher_disagreement_score":0.95352983,"about_ca_system_score_codex":0.00005902326,"about_ca_system_score_gemma":0.00025270917,"threshold_uncertainty_score":0.79422534},"labels":[],"label_agreement":null},{"id":"W2039409516","doi":"10.1109/tencon.2013.6718492","title":"A framework for spatio-temporal context query in open space based on GPS data","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Dalhousie University","funders":"","keywords":"Computer science; Global Positioning System; Search engine indexing; Information retrieval; Context (archaeology); Web search query; Interface (matter); Query expansion; Web query classification; Query language; Query optimization; World Wide Web; Data mining; Search engine; Geography","score_opus":0.13070871740005627,"score_gpt":0.3433273921432652,"score_spread":0.21261867474320892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039409516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029881168,0.000013682193,0.97659785,0.013656433,0.0004167233,0.0022061141,0.000035971116,0.00013064567,0.0039544413],"genre_scores_gemma":[0.86918074,4.6049783e-7,0.12662552,0.0030413202,0.000088337554,0.00038854615,0.000063703555,0.00001509553,0.0005962836],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981514,0.00015261881,0.00034184984,0.00077487173,0.00026375873,0.00031548314],"domain_scores_gemma":[0.9961516,0.0014584067,0.00016015272,0.001959113,0.0001490539,0.0001216254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081538595,0.00018140995,0.00032196066,0.00016298548,0.00007800457,0.0009146782,0.0029783589,0.00011921976,0.00036192255],"category_scores_gemma":[0.0004959113,0.00016359561,0.000047471927,0.00037083327,0.000027108108,0.0023641458,0.00096397754,0.00018682721,0.00055679487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015936598,0.00075823395,0.030899294,0.00009745278,0.000052567477,0.000020450649,0.00089858304,0.000065647255,0.00006828881,0.16715236,0.15202966,0.6477981],"study_design_scores_gemma":[0.002610086,0.0003500894,0.005769485,0.0004636949,0.0000053667072,0.0000064945493,0.00039700847,0.86829966,0.00069480285,0.027658217,0.09302879,0.0007163151],"about_ca_topic_score_codex":0.0071762176,"about_ca_topic_score_gemma":0.0058431886,"teacher_disagreement_score":0.868234,"about_ca_system_score_codex":0.00007417542,"about_ca_system_score_gemma":0.00022245628,"threshold_uncertainty_score":0.99943507},"labels":[],"label_agreement":null},{"id":"W2039437442","doi":"10.1145/2769493.2769519","title":"Smart homes and the challenges of data","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Big data; Data science; Data warehouse; Context (archaeology); Computer science; Smart city; Home automation; Internet of Things; Computer security; Database; Data mining; Telecommunications; Geography","score_opus":0.20077704073600317,"score_gpt":0.30878871076277886,"score_spread":0.1080116700267757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039437442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028894678,0.021867998,0.6314934,0.08224431,0.0012977946,0.0009578508,0.000026724905,0.00045394295,0.23276332],"genre_scores_gemma":[0.99784106,0.000086408974,0.001752422,0.000092493654,0.000024716534,0.00000367959,6.0266717e-7,0.0000018740832,0.00019673406],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993875,0.00010870902,0.00010209116,0.0001806405,0.00015639902,0.000064651715],"domain_scores_gemma":[0.9987493,0.0002925527,0.000049702754,0.00080157514,0.00006559126,0.000041286443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092093885,0.000044512486,0.0001197233,0.000026117348,0.000018206467,0.00004619425,0.0007666488,0.000016891168,0.0000032468058],"category_scores_gemma":[0.00007908955,0.000025721085,0.000011157883,0.000059223046,0.00007295289,0.0006185616,0.0007275809,0.000032203076,0.000019430929],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001535498,0.00003967414,0.00062129635,0.000022322005,0.000043193744,0.0000018838856,0.0029337222,1.6107035e-7,0.000018107396,0.22724797,0.006414103,0.7626422],"study_design_scores_gemma":[0.02584497,0.0007642523,0.038158067,0.00038369803,0.000108358014,0.0008876192,0.021347437,0.24280159,0.004040364,0.18377964,0.48002064,0.0018633592],"about_ca_topic_score_codex":0.00015399314,"about_ca_topic_score_gemma":0.00018203365,"teacher_disagreement_score":0.9689464,"about_ca_system_score_codex":0.0000033687238,"about_ca_system_score_gemma":0.000033698947,"threshold_uncertainty_score":0.1424637},"labels":[],"label_agreement":null},{"id":"W2040879679","doi":"10.1109/tnsre.2011.2162250","title":"An Analysis of the Accuracy of Wearable Sensors for Classifying the Causes of Falls in Humans","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Accelerometer; Wearable computer; Physical medicine and rehabilitation; Poison control; Sensitivity (control systems); Medicine; Computer science; Ankle; Linear discriminant analysis; Artificial intelligence; Medical emergency; Engineering; Embedded system","score_opus":0.03330020254826666,"score_gpt":0.25718888823402936,"score_spread":0.2238886856857627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040879679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66690636,0.00003043452,0.33242065,0.00003053113,0.00022627719,0.0003514861,0.000014777778,0.000015218087,0.000004251458],"genre_scores_gemma":[0.99938834,0.0000045999445,0.00051806297,0.00000245321,0.0000052176238,0.000066518376,1.7701926e-7,0.0000063333173,0.0000083279965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989872,0.00013233806,0.00045796862,0.00017155579,0.00015226567,0.00009867626],"domain_scores_gemma":[0.9980253,0.0012726408,0.00020143665,0.00034617577,0.00013069628,0.000023755223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040222946,0.00009055409,0.0002729506,0.00031416726,0.000058237983,0.000016736172,0.0002050422,0.000044773966,0.0000012387476],"category_scores_gemma":[0.00004549745,0.000060413044,0.00015678308,0.00070495135,0.000053453336,0.0003249068,0.0000019550139,0.00008270174,7.328234e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021764905,0.00016457404,0.0041149124,0.0004966344,0.00021209421,1.566578e-7,0.015489903,0.93033725,0.045436636,0.0006617514,9.1848136e-7,0.0030633758],"study_design_scores_gemma":[0.00028519752,0.00022483298,0.067657754,0.00024412142,0.00010155908,0.0000019270883,0.0022105067,0.91871846,0.010426454,0.000011758109,0.000010431644,0.000106982196],"about_ca_topic_score_codex":0.00075914507,"about_ca_topic_score_gemma":0.00023247901,"teacher_disagreement_score":0.33248192,"about_ca_system_score_codex":0.000023524257,"about_ca_system_score_gemma":0.000015336727,"threshold_uncertainty_score":0.24635725},"labels":[],"label_agreement":null},{"id":"W2045200664","doi":"10.1049/cp:20081150","title":"Ontology-based negotiation protocol and context-level agreements","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Protocol (science); Negotiation; Ontology; Context (archaeology); World Wide Web; Information retrieval; Political science; History; Philosophy; Epistemology","score_opus":0.10798878304194932,"score_gpt":0.290075831553599,"score_spread":0.1820870485116497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045200664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01567275,0.000006813964,0.9472172,0.0011712211,0.00016691319,0.030703915,0.000004106659,0.0002930287,0.004764031],"genre_scores_gemma":[0.97117466,2.7400597e-7,0.007926883,0.0012172639,0.000030642128,0.018850952,0.0000021686367,0.0000058775818,0.0007912973],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988684,0.00010934137,0.00024014847,0.00035267806,0.00023870352,0.00019073943],"domain_scores_gemma":[0.9992111,0.00012263538,0.00012192806,0.0003152962,0.00013944226,0.00008961376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017458762,0.00012554608,0.00016995896,0.00010643231,0.00015918602,0.00006598701,0.00026163008,0.000065351385,0.00006951958],"category_scores_gemma":[0.000046783996,0.00011296137,0.00003867092,0.00016637464,0.000055759912,0.00059173757,0.00008885493,0.00007535611,0.00013323549],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009222851,0.00063868426,0.1310275,0.00011962128,0.00008718854,0.00008792472,0.0022547743,0.000007632032,0.0024592874,0.008234968,0.015010505,0.8399797],"study_design_scores_gemma":[0.02152505,0.0014061916,0.6401451,0.00023000535,0.000022279577,0.0008764217,0.00031339715,0.14523223,0.03582368,0.002548795,0.14986734,0.0020094966],"about_ca_topic_score_codex":0.0002532288,"about_ca_topic_score_gemma":0.00035972122,"teacher_disagreement_score":0.9555019,"about_ca_system_score_codex":0.000043535,"about_ca_system_score_gemma":0.000113144146,"threshold_uncertainty_score":0.46064308},"labels":[],"label_agreement":null},{"id":"W2046217253","doi":"10.1145/2674396.2674399","title":"A new system for assistance and guidance in smart homes based on electrical devices identification","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Exploit; Computer science; Identification (biology); Task (project management); Analyser; Autonomy; Human–computer interaction; Cognition; Home automation; Activities of daily living; Computer security; Engineering; Psychology; Telecommunications","score_opus":0.018166343102155966,"score_gpt":0.2531966206389893,"score_spread":0.2350302775368333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046217253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007500794,0.00009612151,0.9895368,0.00084182445,0.00019721204,0.00035549956,0.0000013410183,0.00015217927,0.0013182005],"genre_scores_gemma":[0.9880881,4.2232847e-7,0.011117582,0.00022619255,0.00004725858,0.00008855087,0.0000015400987,0.000006246839,0.0004241124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987804,0.0001315308,0.00028278749,0.00042643098,0.00020234592,0.00017647204],"domain_scores_gemma":[0.99871635,0.00064872066,0.00013086262,0.00034611017,0.00008486751,0.0000730783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071894913,0.00010380187,0.00018781818,0.00015784282,0.000060745973,0.000251346,0.00029604574,0.00005031692,0.0000018194527],"category_scores_gemma":[0.00013838394,0.00009555476,0.00003553799,0.00030873783,0.000008825216,0.00036848648,0.000024301758,0.00005269686,0.00002197819],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101583035,0.00015117456,0.068736255,0.00046821337,0.000020356134,0.0000031482843,0.0002279879,0.00005828223,0.0033488818,0.076517195,0.0046656295,0.8457013],"study_design_scores_gemma":[0.0010418597,0.00012726219,0.16316366,0.00022531183,0.0000045548877,0.0000066414846,0.00002050783,0.8251583,0.0038653188,0.0004512661,0.0056947824,0.00024051784],"about_ca_topic_score_codex":0.00009757243,"about_ca_topic_score_gemma":0.00039054145,"teacher_disagreement_score":0.9805873,"about_ca_system_score_codex":0.00009211837,"about_ca_system_score_gemma":0.00006509635,"threshold_uncertainty_score":0.38966098},"labels":[],"label_agreement":null},{"id":"W2046372943","doi":"10.1007/s11042-009-0285-9","title":"A framework for human-centered provisioning of ambient media services","year":2009,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Ottawa","funders":"","keywords":"Computer science; Provisioning; Context (archaeology); Ambient intelligence; Premise; Human–computer interaction; Multimedia; Process (computing); Context awareness; Volume (thermodynamics); Service (business); Computer network; Operating system","score_opus":0.0596019347776651,"score_gpt":0.3233684256432144,"score_spread":0.26376649086554926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046372943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030522387,0.00014146106,0.967044,0.00074487366,0.000066223096,0.0011570603,0.000077655284,0.00010914043,0.00013722814],"genre_scores_gemma":[0.8984639,0.0000097530055,0.10073997,0.00016140314,0.000117936,0.00044843837,0.000038757087,0.0000053606277,0.000014467894],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999075,0.000019945866,0.0002804547,0.00031619694,0.00014931326,0.00015910462],"domain_scores_gemma":[0.9987522,0.00046791843,0.00017906284,0.00036962255,0.00014134024,0.00008985473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014590395,0.00010484628,0.00018785018,0.000067417444,0.00015835545,0.00013301062,0.00035507517,0.00006782252,0.0000047345065],"category_scores_gemma":[0.00003807589,0.00009849256,0.00005635509,0.00019462492,0.000029818142,0.00030615705,0.00006730312,0.000075154705,0.000008390546],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005320919,0.0002274141,0.0005468631,0.000064365435,0.000014406097,2.513955e-7,0.0020122523,0.000002431301,0.010777941,0.042639706,0.000049129936,0.9436599],"study_design_scores_gemma":[0.007823337,0.0011464041,0.27270055,0.0019874682,0.00017309918,0.000047362097,0.0023580866,0.1761631,0.08036343,0.36618948,0.08861074,0.002436948],"about_ca_topic_score_codex":0.000022092943,"about_ca_topic_score_gemma":0.000018021401,"teacher_disagreement_score":0.94122297,"about_ca_system_score_codex":0.0000147423625,"about_ca_system_score_gemma":0.000019185738,"threshold_uncertainty_score":0.40164098},"labels":[],"label_agreement":null},{"id":"W2046457489","doi":"10.1519/jpt.0b013e318224aa23","title":"A Review of Monitoring Technology for Use With Older Adults","year":2011,"lang":"en","type":"review","venue":"Journal of Geriatric Physical Therapy","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Clothing; Wearable computer; Narrative review; Wearable technology; Medicine; Older people; Gerontology; Computer science","score_opus":0.07712283134315968,"score_gpt":0.342915698265908,"score_spread":0.26579286692274834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046457489","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010492783,0.97567743,0.022512538,0.00003413543,0.00047446694,0.0011449265,0.000009543218,0.00003104037,0.000010974063],"genre_scores_gemma":[0.00026810347,0.99432594,0.004451092,0.00002343858,0.0007785458,0.000103007136,8.346604e-7,0.000038284612,0.000010779577],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978106,0.0002151349,0.0010638781,0.00032941668,0.00033031031,0.0002506742],"domain_scores_gemma":[0.9944577,0.0006464235,0.003247986,0.00061841303,0.000930603,0.000098825345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047225173,0.00037736958,0.0028408482,0.00049887516,0.00003809379,0.000049297843,0.0012498581,0.00016847248,0.0000030130807],"category_scores_gemma":[0.000089183544,0.0002320259,0.0010643679,0.0013047246,0.0000450478,0.00061848,0.00006698472,0.00045175038,0.000004851443],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003246392,0.00025509286,0.000002338544,0.016653739,0.0003076323,0.0000059459567,0.000070522015,1.46637e-8,0.0000023606356,0.00006772333,0.00014899154,0.98245317],"study_design_scores_gemma":[0.0011035074,0.0014519965,0.0000062245654,0.20075294,0.00033808057,0.00042768972,0.0000066831544,0.0000050959534,0.00006293401,0.0002166269,0.7952591,0.00036909938],"about_ca_topic_score_codex":0.0000042041474,"about_ca_topic_score_gemma":8.982527e-8,"teacher_disagreement_score":0.9820841,"about_ca_system_score_codex":0.00007844324,"about_ca_system_score_gemma":0.0004551359,"threshold_uncertainty_score":0.94617414},"labels":[],"label_agreement":null},{"id":"W2046718994","doi":"10.1109/mwc.2002.1160084","title":"Facilitating the programming of the smart home","year":2002,"lang":"en","type":"article","venue":"IEEE Wireless Communications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Home automation; Software; Programming paradigm; Embedded system; Visual programming language; Component (thermodynamics); Software engineering; Multimedia; Telecommunications; Operating system; Programming language","score_opus":0.07640354765809168,"score_gpt":0.27311454974721744,"score_spread":0.19671100208912576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046718994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5267454,0.0029746254,0.37673753,0.035979148,0.0015791461,0.0031007596,0.000049482107,0.00093345036,0.05190047],"genre_scores_gemma":[0.9955161,0.000023385575,0.0029965471,0.00011669542,0.0000131099705,0.00011956806,7.0654175e-7,0.0000071958452,0.0012066617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987003,0.00040988703,0.0003170348,0.00015724247,0.00025147575,0.00016404722],"domain_scores_gemma":[0.99490905,0.0013026242,0.00023353187,0.0033448797,0.00017706509,0.000032823948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040800893,0.00009660382,0.00013829132,0.000042046435,0.00058151834,0.00011308613,0.0038014457,0.000038951366,0.0000070910264],"category_scores_gemma":[0.00007416497,0.00006369229,0.000116599804,0.00052882114,0.00027453338,0.0002920601,0.00061165466,0.0002590703,0.00007528294],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.87722e-7,0.00025898695,0.0029213233,0.000028812372,0.00006835315,3.7623462e-7,0.019536402,0.00003195362,0.0027259456,0.025614308,0.0007355861,0.9480771],"study_design_scores_gemma":[0.001845535,0.00018998054,0.022625659,0.001043032,0.00010934992,0.00022249685,0.00797859,0.7344199,0.008740455,0.008788329,0.2124559,0.0015807758],"about_ca_topic_score_codex":0.00007416839,"about_ca_topic_score_gemma":0.00021249533,"teacher_disagreement_score":0.9464963,"about_ca_system_score_codex":0.00003843049,"about_ca_system_score_gemma":0.000031093616,"threshold_uncertainty_score":0.7064095},"labels":[],"label_agreement":null},{"id":"W2047262165","doi":"10.1109/cicomms.2014.7014391","title":"Modeling and reasoning in context-aware systems based on relational concept analysis and description logic","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Context (archaeology); Context model; Formal concept analysis; Ubiquitous computing; Context awareness; Data science; Context management; Knowledge base; Human–computer interaction; World Wide Web; Knowledge management; Artificial intelligence; Object (grammar); Algorithm","score_opus":0.04015206946767178,"score_gpt":0.24446871795691724,"score_spread":0.20431664848924547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047262165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11745665,0.00009110998,0.88111883,0.00024008253,0.000097334894,0.00017614278,0.0000027322906,0.00009593574,0.00072116667],"genre_scores_gemma":[0.99670947,0.0000022930576,0.0028292283,0.00030900256,0.000029856097,0.000024830451,0.00001092756,0.0000060875564,0.000078312856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838555,0.00034135737,0.00032319408,0.00050185894,0.0002704264,0.00017763294],"domain_scores_gemma":[0.9990108,0.00041589752,0.00009766208,0.00026262665,0.00012276424,0.000090269175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000805636,0.00014649158,0.00031383382,0.00045095946,0.00011311336,0.0002681173,0.00012816237,0.00009452684,0.000008613714],"category_scores_gemma":[0.00012710238,0.00013539054,0.000048323403,0.00053460477,0.000033500488,0.0006148702,0.000058888076,0.00013642685,0.0000064473934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006854766,0.00017193641,0.18934718,0.00009409334,0.00023743893,0.000016286122,0.0023982981,0.5512517,0.0002788021,0.17394987,0.00007279758,0.082113035],"study_design_scores_gemma":[0.0005468884,0.00005210615,0.007913759,0.00009568501,0.0000221342,0.000007817705,0.000219128,0.99067086,0.0000133392095,0.00026622336,0.000031300708,0.00016075434],"about_ca_topic_score_codex":0.0009286612,"about_ca_topic_score_gemma":0.00057690917,"teacher_disagreement_score":0.8792528,"about_ca_system_score_codex":0.00006979514,"about_ca_system_score_gemma":0.000028259296,"threshold_uncertainty_score":0.55210656},"labels":[],"label_agreement":null},{"id":"W2047687078","doi":"10.1002/wcm.231","title":"Personal and service mobility in ubiquitous computing environments","year":2004,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Ubiquitous computing; Service discovery; Bluetooth; Service (business); Personal mobility; Computer network; Quality of service; Personal area network; Wireless; World Wide Web; Human–computer interaction; Web service; Telecommunications","score_opus":0.027338031977106117,"score_gpt":0.27088074036945775,"score_spread":0.24354270839235165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047687078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8752062,0.0010386184,0.12222853,0.0007736802,0.0000678964,0.00041318822,0.000003421249,0.00009785512,0.00017062537],"genre_scores_gemma":[0.9901658,0.00016346472,0.009272369,0.00031729098,0.00002444547,0.000031902393,0.0000075309244,0.000011901335,0.000005292965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843806,0.00022193362,0.00040607885,0.00047488182,0.00017181835,0.0002871987],"domain_scores_gemma":[0.9982537,0.00044615392,0.00017667879,0.0009626171,0.00005135908,0.00010948703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006308537,0.00018323777,0.00027679116,0.000121609366,0.0004812736,0.000192014,0.0008292662,0.000078658486,0.0000012898779],"category_scores_gemma":[0.000014837802,0.0002056333,0.000034449124,0.00036648544,0.00014309728,0.00036362567,0.001969337,0.00032004327,0.000008027398],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061909623,0.00077598117,0.015904296,0.00012457935,0.000037605198,0.000008315032,0.025829254,0.0010261296,0.003382571,0.004126595,0.0000039943434,0.9487745],"study_design_scores_gemma":[0.002048564,0.00012669379,0.05957997,0.00054321805,0.000011521359,0.00020796034,0.0028337587,0.93072206,0.00036005635,0.00079633464,0.0021516816,0.0006182012],"about_ca_topic_score_codex":0.00069418136,"about_ca_topic_score_gemma":0.00034598174,"teacher_disagreement_score":0.9481563,"about_ca_system_score_codex":0.00012236202,"about_ca_system_score_gemma":0.000059195587,"threshold_uncertainty_score":0.8385483},"labels":[],"label_agreement":null},{"id":"W2048682232","doi":"10.1007/s11390-009-9232-5","title":"A Scalable Testing Framework for Location-Based Services","year":2009,"lang":"en","type":"article","venue":"Journal of Computer Science and Technology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Alberta","funders":"","keywords":"Computer science; Scalability; Integration testing; Automation; Architecture; Model-based testing; Service (business); System testing; Unit testing; Test strategy; Software engineering; Distributed computing; Test case; Embedded system; Operating system; Software; Machine learning","score_opus":0.021245236870565853,"score_gpt":0.27247164064755136,"score_spread":0.2512264037769855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048682232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046286426,0.00015774646,0.9443109,0.008661198,0.000350833,0.00011849263,2.6680038e-7,0.00008618687,0.000027950373],"genre_scores_gemma":[0.61953133,0.000001069066,0.37972942,0.0006599555,0.00007338933,0.0000019078018,3.0788204e-8,0.0000015940901,0.0000012960411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880517,0.000016654612,0.00032479237,0.0002542248,0.0003485582,0.0002506065],"domain_scores_gemma":[0.99676245,0.00034777666,0.0003929378,0.00028950567,0.002121273,0.00008603917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010502591,0.00009561612,0.00021728917,0.000785774,0.00024126336,0.000319305,0.0012202824,0.000089610156,4.4323212e-7],"category_scores_gemma":[0.00027890204,0.00008116328,0.000028353285,0.0026006817,0.00017479197,0.0010056122,0.0001014852,0.0001767123,0.0000022384093],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004571772,0.000073877294,0.0015135107,0.00002429044,0.0000059049075,0.000008712211,0.00010676531,0.00011721205,0.0019166642,0.02409108,0.00006107898,0.97207636],"study_design_scores_gemma":[0.00081125024,0.0019522385,0.004463417,0.0006396179,0.000011853722,0.00075820414,0.000051747975,0.7736432,0.012097671,0.20335332,0.0019385612,0.00027892683],"about_ca_topic_score_codex":0.000002139475,"about_ca_topic_score_gemma":0.0000010263433,"teacher_disagreement_score":0.9717974,"about_ca_system_score_codex":0.000046359295,"about_ca_system_score_gemma":0.00045711876,"threshold_uncertainty_score":0.33097425},"labels":[],"label_agreement":null},{"id":"W2048736470","doi":"10.1007/s11036-014-0531-x","title":"Context-Aware Systems and Applications","year":2014,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Context (archaeology); Data science; Engineering ethics; State (computer science); Engineering management; Management science","score_opus":0.010073271213082009,"score_gpt":0.2289391790902038,"score_spread":0.2188659078771218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048736470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064976723,0.0019437668,0.9936091,0.00017195026,0.0000857638,0.0016601678,0.000011840956,0.0002654666,0.0016022172],"genre_scores_gemma":[0.99003726,0.00017980294,0.00016456954,0.00024095413,0.0005154245,0.008533512,0.000019338106,0.000015556509,0.00029356673],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986849,0.00008608569,0.00029398096,0.0005570408,0.00013374118,0.0002442772],"domain_scores_gemma":[0.99850833,0.00036785795,0.00014716589,0.0006590523,0.00012772607,0.00018985779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030219872,0.00017160652,0.00024694626,0.00006997423,0.00042290884,0.00037005986,0.00034466814,0.00010765613,0.0000043271107],"category_scores_gemma":[0.0000044702406,0.00016837525,0.000038995862,0.0003425762,0.000102128804,0.00026755797,0.00018768298,0.00015118031,0.000038338483],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015183504,0.00008126788,0.0012935085,0.000048642327,0.000026160013,2.7321408e-7,0.000053871616,0.0022326582,0.00003277813,0.15011702,0.0012886723,0.84482366],"study_design_scores_gemma":[0.00030136152,0.00004461544,0.00086688204,0.000032131786,0.000015962987,0.000051414394,0.00014810268,0.25285286,0.0000115445455,0.0009942459,0.74440825,0.00027265438],"about_ca_topic_score_codex":0.00006685048,"about_ca_topic_score_gemma":0.000030884054,"teacher_disagreement_score":0.9934445,"about_ca_system_score_codex":0.000021153066,"about_ca_system_score_gemma":0.00001891467,"threshold_uncertainty_score":0.68661433},"labels":[],"label_agreement":null},{"id":"W2048889456","doi":"10.1016/j.procs.2013.06.069","title":"Towards a Real-time Error Detection within a Smart Home by Using Activity Recognition with a Shoe-mounted Accelerometer","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition 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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Activity recognition; Accelerometer; Histogram; Home automation; Set (abstract data type); Artificial intelligence; Real-time computing; Fuzzy logic; Smart environment; State (computer science); Machine learning; Computer vision; Human–computer interaction; Embedded system; Internet of Things; Algorithm","score_opus":0.03398339952496479,"score_gpt":0.2609805559961663,"score_spread":0.22699715647120153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048889456","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53487873,0.000005326671,0.463329,0.00014954126,0.00044101945,0.00065727957,0.000005565728,0.00041876853,0.00011479552],"genre_scores_gemma":[0.92485017,0.000002194572,0.074498974,0.00022180173,0.00013791077,0.00021461622,0.0000036405718,0.000029822124,0.000040885017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956835,0.0001777128,0.00046789486,0.0015629119,0.0012593219,0.00084868947],"domain_scores_gemma":[0.9969829,0.00015350866,0.00048823908,0.00079863396,0.0011567883,0.0004199344],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011215393,0.00045117966,0.00047273203,0.00066305953,0.0005658251,0.0018204002,0.0015397684,0.00013673946,0.000034843397],"category_scores_gemma":[0.00008148854,0.00039353815,0.00009378695,0.0032586362,0.00039479573,0.008382313,0.0006588742,0.00034609932,0.00039513689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047321388,0.00027833707,0.0005856758,0.000072690054,0.000043499247,0.000010933939,0.0018628484,0.000045010493,0.45171604,0.00000851294,0.0001858294,0.5451433],"study_design_scores_gemma":[0.00072178827,0.0005234112,0.0063995416,0.00016800915,0.000017607834,0.00044430682,0.000023970937,0.9107816,0.07978669,0.0003373177,0.000034945635,0.00076081045],"about_ca_topic_score_codex":0.0011049791,"about_ca_topic_score_gemma":0.00006724372,"teacher_disagreement_score":0.9107366,"about_ca_system_score_codex":0.00043608376,"about_ca_system_score_gemma":0.00064421195,"threshold_uncertainty_score":0.99985164},"labels":[],"label_agreement":null},{"id":"W2049423136","doi":"10.1016/j.jalz.2008.05.403","title":"S3‐05–06: An intelligent enviornment to support aging‐in‐place and independence","year":2008,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"","keywords":"Dementia; Activities of daily living; Psychology; Computer science; Tracking (education); Disease; Human–computer interaction; Applied psychology; Gerontology; Medicine; Psychiatry","score_opus":0.05747210864721951,"score_gpt":0.2868046317533971,"score_spread":0.22933252310617758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049423136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72953606,0.015034112,0.24502961,0.0032659909,0.0019064356,0.0022998208,0.000021685535,0.00055527955,0.002350973],"genre_scores_gemma":[0.9938288,0.00006490288,0.0048121493,0.0010796651,0.000056100696,0.00008550467,0.0000064619703,0.000017648847,0.000048801096],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974931,0.00016254578,0.0004523159,0.00080610724,0.0006167438,0.0004691946],"domain_scores_gemma":[0.99868464,0.00007876253,0.00012113653,0.0007132454,0.00008358335,0.0003186572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063847756,0.00025098803,0.00027179258,0.0002321455,0.00016233292,0.00015302395,0.00073932466,0.00008321274,0.00011963976],"category_scores_gemma":[0.000015727253,0.00027125672,0.00005249785,0.00033918172,0.000059157635,0.0010528205,0.00053697283,0.00019684398,0.00043771666],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004035335,0.00090532517,0.11456721,0.000014965989,0.0022674527,0.00072409207,0.017064678,0.00009735789,0.0035333505,0.0014458885,0.011447217,0.8478921],"study_design_scores_gemma":[0.0048723486,0.0034677356,0.2827461,0.0003161345,0.0027884583,0.0034910152,0.0020659151,0.013286598,0.2114166,0.0014205817,0.46866477,0.005463741],"about_ca_topic_score_codex":0.00046116626,"about_ca_topic_score_gemma":0.00043433797,"teacher_disagreement_score":0.8424284,"about_ca_system_score_codex":0.000027615615,"about_ca_system_score_gemma":0.00011522114,"threshold_uncertainty_score":0.99997395},"labels":[],"label_agreement":null},{"id":"W2051235428","doi":"10.3389/fsurg.2014.00039","title":"The Application of Wearable Technology in Surgery: Ensuring the Positive Impact of the Wearable Revolution on Surgical Patients","year":2014,"lang":"en","type":"article","venue":"Frontiers in Surgery","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; University of Toronto","funders":"","keywords":"Wearable computer; Wearable technology; Workflow; Medicine; Electronics; Surgical robot; Human–computer interaction; Computer science; Embedded system; Engineering; Electrical engineering; Artificial intelligence","score_opus":0.008907348305559719,"score_gpt":0.22604507153887426,"score_spread":0.21713772323331454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051235428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9783221,0.00054059393,0.018283818,0.0009359547,0.001016906,0.00053582585,0.0000071305135,0.00003462724,0.000323037],"genre_scores_gemma":[0.99967283,0.000101115504,0.00008089531,0.000015272479,0.000024344581,0.000066661545,0.0000014693788,0.000009407809,0.00002798276],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99761164,0.0007986643,0.00058407284,0.00030499283,0.00037016853,0.0003304815],"domain_scores_gemma":[0.9957554,0.0027575572,0.00050252426,0.0007952564,0.00016158768,0.000027722917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027309046,0.0001427496,0.0004337399,0.00037809857,0.00014991837,0.000030390709,0.0005708776,0.00012831969,9.076457e-7],"category_scores_gemma":[0.00069829257,0.00008314525,0.00023281372,0.0015002305,0.00021014815,0.0002217806,0.00015083057,0.0002796053,0.0000040760674],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057996665,0.000114301394,0.85375386,0.000011625793,0.000022916674,6.6292483e-7,0.00012607643,0.00017430831,0.00006193993,0.00023520755,0.0016062737,0.14383483],"study_design_scores_gemma":[0.00036786793,0.000056992372,0.9715432,0.000634102,0.000006635321,0.000008345457,0.00011845402,0.01863186,0.0031295128,0.0040826895,0.0012143106,0.00020604096],"about_ca_topic_score_codex":0.0006179995,"about_ca_topic_score_gemma":0.000058310634,"teacher_disagreement_score":0.14362879,"about_ca_system_score_codex":0.00024501202,"about_ca_system_score_gemma":0.00013595904,"threshold_uncertainty_score":0.3390565},"labels":[],"label_agreement":null},{"id":"W2051500356","doi":"10.1007/s11042-011-0940-9","title":"Survey on context-awareness in ubiquitous media","year":2011,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Context (archaeology); Context awareness; Ubiquitous computing; Task (project management); Human–computer interaction; Key (lock); Data science; World Wide Web; Computer security","score_opus":0.13493574146946127,"score_gpt":0.2909158222357411,"score_spread":0.1559800807662798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051500356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30042425,0.0008893299,0.6803071,0.0007814093,0.001088802,0.0039358228,0.0008411807,0.00083876087,0.010893361],"genre_scores_gemma":[0.9972456,0.000038577684,0.0016192658,0.0002137686,0.00007865206,0.0006908599,0.00005501701,0.0000117195095,0.000046550784],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99866766,0.00013604392,0.0002977637,0.0004819897,0.000177899,0.00023862656],"domain_scores_gemma":[0.9980297,0.0010958124,0.0001016166,0.0005205163,0.00010835227,0.00014398906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043390802,0.00015783214,0.00023635407,0.00014132829,0.00010485989,0.000104409395,0.00048277495,0.000093322604,0.000025753785],"category_scores_gemma":[0.0001306914,0.00015182208,0.000034654724,0.00040429566,0.000074644166,0.00035839464,0.00012643213,0.00015595689,0.0002128771],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009726797,0.00024235786,0.035308246,0.000007504386,0.000009520444,0.000004502398,0.0019092829,7.7322414e-7,0.00009339179,0.0030764001,0.00024236081,0.95909595],"study_design_scores_gemma":[0.0007612188,0.000041848627,0.98585933,0.00003693132,0.0000039891142,0.0000047745816,0.00012587263,0.00429354,0.0015145736,0.0008803491,0.0061606644,0.00031687645],"about_ca_topic_score_codex":0.0012801337,"about_ca_topic_score_gemma":0.0033815552,"teacher_disagreement_score":0.95877904,"about_ca_system_score_codex":0.00003192239,"about_ca_system_score_gemma":0.00006454085,"threshold_uncertainty_score":0.6191125},"labels":[],"label_agreement":null},{"id":"W2053073375","doi":"10.1007/s12652-013-0200-0","title":"Advances in ambient intelligence technologies","year":2013,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Ambient intelligence; Computer science; Context (archaeology); Computational intelligence; Wireless sensor network; Process (computing); Artificial intelligence; Cover (algebra); Data science; Engineering","score_opus":0.033055223576779966,"score_gpt":0.282866476304651,"score_spread":0.24981125272787102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053073375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19980541,0.0034800677,0.79458076,0.0005437946,0.00073808554,0.00030725103,3.3221394e-7,0.00012459325,0.0004196964],"genre_scores_gemma":[0.9857882,0.0012465355,0.012730141,0.000120850316,0.000076475546,0.0000062973245,2.2772397e-7,0.000010452236,0.000020780133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735546,0.00012526356,0.0012100001,0.00039666294,0.00048367304,0.0004289582],"domain_scores_gemma":[0.9977008,0.00047107766,0.0008372653,0.00034132643,0.0005475657,0.000101944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009679344,0.00024696733,0.0005161359,0.0006763077,0.00017569805,0.00047906948,0.0012086716,0.00009751716,0.00002775208],"category_scores_gemma":[0.0002473797,0.0002128887,0.00012761132,0.0006641592,0.00015030432,0.0021034621,0.00052163604,0.00054276147,0.00006200208],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009838678,0.00013483629,0.0016640285,0.000040859628,0.000015996657,0.000038785838,0.0022510195,0.00043594543,0.00091284147,0.008141251,0.000044543576,0.98631006],"study_design_scores_gemma":[0.0017679569,0.004146646,0.010290591,0.0068213097,0.00006988512,0.0037606382,0.04630921,0.41489053,0.18434365,0.30842283,0.015952397,0.0032243687],"about_ca_topic_score_codex":0.00006201681,"about_ca_topic_score_gemma":0.0000311228,"teacher_disagreement_score":0.9830857,"about_ca_system_score_codex":0.00011568125,"about_ca_system_score_gemma":0.00006444896,"threshold_uncertainty_score":0.8681349},"labels":[],"label_agreement":null},{"id":"W2053771393","doi":"10.2196/mhealth.3123","title":"Privacy-Related Context Information for Ubiquitous Health","year":2014,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Internet privacy; Ubiquitous computing; Computer science; Health information; mHealth; Business; World Wide Web; Health care; Medicine; Geography; Human–computer interaction; Psychological intervention; Nursing; Political science","score_opus":0.046612896417274036,"score_gpt":0.34064924541419017,"score_spread":0.2940363489969161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053771393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094699375,0.00094133127,0.8500201,0.0461103,0.0019485963,0.0039362637,0.000058902,0.00086927443,0.0014158767],"genre_scores_gemma":[0.98517483,0.00011325033,0.0028884364,0.0113091245,0.00014120442,0.00023070403,0.00004246135,0.000013682565,0.000086275686],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974408,0.00029884718,0.00086970255,0.00039565624,0.0002897765,0.0007051789],"domain_scores_gemma":[0.9975709,0.00039223424,0.0006630163,0.0005188338,0.0002048597,0.0006501558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018157163,0.00021200105,0.00046751145,0.00024415445,0.0005277545,0.00019251514,0.00039419567,0.00013112724,0.000005460989],"category_scores_gemma":[0.00014894291,0.00020346564,0.000068458285,0.00032842264,0.000042999254,0.0012892566,0.00011253587,0.00023439767,0.000079745536],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033008997,0.00005346476,0.00031505388,0.0010003792,0.0000080484915,1.5183389e-7,0.0045659808,0.0000012891335,0.0000023294524,0.029043285,0.0087134065,0.9562636],"study_design_scores_gemma":[0.007173528,0.003272944,0.0318822,0.0003883404,0.000019587553,0.00017808247,0.00075833563,0.07339666,0.000050449362,0.011774999,0.8702443,0.000860544],"about_ca_topic_score_codex":0.0003622917,"about_ca_topic_score_gemma":0.000102007434,"teacher_disagreement_score":0.95540303,"about_ca_system_score_codex":0.0001897545,"about_ca_system_score_gemma":0.0006747236,"threshold_uncertainty_score":0.8297088},"labels":[],"label_agreement":null},{"id":"W2054097007","doi":"10.1145/2684380.2684382","title":"ISA 2013 workshop report: a report on the Fifth International Workshop on Indoor Spatial Awareness","year":2014,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Context-Aware Activity Recognition Systems","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 Saskatchewan","funders":"","keywords":"Computer science; The Internet; Population; Human–computer interaction; Multimedia; Data science; World Wide Web","score_opus":0.03639649305360779,"score_gpt":0.28469514470301865,"score_spread":0.24829865164941087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054097007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11989427,0.000010337203,0.7480951,0.03887512,0.03130105,0.0014274237,0.000041554464,0.00057285326,0.059782267],"genre_scores_gemma":[0.97575194,0.0000033909498,0.00027614404,0.0013402022,0.020212807,0.00015194273,0.000071072216,0.000037457325,0.0021550518],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954584,0.00045396166,0.0009082398,0.0011280196,0.0015419343,0.00050939416],"domain_scores_gemma":[0.99529773,0.0017456084,0.0008131361,0.0015738102,0.00036988917,0.00019982718],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014493765,0.0004124537,0.00045948583,0.00024886028,0.00041764544,0.00068419037,0.0016459583,0.00024344838,0.0012369874],"category_scores_gemma":[0.002705392,0.00032378352,0.0002753514,0.0004615893,0.00012139604,0.00048617457,0.0004993034,0.00064098625,0.0009947196],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050065183,0.00086297013,0.008330008,0.000014975986,0.00024959276,0.002173531,0.0023863176,0.00042017022,0.00018664029,0.017115386,0.13152654,0.8362332],"study_design_scores_gemma":[0.0018331027,0.000315809,0.021914128,0.00044662133,0.000030742853,0.0010158372,0.00013504758,0.0198262,0.0016809453,0.0036076913,0.9479395,0.0012543473],"about_ca_topic_score_codex":0.0010117949,"about_ca_topic_score_gemma":0.0040443297,"teacher_disagreement_score":0.8558577,"about_ca_system_score_codex":0.0002288676,"about_ca_system_score_gemma":0.00026473287,"threshold_uncertainty_score":0.99992144},"labels":[],"label_agreement":null},{"id":"W2056659762","doi":"10.1109/wac.2014.6936003","title":"&amp;#x201C;CANthings&amp;#x201D;: Context-Aware Networks for the Design of Connected Things","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; World Wide Web; The Internet; Semantics (computer science); Context (archaeology); Intuition; Focus (optics); Software engineering; Data science; Human–computer interaction; Knowledge management; Programming language; Cognitive science","score_opus":0.06538430987919606,"score_gpt":0.26876642679597285,"score_spread":0.20338211691677677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056659762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008592651,0.0002183824,0.9930086,0.0024559728,0.0011023326,0.0013377625,0.000008012051,0.000448819,0.00056086027],"genre_scores_gemma":[0.94544595,0.00001755504,0.049716197,0.0026698639,0.00024771827,0.00020545752,0.000011636742,0.000039818893,0.0016457887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970652,0.0005417673,0.0006987243,0.0006660531,0.00046339916,0.0005648401],"domain_scores_gemma":[0.98863506,0.008622649,0.0005280845,0.0013192255,0.0007375405,0.00015742378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002623331,0.0003467574,0.0005943972,0.0001404054,0.00039497387,0.00031250855,0.0017682574,0.00021456127,0.00010425013],"category_scores_gemma":[0.0009280096,0.0002451205,0.00025094382,0.00046946038,0.00017246432,0.0009880258,0.00030544866,0.00029776365,0.00010178246],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045732642,0.00060633355,0.0013372129,0.0003158297,0.0010973787,0.0000015860811,0.012190939,0.006584451,0.011466636,0.19404697,0.1049456,0.66694975],"study_design_scores_gemma":[0.0021335839,0.00030305612,0.0005316799,0.00023951454,0.000088981455,0.000081508086,0.00034855134,0.6089365,0.0014225973,0.004569268,0.38043338,0.00091136136],"about_ca_topic_score_codex":0.00058850873,"about_ca_topic_score_gemma":0.0003871346,"teacher_disagreement_score":0.9445867,"about_ca_system_score_codex":0.00005613228,"about_ca_system_score_gemma":0.00012092785,"threshold_uncertainty_score":0.9995724},"labels":[],"label_agreement":null},{"id":"W2057590667","doi":"10.1007/s11036-008-0092-y","title":"Gain-based Selection of Ambient Media Services in Pervasive Environments","year":2008,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Ottawa","funders":"","keywords":"Computer science; Context (archaeology); Service (business); Reputation; Energy consumption; Selection (genetic algorithm); Constraint (computer-aided design); Human–computer interaction; Artificial intelligence; Business","score_opus":0.011061155781348244,"score_gpt":0.21451827418571487,"score_spread":0.20345711840436662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057590667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23526764,0.00025451276,0.763509,0.00006104352,0.00004037194,0.00066806225,0.0000052591677,0.00004472671,0.00014941432],"genre_scores_gemma":[0.9984564,0.00010032436,0.00034375957,0.00007938847,0.00005421774,0.00092537655,0.000013012016,0.0000051432676,0.000022381058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992591,0.0000416999,0.00019741949,0.00025387475,0.00012312757,0.00012479068],"domain_scores_gemma":[0.99946904,0.00013833606,0.0001070568,0.00020734963,0.000026658734,0.00005158223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009624039,0.00008010684,0.0001289752,0.000069109745,0.00009841681,0.000016647524,0.00017230528,0.000051386713,0.000008548976],"category_scores_gemma":[0.0000013473862,0.00008262308,0.000028433315,0.00033371165,0.00004582141,0.00012485558,0.000049934053,0.00007621542,0.000006966686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002986275,0.0019198859,0.24383211,0.00014646699,0.00006486718,0.000008528518,0.0021824972,0.35800698,0.0070683504,0.0026382178,0.00044540505,0.38365683],"study_design_scores_gemma":[0.0008977854,0.00010639752,0.15407139,0.00007426922,0.000010792001,0.000026987105,0.00022095208,0.82354534,0.0036197295,0.00019887999,0.01692637,0.00030109618],"about_ca_topic_score_codex":0.00008469097,"about_ca_topic_score_gemma":0.00013225242,"teacher_disagreement_score":0.7631888,"about_ca_system_score_codex":0.000030116165,"about_ca_system_score_gemma":0.000022231856,"threshold_uncertainty_score":0.33692715},"labels":[],"label_agreement":null},{"id":"W2057698772","doi":"10.1109/ia.2014.7009454","title":"Human activity recognition in smart homes: Combining passive RFID and load signatures of electrical devices","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Scalability; Computer science; Adaptability; Autonomy; Home automation; Activity recognition; Cognition; Human–computer interaction; Computer security; Telecommunications; Artificial intelligence; Medicine","score_opus":0.024006275445627463,"score_gpt":0.2626134516228088,"score_spread":0.23860717617718133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057698772","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9421529,0.000058910555,0.05324818,0.00022799739,0.00009985723,0.00022091916,0.0000019882816,0.0001040347,0.003885187],"genre_scores_gemma":[0.9989675,0.0000039862125,0.00077894726,0.00014985763,0.000027382408,0.000022009532,0.000002104683,0.0000069856765,0.00004117473],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984608,0.0003351572,0.0002691693,0.00040329472,0.00030537858,0.00022617185],"domain_scores_gemma":[0.9987121,0.00058577384,0.00020861358,0.00024051296,0.0001783199,0.000074662705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005568238,0.0001472564,0.00033042315,0.00022607837,0.000079125544,0.00010334908,0.00026401895,0.00011708526,0.000017504355],"category_scores_gemma":[0.00016890313,0.00013747245,0.00004757329,0.00043131594,0.0000501394,0.0007182086,0.00013483585,0.00023111788,0.000012268044],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027521197,0.0003309972,0.018233921,0.000094912146,0.000043791853,0.000007477271,0.0009976927,0.0000027590902,0.03611489,0.0022782383,0.00030904423,0.9415588],"study_design_scores_gemma":[0.0055462266,0.001803637,0.77493304,0.0007568707,0.00004334693,0.00008495922,0.00037525897,0.06166681,0.13348576,0.018214472,0.0015380323,0.001551592],"about_ca_topic_score_codex":0.00063571543,"about_ca_topic_score_gemma":0.001411841,"teacher_disagreement_score":0.94000715,"about_ca_system_score_codex":0.00006373166,"about_ca_system_score_gemma":0.000056571676,"threshold_uncertainty_score":0.5605964},"labels":[],"label_agreement":null},{"id":"W2057881048","doi":"10.1016/j.ijar.2013.03.006","title":"Relational approach to knowledge engineering for POMDP-based assistance systems as a translation of a psychological model","year":2013,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Partially observable Markov decision process; Computer science; Artificial intelligence; Probabilistic logic; Context (archaeology); Process (computing); Machine learning; Human–computer interaction; Markov model; Markov chain; Programming language","score_opus":0.0682950844509946,"score_gpt":0.31340606586224873,"score_spread":0.24511098141125415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057881048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04905911,0.00017406253,0.94728637,0.0003665935,0.0005476967,0.00045170565,0.000012683302,0.00004129197,0.0020605035],"genre_scores_gemma":[0.7082067,0.0000017142756,0.29151514,0.000030521067,0.000117643656,0.000078512734,0.0000048860975,0.00001166684,0.000033168148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811405,0.00006273264,0.00075557403,0.00026131416,0.0006173223,0.00018898139],"domain_scores_gemma":[0.99724114,0.00039015998,0.0005862697,0.00017007715,0.0014822528,0.00013011807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007932497,0.00015911592,0.00032053742,0.00039960566,0.00004107938,0.0001961231,0.0007396858,0.00009364759,0.0000041765834],"category_scores_gemma":[0.0002860938,0.00014533187,0.00019978882,0.0002393673,0.000018266319,0.00088097434,0.000042556723,0.00017261859,0.000008295066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005192569,0.0018248254,0.00084856746,0.00036809937,0.00061420887,0.00001243463,0.0039173285,0.62739545,0.0323624,0.24204165,0.0009810879,0.08911472],"study_design_scores_gemma":[0.0009113878,0.00010713912,0.0005646673,0.00041429643,0.000009920164,0.00016851375,0.00005095879,0.9961518,0.00056063547,0.0006301505,0.00028356307,0.00014695994],"about_ca_topic_score_codex":0.000014253346,"about_ca_topic_score_gemma":3.9046066e-7,"teacher_disagreement_score":0.6591476,"about_ca_system_score_codex":0.00015578288,"about_ca_system_score_gemma":0.00012976844,"threshold_uncertainty_score":0.5926462},"labels":[],"label_agreement":null},{"id":"W2059300352","doi":"10.1007/s11042-012-1302-y","title":"A context-aware multimedia framework toward personal social network services","year":2012,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada; University of Ottawa","funders":"","keywords":"Computer science; Context (archaeology); The Internet; Recreation; World Wide Web; Multimedia; Internet privacy; Social network (sociolinguistics); Context awareness; Social media","score_opus":0.04424634539792051,"score_gpt":0.28472970421315463,"score_spread":0.24048335881523414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059300352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011788359,0.0021892248,0.9789909,0.0030919022,0.00074813096,0.0015471256,0.00023291595,0.0006090916,0.00080231245],"genre_scores_gemma":[0.9714445,0.000030825173,0.023509718,0.0009640446,0.0027342455,0.0011239052,0.00007762706,0.000023906858,0.000091242546],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99809855,0.00010383943,0.00034772113,0.0004949582,0.00033681976,0.00061811187],"domain_scores_gemma":[0.99814284,0.0008114264,0.00020183121,0.00034468767,0.00015438408,0.0003448146],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034911605,0.00026886482,0.0003440502,0.000065612985,0.00054044754,0.00034788842,0.000541176,0.00023253664,0.000074667834],"category_scores_gemma":[0.00002997707,0.00026465257,0.000120306555,0.0004283162,0.00012343415,0.0010848525,0.0002877615,0.0003326519,0.0004046803],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007982516,0.00020592929,0.014736227,0.00006957438,0.00007582357,0.0000011305403,0.012593438,0.0000012896768,0.00010703497,0.0054308716,0.0013698644,0.9654008],"study_design_scores_gemma":[0.002811231,0.000104251594,0.26522234,0.00032294242,0.00018425267,0.0001276689,0.0065344106,0.15436445,0.00049138174,0.0052242023,0.5621228,0.0024900308],"about_ca_topic_score_codex":0.000076616416,"about_ca_topic_score_gemma":0.000030553005,"teacher_disagreement_score":0.96291083,"about_ca_system_score_codex":0.000049449955,"about_ca_system_score_gemma":0.00005204736,"threshold_uncertainty_score":0.99998057},"labels":[],"label_agreement":null},{"id":"W205944203","doi":"10.1007/978-1-4419-9893-4_102","title":"ZOGIM-A (Alertness Questionnaire)","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Alertness; Psychology; Psychiatry","score_opus":0.050220470355774774,"score_gpt":0.2341840574442736,"score_spread":0.1839635870884988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W205944203","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000016620776,0.00021409756,0.364538,0.00017789246,0.0010547924,0.00021670772,0.000004458401,0.0005720151,0.6332204],"genre_scores_gemma":[0.047527548,0.000051291718,0.0014522797,0.00042023114,0.00030886446,0.000030308875,0.0000080840555,0.000051582483,0.95014983],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803597,0.000041337324,0.00043400165,0.0007849716,0.0004338954,0.00026982854],"domain_scores_gemma":[0.9979006,0.00008754411,0.00030084388,0.0012196021,0.00032118204,0.00017021691],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00026259088,0.00041550153,0.00047961075,0.00027015797,0.00009808116,0.00018537145,0.0012117567,0.00041397082,0.0013086312],"category_scores_gemma":[0.000021999196,0.00039213695,0.0002353302,0.000051611998,0.00007682679,0.00065131683,0.000436613,0.00035486114,0.005941022],"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":[0.0000025177549,0.00001906091,0.000013579887,0.000033783195,0.000055264216,0.000041728967,0.00014052556,8.4592145e-8,0.0000061307874,0.7631533,0.0063433833,0.23019063],"study_design_scores_gemma":[0.00033792452,0.00010997766,0.00019384862,0.0008128909,0.00003571066,0.0002795816,0.0000032417213,0.00027597084,0.00025028439,0.17626369,0.820108,0.0013288688],"about_ca_topic_score_codex":0.00015939603,"about_ca_topic_score_gemma":0.0001661488,"teacher_disagreement_score":0.81376463,"about_ca_system_score_codex":0.000102977814,"about_ca_system_score_gemma":0.00018055747,"threshold_uncertainty_score":0.9998531},"labels":[],"label_agreement":null},{"id":"W2060443659","doi":"10.2196/mhealth.2536","title":"User Behavior Shift Detection in Ambient Assisted Living Environments","year":2013,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Eusko Jaurlaritza; Middlesex University; Washington State University","keywords":"Assisted living; Computer science; Activities of daily living; Ambient intelligence; Key (lock); Depression (economics); Human–computer interaction; Psychology; Medicine; Computer security; Gerontology; Psychiatry","score_opus":0.04721506972740363,"score_gpt":0.3205489651985068,"score_spread":0.27333389547110315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060443659","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9792885,0.000114409806,0.018215016,0.0007645167,0.00037771568,0.0010356645,0.00000247446,0.000095162075,0.00010650995],"genre_scores_gemma":[0.99804777,0.000049256447,0.0004406061,0.00066646805,0.000057603047,0.0005881216,0.000001918135,0.000012593998,0.00013567744],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99797136,0.0002860348,0.00044824617,0.0004982572,0.0002857406,0.0005103589],"domain_scores_gemma":[0.99888366,0.00014744267,0.00020928746,0.00035762182,0.000019566496,0.00038238842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005002581,0.00016272684,0.00024468024,0.00022760464,0.00019580618,0.00014374891,0.00021456023,0.00011000903,0.00003643352],"category_scores_gemma":[0.000022694758,0.00016264018,0.000034526765,0.00028981356,0.000029832463,0.0007850146,0.00013324305,0.00026884867,0.00016715514],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000095044,0.00061525544,0.12422573,0.000240852,0.0000052462033,0.000008870885,0.0024254231,8.8966976e-7,0.001031383,0.0001179183,0.00017878917,0.8711401],"study_design_scores_gemma":[0.00034123243,0.0001448305,0.996767,0.00006521232,0.0000043313053,0.00002600605,0.00008381125,0.0011031759,0.00009055977,0.0000455528,0.0011800261,0.00014827639],"about_ca_topic_score_codex":0.0022468127,"about_ca_topic_score_gemma":0.0009820944,"teacher_disagreement_score":0.87254125,"about_ca_system_score_codex":0.00026728082,"about_ca_system_score_gemma":0.00009688856,"threshold_uncertainty_score":0.66322744},"labels":[],"label_agreement":null},{"id":"W2061191276","doi":"10.1145/2491465.2491469","title":"Adaptive Composition of Distributed Pervasive Applications in Heterogeneous Environments","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Autonomous and Adaptive Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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 British Columbia","funders":"","keywords":"Computer science; Distributed computing; Unavailability; Latency (audio); Ubiquitous computing; Reuse; Distributed Computing Environment; Process (computing); Human–computer interaction; Operating system","score_opus":0.022077894232120157,"score_gpt":0.22347312904037264,"score_spread":0.2013952348082525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061191276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04270042,0.0002135537,0.9549375,0.00011933683,0.00012612286,0.0015128161,0.00018914447,0.00007065023,0.00013045914],"genre_scores_gemma":[0.9971328,0.0000265181,0.001792406,0.00002467329,0.00001567938,0.0008916494,0.000019459585,0.000014840464,0.000082012746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831456,0.0002018908,0.00049254467,0.00050800777,0.0002329974,0.0002500045],"domain_scores_gemma":[0.99864537,0.00030389082,0.00024368151,0.00059521076,0.000087662236,0.00012416727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012596296,0.00023586348,0.00038820828,0.00022437566,0.00016951779,0.000091107184,0.00040803457,0.00010928869,0.000018113344],"category_scores_gemma":[0.0000043106484,0.00023103565,0.00009806644,0.00030174735,0.00009807385,0.00054630474,0.000027385819,0.00018374978,0.0000633945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029453007,0.007679505,0.0035179816,0.00045282743,0.0018077687,0.00006999018,0.012523335,0.10820065,0.05251816,0.018255817,0.00013673224,0.79454273],"study_design_scores_gemma":[0.010999267,0.0061234715,0.08730511,0.001990348,0.00032868332,0.00093380816,0.017558789,0.7909296,0.06274568,0.007112222,0.009009842,0.004963154],"about_ca_topic_score_codex":0.0010750124,"about_ca_topic_score_gemma":0.0000522964,"teacher_disagreement_score":0.95443237,"about_ca_system_score_codex":0.0002046749,"about_ca_system_score_gemma":0.000044727705,"threshold_uncertainty_score":0.94213605},"labels":[],"label_agreement":null},{"id":"W2061691239","doi":"10.1145/1864431.1864467","title":"ALIS","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Ecological footprint; Key (lock); Resource (disambiguation); Computer science; Sustainable living; Interface (matter); Sustainable development; Assisted living; Business; Environmental resource management; Sustainability; Environmental science; Ecology; Computer security","score_opus":0.010899051613511065,"score_gpt":0.23214249128220857,"score_spread":0.2212434396686975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061691239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05908765,0.000004504087,0.65769035,0.0025498052,0.001631143,0.000101508616,9.4954623e-7,0.000537617,0.27839646],"genre_scores_gemma":[0.98824924,4.3423785e-7,0.00943638,0.00043151143,0.00007892697,0.000007271756,3.332372e-7,0.0000025777372,0.0017933137],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952877,0.000016352105,0.000077189645,0.00015976999,0.00011559026,0.00010233467],"domain_scores_gemma":[0.9994158,0.00006932037,0.00002309389,0.0003811172,0.000054200726,0.000056472156],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014506611,0.000045757362,0.000057766152,0.000040827956,0.00003769652,0.000111316964,0.00038667608,0.000032295167,0.0002804585],"category_scores_gemma":[0.000030514408,0.000039100538,0.00003039947,0.0001298345,0.000012403599,0.0008059364,0.00009637972,0.00009486504,0.0009953503],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010480433,0.00007731672,0.0029811251,0.00000504735,0.000015336951,0.000013936801,0.0004562523,1.9676945e-7,0.059140373,0.34006155,0.015364593,0.58188325],"study_design_scores_gemma":[0.00049331196,0.00004678485,0.010156843,0.000007395556,0.0000028544955,0.00023802157,0.000040824907,0.010516105,0.06262925,0.0051460583,0.91027844,0.00044408653],"about_ca_topic_score_codex":0.00009268648,"about_ca_topic_score_gemma":0.00024438568,"teacher_disagreement_score":0.9291616,"about_ca_system_score_codex":0.000003833051,"about_ca_system_score_gemma":0.000022366576,"threshold_uncertainty_score":0.9997825},"labels":[],"label_agreement":null},{"id":"W2061834800","doi":"10.46298/jips.663","title":"An empirical study on the user's context in mobile videoconferencing devices","year":2015,"lang":"en","type":"article","venue":"Journal d Interaction Personne-Système","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Université de Montréal","keywords":"Videoconferencing; Context (archaeology); Computer science; Mobile device; Empirical research; Multimedia; Human–computer interaction; World Wide Web; Geography; Mathematics","score_opus":0.11431694385272374,"score_gpt":0.36298102704032387,"score_spread":0.24866408318760014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061834800","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9766289,0.000075969394,0.016009308,0.0025913038,0.0023307486,0.00072857307,0.000002665852,0.000120781006,0.0015117658],"genre_scores_gemma":[0.9979654,0.0000028313586,0.00008315834,0.0011663304,0.00050398556,0.00012886937,0.0000010250591,0.000021309213,0.00012708767],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99599695,0.0015728292,0.00073314144,0.00050325994,0.0008142075,0.0003796078],"domain_scores_gemma":[0.9972172,0.00076870504,0.00053951546,0.0005929114,0.0005430325,0.00033868218],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0025020875,0.00028416252,0.00041606088,0.00048292303,0.00029807322,0.0013867279,0.001013292,0.00009991294,0.00013613784],"category_scores_gemma":[0.00037199687,0.00020034776,0.00015356173,0.00051806617,0.00002867041,0.003246787,0.000115575596,0.0010033706,0.0004317615],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089787197,0.007605749,0.34360072,0.000044997087,0.0006133933,0.0012719098,0.38836932,0.0014815241,0.002595367,0.0008222913,0.031096028,0.22160085],"study_design_scores_gemma":[0.0063435407,0.008816798,0.10466168,0.0011440754,0.00006899348,0.005061931,0.70366657,0.10031073,0.0012063407,0.0012082442,0.06595898,0.0015521273],"about_ca_topic_score_codex":0.00042808254,"about_ca_topic_score_gemma":0.0014578373,"teacher_disagreement_score":0.31529725,"about_ca_system_score_codex":0.0011749781,"about_ca_system_score_gemma":0.00034926314,"threshold_uncertainty_score":0.99964994},"labels":[],"label_agreement":null},{"id":"W2062668382","doi":"10.1016/j.gaitpost.2013.08.034","title":"Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers","year":2013,"lang":"en","type":"article","venue":"Gait & Posture","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Accelerometer; Wearable computer; Physical medicine and rehabilitation; Computer science; Medicine; Embedded system","score_opus":0.05548488870813528,"score_gpt":0.2790551515394165,"score_spread":0.22357026283128123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062668382","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9878732,0.000100591395,0.010673408,0.00017843005,0.00029067366,0.00037392593,0.000007843659,0.000038952672,0.00046295498],"genre_scores_gemma":[0.9980104,0.0000029961536,0.0017606365,0.00009882314,0.00006699661,0.000012755925,0.0000017867301,0.000010815825,0.00003479132],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99856144,0.0002121409,0.00038402533,0.0002878388,0.0003098041,0.00024474613],"domain_scores_gemma":[0.9986873,0.0002319947,0.0003006518,0.0005240975,0.00020346516,0.000052486143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041495642,0.00013974869,0.00030429952,0.00014021824,0.00008625752,0.0001662723,0.0008167653,0.00008077057,0.000024914872],"category_scores_gemma":[0.0001631587,0.00010608921,0.00007736173,0.00045385238,0.00006618193,0.0009102367,0.00012388594,0.0002070519,0.0000072978155],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024031346,0.00048337821,0.091390826,0.00016324218,0.000103608734,0.000011723913,0.03286024,0.00030804068,0.8397167,0.00031562656,0.00029010844,0.034332477],"study_design_scores_gemma":[0.0023317027,0.00059359556,0.8878996,0.0014073127,0.00006263548,0.000069567,0.00692697,0.00819473,0.08839924,0.0020986514,0.00092444837,0.001091558],"about_ca_topic_score_codex":0.004031099,"about_ca_topic_score_gemma":0.0007825332,"teacher_disagreement_score":0.7965088,"about_ca_system_score_codex":0.00004174475,"about_ca_system_score_gemma":0.00006557631,"threshold_uncertainty_score":0.6093843},"labels":[],"label_agreement":null},{"id":"W2063728828","doi":"10.1186/s12984-015-0026-4","title":"Auto detection and segmentation of physical activities during a Timed-Up-and-Go (TUG) task in healthy older adults using multiple inertial sensors","year":2015,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Université de Sherbrooke; Université du Québec à Montréal","funders":"Université du Québec à Montréal; University of Windsor; Université Laval","keywords":"Segmentation; Inertial measurement unit; Task (project management); Computer science; Artificial intelligence; Trunk; Kinematics; Computer vision; Sitting; Gait; Physical medicine and rehabilitation; Simulation; Medicine; Engineering","score_opus":0.01169955328684873,"score_gpt":0.2441165391243683,"score_spread":0.23241698583751955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063728828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9663021,0.000061010698,0.033098582,0.0001355299,0.00024120284,0.00014270176,0.0000010716047,0.00001704023,7.434059e-7],"genre_scores_gemma":[0.9980507,0.000008075622,0.0018657199,0.0000046013415,0.000058874146,0.0000032758892,1.6450434e-7,0.0000074159593,0.0000012035272],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991418,0.00012227934,0.00030953117,0.00014176282,0.00018794571,0.000096697666],"domain_scores_gemma":[0.9991422,0.00041146632,0.00020897591,0.00006187428,0.000095325355,0.00008015684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020619646,0.00009015016,0.00020225097,0.00031518095,0.000032693286,0.000037271187,0.0000367143,0.000029737761,5.90746e-8],"category_scores_gemma":[0.00024949398,0.00008553772,0.000031829542,0.00016294603,0.000030766598,0.00077450054,0.000030309939,0.00013726905,7.358959e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012029206,0.0002800525,0.015830249,0.0012236742,0.0000491866,0.000020244332,0.06740357,0.034341287,0.8081803,0.000020769234,0.000004707632,0.071443066],"study_design_scores_gemma":[0.0025023776,0.001087292,0.09442667,0.00036411048,0.000010915887,0.00026296615,0.0016908925,0.894798,0.0047119707,0.000027219927,0.000004348964,0.00011326683],"about_ca_topic_score_codex":0.000053645144,"about_ca_topic_score_gemma":0.000012366105,"teacher_disagreement_score":0.8604567,"about_ca_system_score_codex":0.00008274219,"about_ca_system_score_gemma":0.000025918627,"threshold_uncertainty_score":0.3488127},"labels":[],"label_agreement":null},{"id":"W2066286351","doi":"10.4236/jgis.2012.45048","title":"Mobile and Context-Aware GeoBI Applications: A Multilevel Model for Structuring and Sharing of Contextual Information","year":2012,"lang":"en","type":"article","venue":"Journal of Geographic Information System","topic":"Context-Aware Activity Recognition Systems","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":"Centre de Géomatique du Québec; Université Laval","funders":"","keywords":"Structuring; Computer science; Geospatial analysis; Context (archaeology); Business intelligence; Data science; Context awareness; Knowledge management; Mobile computing; Mobile device; Spatial contextual awareness; Contextual design; Mobile mapping; World Wide Web; Human–computer interaction; Geography; Artificial intelligence; Telecommunications; Business; Cartography","score_opus":0.020836673813241266,"score_gpt":0.24449850694672967,"score_spread":0.22366183313348842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066286351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1693186,0.00032001635,0.829125,0.0000142438985,0.00017757165,0.0008900371,0.000050762115,0.000038827093,0.000064972584],"genre_scores_gemma":[0.9952114,0.0000311528,0.004463289,0.000064929976,0.000058430658,0.00015504849,0.0000069252624,0.0000051913203,0.0000035932915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791414,0.000038498954,0.0013663452,0.000084539104,0.00037166165,0.00022481233],"domain_scores_gemma":[0.99653465,0.00014406585,0.0018382083,0.00022750466,0.0010966805,0.00015891201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013340598,0.00016371936,0.00040255746,0.0007498544,0.00016821895,0.00023480467,0.00031197089,0.0001125893,8.13061e-7],"category_scores_gemma":[0.000052315307,0.00014754932,0.000117566226,0.00020209285,0.000052968815,0.01092708,0.00013280056,0.0001448225,0.0000024096441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018934505,0.00006194344,0.05618787,0.004119131,0.00037032532,3.0399832e-7,0.04575536,0.0012641434,0.00016576328,0.0354165,0.00009247758,0.8563768],"study_design_scores_gemma":[0.0036067213,0.00022737215,0.011590237,0.0006747974,0.000066780834,0.00077367335,0.012193219,0.96560174,0.0004361985,0.00027129208,0.0041772607,0.00038072304],"about_ca_topic_score_codex":0.000019603945,"about_ca_topic_score_gemma":0.0000019816753,"teacher_disagreement_score":0.9643376,"about_ca_system_score_codex":0.0000570623,"about_ca_system_score_gemma":0.000060319726,"threshold_uncertainty_score":0.792187},"labels":[],"label_agreement":null},{"id":"W2066338981","doi":"10.1371/journal.pone.0124414","title":"Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":190,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; C4.5 algorithm; Artificial intelligence; Computer science; Accelerometer; Activity recognition; Wearable computer; Naive Bayes classifier; Pattern recognition (psychology); Gyroscope; Support vector machine; Classifier (UML); Population; Feature (linguistics); Decision tree; Machine learning; Medicine; Engineering","score_opus":0.08234508582423877,"score_gpt":0.23966266756549431,"score_spread":0.15731758174125554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066338981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92921704,0.000027981376,0.0681283,0.00051149057,0.000077701414,0.0009802255,0.00003972695,0.00023550804,0.0007819945],"genre_scores_gemma":[0.98546153,0.0000018875066,0.012921025,0.000085002226,0.00008441241,0.00025664814,0.00003366752,0.000021473943,0.0011343497],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998513,0.00011764982,0.00012627186,0.00048767272,0.00045745086,0.00029796705],"domain_scores_gemma":[0.99856,0.00013202957,0.00018232717,0.00025672215,0.0006958055,0.00017312737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031691414,0.00018527322,0.00028371863,0.00015127858,0.00024503825,0.00025431023,0.00016283957,0.00011815049,0.0000059687354],"category_scores_gemma":[0.00008552525,0.00017920708,0.000038441947,0.00024548618,0.000030829044,0.000966226,0.00004577126,0.00018682289,0.000026552445],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027550492,0.018046211,0.28167817,0.0015913286,0.0014022222,0.000008043244,0.0020562017,0.00004457947,0.13151683,0.000097204,0.011278736,0.54952544],"study_design_scores_gemma":[0.031916052,0.016327111,0.0359411,0.0023571812,0.00055454747,0.000025732375,0.00018796527,0.09108598,0.8131004,0.002278455,0.0036137337,0.002611777],"about_ca_topic_score_codex":0.00024058927,"about_ca_topic_score_gemma":0.0007299243,"teacher_disagreement_score":0.6815835,"about_ca_system_score_codex":0.00013708546,"about_ca_system_score_gemma":0.00011702153,"threshold_uncertainty_score":0.73078525},"labels":[],"label_agreement":null},{"id":"W2069619006","doi":"10.1016/j.pmcj.2013.09.006","title":"RRACE: Robust realtime algorithm for cadence estimation","year":2013,"lang":"en","type":"article","venue":"Pervasive and Mobile Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Cadence; Computer science; A priori and a posteriori; Accelerometer; Orientation (vector space); Algorithm; Real-time computing; Latency (audio); Robustness (evolution); Artificial intelligence; Telecommunications","score_opus":0.024722039231056408,"score_gpt":0.2670118799789327,"score_spread":0.24228984074787632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069619006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059265915,0.00026702587,0.9386069,0.00025705626,0.0002779279,0.0009872931,0.000004804756,0.00017172836,0.0001613114],"genre_scores_gemma":[0.76738536,0.000013251458,0.23174463,0.0002358279,0.00016507487,0.00025060197,0.000009348206,0.000012380853,0.00018353222],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987527,0.00006363753,0.0002689197,0.00046118215,0.00016258591,0.00029099116],"domain_scores_gemma":[0.998555,0.00055872445,0.00016603881,0.00026760364,0.00033983006,0.00011279619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030533088,0.00015160546,0.00021758948,0.00008217799,0.00029748166,0.00032957218,0.0002971068,0.000067101624,0.000012676143],"category_scores_gemma":[0.000085951266,0.00015024528,0.00006087443,0.00017257222,0.000036866193,0.00083241443,0.0002190626,0.00010625761,0.000068696325],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.827663e-7,0.000022402435,0.00014235551,0.000032420638,0.000010935952,0.0000013860982,0.0010871241,0.0005997226,0.00030635818,0.0001167253,0.0007112957,0.9969688],"study_design_scores_gemma":[0.00030505058,0.000100971076,0.0013004456,0.0000843907,0.000005999201,0.00006658362,0.00021351558,0.99549514,0.00059487444,0.00046448066,0.0011761223,0.00019244761],"about_ca_topic_score_codex":0.00025256636,"about_ca_topic_score_gemma":0.0000053179706,"teacher_disagreement_score":0.99677634,"about_ca_system_score_codex":0.0000423028,"about_ca_system_score_gemma":0.00004989898,"threshold_uncertainty_score":0.61268246},"labels":[],"label_agreement":null},{"id":"W2070885583","doi":"10.1109/cica.2014.7013250","title":"Ensuring safe prevention and reaction in smart home systems dedicated to people becoming disabled","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Home automation; Controller (irrigation); Risk analysis (engineering); Control (management); Formal verification; Computer security; Human–computer interaction; Embedded system; Artificial intelligence; Telecommunications","score_opus":0.022567397247582822,"score_gpt":0.2518773799076067,"score_spread":0.2293099826600239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070885583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67621297,0.000013041632,0.320266,0.00044436293,0.00046105168,0.00037057386,4.6812008e-7,0.00015898807,0.0020725627],"genre_scores_gemma":[0.99828994,0.0000032337844,0.00090339995,0.000051893065,0.000056366127,0.000070666654,0.0000029866505,0.000009132597,0.0006123566],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985494,0.0002448046,0.00034335922,0.00041583402,0.0002160006,0.00023062491],"domain_scores_gemma":[0.99913144,0.00025398785,0.00010967536,0.0003183479,0.000063254105,0.00012327121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001014186,0.00012141733,0.00025054428,0.00025576298,0.0000834294,0.00021223433,0.00020356006,0.00006623193,0.0000046189048],"category_scores_gemma":[0.000112399415,0.000121840385,0.000030305617,0.00045491938,0.000007982623,0.0007570966,0.00015843754,0.00009800993,0.00010394192],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112399895,0.00070269033,0.2823904,0.0007305453,0.0001283875,0.000014478115,0.008144878,0.00030635225,0.12291695,0.020726426,0.0011874051,0.5626391],"study_design_scores_gemma":[0.002811419,0.00034531293,0.6877304,0.0009323273,0.000020365114,0.00020031292,0.0007057434,0.29584628,0.0025276705,0.0015730527,0.006329085,0.0009780342],"about_ca_topic_score_codex":0.0014999622,"about_ca_topic_score_gemma":0.0027813443,"teacher_disagreement_score":0.56166106,"about_ca_system_score_codex":0.00010344033,"about_ca_system_score_gemma":0.000023765417,"threshold_uncertainty_score":0.49685067},"labels":[],"label_agreement":null},{"id":"W2070962496","doi":"10.1145/1414471.1414473","title":"Designing technology to aid cognition","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"","keywords":"Cognition; Computer science; Term (time); Cognitive science; Psychology; Cognitive psychology","score_opus":0.043781064236954624,"score_gpt":0.2715997634111374,"score_spread":0.22781869917418276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070962496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033811692,0.000016585098,0.94783556,0.0019412022,0.00019307635,0.0002139999,6.6995716e-7,0.0007950993,0.015192123],"genre_scores_gemma":[0.9213251,0.0000019255203,0.07705048,0.0007677647,0.000036910977,0.000052640473,7.374185e-7,0.000006089478,0.00075834064],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990851,0.000041921965,0.00016209231,0.0003189811,0.00017670389,0.00021518675],"domain_scores_gemma":[0.9993038,0.0000879197,0.000042624677,0.00032717126,0.00015405528,0.000084432875],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013969644,0.000092767725,0.00013158057,0.00034445451,0.00015290982,0.000044534703,0.0004066574,0.00007017934,0.00004971263],"category_scores_gemma":[0.0000832385,0.00009224751,0.000033484906,0.0008119169,0.000029645196,0.0004492238,0.00017380169,0.00008842748,0.0016782595],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024670113,0.0003250882,0.008404197,0.000023833536,0.00008848084,0.0003709947,0.0027577912,0.000025375139,0.18753876,0.021857817,0.025238061,0.75334495],"study_design_scores_gemma":[0.0019691787,0.0007202384,0.0036699304,0.00015794713,0.000015051853,0.0035897035,0.0005694083,0.0051972694,0.9052542,0.010561115,0.06696364,0.0013323419],"about_ca_topic_score_codex":0.00001794548,"about_ca_topic_score_gemma":0.000014907953,"teacher_disagreement_score":0.8875134,"about_ca_system_score_codex":0.000031421016,"about_ca_system_score_gemma":0.000050760227,"threshold_uncertainty_score":0.9990991},"labels":[],"label_agreement":null},{"id":"W2071481589","doi":"10.1145/1328202.1328210","title":"Sensor networks as video game input devices","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Computer science; Context (archaeology); Set (abstract data type); Human–computer interaction; Video game; Cheating; Work (physics); Haptic technology; Motion sensors; Real-time computing; Simple (philosophy); Multimedia; Simulation; Artificial intelligence; Engineering","score_opus":0.020978297412079003,"score_gpt":0.2696297478748622,"score_spread":0.2486514504627832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071481589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042319525,0.000096495765,0.9087995,0.0007136768,0.0006843983,0.0001798318,3.854989e-7,0.000481678,0.046724487],"genre_scores_gemma":[0.98837024,0.0000039379,0.005807874,0.0026379812,0.0002770926,0.0000064425612,0.000001141734,0.000010304779,0.0028849964],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985005,0.00007529232,0.00030707155,0.00041324575,0.00030704887,0.00039681082],"domain_scores_gemma":[0.9985198,0.00052522647,0.00011393345,0.0005177342,0.00015104418,0.00017225413],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00078741927,0.00015027412,0.00019146728,0.00013252215,0.00008657544,0.00025825005,0.0005650515,0.00010011964,0.00014011459],"category_scores_gemma":[0.00005889106,0.000134946,0.000080915415,0.00047696554,0.000029209346,0.0007357855,0.00021564709,0.00015562699,0.0011399952],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027427386,0.00016127514,0.0123511385,0.000026293079,0.00009336062,0.00022672332,0.0011409757,0.00019542211,0.0009652365,0.011647281,0.00671782,0.96644706],"study_design_scores_gemma":[0.0025677874,0.00052839593,0.078397,0.00028372675,0.000038953982,0.0016837266,0.0010420047,0.23900197,0.019688405,0.0027895097,0.65146834,0.0025101698],"about_ca_topic_score_codex":0.00027326104,"about_ca_topic_score_gemma":0.00053967565,"teacher_disagreement_score":0.96393687,"about_ca_system_score_codex":0.000047801823,"about_ca_system_score_gemma":0.000043395106,"threshold_uncertainty_score":0.9996377},"labels":[],"label_agreement":null},{"id":"W2072137155","doi":"10.1080/08839514.2010.481486","title":"DEEPKOVER – AN ADAPTIVE ARTFUL INTELLIGENT ASSISTANCE SYSTEM FOR COGNITIVELY IMPAIRED PEOPLE","year":2010,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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 de Moncton; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Computer science; Realisation; Modular design; Activities of daily living; Key (lock); Order (exchange); Human–computer interaction; Psychology; Computer security; Operating system; Business","score_opus":0.05728428071159727,"score_gpt":0.28531421962465586,"score_spread":0.2280299389130586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072137155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05943619,0.000018680965,0.9327764,0.00018603714,0.0019673954,0.0017048828,0.000072171744,0.00068583916,0.0031524354],"genre_scores_gemma":[0.9783924,0.0000018251835,0.01985575,0.00018503689,0.00059559266,0.0008412137,0.000022498803,0.000050412513,0.00005525511],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962803,0.000117766125,0.00092543144,0.0013222676,0.0005773421,0.0007768796],"domain_scores_gemma":[0.99647355,0.0009103296,0.00045389982,0.0010676506,0.0007065099,0.0003880715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011146888,0.0004676982,0.00057626417,0.00023296555,0.000497392,0.00058832933,0.0014540303,0.00025434291,0.000063191634],"category_scores_gemma":[0.00013696357,0.00048393046,0.00021788846,0.0007744466,0.0001840442,0.0009257871,0.0002436283,0.00047894032,0.0007292449],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027447368,0.00037001894,0.00004336352,0.00005392318,0.000063450956,0.000009083331,0.0029260954,0.00006450447,0.035945326,0.50763416,0.00012742128,0.45248818],"study_design_scores_gemma":[0.00023687577,0.00066184497,0.0005746599,0.000118758384,0.00006800479,0.000097188604,0.008379362,0.1874394,0.74283606,0.05418348,0.0037230947,0.0016812476],"about_ca_topic_score_codex":0.00014266351,"about_ca_topic_score_gemma":0.0022506043,"teacher_disagreement_score":0.9189562,"about_ca_system_score_codex":0.00014891021,"about_ca_system_score_gemma":0.00021859552,"threshold_uncertainty_score":0.9997612},"labels":[],"label_agreement":null},{"id":"W2073818594","doi":"10.1109/iscc.2010.5546611","title":"Using heterogeneous and Social Contexts to create a smart space architecture","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Ubiquitous computing; Computer science; Smart environment; Architecture; Context (archaeology); Context-aware pervasive systems; Ambient intelligence; Wireless sensor network; Space (punctuation); Context awareness; Human–computer interaction; Mobile device; Ubiquitous robot; Data science; Mobile computing; World Wide Web; Internet of Things; Telecommunications; Artificial intelligence; Computer network; Geography","score_opus":0.030649115444993973,"score_gpt":0.279224785128449,"score_spread":0.24857566968345504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073818594","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61958027,0.0000089982295,0.37628356,0.0019990213,0.00034018123,0.00022079734,0.0000038921244,0.00015301768,0.0014102812],"genre_scores_gemma":[0.9811481,1.5904978e-7,0.017420087,0.0008929866,0.00013604885,0.000010246265,4.784887e-7,0.000010860139,0.000381012],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989496,0.0000693778,0.0001440531,0.00039518424,0.00018392652,0.0002578614],"domain_scores_gemma":[0.9993405,0.000098844,0.000054467007,0.000278211,0.00008650131,0.00014147606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018330346,0.00014646685,0.00019786824,0.00011375728,0.00018820725,0.00029597478,0.0002912806,0.00008264916,0.000036525384],"category_scores_gemma":[0.000030745752,0.00013315963,0.00005642536,0.000157907,0.00004253106,0.00019511231,0.00027573068,0.00018700886,0.000056906716],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054161217,0.00013539413,0.003095749,0.000050479703,0.000120144825,0.00011979442,0.010459902,0.000034567394,0.4184217,0.0084482385,0.0009129977,0.5581469],"study_design_scores_gemma":[0.008888412,0.0014161775,0.032009084,0.00030397525,0.00016099222,0.014173762,0.0007910302,0.09325933,0.38051987,0.01666216,0.44470122,0.0071139988],"about_ca_topic_score_codex":0.00027826938,"about_ca_topic_score_gemma":0.0013491285,"teacher_disagreement_score":0.5510329,"about_ca_system_score_codex":0.000017135957,"about_ca_system_score_gemma":0.000043239525,"threshold_uncertainty_score":0.5430092},"labels":[],"label_agreement":null},{"id":"W2074582715","doi":"10.1016/j.procs.2011.07.077","title":"An Adaptive Context-Aware and Event-Based Framework Design Model","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Event (particle physics); Publication; Context (archaeology); Leverage (statistics); Context model; Context awareness; Adaptation (eye); Ubiquitous computing; Data science; World Wide Web; Human–computer interaction; Artificial intelligence","score_opus":0.08759148297205874,"score_gpt":0.27435972195438035,"score_spread":0.18676823898232162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074582715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077924537,0.00006420911,0.9902751,0.00015279317,0.00062867696,0.0005438063,0.0000033273109,0.0004534196,0.000086231434],"genre_scores_gemma":[0.6183885,0.0000014523533,0.38068068,0.0008006801,0.000058246387,0.000054054904,3.490558e-7,0.000010627314,0.000005472761],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99680203,0.00014969255,0.0003580875,0.0013281764,0.0007292288,0.00063278375],"domain_scores_gemma":[0.99747264,0.00027998097,0.0002172392,0.0009126035,0.0006160273,0.00050148147],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012928752,0.00031665197,0.0003159473,0.0003570065,0.00045550396,0.0005454079,0.002283219,0.000117439515,0.0000073255433],"category_scores_gemma":[0.000077393,0.00030140253,0.00005959782,0.0011231657,0.0005303491,0.0034225509,0.00046558087,0.00028155177,0.000044999506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015779154,0.0009565968,0.0032216867,0.00008633817,0.000042844178,0.00007323669,0.024756707,0.009286654,0.0013815223,0.060343083,0.00043701878,0.8992565],"study_design_scores_gemma":[0.00028490287,0.0005063609,0.0010956607,0.00009804932,0.000005596791,0.000040990835,0.00005084251,0.98205185,0.0046514887,0.010802101,0.000013556448,0.00039860452],"about_ca_topic_score_codex":0.00003653964,"about_ca_topic_score_gemma":0.000010862287,"teacher_disagreement_score":0.9727652,"about_ca_system_score_codex":0.00008895158,"about_ca_system_score_gemma":0.0009014186,"threshold_uncertainty_score":0.9999438},"labels":[],"label_agreement":null},{"id":"W2075096900","doi":"10.1145/1148550.1148598","title":"Virtualisation d'interfaces matérielles par l'intermédiaire d'un ordinateur porté","year":2005,"lang":"fr","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Polytechnique Montréal","funders":"","keywords":"Virtualization; Computer science; Wearable computer; Complementarity (molecular biology); Operating system; Embedded system; Full virtualization; Computer architecture; Human–computer interaction; Cloud computing","score_opus":0.03723234508068463,"score_gpt":0.2560182582757745,"score_spread":0.2187859131950899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075096900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23429704,0.004321216,0.61408514,0.06550025,0.0065008523,0.0008662352,0.000057419562,0.0008840723,0.07348779],"genre_scores_gemma":[0.92051905,0.0001326394,0.005286108,0.0016122827,0.00084748334,0.000043638414,0.000011847018,0.000042334555,0.07150462],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99625385,0.0005196637,0.000941847,0.0009845558,0.00058097567,0.00071911793],"domain_scores_gemma":[0.99757737,0.00037825326,0.00042957207,0.00090674707,0.00039803152,0.0003100524],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008373308,0.0005019189,0.000531835,0.00024036919,0.00029987213,0.0009321674,0.0010143114,0.00030080584,0.0044810893],"category_scores_gemma":[0.00013700395,0.00051804865,0.00025101035,0.0005306645,0.00022155848,0.00368211,0.00051818293,0.00036576804,0.0069760047],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001843415,0.0003178317,0.0011310689,0.00005873787,0.00011765724,0.0000515186,0.0034924257,0.000055695582,0.00096622383,0.017887173,0.030954473,0.94494873],"study_design_scores_gemma":[0.001621233,0.00048168915,0.008120322,0.0007622042,0.000093921786,0.0011640996,0.0020103534,0.08862282,0.11549243,0.0021783866,0.7777875,0.0016650363],"about_ca_topic_score_codex":0.0009414709,"about_ca_topic_score_gemma":0.0010716077,"teacher_disagreement_score":0.94328374,"about_ca_system_score_codex":0.00020674527,"about_ca_system_score_gemma":0.00017652704,"threshold_uncertainty_score":0.99972713},"labels":[],"label_agreement":null},{"id":"W2075457858","doi":"10.1145/2395123.2395125","title":"People, sensors, decisions","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Interactive Intelligent Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Toronto Rehabilitation Institute; Canadian Institutes of Health Research; Research Councils UK; Alzheimer's Association","keywords":"Telecare; Health care; Population; Population ageing; Internet privacy; Business; Computer science; Knowledge management; Medicine; Telemedicine; Political science; Environmental health","score_opus":0.05486439013506324,"score_gpt":0.3046270733675481,"score_spread":0.24976268323248485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075457858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02698001,0.00023697458,0.9589809,0.000609992,0.009148598,0.0008702565,0.00005437785,0.00052163913,0.0025972317],"genre_scores_gemma":[0.9948923,0.00007014393,0.0021424508,0.00017464852,0.00030980996,0.0003994134,0.0000076159104,0.00004917115,0.001954461],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99625474,0.00059726776,0.000867401,0.0007242985,0.0007725507,0.0007837338],"domain_scores_gemma":[0.9926002,0.0041621495,0.0003467825,0.0019687803,0.0004524986,0.00046959345],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007114522,0.00047154867,0.0005722484,0.00071473356,0.00040646922,0.00038771558,0.0015257683,0.00019615355,0.00029460815],"category_scores_gemma":[0.00046493567,0.00044092909,0.00040329443,0.0009486373,0.00005070132,0.0025017662,0.00006400144,0.00067694415,0.0049958467],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006048407,0.008874303,0.0062457537,0.00021329781,0.0029199978,0.000083722334,0.050119787,0.008314295,0.008253543,0.016122244,0.009986693,0.8882615],"study_design_scores_gemma":[0.004005825,0.002570444,0.012725852,0.004730488,0.0005466814,0.007103692,0.046465844,0.072142504,0.23769553,0.0028844823,0.6010886,0.0080400705],"about_ca_topic_score_codex":0.00050164195,"about_ca_topic_score_gemma":0.00008821881,"teacher_disagreement_score":0.96791226,"about_ca_system_score_codex":0.00050532277,"about_ca_system_score_gemma":0.0000760856,"threshold_uncertainty_score":0.99980426},"labels":[],"label_agreement":null},{"id":"W2076056344","doi":"10.1155/2013/936816","title":"Accurate RFID Trilateration to Learn and Recognize Spatial Activities in Smart Environment","year":2013,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec","keywords":"Trilateration; Exploit; Computer science; Human–computer interaction; Field (mathematics); Smart environment; Activity recognition; Home automation; Ubiquitous computing; Wireless; Smart objects; Computer security; Embedded system; Artificial intelligence; Telecommunications; Internet of Things; Node (physics)","score_opus":0.01450483579319927,"score_gpt":0.2356283795930904,"score_spread":0.22112354379989113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076056344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36862034,0.000044490313,0.6277517,0.0023780945,0.00092930277,0.00018675491,0.000020003316,0.00001599683,0.00005331039],"genre_scores_gemma":[0.9977063,0.000069637856,0.0015272946,0.00023812364,0.0003580814,0.000013654958,0.000021401163,0.00000893735,0.000056588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983231,0.00018671522,0.0006011391,0.00022898761,0.00044967007,0.00021039984],"domain_scores_gemma":[0.99873954,0.0003002878,0.00040009592,0.00013811709,0.0002752672,0.00014667324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035571144,0.0001550239,0.00025566886,0.0002456556,0.00004435757,0.00048284183,0.00042777945,0.000087222645,0.00008666316],"category_scores_gemma":[0.00010969965,0.00014677821,0.00007476261,0.00013855114,0.000027405784,0.0011637416,0.0001581852,0.00029391111,0.000035629142],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005137258,0.00052164536,0.035283443,0.000013948948,0.0005378455,0.00036525764,0.0018626803,0.068115786,0.007148185,0.00019754558,0.005616464,0.87982345],"study_design_scores_gemma":[0.0060763247,0.000872406,0.34189534,0.0006569366,0.00004225746,0.0017412578,0.0007652228,0.6211037,0.0038766894,0.0012917455,0.020518918,0.0011591977],"about_ca_topic_score_codex":0.00015730486,"about_ca_topic_score_gemma":0.000052257437,"teacher_disagreement_score":0.87866426,"about_ca_system_score_codex":0.000220405,"about_ca_system_score_gemma":0.00004025297,"threshold_uncertainty_score":0.5985442},"labels":[],"label_agreement":null},{"id":"W2078099242","doi":"10.5539/nct.v1n1p79","title":"A Complete Interoperable eHealth System Based on ZigBee Pro Standard","year":2012,"lang":"en","type":"article","venue":"Network and Communication Technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"eHealth; Interoperability; Telemedicine; Computer science; Wireless sensor network; Software; Set (abstract data type); World Wide Web; Telecommunications; Computer network; Health care; Operating system","score_opus":0.040618673950727525,"score_gpt":0.2648189621668866,"score_spread":0.2242002882161591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078099242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020979548,0.0160309,0.9170695,0.016005099,0.0007692859,0.00181733,0.00002854694,0.009510473,0.017789308],"genre_scores_gemma":[0.9819397,0.00021806337,0.017365927,0.00023398573,0.000027194437,0.00017429206,0.000007374275,0.000010059732,0.000023452574],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99863,0.00029446985,0.00028065193,0.00023094313,0.00020594761,0.00035801847],"domain_scores_gemma":[0.99773115,0.00034595843,0.00018998513,0.00157171,0.00010824713,0.000052922762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085202674,0.00015569133,0.00026438566,0.00012117631,0.00043348837,0.000171223,0.0011069486,0.000124293,0.0000026789382],"category_scores_gemma":[0.000054804426,0.00013478154,0.000032875032,0.00041219348,0.00011977878,0.00042778085,0.00056911714,0.00031830394,0.0000359827],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055049695,0.00012379642,0.0099795135,0.00013477201,0.000033962784,0.0000010770163,0.0005367787,0.00013911507,0.000065042404,0.13168532,0.006240856,0.8510047],"study_design_scores_gemma":[0.0034634708,0.0019729517,0.007164666,0.004320636,0.000051538158,0.00016663721,0.007624143,0.34100026,0.0021888372,0.007477731,0.6226482,0.0019209654],"about_ca_topic_score_codex":0.00003300588,"about_ca_topic_score_gemma":0.00002285201,"teacher_disagreement_score":0.9609601,"about_ca_system_score_codex":0.0001312058,"about_ca_system_score_gemma":0.000036683417,"threshold_uncertainty_score":0.5496232},"labels":[],"label_agreement":null},{"id":"W2078141940","doi":"10.1007/s11277-009-9885-y","title":"Editorial: Special Issue on “Trustworthy and Intelligent Services for Ubiquitous Computing”","year":2009,"lang":"en","type":"editorial","venue":"Wireless Personal Communications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Trustworthiness; Ubiquitous computing; Computer security; Data science; World Wide Web; Internet privacy; Human–computer interaction","score_opus":0.024140228808599902,"score_gpt":0.30702338551736374,"score_spread":0.28288315670876385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078141940","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004365472,0.000711801,0.0060833376,0.0023620725,0.9865122,0.0010692104,0.0004411863,0.00034418982,0.002432359],"genre_scores_gemma":[0.004215111,0.0007500956,0.00224229,0.00024257554,0.99110234,0.0001469338,0.0007327846,0.00006102189,0.00050682307],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99620926,0.0004291185,0.0007150237,0.0009698656,0.0011682389,0.00050851825],"domain_scores_gemma":[0.99091893,0.0047058365,0.00067090464,0.0023457864,0.0011267372,0.00023182904],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009289911,0.0005815351,0.0008354699,0.00031960665,0.00096812355,0.0008753669,0.004203492,0.000892961,0.000014625795],"category_scores_gemma":[0.00019470367,0.0006328067,0.00028026974,0.0003659397,0.00019893757,0.00040879849,0.001223154,0.0014638341,0.00011040483],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036442856,0.00018885754,0.0000023075534,0.0001141844,0.00007269465,9.283512e-7,0.0020619666,0.000001601086,0.000005093841,0.0006987486,0.8039012,0.19291598],"study_design_scores_gemma":[0.0005305876,0.00031015,0.000009793722,0.00052157737,0.00005150472,0.0000049217656,0.00021836681,0.008607941,0.000016003449,0.0004879823,0.98864055,0.0006006347],"about_ca_topic_score_codex":0.00022826646,"about_ca_topic_score_gemma":0.0008502046,"teacher_disagreement_score":0.19231534,"about_ca_system_score_codex":0.00029218587,"about_ca_system_score_gemma":0.00045103914,"threshold_uncertainty_score":0.99961233},"labels":[],"label_agreement":null},{"id":"W2078553168","doi":"10.1109/aina.2013.109","title":"Causality-Based Model for User Profile Construction from Behavior Sequences","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Causality (physics); Construct (python library); Computer science; Cluster analysis; Transfer entropy; Causal model; Entropy (arrow of time); Data mining; Sequence (biology); Artificial intelligence; Variety (cybernetics); Machine learning; Principle of maximum entropy; Mathematics; Statistics","score_opus":0.05349435399899848,"score_gpt":0.27805078075105505,"score_spread":0.22455642675205656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078553168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16346057,0.0000048032243,0.83377784,0.00059215917,0.00033722605,0.000992554,0.000062175386,0.0002868423,0.0004858509],"genre_scores_gemma":[0.8005007,2.0797961e-7,0.19720432,0.00032077724,0.000048219474,0.0011207345,0.000024248033,0.000007867207,0.00077294104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988242,0.00006278159,0.0002559086,0.00042403367,0.00021985776,0.00021322338],"domain_scores_gemma":[0.9988709,0.0002027605,0.00011632711,0.0004111705,0.00030607378,0.00009274253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014709901,0.00013345295,0.00017671038,0.00007159078,0.00011173081,0.0003063568,0.00036522213,0.00008456389,0.00027324588],"category_scores_gemma":[0.000026356249,0.0001177838,0.00008100022,0.000124709,0.000057782265,0.0012202606,0.000056211375,0.00006884932,0.00024326815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007301666,0.001189058,0.121228166,0.0002005775,0.00023185101,0.000013081646,0.0023932455,0.0016033811,0.096668825,0.05595581,0.038342856,0.6821001],"study_design_scores_gemma":[0.0005147127,0.00004493629,0.0021424901,0.000017725846,0.000013991725,0.0000058140668,0.00007867912,0.97666955,0.015849877,0.0040605078,0.00034869427,0.00025300175],"about_ca_topic_score_codex":0.0018619708,"about_ca_topic_score_gemma":0.00041157496,"teacher_disagreement_score":0.9750662,"about_ca_system_score_codex":0.0000539655,"about_ca_system_score_gemma":0.00016547363,"threshold_uncertainty_score":0.4803084},"labels":[],"label_agreement":null},{"id":"W2079358902","doi":"10.1109/iit.2007.4430415","title":"A Hybrid Framework to Program Smart Environments","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 British Columbia","funders":"","keywords":"Computer science; Human–computer interaction; Usability; Session (web analytics); User interface; Correctness; Flexibility (engineering); Smart environment; Internet of Things; World Wide Web; Programming language","score_opus":0.02149551069238909,"score_gpt":0.28395916116811243,"score_spread":0.26246365047572334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079358902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04783903,0.000011000463,0.939014,0.0005875342,0.00044030466,0.00054550334,9.836323e-7,0.00038784128,0.011173804],"genre_scores_gemma":[0.8662611,6.514586e-7,0.13059288,0.0014124041,0.00007935472,0.000056467386,9.4001746e-7,0.000008683577,0.0015874768],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985982,0.000036441965,0.0002256865,0.00041174574,0.00035445628,0.0003734506],"domain_scores_gemma":[0.99891126,0.0001880884,0.000049587383,0.0005877095,0.000020896614,0.00024243243],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005103964,0.00012496805,0.00013782922,0.00011418302,0.00007247788,0.00016301904,0.00056526356,0.000047939022,0.00009448547],"category_scores_gemma":[0.00006509587,0.00011840124,0.000060946237,0.0002520714,0.000018205996,0.00034104523,0.0002774874,0.0001280897,0.0023019325],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055682544,0.00021340237,0.0027109112,0.000003352642,0.000014132517,0.000036198613,0.00025234575,9.806425e-7,0.0004713008,0.0029628903,0.0018303255,0.9914986],"study_design_scores_gemma":[0.00043352958,0.00050069677,0.04142119,0.00008415815,0.000006761856,0.00014671883,0.00008261773,0.0016941909,0.032618582,0.004777799,0.9175122,0.00072155823],"about_ca_topic_score_codex":0.00004040366,"about_ca_topic_score_gemma":0.000022938993,"teacher_disagreement_score":0.990777,"about_ca_system_score_codex":0.00006784453,"about_ca_system_score_gemma":0.000017334281,"threshold_uncertainty_score":0.9984749},"labels":[],"label_agreement":null},{"id":"W2080321330","doi":"10.1145/1515747.1515768","title":"Data-centric support of a smart walker in a ubiquitous-computing environment","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Ubiquitous computing; Assisted living; Independent living; Human–computer interaction; Health care; Assistive technology; Population; Intelligent sensor; Smart environment; Internet privacy; Wireless sensor network; Internet of Things; Gerontology; Medicine; Computer network","score_opus":0.06627037847048961,"score_gpt":0.26199497066438077,"score_spread":0.19572459219389116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080321330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35902447,0.000087381406,0.63183904,0.00052900834,0.00038100657,0.00050033693,0.00002003544,0.00016421649,0.0074545396],"genre_scores_gemma":[0.99305505,0.000020410627,0.006200329,0.00021398335,0.000036114958,0.000004680187,0.0000097231305,0.000007787337,0.00045191546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822,0.00010494506,0.00047507437,0.0005235642,0.00038687055,0.00028952875],"domain_scores_gemma":[0.99849516,0.00017477965,0.00015523503,0.001072028,0.000026839409,0.000075968914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005426524,0.00012991004,0.00027227608,0.00020426788,0.00004898872,0.000024829504,0.0010431597,0.000051627914,0.00015645653],"category_scores_gemma":[0.00003260359,0.00012439866,0.000047529415,0.00036144097,0.000044666333,0.000601685,0.0009241234,0.00011388815,0.0002865706],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006975753,0.0036496064,0.29256067,0.00021831831,0.00021140257,0.0013318778,0.01145211,0.00036890918,0.006537216,0.0018207042,0.04165526,0.64012414],"study_design_scores_gemma":[0.0073271464,0.0009663842,0.19916938,0.00022102731,0.000039228707,0.00281807,0.00055737956,0.4541413,0.027984066,0.00045888056,0.3039467,0.002370462],"about_ca_topic_score_codex":0.00023077938,"about_ca_topic_score_gemma":0.000067164336,"teacher_disagreement_score":0.6377537,"about_ca_system_score_codex":0.0000667506,"about_ca_system_score_gemma":0.000099003766,"threshold_uncertainty_score":0.50728303},"labels":[],"label_agreement":null},{"id":"W2080872143","doi":"10.3166/ria.22.589-608","title":"Reasoning about Models of Context. A Context-Oriented Logical Language for Knowledge-Based Context-Aware Applications","year":2008,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Computer science; Cognitive science; Linguistics; Natural language processing; Psychology; History; Philosophy","score_opus":0.07886819843786519,"score_gpt":0.3041189675546598,"score_spread":0.2252507691167946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080872143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012541111,0.0026573574,0.98037755,0.0002961372,0.00030141918,0.0018328483,0.00009721334,0.00032975714,0.0015666018],"genre_scores_gemma":[0.99285805,0.000036793495,0.004485574,0.00030454143,0.00012779691,0.00093803107,0.000043534284,0.00003804557,0.0011676236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99682057,0.00020702038,0.0010963922,0.00096086797,0.00032615417,0.00058899145],"domain_scores_gemma":[0.9954035,0.0015721155,0.0005282165,0.001200775,0.0010469588,0.00024839898],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067057036,0.00037634038,0.0007353451,0.0002778426,0.00044902216,0.00007809401,0.0011208163,0.00021537926,0.00006972326],"category_scores_gemma":[0.00032594908,0.0003821841,0.00041244863,0.00092520146,0.00035189884,0.000553541,0.00018863795,0.00028531277,0.00021994371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002123029,0.0018372823,0.0006240786,0.00048239084,0.00015156563,0.000044352713,0.022134341,0.007564676,0.005588107,0.16083069,0.0017915191,0.79873866],"study_design_scores_gemma":[0.00046221766,0.0002703235,0.00003874921,0.00041289636,0.000025224319,0.00009101289,0.0048016394,0.9171755,0.05587164,0.0009090157,0.019418046,0.0005237701],"about_ca_topic_score_codex":0.0001827796,"about_ca_topic_score_gemma":0.00019164961,"teacher_disagreement_score":0.98031694,"about_ca_system_score_codex":0.00013442438,"about_ca_system_score_gemma":0.00030889185,"threshold_uncertainty_score":0.999863},"labels":[],"label_agreement":null},{"id":"W2081699596","doi":"10.1115/detc2009-87639","title":"An Human-Robot Interaction Control Architecture for an Intelligent Assistive Robot","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"","keywords":"Robot; Computer science; Human–computer interaction; Pace; Health care; Architecture; Control (management); Quality (philosophy); Task (project management); Social robot; Artificial intelligence; Mobile robot; Robot control; Engineering; Systems engineering","score_opus":0.05821956900158581,"score_gpt":0.3474912704989184,"score_spread":0.2892717014973326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081699596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008085826,0.000013089628,0.9870786,0.0011642028,0.00047757485,0.00079387246,0.000008946476,0.00044094326,0.00193693],"genre_scores_gemma":[0.98280907,5.260333e-7,0.015136295,0.0014259731,0.00028302957,0.00007687425,0.0000231648,0.000012462117,0.00023261899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981883,0.00022952071,0.00036408406,0.00065078196,0.00024539937,0.00032187844],"domain_scores_gemma":[0.99847406,0.00023084646,0.00018447901,0.0006523887,0.0002514548,0.00020675058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004006771,0.000231173,0.00030633388,0.00020759559,0.00022325652,0.0004403671,0.0006756095,0.00009978583,0.000042947333],"category_scores_gemma":[0.000046145662,0.0002054646,0.00014176633,0.00018590427,0.00002569857,0.0011423619,0.00003166551,0.00020977341,0.000041779458],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009493419,0.0006412205,0.000120228506,0.000009970037,0.000054455355,0.0000047811145,0.0010889939,0.0023092402,0.039849013,0.006438101,0.00058468303,0.9488044],"study_design_scores_gemma":[0.007618512,0.018200258,0.046892956,0.00033256,0.00015943155,0.0005119239,0.0017156034,0.7454264,0.10429693,0.03214503,0.039263953,0.0034364134],"about_ca_topic_score_codex":0.00010613654,"about_ca_topic_score_gemma":0.000566683,"teacher_disagreement_score":0.9747232,"about_ca_system_score_codex":0.000115927236,"about_ca_system_score_gemma":0.000045197194,"threshold_uncertainty_score":0.83786035},"labels":[],"label_agreement":null},{"id":"W2082533019","doi":"10.1007/s11042-013-1595-5","title":"Context-aware multimedia services modeling: an e-Health perspective","year":2013,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Technische Universität Darmstadt; University of Ottawa","keywords":"Computer science; Context (archaeology); Multimedia; Perspective (graphical); The Internet; World Wide Web; Context awareness; Human–computer interaction; Artificial intelligence","score_opus":0.045142642845784176,"score_gpt":0.29656647906089756,"score_spread":0.2514238362151134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082533019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018271973,0.000804475,0.9697641,0.006137753,0.00019173855,0.0031323393,0.00015576965,0.00069225696,0.00084956613],"genre_scores_gemma":[0.9773857,0.000067938345,0.018898407,0.0010586188,0.00027643863,0.0020830738,0.00008589239,0.000023493785,0.00012042601],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980178,0.000112273316,0.00040028678,0.0007861477,0.00027481728,0.00040866033],"domain_scores_gemma":[0.99780923,0.00025253693,0.00019054709,0.0007486472,0.0005139723,0.00048503885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002268757,0.0002513166,0.00034579006,0.00012689528,0.00040351914,0.00057025655,0.0006713552,0.00011136115,0.00005690047],"category_scores_gemma":[0.00002199331,0.00024034493,0.00006643169,0.00031403228,0.00007730575,0.0019671728,0.0001927523,0.00021813351,0.00048265775],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020546336,0.00020220333,0.00036681423,0.000036015015,0.00003120665,5.993592e-7,0.009047944,0.000039987866,0.00030865922,0.0036620519,0.00021730903,0.9860852],"study_design_scores_gemma":[0.0005946475,0.00006130909,0.0024520939,0.000037070164,0.0000077030145,0.000017610144,0.008067786,0.9803082,0.000083392006,0.0030550053,0.004958861,0.00035632652],"about_ca_topic_score_codex":0.0038258429,"about_ca_topic_score_gemma":0.0006363191,"teacher_disagreement_score":0.9857288,"about_ca_system_score_codex":0.0000998594,"about_ca_system_score_gemma":0.00014794999,"threshold_uncertainty_score":0.9800982},"labels":[],"label_agreement":null},{"id":"W2083068858","doi":"10.1089/tmj.2007.0007","title":"User-Based Motion Sensing and Fuzzy Logic for Automated Fall Detection in Older Adults","year":2007,"lang":"en","type":"article","venue":"Telemedicine Journal and e-Health","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health and Social Services Centre University Institute of Geriatrics of Sherbrooke; Université de Sherbrooke","funders":"","keywords":"Accelerometer; Trunk; Falling (accident); Fall prevention; Physical medicine and rehabilitation; Medicine; Artificial intelligence; Computer science; Poison control; Motion (physics); Simulation; Injury prevention; Medical emergency","score_opus":0.0311711229023863,"score_gpt":0.31207723384612407,"score_spread":0.28090611094373774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083068858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32684258,0.0004197759,0.6663347,0.0055669523,0.00032895335,0.00036782236,9.221701e-7,0.0001186314,0.000019679925],"genre_scores_gemma":[0.9918773,0.00006980056,0.0059814057,0.0018254525,0.00022375649,0.0000020608459,0.00000249594,0.0000075329253,0.000010197659],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985648,0.000106933745,0.0005235843,0.00023850778,0.00021658985,0.00034960397],"domain_scores_gemma":[0.9990206,0.00015336106,0.00034886267,0.0000963452,0.00013914987,0.0002417218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030153452,0.00012329787,0.0002789163,0.00036864143,0.00023352791,0.000066941604,0.00007132541,0.000058398775,0.0000013006586],"category_scores_gemma":[0.000068690024,0.000100861165,0.000028351305,0.00028560084,0.000025283012,0.00027061484,0.000022355907,0.00023840863,0.0000010831398],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004703536,0.000047659112,0.0030231439,0.00014626603,0.000006019387,0.000014195847,0.0010034742,0.0000051078846,0.00026269665,0.000016679953,0.00036014055,0.9950676],"study_design_scores_gemma":[0.011222779,0.0024929878,0.5695547,0.0022367889,0.000011346561,0.0025315422,0.0019839904,0.40713432,0.0005702692,0.0008342915,0.0010403871,0.0003866286],"about_ca_topic_score_codex":0.0006113701,"about_ca_topic_score_gemma":0.0022468904,"teacher_disagreement_score":0.99468094,"about_ca_system_score_codex":0.00015613773,"about_ca_system_score_gemma":0.00010096873,"threshold_uncertainty_score":0.4112999},"labels":[],"label_agreement":null},{"id":"W2083106564","doi":"10.1109/ccnc.2011.5766464","title":"Recognition of false alarms in fall detection systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"ALARM; Accelerometer; Wearable computer; Computer science; False alarm; Population; Poison control; Artificial intelligence; Computer security; Medical emergency; Medicine; Engineering; Embedded system","score_opus":0.07748503121588851,"score_gpt":0.23657758248127433,"score_spread":0.15909255126538582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083106564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4001107,0.000059066457,0.58435345,0.000019324367,0.00076925225,0.00039228314,0.000003486875,0.00018869863,0.01410376],"genre_scores_gemma":[0.9983214,0.000006572332,0.0014163736,0.000027520759,0.000026970025,0.000057384554,0.0000014545026,0.0000073768597,0.00013496021],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871826,0.00017569585,0.0004180199,0.00029999443,0.00020955502,0.00017846086],"domain_scores_gemma":[0.9991518,0.00008929557,0.00018091491,0.00034447198,0.00017780028,0.000055699635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005360669,0.00010961296,0.0002155461,0.00030640425,0.000024972313,0.000042209347,0.00030623426,0.000090158814,0.00002531684],"category_scores_gemma":[0.000051304854,0.0001046584,0.00005871649,0.00046811876,0.00002101123,0.0008388828,0.00007954442,0.00009990558,0.00019259776],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060701143,0.0006211395,0.016467206,0.0002027408,0.00006306198,0.00003400152,0.0050986097,0.000008855694,0.025035875,0.0023597803,0.00012094563,0.9499271],"study_design_scores_gemma":[0.00646243,0.0018540614,0.15206668,0.001384382,0.000058650545,0.0008071403,0.0047032917,0.12148427,0.68670946,0.018379956,0.0034126136,0.0026770781],"about_ca_topic_score_codex":0.0041845376,"about_ca_topic_score_gemma":0.0017163763,"teacher_disagreement_score":0.94725,"about_ca_system_score_codex":0.00005362641,"about_ca_system_score_gemma":0.000028015056,"threshold_uncertainty_score":0.63257974},"labels":[],"label_agreement":null},{"id":"W2083906930","doi":"10.1109/cnsr.2010.14","title":"Middleware Framework for Disconnection Tolerant Mobile Application Services","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Disconnection; Computer science; Middleware (distributed applications); Computer network; Mobile computing; Distributed computing; Blocking (statistics); Service (business)","score_opus":0.011390224249421169,"score_gpt":0.2720008262536272,"score_spread":0.26061060200420605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083906930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077815786,0.000015276515,0.9184476,0.00049474346,0.0014152661,0.0008344926,0.0000074924583,0.0003701518,0.0005991607],"genre_scores_gemma":[0.94949985,8.7455584e-7,0.048590504,0.0002950785,0.0002636353,0.0010426558,0.0000117362915,0.00001019418,0.00028544987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903804,0.000022535554,0.00019732844,0.00040887546,0.00015306688,0.00018012674],"domain_scores_gemma":[0.9987623,0.00030657215,0.00011259504,0.0005839225,0.0001620111,0.00007258755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021470629,0.00011437494,0.00013765771,0.000068165165,0.00015416778,0.00022531142,0.00048348375,0.00012200891,0.00003823829],"category_scores_gemma":[0.00001994244,0.00010171811,0.00007625227,0.0002201903,0.000014871781,0.0007094119,0.000083577535,0.00014961569,0.00013403315],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020539124,0.0002721056,0.0021970386,0.00019443658,0.000039240644,7.592848e-7,0.0020728025,0.000040330175,0.03275044,0.21704999,0.0003510336,0.74501127],"study_design_scores_gemma":[0.001265998,0.0003850971,0.0046372656,0.00016189352,0.00003074144,0.00010029289,0.0013583758,0.5297921,0.076123334,0.14787364,0.23702216,0.0012490962],"about_ca_topic_score_codex":0.00014779686,"about_ca_topic_score_gemma":0.0007126734,"teacher_disagreement_score":0.8716841,"about_ca_system_score_codex":0.00001666736,"about_ca_system_score_gemma":0.0000238995,"threshold_uncertainty_score":0.41479442},"labels":[],"label_agreement":null},{"id":"W2083983761","doi":"10.1109/percomw.2007.65","title":"Managing an Integrated Ubicomp Environment Using Ontologies and Reasoning","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of British Columbia","funders":"","keywords":"Ubiquitous computing; Computer science; Ontology; Middleware (distributed applications); Software deployment; Human–computer interaction; Software engineering; World Wide Web; Data science; Distributed computing","score_opus":0.04492097505894752,"score_gpt":0.2758995148226414,"score_spread":0.2309785397636939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083983761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25163895,0.00007002236,0.7456402,0.00010159321,0.00009904332,0.00008469248,3.181472e-7,0.00020048162,0.0021647073],"genre_scores_gemma":[0.89158046,0.0000068884106,0.10816408,0.00011249158,0.000022662214,0.0000017460368,0.0000010522203,0.000005834122,0.00010477217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989613,0.00007200093,0.00019157049,0.00036117676,0.0001627589,0.00025113524],"domain_scores_gemma":[0.99937856,0.00011228575,0.00007445447,0.00031585532,0.00002376023,0.00009509842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063172786,0.00012109486,0.00014819497,0.0001288029,0.00012999802,0.00020189257,0.0002616742,0.00004634978,0.000020503117],"category_scores_gemma":[0.000019309342,0.00010766812,0.000022714488,0.00013016108,0.00004390908,0.000871085,0.00020552863,0.00010060832,0.000015219785],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008156819,0.000052549854,0.008022524,0.000008525397,0.000022731077,0.0000505027,0.0010410658,0.000069586065,0.0053010248,0.004051047,0.000018802459,0.98135346],"study_design_scores_gemma":[0.0012109149,0.00030266575,0.062173396,0.00024061909,0.000027898539,0.0008929979,0.0074080667,0.88914347,0.02199821,0.0031338045,0.0121922055,0.001275769],"about_ca_topic_score_codex":0.0005963254,"about_ca_topic_score_gemma":0.00021120335,"teacher_disagreement_score":0.98007774,"about_ca_system_score_codex":0.00009370722,"about_ca_system_score_gemma":0.000015331732,"threshold_uncertainty_score":0.43905786},"labels":[],"label_agreement":null},{"id":"W2084805558","doi":"10.1145/2674396.2674403","title":"Human activity recognition in smart homes based on passive RFID localization","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Trilateration; Computer science; Autonomy; Activity recognition; Cognition; Human–computer interaction; Implementation; Activities of daily living; Home automation; Artificial intelligence; Engineering; Telecommunications; Medicine; Software engineering","score_opus":0.028860338415092893,"score_gpt":0.25806885566907006,"score_spread":0.22920851725397717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084805558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095310435,0.0000012070493,0.88962215,0.0007670507,0.00027751003,0.00030270327,0.000003142813,0.00027212265,0.013443651],"genre_scores_gemma":[0.9980648,4.852156e-7,0.00068279624,0.0009369884,0.0000620188,0.00006548114,0.00001588968,0.000011817151,0.00015971856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982412,0.000455386,0.00023635337,0.00049539615,0.0003353115,0.00023636263],"domain_scores_gemma":[0.9988625,0.00030255076,0.0001490176,0.00046302247,0.00014173232,0.00008114863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004936829,0.0001673648,0.00022562359,0.0003487745,0.00012467412,0.00016860878,0.00029199608,0.000103495295,0.000096081065],"category_scores_gemma":[0.00014294885,0.00016367304,0.00006454234,0.0005260277,0.000029117708,0.0007364713,0.00006321431,0.00015148257,0.00026966457],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025071327,0.00052728946,0.008502975,0.000048585505,0.000011463629,0.0000080410655,0.00022635501,0.00040462028,0.0022006845,0.0018391695,0.0013794819,0.98482627],"study_design_scores_gemma":[0.0022082876,0.0005168467,0.07484525,0.0002606912,0.000007367794,0.0000072200937,0.00004554299,0.88304,0.03058227,0.004689098,0.0031118197,0.0006856255],"about_ca_topic_score_codex":0.00037777983,"about_ca_topic_score_gemma":0.00095892797,"teacher_disagreement_score":0.98414063,"about_ca_system_score_codex":0.00013134012,"about_ca_system_score_gemma":0.000044325967,"threshold_uncertainty_score":0.6674393},"labels":[],"label_agreement":null},{"id":"W2085379519","doi":"10.1007/s11042-012-1006-3","title":"Ant-based service selection framework for a smart home monitoring environment","year":2012,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Context (archaeology); Selection (genetic algorithm); Fuzzy logic; Smart environment; Service (business); Ant colony optimization algorithms; Ubiquitous computing; Home automation; Genetic algorithm; Ant colony; Distributed computing; Human–computer interaction; Artificial intelligence; World Wide Web; Machine learning; Internet of Things; Telecommunications","score_opus":0.054261127713854024,"score_gpt":0.2824549059057962,"score_spread":0.22819377819194217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085379519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013091146,0.00015002076,0.9847481,0.0007304801,0.0001847498,0.0008858244,0.00003521448,0.0001379148,0.000036511934],"genre_scores_gemma":[0.77148515,0.000012289721,0.22520196,0.0001370976,0.0005314719,0.002578983,0.00001798513,0.000011463144,0.000023612605],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991487,0.000028499733,0.00016726027,0.00027002,0.0001340979,0.0002513778],"domain_scores_gemma":[0.9988237,0.0006069191,0.000090253845,0.00027628706,0.000054429096,0.00014839407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019370325,0.000115784635,0.00012620667,0.000053176107,0.0002303337,0.00013238516,0.00018641746,0.00008299531,0.00000809801],"category_scores_gemma":[0.000023656708,0.000119342505,0.00003846899,0.00020652515,0.000017235416,0.00047406554,0.00005561083,0.00009813556,0.000105557396],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009033144,0.00037532568,0.04165031,0.000084489635,0.00003539985,1.0385636e-7,0.0008449209,0.000050853054,0.007463448,0.0043470906,0.00009334445,0.9450457],"study_design_scores_gemma":[0.0027180137,0.0001805793,0.31073338,0.0002203343,0.000118647644,0.000034040542,0.0004362597,0.27397946,0.051180124,0.005865719,0.3528779,0.0016555459],"about_ca_topic_score_codex":0.000024179977,"about_ca_topic_score_gemma":0.0000027266606,"teacher_disagreement_score":0.94339013,"about_ca_system_score_codex":0.000054380493,"about_ca_system_score_gemma":0.000022719045,"threshold_uncertainty_score":0.4866646},"labels":[],"label_agreement":null},{"id":"W2085450850","doi":"10.4018/jbdcn.2011040104","title":"Micro Context-Awareness for Autonomic Pervasive Computing","year":2011,"lang":"en","type":"article","venue":"International Journal of Business Data Communications and Networking","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Systems, Applications & Products in Data Processing (Canada); Université de Sherbrooke","funders":"","keywords":"Ubiquitous computing; Computer science; Autonomic computing; Context awareness; Context (archaeology); Context-aware pervasive systems; Macro; End-user computing; Software; Human–computer interaction; Context model; Software engineering; Data science; Computer security; Utility computing; Artificial intelligence; Cloud computing; Operating system; Programming language; Cloud computing security","score_opus":0.2338503335075809,"score_gpt":0.34180641097452,"score_spread":0.10795607746693914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085450850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011535179,0.0033442324,0.9805342,0.0020798994,0.0020316257,0.00016723819,0.00005874344,0.000033859178,0.00021505283],"genre_scores_gemma":[0.9445699,0.00094412634,0.05372004,0.0002651559,0.00041094673,0.0000047342437,0.00006569737,0.000011248744,0.000008138059],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872315,0.00011504844,0.0006123905,0.00022007497,0.0001834338,0.00014590632],"domain_scores_gemma":[0.9959137,0.0007015024,0.00081759907,0.0011226179,0.0013841942,0.000060427657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009264492,0.00012395895,0.00025377268,0.0002056999,0.00023650043,0.00030619485,0.0050490783,0.000049083184,0.0000046239124],"category_scores_gemma":[0.00007304124,0.00012147324,0.000059312275,0.00017472285,0.0000983671,0.0015178195,0.0021170902,0.00016773025,0.0000021747733],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049967122,0.00016760362,0.008245595,0.000012724523,0.000314098,0.000009899264,0.0014185447,0.000014867402,0.00023950284,0.0031811742,0.00070113357,0.9856449],"study_design_scores_gemma":[0.0041781133,0.00014869294,0.04652557,0.002060867,0.00015864515,0.0025339478,0.0012472519,0.5512928,0.00036943975,0.004920391,0.38566902,0.00089531485],"about_ca_topic_score_codex":0.00015404544,"about_ca_topic_score_gemma":0.00011679759,"teacher_disagreement_score":0.98474956,"about_ca_system_score_codex":0.00005999581,"about_ca_system_score_gemma":0.00020418885,"threshold_uncertainty_score":0.9382528},"labels":[],"label_agreement":null},{"id":"W2085478833","doi":"10.1109/tnsre.2014.2357806","title":"Inertial Sensing-Based Pre-Impact Detection of Falls Involving Near-Fall Scenarios","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Acceleration; Accelerometer; Fall prevention; Poison control; Simulation; Inertial frame of reference; Computer science; Injury prevention; Medicine; Physics","score_opus":0.007962471941868365,"score_gpt":0.21642285328769234,"score_spread":0.20846038134582398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085478833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45019257,0.000025681307,0.5486021,0.00005471662,0.00071020564,0.00026088193,0.0000035745716,0.00014695265,0.0000033290676],"genre_scores_gemma":[0.9981362,0.0000012658548,0.0017301679,0.00001418819,0.000053067088,0.000030481477,7.136331e-7,0.000022084705,0.000011833855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984267,0.00020525971,0.00048820488,0.0003646222,0.00029545432,0.00021981154],"domain_scores_gemma":[0.9981971,0.0009963452,0.00015788524,0.00034232228,0.00018507974,0.00012125923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044884192,0.0002196967,0.00032388975,0.00031548558,0.00015371798,0.00017136971,0.00013102363,0.00011510265,0.0000010642029],"category_scores_gemma":[0.000081343525,0.00020366583,0.00016326435,0.00040943807,0.00004975366,0.0005469848,0.000002693684,0.00021315822,0.0000031147727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025708681,0.000052443404,0.00009721713,0.0002828082,0.000028602446,5.9099426e-7,0.0010389597,0.88351536,0.08524857,0.000018726892,0.0000023552068,0.02968867],"study_design_scores_gemma":[0.0004860944,0.0005301913,0.00237032,0.0002467871,0.000010178659,0.000023459683,0.00005391828,0.9909309,0.005072825,0.000003525631,0.00007000199,0.00020179042],"about_ca_topic_score_codex":0.00086887024,"about_ca_topic_score_gemma":0.00010901008,"teacher_disagreement_score":0.5479436,"about_ca_system_score_codex":0.000107907974,"about_ca_system_score_gemma":0.000034186683,"threshold_uncertainty_score":0.83052516},"labels":[],"label_agreement":null},{"id":"W2085786601","doi":"10.1109/services.2014.15","title":"Personalized Web-Tasking Applications: An Online Grocery Shopping Prototype","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada); University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Universidad ICESI; University of Victoria; Rice University","keywords":"Computer science; Web modeling; World Wide Web; Web navigation; Human multitasking; Context (archaeology); Web development; Human–computer interaction; Web service; Web application","score_opus":0.06102443953655445,"score_gpt":0.2909280036603555,"score_spread":0.22990356412380103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085786601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006877185,0.000051473024,0.9810595,0.00070712785,0.0001089781,0.00094158977,0.0000033600697,0.0006813861,0.009569398],"genre_scores_gemma":[0.9631848,0.000004598047,0.03448388,0.0010394824,0.0002737383,0.00048976176,0.000017213339,0.0000157019,0.00049084565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857426,0.0001517135,0.00025580174,0.0004986135,0.0002857526,0.00023386789],"domain_scores_gemma":[0.9987931,0.00016908329,0.0001158949,0.00060801423,0.00018640075,0.00012746811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045719126,0.00014372109,0.00019560987,0.00013433353,0.0001661666,0.00024988447,0.00065749086,0.00006283523,0.000059627582],"category_scores_gemma":[0.00004026329,0.00013535623,0.00006251571,0.000383752,0.000034983168,0.0012724543,0.00014217525,0.00012404165,0.00014018499],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019023077,0.0003892142,0.0009990676,0.00009565066,0.00003831155,0.000002271713,0.00076264184,0.000016756823,0.0046873256,0.10107302,0.0004033275,0.8915134],"study_design_scores_gemma":[0.0015775753,0.00027386573,0.0012307962,0.00014131566,0.000016065456,0.00013148205,0.00048226627,0.7154576,0.000608727,0.0091792755,0.27005517,0.00084582594],"about_ca_topic_score_codex":0.0000403142,"about_ca_topic_score_gemma":0.00012972888,"teacher_disagreement_score":0.9563076,"about_ca_system_score_codex":0.000043717606,"about_ca_system_score_gemma":0.000082147475,"threshold_uncertainty_score":0.55196667},"labels":[],"label_agreement":null},{"id":"W2088304747","doi":"10.1155/2014/978951","title":"Framework for NFC-Based Intelligent Agents: A Context-Awareness Enabler for Social Internet of Things","year":2014,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Enabling; Ontology; Context (archaeology); Adaptation (eye); Intelligent agent; Context awareness; Human–computer interaction; The Internet; Ubiquitous computing; Smart objects; Action (physics); World Wide Web; Knowledge management; Computer security; Artificial intelligence","score_opus":0.05309212331349933,"score_gpt":0.3209925026867606,"score_spread":0.2679003793732613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088304747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074145,0.00007776454,0.98608214,0.002276021,0.0035217186,0.0003978114,0.0001556981,0.00004297741,0.00003134758],"genre_scores_gemma":[0.9745041,0.000006951178,0.023161577,0.001043361,0.0010773806,0.000037022935,0.000113088885,0.000025628193,0.000030885392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972969,0.0001975131,0.0011780978,0.00032522297,0.00066109805,0.00034112364],"domain_scores_gemma":[0.99248314,0.0023478954,0.0016602277,0.00022870569,0.003144253,0.00013575137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011419923,0.00025447394,0.00059418957,0.00024562728,0.00009320157,0.00026005006,0.0015521385,0.00024033972,0.00003027186],"category_scores_gemma":[0.001046759,0.00023962649,0.0006031633,0.00021708127,0.00008584818,0.000561189,0.00013246012,0.00034527693,0.0000028886504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0051745875,0.0025846532,0.005809274,0.0004180597,0.0051046214,0.0000743997,0.0050114486,0.03714401,0.0009829198,0.17350046,0.060193602,0.70400196],"study_design_scores_gemma":[0.0038521888,0.0006458965,0.0007473346,0.0010111851,0.000110730485,0.0000936439,0.00029675133,0.9164152,0.0063862377,0.0125216,0.05741424,0.00050499674],"about_ca_topic_score_codex":0.000025509724,"about_ca_topic_score_gemma":0.000006356965,"teacher_disagreement_score":0.9670896,"about_ca_system_score_codex":0.00022882115,"about_ca_system_score_gemma":0.00015414295,"threshold_uncertainty_score":0.97716844},"labels":[],"label_agreement":null},{"id":"W2089142163","doi":"10.1145/1460563.1460596","title":"Evaluating the deployment of a mobile technology in a hospital ward","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software deployment; Work (physics); Mobile technology; Mobile device; Health care; Information technology; Computer science; Nursing; Knowledge management; Medicine; Engineering; World Wide Web; Political science","score_opus":0.05595653873005501,"score_gpt":0.32392079404392216,"score_spread":0.26796425531386714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089142163","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9530809,0.00009952573,0.04393113,0.0014838568,0.00012405205,0.00042783757,4.3903654e-7,0.00012305695,0.00072920864],"genre_scores_gemma":[0.9953625,0.0000050806552,0.0042491686,0.000047539244,0.000010128381,0.00019723244,1.1794929e-7,0.000003885616,0.00012434313],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990689,0.000077370634,0.00024954657,0.00021272396,0.00024085866,0.00015060599],"domain_scores_gemma":[0.99922025,0.00015654588,0.000096934345,0.0004179221,0.00009069956,0.00001766105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039027838,0.00006914622,0.00014785725,0.00014725365,0.000051229337,0.000012125432,0.0005237743,0.000043116164,0.000015357009],"category_scores_gemma":[0.00008137242,0.000048014597,0.000040378854,0.000583549,0.00006797776,0.00017488236,0.00024948388,0.00009748338,0.00003706927],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014121316,0.0010683626,0.059624303,0.00003109172,0.00006732162,0.000064991036,0.013585259,0.00027018346,0.016893696,0.007865068,0.0010436199,0.899472],"study_design_scores_gemma":[0.009630404,0.014107844,0.07311915,0.00061661133,0.00003869376,0.0018525085,0.009820059,0.571033,0.28472877,0.024146883,0.008581578,0.0023245083],"about_ca_topic_score_codex":0.00014955485,"about_ca_topic_score_gemma":0.000039832183,"teacher_disagreement_score":0.8971475,"about_ca_system_score_codex":0.000040914736,"about_ca_system_score_gemma":0.00008224529,"threshold_uncertainty_score":0.19579785},"labels":[],"label_agreement":null},{"id":"W2089638258","doi":"10.1016/j.pmcj.2010.11.007","title":"Rapid specification and automated generation of prompting systems to assist people with dementia","year":2010,"lang":"en","type":"article","venue":"Pervasive and Mobile Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Engineering and Physical Sciences Research Council","keywords":"Partially observable Markov decision process; Computer science; Task (project management); Context (archaeology); Process (computing); Set (abstract data type); Human–computer interaction; Artificial intelligence; Key (lock); Machine learning; Markov chain; Markov model; Systems engineering; Computer security; Programming language","score_opus":0.029203914785360646,"score_gpt":0.2539705770262926,"score_spread":0.22476666224093197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089638258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81116873,0.00019983269,0.18756948,0.000059826663,0.00023958806,0.0005320228,0.0000024219557,0.00014467367,0.00008341482],"genre_scores_gemma":[0.9907674,0.0000063763214,0.00899194,0.000023077902,0.00013605328,0.00005094234,0.0000071846257,0.000008900823,0.0000081344615],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894094,0.00007469344,0.00027778817,0.00037987347,0.00017087515,0.00015579938],"domain_scores_gemma":[0.99903387,0.0001263828,0.00022284208,0.00024101707,0.00029054264,0.000085361484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004125218,0.0001188919,0.0002113639,0.000105217056,0.00019566083,0.00024567588,0.0001439883,0.000043382704,0.0000026529692],"category_scores_gemma":[0.00003889945,0.00010750326,0.000018093608,0.00023606325,0.000026115556,0.00028431704,0.00014103751,0.000091444585,0.0000032642283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014794981,0.0001656365,0.08627211,0.0004120379,0.00015878012,0.000006081572,0.013362913,0.00097350293,0.46329123,0.0013188076,0.00034911244,0.433675],"study_design_scores_gemma":[0.00054391456,0.00028109312,0.11020973,0.00019188094,0.000028317283,0.0001653506,0.0006812839,0.87388766,0.012401667,0.000005133439,0.0012995062,0.00030446894],"about_ca_topic_score_codex":0.00013827617,"about_ca_topic_score_gemma":0.00012526567,"teacher_disagreement_score":0.87291414,"about_ca_system_score_codex":0.00001210318,"about_ca_system_score_gemma":0.00003789795,"threshold_uncertainty_score":0.43838558},"labels":[],"label_agreement":null},{"id":"W2089850870","doi":"10.1145/2413097.2413134","title":"Guidelines to efficient smart home design for rapid AI prototyping","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Home automation; Computer science; Set (abstract data type); Component (thermodynamics); Key (lock); Architecture; Rapid prototyping; Everyday life; Smart environment; Ambient intelligence; Human–computer interaction; Embedded system; Engineering; Computer security; Internet of Things; Telecommunications","score_opus":0.1451291393622922,"score_gpt":0.34506550864664864,"score_spread":0.19993636928435646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089850870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020764796,0.000114761824,0.98904914,0.0037862533,0.0009763745,0.0029180818,0.0000023903146,0.00031922082,0.00075730914],"genre_scores_gemma":[0.691975,0.0000011092676,0.30107442,0.0041569932,0.00043895724,0.0010453346,0.0000014585635,0.00002173972,0.0012850108],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984848,0.00010401997,0.00035764737,0.0003358722,0.0002510484,0.0004666346],"domain_scores_gemma":[0.9984725,0.00030670955,0.00007208589,0.00046056634,0.0004648508,0.00022324861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015681374,0.00015220766,0.00020647387,0.00016200314,0.00011656454,0.0001727579,0.0004890894,0.000051729345,0.000035973284],"category_scores_gemma":[0.00025240285,0.00012927504,0.00009193082,0.00034251632,0.000010740388,0.0004939036,0.0001750107,0.000055461303,0.0005599048],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008350311,0.00041368816,0.00093265047,0.000110520145,0.00007523665,0.0000016971303,0.002310468,0.00075876684,0.007139257,0.014328133,0.089342676,0.8845034],"study_design_scores_gemma":[0.0014933815,0.0005443788,0.0013870825,0.00021020154,0.000019183894,0.0000858378,0.00015514073,0.3122147,0.06271494,0.0005800751,0.6194342,0.0011608845],"about_ca_topic_score_codex":0.000024838153,"about_ca_topic_score_gemma":0.000005275841,"teacher_disagreement_score":0.8833425,"about_ca_system_score_codex":0.00005769039,"about_ca_system_score_gemma":0.00009541638,"threshold_uncertainty_score":0.71966296},"labels":[],"label_agreement":null},{"id":"W2090764929","doi":"10.1145/1370256.1370292","title":"Context-aware application programming for mobile devices","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Software engineering; Software development; Programming paradigm; Context (archaeology); Functional reactive programming; Software development process; Software; Programming domain; Programming language; Inductive programming","score_opus":0.037159543778532124,"score_gpt":0.27756332971633363,"score_spread":0.2404037859378015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090764929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016291935,0.00010678108,0.9803425,0.0002762537,0.00015610954,0.0014889333,0.000004000701,0.0005528109,0.0007806688],"genre_scores_gemma":[0.9811041,0.0000031454388,0.015813235,0.00036199164,0.00009175912,0.0019206541,0.000010264232,0.000011208803,0.0006836748],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99885076,0.000035530476,0.00024886616,0.00042542783,0.00019408032,0.00024532562],"domain_scores_gemma":[0.99887127,0.00024035116,0.0001274316,0.00041968515,0.00025586062,0.000085410284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020738687,0.00012825541,0.0001822926,0.00007856208,0.00024490734,0.000099622775,0.0004937473,0.0000635524,0.000011589256],"category_scores_gemma":[0.000019555786,0.000118950644,0.00009332707,0.00026233273,0.000037429592,0.0007142201,0.000090045534,0.000058485242,0.00014795572],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004893633,0.00009573975,0.002761698,0.000032791373,0.000018996316,0.0000020070786,0.0005871856,0.0000038503463,0.0002821034,0.0027660243,0.0013014213,0.9921433],"study_design_scores_gemma":[0.0013330455,0.00034082765,0.002536407,0.000044975553,0.000012540017,0.0002429708,0.0007586038,0.063285254,0.010165685,0.0005311471,0.9200681,0.00068044756],"about_ca_topic_score_codex":0.00011839455,"about_ca_topic_score_gemma":0.00020407715,"teacher_disagreement_score":0.9914628,"about_ca_system_score_codex":0.000042587017,"about_ca_system_score_gemma":0.00006883358,"threshold_uncertainty_score":0.48506662},"labels":[],"label_agreement":null},{"id":"W2091180312","doi":"10.1109/icmew.2013.6618323","title":"SmartInsole: A foot-based activity and gait measurement device","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Ottawa","funders":"","keywords":"Computer science; Wireless; Transceiver; Gait; Acceleration; Battery (electricity); Gait analysis; Power (physics); Simulation; Embedded system; Telecommunications; Physical medicine and rehabilitation","score_opus":0.054834524481313776,"score_gpt":0.24289463180208276,"score_spread":0.188060107320769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091180312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31492448,0.000052944022,0.67319113,0.0036008435,0.00019371389,0.0005354939,9.281157e-7,0.00033839597,0.0071620853],"genre_scores_gemma":[0.9951672,7.1686287e-7,0.0035924576,0.0008945625,0.00003130161,0.00012283385,2.4565188e-7,0.000007802001,0.00018289764],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99850005,0.00015249097,0.00017511388,0.00041913876,0.0004907115,0.00026250436],"domain_scores_gemma":[0.99878657,0.00014876154,0.0000940043,0.0004657899,0.00034050317,0.00016438837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005136732,0.00015164752,0.00018438949,0.000089542016,0.00011895727,0.00036115412,0.00031575697,0.000053078133,0.000111721405],"category_scores_gemma":[0.00008577498,0.00013302236,0.000049342114,0.00023739258,0.000029745588,0.00088109245,0.00015075636,0.000100861595,0.0003892033],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008531057,0.0002615475,0.004901434,0.00006818687,0.000049480725,0.000005054521,0.00022318061,0.0000034679474,0.054884106,0.00076870486,0.004722928,0.93410337],"study_design_scores_gemma":[0.0042749103,0.0005978553,0.38040245,0.00029754767,0.00004341983,0.0000927457,0.00018374319,0.33026728,0.24625528,0.0021713458,0.03344354,0.0019699053],"about_ca_topic_score_codex":0.001019488,"about_ca_topic_score_gemma":0.0005563973,"teacher_disagreement_score":0.9321335,"about_ca_system_score_codex":0.00007882977,"about_ca_system_score_gemma":0.000106102736,"threshold_uncertainty_score":0.5424495},"labels":[],"label_agreement":null},{"id":"W2092044338","doi":"10.1089/tmj.2006.12.622","title":"A Telemedicine System for Remote Health and Activity Monitoring for the Elderly","year":2006,"lang":"en","type":"article","venue":"Telemedicine Journal and e-Health","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Hong Kong Government","keywords":"Telemedicine; Telecare; Adaptation (eye); Telehealth; Computer science; Health care; Service (business); Business","score_opus":0.05660658304031494,"score_gpt":0.3311133766110271,"score_spread":0.2745067935707121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092044338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022891048,0.012740164,0.85293466,0.10811223,0.001521717,0.0016313387,0.0000138032465,0.00011545993,0.000039604685],"genre_scores_gemma":[0.98522073,0.0012101562,0.008590224,0.0012301361,0.0034935782,0.000042143885,0.0000033192398,0.000023327992,0.00018636229],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975464,0.00018018862,0.0007802701,0.00038764472,0.0004374409,0.0006680778],"domain_scores_gemma":[0.99735594,0.00074747106,0.00088043074,0.0002883834,0.00022059833,0.00050714886],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004769592,0.00023778495,0.00069123047,0.00018892052,0.0013155448,0.00016856748,0.00030112578,0.000053514974,0.0000010450763],"category_scores_gemma":[0.00009007696,0.00015184842,0.0000850124,0.0002516515,0.00007025269,0.00034515891,0.000070723065,0.00037869532,7.1046384e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004842253,0.000031607615,0.00086200406,0.00073177746,0.00003969529,0.0000030841595,0.0008858835,0.0000018701968,0.000100052355,0.00026327872,0.012980971,0.98405135],"study_design_scores_gemma":[0.048914477,0.05566284,0.12287964,0.020291585,0.00023998081,0.025045007,0.026811514,0.17341419,0.0009770535,0.008580794,0.5146302,0.0025527251],"about_ca_topic_score_codex":0.0022328456,"about_ca_topic_score_gemma":0.00025487752,"teacher_disagreement_score":0.9814986,"about_ca_system_score_codex":0.00029183252,"about_ca_system_score_gemma":0.0005897465,"threshold_uncertainty_score":0.9999846},"labels":[],"label_agreement":null},{"id":"W2094472262","doi":"10.4018/jmhci.2009062601","title":"How It Started","year":2009,"lang":"en","type":"article","venue":"International Journal of Mobile Human Computer Interaction","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Thriving; The Internet; Computer science; Mobile device; Quarter (Canadian coin); Great recession; Internet access; Telecommunications; Internet privacy; World Wide Web; Geography; Economics; Psychology; Keynesian economics","score_opus":0.035033096899765005,"score_gpt":0.32034745017579924,"score_spread":0.2853143532760342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094472262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1115956,0.000045346886,0.8729301,0.0068528196,0.0074898526,0.00016566954,0.0000029545795,0.00009360461,0.00082405895],"genre_scores_gemma":[0.9909525,0.000010111237,0.005530243,0.0011580763,0.002030684,0.0000052846804,0.000005548755,0.000008444542,0.00029913711],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997934,0.00014334328,0.0006589723,0.00026126872,0.000828278,0.00017413785],"domain_scores_gemma":[0.99707526,0.0001619713,0.00091159483,0.00026838333,0.001470538,0.00011228173],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00039606876,0.00018134908,0.00027817878,0.00057731953,0.000080334365,0.0011650943,0.0013722599,0.0000663844,0.000060978542],"category_scores_gemma":[0.000033169556,0.00017195445,0.00025975736,0.00015311805,0.000021506798,0.0034025975,0.00012179388,0.00037307345,0.000039626422],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008260051,0.0006666907,0.00018884227,0.0000059971107,0.0003398225,0.00033480374,0.0015380795,0.00058973377,0.01761141,0.002739466,0.03142957,0.94447297],"study_design_scores_gemma":[0.0074008126,0.00699027,0.015416066,0.0014863291,0.00008085382,0.012925082,0.00090699707,0.08361676,0.029868538,0.011953578,0.8277557,0.0015990267],"about_ca_topic_score_codex":0.000008713349,"about_ca_topic_score_gemma":0.000008903062,"teacher_disagreement_score":0.94287395,"about_ca_system_score_codex":0.00029885865,"about_ca_system_score_gemma":0.00006264405,"threshold_uncertainty_score":0.9998718},"labels":[],"label_agreement":null},{"id":"W2094481865","doi":"10.1109/memea.2010.5480206","title":"Mobility change-of-state detection using a smartphone-based approach","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Ottawa Hospital; University of Ottawa","funders":"","keywords":"Accelerometer; Wearable computer; Computer science; State (computer science); Human–computer interaction; Wearable technology; Real-time computing; Simulation; Embedded system","score_opus":0.07120529313283257,"score_gpt":0.2720530086090499,"score_spread":0.20084771547621733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094481865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46185786,0.0000032667779,0.53690606,0.00002450867,0.00039312465,0.00022220593,0.0000018338221,0.0001187115,0.00047242118],"genre_scores_gemma":[0.9723689,1.5493737e-7,0.027395017,0.00007866164,0.00006058145,0.00005425614,9.766953e-7,0.000007622409,0.00003384834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988375,0.00010458553,0.0002458291,0.00037493437,0.00024315284,0.00019401338],"domain_scores_gemma":[0.99885607,0.00011127506,0.00014866384,0.0006141984,0.00018964965,0.000080169826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006088786,0.00011974081,0.00018698603,0.00015249323,0.000078703044,0.00007905617,0.000340298,0.000073326126,0.000021175285],"category_scores_gemma":[0.000048259666,0.00011099719,0.0000887165,0.00045825442,0.00005402727,0.00061768235,0.000097355005,0.00018164262,0.000018702616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003261138,0.00060690567,0.009631151,0.00013896704,0.000029599241,0.000002454531,0.0012234888,0.000084332954,0.4211988,0.00023827578,0.000009982179,0.56680346],"study_design_scores_gemma":[0.000484733,0.00006190072,0.010314152,0.000013011024,0.0000059013664,0.000027177013,0.000034363376,0.73037493,0.25779074,0.0002801037,0.00037245633,0.00024052779],"about_ca_topic_score_codex":0.0011701655,"about_ca_topic_score_gemma":0.00059144886,"teacher_disagreement_score":0.7302906,"about_ca_system_score_codex":0.000033058837,"about_ca_system_score_gemma":0.000071860384,"threshold_uncertainty_score":0.4526334},"labels":[],"label_agreement":null},{"id":"W2095408141","doi":"10.1109/mprv.2006.58","title":"Real-World Ubicomp Deployments: Lessons Learned","year":2006,"lang":"en","type":"article","venue":"IEEE Pervasive Computing","topic":"Context-Aware Activity Recognition Systems","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":"University of Toronto","funders":"","keywords":"Ubiquitous computing; Computer science; Context-aware pervasive systems; Focus (optics); Human–computer interaction; Embedding; World Wide Web; Data science; Artificial intelligence","score_opus":0.07699517673647575,"score_gpt":0.33190141971143594,"score_spread":0.2549062429749602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095408141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32668155,0.00010604593,0.64497024,0.0035076926,0.002257427,0.00046231615,0.000009908289,0.001403424,0.020601425],"genre_scores_gemma":[0.9895798,0.000008590007,0.007900565,0.00038840066,0.000695884,0.00001581577,0.000008507281,0.000038430324,0.0013639876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702746,0.00030598525,0.0005848706,0.0008930091,0.00052791863,0.00066077814],"domain_scores_gemma":[0.9977419,0.00062990707,0.00036241216,0.0007873417,0.00033730597,0.00014115845],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005319721,0.00032715348,0.0004428442,0.0003343962,0.0004632694,0.00047128735,0.0010872782,0.00008420443,0.000022029091],"category_scores_gemma":[0.00005222115,0.00034982857,0.00020794496,0.0008930506,0.00006796378,0.00066514005,0.00037432942,0.000323589,0.00045865288],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035064873,0.0011367003,0.058049873,0.00020696019,0.0003235796,0.0005347313,0.0032166678,0.006543124,0.062484443,0.04519584,0.033776216,0.7884968],"study_design_scores_gemma":[0.007085963,0.00042977362,0.21948701,0.0016938116,0.00013644395,0.0005648232,0.0006625386,0.5664951,0.090102114,0.020812709,0.087462984,0.0050667305],"about_ca_topic_score_codex":0.0025794732,"about_ca_topic_score_gemma":0.0011493196,"teacher_disagreement_score":0.78343004,"about_ca_system_score_codex":0.00024738978,"about_ca_system_score_gemma":0.00013717209,"threshold_uncertainty_score":0.9998954},"labels":[],"label_agreement":null},{"id":"W2095728036","doi":"10.1109/iembs.2003.1279569","title":"An architecture of a scalable wireless monitoring system","year":2004,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Scalability; Computer science; Wireless; Telemedicine; Remote patient monitoring; Wireless sensor network; Real-time computing; Architecture; Wireless transmission; Embedded system; Wireless network; Data acquisition; Transmission (telecommunications); Health care; Computer network; Telecommunications; Operating system; Medicine","score_opus":0.017163383244371888,"score_gpt":0.24775598730314874,"score_spread":0.23059260405877685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095728036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38908184,0.000020237645,0.60788417,0.00008046923,0.00030277367,0.000119420554,0.0000012804359,0.00028560052,0.0022242288],"genre_scores_gemma":[0.98624724,5.502033e-7,0.013551913,0.000012144835,0.0000980098,0.000015803289,4.1267757e-7,0.000008203016,0.00006574074],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99892503,0.00006567359,0.00023043696,0.0002973274,0.000286131,0.00019542189],"domain_scores_gemma":[0.99904335,0.000050751707,0.00009947352,0.0005790881,0.00012374438,0.00010357486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022207924,0.00010937489,0.00020984716,0.000118550495,0.00006305897,0.00009983465,0.0006069736,0.000056104676,0.0000033455317],"category_scores_gemma":[0.0000067397996,0.000096133925,0.000056977817,0.0003463625,0.00002219958,0.0005161044,0.00008848751,0.00009433643,0.000038620892],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003310972,0.0006913605,0.014226715,0.00079664495,0.0001402764,0.00009854933,0.007410258,0.004624051,0.24191107,0.09274717,0.000043863307,0.63727695],"study_design_scores_gemma":[0.0024260327,0.00052652025,0.009011502,0.0014443427,0.000019757039,0.00055874203,0.0022811373,0.01746881,0.9627588,0.0021593259,0.00047845815,0.00086660887],"about_ca_topic_score_codex":0.0004952975,"about_ca_topic_score_gemma":0.00003438212,"teacher_disagreement_score":0.72084767,"about_ca_system_score_codex":0.00008694237,"about_ca_system_score_gemma":0.00007980744,"threshold_uncertainty_score":0.3920228},"labels":[],"label_agreement":null},{"id":"W2098557952","doi":"10.1109/ijcnn.2010.5596770","title":"Complex situation modeling in distributed sensor networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Inference; Computer science; Representation (politics); Perspective (graphical); Domain (mathematical analysis); Decision support system; Wireless sensor network; Artificial intelligence; Knowledge representation and reasoning; Domain knowledge; Machine learning; Data mining","score_opus":0.06324628188571148,"score_gpt":0.27024070735359607,"score_spread":0.2069944254678846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098557952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14983794,0.000002569637,0.8478856,0.0005785756,0.00031998038,0.0001465281,0.0000025160832,0.00018488905,0.0010413724],"genre_scores_gemma":[0.9881061,6.858165e-7,0.011567195,0.00015463676,0.00008236574,0.000012906278,0.000025032141,0.0000051222323,0.000045987537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990545,0.00006273556,0.00023844161,0.00027658648,0.00016291965,0.00020481888],"domain_scores_gemma":[0.99929935,0.00012339893,0.0000533014,0.0003467085,0.000113626396,0.000063635816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033257852,0.00009327757,0.00013667163,0.00008907716,0.000059190883,0.00015442421,0.00030704064,0.000077645615,0.000053332325],"category_scores_gemma":[0.000053217052,0.000091048,0.000036869904,0.00035753864,0.000012268559,0.00050697784,0.000109801214,0.00022522148,0.00006832996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003886369,0.0006413544,0.01846835,0.000036348196,0.000060234044,0.00006887958,0.0020001146,0.25011742,0.056667652,0.02943834,0.0029778176,0.63948464],"study_design_scores_gemma":[0.00024092325,0.0000070523643,0.0024731227,0.0000064343694,9.189036e-7,0.000014957741,0.000030190507,0.9962879,0.00010178777,0.00039754758,0.00032080413,0.000118375676],"about_ca_topic_score_codex":0.00028015202,"about_ca_topic_score_gemma":0.0014956337,"teacher_disagreement_score":0.83826816,"about_ca_system_score_codex":0.000027659187,"about_ca_system_score_gemma":0.000026034828,"threshold_uncertainty_score":0.37128296},"labels":[],"label_agreement":null},{"id":"W2098800931","doi":"10.1109/compsac.2009.136","title":"Context Analysis of Intensional Languages","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 New Brunswick","funders":"","keywords":"Computer science; Meaning (existential); Construct (python library); Context (archaeology); Semantics (computer science); Graph; Formal language; Value (mathematics); Space (punctuation); Programming language; Theoretical computer science; Geography","score_opus":0.019864526030365297,"score_gpt":0.2808640708184787,"score_spread":0.2609995447881134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098800931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18635103,0.00017284659,0.7837666,0.0026858416,0.00013336548,0.00011633304,0.000007822793,0.0002187748,0.026547384],"genre_scores_gemma":[0.9960829,0.0000013386938,0.0019953172,0.0012457069,0.0000134586235,0.0000012983947,0.0000033187498,0.0000013110125,0.0006553516],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992604,0.00004495076,0.00019637046,0.00019161151,0.00020652266,0.000100133526],"domain_scores_gemma":[0.99918896,0.00014861373,0.00008329094,0.00033014372,0.000203135,0.000045882567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017033986,0.000066043765,0.00024358825,0.00031369962,0.000020939891,0.000033904398,0.00031359476,0.000029717912,0.00015431989],"category_scores_gemma":[0.000058222493,0.00005448972,0.0001472004,0.0008969524,0.000016918155,0.00025404125,0.000050488445,0.000043873628,0.000044767854],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010131162,0.00019537519,0.0047107367,0.000004110522,0.0006251655,0.000019651596,0.0010987865,0.000026101397,0.009494386,0.07174613,0.0041468283,0.9079226],"study_design_scores_gemma":[0.0016962163,0.0006492939,0.70913756,0.000106358486,0.0006630285,0.000089517715,0.0029534388,0.21209702,0.054742184,0.0036741039,0.013071616,0.0011196698],"about_ca_topic_score_codex":0.00012500341,"about_ca_topic_score_gemma":0.00012883959,"teacher_disagreement_score":0.90680295,"about_ca_system_score_codex":0.000012901508,"about_ca_system_score_gemma":0.000022181313,"threshold_uncertainty_score":0.22220261},"labels":[],"label_agreement":null},{"id":"W2098992914","doi":"10.1109/comswa.2006.1665203","title":"Mobile middleware service architecture for EMS application.","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Middleware (distributed applications); Server; Scalability; Service (business); Mobile computing; Software architecture; Operating system; Computer network; Software; Embedded system","score_opus":0.014329739222138845,"score_gpt":0.23900444802922108,"score_spread":0.22467470880708224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098992914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036896253,0.000107630985,0.98747134,0.0009519437,0.00015012005,0.0010008687,0.000018428378,0.0004581609,0.0061518564],"genre_scores_gemma":[0.9530225,6.031672e-7,0.04105561,0.0015747582,0.0002707988,0.0014449815,0.000036588488,0.000016508422,0.0025776844],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896026,0.000029387558,0.00020776916,0.00040804222,0.0001753372,0.00021922226],"domain_scores_gemma":[0.99889916,0.00016712659,0.00008493867,0.00056036515,0.00023392068,0.00005447482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013754438,0.00012716342,0.0001467126,0.00007808501,0.000113220056,0.0001394687,0.0006015065,0.00006560092,0.000017789895],"category_scores_gemma":[0.0000072493253,0.00011497619,0.00007603533,0.00038585288,0.00001159912,0.00025833558,0.00010492583,0.00006701732,0.00019974355],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022630047,0.00047787375,0.0011970701,0.00028690256,0.000053640615,0.0000033530532,0.0013761033,0.00161443,0.017476866,0.039366193,0.04445147,0.8936735],"study_design_scores_gemma":[0.00086256687,0.0001140336,0.0007481141,0.00002594404,0.000009186663,0.00006277672,0.00011052356,0.060001217,0.011122165,0.023836799,0.9026049,0.00050177734],"about_ca_topic_score_codex":0.0004949919,"about_ca_topic_score_gemma":0.00095629,"teacher_disagreement_score":0.94933283,"about_ca_system_score_codex":0.00003193765,"about_ca_system_score_gemma":0.000041607473,"threshold_uncertainty_score":0.4688593},"labels":[],"label_agreement":null},{"id":"W2100429301","doi":"","title":"UHYPERLINK: AN ORGANIZED METHOD TO COLLECT, MANAGE AND STORE OBJECT HYPERLINKS USING RFID","year":2012,"lang":"en","type":"article","venue":"Library and Archives Canada (Government of Canada)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Object (grammar); Hyperlink; Database; World Wide Web; Computer security; Artificial intelligence; Web page","score_opus":0.010839179356467806,"score_gpt":0.19070108594279112,"score_spread":0.1798619065863233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100429301","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81329674,0.0005319988,0.13933378,0.0032775924,0.0007896203,0.0007633816,0.00014715423,0.00008373912,0.041775998],"genre_scores_gemma":[0.93896896,0.000009573821,0.058027014,0.0015850426,0.00008946776,0.000006379468,0.0000017814655,0.000020209456,0.0012915576],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997768,0.00023939925,0.00025453584,0.00034290415,0.0010137202,0.0003814376],"domain_scores_gemma":[0.9986344,0.0003612923,0.00012489568,0.00031889178,0.000001803415,0.00055869075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000058662958,0.00019853639,0.0003178323,0.000055459917,0.0002394109,0.000082349245,0.00035466647,0.000033677432,0.000014397045],"category_scores_gemma":[0.000007200112,0.00019844418,0.000020805353,0.00023368507,0.000029896893,0.0013161381,0.00035202724,0.00012815236,7.661568e-9],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006771894,0.00028742006,0.16077183,0.00056334416,0.0004121182,0.00026164876,0.0061265314,0.000133608,0.37897655,0.08495034,0.0015025442,0.36533687],"study_design_scores_gemma":[0.0035201272,0.00072466227,0.30000746,0.000501964,0.00016801461,0.0006911051,0.013195558,0.09976699,0.44164494,0.0020027105,0.13484524,0.0029312347],"about_ca_topic_score_codex":0.003947215,"about_ca_topic_score_gemma":0.011541292,"teacher_disagreement_score":0.36240563,"about_ca_system_score_codex":0.000017812725,"about_ca_system_score_gemma":0.0007629863,"threshold_uncertainty_score":0.8092319},"labels":[],"label_agreement":null},{"id":"W2101039650","doi":"10.1007/3-540-44963-9_56","title":"Purpose-Based User Modelling in a Multi-agent Portfolio Management System","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Variety (cybernetics); Representation (politics); User modeling; Context (archaeology); Distributed computing; Human–computer interaction; User agent; Scheme (mathematics); User interface; Artificial intelligence; World Wide Web; Programming language","score_opus":0.04567397486799251,"score_gpt":0.2512206922072559,"score_spread":0.20554671733926339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101039650","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008846473,0.00039554635,0.99179786,0.0002059705,0.0023789569,0.0014707529,0.0000059444174,0.00026806595,0.0033884295],"genre_scores_gemma":[0.37802273,0.000025424786,0.61896104,0.001647867,0.00014355802,0.00011906039,0.0000073389638,0.000080652484,0.000992368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939002,0.00014064534,0.0011034587,0.0023916415,0.0014894395,0.0009746461],"domain_scores_gemma":[0.99665326,0.00032480335,0.00053118396,0.0019538228,0.0002849198,0.0002520354],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018195091,0.0007962321,0.00090444833,0.0022542104,0.00021138703,0.0008080378,0.0030484898,0.00039515324,0.000016206011],"category_scores_gemma":[0.000020410356,0.0008026302,0.00023748653,0.0011907155,0.00026137673,0.00075152854,0.00087594584,0.00086997024,0.000121897705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014888934,0.00021931707,0.00024985004,0.00054225954,0.000045856206,0.0015407301,0.0006166548,0.53464746,0.00003116268,0.011837695,0.000047722744,0.45020643],"study_design_scores_gemma":[0.0008541935,0.00006832959,0.000036651683,0.0018432018,0.000012616615,0.00009960378,0.0000010052117,0.9896067,0.0003412485,0.0021148063,0.004119912,0.00090178044],"about_ca_topic_score_codex":0.00008944074,"about_ca_topic_score_gemma":0.0001326836,"teacher_disagreement_score":0.4549592,"about_ca_system_score_codex":0.0013848803,"about_ca_system_score_gemma":0.00040856662,"threshold_uncertainty_score":0.99944246},"labels":[],"label_agreement":null},{"id":"W2101160373","doi":"10.1109/iembs.2008.4649600","title":"Monitoring activity patterns and trends of older adults","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Task (project management); Sitting; Everyday life; Physical activity; Key (lock); Applied psychology; Cognitive impairment; Cognition; Activities of daily living; Computer science; Psychology; Gerontology; Human–computer interaction; Physical medicine and rehabilitation; Engineering; Medicine; Computer security","score_opus":0.03136323198437408,"score_gpt":0.257011809943871,"score_spread":0.22564857795949694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101160373","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.910039,0.000024306311,0.0882001,0.00009104862,0.00020291451,0.00004956885,0.0000022425008,0.00008807634,0.0013027086],"genre_scores_gemma":[0.9986824,0.000018205596,0.00070582924,0.000012972147,0.00004976001,0.000008318573,1.8228927e-7,0.0000044351073,0.00051788794],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992587,0.000044938035,0.00012684253,0.0002503522,0.00018838457,0.00013076572],"domain_scores_gemma":[0.9994253,0.00009115624,0.00007686109,0.00027778893,0.0000644232,0.00006445902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008079332,0.0000880216,0.00015694591,0.00012104797,0.000057221456,0.000023194107,0.00018858403,0.000038942726,0.000019853129],"category_scores_gemma":[0.000009095878,0.00008037418,0.00004121807,0.00019862567,0.000024193407,0.0005752275,0.00013542127,0.000064303116,0.00000568192],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057773937,0.00007541344,0.32799774,0.000020968233,0.000012581303,0.000009865826,0.0012953499,2.508598e-7,0.0007884397,0.000020895131,0.00006463824,0.6697081],"study_design_scores_gemma":[0.0003911772,0.00003731061,0.9616649,0.000069840906,0.0000015870271,0.00006927804,0.00003569922,0.0011540654,0.03641827,0.000004334331,0.00005066816,0.000102845035],"about_ca_topic_score_codex":0.0006721091,"about_ca_topic_score_gemma":0.000045314206,"teacher_disagreement_score":0.6696052,"about_ca_system_score_codex":0.000014012257,"about_ca_system_score_gemma":0.000012245361,"threshold_uncertainty_score":0.32775638},"labels":[],"label_agreement":null},{"id":"W2101478909","doi":"10.1109/hpcc.2011.133","title":"Tyche Project: A Context Aware Self-Organization Middleware for Ubiquitous Environment","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Middleware (distributed applications); Computer science; Ubiquitous computing; Context (archaeology); Architecture; Software architecture; Message oriented middleware; Software; Software engineering; Distributed computing; Human–computer interaction; Operating system","score_opus":0.06600569855965319,"score_gpt":0.22934127898953857,"score_spread":0.16333558042988539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101478909","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012846913,0.000045474757,0.98194474,0.0002333572,0.00041984083,0.001552421,0.000016798927,0.0007818737,0.0021586071],"genre_scores_gemma":[0.9755028,0.000008613572,0.022572549,0.00043671034,0.000091713206,0.00023477472,0.000016203754,0.000029799761,0.0011068427],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998508,0.00008034931,0.00031125083,0.00054938655,0.0002465741,0.0003044798],"domain_scores_gemma":[0.9988577,0.00011381393,0.00015596717,0.00057731103,0.00020606609,0.0000891503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023402275,0.00019277712,0.00022335896,0.00012233848,0.00015965833,0.00011590304,0.0006033045,0.000111967805,0.00015626042],"category_scores_gemma":[0.000038684502,0.00018588055,0.00008079238,0.00029214894,0.000023061992,0.0006622885,0.00021286563,0.00007814678,0.00035344777],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009607823,0.003297483,0.019092297,0.00058640586,0.0006460002,0.000058896625,0.08504974,0.000004390211,0.0039769127,0.029059596,0.03180729,0.8263249],"study_design_scores_gemma":[0.015913773,0.004713508,0.031120595,0.00057771814,0.0004021769,0.0010924841,0.017906025,0.06428112,0.47094125,0.010153634,0.37534133,0.0075564156],"about_ca_topic_score_codex":0.00016895603,"about_ca_topic_score_gemma":0.000067212524,"teacher_disagreement_score":0.9626559,"about_ca_system_score_codex":0.0001269977,"about_ca_system_score_gemma":0.00012248402,"threshold_uncertainty_score":0.7579989},"labels":[],"label_agreement":null},{"id":"W2103346825","doi":"10.1186/2192-1962-3-1","title":"Publishing and discovering context-dependent services","year":2013,"lang":"en","type":"article","venue":"Human-centric Computing and Information Sciences","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":115,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Correctness; Service (business); Computer science; Service provider; Context (archaeology); Service discovery; Differentiated service; Service design; Service delivery framework; World Wide Web; Service level objective; Matching (statistics); Ranking (information retrieval); Internet privacy; Business; Information retrieval; Web service; Marketing","score_opus":0.023223260116564375,"score_gpt":0.2531672897771981,"score_spread":0.22994402966063374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103346825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8456184,0.00048273744,0.14469731,0.00096817006,0.0004225682,0.0003614622,0.0000022149793,0.00025132336,0.0071957535],"genre_scores_gemma":[0.9984689,0.00002277634,0.00070491893,0.00071349926,0.000054425076,0.00000634757,0.000002601098,0.0000022226181,0.000024330375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983829,0.00006559764,0.00043811643,0.00028527318,0.0005183866,0.00030968248],"domain_scores_gemma":[0.9989847,0.00021789271,0.00029676984,0.00018810862,0.00018264388,0.00012987555],"candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0010318968,0.00014085256,0.00018378551,0.00033157182,0.00092049467,0.008671135,0.00058120134,0.00004695616,0.000017011778],"category_scores_gemma":[0.00006953212,0.00011975279,0.00003126483,0.0005135565,0.00011285398,0.027359245,0.0006330204,0.00013182768,0.00004839151],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.867278e-7,0.000016819982,0.009974067,0.00009452221,0.000014076845,5.6439086e-7,0.008750565,0.000099671364,0.00004102453,0.015353522,0.00025044943,0.96540403],"study_design_scores_gemma":[0.0014103578,0.0001697438,0.08073662,0.00033823302,0.0000111041845,0.00017987209,0.012984698,0.88448703,0.00023141556,0.0021905943,0.016529435,0.0007309169],"about_ca_topic_score_codex":0.0014499567,"about_ca_topic_score_gemma":0.0000304795,"teacher_disagreement_score":0.9646731,"about_ca_system_score_codex":0.000030525596,"about_ca_system_score_gemma":0.00003815887,"threshold_uncertainty_score":0.99235797},"labels":[],"label_agreement":null},{"id":"W2103442193","doi":"10.1145/1134680.1134686","title":"Context-aware interactive content adaptation","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adaptation (eye); Computer science; Content adaptation; Context (archaeology); Exploit; Mobile device; Human–computer interaction; Semantics (computer science); Process (computing); Context awareness; Ubiquitous computing; Multimedia; World Wide Web; Computer security","score_opus":0.057256197685632934,"score_gpt":0.2433807007884202,"score_spread":0.18612450310278728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103442193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01656288,0.000054117576,0.9587953,0.0012127861,0.0006567638,0.00032484654,0.0000060884827,0.0004681391,0.021919064],"genre_scores_gemma":[0.9933501,0.0000010827176,0.0020500752,0.0005903098,0.00011232429,0.00005675554,0.000011344212,0.000010274484,0.0038177164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857754,0.00012534742,0.0003409398,0.00041940957,0.00029651256,0.000240267],"domain_scores_gemma":[0.99873286,0.00029097087,0.00017049434,0.00037760974,0.00035967442,0.00006839884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019179708,0.0001658298,0.00021319996,0.00013997729,0.00010022916,0.00024921636,0.00041839003,0.000061902276,0.00010936218],"category_scores_gemma":[0.00003628594,0.0001510058,0.00010496344,0.00025005153,0.000032722066,0.0014324222,0.00012710756,0.0001261671,0.0005979035],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062099796,0.00055999117,0.0035695555,0.000027599848,0.00012042823,0.00007585889,0.002502227,0.00010529881,0.0051659904,0.13886856,0.020580707,0.8283617],"study_design_scores_gemma":[0.008700589,0.00092506583,0.07246813,0.00047890202,0.00006501841,0.0006891815,0.01519273,0.63039315,0.086829074,0.02008366,0.16085874,0.0033157864],"about_ca_topic_score_codex":0.0026049248,"about_ca_topic_score_gemma":0.0017366667,"teacher_disagreement_score":0.9767872,"about_ca_system_score_codex":0.000118945274,"about_ca_system_score_gemma":0.00006332521,"threshold_uncertainty_score":0.76850396},"labels":[],"label_agreement":null},{"id":"W2103587716","doi":"10.1186/1743-0003-4-44","title":"A flexible routing scheme for patients with topographical disorientation","year":2007,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Scheme (mathematics); Neurology; Routing (electronic design automation); Computer science; Neuroscience; Physical medicine and rehabilitation; Medicine; Computer network; Psychology; Mathematics","score_opus":0.005316070793229844,"score_gpt":0.22461009139654267,"score_spread":0.21929402060331282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103587716","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50362474,0.000009388231,0.49590236,0.00018176573,0.00016698253,0.00009188949,3.6935114e-7,0.000017291932,0.000005188453],"genre_scores_gemma":[0.9411317,0.000001394664,0.058784675,0.000015904288,0.00005252475,0.0000030706713,6.2150195e-7,0.000006143641,0.000003971118],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992736,0.000020093703,0.00028843316,0.00011088706,0.00019302013,0.000113942835],"domain_scores_gemma":[0.9986316,0.0007328382,0.00017340633,0.00007457673,0.00032345756,0.00006414173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041594479,0.00006822948,0.00011519903,0.00021784747,0.00005245058,0.000055895463,0.00007595992,0.000022426102,1.8117922e-7],"category_scores_gemma":[0.00031274013,0.000054719316,0.000052345094,0.00023897209,0.000017204155,0.00056991755,0.000012872546,0.0000868085,1.4459008e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079667923,0.00078120735,0.66402495,0.000539375,0.00013309119,0.000013086213,0.0074452246,0.001882738,0.008438613,0.008852538,0.00019080941,0.30690166],"study_design_scores_gemma":[0.0025362256,0.0038792777,0.9755153,0.0002307808,0.000015006689,0.000051606217,0.0002716159,0.015530265,0.0003430182,0.00050266937,0.0009211092,0.00020309197],"about_ca_topic_score_codex":0.0000011291748,"about_ca_topic_score_gemma":8.02526e-7,"teacher_disagreement_score":0.43750694,"about_ca_system_score_codex":0.000022479944,"about_ca_system_score_gemma":0.000013177217,"threshold_uncertainty_score":0.2231389},"labels":[],"label_agreement":null},{"id":"W2103691021","doi":"10.1164/ajrccm.187.6.660","title":"The Usefulness of K <scp>co</scp> Is Questionable","year":2013,"lang":"en","type":"article","venue":"American Journal of Respiratory and Critical Care Medicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Medicine; Intensive care medicine","score_opus":0.030479584032268293,"score_gpt":0.29946613150617346,"score_spread":0.26898654747390516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103691021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92259467,0.0074529736,0.05893886,0.007961854,0.00067738636,0.00019032895,0.0000056600015,0.000029441118,0.0021488466],"genre_scores_gemma":[0.99733895,0.000033210476,0.00027579407,0.0020541253,0.00025171528,0.000008380381,1.424003e-7,0.000006840356,0.0000308259],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99815303,0.00035994375,0.000537275,0.00016510209,0.0005620198,0.00022263263],"domain_scores_gemma":[0.99500376,0.0027713825,0.0003135298,0.00027561106,0.0013696911,0.0002660093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007983326,0.0001231526,0.00040564028,0.00012219757,0.00016261806,0.000069226706,0.00045277554,0.000034505294,0.000019496612],"category_scores_gemma":[0.0017875866,0.000075401345,0.00007300125,0.0003206291,0.0012032097,0.000490236,0.000063063584,0.00026511663,0.000008735792],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015237991,0.00007135406,0.018589508,0.00014459742,0.00008548858,0.00006998044,0.006722294,9.4295376e-7,0.002575024,0.011580412,0.014680512,0.9454647],"study_design_scores_gemma":[0.005147715,0.028988058,0.13123804,0.002865568,0.0003584071,0.0024163325,0.19971707,0.0005256242,0.042942572,0.016776,0.568332,0.00069259555],"about_ca_topic_score_codex":0.000071442286,"about_ca_topic_score_gemma":0.000003114117,"teacher_disagreement_score":0.94477206,"about_ca_system_score_codex":0.000029335599,"about_ca_system_score_gemma":0.0001390314,"threshold_uncertainty_score":0.44332764},"labels":[],"label_agreement":null},{"id":"W2104640696","doi":"10.12688/f1000research.4790.2","title":"Combining low sampling frequency smartphone sensors and video for a Wearable Mobility Monitoring System","year":2015,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Ottawa Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stairs; Accelerometer; False positive paradox; Wearable computer; Sitting; Computer science; Gait; Context (archaeology); Real-time computing; Activities of daily living; Activity recognition; Simulation; Physical medicine and rehabilitation; Artificial intelligence; Embedded system; Medicine; Engineering; Physical therapy; Geography","score_opus":0.1511383319480872,"score_gpt":0.3654253436496069,"score_spread":0.21428701170151968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104640696","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.688416,0.0025044885,0.29893494,0.00037922262,0.0033575373,0.0039318996,0.00016438054,0.0009866006,0.0013249358],"genre_scores_gemma":[0.97654176,0.000050115435,0.02117625,0.000007846748,0.00056860864,0.0012197585,0.000017918699,0.00007087286,0.00034685162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944417,0.0007666563,0.0008004399,0.0016751077,0.0013082783,0.0010078064],"domain_scores_gemma":[0.9936355,0.0020056558,0.00032881123,0.0018073949,0.0016715198,0.0005511304],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00648241,0.00047151506,0.0009190867,0.0005153513,0.000448296,0.0013962942,0.0016671623,0.0004693216,0.0000059456115],"category_scores_gemma":[0.0010442545,0.00050475023,0.0002022056,0.000494104,0.00014943947,0.0006416534,0.0032634418,0.0013223928,0.00008098202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012370718,0.0029880393,0.17142406,0.081290565,0.0033136262,0.00079906546,0.04863362,0.0097356895,0.061480206,0.011409737,0.007786292,0.59990203],"study_design_scores_gemma":[0.009530679,0.001383033,0.025297675,0.029886091,0.00018453723,0.000670845,0.008984696,0.8389498,0.030927537,0.045269504,0.0030820195,0.0058335867],"about_ca_topic_score_codex":0.001631658,"about_ca_topic_score_gemma":0.00003512299,"teacher_disagreement_score":0.8292141,"about_ca_system_score_codex":0.00093722926,"about_ca_system_score_gemma":0.0009865605,"threshold_uncertainty_score":0.9997404},"labels":[],"label_agreement":null},{"id":"W2106309798","doi":"10.1109/scc.2011.92","title":"An Architecture for Managing and Delivering Trustworthy Context-Dependent Services","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Trustworthiness; Computer science; Publication; Context (archaeology); Architecture; Order (exchange); Selection (genetic algorithm); World Wide Web; Knowledge management; Computer security; Business; Artificial intelligence","score_opus":0.028995287233572125,"score_gpt":0.23766894112406545,"score_spread":0.20867365389049333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106309798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14823258,0.00018027112,0.84761214,0.0001291471,0.00018723999,0.00035785933,0.0000040706177,0.00027880754,0.0030179012],"genre_scores_gemma":[0.9814481,0.000005990465,0.017851556,0.00051004655,0.00004881621,0.00004527181,0.0000014739643,0.0000111628315,0.0000775901],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897546,0.000049081256,0.00016770187,0.0004363746,0.00014089912,0.00023048886],"domain_scores_gemma":[0.99925184,0.00007535523,0.00006736421,0.00040419647,0.000078222205,0.00012301505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026103918,0.00013736461,0.00017686195,0.00010902409,0.0001266506,0.00018648965,0.00047761702,0.000045092922,0.00002420672],"category_scores_gemma":[0.0000035042062,0.00012366057,0.00005346038,0.00008515388,0.000018506707,0.0008094027,0.0001587556,0.00007331827,0.000010860377],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020903637,0.00004075319,0.0019437406,0.000080290585,0.000049545484,0.000008044652,0.005437118,0.000009587457,0.00041897054,0.0060363575,0.000008477485,0.98594624],"study_design_scores_gemma":[0.010699156,0.0030844521,0.05840166,0.0008137523,0.0002522796,0.0014212963,0.015836434,0.7079305,0.052813984,0.10434536,0.039329518,0.0050715785],"about_ca_topic_score_codex":0.00096115697,"about_ca_topic_score_gemma":0.0020407692,"teacher_disagreement_score":0.98087466,"about_ca_system_score_codex":0.0000143985635,"about_ca_system_score_gemma":0.000012531734,"threshold_uncertainty_score":0.5042732},"labels":[],"label_agreement":null},{"id":"W2106627347","doi":"10.1109/mwc.2012.6393513","title":"Enabling pervasive healthcare through continuous remote health monitoring","year":2012,"lang":"en","type":"article","venue":"IEEE Wireless Communications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Natural Resources; Natural Sciences and Engineering Research Council of Canada; University of Waterloo","funders":"","keywords":"Computer science; Ubiquitous computing; Health care; Architecture; Healthcare delivery; Computer security; Risk analysis (engineering); Human–computer interaction; Business","score_opus":0.1312335183405074,"score_gpt":0.3629642364834736,"score_spread":0.2317307181429662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106627347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09677187,0.019992227,0.83521324,0.038316786,0.003948567,0.0014341355,0.00004202367,0.0014526781,0.0028284749],"genre_scores_gemma":[0.96391886,0.0015640603,0.033254344,0.0007021334,0.0003269151,0.00006907645,0.000011784347,0.000030059498,0.00012276531],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99718225,0.0008233123,0.0005878128,0.00034854028,0.00036235515,0.0006957425],"domain_scores_gemma":[0.9947638,0.0006899266,0.00044376028,0.0034490903,0.00036566216,0.00028777696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00083270913,0.00022820313,0.00042558543,0.00012763766,0.0010096319,0.00020658658,0.0024568865,0.00010822759,0.0000032514865],"category_scores_gemma":[0.00006407591,0.00025249537,0.00012908045,0.0006517797,0.00010495102,0.0017758101,0.0006182772,0.00053369504,0.00016907697],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064790634,0.00047038065,0.015130386,0.00015396131,0.00011456787,0.0000028161764,0.042487692,0.00001230109,0.0021285492,0.023201,0.0008264705,0.9154654],"study_design_scores_gemma":[0.008312079,0.0012491264,0.07129446,0.009699961,0.00017767085,0.0020305377,0.05573102,0.070926666,0.058594618,0.011829191,0.7014459,0.008708809],"about_ca_topic_score_codex":0.0026423854,"about_ca_topic_score_gemma":0.0002315356,"teacher_disagreement_score":0.9067566,"about_ca_system_score_codex":0.0003394772,"about_ca_system_score_gemma":0.0002708331,"threshold_uncertainty_score":0.9999927},"labels":[],"label_agreement":null},{"id":"W2106628769","doi":"10.1007/978-3-319-05939-6_4","title":"Storing and Managing Context and Context History","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Context (archaeology); Computer science; Structuring; Scalability; Personalization; Context model; Data science; Dimension (graph theory); Work (physics); Service (business); World Wide Web; Database; Engineering; Artificial intelligence","score_opus":0.031974618291975586,"score_gpt":0.22496692986723782,"score_spread":0.19299231157526223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106628769","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031530636,0.0030149564,0.99288684,0.0008695986,0.00075263885,0.00043421084,0.000014761711,0.0000742944,0.0016373671],"genre_scores_gemma":[0.3951879,0.00043440075,0.6031026,0.0007003198,0.00023143731,0.000038258204,0.000012282219,0.00003052956,0.00026222283],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988852,0.000017774904,0.0005004305,0.00020070247,0.00020401133,0.00019189449],"domain_scores_gemma":[0.9983396,0.0005120241,0.00046063666,0.00048174956,0.00014295487,0.00006301843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047566014,0.0002637847,0.00044932106,0.00022244181,0.00059562863,0.00022327226,0.0010408403,0.00015836071,6.163719e-7],"category_scores_gemma":[0.000053427444,0.00023323126,0.00010351227,0.000082308645,0.00046445662,0.00045000835,0.0009301757,0.00034080184,4.4695267e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025030722,0.000009572233,0.000016924021,0.00059879315,0.0001528672,2.1385964e-7,0.0077722166,0.018392794,0.000015811454,0.21176201,0.00021621623,0.76106006],"study_design_scores_gemma":[0.00042031906,0.00008520482,0.00006293349,0.00062684354,0.000055237113,0.00005343377,0.000025193993,0.7057927,0.00003358586,0.0013648516,0.2909582,0.00052146026],"about_ca_topic_score_codex":0.00008342484,"about_ca_topic_score_gemma":0.00017222026,"teacher_disagreement_score":0.76053864,"about_ca_system_score_codex":0.00012130352,"about_ca_system_score_gemma":0.00010648672,"threshold_uncertainty_score":0.95108944},"labels":[],"label_agreement":null},{"id":"W2107123088","doi":"10.1109/ictai.2011.36","title":"Simultaneous Tracking and Activity Recognition","year":2011,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Regina; University of Toronto","funders":"","keywords":"Tracking (education); Activity recognition; Computer science; Artificial intelligence; Bayesian probability; Identity (music); Contrast (vision); Pattern recognition (psychology); Machine learning; Video tracking; Computer vision; Object (grammar)","score_opus":0.08856410063087836,"score_gpt":0.2767940854478743,"score_spread":0.18822998481699596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107123088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15773487,0.00011856719,0.8205499,0.00025804059,0.0013121039,0.000667008,0.000024632078,0.0008406843,0.018494207],"genre_scores_gemma":[0.991155,0.00005142657,0.008032234,0.00013358518,0.00014116176,0.00006146551,0.000009377917,0.000022132133,0.00039358533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785596,0.00023737486,0.00029627667,0.0010295367,0.00028491154,0.00029596762],"domain_scores_gemma":[0.99784505,0.0006100575,0.0003197088,0.0007982409,0.00027307053,0.0001538545],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043840945,0.0003416175,0.0004448378,0.00020842813,0.000114572686,0.0005369689,0.00062317844,0.00038076538,0.000083609884],"category_scores_gemma":[0.00020436817,0.0003467906,0.0001266609,0.00013271107,0.000051691204,0.00071804516,0.0014553808,0.00066395383,0.00018714769],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010131019,0.000083114326,0.000072198694,0.000106313695,0.000047745074,0.000042300544,0.000695575,0.000006559839,0.00042430274,0.000112225935,0.000078274425,0.99832124],"study_design_scores_gemma":[0.0039177015,0.0008938185,0.010857748,0.0036545522,0.00038884417,0.0023333027,0.00042523356,0.5695896,0.13188446,0.2574177,0.0088639045,0.0097731445],"about_ca_topic_score_codex":0.00065702194,"about_ca_topic_score_gemma":0.00022183973,"teacher_disagreement_score":0.9885481,"about_ca_system_score_codex":0.00007888686,"about_ca_system_score_gemma":0.00011603899,"threshold_uncertainty_score":0.99989843},"labels":[],"label_agreement":null},{"id":"W2107307281","doi":"10.1109/iscc.2006.166","title":"Using Web Services for Bridging End-User Applications and Wireless Sensor Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Ericsson (Canada); Concordia University","funders":"","keywords":"Bridging (networking); Web service; Computer science; Sensor web; Wireless sensor network; World Wide Web; WS-Policy; Computer network; Wireless network; Services computing; WS-Addressing; End user; Wireless; Web development; Key distribution in wireless sensor networks; Web application security; Telecommunications","score_opus":0.023795747881658634,"score_gpt":0.26102771725883633,"score_spread":0.2372319693771777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107307281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09045243,0.000070304006,0.90755266,0.00026123953,0.000092488364,0.00046735138,0.000006668078,0.00020974575,0.00088711304],"genre_scores_gemma":[0.970388,0.0000029517626,0.028652405,0.00019303878,0.0002875459,0.0000958169,0.000006191551,0.000011376712,0.00036267258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991046,0.00003156452,0.00019304121,0.0003438526,0.00010867734,0.00021822989],"domain_scores_gemma":[0.99928206,0.00018475975,0.00009533614,0.00027316128,0.000114225964,0.00005046518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016991593,0.000110746136,0.00014542206,0.00007130285,0.00020547712,0.00030594273,0.00022315397,0.00005288007,0.000005323049],"category_scores_gemma":[0.0000011358811,0.00011014993,0.000041158848,0.00021616912,0.000021474556,0.000488669,0.00011260077,0.000052117506,0.0000066334596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024434383,0.0003603137,0.036106046,0.00058117096,0.00015058435,0.00001092966,0.0005342376,0.0059940848,0.081544034,0.07940278,0.0017809474,0.79351044],"study_design_scores_gemma":[0.00027608735,0.000006849594,0.0009329181,0.000027012074,0.000008424121,0.000042301195,0.00004563277,0.9872917,0.00087024254,0.00022325583,0.0101026045,0.00017300007],"about_ca_topic_score_codex":0.00045607897,"about_ca_topic_score_gemma":0.000462446,"teacher_disagreement_score":0.9812976,"about_ca_system_score_codex":0.00002557161,"about_ca_system_score_gemma":0.00002675641,"threshold_uncertainty_score":0.44917837},"labels":[],"label_agreement":null},{"id":"W2107851178","doi":"10.1109/cloud.2009.73","title":"SOA's Last Mile-Connecting Smartphones to the Service Cloud","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Cloud computing; Computer science; Middleware (distributed applications); Software as a service; Key (lock); Service (business); Service provider; Mobile cloud computing; World Wide Web; Computer network; Software; Computer security; Operating system; Software development","score_opus":0.026168260911685812,"score_gpt":0.2503477670618299,"score_spread":0.22417950615014406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107851178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25194976,0.000051023682,0.64799047,0.07416778,0.001962624,0.00051224127,0.0000030079696,0.0007772579,0.022585811],"genre_scores_gemma":[0.9826188,7.390725e-7,0.003620676,0.012716812,0.000410263,0.000013125051,8.858705e-7,0.0000070104143,0.0006116579],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99871343,0.00011546677,0.00022262335,0.0003836828,0.00026618835,0.0002986213],"domain_scores_gemma":[0.99864244,0.0003181618,0.000071735754,0.00067070767,0.00018270608,0.00011421564],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00047821953,0.0001434733,0.00016121264,0.00007542973,0.0002634228,0.0003605188,0.0009878132,0.00004497368,0.00006046913],"category_scores_gemma":[0.00008176593,0.000101699196,0.00006376173,0.0007710244,0.0000055399364,0.00044391214,0.00021898735,0.00012496198,0.0017628019],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013934883,0.00013383858,0.00039600232,0.000015417865,0.000035943478,0.00001671824,0.010520946,0.00013813857,0.0055934885,0.007270263,0.028316366,0.9475489],"study_design_scores_gemma":[0.0019243864,0.0006721664,0.029737411,0.0003110347,0.000033021155,0.00082086964,0.0055736965,0.050811075,0.04534798,0.0060095116,0.8566863,0.0020725704],"about_ca_topic_score_codex":0.0002824347,"about_ca_topic_score_gemma":0.0026901092,"teacher_disagreement_score":0.94547635,"about_ca_system_score_codex":0.000033554832,"about_ca_system_score_gemma":0.000037899223,"threshold_uncertainty_score":0.99901444},"labels":[],"label_agreement":null},{"id":"W2108362750","doi":"10.1109/hpcsim.2011.5999857","title":"Automated inventory tracking system prototype in cloud","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Okanagan College","funders":"","keywords":"Cloud computing; Computer science; Point of sale; Tracking system; Process (computing); Order processing; Audit; Business; Supply chain; World Wide Web; Marketing; Operating system","score_opus":0.07570242374295638,"score_gpt":0.26338525759694553,"score_spread":0.18768283385398915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108362750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39173827,0.0001180455,0.38908002,0.00015668407,0.0032550455,0.0035505514,0.0000024513363,0.0107388515,0.20136009],"genre_scores_gemma":[0.99772704,3.165047e-7,0.0018549166,0.00006189498,0.000043032695,0.00013968983,5.1823844e-7,0.0000096940885,0.00016292093],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987002,0.00018922775,0.0003260016,0.00033476314,0.00020396459,0.00024583415],"domain_scores_gemma":[0.99926716,0.00004029167,0.00010400128,0.00042024403,0.00009084441,0.0000774772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000541475,0.00012006019,0.00019249311,0.00019606495,0.000043081596,0.00008240581,0.000536951,0.00007588455,0.000040179195],"category_scores_gemma":[0.00002505288,0.0001114621,0.00004860904,0.00045191456,0.000018016477,0.00072751974,0.00012130516,0.000113573245,0.00043278869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002517733,0.0022542616,0.27429542,0.002079253,0.0003013062,0.0014735267,0.063962825,0.000029208011,0.0072569875,0.30225506,0.008285069,0.3375553],"study_design_scores_gemma":[0.003512208,0.0006771172,0.10023319,0.0016899976,0.00001821249,0.00064397475,0.0026843157,0.8512766,0.030308248,0.0013395554,0.005732316,0.0018842781],"about_ca_topic_score_codex":0.00060535735,"about_ca_topic_score_gemma":0.00022655222,"teacher_disagreement_score":0.8512474,"about_ca_system_score_codex":0.00012709897,"about_ca_system_score_gemma":0.00007519573,"threshold_uncertainty_score":0.55627674},"labels":[],"label_agreement":null},{"id":"W2108732404","doi":"10.1504/ijhtm.2009.023726","title":"Ubiquitous and personalised contextual biomonitoring","year":2009,"lang":"en","type":"article","venue":"International Journal of Healthcare Technology and Management","topic":"Context-Aware Activity Recognition 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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Biomonitoring; Context (archaeology); Computer science; Adaptation (eye); Quality (philosophy); Inference; Data science; Geography; Ecology; Artificial intelligence; Psychology","score_opus":0.017785637338280334,"score_gpt":0.3071358654828529,"score_spread":0.28935022814457256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108732404","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.532059,0.006341372,0.15843801,0.29807737,0.0033492288,0.00043566577,0.0000063147017,0.0002671615,0.0010258586],"genre_scores_gemma":[0.9950715,0.0006863881,0.0032536052,0.000819494,0.000114427574,0.0000032396922,3.4024652e-7,0.0000027424774,0.000048288926],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990913,0.00003575183,0.0002985276,0.00016880519,0.00027240417,0.00013317891],"domain_scores_gemma":[0.99925923,0.00003871277,0.00022618151,0.000113206734,0.0002963674,0.00006631216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029845035,0.00008915389,0.00015949582,0.00075481716,0.000066703244,0.0000843224,0.00047053694,0.00007212772,0.000001681164],"category_scores_gemma":[0.000024220933,0.00008253348,0.000032585518,0.00017319714,0.00005131373,0.00034672685,0.00015816234,0.00017154956,0.000002034304],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002888656,0.000043851025,0.0016196648,0.00001612506,0.00010087129,0.00031443374,0.00024877727,2.8957254e-7,0.00024543714,0.13874437,0.00017267623,0.8584646],"study_design_scores_gemma":[0.02040773,0.00735426,0.21492244,0.0040939567,0.00020815482,0.030846126,0.014339483,0.0034507553,0.012166057,0.3539588,0.33589306,0.0023592012],"about_ca_topic_score_codex":0.000015280535,"about_ca_topic_score_gemma":0.000007668712,"teacher_disagreement_score":0.85610545,"about_ca_system_score_codex":0.000067546585,"about_ca_system_score_gemma":0.000023473895,"threshold_uncertainty_score":0.33656177},"labels":[],"label_agreement":null},{"id":"W2108973640","doi":"10.1109/cnsm.2010.5691258","title":"Adaptive context monitoring in heterogeneous environments","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa; Cistel Technology (Canada)","funders":"","keywords":"Computer science; Scalability; Context (archaeology); Bluetooth; Context management; Personalization; Distributed computing; Context awareness; Adaptation (eye); Overlay; Global Positioning System; Wireless sensor network; Ubiquitous computing; Context model; Computer network; Real-time computing; Human–computer interaction; Database; Wireless; World Wide Web; Artificial intelligence; Telecommunications","score_opus":0.028674744539982703,"score_gpt":0.2435526617864884,"score_spread":0.2148779172465057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108973640","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81726813,0.000041091607,0.17636316,0.00017011339,0.0015129353,0.00027918196,0.0000016049817,0.00013274388,0.00423102],"genre_scores_gemma":[0.99646944,0.000002263658,0.0028150436,0.000078002966,0.0000780769,0.0000359789,2.7067404e-7,0.0000075857183,0.0005133477],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998978,0.00005435523,0.00019392373,0.0003401216,0.00020836768,0.00022521614],"domain_scores_gemma":[0.99931675,0.000116241004,0.00005841655,0.00040803428,0.000014760252,0.00008576712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017778383,0.0001154092,0.0001432131,0.00009965041,0.000043662138,0.000084179126,0.0004568995,0.00006927018,0.000055478118],"category_scores_gemma":[0.000020815101,0.00011354121,0.000045001907,0.00012704126,0.000026906087,0.0005070892,0.0001864275,0.00021705686,0.000442673],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012934994,0.0002363757,0.046044625,0.0000041644444,0.0000324942,0.00011264823,0.001431704,0.000029526671,0.06540483,0.0020629433,0.00006655236,0.8845612],"study_design_scores_gemma":[0.0046178563,0.00052412023,0.14066169,0.00015954727,0.000012674845,0.0006547793,0.0011774906,0.057597637,0.74559426,0.0033567206,0.0434602,0.002183043],"about_ca_topic_score_codex":0.00021961659,"about_ca_topic_score_gemma":0.0003851671,"teacher_disagreement_score":0.88237816,"about_ca_system_score_codex":0.000045379333,"about_ca_system_score_gemma":0.000019697314,"threshold_uncertainty_score":0.56898135},"labels":[],"label_agreement":null},{"id":"W2109933726","doi":"10.1109/elml.2009.23","title":"Space-Mediated Learning at the Locus of Action in a Heterogeneous Team of Mobile Workers","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke","funders":"","keywords":"Computer science; Knowledge management; Human–computer interaction; Knowledge base; Task (project management); Space (punctuation); Collaborative learning; Context (archaeology); Excellence; World Wide Web; Engineering","score_opus":0.020429506863022587,"score_gpt":0.26297185770281206,"score_spread":0.24254235083978948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109933726","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9742057,0.00014237897,0.022698553,0.0002469842,0.00009373708,0.00029390785,0.0000010717537,0.00007405804,0.0022436294],"genre_scores_gemma":[0.99910635,0.0000123753425,0.00021389987,0.000038165745,0.000012309045,0.000013690278,0.0000011887621,0.0000038798225,0.00059815036],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896383,0.00017186608,0.00027835552,0.00020024,0.00023234301,0.0001533794],"domain_scores_gemma":[0.9991273,0.0002756833,0.00021001155,0.00027914593,0.000073931544,0.000033916738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003448402,0.00008761656,0.0001940219,0.00012253289,0.000038854312,0.000021006028,0.00026560572,0.000055330394,0.000044277476],"category_scores_gemma":[0.000048763497,0.000065143584,0.000068082845,0.00048793366,0.000035757595,0.00020059219,0.00008249401,0.00012342566,0.00003410512],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111588495,0.00041736665,0.040499818,0.00004053538,0.00005994788,0.000014438633,0.0046964097,0.014233845,0.12170831,0.00007000689,0.0009437935,0.81720394],"study_design_scores_gemma":[0.004177356,0.0028459688,0.0484125,0.0005633884,0.00003385085,0.0003574321,0.0050285095,0.12653612,0.7855158,0.0004375614,0.024984578,0.001106934],"about_ca_topic_score_codex":0.0002860198,"about_ca_topic_score_gemma":0.0006389421,"teacher_disagreement_score":0.816097,"about_ca_system_score_codex":0.00007703991,"about_ca_system_score_gemma":0.000040140658,"threshold_uncertainty_score":0.26564783},"labels":[],"label_agreement":null},{"id":"W2111082879","doi":"","title":"What is an address in South Africa","year":2007,"lang":"en","type":"article","venue":"UpSpace Institutional Repository (University of Pretoria)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Society of Intestinal Research","funders":"","keywords":"Standardization; Variety (cybernetics); Set (abstract data type); Political science; Standardized test; Computer science; Law; Psychology","score_opus":0.03461030240406364,"score_gpt":0.23614843648275272,"score_spread":0.2015381340786891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111082879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7273549,0.00042244463,0.24959907,0.00038629293,0.0051128063,0.000387038,0.000013095764,0.00020568493,0.0165187],"genre_scores_gemma":[0.99592185,0.000007637215,0.0027921968,0.000023307495,0.00014354615,4.585416e-7,0.0000030310032,0.0000044641565,0.0011035295],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9984515,0.00009571512,0.00018235623,0.00045869028,0.0005600092,0.000251745],"domain_scores_gemma":[0.99876255,0.00011391171,0.00019959263,0.00048534267,0.00026589102,0.00017271089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051945134,0.00013333689,0.00021284257,0.000258034,0.0003101286,0.00011148279,0.0007516672,0.00014902704,0.000019170679],"category_scores_gemma":[0.000024609544,0.00017735161,0.000096554344,0.0004857366,0.00019212137,0.0039832364,0.0002042398,0.00018820635,0.000030010127],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013321564,0.0028945012,0.13344136,0.00043070223,0.00055920007,0.005566617,0.59929377,0.0021794126,0.049280215,0.1524844,0.0039775968,0.048560083],"study_design_scores_gemma":[0.013215848,0.0016493263,0.47596273,0.003294449,0.00018005117,0.0010786873,0.14955889,0.03553195,0.044175632,0.0060724323,0.26419505,0.005084919],"about_ca_topic_score_codex":0.00024412999,"about_ca_topic_score_gemma":0.00022158836,"teacher_disagreement_score":0.44973487,"about_ca_system_score_codex":0.00029282377,"about_ca_system_score_gemma":0.00032521042,"threshold_uncertainty_score":0.7232189},"labels":[],"label_agreement":null},{"id":"W2111769930","doi":"10.1016/j.compbiomed.2004.07.007","title":"Hypoplastic left heart syndrome: knowledge discovery with a data mining approach","year":2004,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; University of Toronto; SickKids Foundation","funders":"","keywords":"Hypoplastic left heart syndrome; Data collection; Data mining; Computer science; Metric (unit); Measure (data warehouse); Knowledge extraction; Medicine; Data acquisition; Machine learning; Artificial intelligence; Medical physics; Heart disease; Statistics; Internal medicine; Operations management","score_opus":0.05144625212388019,"score_gpt":0.31263403939511136,"score_spread":0.26118778727123115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111769930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17284372,0.0011942596,0.82332367,0.0012527216,0.0006983961,0.0001953648,0.000004256135,0.00007249937,0.0004150971],"genre_scores_gemma":[0.9694063,0.00003056031,0.029962238,0.00041026674,0.00010588413,0.00000768921,0.00004124783,0.0000066149146,0.000029193872],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99862003,0.00012691149,0.00024125341,0.0006647249,0.00008141366,0.00026568488],"domain_scores_gemma":[0.99871093,0.00047807445,0.000070870956,0.00061554357,0.00002988601,0.00009470695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048440296,0.00017118515,0.00041212526,0.00021965744,0.00008185031,0.000032073207,0.0006572197,0.0000887887,0.0000013320804],"category_scores_gemma":[0.000068213645,0.00012174746,0.000012840963,0.00028059364,0.00034192638,0.0005973447,0.00054516963,0.00018414963,0.0000054955253],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004930015,0.0029375595,0.33783844,0.0013364566,0.00097297225,0.001519381,0.03957241,0.00049406895,0.0032019943,0.07637796,0.010880359,0.5243754],"study_design_scores_gemma":[0.058378696,0.015607138,0.5321745,0.013867303,0.0003153038,0.08426964,0.004275414,0.21361876,0.00026583258,0.023263462,0.048652552,0.0053113927],"about_ca_topic_score_codex":0.00005713962,"about_ca_topic_score_gemma":0.00008204904,"teacher_disagreement_score":0.79656255,"about_ca_system_score_codex":0.00003884315,"about_ca_system_score_gemma":0.00010362286,"threshold_uncertainty_score":0.49647173},"labels":[],"label_agreement":null},{"id":"W2112370044","doi":"10.1109/have.2006.283799","title":"A Pressure Sensitive Home Environment","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Élisabeth Bruyère Hospital; University of Ottawa; Carleton University","funders":"","keywords":"Pressure sensor; Computer science; Real-time computing; Interface (matter); Embedded system; Engineering; Operating system","score_opus":0.008923836948332976,"score_gpt":0.18796300648617464,"score_spread":0.17903916953784166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112370044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013443017,0.00007207093,0.9551861,0.0009440963,0.0001890576,0.00021381852,0.0000070313085,0.00028093177,0.029663844],"genre_scores_gemma":[0.9874128,0.0000013363293,0.0040712864,0.0001710581,0.00008495198,0.000014269222,0.0000025129675,0.000005069102,0.00823668],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99911153,0.000068804016,0.00013173925,0.0002965732,0.00022437723,0.00016698198],"domain_scores_gemma":[0.9994575,0.000080950864,0.00004967385,0.00034376502,0.000025916175,0.000042214953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012212504,0.00009317361,0.000115608294,0.000050266077,0.00005384856,0.00009685746,0.00020989438,0.00003914264,0.000070076865],"category_scores_gemma":[0.000003282617,0.00008434683,0.000049436712,0.000086887136,0.000021603195,0.0003716825,0.00014664955,0.000058554968,0.0007437486],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046799436,0.0018654518,0.014353038,0.00012161982,0.00049051497,0.0005813406,0.0032847929,0.001525831,0.081725545,0.23406233,0.08882888,0.57311386],"study_design_scores_gemma":[0.0023293735,0.00027536575,0.12874709,0.000057773977,0.000054155273,0.00053400145,0.00022125868,0.11973927,0.085542694,0.012719424,0.64801586,0.0017637275],"about_ca_topic_score_codex":0.00024850576,"about_ca_topic_score_gemma":0.000019260677,"teacher_disagreement_score":0.9739698,"about_ca_system_score_codex":0.00002472854,"about_ca_system_score_gemma":0.000014793132,"threshold_uncertainty_score":0.95596313},"labels":[],"label_agreement":null},{"id":"W2112759888","doi":"10.1109/ainaw.2007.209","title":"Integration of Smart Home Technologies in a Health Monitoring System for the Elderly","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":166,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Élisabeth Bruyère Hospital; Carleton University","funders":"","keywords":"Residence; Perspective (graphical); Assisted living; Function (biology); Home automation; Computer science; Cognition; Elderly people; Health care; Independent living; Human–computer interaction; Risk analysis (engineering); Gerontology; Business; Medicine; Telecommunications","score_opus":0.04993743631752613,"score_gpt":0.3043630397847111,"score_spread":0.25442560346718496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112759888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04372724,0.00025184522,0.953311,0.0010997927,0.0005378813,0.00054782117,0.0000010992328,0.00032665525,0.00019663686],"genre_scores_gemma":[0.9912399,0.000007137569,0.008601252,0.0000068761315,0.000023905419,0.000060922528,2.0584478e-7,0.0000038430003,0.00005596385],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906504,0.000036606594,0.00037926077,0.00017627515,0.00015346712,0.00018937846],"domain_scores_gemma":[0.99870175,0.00067125954,0.00017149006,0.00033147103,0.00010727776,0.000016748596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015751163,0.000073335796,0.00016833511,0.00020172322,0.00006509683,0.00005180227,0.00043048433,0.00005228328,2.4268311e-7],"category_scores_gemma":[0.00007772266,0.000049528204,0.00004426399,0.00045259634,0.000021688427,0.00027995714,0.000068939255,0.00008781217,0.0000034595582],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008850592,0.00002013591,0.0019304993,0.000088550136,0.0000071470326,6.61314e-7,0.00056393637,0.0000034011578,0.0018980725,0.010370851,0.000026426886,0.9850815],"study_design_scores_gemma":[0.0035862157,0.00340219,0.054364514,0.003833224,0.000014814243,0.0001722419,0.124002896,0.059424344,0.73881304,0.0045194044,0.0068479357,0.001019178],"about_ca_topic_score_codex":0.00053856545,"about_ca_topic_score_gemma":0.000740656,"teacher_disagreement_score":0.9840623,"about_ca_system_score_codex":0.00014028906,"about_ca_system_score_gemma":0.000051568695,"threshold_uncertainty_score":0.20197016},"labels":[],"label_agreement":null},{"id":"W2113716368","doi":"10.1145/1216295.1216315","title":"Unsupervised and supervised machine learning in user modeling for intelligent learning environments","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Unsupervised learning; Artificial intelligence; Machine learning; Human–computer interaction","score_opus":0.03989500359196595,"score_gpt":0.2600181468583824,"score_spread":0.22012314326641647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113716368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30079117,0.00013218001,0.69816655,0.0001237212,0.00007096213,0.00029970793,5.001636e-7,0.00010015585,0.00031504332],"genre_scores_gemma":[0.98691916,0.000043748234,0.011814217,0.00014444444,0.000032226195,0.000026538166,0.000007871258,0.00002025337,0.0009915151],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983532,0.00010986575,0.000406895,0.00049813767,0.00023256808,0.00039934643],"domain_scores_gemma":[0.999194,0.00038826378,0.00006137793,0.00020207913,0.000030681764,0.00012356637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001345025,0.0001813176,0.00023944955,0.00025353293,0.00014682437,0.00013378295,0.00028230605,0.000085318075,0.00002985781],"category_scores_gemma":[0.00011308265,0.00017996698,0.00006158029,0.00021125778,0.000018361361,0.00057395565,0.00022737951,0.00028816907,0.000025602203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011656977,0.00024891447,0.13705908,0.000088240595,0.000063683874,0.000026571015,0.0063602123,0.028497789,0.014253231,0.0014259402,0.00001184585,0.8118479],"study_design_scores_gemma":[0.00089178886,0.000094682786,0.0010371055,0.00003666907,0.0000038178005,0.0000114150125,0.00051705266,0.9908701,0.0021194518,0.000131306,0.004047152,0.00023942016],"about_ca_topic_score_codex":0.00025565576,"about_ca_topic_score_gemma":0.0002650831,"teacher_disagreement_score":0.96237236,"about_ca_system_score_codex":0.00008514326,"about_ca_system_score_gemma":0.000019171657,"threshold_uncertainty_score":0.73388404},"labels":[],"label_agreement":null},{"id":"W2114799397","doi":"10.1109/iros.1992.587359","title":"A Blackboard, Activity-based Control Architecture For A Mobile Platform","year":2005,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University; National Research Council Canada","funders":"","keywords":"Blackboard system; Blackboard (design pattern); Computer science; Architecture; Abstraction; Action (physics); Perception; Control (management); Debugging; Embedded system; Human–computer interaction; Controller (irrigation); Artificial intelligence; Software engineering; Operating system","score_opus":0.0193215975384178,"score_gpt":0.25986532492309766,"score_spread":0.24054372738467986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114799397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027892323,0.000026484253,0.96430814,0.0029257678,0.00018040101,0.0013674021,0.000031473395,0.00048843655,0.0027795895],"genre_scores_gemma":[0.97194004,2.702476e-7,0.024423735,0.002004481,0.00022642036,0.0007444013,0.0000030952983,0.00001702471,0.0006405314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855375,0.00005261168,0.00022324665,0.0004877944,0.00028222613,0.00040035392],"domain_scores_gemma":[0.99824417,0.00076160184,0.00012264834,0.00058511365,0.00014013289,0.00014636162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035437135,0.0002102434,0.00030996065,0.00017114662,0.00013195677,0.00020713106,0.0005746338,0.00010291052,0.00006776929],"category_scores_gemma":[0.000049201233,0.00018086996,0.00022543102,0.00023012114,0.000036486294,0.0006405041,0.000045347842,0.00015948953,0.00017145748],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012058521,0.0002555564,0.000060513685,0.000037239945,0.000055279794,0.0000027993347,0.00034711187,0.0015878119,0.004703966,0.0013100664,0.002876114,0.98864293],"study_design_scores_gemma":[0.007081868,0.0006507013,0.00018125854,0.000047412468,0.000025763673,0.000051072653,0.00004369588,0.545421,0.06200712,0.0012126047,0.3825526,0.0007248985],"about_ca_topic_score_codex":0.000036204878,"about_ca_topic_score_gemma":0.00031313958,"teacher_disagreement_score":0.9879181,"about_ca_system_score_codex":0.000094509625,"about_ca_system_score_gemma":0.00015167144,"threshold_uncertainty_score":0.7375663},"labels":[],"label_agreement":null},{"id":"W2115079213","doi":"10.1109/icce.2011.5722733","title":"Optimized, practical firmware design for a novel flexible wireless multi-sensor platform for body activity and vitals monitoring","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Firmware; Computer science; Wireless; Wireless sensor network; Embedded system; Real-time computing; Computer network; Computer hardware; Telecommunications","score_opus":0.2757156681102287,"score_gpt":0.35484506570439706,"score_spread":0.07912939759416837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115079213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02196675,0.000019259345,0.9745408,0.00017772587,0.00044506686,0.0022303262,0.00003476465,0.00037460754,0.0002106984],"genre_scores_gemma":[0.49335715,0.000003078001,0.505589,0.000029658857,0.000076286095,0.00051744014,0.000001100956,0.000021117958,0.0004051678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981335,0.00005238327,0.00033086966,0.0007289957,0.0002346599,0.0005196266],"domain_scores_gemma":[0.99658024,0.0021559738,0.00022542311,0.00047392223,0.00034098254,0.00022345026],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007680894,0.000289515,0.00044806258,0.00013742968,0.00028514292,0.00029014677,0.0003621555,0.00018067603,0.0000092758555],"category_scores_gemma":[0.00036882336,0.00026624315,0.00014662008,0.00017030047,0.000053635555,0.0023607959,0.00021773533,0.00014988017,0.000012048389],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007534231,0.007968645,0.0034298298,0.0014500694,0.0015610111,0.00006927029,0.01083136,0.0002018584,0.39714193,0.018828433,0.0028016828,0.5481817],"study_design_scores_gemma":[0.0069865123,0.0006877352,0.001527068,0.000120656856,0.000059131522,0.00018899656,0.0005991739,0.5402008,0.44780052,0.00038737486,0.0006184593,0.0008235485],"about_ca_topic_score_codex":0.00014734775,"about_ca_topic_score_gemma":0.000010116155,"teacher_disagreement_score":0.54735816,"about_ca_system_score_codex":0.00008195498,"about_ca_system_score_gemma":0.00013638663,"threshold_uncertainty_score":0.99997896},"labels":[],"label_agreement":null},{"id":"W2115286752","doi":"10.1145/2638728.2641716","title":"Recording events, interactions, and annotations to communicate reasoning in medical situations","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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; Process (computing); Context (archaeology); Reliability (semiconductor); Data collection; Human–computer interaction; Order (exchange); Data science","score_opus":0.0288526575367859,"score_gpt":0.3145374078944112,"score_spread":0.2856847503576253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115286752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063757695,0.000015773534,0.90725344,0.021507928,0.00024092087,0.00017559495,0.0000010344123,0.00013861546,0.0069090044],"genre_scores_gemma":[0.97194004,0.0000105255995,0.026758522,0.0009405029,0.000029222112,0.000052886073,0.000003323287,0.0000051251777,0.0002598251],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884033,0.00029647726,0.000269558,0.00023656043,0.00020850833,0.00014857193],"domain_scores_gemma":[0.99843955,0.0008784953,0.000059358696,0.000394872,0.00007836555,0.00014936042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009126713,0.00007734562,0.00013482866,0.00024802462,0.00015307641,0.00010647098,0.00040766367,0.000035503293,0.000054855882],"category_scores_gemma":[0.0008963015,0.000079345096,0.000023140801,0.0004599897,0.000014498173,0.00066535233,0.0003396069,0.00018312098,0.0001062399],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000442464,0.0001284809,0.017403305,0.000009280559,0.000018485161,0.0000030043038,0.0025144657,0.000032151085,0.0002914575,0.038349327,0.0021906157,0.939055],"study_design_scores_gemma":[0.0016318442,0.00016257151,0.103972174,0.0010780459,0.000012694487,0.00030734553,0.0013449496,0.78339046,0.0003335954,0.012155291,0.094790034,0.0008209866],"about_ca_topic_score_codex":0.00068054884,"about_ca_topic_score_gemma":0.0042853244,"teacher_disagreement_score":0.93823403,"about_ca_system_score_codex":0.00005132534,"about_ca_system_score_gemma":0.000042554726,"threshold_uncertainty_score":0.3235599},"labels":[],"label_agreement":null},{"id":"W2115303515","doi":"10.2196/mhealth.2730","title":"Indoor and Outdoor Social Alarms: Understanding Users' Perspectives","year":2014,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Länsförsäkringar; VINNOVA","keywords":"ALARM; Wearable computer; Work (physics); Internet privacy; Phone; Computer science; Applied psychology; Computer security; Psychology; Engineering","score_opus":0.10527427834314168,"score_gpt":0.3609849081287182,"score_spread":0.2557106297855765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115303515","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6765428,0.0012375393,0.2925267,0.023507359,0.0006515832,0.0009953622,0.000018398989,0.0004745602,0.0040456722],"genre_scores_gemma":[0.9973995,0.00014554647,0.0007581739,0.0012689334,0.000307166,0.00003236531,0.0000018821578,0.000014045597,0.0000723848],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99805194,0.00031646475,0.00029958112,0.0005630813,0.00026361825,0.00050531037],"domain_scores_gemma":[0.99883866,0.00027101787,0.00020771206,0.00021898156,0.00006680083,0.0003968384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088967115,0.00018358823,0.00034293233,0.00018537094,0.0006873133,0.00021881677,0.00019925256,0.00011281657,0.0000062438285],"category_scores_gemma":[0.00005401956,0.00017632009,0.00004019096,0.00025496894,0.00011575527,0.00053264224,0.00013149886,0.00026887283,0.000013255964],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016749618,0.00037305834,0.03541991,0.0020819667,0.000074215655,0.000010989557,0.12068756,3.798449e-7,0.000088353445,0.49512634,0.0065496624,0.33942005],"study_design_scores_gemma":[0.016423343,0.0036293212,0.7014571,0.00067885016,0.00015704679,0.0006339782,0.10152543,0.0287711,0.00009227862,0.076550364,0.06662667,0.0034545357],"about_ca_topic_score_codex":0.00021001235,"about_ca_topic_score_gemma":0.00015485342,"teacher_disagreement_score":0.66603714,"about_ca_system_score_codex":0.00023718238,"about_ca_system_score_gemma":0.00020392594,"threshold_uncertainty_score":0.7190125},"labels":[],"label_agreement":null},{"id":"W2116515896","doi":"10.1109/icc.2002.997291","title":"VELVET: an adaptive hybrid architecture for very large virtual environments","year":2003,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Ottawa","funders":"Ministério da Educação; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Velvet; Architecture; Computer science; Materials science","score_opus":0.024715828075852358,"score_gpt":0.24143665435914494,"score_spread":0.2167208262832926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116515896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025370492,0.000023590344,0.9706263,0.000096066025,0.0003980577,0.00043162607,0.00004240299,0.000118178876,0.0028933028],"genre_scores_gemma":[0.98403484,0.0000012042165,0.013040738,0.0006354898,0.000067087305,0.00008506068,0.000009459515,0.000015997599,0.0021101371],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99852633,0.0001704427,0.00018722285,0.00050791405,0.0002539058,0.0003542088],"domain_scores_gemma":[0.99905014,0.00019936873,0.000076909535,0.000503931,0.000025928428,0.00014373631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035493224,0.00017173466,0.00018729744,0.00008834629,0.00014424314,0.000101164405,0.00042678983,0.000050001978,0.00007754558],"category_scores_gemma":[0.000050117767,0.00015983646,0.00009641183,0.00009961384,0.000026162837,0.00062106096,0.00008811098,0.000113803166,0.00016090494],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021851601,0.0022062792,0.0009966154,0.000031725616,0.00036003234,0.00007918442,0.0042664628,0.00032272667,0.014594047,0.2817581,0.008885255,0.6862811],"study_design_scores_gemma":[0.004914396,0.00270314,0.0015342426,0.000049057588,0.00003099793,0.0002967562,0.0008375696,0.027870933,0.075734824,0.013295824,0.8712595,0.0014727425],"about_ca_topic_score_codex":0.000010255147,"about_ca_topic_score_gemma":0.000037144197,"teacher_disagreement_score":0.95866436,"about_ca_system_score_codex":0.00005731797,"about_ca_system_score_gemma":0.000052973377,"threshold_uncertainty_score":0.65179414},"labels":[],"label_agreement":null},{"id":"W2116665252","doi":"10.1145/1462039.1462042","title":"Web 3.0","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; World Wide Web; Web intelligence; Web application; Numbering; Web modeling; Web standards; Web engineering; Web development; Convergence (economics); Ubiquitous computing; Web navigation; Web service; Human–computer interaction","score_opus":0.04567761560295981,"score_gpt":0.23956646187456548,"score_spread":0.19388884627160569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116665252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08472106,0.00003595635,0.70170134,0.0012830307,0.00047186596,0.00010215836,6.990971e-7,0.0006539612,0.2110299],"genre_scores_gemma":[0.9886878,0.0000043616988,0.004855742,0.0004700893,0.000044785884,0.0000070517726,2.3029715e-7,0.0000026358125,0.0059273075],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994591,0.000028914292,0.00008938087,0.00016655303,0.00014505244,0.00011098942],"domain_scores_gemma":[0.9995186,0.000059743525,0.000023316124,0.00029689426,0.000049093716,0.000052381307],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000759218,0.000050043745,0.00007223753,0.000050941264,0.0000732502,0.00003226335,0.0003213236,0.000021869106,0.000117888114],"category_scores_gemma":[0.00001554137,0.000043906057,0.000035756002,0.0001852404,0.000017280965,0.00043263522,0.00009261411,0.00004196059,0.0016000589],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000083536515,0.00039339025,0.02998566,0.000021025458,0.00007614219,0.00043675842,0.0029230358,0.000005926609,0.015147985,0.087018125,0.27958795,0.58439565],"study_design_scores_gemma":[0.0020071138,0.00017607471,0.03204832,0.000030185156,0.000004108473,0.0030638475,0.0000860968,0.0630597,0.021851892,0.0025947576,0.8740919,0.000985972],"about_ca_topic_score_codex":0.0000268849,"about_ca_topic_score_gemma":0.000016468786,"teacher_disagreement_score":0.9039667,"about_ca_system_score_codex":0.000013608027,"about_ca_system_score_gemma":0.000055787405,"threshold_uncertainty_score":0.99917734},"labels":[],"label_agreement":null},{"id":"W2117751530","doi":"10.1109/vr.2007.352472","title":"Variability-Aware Latency Amelioration in Distributed Environments","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"University of New South Wales; Ontario Centres of Excellence","keywords":"Computer science; Latency (audio); Distributed computing; Telecommunications","score_opus":0.015310620214895973,"score_gpt":0.24520200975492257,"score_spread":0.22989138954002658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117751530","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0808156,0.0000048818424,0.9161067,0.0002947543,0.0002128198,0.00023409762,0.0000059755466,0.00011147723,0.0022136678],"genre_scores_gemma":[0.99762774,0.0000017099645,0.0019885015,0.00011645761,0.0000295073,0.000012901571,0.000021449525,0.000004903366,0.00019686043],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986123,0.00012438912,0.00035644163,0.00038124953,0.00026440935,0.00026124922],"domain_scores_gemma":[0.99912655,0.00023860537,0.000082918,0.00044121282,0.000028955354,0.00008177885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012303356,0.00011207368,0.00014491558,0.00012889545,0.00004910962,0.00007568882,0.00034123525,0.000082520855,0.000071224764],"category_scores_gemma":[0.00007699616,0.00010962381,0.00003825045,0.0005101569,0.00002195841,0.0007852146,0.00013392515,0.00012100841,0.00023859284],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048416827,0.0015101065,0.45165056,0.000048166286,0.00004598125,0.00015210581,0.0019204332,0.00013056108,0.01379356,0.018967424,0.0010147188,0.510718],"study_design_scores_gemma":[0.0019414969,0.00016595484,0.9086478,0.000068867346,0.0000079360125,0.00004651606,0.00029874002,0.045535166,0.014700812,0.010502475,0.017186673,0.000897564],"about_ca_topic_score_codex":0.00013949958,"about_ca_topic_score_gemma":0.00023833996,"teacher_disagreement_score":0.9168121,"about_ca_system_score_codex":0.0001822576,"about_ca_system_score_gemma":0.000028532213,"threshold_uncertainty_score":0.44703293},"labels":[],"label_agreement":null},{"id":"W2118521008","doi":"10.1109/mprv.2005.76","title":"Evaluating Early Prototypes in Context: Trade-offs, Challenges, and Successes","year":2005,"lang":"en","type":"article","venue":"IEEE Pervasive Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Dalhousie University","keywords":"Impartiality; Computer science; Context (archaeology); Realism; Wizard; Ubiquitous computing; Human–computer interaction; Data science; World Wide Web; Epistemology","score_opus":0.09588784076188253,"score_gpt":0.3332429163422282,"score_spread":0.23735507558034566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118521008","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9642539,0.006054757,0.023050388,0.004057234,0.00041538908,0.0011205983,0.0000023013813,0.00027393663,0.0007715132],"genre_scores_gemma":[0.99370325,0.00008611521,0.0054637585,0.00030102144,0.00035034236,0.00005490045,8.262914e-7,0.000020204032,0.000019587154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997508,0.00038226208,0.0005121441,0.00074414065,0.0003971133,0.0004563728],"domain_scores_gemma":[0.9982571,0.00086092105,0.00023779021,0.0003750787,0.00014899757,0.000120109726],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010842652,0.00025961467,0.00040638915,0.00021261703,0.00018322354,0.00025290556,0.00056226633,0.00010061278,0.000005486384],"category_scores_gemma":[0.00020472141,0.0002701826,0.00006895929,0.0002774683,0.00006224951,0.0010295156,0.00019987875,0.0003119752,0.000038977923],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000826629,0.00007238702,0.002924281,0.00008174679,0.00002042437,0.00001927907,0.01231428,0.00012668496,0.0011560993,0.0007934474,0.00001690821,0.9824662],"study_design_scores_gemma":[0.0071475036,0.0013524732,0.28972292,0.003370277,0.000046372592,0.0007898699,0.0037089214,0.6585975,0.019348133,0.0019159405,0.011261297,0.0027387547],"about_ca_topic_score_codex":0.00020791369,"about_ca_topic_score_gemma":0.0009973971,"teacher_disagreement_score":0.97972745,"about_ca_system_score_codex":0.00010298936,"about_ca_system_score_gemma":0.000106899264,"threshold_uncertainty_score":0.999975},"labels":[],"label_agreement":null},{"id":"W2119209671","doi":"10.1109/percomw.2011.5766965","title":"Pervasive safety application with model checking in smart houses: The INOVUS intelligent oven","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke","funders":"","keywords":"Computer science; Process (computing); Model checking; Software engineering; Software; Computer security; Systems engineering; Engineering; Operating system; Programming language","score_opus":0.059314170134486144,"score_gpt":0.2340392910730008,"score_spread":0.17472512093851467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119209671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030573381,0.000021396483,0.95282125,0.00034323536,0.00004782146,0.0005449251,0.0000012669966,0.00014467284,0.015502032],"genre_scores_gemma":[0.9922328,0.000009085638,0.0069819163,0.00041403977,0.000018005823,0.00014577282,0.0000012004557,0.000011000723,0.0001861735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887544,0.00007352829,0.00025669526,0.00036425164,0.00022714806,0.00020293547],"domain_scores_gemma":[0.9989898,0.00010200192,0.00011750428,0.000625917,0.00011555343,0.000049178478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004196163,0.00013018562,0.00015154037,0.00009110257,0.00008755272,0.00007543865,0.0006942064,0.000047550697,0.000014657602],"category_scores_gemma":[0.00001673918,0.00008465563,0.00004050499,0.00037740835,0.000042939104,0.00051264727,0.00019975877,0.0001512278,0.000085124586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018094291,0.00075198105,0.06880552,0.000065634886,0.00013530822,0.000022158885,0.08043933,0.002409742,0.0010693028,0.10362707,0.00028670198,0.7422063],"study_design_scores_gemma":[0.00073635246,0.00015269537,0.037287846,0.00012354967,0.000014925534,0.0000889404,0.0021242101,0.9407032,0.010820735,0.005523976,0.0018344681,0.0005890668],"about_ca_topic_score_codex":0.0012301032,"about_ca_topic_score_gemma":0.0025690205,"teacher_disagreement_score":0.96165943,"about_ca_system_score_codex":0.00010308113,"about_ca_system_score_gemma":0.00010004204,"threshold_uncertainty_score":0.34521565},"labels":[],"label_agreement":null},{"id":"W2120222693","doi":"10.3233/mis-130159","title":"Integrated Context-Aware Driver Assistance System Architecture","year":2013,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Context-Aware Activity Recognition Systems","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":"Acadia University","funders":"","keywords":"Context (archaeology); Computer science; Process (computing); Advanced driver assistance systems; Hazard; Key (lock); Architecture; Systems architecture; Set (abstract data type); Embedded system; Computer security; Real-time computing; Artificial intelligence","score_opus":0.1731493440134916,"score_gpt":0.4689401865064602,"score_spread":0.2957908424929686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120222693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49352613,0.008894817,0.47787985,0.0011407217,0.0034662993,0.0028379902,0.0001358381,0.0007320345,0.011386332],"genre_scores_gemma":[0.9969526,0.0002154574,0.0009835664,0.0005151028,0.00018450143,0.00022538377,0.000011428309,0.00006110447,0.0008508682],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99475336,0.0009462692,0.0013674395,0.0010206528,0.0012181342,0.0006941563],"domain_scores_gemma":[0.99428964,0.00080569397,0.0015350871,0.001380256,0.0014588216,0.00053051196],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012819781,0.000603304,0.001326203,0.0010708115,0.00041161582,0.0050775236,0.0077057346,0.00023359295,0.0034712453],"category_scores_gemma":[0.0002658333,0.00051527156,0.00036896285,0.002033995,0.00019027147,0.0070291227,0.0017923233,0.00086916966,0.00035114883],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001207506,0.00047017582,0.1590314,0.0004708591,0.00074424956,0.00020682199,0.0012008805,0.00011146611,0.05850258,0.0013345596,0.13150609,0.6463002],"study_design_scores_gemma":[0.003667238,0.00007979743,0.80962974,0.007793941,0.00018517597,0.00073762046,0.0019994262,0.01359259,0.04678886,0.008906319,0.10291082,0.0037084739],"about_ca_topic_score_codex":0.0030349956,"about_ca_topic_score_gemma":0.00030958676,"teacher_disagreement_score":0.65059835,"about_ca_system_score_codex":0.000379116,"about_ca_system_score_gemma":0.00033304997,"threshold_uncertainty_score":0.9997299},"labels":[],"label_agreement":null},{"id":"W2120738315","doi":"10.3233/978-1-60750-946-2-101","title":"MET System: a New Approach to m-Health in Emergency Triage","year":2004,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Triage; Medical emergency; Computer science; Medicine","score_opus":0.09697689674067783,"score_gpt":0.36718752276651484,"score_spread":0.270210626025837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120738315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042473476,0.01085554,0.88550794,0.04654366,0.0023309125,0.0043936013,0.000014721011,0.0010632961,0.006816846],"genre_scores_gemma":[0.91148806,0.002649769,0.08406911,0.0014814124,0.000031246327,0.00021519272,0.000002054852,0.000008805775,0.000054331646],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9975897,0.00008780709,0.0014250949,0.00023081992,0.00015184008,0.0005147531],"domain_scores_gemma":[0.998887,0.000066929446,0.00040113382,0.00045535542,0.00006335122,0.0001262458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017085474,0.00017409984,0.00066726806,0.0012880991,0.00018832416,0.000018113344,0.0004670253,0.00013496434,7.264764e-7],"category_scores_gemma":[0.00029929483,0.00016164193,0.00002534085,0.002341486,0.000070738,0.00045794086,0.000459511,0.0003625301,0.000030304103],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022353488,0.00022555368,0.0033356715,0.005474597,0.00007742475,0.000007373362,0.104922146,0.00031161032,7.0719705e-7,0.39946976,0.0037481044,0.48240468],"study_design_scores_gemma":[0.027894367,0.007750874,0.012060179,0.012350795,0.000029413624,0.0023837131,0.58734447,0.027460774,0.00012550857,0.19972713,0.11881312,0.0040596332],"about_ca_topic_score_codex":0.00032488478,"about_ca_topic_score_gemma":0.00040275307,"teacher_disagreement_score":0.8690146,"about_ca_system_score_codex":0.0005645177,"about_ca_system_score_gemma":0.00038768508,"threshold_uncertainty_score":0.6591567},"labels":[],"label_agreement":null},{"id":"W2121084152","doi":"10.1109/conielecomp.2007.41","title":"A Cyberforaging Infrastructure Based on Web Services","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Western University","funders":"","keywords":"Computer science; Web service; Architecture; Mobile device; Distributed computing; Services computing; Load balancing (electrical power); Extension (predicate logic); Mobile computing; Service-oriented architecture; World Wide Web; Operating system","score_opus":0.007871844205175756,"score_gpt":0.23462273487236002,"score_spread":0.22675089066718426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121084152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15788837,0.000009598149,0.75803834,0.000804403,0.0007285847,0.00018980491,0.000002364028,0.000586209,0.08175235],"genre_scores_gemma":[0.98846704,2.2132033e-7,0.008031693,0.003145914,0.00008909392,0.0000042741394,0.0000018721219,0.000007741645,0.00025217803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988538,0.000037868555,0.0001871981,0.00032636145,0.0003279907,0.00026677814],"domain_scores_gemma":[0.9990411,0.00021612245,0.00007808864,0.00047382907,0.00008602816,0.000104846724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004621353,0.00013141922,0.0001293798,0.00021265108,0.000093330375,0.00018732436,0.0005368786,0.00006263304,0.00011766187],"category_scores_gemma":[0.000010591786,0.000112188354,0.00005443584,0.00042735424,0.000012489588,0.00046981746,0.00009815249,0.00013849982,0.0002068272],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052840467,0.00018634237,0.039507248,0.00014176319,0.000046992453,0.00013512473,0.0017341512,0.00063649734,0.008601139,0.008309488,0.003784469,0.93686396],"study_design_scores_gemma":[0.0013141212,0.00015170239,0.040031686,0.0001798299,0.000006486305,0.00005971225,0.0003526026,0.8930874,0.013043214,0.0010302701,0.05012752,0.0006154647],"about_ca_topic_score_codex":0.000061635896,"about_ca_topic_score_gemma":0.00021866134,"teacher_disagreement_score":0.9362485,"about_ca_system_score_codex":0.00006158264,"about_ca_system_score_gemma":0.000059080605,"threshold_uncertainty_score":0.45749083},"labels":[],"label_agreement":null},{"id":"W2121652606","doi":"10.1109/ccece.2006.277551","title":"Attribute-Driven Design of Incremental Learning Component of a Ubiquitous Multimodal Multimedia Computing System","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Component (thermodynamics); Context (archaeology); Modality (human–computer interaction); Human–computer interaction; User interface; Usability; Interface (matter); Process (computing); Software engineering; Multimedia; Artificial intelligence; Programming language; Operating system","score_opus":0.025572055971110747,"score_gpt":0.23925418854978997,"score_spread":0.2136821325786792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121652606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2525667,0.000027374548,0.7460722,0.000020324878,0.00022749417,0.00045278392,0.000006698144,0.00024927635,0.00037720264],"genre_scores_gemma":[0.9211106,5.157009e-7,0.07873258,0.000007469968,0.00007570738,0.000010036955,0.000012267068,0.000014337894,0.0000365286],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972709,0.00053021265,0.0008560337,0.00042161683,0.0005853751,0.00033587727],"domain_scores_gemma":[0.997783,0.00081920193,0.00063941017,0.00035796757,0.0003213528,0.00007903764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007927771,0.00021673573,0.0005651852,0.00022685829,0.00011712527,0.00005428945,0.00062289735,0.00009309643,0.00001086444],"category_scores_gemma":[0.000045864588,0.00021328196,0.00013979798,0.00036601385,0.000076179465,0.0003153774,0.00040870084,0.00017699943,0.000033498392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009126031,0.0011060007,0.08317369,0.00073225226,0.0002756113,0.00007581512,0.0027878513,0.12734464,0.74361116,0.002204959,0.0005157752,0.038080994],"study_design_scores_gemma":[0.0011147606,0.00015541015,0.0070028957,0.0002319568,0.000012749558,0.000052662686,0.00044478706,0.91570973,0.07503469,0.0000035351436,0.000025717105,0.00021110251],"about_ca_topic_score_codex":0.00243237,"about_ca_topic_score_gemma":0.000035004134,"teacher_disagreement_score":0.7883651,"about_ca_system_score_codex":0.00016504145,"about_ca_system_score_gemma":0.000076917735,"threshold_uncertainty_score":0.86973864},"labels":[],"label_agreement":null},{"id":"W2122046532","doi":"10.1109/ccnc.2007.104","title":"PACMAN: A Policy-Based Architecture for Context Management in Ambient Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Computer science; Architecture; Context (archaeology); Context management; Computer architecture; Distributed computing; Ubiquitous computing; Human–computer interaction","score_opus":0.020080068130210803,"score_gpt":0.2768533582741962,"score_spread":0.2567732901439854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122046532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037029218,0.000039225575,0.9854613,0.0029710112,0.00029082896,0.0010521384,0.0000023359562,0.00018729699,0.0062929355],"genre_scores_gemma":[0.9767522,0.000001260031,0.018282788,0.0040446776,0.00013073169,0.00012579093,0.0000043606383,0.000012392721,0.00064580323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841017,0.00005714038,0.00034082946,0.0004446306,0.00023400299,0.0005132203],"domain_scores_gemma":[0.99884444,0.0003672658,0.00009177616,0.00050548365,0.00006852505,0.00012251838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008326558,0.00016955324,0.00021271329,0.00047087012,0.00006610394,0.000121120036,0.0005387923,0.00007533805,0.000011062678],"category_scores_gemma":[0.00002679903,0.0001552732,0.00011533373,0.0006642086,0.00002720939,0.00014402781,0.00014472258,0.00012694029,0.000022209397],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000643105,0.00015911821,0.0004620386,0.000039135466,0.000032644155,0.000037605027,0.0004169356,0.0012224473,0.00004479972,0.037487805,0.0015680199,0.95846516],"study_design_scores_gemma":[0.014172346,0.00085108384,0.02914974,0.00050556485,0.000032544165,0.000088380344,0.0014464698,0.5841866,0.0064776037,0.014616016,0.3463527,0.0021209244],"about_ca_topic_score_codex":0.00032477008,"about_ca_topic_score_gemma":0.0030113368,"teacher_disagreement_score":0.9730493,"about_ca_system_score_codex":0.00015336428,"about_ca_system_score_gemma":0.000047876085,"threshold_uncertainty_score":0.6331857},"labels":[],"label_agreement":null},{"id":"W2122069771","doi":"10.1109/icdim.2008.4746831","title":"Mobile Ontology-based Reasoning and Feedback (MORF) Health Monitoring System","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Lakehead University","funders":"","keywords":"Ontology; Computer science; Focus (optics); Human–computer interaction; Ubiquitous computing; Data science; Software","score_opus":0.03798056511767437,"score_gpt":0.27135340961073995,"score_spread":0.23337284449306558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122069771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4074995,0.0013183203,0.5820206,0.0004319227,0.0012122608,0.0006704417,0.0000034629197,0.0012546836,0.00558882],"genre_scores_gemma":[0.97749066,0.000016025053,0.021809014,0.00013778053,0.000109208144,0.00007514711,9.564981e-7,0.000010931518,0.00035029987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984275,0.00018318655,0.00030818162,0.000478377,0.00025187453,0.00035084036],"domain_scores_gemma":[0.99891007,0.00016269417,0.00016224117,0.00046240352,0.00009910831,0.00020350344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038350848,0.00016039716,0.00033046675,0.00012608462,0.0003514716,0.000096473435,0.00031808074,0.00006833083,0.000005027982],"category_scores_gemma":[0.00002180869,0.000153479,0.00005330127,0.00026169218,0.000048196714,0.00044353286,0.00011762965,0.00013609565,0.00007482719],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063896034,0.00046608812,0.3378901,0.001177325,0.00018109595,0.00048412214,0.013117898,0.00026096555,0.0018752835,0.010251453,0.006169786,0.628062],"study_design_scores_gemma":[0.0145765515,0.004637515,0.3379661,0.005979408,0.00005227657,0.014741987,0.013345354,0.5185656,0.03680421,0.00020157693,0.04795095,0.005178432],"about_ca_topic_score_codex":0.0006268441,"about_ca_topic_score_gemma":0.000040161565,"teacher_disagreement_score":0.62288356,"about_ca_system_score_codex":0.00018414532,"about_ca_system_score_gemma":0.00020950787,"threshold_uncertainty_score":0.6258692},"labels":[],"label_agreement":null},{"id":"W2123268655","doi":"10.1007/s11257-007-9032-y","title":"Modeling the progression of Alzheimer’s disease for cognitive assistance in smart homes","year":2007,"lang":"en","type":"article","venue":"User Modeling and User-Adapted Interaction","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Dementia; Activities of daily living; Cognition; Task (project management); Disease; Gerontology; Psychology; Alzheimer's disease; Population; Cognitive architecture; Cognitive impairment; Medicine; Physical medicine and rehabilitation; Cognitive psychology; Psychiatry; Engineering","score_opus":0.06851222218680152,"score_gpt":0.3352329348173985,"score_spread":0.266720712630597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123268655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41132116,0.0004336316,0.58721936,0.00021441004,0.00027087875,0.0004130192,0.000006879146,0.000056727695,0.00006391699],"genre_scores_gemma":[0.99709153,0.000039015988,0.002568046,0.000094966395,0.00006540002,0.000086670625,0.000013481797,0.000018255094,0.00002265385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834126,0.00009965789,0.00054659205,0.00045223333,0.00028452088,0.00027573365],"domain_scores_gemma":[0.99862546,0.00038101504,0.0002020539,0.00027430922,0.0004176309,0.000099561155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090499566,0.00018710579,0.00025069006,0.0002453694,0.00016847262,0.00014330538,0.00021268833,0.00007814325,0.000002155136],"category_scores_gemma":[0.00016217161,0.00015076796,0.000096142205,0.0002629763,0.000026498585,0.001096479,0.00009175267,0.00022739726,0.0000021142105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005535631,0.0015909367,0.019298352,0.0004520146,0.0005615106,0.00003932352,0.012155499,0.14577577,0.0057970574,0.0068135937,0.00023385268,0.8017465],"study_design_scores_gemma":[0.00064427394,0.00007107913,0.00069267437,0.0007303194,0.000041804877,0.0000066175858,0.00096862967,0.9953397,0.00082715636,0.0002964435,0.00020114775,0.00018016122],"about_ca_topic_score_codex":0.0002819017,"about_ca_topic_score_gemma":0.00042411868,"teacher_disagreement_score":0.84956396,"about_ca_system_score_codex":0.000044079945,"about_ca_system_score_gemma":0.00006207747,"threshold_uncertainty_score":0.61481386},"labels":[],"label_agreement":null},{"id":"W2123874805","doi":"10.1109/aina.2011.13","title":"A Frequent Pattern Mining Approach for ADLs Recognition in Smart Environments","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Activity recognition; Hidden Markov model; Artificial intelligence; Pattern recognition (psychology); Activities of daily living; Smart environment; Event (particle physics); Machine learning; Data mining; Internet of Things","score_opus":0.12260485498414733,"score_gpt":0.24295158914526335,"score_spread":0.12034673416111602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123874805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06317041,0.000012594415,0.9255471,0.00004133261,0.00017232978,0.0005878187,0.000007988246,0.000077416014,0.010383011],"genre_scores_gemma":[0.9267545,0.0000022010001,0.0722027,0.00029730686,0.000030628118,0.00040586587,0.000019360315,0.000011424325,0.00027601072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987089,0.000093372015,0.00029993983,0.0004575332,0.00017233666,0.00026794284],"domain_scores_gemma":[0.9993875,0.000091796195,0.00010659736,0.00032067663,0.000021398713,0.0000720362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042750168,0.00013471184,0.00017571003,0.00015119217,0.000045232006,0.000053762567,0.00034841648,0.000072769544,0.00008063311],"category_scores_gemma":[0.000025284919,0.00013345965,0.00006945191,0.00013468134,0.000017839324,0.0006279983,0.00010885837,0.00007117819,0.00010869512],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014041153,0.00046806093,0.023152847,0.00003565903,0.000030111018,0.00000758776,0.0037550193,0.0000010920016,0.00086086005,0.00008141284,0.00029430402,0.971299],"study_design_scores_gemma":[0.021048283,0.0027479345,0.3247815,0.0009494303,0.00012978783,0.0005050951,0.00771181,0.46904206,0.13600484,0.01455663,0.0160749,0.0064477124],"about_ca_topic_score_codex":0.00031803775,"about_ca_topic_score_gemma":0.00010263904,"teacher_disagreement_score":0.9648513,"about_ca_system_score_codex":0.000075158314,"about_ca_system_score_gemma":0.000018854043,"threshold_uncertainty_score":0.54423267},"labels":[],"label_agreement":null},{"id":"W2124841413","doi":"10.1109/ccnc.2011.5766439","title":"Virtual Remote Nursing system","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"eHealth; Workflow; Task (project management); Computer science; Onboarding; Health care; Health informatics; Phone; Nursing; Medicine; Database","score_opus":0.06082861778078024,"score_gpt":0.2510997749893329,"score_spread":0.19027115720855264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124841413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058975047,0.000013973972,0.7761419,0.000070952425,0.00080364116,0.000105045445,4.652687e-7,0.0006192225,0.21634734],"genre_scores_gemma":[0.97969806,3.6230904e-7,0.018785095,0.000083629,0.00006822919,0.0000024044664,3.0073844e-7,0.000007506645,0.0013544273],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897856,0.000095013456,0.00019985593,0.0003091023,0.0002087616,0.00020871083],"domain_scores_gemma":[0.9991722,0.00005657621,0.00007359818,0.00051225536,0.00008778567,0.00009758823],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025511993,0.000102618826,0.00014568972,0.00009892792,0.000083838095,0.00008923496,0.00052628375,0.00005230529,0.000049387945],"category_scores_gemma":[0.000014830696,0.000093055256,0.00006007477,0.0002328469,0.000027270207,0.0006222485,0.00008223548,0.000076373544,0.0009965022],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056102303,0.000051914893,0.00009366313,0.000011435947,0.000017150776,0.000030282672,0.0031494112,3.347495e-7,0.00050965743,0.12296914,0.0010856532,0.87207574],"study_design_scores_gemma":[0.0051041716,0.0018681663,0.020456512,0.004241137,0.000100913734,0.0057999264,0.018174537,0.76111954,0.14128122,0.014699247,0.022471959,0.0046826922],"about_ca_topic_score_codex":0.00019708628,"about_ca_topic_score_gemma":0.000012784242,"teacher_disagreement_score":0.97380054,"about_ca_system_score_codex":0.000094876006,"about_ca_system_score_gemma":0.00004229758,"threshold_uncertainty_score":0.9997813},"labels":[],"label_agreement":null},{"id":"W2125422609","doi":"10.1109/iembs.2009.5334690","title":"Preliminary results on the effect of sensor position on unobtrusive rollover detection for sleep monitoring in smart homes","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Rollover (web design); Sleep (system call); Physical medicine and rehabilitation; Center of gravity; Computer science; Audiology; Psychology; Real-time computing; Artificial intelligence; Simulation; Computer vision; Medicine","score_opus":0.015952011684162378,"score_gpt":0.2575523333177489,"score_spread":0.24160032163358652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125422609","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9644525,0.000012158791,0.03071785,0.001233619,0.00053225603,0.0011374386,0.0000072333623,0.000095221905,0.0018117077],"genre_scores_gemma":[0.9994142,0.0000014346614,0.00021439398,0.00008963428,0.00009456747,0.000064135806,0.0000017082926,0.0000062235845,0.00011372172],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869215,0.0002715345,0.00027059866,0.0003320239,0.00025630754,0.00017739493],"domain_scores_gemma":[0.9974292,0.0019408033,0.00015318712,0.00037079857,0.00007570728,0.00003028528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006453969,0.00014373109,0.00019942144,0.0001938499,0.00008405656,0.000057444788,0.00021669401,0.000076393124,0.0000013029397],"category_scores_gemma":[0.00023949947,0.000099431825,0.00010171614,0.00029404185,0.000012107947,0.00029862224,0.000028977749,0.00012535218,0.000019262929],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008527738,0.00031504247,0.0014927816,0.00006764956,0.00006666566,0.000018674773,0.0015526199,0.0013746852,0.069913715,0.00063367054,0.00027186162,0.91576487],"study_design_scores_gemma":[0.0020648232,0.010446189,0.063201584,0.00026011784,0.000010794293,0.000015221939,0.000070268914,0.021293756,0.9021354,0.0002509427,0.00006901952,0.00018190828],"about_ca_topic_score_codex":0.000051988853,"about_ca_topic_score_gemma":0.00001304445,"teacher_disagreement_score":0.915583,"about_ca_system_score_codex":0.000112354326,"about_ca_system_score_gemma":0.000009677357,"threshold_uncertainty_score":0.40547124},"labels":[],"label_agreement":null},{"id":"W2127562956","doi":"10.1109/cnsr.2009.32","title":"Applications of Wireless Sensor Networks and RFID in a Smart Home Environment","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Radio-frequency identification; Wireless sensor network; Computer science; Wireless; Focus (optics); Home automation; Identification (biology); Architecture; Telecommunications; Work (physics); Wireless network; Population; Ubiquitous computing; Smart environment; Computer security; Computer network; Human–computer interaction; Internet of Things; Engineering; Medicine; Geography","score_opus":0.009779737491766933,"score_gpt":0.21182318395092822,"score_spread":0.20204344645916128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127562956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09258886,0.00008176696,0.90525496,0.0005528816,0.000022229915,0.00028405155,0.0000011238052,0.0000407351,0.0011734199],"genre_scores_gemma":[0.99679625,0.000031307736,0.0028055569,0.00014967236,0.000016727658,0.000031652497,9.185176e-7,0.0000023561904,0.00016554512],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993199,0.00004192028,0.00018773899,0.00022309115,0.00010900029,0.00011832866],"domain_scores_gemma":[0.9995086,0.000082523664,0.00006132542,0.00028543852,0.000014390992,0.000047708403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015188979,0.000068847745,0.00014134198,0.000078108606,0.000023468234,0.00002925918,0.00016729216,0.000039435265,0.000009563894],"category_scores_gemma":[0.0000017430328,0.00006651558,0.000021137243,0.00017997833,0.000024554394,0.00016636639,0.000061011022,0.000060549883,0.000015877351],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051570823,0.00021249699,0.011002315,0.000010366381,0.000009338898,0.000003857146,0.00033531856,0.00016729678,0.0015628687,0.0062512625,0.0000916915,0.98034805],"study_design_scores_gemma":[0.0027451378,0.00044331502,0.5039849,0.00013934172,0.00001754076,0.00016298784,0.00043686107,0.46144634,0.004173352,0.005248652,0.020114077,0.0010874725],"about_ca_topic_score_codex":0.000046271794,"about_ca_topic_score_gemma":0.000026954871,"teacher_disagreement_score":0.97926056,"about_ca_system_score_codex":0.000022299775,"about_ca_system_score_gemma":0.000009445543,"threshold_uncertainty_score":0.27124268},"labels":[],"label_agreement":null},{"id":"W2128427171","doi":"10.1145/2638728.2641324","title":"Activity tracking","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Victoria","funders":"","keywords":"Accelerometer; Gyroscope; Activity tracker; BitTorrent tracker; Computer science; Tracking (education); Raw data; Artificial intelligence; Computer vision; Eye tracking; Engineering; Embedded system; Wearable computer; Psychology","score_opus":0.03177536590813239,"score_gpt":0.25349877709572616,"score_spread":0.22172341118759376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128427171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05239356,0.0000031726693,0.8863918,0.00076632824,0.00030339352,0.00006319442,1.942254e-7,0.00035324204,0.059725106],"genre_scores_gemma":[0.9953361,3.2598913e-7,0.003414531,0.00031083942,0.0000808842,0.0000072098783,1.3953048e-7,0.000004280201,0.000845652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992291,0.000105969164,0.000087988454,0.00024894672,0.00017104726,0.00015696384],"domain_scores_gemma":[0.9992152,0.00021757965,0.00004859699,0.00039777067,0.000057788788,0.00006308816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036237403,0.00007371605,0.00011109822,0.000056591416,0.000069319365,0.00017815131,0.00037905006,0.000034549295,0.000046511377],"category_scores_gemma":[0.00007053287,0.00006636278,0.000048988575,0.00017011772,0.000012096714,0.0008634525,0.000107821055,0.00007561125,0.0004394906],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.114459e-7,0.000030154748,0.0006952591,0.0000039910065,0.0000048858033,9.3604257e-7,0.00009544734,0.0000012853498,0.004145876,0.012452129,0.0004997883,0.9820693],"study_design_scores_gemma":[0.0018729777,0.0003232496,0.12617724,0.000093219714,0.000013125032,0.00022746746,0.00006529717,0.37741628,0.21958077,0.0155112,0.25719613,0.0015230557],"about_ca_topic_score_codex":0.00006553218,"about_ca_topic_score_gemma":0.00004731772,"teacher_disagreement_score":0.9805463,"about_ca_system_score_codex":0.000020417338,"about_ca_system_score_gemma":0.000017108821,"threshold_uncertainty_score":0.5648909},"labels":[],"label_agreement":null},{"id":"W2129004304","doi":"10.3390/mi6081100","title":"Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application","year":2015,"lang":"en","type":"article","venue":"Micromachines","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Activity recognition; Feature selection; Naive Bayes classifier; Artificial intelligence; Computer science; Decision tree; Machine learning; Classifier (UML); Artificial neural network; Pattern recognition (psychology); Feature (linguistics); Support vector machine; Computation; Inference; Feature extraction","score_opus":0.060862881707957284,"score_gpt":0.3119554220206637,"score_spread":0.2510925403127064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129004304","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66917247,0.000016189082,0.32941628,0.000050352555,0.0002505921,0.00089889416,0.000055414963,0.00008729173,0.0000525194],"genre_scores_gemma":[0.99259436,0.0000015940831,0.006892663,0.000035356756,0.00011816143,0.00022777432,0.000090224836,0.000017501387,0.000022365468],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99874216,0.00014435644,0.00028592555,0.00041107374,0.00023913509,0.00017737043],"domain_scores_gemma":[0.9985583,0.00020556115,0.0003525359,0.00038785487,0.0004099523,0.000085794134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005246323,0.00016962687,0.00024941028,0.00016294623,0.00010700775,0.0000622424,0.0002270598,0.000096978045,0.0000021932103],"category_scores_gemma":[0.00007538091,0.0001673745,0.00009607852,0.00036503933,0.000031630087,0.00053685106,0.00007537752,0.000092814975,0.000042461703],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012193188,0.0002730991,0.0005026652,0.00009084401,0.000014585558,8.623008e-7,0.0006400041,0.00024133563,0.5824277,0.000014254465,0.00011627895,0.4155565],"study_design_scores_gemma":[0.0020996218,0.0003425261,0.0021281254,0.00031425583,0.00002543443,0.00006920208,0.000106097075,0.25245896,0.739618,0.0012903423,0.0010950178,0.00045244797],"about_ca_topic_score_codex":0.00033925468,"about_ca_topic_score_gemma":0.000034845176,"teacher_disagreement_score":0.41510403,"about_ca_system_score_codex":0.00013207525,"about_ca_system_score_gemma":0.0000697889,"threshold_uncertainty_score":0.6825334},"labels":[],"label_agreement":null},{"id":"W2130040955","doi":"10.1109/iros.2000.893122","title":"e-service robot in home healthcare","year":2002,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Teleoperation; The Internet; Health care; Healthcare service; Computer science; Service (business); Robot; Population; Internet privacy; World Wide Web; Business; Medicine; Artificial intelligence; Marketing","score_opus":0.07288446717553108,"score_gpt":0.2607981097629438,"score_spread":0.18791364258741272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130040955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14823422,0.0014982139,0.6243283,0.16017501,0.0023195387,0.0011930495,0.000007959148,0.0017961997,0.060447477],"genre_scores_gemma":[0.9931422,0.000009858705,0.0026541932,0.0033528223,0.00004049609,0.000021262173,6.029806e-7,0.000005768116,0.0007727883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895895,0.000093310126,0.00020638846,0.00030553003,0.00019777409,0.00023805625],"domain_scores_gemma":[0.9992509,0.000094770105,0.000045631954,0.0004314116,0.000087472894,0.000089818685],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016649852,0.000091080234,0.00015174915,0.00013901613,0.00003952714,0.000095688505,0.0004998024,0.000052906533,0.00022462648],"category_scores_gemma":[0.0000125435945,0.00008824088,0.000030862735,0.00072233076,0.000006416678,0.00059487537,0.00014181266,0.00011728066,0.0015179679],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040052237,0.00032512975,0.016542276,0.00011728342,0.000015547519,0.000090815156,0.0047093146,0.000044414162,0.000526581,0.0110442,0.007383002,0.9591974],"study_design_scores_gemma":[0.0052297018,0.00047062812,0.14909391,0.00052275567,0.000007392046,0.00059811777,0.0015381824,0.74001044,0.0027099515,0.0089564435,0.08821564,0.0026468395],"about_ca_topic_score_codex":0.0015891424,"about_ca_topic_score_gemma":0.0049983375,"teacher_disagreement_score":0.9565506,"about_ca_system_score_codex":0.00006207612,"about_ca_system_score_gemma":0.000017154769,"threshold_uncertainty_score":0.9992595},"labels":[],"label_agreement":null},{"id":"W2130053764","doi":"10.1109/ccece.2007.57","title":"SoPC Based Smart Home Embedded Computer Capable of Caring for the Home Occupants","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Home automation; Computer science; Embedded system; Operating system","score_opus":0.040495964045779424,"score_gpt":0.2750635365078024,"score_spread":0.23456757246202298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130053764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05045233,0.00004292148,0.94655025,0.00028844117,0.000903142,0.0005341571,0.000007896997,0.00016343093,0.0010574494],"genre_scores_gemma":[0.9711953,7.449491e-7,0.027591173,0.0006347045,0.00014536595,0.000027596354,0.000002633675,0.000013153233,0.00038933314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983995,0.000054503616,0.00041058316,0.00037703622,0.00034289784,0.00041547755],"domain_scores_gemma":[0.99713755,0.0015979313,0.00017410357,0.0007176301,0.00027718465,0.00009559445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012367021,0.00016527106,0.0003025335,0.00016180536,0.00015595387,0.0001330015,0.0008475365,0.00007385846,0.000028745533],"category_scores_gemma":[0.000028644781,0.00012075844,0.00016551463,0.0003718342,0.000059338483,0.00040890096,0.00017078481,0.000101660116,0.00004099592],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003410026,0.0009786008,0.050453093,0.00062288094,0.00067805976,0.000050445837,0.0057232524,0.0012949858,0.01694375,0.015929794,0.025679298,0.88130486],"study_design_scores_gemma":[0.0039360435,0.00040298494,0.059624642,0.00019152965,0.00005119227,0.00005867117,0.000367604,0.86477596,0.056756783,0.0010245476,0.01190214,0.0009078982],"about_ca_topic_score_codex":0.0003631383,"about_ca_topic_score_gemma":0.00033152752,"teacher_disagreement_score":0.920743,"about_ca_system_score_codex":0.000054324275,"about_ca_system_score_gemma":0.000105484185,"threshold_uncertainty_score":0.49243864},"labels":[],"label_agreement":null},{"id":"W2131733845","doi":"10.1109/icme.2011.6012218","title":"A context-aware e-health framework for students with moderate intellectual and learning disabilities","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Context (archaeology); Intellectual disability; Computer science; Human–computer interaction; Multimedia; Learning disability; Context awareness; Interface (matter); User interface; Knowledge management; World Wide Web; Psychology; Developmental psychology","score_opus":0.08584469581469925,"score_gpt":0.32032927171102754,"score_spread":0.2344845758963283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131733845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13731165,0.00007401166,0.86090153,0.00025761346,0.00010996109,0.00058351667,0.0000033306271,0.00024794295,0.0005104247],"genre_scores_gemma":[0.9711957,0.00000947616,0.027070634,0.00056720973,0.000031976622,0.0001444448,0.0000016534443,0.000017597746,0.00096129003],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9984225,0.0002105882,0.0002644698,0.00049968966,0.0002627929,0.0003399401],"domain_scores_gemma":[0.997409,0.00185917,0.00011678928,0.00029518502,0.00016258127,0.0001572632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005345835,0.00019121447,0.00032911412,0.000083087085,0.0003351281,0.0002735915,0.00041922918,0.00006851606,0.00006593475],"category_scores_gemma":[0.00045052165,0.00014962378,0.000045460558,0.0001606727,0.00010398678,0.00057952374,0.00021776548,0.0002623029,0.000028484455],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062935497,0.0006178374,0.054687455,0.00035315976,0.00036253408,0.000006130099,0.3662414,0.000013985678,0.000008471167,0.08411005,0.0009837213,0.4919859],"study_design_scores_gemma":[0.016679427,0.025419334,0.03137314,0.0042361086,0.00014723913,0.0008217687,0.4503713,0.28191346,0.0052675167,0.15430583,0.022559695,0.0069051804],"about_ca_topic_score_codex":0.0002878336,"about_ca_topic_score_gemma":0.00028128375,"teacher_disagreement_score":0.83388406,"about_ca_system_score_codex":0.000054225125,"about_ca_system_score_gemma":0.0000991103,"threshold_uncertainty_score":0.6101481},"labels":[],"label_agreement":null},{"id":"W2132052807","doi":"10.1007/978-3-540-74377-4_84","title":"Security Contexts in Autonomic Systems","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Autonomic computing; Computer science; Context (archaeology); Computer security; Computer security model; Risk analysis (engineering); Business; Cloud computing; Geography","score_opus":0.027926840302810944,"score_gpt":0.2656843266129577,"score_spread":0.23775748631014676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132052807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059524225,0.001134306,0.9817042,0.0002827332,0.0049409224,0.000985541,0.000012300363,0.0002821643,0.010062612],"genre_scores_gemma":[0.98726165,0.000026009153,0.010821817,0.0007994681,0.00059308513,0.000026252867,0.0000055146224,0.000048750906,0.00041747608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99433595,0.00011700737,0.0011469615,0.0021846357,0.0011845555,0.0010308921],"domain_scores_gemma":[0.99585074,0.0011772591,0.0005730047,0.0017874482,0.00033917543,0.0002723737],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028641894,0.0007288602,0.0010672754,0.0021220353,0.00017620545,0.0009526586,0.003729494,0.00061732077,0.000020374428],"category_scores_gemma":[0.000124094,0.00074729533,0.00018746991,0.00092719746,0.00057040487,0.0011885789,0.0013668652,0.0014057294,0.00021573421],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017527751,0.00010460571,0.00059753784,0.0001457387,0.000028660968,0.00062107225,0.0023864654,0.011362301,0.000099506055,0.031607885,0.000058035053,0.9529707],"study_design_scores_gemma":[0.0014295966,0.0002866706,0.0009871201,0.0027094893,0.000011865483,0.0006608732,0.0000016288814,0.8997069,0.00069308036,0.07793322,0.013136994,0.0024425695],"about_ca_topic_score_codex":0.00038350408,"about_ca_topic_score_gemma":0.0014694892,"teacher_disagreement_score":0.9866664,"about_ca_system_score_codex":0.0013057728,"about_ca_system_score_gemma":0.00096741616,"threshold_uncertainty_score":0.99949783},"labels":[],"label_agreement":null},{"id":"W2132168989","doi":"10.1145/2047196.2047238","title":"The proximity toolkit","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":273,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Proxemics; Human–computer interaction; Computer science; Ubiquitous computing; Orientation (vector space); Key (lock); Computer security; Mathematics","score_opus":0.06882306333130098,"score_gpt":0.230261115992285,"score_spread":0.16143805266098402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132168989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007258612,0.00003543357,0.7280161,0.0011335983,0.00062781683,0.00024224364,4.2880123e-7,0.00037599367,0.26230982],"genre_scores_gemma":[0.99172616,0.0000022824117,0.003814338,0.00023945217,0.000031357064,0.000028022605,8.411739e-8,0.0000028831591,0.004155405],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993447,0.00006588597,0.00011670707,0.00017329701,0.00014451407,0.00015487555],"domain_scores_gemma":[0.9992254,0.00010464502,0.000043299842,0.0005050563,0.00007561003,0.000046029294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034805347,0.000058665333,0.000059998325,0.000019018522,0.00016105828,0.00013779725,0.00071100914,0.00002356674,0.000049015183],"category_scores_gemma":[0.000043864937,0.000035131852,0.000040736697,0.00014134803,0.000023932344,0.0006331571,0.00018520762,0.00006343648,0.00060231134],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058901405,0.0000784845,0.0027797115,0.000004022342,0.000025355675,0.00000754646,0.0014726577,3.569974e-8,0.00015246829,0.20145679,0.007916881,0.78610015],"study_design_scores_gemma":[0.0016121317,0.0005014366,0.13388616,0.000060164657,0.000020641817,0.00041805062,0.0013118074,0.019852577,0.061697487,0.14151989,0.6375006,0.0016190637],"about_ca_topic_score_codex":0.00011547065,"about_ca_topic_score_gemma":0.00013038347,"teacher_disagreement_score":0.98446757,"about_ca_system_score_codex":0.0000142203435,"about_ca_system_score_gemma":0.00003428672,"threshold_uncertainty_score":0.7741695},"labels":[],"label_agreement":null},{"id":"W2134687011","doi":"10.1109/cnsr.2011.29","title":"Context Aware Middleware Services for Disconnection Tolerant Mobile Applications","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 New Brunswick","funders":"","keywords":"Middleware (distributed applications); Disconnection; Computer science; Context (archaeology); Mobile computing; Server; Computer network; Context awareness; Services computing; World Wide Web; Distributed computing; Web service","score_opus":0.04564250217321928,"score_gpt":0.25368606178319447,"score_spread":0.2080435596099752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134687011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008350898,0.00009200669,0.9851175,0.00014717724,0.00034354822,0.0017763488,0.00003420125,0.00048947986,0.0036488248],"genre_scores_gemma":[0.99036473,0.000002773928,0.004860028,0.00037437963,0.00010557301,0.003367783,0.000019322531,0.0000141293,0.0008912997],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988071,0.000042809526,0.00028278615,0.0004822935,0.00014761556,0.00023742422],"domain_scores_gemma":[0.9987517,0.00014793736,0.00013384376,0.00059185084,0.00027301043,0.00010167283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016776855,0.00015584711,0.00020055637,0.00009961482,0.00021825498,0.00013620236,0.00060642586,0.000072551564,0.00011452064],"category_scores_gemma":[0.0000041416665,0.00013939838,0.00011856786,0.000249149,0.0000238513,0.0009279724,0.00011926187,0.00006349124,0.00022927378],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042723073,0.0006365157,0.002757691,0.0003787115,0.00012552588,0.0000029433934,0.010542409,0.000010704881,0.00084276346,0.0396989,0.0010519467,0.94390917],"study_design_scores_gemma":[0.006276769,0.0018120945,0.008091777,0.0005110384,0.000152623,0.00033196597,0.034679048,0.20239021,0.093247496,0.029518453,0.6193534,0.0036351427],"about_ca_topic_score_codex":0.0005321633,"about_ca_topic_score_gemma":0.0010871455,"teacher_disagreement_score":0.9820138,"about_ca_system_score_codex":0.000040586947,"about_ca_system_score_gemma":0.00004168148,"threshold_uncertainty_score":0.56845015},"labels":[],"label_agreement":null},{"id":"W2135283631","doi":"10.1109/iembs.1995.579615","title":"Gait event and user intention detection for FES-control: selecting sensors","year":2002,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Alberta","funders":"","keywords":"Event (particle physics); Computer science; Intuition; Artificial intelligence; Detector; Decision tree; Ranking (information retrieval); Machine learning; Robot; Data mining; Psychology","score_opus":0.027337796969009884,"score_gpt":0.24031582071348426,"score_spread":0.2129780237444744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135283631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14243692,0.00002828212,0.85555655,0.00045128787,0.0003235033,0.0003924181,0.0000010658972,0.0002231496,0.00058684475],"genre_scores_gemma":[0.9956697,0.0000031353568,0.0021659741,0.00016259504,0.00007197152,0.00006164389,3.749616e-7,0.000008803124,0.0018558089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905866,0.00007972736,0.0002068864,0.00032431187,0.00013342402,0.00019699843],"domain_scores_gemma":[0.9992881,0.00022930099,0.000099822304,0.00016529263,0.00015948084,0.00005802329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034230383,0.00010421627,0.00013754204,0.00011319807,0.00015807021,0.00017875446,0.00010951489,0.000054798405,0.000025920966],"category_scores_gemma":[0.00011218505,0.00009867465,0.000073630596,0.00018359374,0.000010697652,0.00062413706,0.000037792786,0.000075827375,0.00004447434],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019009103,0.00008650265,0.0030212828,0.000043932017,0.00007037907,0.000002307,0.0005494441,0.000024639581,0.021207262,0.00068657356,0.00088836194,0.9734003],"study_design_scores_gemma":[0.0019592568,0.00028703304,0.00740126,0.000050708903,0.000024526735,0.00016645656,0.00018796578,0.9597434,0.02203879,0.0005877203,0.0071798926,0.0003730126],"about_ca_topic_score_codex":0.0000793353,"about_ca_topic_score_gemma":0.00017137679,"teacher_disagreement_score":0.9730273,"about_ca_system_score_codex":0.000054108255,"about_ca_system_score_gemma":0.000005175844,"threshold_uncertainty_score":0.40238357},"labels":[],"label_agreement":null},{"id":"W2136282990","doi":"10.1109/nof.2015.7333282","title":"Synthesizing social context for making Internet of Things environments more immersive","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Smart objects; Context (archaeology); Internet of Things; Ontology; Ubiquitous computing; Architecture; The Internet; World Wide Web; Context management; Web of Things; Human–computer interaction; Multimedia","score_opus":0.07908176881776917,"score_gpt":0.29482190240880873,"score_spread":0.21574013359103955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136282990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017321952,0.000041323507,0.9776688,0.00080073386,0.0002995281,0.00031985957,0.0000036611484,0.000041858628,0.003502266],"genre_scores_gemma":[0.99278647,3.303966e-7,0.0057552736,0.000602292,0.00005056573,0.000030996685,0.0000015801062,0.0000102507,0.0007622718],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998933,0.000061979146,0.00025938932,0.00028170604,0.00026925633,0.0001946707],"domain_scores_gemma":[0.9992099,0.00024414854,0.00022876091,0.00018697589,0.00007419934,0.000056032364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039628195,0.000116993164,0.00023785426,0.000077345256,0.000046857538,0.00006374861,0.00049861893,0.00006320358,0.0000151029135],"category_scores_gemma":[0.00010039431,0.000112631875,0.00010758265,0.00007908113,0.000053550564,0.00077179074,0.00024318247,0.0000643466,0.000037880414],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013819251,0.00029168668,0.0018742718,0.00011473105,0.00036805688,0.000014272117,0.10775836,0.0000059020476,0.0133458255,0.031299137,0.011718034,0.83307153],"study_design_scores_gemma":[0.010973157,0.0012807082,0.0035569223,0.0011047483,0.00020060843,0.00020596254,0.08642769,0.28399146,0.43416363,0.011944521,0.16298915,0.0031614231],"about_ca_topic_score_codex":0.0001074787,"about_ca_topic_score_gemma":0.000006750306,"teacher_disagreement_score":0.97546446,"about_ca_system_score_codex":0.00010934154,"about_ca_system_score_gemma":0.000044635515,"threshold_uncertainty_score":0.45929945},"labels":[],"label_agreement":null},{"id":"W2136837147","doi":"10.1145/2030045.2030063","title":"Goal-oriented sensor selection for intelligent phones","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Selection (genetic algorithm); Human–computer interaction; Artificial intelligence","score_opus":0.05549250457637247,"score_gpt":0.26542525846573967,"score_spread":0.2099327538893672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136837147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01762652,0.0000114544655,0.9745703,0.00013255658,0.0008791838,0.00042667624,0.0000022578004,0.00041026605,0.005940794],"genre_scores_gemma":[0.94399565,0.0000024232115,0.052409515,0.00020116013,0.00009141307,0.00010814103,0.0000020491455,0.000010179217,0.003179493],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999041,0.00005142403,0.00021545666,0.0003353045,0.00013990671,0.00021687865],"domain_scores_gemma":[0.9992162,0.00011171202,0.00008170591,0.00024631037,0.00026661955,0.000077441226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022818563,0.00011166045,0.00013579507,0.00011473795,0.00009532127,0.000054993565,0.00023965242,0.00005252424,0.00013860861],"category_scores_gemma":[0.000053652115,0.00010023759,0.00008598694,0.00031391953,0.000016223807,0.0004557852,0.00007114523,0.00005547147,0.00030037996],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017864139,0.0010141418,0.014971107,0.0001120201,0.00025343875,0.0000073900515,0.012343737,0.0000067719047,0.024066214,0.08369135,0.015095626,0.84825957],"study_design_scores_gemma":[0.0012885934,0.00078753097,0.004373457,0.000060212544,0.000029246137,0.0002041294,0.0011019617,0.08976284,0.76524574,0.004722155,0.1314968,0.00092735165],"about_ca_topic_score_codex":0.00025746843,"about_ca_topic_score_gemma":0.00016774269,"teacher_disagreement_score":0.92636913,"about_ca_system_score_codex":0.000048230544,"about_ca_system_score_gemma":0.000036652476,"threshold_uncertainty_score":0.40875703},"labels":[],"label_agreement":null},{"id":"W2137573643","doi":"10.4108/icst.mobiquitous2009.6836","title":"Temporal addressing for mobile context-aware communication","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Downtown; Context (archaeology); Decoupling (probability); Mobile telephony; Context awareness; Mobile computing; Internet privacy; World Wide Web; Mobile radio; Telecommunications; Geography; Engineering; Phone","score_opus":0.06869144024445588,"score_gpt":0.3247655170499193,"score_spread":0.2560740768054634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137573643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007791474,0.00030116603,0.9833036,0.0022022962,0.00017766429,0.00086311257,0.000008013672,0.00052972004,0.004822956],"genre_scores_gemma":[0.9837936,0.000004238661,0.013900516,0.0012641358,0.000049483977,0.00013666511,0.000020054335,0.0000073433857,0.0008239356],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988997,0.000107029344,0.00028661164,0.00030610326,0.00017704497,0.00022352958],"domain_scores_gemma":[0.99847186,0.0002892822,0.0001485164,0.00077117747,0.00023771967,0.00008145748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039174405,0.00013089087,0.00020660949,0.00009078626,0.00023812219,0.00029707118,0.0007714651,0.00007373163,0.000030907362],"category_scores_gemma":[0.000037620895,0.00012419686,0.00010015748,0.00020217185,0.000028207342,0.001032859,0.000094313116,0.0000985581,0.000060166236],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011511868,0.00012909339,0.00034301422,0.000012561913,0.000013457572,0.000001355797,0.00070327683,0.0000066838,0.00062133675,0.008709273,0.009914742,0.9795337],"study_design_scores_gemma":[0.008671763,0.0022769405,0.009973943,0.0009561406,0.00005656882,0.00026359563,0.004150534,0.33240157,0.043532662,0.04204881,0.55268,0.0029874658],"about_ca_topic_score_codex":0.00007471737,"about_ca_topic_score_gemma":0.00009780677,"teacher_disagreement_score":0.9765462,"about_ca_system_score_codex":0.000054886248,"about_ca_system_score_gemma":0.0000642934,"threshold_uncertainty_score":0.5064601},"labels":[],"label_agreement":null},{"id":"W2138875386","doi":"10.1109/wimob.2007.4390864","title":"Managing and Deployment of Applications with OSGi in the Context of Smart Home","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Software deployment; Computer science; Context (archaeology); Ubiquitous computing; Smart environment; Resource (disambiguation); Home automation; Embedded system; Software engineering; Internet of Things; Human–computer interaction; Operating system; Computer network","score_opus":0.019454477288642115,"score_gpt":0.2476579325712736,"score_spread":0.2282034552826315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138875386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15290675,0.00008277017,0.84069806,0.00070636155,0.000015896023,0.0004120943,0.0000010324394,0.000023591627,0.0051534586],"genre_scores_gemma":[0.9980385,0.00000475796,0.0017137658,0.00016425272,0.000005113953,0.000028290806,2.496342e-7,0.0000024680096,0.000042657994],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99939615,0.000033352757,0.00018624864,0.00013382836,0.00016003793,0.00009035741],"domain_scores_gemma":[0.99930865,0.00025709398,0.00008623962,0.00027156636,0.000055830686,0.00002064225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005661667,0.000050426963,0.0001098534,0.000097352255,0.000021596616,0.000018946792,0.00024707147,0.000014316349,0.0000022709855],"category_scores_gemma":[0.0000031925354,0.0000330108,0.000014333334,0.00029870344,0.000046382596,0.0001454866,0.000056232817,0.000041734875,0.0000026154669],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031951444,0.0002636684,0.076604515,0.00009436281,0.000042131443,0.0000068729837,0.0063838386,0.000003197097,0.0018178057,0.08039082,0.00010412909,0.8342567],"study_design_scores_gemma":[0.008166861,0.0016669155,0.7763353,0.00074047624,0.0000796483,0.00055765454,0.045586523,0.012679317,0.10880384,0.015048601,0.028893083,0.0014417943],"about_ca_topic_score_codex":0.0003337713,"about_ca_topic_score_gemma":0.0014798352,"teacher_disagreement_score":0.8451317,"about_ca_system_score_codex":0.000011031133,"about_ca_system_score_gemma":0.000015010874,"threshold_uncertainty_score":0.13461414},"labels":[],"label_agreement":null},{"id":"W2138972933","doi":"10.1109/icdim.2010.5662575","title":"Dynamic user-centric mobile context model","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 model; Context (archaeology); Human–computer interaction; Context awareness; Mobile computing; Focus (optics); Context management; Ubiquitous computing; Data science; Artificial intelligence","score_opus":0.012960436068804815,"score_gpt":0.25036382254470224,"score_spread":0.23740338647589743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138972933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19438103,0.000032099222,0.7952016,0.00033071937,0.0008448175,0.00035846292,0.0000054274756,0.00048202454,0.008363813],"genre_scores_gemma":[0.9817525,0.0000036335641,0.011644704,0.00050109584,0.00002693857,0.00008260304,0.0000023355212,0.000012707315,0.005973454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986744,0.00004090517,0.0002470036,0.00044757425,0.00028074248,0.00030936982],"domain_scores_gemma":[0.9986571,0.00014453837,0.00008917921,0.00079223665,0.00016340734,0.0001535567],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002470036,0.00015586535,0.00019752249,0.0001476858,0.00010173959,0.00023102,0.0008571873,0.00010111984,0.0001817334],"category_scores_gemma":[0.000037938647,0.0001416317,0.000095897325,0.00034209833,0.000036777274,0.00088434824,0.0002510679,0.00026882376,0.0010357595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009446423,0.0004504022,0.0013403584,0.000026075864,0.00005133133,0.000024612718,0.0010640183,0.00039295727,0.031253528,0.051176794,0.007432235,0.9067782],"study_design_scores_gemma":[0.00045506423,0.000035056233,0.00026063758,0.000006692633,0.0000037109157,0.000059532278,0.000056636327,0.9795994,0.0016278952,0.0011208277,0.016513363,0.00026119722],"about_ca_topic_score_codex":0.0000911279,"about_ca_topic_score_gemma":0.00065479433,"teacher_disagreement_score":0.97920644,"about_ca_system_score_codex":0.000038420832,"about_ca_system_score_gemma":0.000114293056,"threshold_uncertainty_score":0.99974203},"labels":[],"label_agreement":null},{"id":"W2139099332","doi":"10.1007/11839569_37","title":"Ubisafe Computing: Vision and Challenges (I)","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Ubiquitous computing; Computer science; End-user computing; Variety (cybernetics); Human–computer interaction; Construct (python library); Data science; Internet privacy; Artificial intelligence; Utility computing; Cloud computing; Cloud computing security","score_opus":0.0349413565202185,"score_gpt":0.2588513379474491,"score_spread":0.22390998142723056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139099332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012105426,0.004319396,0.9801628,0.0025355257,0.0016758324,0.00044292124,0.000004580955,0.0002996346,0.010438242],"genre_scores_gemma":[0.8560405,0.00059954915,0.1401598,0.0012459374,0.001201002,0.000009594682,0.000008376738,0.000082421204,0.0006528648],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99541765,0.00007876457,0.00064908504,0.0020874138,0.0010988431,0.00066824746],"domain_scores_gemma":[0.9967666,0.0009932425,0.0004161462,0.0013226276,0.0002954156,0.00020595903],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012309757,0.0006546038,0.0007665371,0.0009786454,0.00030789105,0.0007976467,0.0021847722,0.00041014497,0.000008902732],"category_scores_gemma":[0.000066263325,0.0006069686,0.00013226364,0.00038574083,0.00068434095,0.0008743517,0.0019962883,0.0007527257,0.00006053375],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025592783,0.00002882491,0.000028027867,0.000077570876,0.000008457533,0.00006709058,0.00046518404,0.0009707082,0.000055523873,0.00581448,0.000060762257,0.9924208],"study_design_scores_gemma":[0.0011376393,0.0006698197,0.002431371,0.0024464137,0.000022456195,0.0008652941,0.0000010128282,0.8101684,0.0010319292,0.13751264,0.041089192,0.0026238393],"about_ca_topic_score_codex":0.000048904054,"about_ca_topic_score_gemma":0.00016400687,"teacher_disagreement_score":0.989797,"about_ca_system_score_codex":0.00024201446,"about_ca_system_score_gemma":0.00028547365,"threshold_uncertainty_score":0.99963814},"labels":[],"label_agreement":null},{"id":"W2140900423","doi":"10.1504/ijmc.2007.011492","title":"Identifying the differences between stationary office support and mobile work support: a conceptual framework","year":2006,"lang":"en","type":"article","venue":"International Journal of Mobile Communications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Work (physics); Computer science; Mobile business development; Mobile computing; Mobile technology; Conceptual framework; Context (archaeology); Knowledge management; Mobile telephony; Process management; Mobile Web; Telecommunications; Business; Mobile radio; Engineering","score_opus":0.0613531330578789,"score_gpt":0.3355660501456012,"score_spread":0.27421291708772233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140900423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41891086,0.0033068834,0.56588924,0.006860111,0.0017577569,0.00075570686,0.00014285583,0.00012370567,0.0022528963],"genre_scores_gemma":[0.98722225,0.00026799546,0.011654783,0.00021318976,0.0003155215,0.000095305215,0.000033574106,0.000010742705,0.000186646],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99769473,0.00036221894,0.0008547333,0.00018902146,0.00073009194,0.00016918869],"domain_scores_gemma":[0.99436563,0.0029823096,0.00086834654,0.00077614613,0.0009251154,0.00008243982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081796024,0.00014947054,0.00025901702,0.00021949198,0.00028951568,0.00047947577,0.0034769026,0.00007797197,0.000098673576],"category_scores_gemma":[0.00013246368,0.0001204345,0.00013445699,0.00031684616,0.00036178206,0.0010040906,0.0008162456,0.00050833955,0.000043471944],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011972453,0.0016862871,0.30588222,0.000031647884,0.0016138317,0.00011108061,0.035872187,0.0004917293,0.0008622752,0.0878832,0.018666856,0.546779],"study_design_scores_gemma":[0.002470018,0.00089390285,0.5806888,0.00082058343,0.00021182516,0.0013304324,0.010188449,0.0012378014,0.00061557716,0.039090656,0.36136347,0.0010885465],"about_ca_topic_score_codex":0.000069166585,"about_ca_topic_score_gemma":0.000026423562,"teacher_disagreement_score":0.5683114,"about_ca_system_score_codex":0.00009222117,"about_ca_system_score_gemma":0.00020600183,"threshold_uncertainty_score":0.6461008},"labels":[],"label_agreement":null},{"id":"W2144027965","doi":"10.1109/imtc.2010.5488279","title":"Context-aware social networks mashup: A personalized web perspective","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mashup; Computer science; World Wide Web; Context (archaeology); Perspective (graphical); Social network (sociolinguistics); The Internet; Context awareness; Architecture; Ubiquitous computing; Estate; Human–computer interaction; Web service; Internet of Things; Data science; Web 2.0; Social media; Artificial intelligence; Business","score_opus":0.018798944192796513,"score_gpt":0.2658789332781896,"score_spread":0.2470799890853931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144027965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019957436,0.00013780227,0.8816395,0.009132167,0.0028287023,0.00071906357,0.000017973089,0.0013787191,0.08418866],"genre_scores_gemma":[0.9933961,0.0000034843492,0.0010887929,0.0013155972,0.00064913754,0.00007155089,0.0000036469735,0.00002306411,0.0034486272],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793,0.00017572595,0.00028713053,0.0006966619,0.00043068745,0.00047981535],"domain_scores_gemma":[0.99835134,0.00027776742,0.00015765293,0.0004686748,0.00056594174,0.00017862685],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044548788,0.00026949184,0.00037472573,0.00014404384,0.00038140436,0.00040658994,0.00090144837,0.00023349794,0.00064860645],"category_scores_gemma":[0.000061810584,0.00025355307,0.00024947253,0.00047110475,0.00018355866,0.0010819011,0.0002892785,0.0006298208,0.00028987974],"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":[0.000055991262,0.00028066963,0.0007798442,0.0000136886965,0.00020426417,0.00006240398,0.010189232,0.0000045239067,0.0021152208,0.8568387,0.031238545,0.09821691],"study_design_scores_gemma":[0.009588725,0.00034584053,0.0047333688,0.000087124994,0.00009025186,0.0008009061,0.024990492,0.72348297,0.00094992423,0.027657825,0.20399597,0.0032766121],"about_ca_topic_score_codex":0.00036780775,"about_ca_topic_score_gemma":0.001837684,"teacher_disagreement_score":0.9734387,"about_ca_system_score_codex":0.00011261697,"about_ca_system_score_gemma":0.0002223707,"threshold_uncertainty_score":0.99999166},"labels":[],"label_agreement":null},{"id":"W2144373618","doi":"10.4108/icst.pervasivehealth2009.6020","title":"The smart condo: visualizing independent living environments in a virtual world","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Advanced Education, Government of Alberta","keywords":"Computer science; Variety (cybernetics); Assisted living; Wireless sensor network; Wireless; Computer security; Telecommunications; Computer network; Human–computer interaction; Artificial intelligence; Gerontology; Medicine","score_opus":0.02040046738004776,"score_gpt":0.26194399169159316,"score_spread":0.2415435243115454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144373618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2752566,0.00027071155,0.67768735,0.0036299548,0.0012087573,0.00094824587,0.0000015178892,0.00037976433,0.040617093],"genre_scores_gemma":[0.9936233,0.0000084992835,0.0002050131,0.00089469197,0.000037121084,0.00001965743,3.3509374e-7,0.0000056577082,0.00520569],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983176,0.00022858028,0.00032933662,0.00036257048,0.00042470207,0.00033721435],"domain_scores_gemma":[0.99868906,0.0006778967,0.00010099991,0.00043848855,0.0000131903,0.00008037586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000871678,0.00013923607,0.00015961885,0.00015818291,0.00017388932,0.00036069035,0.0006750242,0.00004061976,0.000042589378],"category_scores_gemma":[0.000076277385,0.00010780196,0.00005765223,0.00035118018,0.000030804804,0.00066385674,0.00024452174,0.00019670362,0.00023826328],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016364424,0.00045538077,0.023595065,0.0000036590343,0.000041374537,0.00006368087,0.003124374,0.000046734476,0.0075071203,0.043279793,0.0013592378,0.9205072],"study_design_scores_gemma":[0.0035738396,0.00089081156,0.69501156,0.0007292565,0.000019184443,0.00021631324,0.002864351,0.1108327,0.013418519,0.006126252,0.16405202,0.0022651649],"about_ca_topic_score_codex":0.00008833387,"about_ca_topic_score_gemma":0.001102605,"teacher_disagreement_score":0.91824204,"about_ca_system_score_codex":0.00013311146,"about_ca_system_score_gemma":0.000034715118,"threshold_uncertainty_score":0.43960366},"labels":[],"label_agreement":null},{"id":"W2144524535","doi":"10.1109/i-span.2008.33","title":"Management of Ambient Media Preferences in Distributed Environments for Service Personalization","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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 Ottawa","funders":"","keywords":"Personalization; Computer science; Service (business); Ambient intelligence; World Wide Web; Multimedia; Human–computer interaction; Business; Marketing","score_opus":0.052240201987174884,"score_gpt":0.24562489485469127,"score_spread":0.1933846928675164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144524535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21514565,0.000021577951,0.7835244,0.00021739886,0.00010397264,0.0003948286,0.000023255112,0.00002883533,0.0005400859],"genre_scores_gemma":[0.99557585,0.00002526868,0.004075253,0.000057458645,0.000007267456,0.0000830689,0.000045035056,0.0000030625174,0.00012771957],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991577,0.000038281454,0.00021367909,0.00022540639,0.00024391492,0.00012101784],"domain_scores_gemma":[0.99958235,0.00007756549,0.00008885653,0.00018306739,0.000034963814,0.00003321608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012890193,0.000073709154,0.000120150806,0.000062781146,0.000034247456,0.000012653122,0.00028630169,0.000028350576,0.000019688716],"category_scores_gemma":[0.000006489468,0.000069339054,0.000028251488,0.00027404807,0.00001553315,0.00028349424,0.00010009583,0.0000226914,0.000014917761],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039509006,0.007223379,0.3110013,0.00344697,0.0009978174,0.00018130853,0.06755414,0.0016789619,0.015823023,0.08801714,0.0056222575,0.49805862],"study_design_scores_gemma":[0.0042033447,0.0002003613,0.8725649,0.00038662908,0.000028284077,0.000028058463,0.0022554123,0.08920202,0.018892428,0.0033012095,0.008247248,0.00069010025],"about_ca_topic_score_codex":0.000085793814,"about_ca_topic_score_gemma":0.00010500751,"teacher_disagreement_score":0.7804302,"about_ca_system_score_codex":0.000047539896,"about_ca_system_score_gemma":0.000013326897,"threshold_uncertainty_score":0.28275645},"labels":[],"label_agreement":null},{"id":"W2145131854","doi":"10.1109/iri.2012.6303015","title":"Recognition of fuzzy contexts from temporal data under uncertainty case study: Activity recognition in smart homes","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Normality; Intelligent decision support system; Context (archaeology); Artificial intelligence; Realization (probability); Computational intelligence; Fuzzy logic; Machine learning; Fuzzy control system; Mathematics","score_opus":0.14874222288584674,"score_gpt":0.3298542391241626,"score_spread":0.18111201623831588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145131854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95890385,0.00009068925,0.037566748,0.0002074901,0.00078278995,0.0009976088,0.00036541643,0.00018940914,0.00089601893],"genre_scores_gemma":[0.9973088,0.000007352201,0.0019872533,0.00012409403,0.00015161629,0.0000732299,0.00028204583,0.000020854584,0.00004475432],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9964986,0.0010117254,0.00067842,0.0008356982,0.0005013124,0.00047426316],"domain_scores_gemma":[0.9965784,0.0010323523,0.00043500678,0.0014851097,0.00026594155,0.00020320325],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001887961,0.0003085923,0.00058494805,0.00035473844,0.0001091814,0.0001408401,0.0007647592,0.00016244156,0.00015903283],"category_scores_gemma":[0.00020894913,0.00030052912,0.00008457729,0.0007047766,0.000071732095,0.004735833,0.00071933196,0.0003116119,0.00018704784],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109912144,0.0029320933,0.13161209,0.000036767262,0.00016433178,0.00023373759,0.0028865868,0.0000036252356,0.00065891445,0.000020358613,0.000514514,0.8608271],"study_design_scores_gemma":[0.031593375,0.0026716876,0.7943724,0.0015627919,0.00064950023,0.005259219,0.07418861,0.0410926,0.016172,0.023260342,0.0020299666,0.007147498],"about_ca_topic_score_codex":0.047158193,"about_ca_topic_score_gemma":0.037319116,"teacher_disagreement_score":0.8536796,"about_ca_system_score_codex":0.00016235044,"about_ca_system_score_gemma":0.00014206728,"threshold_uncertainty_score":0.9999447},"labels":[],"label_agreement":null},{"id":"W2145552549","doi":"10.1109/tmc.2013.132","title":"Adaptive Context Dissemination in Heterogeneous Environments","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Context-Aware Activity Recognition 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 Ottawa; Blackberry (Canada)","funders":"","keywords":"Computer science; Middleware (distributed applications); Context (archaeology); Distributed computing; Overlay network; Overlay; Context model; Protocol (science); Computer network; Context awareness; Personalization; Ubiquitous computing; World Wide Web; Human–computer interaction; The Internet; Operating system; Artificial intelligence","score_opus":0.016194441889625353,"score_gpt":0.2438644258242591,"score_spread":0.22766998393463375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145552549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21968469,0.000034983677,0.7787832,0.0000511846,0.00048728852,0.00067542144,0.0000037322634,0.00012255345,0.00015694759],"genre_scores_gemma":[0.9979741,0.0000060605294,0.0014183697,0.00011670423,0.000029207495,0.0002427947,0.0000012370575,0.000018444733,0.00019308225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825215,0.00018840622,0.00040091967,0.0005299313,0.00029721315,0.00033138326],"domain_scores_gemma":[0.9988379,0.0004664114,0.0001358673,0.00041002332,0.000046824953,0.00010297891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020128921,0.00021132668,0.00023935568,0.00026238622,0.00017754242,0.00015124983,0.00040294163,0.00009104387,0.000084340216],"category_scores_gemma":[0.000004675357,0.00022975125,0.000108271764,0.0003431471,0.00004045745,0.00065122626,0.000008339709,0.00027417243,0.0005904774],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054698335,0.0003025355,0.000060920334,0.0000063323478,0.000023273491,0.000009859551,0.0012568831,0.02877774,0.0032571608,0.000016618276,0.000033745342,0.96624947],"study_design_scores_gemma":[0.0008886816,0.00042002974,0.0015061039,0.00026448793,0.000009539772,0.00009007935,0.00067377416,0.93292224,0.06207458,0.00015925935,0.00040584602,0.00058540265],"about_ca_topic_score_codex":0.00018078418,"about_ca_topic_score_gemma":0.00006530505,"teacher_disagreement_score":0.965664,"about_ca_system_score_codex":0.00022929422,"about_ca_system_score_gemma":0.000022008142,"threshold_uncertainty_score":0.9368984},"labels":[],"label_agreement":null},{"id":"W2145767089","doi":"10.1109/health.2011.6026796","title":"Remote health, activity, and asset monitoring with wireless sensor networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Computer science; Wireless sensor network; Asset (computer security); Visualization; Wearable computer; Wireless; Embedded system; Computer network; Real-time computing; Computer security; Telecommunications; Data mining","score_opus":0.059563217238398934,"score_gpt":0.2673812593579896,"score_spread":0.20781804211959068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145767089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2470018,0.000052555937,0.74920017,0.00036956815,0.00030210696,0.00020632033,7.533974e-7,0.0002667679,0.002599963],"genre_scores_gemma":[0.9765098,0.000030199702,0.022926083,0.00011309315,0.000107004,0.0000052333953,2.8998682e-7,0.000013362356,0.0002949286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987089,0.00014850215,0.00015934861,0.0004368501,0.00021657922,0.00032979515],"domain_scores_gemma":[0.999009,0.00013054335,0.00013966286,0.0004469338,0.000084533596,0.00018932995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034564539,0.00015991158,0.0002490277,0.000079778,0.00015217216,0.0001529327,0.00027078006,0.00006337024,0.0000074605246],"category_scores_gemma":[0.000006256124,0.00013256601,0.00002719051,0.0002553186,0.000037824062,0.00075338193,0.00018943945,0.00018535495,0.00001528413],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027618787,0.00006215163,0.015357623,0.000031520864,0.000043545802,0.000032753924,0.0011768789,0.0000061906235,0.00027734457,0.0002564036,0.00013148134,0.9825965],"study_design_scores_gemma":[0.0039562755,0.002144532,0.47656175,0.001187539,0.000038061222,0.0022497065,0.0012242205,0.47802314,0.028424509,0.00078688073,0.0027037207,0.0026996534],"about_ca_topic_score_codex":0.0015673866,"about_ca_topic_score_gemma":0.00030648563,"teacher_disagreement_score":0.97989684,"about_ca_system_score_codex":0.000046545923,"about_ca_system_score_gemma":0.000064130145,"threshold_uncertainty_score":0.5405885},"labels":[],"label_agreement":null},{"id":"W2147201860","doi":"10.3138/carto.46.2.101","title":"Improving Accessibility Information in Pedestrian Maps and Databases","year":2011,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"FP7 Information and Communication Technologies","keywords":"Geospatial analysis; Pedestrian; Computer science; Stairs; Database; Spatial database; Spatial analysis; Information retrieval; Geographic information system; World Wide Web; Data science; Geography; Transport engineering; Cartography; Engineering; Remote sensing","score_opus":0.037343147604046154,"score_gpt":0.29163280642197537,"score_spread":0.2542896588179292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147201860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22767326,0.00018871584,0.76787484,0.0011823868,0.0015936476,0.0008224014,0.000086370215,0.00010687139,0.00047151785],"genre_scores_gemma":[0.9969965,0.00039118688,0.0014601137,0.0008633243,0.000064248634,0.0000641651,0.0001514936,0.0000045013894,0.0000044489075],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99832666,0.00010426202,0.00077645935,0.000151982,0.00043441475,0.00020623312],"domain_scores_gemma":[0.99808,0.00016273819,0.0006129781,0.0002104982,0.0008329463,0.00010080197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016253792,0.00016069523,0.00015067465,0.0011424548,0.00035812217,0.0010268298,0.00055262237,0.00007400964,0.00000920789],"category_scores_gemma":[0.0003921174,0.00012781225,0.000089236106,0.00055257743,0.0001024596,0.010645093,0.00020495414,0.00019868079,0.0000025880024],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033627907,0.00014276331,0.24025394,0.0002053946,0.00019615369,0.000004097617,0.01490523,0.000030340507,0.0000627224,0.10716625,0.0007484506,0.63594836],"study_design_scores_gemma":[0.010283303,0.0006305991,0.5645869,0.0005101236,0.00012630013,0.0015850308,0.009528516,0.19833507,0.00065849634,0.05326296,0.15889584,0.0015968957],"about_ca_topic_score_codex":0.00044819008,"about_ca_topic_score_gemma":0.00020213895,"teacher_disagreement_score":0.7693233,"about_ca_system_score_codex":0.00003066062,"about_ca_system_score_gemma":0.00007990931,"threshold_uncertainty_score":0.99017435},"labels":[],"label_agreement":null},{"id":"W2148492800","doi":"10.3390/s150924343","title":"Implementation and Evaluation of Four Interoperable Open Standards for the Internet of Things","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Interoperability; Computer science; Open standard; The Internet; Internet of Things; World Wide Web; Bluetooth; Operating system; Wireless","score_opus":0.17472987050224695,"score_gpt":0.40349500631119317,"score_spread":0.22876513580894622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148492800","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81553364,0.00010474706,0.18113996,0.00057524897,0.00027438204,0.0011323531,0.000020057556,0.000013299642,0.0012063312],"genre_scores_gemma":[0.9979036,0.0000023834898,0.0019024496,0.00004375439,0.000011152757,0.00003258585,0.0000018380555,0.0000037278357,0.00009850724],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902856,0.00017206486,0.00020530392,0.0001317389,0.00039471057,0.00006763623],"domain_scores_gemma":[0.99843097,0.00016925379,0.00017341583,0.00018472206,0.0010166481,0.000024997164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035542743,0.000049774542,0.00011736407,0.00004075836,0.000023342593,0.000088862216,0.0003214172,0.000016735949,0.000012449788],"category_scores_gemma":[0.00017550869,0.00003567909,0.000022402564,0.00008312866,0.000024940402,0.0004780022,0.0001916729,0.000028027525,9.1015687e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009955889,0.000036219688,0.00256387,0.000054513966,0.0001245383,2.7847022e-7,0.027487293,0.000144197,0.0013897015,0.0024501437,0.005792508,0.95985717],"study_design_scores_gemma":[0.0060496638,0.0008450291,0.0037771987,0.00019094463,0.000121052835,0.00003227305,0.01665443,0.8759222,0.07789595,0.0059202737,0.012339236,0.00025172663],"about_ca_topic_score_codex":0.0011971834,"about_ca_topic_score_gemma":0.00022204651,"teacher_disagreement_score":0.95960546,"about_ca_system_score_codex":0.0000610988,"about_ca_system_score_gemma":0.00015447498,"threshold_uncertainty_score":0.18097912},"labels":[],"label_agreement":null},{"id":"W2149294428","doi":"10.1109/icmlc.2010.5581067","title":"Distributed agent system for behavior pattern recognition","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Computer science; Cluster analysis; Set (abstract data type); Resource (disambiguation); Modality (human–computer interaction); Hidden Markov model; Distributed computing; Wireless sensor network; Markov chain; Multi-agent system; Artificial intelligence; Data mining; Machine learning; Computer network","score_opus":0.043330548578959606,"score_gpt":0.26767923401690835,"score_spread":0.22434868543794875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149294428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13510983,0.0000029465155,0.86024565,0.00027107866,0.0021700277,0.0008046819,0.00018911644,0.0005829567,0.0006237362],"genre_scores_gemma":[0.9934609,2.7351902e-7,0.0052903905,0.000098891076,0.00017992518,0.0007069757,0.00012194021,0.000012825853,0.00012789476],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99874955,0.000051104358,0.0003013476,0.00041708606,0.00021582737,0.00026507775],"domain_scores_gemma":[0.99875915,0.00017339244,0.00013526251,0.0005047161,0.00029537303,0.00013209961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000353141,0.0001463743,0.00018591211,0.00008509087,0.00013529466,0.00025745892,0.0004541942,0.00010627753,0.000058769227],"category_scores_gemma":[0.000046959874,0.00013574352,0.0001247776,0.00017378942,0.000018124192,0.0005117341,0.00010588561,0.00014095935,0.00039542027],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047351473,0.00015373614,0.0018080851,0.000068344125,0.00002018499,0.000013628053,0.0001033195,1.645417e-7,0.010377041,0.00044704578,0.0019961782,0.9850075],"study_design_scores_gemma":[0.015507242,0.0016447542,0.109422415,0.0008677567,0.0005252609,0.003247457,0.002203864,0.17942452,0.4903034,0.0020853623,0.18809111,0.0066768643],"about_ca_topic_score_codex":0.00011662181,"about_ca_topic_score_gemma":0.00026166366,"teacher_disagreement_score":0.9783307,"about_ca_system_score_codex":0.000059250746,"about_ca_system_score_gemma":0.00004090301,"threshold_uncertainty_score":0.553546},"labels":[],"label_agreement":null},{"id":"W2149643171","doi":"10.1109/iembs.2008.4649682","title":"Real-time signal processing of accelerometer data for wearable medical patient monitoring devices","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Accelerometer; Wearable computer; Computer science; Bluetooth; Gateway (web page); Real-time computing; Default gateway; Event (particle physics); Remote patient monitoring; Wireless sensor network; Embedded system; Computer security; Telecommunications; Wireless; Computer network; Medicine; Operating system","score_opus":0.1078707546089016,"score_gpt":0.3136105062655986,"score_spread":0.205739751656697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149643171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43552878,0.00018133012,0.559417,0.00033224502,0.00027732545,0.0005332972,0.000020992122,0.0002799397,0.0034290717],"genre_scores_gemma":[0.96146417,0.00001727919,0.038123924,0.000032988177,0.000115184186,0.000030582756,0.000006412737,0.000010045855,0.00019941523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982106,0.00005971633,0.00039264606,0.00044208247,0.0006507344,0.00024422118],"domain_scores_gemma":[0.9985108,0.00036058616,0.00019001639,0.00058705587,0.00022646881,0.00012511108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045581386,0.00011709419,0.00025575148,0.00010096321,0.00013846008,0.00007663684,0.00123543,0.000073208415,0.00010515058],"category_scores_gemma":[0.00007857167,0.000099634606,0.000045397344,0.00026819744,0.000044534274,0.0015192282,0.0005587172,0.00007600887,0.000031695683],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022588267,0.00025478884,0.0056005884,0.00017413711,0.00005658532,0.000018543638,0.0009985776,0.0000049879463,0.010736058,0.00002272734,0.0019770951,0.9801333],"study_design_scores_gemma":[0.0030272987,0.0010474417,0.0132256355,0.0021482622,0.00005462877,0.00056444126,0.00057965476,0.78867835,0.17366086,0.0002901059,0.015247841,0.0014754572],"about_ca_topic_score_codex":0.00016143009,"about_ca_topic_score_gemma":0.0000085470665,"teacher_disagreement_score":0.97865784,"about_ca_system_score_codex":0.000025701853,"about_ca_system_score_gemma":0.0002585043,"threshold_uncertainty_score":0.40629813},"labels":[],"label_agreement":null},{"id":"W2150216628","doi":"10.1109/titb.2010.2087385","title":"Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":256,"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 Universitaire de Gériatrie de Montréal; Université de Montréal","funders":"","keywords":"Silhouette; Computer vision; Computer science; Artificial intelligence; Sitting; Position (finance); Sensitivity (control systems); Medicine; Engineering; Business","score_opus":0.00776068218227131,"score_gpt":0.24995854664394765,"score_spread":0.24219786446167635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150216628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17436221,0.0000012147128,0.82086295,0.002920573,0.00064886495,0.00048094545,0.0000436938,0.00063487445,0.00004465],"genre_scores_gemma":[0.9920746,0.0000019573322,0.007091732,0.0005633762,0.000024423549,0.00018504857,0.000043092383,0.000010805017,0.0000049693917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980844,0.00010504465,0.0006006356,0.0003631049,0.0005078319,0.0003389336],"domain_scores_gemma":[0.99835163,0.00032329498,0.00015986069,0.0007807631,0.0002525808,0.00013184683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060947455,0.00025792493,0.0002926837,0.0016947318,0.00028994938,0.00007360558,0.0004679523,0.00041468756,0.00001649966],"category_scores_gemma":[0.00008093442,0.00021652239,0.000053216612,0.0020807248,0.00021070866,0.0014275385,0.0000053759777,0.0009301181,0.000080294645],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090212096,0.0010038681,0.00050721946,0.00006660612,0.000052495554,0.00003053816,0.00085944845,0.0031312557,0.11399204,0.00069375685,0.00006682702,0.8786938],"study_design_scores_gemma":[0.0038244366,0.002234897,0.002691588,0.00013886436,0.000025094061,0.00014383969,0.00031832146,0.7667801,0.2212373,0.0002610865,0.0019382053,0.00040626118],"about_ca_topic_score_codex":0.0002938583,"about_ca_topic_score_gemma":0.0015897102,"teacher_disagreement_score":0.87828755,"about_ca_system_score_codex":0.00022536717,"about_ca_system_score_gemma":0.000102920734,"threshold_uncertainty_score":0.8829527},"labels":[],"label_agreement":null},{"id":"W2150266240","doi":"10.1109/itng.2009.60","title":"Implementation of a Physical Activity Monitoring System for the Elderly People with Built-in Vital Sign and Fall Detection","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wearable computer; Computer science; Remote patient monitoring; Wireless; Heart beat; Real-time computing; Embedded system; Human–computer interaction; Computer security; Telecommunications; Medicine","score_opus":0.021507148856761103,"score_gpt":0.2898479514445397,"score_spread":0.2683408025877786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150266240","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57123524,0.000005126347,0.4281886,0.00008828264,0.000046076562,0.00037630656,0.0000012352717,0.000037997917,0.000021158363],"genre_scores_gemma":[0.9993185,6.2679516e-7,0.0005241595,0.0000026258656,0.00006001281,0.00008429395,1.7735361e-7,0.0000039504407,0.0000056565013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993064,0.000056498804,0.000112296184,0.00021885704,0.00017020846,0.00013576272],"domain_scores_gemma":[0.9992653,0.00034225595,0.00010577541,0.00018317766,0.00007673473,0.000026751808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020835467,0.000089793306,0.0001555186,0.00006389937,0.000077516575,0.00008675723,0.00014082789,0.000022886961,2.1343014e-7],"category_scores_gemma":[0.000006969436,0.00006218649,0.00003601325,0.00021989139,0.000009726953,0.0005931891,0.000026340382,0.000055236604,6.036128e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003233197,0.000054165328,0.003915963,0.00003287691,0.000014037288,4.933636e-7,0.0012054312,0.000016369873,0.04260429,0.00020076247,0.0000013282162,0.95192194],"study_design_scores_gemma":[0.0021173884,0.0026788572,0.40644172,0.0001062366,0.000030375368,0.00005000306,0.005736814,0.055887233,0.52651715,0.00013787867,0.000024291834,0.00027206293],"about_ca_topic_score_codex":0.0014843271,"about_ca_topic_score_gemma":0.0028472303,"teacher_disagreement_score":0.9516499,"about_ca_system_score_codex":0.000060069808,"about_ca_system_score_gemma":0.000027329444,"threshold_uncertainty_score":0.25358912},"labels":[],"label_agreement":null},{"id":"W2151870834","doi":"10.1109/icme.2009.5202853","title":"A Framework to bridge social network and body sensor network: An e-Health perspective","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Ottawa","funders":"","keywords":"Computer science; Event (particle physics); Bridge (graph theory); Raw data; Process (computing); Context (archaeology); Body area network; Perspective (graphical); Wireless sensor network; Human–computer interaction; Social network (sociolinguistics); Service (business); Social media; Data science; World Wide Web; Artificial intelligence; Computer network; Medicine","score_opus":0.043187612580038226,"score_gpt":0.3358837760197196,"score_spread":0.29269616343968136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151870834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017002089,0.00019417264,0.9458483,0.02993695,0.0005589713,0.00064347516,0.0000045650513,0.000562028,0.005249443],"genre_scores_gemma":[0.9364953,0.000008232757,0.04684154,0.014326587,0.0020180654,0.0000137444695,0.0000016720649,0.000012757718,0.00028211976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9976717,0.00030792237,0.00028874588,0.00071703194,0.00030580512,0.00070876157],"domain_scores_gemma":[0.99869233,0.00017712371,0.00012840076,0.00042176642,0.00019407486,0.00038627608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000699292,0.00021533505,0.00038653755,0.00007270751,0.0005156335,0.00040127317,0.00040843987,0.00012436563,0.000029012885],"category_scores_gemma":[0.00005411287,0.00021682419,0.00007099673,0.0007316261,0.000029915269,0.0005805419,0.00015664328,0.0002854306,0.00009510662],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007914456,0.00031091672,0.0017332151,0.000012970014,0.00008138286,0.000038792637,0.020480793,0.00016402102,0.000041564734,0.43859804,0.100412846,0.43804634],"study_design_scores_gemma":[0.0015813921,0.0034968115,0.68405837,0.00034236253,0.000029767403,0.00036437594,0.002569803,0.028177362,0.000036346417,0.23057467,0.046469152,0.0022996012],"about_ca_topic_score_codex":0.00036836183,"about_ca_topic_score_gemma":0.00022285912,"teacher_disagreement_score":0.9194932,"about_ca_system_score_codex":0.00017098027,"about_ca_system_score_gemma":0.00013826824,"threshold_uncertainty_score":0.8841834},"labels":[],"label_agreement":null},{"id":"W2151940868","doi":"10.1109/giis.2009.5307048","title":"A context dissemination scheme for adaptive and fault-tolerant services in heterogeneous environments","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Computer science; Middleware (distributed applications); Context (archaeology); Distributed computing; Fault tolerance; Overlay network; Protocol (science); Context model; Dissemination; Personalization; Computer network; Ubiquitous computing; Overlay; Context awareness; Adaptation (eye); Heterogeneous network; World Wide Web; Human–computer interaction; Wireless network; The Internet; Wireless; Artificial intelligence; Operating system; Telecommunications","score_opus":0.015770036871226346,"score_gpt":0.2549727695740354,"score_spread":0.23920273270280906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151940868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54773515,0.00021145727,0.45019567,0.0006796965,0.00007404032,0.00067967625,0.000008135619,0.00006535156,0.00035079653],"genre_scores_gemma":[0.9954581,0.0000071813533,0.0038088073,0.00041595232,0.000015987027,0.00005304478,0.0000041237104,0.000004436237,0.00023235443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990983,0.000039285464,0.00019838441,0.00034547833,0.00014530675,0.00017322488],"domain_scores_gemma":[0.9995244,0.00013159981,0.000077694676,0.00018351055,0.000024552393,0.00005824541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015020069,0.00011772724,0.00016440947,0.000087080174,0.000052169253,0.00009815799,0.00020420423,0.00005253343,0.0000055887754],"category_scores_gemma":[0.0000077124905,0.000110876215,0.00003408536,0.000091191505,0.000013570569,0.00056614843,0.000052922485,0.000046286717,0.000014003501],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005988925,0.00026112786,0.0011252483,0.000025807636,0.000024381105,0.000014487539,0.00353101,0.000018745355,0.016318606,0.0019530295,0.0000553125,0.9766123],"study_design_scores_gemma":[0.0033589243,0.001022241,0.034106188,0.00037728657,0.000012388463,0.00013629772,0.0014194825,0.9031216,0.04659855,0.0033138343,0.0056724483,0.00086076127],"about_ca_topic_score_codex":0.000042745607,"about_ca_topic_score_gemma":0.00019056466,"teacher_disagreement_score":0.9757516,"about_ca_system_score_codex":0.00004654794,"about_ca_system_score_gemma":0.000007812085,"threshold_uncertainty_score":0.4521401},"labels":[],"label_agreement":null},{"id":"W2152125372","doi":"10.1016/j.jnca.2004.04.002","title":"ACAI: agent-based context-aware infrastructure for spontaneous applications","year":2004,"lang":"en","type":"article","venue":"Journal of Network and Computer Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Context (archaeology); Provisioning; Context management; Ubiquitous computing; Ontology; Context model; Service (business); Parallels; Context awareness; State (computer science); Human–computer interaction; Computer security; Knowledge management; Artificial intelligence; Computer network; Object (grammar); Business; Phone","score_opus":0.013295310568057704,"score_gpt":0.24380701498108692,"score_spread":0.2305117044130292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152125372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015550487,0.00050380005,0.99508244,0.001510925,0.00023688874,0.0009681294,0.00001640584,0.00007528164,0.000051112187],"genre_scores_gemma":[0.8686345,0.000036336973,0.12797324,0.0013736609,0.0015604165,0.00036988535,0.000012095843,0.000018320972,0.000021511156],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99855644,0.000048688904,0.00057771,0.0003152738,0.00023388052,0.0002679957],"domain_scores_gemma":[0.9980057,0.00033728944,0.00054381666,0.00041756986,0.00048304017,0.00021261173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028163128,0.00019365753,0.00034520632,0.00013406973,0.00033666368,0.00022768654,0.00071458484,0.00010626132,0.000004043404],"category_scores_gemma":[0.00000417506,0.0001804628,0.00018035005,0.00045587955,0.00006038347,0.00030262175,0.00010301551,0.00023761693,0.000009422855],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039539595,0.00023093304,0.00029926284,0.0000681256,0.00014488393,0.000028913773,0.00027782272,0.023358932,0.00006267347,0.04139367,0.006038934,0.9280563],"study_design_scores_gemma":[0.0050813467,0.00076718896,0.004730422,0.00029083545,0.00015648591,0.0055869413,0.00011988648,0.087077975,0.00019208076,0.068709806,0.82641184,0.0008752039],"about_ca_topic_score_codex":0.0000042980614,"about_ca_topic_score_gemma":0.00001596056,"teacher_disagreement_score":0.9271811,"about_ca_system_score_codex":0.000102024365,"about_ca_system_score_gemma":0.00023077018,"threshold_uncertainty_score":0.73590595},"labels":[],"label_agreement":null},{"id":"W2152919925","doi":"10.1109/ubicomm.2008.70","title":"Wearable Device for Patients Affected by Neurodegenerative Diseases","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wearable computer; European commission; Czech; Affect (linguistics); Work (physics); Population; Wearable technology; Quarter (Canadian coin); Elderly people; Commission; Population ageing; European population; Computer science; Medicine; Internet privacy; Business; Gerontology; Environmental health; Geography; Psychology; Engineering; European union; Embedded system","score_opus":0.022895288556316763,"score_gpt":0.2368013432780066,"score_spread":0.21390605472168983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152919925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5031875,0.00014761447,0.49260816,0.00034111337,0.0005411007,0.0009417648,0.000092355345,0.00044984007,0.0016906067],"genre_scores_gemma":[0.9950407,0.000007676215,0.0020941312,0.00054511236,0.000042079788,0.00014487628,0.000024654591,0.000010641303,0.002090126],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9989026,0.0001034372,0.00016053327,0.00039446662,0.00021467246,0.0002242891],"domain_scores_gemma":[0.99898505,0.00025894647,0.00007496065,0.0002930323,0.00026969364,0.00011830264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047577018,0.00012980275,0.00016717825,0.000052242583,0.0002259086,0.000079939,0.00033892697,0.00003373476,0.000022636666],"category_scores_gemma":[0.00011861632,0.00011619481,0.00007147015,0.00024800436,0.000025010815,0.0007193344,0.00008963604,0.000044885062,0.00009489747],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098107754,0.0044293283,0.07396883,0.0001314278,0.0002007264,0.000022446062,0.001896784,0.000035216122,0.012800238,0.0016203425,0.72363997,0.18115659],"study_design_scores_gemma":[0.019564403,0.0041644047,0.29088995,0.00026487157,0.00012443488,0.00011178565,0.00017645265,0.2040745,0.21110019,0.0014669462,0.2632378,0.004824252],"about_ca_topic_score_codex":0.000049801554,"about_ca_topic_score_gemma":0.000016996919,"teacher_disagreement_score":0.49185324,"about_ca_system_score_codex":0.000030837924,"about_ca_system_score_gemma":0.000056800956,"threshold_uncertainty_score":0.47382867},"labels":[],"label_agreement":null},{"id":"W2152975774","doi":"10.1109/mis.2015.18","title":"Exploiting Passive RFID Technology for Activity Recognition in Smart Homes","year":2015,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec","keywords":"Trilateration; Computer science; Activity recognition; Set (abstract data type); Home automation; Human–computer interaction; Cognition; Smart environment; Representation (politics); Computer security; Artificial intelligence; Internet of Things; Telecommunications; Engineering","score_opus":0.11935698790469843,"score_gpt":0.30585456908098735,"score_spread":0.18649758117628892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152975774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29510173,0.00024338158,0.69701576,0.00058078894,0.004536022,0.0014890248,0.000026398477,0.00046159848,0.0005452772],"genre_scores_gemma":[0.9970839,0.000015373047,0.0010036804,0.00004759042,0.0003031003,0.0012559978,0.000008646439,0.000031560296,0.0002501296],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972512,0.00029489485,0.00069105724,0.00076407596,0.0004284009,0.00057035405],"domain_scores_gemma":[0.99749583,0.00055578595,0.00045937503,0.0006378732,0.00066312123,0.00018800367],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011737641,0.00030106877,0.0005778863,0.0008462465,0.00009890495,0.0002708635,0.0007431378,0.00027777217,0.000003249892],"category_scores_gemma":[0.00048629974,0.00030989558,0.00013312772,0.00094693346,0.00006021549,0.0010304864,0.00012806979,0.00027943368,0.00034962245],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015201597,0.0006343899,0.008668421,0.0004665359,0.00018585807,0.00011030323,0.0041333074,0.0003756492,0.010269265,0.0021821153,0.0045353584,0.96828675],"study_design_scores_gemma":[0.00650655,0.0023092353,0.00083689316,0.004476646,0.00008658913,0.0014514948,0.020747162,0.22009939,0.6334565,0.024579518,0.08114766,0.0043023243],"about_ca_topic_score_codex":0.0005762035,"about_ca_topic_score_gemma":0.0002697526,"teacher_disagreement_score":0.96398443,"about_ca_system_score_codex":0.0005167215,"about_ca_system_score_gemma":0.00019852023,"threshold_uncertainty_score":0.9999353},"labels":[],"label_agreement":null},{"id":"W2153524372","doi":"10.1109/sehc.2009.5069602","title":"Software engineering for health education and care delivery systems: The Smart Condo project","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Center for Advanced Study, University of Illinois at Urbana-Champaign","keywords":"Variety (cybernetics); Geographic information system; Visualization; Computer science; Wireless sensor network; Data visualization; Sensor web; Software; Environmental data; World Wide Web; Telecommunications; Geography; Computer network; Remote sensing; Key distribution in wireless sensor networks","score_opus":0.022953997876125596,"score_gpt":0.27795862766010726,"score_spread":0.25500462978398164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153524372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024626108,0.004732572,0.9637549,0.002532845,0.0011315306,0.0023821858,0.00001584185,0.0004914915,0.00033254357],"genre_scores_gemma":[0.9911336,0.000014921748,0.007565248,0.0007744398,0.0001035372,0.00016084847,0.0000071984164,0.000006299487,0.00023391162],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992066,0.000056831792,0.00018583618,0.0002498761,0.0001222053,0.00017867617],"domain_scores_gemma":[0.9991595,0.00023597153,0.00008028473,0.00027567442,0.00019616516,0.000052373067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025674736,0.000102784805,0.00014926717,0.00008278792,0.00014999349,0.0002481175,0.00022475682,0.00003280756,5.753443e-7],"category_scores_gemma":[0.00007079889,0.00007642938,0.00003688446,0.00015362477,0.000007528431,0.00037430797,0.000036418707,0.000060109272,0.0000037257237],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009073613,0.00008390035,0.0009096708,0.0005474239,0.00003272811,7.441983e-7,0.008737735,0.000052235617,0.000113880094,0.008530534,0.017456371,0.9635257],"study_design_scores_gemma":[0.0039164997,0.0027121296,0.02994147,0.0024167383,0.00006760044,0.0010941536,0.031268142,0.19007109,0.0015917803,0.00045653852,0.73395896,0.0025049099],"about_ca_topic_score_codex":0.00037204128,"about_ca_topic_score_gemma":0.000049405247,"teacher_disagreement_score":0.9665075,"about_ca_system_score_codex":0.00009302236,"about_ca_system_score_gemma":0.00038945823,"threshold_uncertainty_score":0.31166998},"labels":[],"label_agreement":null},{"id":"W2153744068","doi":"10.1109/icc.2013.6654989","title":"AppaaS: Provisioning of context-aware mobile applications as a service","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Provisioning; Computer science; Context (archaeology); Agile software development; Mobile computing; Service (business); World Wide Web; Context awareness; Mobile device; Mobile service; Telecommunications; Software engineering; Business; Marketing","score_opus":0.013580047332737401,"score_gpt":0.25148935369431924,"score_spread":0.23790930636158183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153744068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03841009,0.00012404345,0.93965954,0.0011974152,0.00012273988,0.0028340123,0.0000072241432,0.00050650846,0.017138463],"genre_scores_gemma":[0.9916283,0.0000023113018,0.0042147776,0.00095895334,0.000038448452,0.002002453,0.0000048561988,0.000011787772,0.0011381363],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99860233,0.00007131372,0.00038407074,0.0004162533,0.00030234628,0.00022369702],"domain_scores_gemma":[0.99794006,0.00027095634,0.00020339535,0.0007707389,0.0006911361,0.00012373623],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018635765,0.00014694565,0.00024586346,0.00012366439,0.00010997713,0.00016777567,0.0008288231,0.00007452151,0.00040752356],"category_scores_gemma":[0.000018156046,0.00013135352,0.00007435552,0.00064704154,0.000031278338,0.0010473382,0.00030638452,0.00010994083,0.0025596677],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031504171,0.00028786054,0.0010720127,0.0001450366,0.000055107863,0.000001400649,0.0019199513,0.000028711092,0.005710341,0.012968757,0.0026225678,0.9751851],"study_design_scores_gemma":[0.0052547455,0.0013240186,0.009148849,0.00088203384,0.00008217372,0.0005062935,0.022305502,0.4115873,0.08696453,0.02190418,0.43656138,0.0034789874],"about_ca_topic_score_codex":0.0020342127,"about_ca_topic_score_gemma":0.00012283985,"teacher_disagreement_score":0.9717061,"about_ca_system_score_codex":0.00003302817,"about_ca_system_score_gemma":0.00012303819,"threshold_uncertainty_score":0.9982169},"labels":[],"label_agreement":null},{"id":"W2154932302","doi":"10.1109/cscwd.2015.7231023","title":"Human activity detection based on multiple smart phone sensors and machine learning algorithms","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Accelerometer; Gyroscope; Computer science; Smart phone; Artificial intelligence; Phone; Step detection; Android (operating system); Machine learning; Mobile phone; Activity recognition; Algorithm; Real-time computing; Computer vision; Engineering","score_opus":0.06040810057479392,"score_gpt":0.26452904274337313,"score_spread":0.2041209421685792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154932302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44849852,0.000007673052,0.54824394,0.00023155058,0.000280054,0.00019331215,0.000002144926,0.00043111123,0.0021117034],"genre_scores_gemma":[0.9972033,3.5341293e-7,0.0018225197,0.00010365173,0.00006278686,0.000018720026,0.000002227756,0.000013413004,0.0007730523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850893,0.00032414024,0.0001515721,0.00045574855,0.00034334557,0.00021623536],"domain_scores_gemma":[0.9989588,0.00030994194,0.00010785879,0.0003166475,0.000117460695,0.00018926666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062612083,0.00016909748,0.00020126667,0.00018039193,0.00022018398,0.00017857067,0.00016320977,0.000077193115,0.000015324853],"category_scores_gemma":[0.0002288041,0.00015845578,0.00004842453,0.0002642982,0.000031692485,0.00050532253,0.00008510334,0.0002688998,0.00008960377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078546786,0.00039370725,0.017654318,0.000029011822,0.00004065372,0.000034331337,0.00094590517,0.0011398994,0.045492496,0.000031346513,0.0002104179,0.93394935],"study_design_scores_gemma":[0.0013075034,0.0002871501,0.008713316,0.000015405396,0.000003906419,0.000027517159,0.00005528979,0.93377286,0.053824805,0.000025008621,0.0017382241,0.00022902645],"about_ca_topic_score_codex":0.0018526233,"about_ca_topic_score_gemma":0.00095157546,"teacher_disagreement_score":0.93372035,"about_ca_system_score_codex":0.00009456218,"about_ca_system_score_gemma":0.000031283886,"threshold_uncertainty_score":0.64616394},"labels":[],"label_agreement":null},{"id":"W2155094613","doi":"10.1109/tim.2010.2084190","title":"Augmenting Context Awareness by Combining Body Sensor Networks and Social Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Context-Aware Activity Recognition Systems","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":"University of Ottawa","funders":"","keywords":"Computer science; Context (archaeology); Context awareness; Social network (sociolinguistics); World Wide Web; Association (psychology); Space (punctuation); Data science; Human–computer interaction; Social media","score_opus":0.03348104498209438,"score_gpt":0.25282371111244895,"score_spread":0.21934266613035458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155094613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16619208,0.000033126296,0.831049,0.0004961865,0.0016292844,0.00033820583,0.0000070996352,0.00013130919,0.00012369912],"genre_scores_gemma":[0.9987564,0.000025258621,0.00046985908,0.00050790346,0.00006287322,0.00009410665,0.000005327615,0.000015695976,0.00006255631],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834466,0.00013403162,0.0003495267,0.00043580343,0.00045608694,0.00027991232],"domain_scores_gemma":[0.99929345,0.0000708676,0.00016837253,0.0001715695,0.00017046776,0.0001252988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006042819,0.00021250497,0.00022040281,0.000113069225,0.0007710535,0.0003682146,0.00013784689,0.00012106176,0.000030381532],"category_scores_gemma":[0.0000037167024,0.00022867516,0.00005844511,0.00016654938,0.00007598656,0.0005413591,0.0000054473653,0.00037312944,0.000005703289],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006439813,0.00031977645,0.00087977573,0.00004074219,0.00019246468,0.0000028245563,0.0018654945,0.00046400932,0.031217968,0.00037962777,0.0005259343,0.964047],"study_design_scores_gemma":[0.01945644,0.001004235,0.009451153,0.00051369454,0.00038454327,0.00030316578,0.0076728575,0.81961226,0.122530386,0.00017823106,0.0158881,0.0030049535],"about_ca_topic_score_codex":0.00013075605,"about_ca_topic_score_gemma":0.00029635284,"teacher_disagreement_score":0.96104205,"about_ca_system_score_codex":0.000076950426,"about_ca_system_score_gemma":0.00004668773,"threshold_uncertainty_score":0.93251026},"labels":[],"label_agreement":null},{"id":"W2156004284","doi":"10.1109/wimob.2006.1696384","title":"AmbiTalk: Enhancing Wireless Communication Services through the Automatic Adaptation of Mobile Communication","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Queen's University","funders":"","keywords":"Computer science; Bluetooth; Session Initiation Protocol; Adaptation (eye); Session (web analytics); Wireless; Mobile device; Mobile telephony; Computer network; Mobile computing; Protocol (science); Architecture; Mobile radio; Telecommunications; Server; Operating system; World Wide Web","score_opus":0.017848308963541632,"score_gpt":0.25310591230162505,"score_spread":0.2352576033380834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156004284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42498034,0.00067687605,0.5656391,0.0006532044,0.00006939274,0.0005721285,0.0000026292385,0.00029336632,0.0071130153],"genre_scores_gemma":[0.98483527,0.00003060792,0.014659943,0.000116084935,0.000014149555,0.00014775629,0.0000285604,0.000009093328,0.00015855576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983681,0.0004492335,0.00054217764,0.0001873937,0.00031243713,0.00014067012],"domain_scores_gemma":[0.99703366,0.0006773547,0.000484258,0.0015091504,0.00027902308,0.000016563374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005955526,0.00012013394,0.0001926847,0.000055642235,0.00027967338,0.00017342668,0.001360637,0.000058297297,0.000021573036],"category_scores_gemma":[0.0000089718105,0.000094683055,0.00006582416,0.00042306544,0.00007712988,0.0014971016,0.0003138303,0.00011592138,0.000046810936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016122514,0.0012619044,0.0026015018,0.00078726537,0.0001782004,0.0000015828116,0.07825104,0.006005777,0.02794205,0.1978427,0.0012422528,0.6838696],"study_design_scores_gemma":[0.00061359635,0.00008391031,0.006389354,0.0004558937,0.000028365512,0.000025812498,0.01186838,0.93616164,0.029795121,0.011834184,0.0023903952,0.00035332743],"about_ca_topic_score_codex":0.010518981,"about_ca_topic_score_gemma":0.008221494,"teacher_disagreement_score":0.9301559,"about_ca_system_score_codex":0.000054600478,"about_ca_system_score_gemma":0.00004510664,"threshold_uncertainty_score":0.9960701},"labels":[],"label_agreement":null},{"id":"W2156821723","doi":"10.1145/1535654.1535684","title":"Getting mobile with mobile devices","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Victoria","funders":"","keywords":"Public transport; Mobile device; Computer science; Population; Visually impaired; Mobile technology; Mobile computing; Internet privacy; Computer security; Business; Telecommunications; Transport engineering; World Wide Web; Engineering; Human–computer interaction; Medicine","score_opus":0.013265264432230413,"score_gpt":0.24978062569636597,"score_spread":0.23651536126413555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156821723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31430742,0.00022522087,0.630428,0.00051675685,0.00017704463,0.00068871037,0.0000012652811,0.0009739629,0.05268161],"genre_scores_gemma":[0.9838869,0.0000015084433,0.014135037,0.00095279,0.00006284441,0.000069040376,8.975605e-7,0.0000051945235,0.0008857705],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99895304,0.00004747842,0.00016798638,0.00034859465,0.00024872023,0.00023415883],"domain_scores_gemma":[0.99919915,0.00010935996,0.00008552815,0.0004161196,0.00010733661,0.00008250925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020052854,0.00012112884,0.00015573081,0.000076527824,0.00010135668,0.00024312637,0.00046150124,0.00003662988,0.00006716],"category_scores_gemma":[0.0000067544443,0.000091942326,0.000039257204,0.00032577844,0.000015164965,0.0009069603,0.00006147409,0.000085180814,0.00023668683],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053365866,0.00013890797,0.0018762387,0.000011632562,0.00001918348,0.000026922102,0.00090871204,0.00012672713,0.0021093178,0.0014188422,0.00095547846,0.9924027],"study_design_scores_gemma":[0.006699417,0.010582349,0.07956587,0.00096394814,0.000085837586,0.0022597432,0.005814232,0.15320016,0.1302691,0.0046161115,0.59990513,0.0060380865],"about_ca_topic_score_codex":0.00004266848,"about_ca_topic_score_gemma":0.00006029216,"teacher_disagreement_score":0.9863646,"about_ca_system_score_codex":0.000027686829,"about_ca_system_score_gemma":0.000045445715,"threshold_uncertainty_score":0.37492993},"labels":[],"label_agreement":null},{"id":"W2156895027","doi":"10.1177/154193120304701231","title":"A Work Domain Analysis for Diabetes Management","year":2003,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Domain (mathematical analysis); Computer science; Work (physics); Resource (disambiguation); Knowledge management; Human–computer interaction; Interface (matter); Process management; Engineering","score_opus":0.01835330489285493,"score_gpt":0.2246091460808625,"score_spread":0.20625584118800755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156895027","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99560344,0.000055816232,0.0029728294,0.000089947345,0.00010637513,0.00031113115,0.000011575364,0.00004564949,0.0008032146],"genre_scores_gemma":[0.98891246,0.000007738144,0.010803808,0.00007645954,0.000027038273,0.000034535282,0.0000010225367,0.000010775844,0.00012618149],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988959,0.000012965385,0.0003115229,0.000363138,0.00013666313,0.0002798431],"domain_scores_gemma":[0.9991633,0.00013135577,0.00035558114,0.00013335705,0.0001562761,0.000060101014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008132267,0.0001701482,0.00031088293,0.00006587786,0.00047092044,0.00020280086,0.00047721367,0.00006756895,0.0000014056359],"category_scores_gemma":[0.0000414696,0.00013369412,0.00044404948,0.0005133517,0.00007360732,0.00039510863,0.00026518517,0.00009421647,3.1532772e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011576658,0.00012789125,0.9209378,0.00042495696,0.0022520814,4.499962e-8,0.024749808,0.000049809016,0.0025225393,0.045440875,0.0019702131,0.0015124119],"study_design_scores_gemma":[0.005042255,0.00049098814,0.7225867,0.0013223526,0.0026147317,0.000004103005,0.099245876,0.0057109846,0.066895485,0.05725382,0.03496603,0.0038666602],"about_ca_topic_score_codex":0.000007486606,"about_ca_topic_score_gemma":0.00000268931,"teacher_disagreement_score":0.19835109,"about_ca_system_score_codex":0.000059887854,"about_ca_system_score_gemma":0.000008400998,"threshold_uncertainty_score":0.5451888},"labels":[],"label_agreement":null},{"id":"W2158928271","doi":"10.1109/icdew.2007.4400972","title":"Multimedia Content Repurposing in Ambient Intelligent Environments","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Repurposing; Computer science; Multimedia; Context (archaeology); Human–computer interaction; Task (project management); World Wide Web; Ubiquitous computing; Architecture; Engineering","score_opus":0.0667383061000671,"score_gpt":0.27112902737409383,"score_spread":0.20439072127402674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158928271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14703593,0.00008998969,0.8485317,0.0001875625,0.0005970831,0.00037874127,5.8029656e-7,0.000092913266,0.003085476],"genre_scores_gemma":[0.9878893,0.000011945977,0.010515819,0.0004046961,0.000036764803,0.000016284812,0.000001633627,0.000007982693,0.0011155541],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99830157,0.00006768832,0.00047101028,0.00043550905,0.00036078464,0.0003634663],"domain_scores_gemma":[0.9990615,0.00021395639,0.00010671407,0.00045299096,0.000023583538,0.00014122587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096828555,0.00013780047,0.00018087245,0.00021485763,0.000043146254,0.000082014856,0.00041290178,0.00006135063,0.000052754473],"category_scores_gemma":[0.00006071898,0.00012969403,0.000064498905,0.00024541182,0.00003322493,0.00043046247,0.00024235384,0.00013938901,0.00039059247],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016648004,0.00039246157,0.020960202,0.0000069100743,0.000019900639,0.000119141754,0.0020580722,0.00001267722,0.03174238,0.0008261736,0.00014429782,0.94370115],"study_design_scores_gemma":[0.0025488893,0.00029403757,0.20779444,0.0002618096,0.000009853141,0.00018096689,0.0018787737,0.05532947,0.65443254,0.00045970356,0.07562656,0.0011829458],"about_ca_topic_score_codex":0.0003588233,"about_ca_topic_score_gemma":0.0003978121,"teacher_disagreement_score":0.9425182,"about_ca_system_score_codex":0.00025909033,"about_ca_system_score_gemma":0.000018341721,"threshold_uncertainty_score":0.5288769},"labels":[],"label_agreement":null},{"id":"W2159342979","doi":"10.1109/cmpsac.2003.1245324","title":"Context aware deployment for mobile users","year":2004,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Software deployment; Computer science; Terminal (telecommunication); Context (archaeology); Ubiquitous computing; Mobile device; Resource (disambiguation); Operating system; Computer network","score_opus":0.032160906120407035,"score_gpt":0.27440886909928214,"score_spread":0.2422479629788751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159342979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034579907,0.00005994234,0.9614016,0.0010562878,0.00052280794,0.0009165152,0.000008521123,0.00044838205,0.0010060449],"genre_scores_gemma":[0.99130183,0.0000025824015,0.006404982,0.0008957989,0.000059855287,0.0004948021,0.0000031001912,0.000012783147,0.0008242324],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99892104,0.00002501608,0.00021553617,0.0003754095,0.00020150916,0.00026147568],"domain_scores_gemma":[0.99909514,0.00014493175,0.00007028818,0.00043006183,0.00014875637,0.0001108266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018383407,0.00012917173,0.00017507804,0.000074758,0.000107570864,0.00015491954,0.00047527225,0.000051429797,0.000036280813],"category_scores_gemma":[0.000020897876,0.000117479394,0.00011535458,0.00016589771,0.000021538726,0.000573261,0.00011179798,0.000055115688,0.00025686444],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003537134,0.0005806767,0.0010026383,0.00008735494,0.00015497983,0.000023381252,0.0030183257,0.00047691236,0.0030747068,0.0599615,0.012807487,0.9187767],"study_design_scores_gemma":[0.016512485,0.0024877859,0.0017095266,0.0003676028,0.000055112312,0.00029677554,0.004656208,0.020618515,0.24332224,0.021856217,0.68545824,0.0026593131],"about_ca_topic_score_codex":0.00023634575,"about_ca_topic_score_gemma":0.00037060873,"teacher_disagreement_score":0.95672196,"about_ca_system_score_codex":0.00013431486,"about_ca_system_score_gemma":0.00011206075,"threshold_uncertainty_score":0.4790671},"labels":[],"label_agreement":null},{"id":"W2159825898","doi":"10.1109/icisa.2011.5772404","title":"A Study on Machine Learning Algorithms for Fall Detection and Movement Classification","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Wearable computer; Statistical classification; Feature selection; Feature (linguistics); Movement (music); Algorithm; Embedded system","score_opus":0.14225861767332004,"score_gpt":0.2968400543053815,"score_spread":0.15458143663206148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159825898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14380161,0.000008259517,0.85333294,0.0000632593,0.00016313167,0.0007224024,9.481848e-7,0.00017471537,0.0017327552],"genre_scores_gemma":[0.9958133,0.0000014771405,0.003428518,0.000091808084,0.00002656179,0.00020339027,9.0559024e-7,0.000006560831,0.00042745046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991715,0.00008771954,0.00015740345,0.0003276305,0.00014282833,0.000112949965],"domain_scores_gemma":[0.9994995,0.00009176366,0.00008582845,0.0001996032,0.00007814472,0.000045176374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039004316,0.00008925006,0.00009939976,0.00010224253,0.00012875159,0.00007827567,0.00013504883,0.000028778715,0.000005065753],"category_scores_gemma":[0.000033096152,0.00007854121,0.000026507316,0.00010995182,0.00000800058,0.00027761536,0.000059604285,0.00007685695,0.000020301422],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002891745,0.00045957952,0.0094317915,0.000008251468,0.000040024443,0.0000016898709,0.004355008,0.0000011399104,0.0027854887,0.0011739265,0.000013382972,0.9817008],"study_design_scores_gemma":[0.0037153368,0.0062133507,0.33661476,0.00003352051,0.000033642304,0.000021125325,0.005369331,0.6188566,0.023739437,0.0021961627,0.0025564034,0.00065031566],"about_ca_topic_score_codex":0.00031907868,"about_ca_topic_score_gemma":0.0003648251,"teacher_disagreement_score":0.9810505,"about_ca_system_score_codex":0.000036214522,"about_ca_system_score_gemma":0.00000827521,"threshold_uncertainty_score":0.32028177},"labels":[],"label_agreement":null},{"id":"W2160381738","doi":"10.1109/glocom.2010.5684103","title":"Counteracting Wireless Congestion in Data Distribution with Adaptive Batching Techniques","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Computer science; Scalability; Wireless; Distributed computing; Exploit; Computer network; Realization (probability); Mobile computing; Channel (broadcasting); Telecommunications; Computer security; Database","score_opus":0.034579897167501,"score_gpt":0.2796055038224108,"score_spread":0.2450256066549098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160381738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22162946,0.0000025392071,0.7758202,0.0005059568,0.00019406936,0.00024255207,0.000026771724,0.00029698884,0.0012814467],"genre_scores_gemma":[0.98795533,0.0000012383433,0.01178051,0.000058513997,0.000061451894,0.000026448532,0.00007773719,0.0000068707523,0.00003191303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883986,0.0000840365,0.00020257253,0.0004565585,0.0002354691,0.00018147998],"domain_scores_gemma":[0.99864817,0.00031299185,0.00013928632,0.0007066582,0.00014571592,0.00004716147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007190515,0.00011778164,0.0001483143,0.00007179139,0.00008290085,0.00023164816,0.0006967216,0.00006860604,0.0000103720895],"category_scores_gemma":[0.00008273951,0.00010042482,0.000012153302,0.00028165744,0.000031836364,0.0028121131,0.0003129937,0.00038165206,0.000015634389],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006152742,0.00020995362,0.017595759,0.000021676842,0.000027600703,0.000053608557,0.0008047397,0.0000038536,0.024214774,0.018605942,0.00059472,0.93780583],"study_design_scores_gemma":[0.0019741673,0.00040412194,0.04720253,0.0011319718,0.000027852837,0.0010329477,0.0016979493,0.8398184,0.083325766,0.0015785076,0.019956153,0.0018496418],"about_ca_topic_score_codex":0.0009258137,"about_ca_topic_score_gemma":0.006217363,"teacher_disagreement_score":0.9359562,"about_ca_system_score_codex":0.00005939048,"about_ca_system_score_gemma":0.00008208074,"threshold_uncertainty_score":0.4095205},"labels":[],"label_agreement":null},{"id":"W2160709886","doi":"10.1109/iembs.2009.5334584","title":"Analysis of commode grab bar usage for the monitoring of older adults in the smart home environment","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Élisabeth Bruyère Hospital; University of Ottawa; Carleton University","funders":"University of Ottawa","keywords":"Toilet; Bathtub; Home automation; Computer science; Balance (ability); Simulation; Engineering; Physical medicine and rehabilitation; Telecommunications; Medicine","score_opus":0.025607571161377303,"score_gpt":0.2550486801090268,"score_spread":0.22944110894764952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160709886","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55955756,0.00012925848,0.43852437,0.0009476159,0.000082177714,0.0004920012,0.00000870486,0.000015249399,0.00024307959],"genre_scores_gemma":[0.9987978,0.000016454436,0.0010042681,0.0000817353,0.000017363276,0.000038753045,0.0000012614702,0.0000023918317,0.00004001407],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989206,0.00012579534,0.000312513,0.0001974131,0.00030407344,0.0001396124],"domain_scores_gemma":[0.99837214,0.00078960095,0.00014150115,0.0006416661,0.00003468324,0.000020438178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074740377,0.00008775508,0.00024627938,0.00014841238,0.000051445222,0.00003314437,0.00074638735,0.000030897678,0.000011798431],"category_scores_gemma":[0.000016578568,0.000051245515,0.00017151094,0.0004747605,0.0000290029,0.00018515889,0.00006125946,0.00006542557,0.0000030626481],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079835976,0.0013710173,0.28501937,0.00006555405,0.000884666,0.0000044739922,0.027693132,0.00280551,0.001932981,0.0030073663,0.00022067812,0.6769154],"study_design_scores_gemma":[0.00054682285,0.00010297067,0.92121404,0.000044403125,0.000106413834,0.0000015186206,0.0011206392,0.07360298,0.0027719478,0.00016480887,0.00021001932,0.000113456976],"about_ca_topic_score_codex":0.00031471468,"about_ca_topic_score_gemma":0.00009246068,"teacher_disagreement_score":0.676802,"about_ca_system_score_codex":0.000025491647,"about_ca_system_score_gemma":0.000009952743,"threshold_uncertainty_score":0.20897314},"labels":[],"label_agreement":null},{"id":"W2161996677","doi":"10.1145/2380116.2380188","title":"PICL","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Waterloo","funders":"","keywords":"Computer science; Arduino; Flash (photography); Computer hardware; Artificial intelligence; Human–computer interaction; Software engineering; Embedded system","score_opus":0.03359927640270363,"score_gpt":0.2532574862296535,"score_spread":0.21965820982694986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161996677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01573166,0.00006655942,0.85577106,0.00060843944,0.00070556777,0.00006301685,2.632309e-7,0.00029772674,0.1267557],"genre_scores_gemma":[0.99124324,5.439748e-7,0.005589685,0.00047922443,0.000121283585,0.0000070381193,2.0319608e-7,0.000002474205,0.0025563287],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950975,0.000032821874,0.000074782074,0.00009420611,0.00011376021,0.00017469822],"domain_scores_gemma":[0.9995421,0.00006200543,0.000022056094,0.00026098828,0.000028301367,0.0000845282],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018881563,0.000044790773,0.000057617286,0.00003601801,0.000033240398,0.000056152076,0.0002454998,0.000020347012,0.000153692],"category_scores_gemma":[0.000016260541,0.000037906757,0.000027874385,0.00013022967,0.000006751642,0.0009706045,0.00010313324,0.000035515895,0.002132395],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013130318,0.00018927264,0.040233735,0.000009144802,0.000022984124,0.0000026324035,0.001452095,3.0547747e-7,0.002670499,0.16172546,0.02775672,0.76593584],"study_design_scores_gemma":[0.0007068244,0.00006809542,0.084110335,0.000022421715,0.000006516448,0.0002761053,0.00019499227,0.0050052064,0.0349887,0.0025584903,0.8713435,0.00071881],"about_ca_topic_score_codex":0.000025284467,"about_ca_topic_score_gemma":0.0000046447462,"teacher_disagreement_score":0.97551155,"about_ca_system_score_codex":0.000014571242,"about_ca_system_score_gemma":0.000010317761,"threshold_uncertainty_score":0.99864453},"labels":[],"label_agreement":null},{"id":"W2162583946","doi":"10.1007/978-3-540-75696-5_12","title":"GlobeCon – A Scalable Framework for Context Aware Computing","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Scalability; Context (archaeology); Context model; Distributed computing; Ubiquitous computing; Context management; Server; Directory; Key (lock); Context awareness; World Wide Web; Database; Human–computer interaction; Computer security; Artificial intelligence; Operating system; Object (grammar); Geography","score_opus":0.046423675226954265,"score_gpt":0.29922563776963074,"score_spread":0.25280196254267645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162583946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000644279,0.0005874381,0.98968554,0.0007802779,0.004647947,0.0013701989,0.000026460224,0.0004376888,0.002400039],"genre_scores_gemma":[0.41711187,0.00001281783,0.576515,0.004240413,0.001516392,0.000026745474,0.000012913366,0.00008912022,0.00047471994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938677,0.000060252347,0.0010440971,0.0024739269,0.0012507874,0.0013032535],"domain_scores_gemma":[0.9923754,0.0038432102,0.00074988947,0.0018267304,0.0008595225,0.00034525906],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002395613,0.0008423976,0.0011166586,0.0011785025,0.0005615145,0.0011818655,0.0039577316,0.0008192124,0.00003706868],"category_scores_gemma":[0.0004066043,0.00085890497,0.00036463732,0.0009255586,0.00063443556,0.00094099937,0.0015324703,0.0012664503,0.0001332556],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012968687,0.00004150406,0.0000675308,0.00009927535,0.00002511176,0.000048274807,0.00060138793,0.0011289397,0.000019402165,0.035654936,0.00006999655,0.9622307],"study_design_scores_gemma":[0.0009263042,0.00041407556,0.00009180433,0.002985962,0.000022576862,0.00027983024,0.0000023171362,0.6422073,0.0017734667,0.33329463,0.01609483,0.0019068634],"about_ca_topic_score_codex":0.000079993115,"about_ca_topic_score_gemma":0.00034967397,"teacher_disagreement_score":0.9603238,"about_ca_system_score_codex":0.0007143127,"about_ca_system_score_gemma":0.00083348545,"threshold_uncertainty_score":0.999855},"labels":[],"label_agreement":null},{"id":"W2163100535","doi":"","title":"Integration and Visualization Issues in Large-Scale Location-Based Facilities Management Systems","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Interoperability; Visualization; Computer science; Information system; Scale (ratio); Systems engineering; Engineering; Data mining; World Wide Web","score_opus":0.014353485330036584,"score_gpt":0.25732788519184274,"score_spread":0.24297439986180616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163100535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02326835,0.0002314366,0.9710701,0.00028827172,0.00017904458,0.0004082252,0.000004266783,0.00019055717,0.0043597133],"genre_scores_gemma":[0.9935336,0.0000062230733,0.0019768463,0.000057448306,0.000030432126,0.000094866744,0.000029383442,0.000005095976,0.004266087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989297,0.00012026714,0.00028837452,0.00029147023,0.00022336135,0.00014684857],"domain_scores_gemma":[0.9994846,0.00006707217,0.00006969838,0.00022304353,0.00013134241,0.000024216723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035899924,0.00010587872,0.00013571569,0.00025292186,0.00006372858,0.00030959785,0.00013967171,0.000044998535,0.00001170609],"category_scores_gemma":[0.000010087074,0.000100815174,0.00001656429,0.0004205867,0.000017210921,0.00061994046,0.000045513887,0.000036532125,0.000032652286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034103636,0.0011234141,0.07405323,0.0014756456,0.00004883286,0.000025257934,0.0072029,0.009171637,0.0013338756,0.8106252,0.0088116545,0.08609424],"study_design_scores_gemma":[0.0006890185,0.000036472007,0.01978769,0.00017499186,0.0000036509955,0.0000038397075,0.0024415331,0.96438295,0.0012828563,0.0008557798,0.010098671,0.00024255745],"about_ca_topic_score_codex":0.0019121866,"about_ca_topic_score_gemma":0.002378962,"teacher_disagreement_score":0.97026527,"about_ca_system_score_codex":0.000074512776,"about_ca_system_score_gemma":0.000017881675,"threshold_uncertainty_score":0.41111237},"labels":[],"label_agreement":null},{"id":"W2163590088","doi":"10.1109/percom.2008.79","title":"Towards an Implementation of Smart Hospital: A Localization System for Mobile Users and Devices","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Context (archaeology); Mobile device; Ubiquitous computing; Mobile computing; Key (lock); Information system; Wireless; Hybrid positioning system; Location-based service; Context awareness; Human–computer interaction; Computer security; Data science; World Wide Web; Positioning system; Telecommunications; Engineering","score_opus":0.028759753911240245,"score_gpt":0.2948746665721917,"score_spread":0.26611491266095144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163590088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39685264,0.000025280779,0.60219175,0.00003661609,0.00012936641,0.0005488858,0.000008143713,0.00010250792,0.000104796294],"genre_scores_gemma":[0.99518555,0.0000044902526,0.004547688,0.000043508964,0.000021736256,0.0001623652,0.000010064253,0.000006874627,0.000017733682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917614,0.00005289239,0.00025060817,0.00024212424,0.00016472086,0.00011351709],"domain_scores_gemma":[0.9993216,0.000051638897,0.00014713986,0.00019453489,0.00022852358,0.00005657753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020493522,0.000082240746,0.00015143589,0.00008482019,0.00009534475,0.000052381853,0.00016005419,0.000033902175,0.000005784712],"category_scores_gemma":[0.000006225346,0.00007709839,0.000032824653,0.00017180949,0.000024138679,0.0009851786,0.0000472695,0.000016309523,0.000002259763],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049007627,0.00043145506,0.25110325,0.001422605,0.00020400742,0.000009559305,0.029207954,0.00013615149,0.0022302214,0.019102467,0.0017666352,0.6943367],"study_design_scores_gemma":[0.009665663,0.009190562,0.1808569,0.00044468534,0.0001333393,0.0004221676,0.06277347,0.4733796,0.23974288,0.00066232955,0.020541785,0.0021866108],"about_ca_topic_score_codex":0.00076395634,"about_ca_topic_score_gemma":0.0004364004,"teacher_disagreement_score":0.69215006,"about_ca_system_score_codex":0.00003893236,"about_ca_system_score_gemma":0.00006455336,"threshold_uncertainty_score":0.31439808},"labels":[],"label_agreement":null},{"id":"W2166169250","doi":"10.1258/jtt.2012.120605","title":"An evaluation of preventive sensor technology for dementia care","year":2013,"lang":"en","type":"article","venue":"Journal of Telemedicine and Telecare","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Intervention (counseling); Dementia; Feeling; Nursing; Warning system; Medicine; Aging in place; Medical emergency; Psychology; Gerontology; Computer science","score_opus":0.022183788120329485,"score_gpt":0.30632954240682164,"score_spread":0.28414575428649214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166169250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90160847,0.0024798478,0.09385027,0.00097239803,0.000258627,0.00067728665,0.0000052876067,0.000018149496,0.00012964215],"genre_scores_gemma":[0.9909833,0.000019700467,0.008763482,0.000047572637,0.000142387,0.000027386186,0.0000026317084,0.0000056187073,0.000007888688],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998759,0.00010507192,0.0004430371,0.00014275125,0.00042255476,0.0001276386],"domain_scores_gemma":[0.9957763,0.000091535214,0.0005218739,0.00019154616,0.0033473403,0.00007143571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006728129,0.00008946847,0.00028080808,0.00033039422,0.00004876047,0.000027434618,0.00026206957,0.000066507964,0.000027919565],"category_scores_gemma":[0.00019044154,0.000069447444,0.00006172946,0.00022902616,0.000040565144,0.00052943785,0.000037236998,0.00010516182,0.0000010271283],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010336354,0.00005751816,0.013708053,0.00008627175,0.0001482654,0.0000025622796,0.0019760618,0.00000597695,0.010599044,0.00009407962,0.00071566436,0.97259617],"study_design_scores_gemma":[0.044174507,0.035142154,0.2658801,0.00390852,0.0042281146,0.0034710318,0.15032502,0.077375196,0.3479397,0.033674598,0.031713508,0.0021675366],"about_ca_topic_score_codex":0.000020569696,"about_ca_topic_score_gemma":0.000019245539,"teacher_disagreement_score":0.97042865,"about_ca_system_score_codex":0.000036400805,"about_ca_system_score_gemma":0.00013509014,"threshold_uncertainty_score":0.28319845},"labels":[],"label_agreement":null},{"id":"W2166477281","doi":"10.1145/2030112.2030185","title":"CAMPUS","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Toronto Metropolitan University","funders":"","keywords":"Computer science","score_opus":0.061634645057725396,"score_gpt":0.22998885450436726,"score_spread":0.16835420944664187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166477281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070197624,0.000011199854,0.71484137,0.00008946179,0.0003370022,0.000055555276,2.2324214e-7,0.0002743784,0.27737108],"genre_scores_gemma":[0.984767,5.573273e-7,0.0125404475,0.00029979585,0.000022945636,0.000007995066,1.300883e-7,0.000002598709,0.002358531],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995168,0.000028561251,0.00008564225,0.00016324999,0.00009440692,0.00011135502],"domain_scores_gemma":[0.99952364,0.000030344967,0.000029348248,0.00031807378,0.000046166424,0.00005241075],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010802533,0.00004901382,0.0000632424,0.00004477774,0.000030979867,0.00003813361,0.00036926125,0.000022984506,0.0002985427],"category_scores_gemma":[0.000012684621,0.0000417112,0.000030490713,0.00013070295,0.000010870169,0.00043102467,0.00010403942,0.000037175225,0.0013341565],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038142787,0.00015693752,0.0074051376,0.000008122695,0.000032458218,0.000048157483,0.0039199493,1.1315727e-7,0.0008789313,0.13312632,0.0103178285,0.8441022],"study_design_scores_gemma":[0.004238383,0.0010266494,0.20641987,0.00014242757,0.00003600782,0.0017805422,0.0014090994,0.03274862,0.30103177,0.13472655,0.3125429,0.0038971696],"about_ca_topic_score_codex":0.00016986679,"about_ca_topic_score_gemma":0.000045065794,"teacher_disagreement_score":0.97774726,"about_ca_system_score_codex":0.0000103439115,"about_ca_system_score_gemma":0.000020368776,"threshold_uncertainty_score":0.9994434},"labels":[],"label_agreement":null},{"id":"W2166631165","doi":"10.1109/wimob.2007.4390863","title":"Reasoning with Contextual Data in Telehealth Applications","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Context (archaeology); Ontology; Context awareness; Inference engine; Ubiquitous computing; Context management; Inference; Context model; Process (computing); Data science; Adaptation (eye); Human–computer interaction; Architecture; Contextual design; World Wide Web; Artificial intelligence","score_opus":0.0479089464721205,"score_gpt":0.31573844481088376,"score_spread":0.26782949833876324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166631165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007585286,0.000047812864,0.9711492,0.00046526807,0.000041868443,0.0003936711,0.0000048469956,0.0001860663,0.02012597],"genre_scores_gemma":[0.9643333,0.0000024283283,0.034877006,0.00038317495,0.000057118268,0.000030659656,0.000014178051,0.0000064873852,0.00029566718],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99874943,0.00004348382,0.00024405742,0.0004626182,0.00022767774,0.0002727198],"domain_scores_gemma":[0.998251,0.00030825505,0.00008727218,0.0011749119,0.00007480707,0.00010375332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001153519,0.00008925203,0.00014006892,0.00014081909,0.00007138711,0.00009093628,0.0010051717,0.000037280788,0.0000132474015],"category_scores_gemma":[0.000034638964,0.000075598706,0.000010122385,0.0006184395,0.000029756216,0.0008295621,0.000280847,0.00013333332,0.000096416275],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017309536,0.00015824001,0.03905126,0.000017098586,0.0000140412,0.00002620582,0.0005403454,0.0000024643068,0.00009072545,0.047629137,0.00072148745,0.91173166],"study_design_scores_gemma":[0.006372148,0.0005477893,0.38865256,0.0005116014,0.000021355481,0.0012595607,0.004757579,0.062086683,0.0024628376,0.0017215317,0.5294195,0.0021868558],"about_ca_topic_score_codex":0.0007913094,"about_ca_topic_score_gemma":0.0076421206,"teacher_disagreement_score":0.956748,"about_ca_system_score_codex":0.00005876325,"about_ca_system_score_gemma":0.0001316347,"threshold_uncertainty_score":0.4264483},"labels":[],"label_agreement":null},{"id":"W2168431821","doi":"10.4017/gt.2011.10.2.004.00","title":"Wireless telesurveillance system for detecting dementia","year":2011,"lang":"en","type":"article","venue":"Gerontechnology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Department of Agriculture","funders":"Agency for Healthcare Research and Quality","keywords":"Dementia; Wireless; Computer science; Telecommunications; Medicine; Internal medicine","score_opus":0.04882548381931368,"score_gpt":0.23934442926921107,"score_spread":0.1905189454498974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168431821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.115729,0.0002039004,0.8793532,0.00010338904,0.0009200818,0.00044599042,0.000005486758,0.0015206268,0.0017182926],"genre_scores_gemma":[0.97863567,0.0000021081737,0.020948913,0.000036922218,0.000046738238,0.00026921797,9.615098e-7,0.000017051534,0.000042441497],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984874,0.00007121718,0.00034029412,0.00053505896,0.000120383345,0.00044564367],"domain_scores_gemma":[0.99869305,0.00013022164,0.0002339995,0.0007317641,0.00015991209,0.000051026425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004816965,0.00016845917,0.00030769597,0.00020449412,0.00016850632,0.00004463282,0.0010063439,0.00021149349,0.0000060015122],"category_scores_gemma":[0.00006379295,0.00017438084,0.0000914643,0.00029221713,0.00006497475,0.00027578295,0.0002712651,0.00014949556,0.000090031775],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003097339,0.00007776016,0.007416749,0.00015826114,0.0001871422,0.000029892246,0.00075184996,4.3004874e-7,0.011887368,0.042904977,0.0002887086,0.9362659],"study_design_scores_gemma":[0.005900345,0.0016376715,0.009400716,0.0005233026,0.00011350561,0.0023578105,0.0047509423,0.04043601,0.88001096,0.008845127,0.043191012,0.002832622],"about_ca_topic_score_codex":0.00010159128,"about_ca_topic_score_gemma":0.00027045916,"teacher_disagreement_score":0.9334333,"about_ca_system_score_codex":0.00008498404,"about_ca_system_score_gemma":0.00004011613,"threshold_uncertainty_score":0.7111044},"labels":[],"label_agreement":null},{"id":"W2168649127","doi":"10.1109/annes.1995.499479","title":"An instructable office clerk","year":2002,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Window (computing); Simple (philosophy); World Wide Web","score_opus":0.039185990043736234,"score_gpt":0.2377933533154639,"score_spread":0.19860736327172765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168649127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15240666,0.00008538514,0.58339715,0.0011300986,0.0009038894,0.00024065816,0.0000027837916,0.0012117996,0.26062155],"genre_scores_gemma":[0.9867054,0.0000028109957,0.0099938335,0.00054309104,0.000055294324,0.000008121396,6.6241597e-7,0.0000061293076,0.0026846204],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906343,0.00007003621,0.00015222299,0.00030970047,0.00020344462,0.0002011716],"domain_scores_gemma":[0.99909145,0.000059600556,0.000047864964,0.00060896494,0.000078068995,0.00011406306],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013720509,0.00008877119,0.000114634175,0.00007678003,0.00008944826,0.00024157323,0.000617755,0.000043036416,0.00082032895],"category_scores_gemma":[0.000016640077,0.00008346834,0.000035664587,0.000334009,0.000017017333,0.0015769287,0.00008407807,0.00007486641,0.0018683727],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002099933,0.00020606875,0.0014881621,0.000008425879,0.000015637934,0.000013884979,0.0006673047,0.0000095996065,0.0027067258,0.020745406,0.011474939,0.96266174],"study_design_scores_gemma":[0.0019027315,0.00048753902,0.0159753,0.00005107199,0.000011019536,0.00054358336,0.0003131717,0.7091836,0.016425025,0.0029920584,0.25073552,0.0013793804],"about_ca_topic_score_codex":0.00012161408,"about_ca_topic_score_gemma":0.000062548075,"teacher_disagreement_score":0.9612824,"about_ca_system_score_codex":0.000025003337,"about_ca_system_score_gemma":0.00001210263,"threshold_uncertainty_score":0.9989088},"labels":[],"label_agreement":null},{"id":"W2169086223","doi":"10.1109/titb.2004.834386","title":"The Use of Computer Vision in an Intelligent Environment to Support Aging-in-Place, Safety, and Independence in the Home","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":205,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto","funders":"","keywords":"Independence (probability theory); Dementia; Aging in place; Independent living; Ubiquitous computing; Health care; Human–computer interaction; Computer science; Gerontology; Medicine","score_opus":0.01944149384030724,"score_gpt":0.2593543807422513,"score_spread":0.23991288690194407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169086223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23476131,0.000009559802,0.7543564,0.010055694,0.00018446849,0.00058370706,0.00000605604,0.00003624341,0.000006568014],"genre_scores_gemma":[0.99755377,0.00017055581,0.0016878024,0.00049941713,0.000004217359,0.000074748,0.0000029635942,0.0000032671082,0.0000032363046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984249,0.00008369101,0.00075126835,0.00019389096,0.0003320696,0.00021418148],"domain_scores_gemma":[0.9991286,0.00021117459,0.00013541282,0.00045379327,0.000034866982,0.00003617623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009517162,0.00013035431,0.00020605256,0.0019866836,0.00006573406,0.00005051487,0.000517046,0.00015268102,0.0000039797455],"category_scores_gemma":[0.000009837462,0.00009307624,0.000018237752,0.0014876478,0.00018077856,0.0012357455,0.000016837863,0.00042208223,0.000020787757],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011936286,0.0003821682,0.0015725564,0.000028961595,0.00000795989,0.00002151398,0.009074795,0.029229917,0.00039127152,0.0010073065,0.00001748866,0.9581467],"study_design_scores_gemma":[0.04053425,0.0289172,0.3333229,0.005840789,0.000059847986,0.0025653348,0.026354222,0.41715315,0.060594715,0.016028369,0.06493261,0.0036965923],"about_ca_topic_score_codex":0.00023263328,"about_ca_topic_score_gemma":0.0013615034,"teacher_disagreement_score":0.95445013,"about_ca_system_score_codex":0.00023900773,"about_ca_system_score_gemma":0.00004647288,"threshold_uncertainty_score":0.37955388},"labels":[],"label_agreement":null},{"id":"W2169588047","doi":"10.1109/iembs.2008.4649671","title":"Data acquisition system using six degree-of-freedom inertia sensor and Zigbee wireless link for fall detection and prevention","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Saskatchewan","funders":"","keywords":"Wireless sensor network; Computer science; Wireless; Transceiver; Data acquisition; Real-time computing; Base station; Wireless network; Embedded system; Computer network; Telecommunications","score_opus":0.13020888538147926,"score_gpt":0.29488806756824987,"score_spread":0.1646791821867706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169588047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.396829,0.0000738851,0.6023194,0.000038026534,0.0001839662,0.00038876984,0.000022618511,0.00009362503,0.00005065668],"genre_scores_gemma":[0.96795595,0.000011553798,0.031777512,0.000011683034,0.00013804848,0.00001787603,0.000016220358,0.000011651916,0.000059502112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986383,0.00012915694,0.00036612505,0.00050641946,0.00019420608,0.00016579355],"domain_scores_gemma":[0.9987043,0.00020506339,0.0002415876,0.0005620471,0.00021554533,0.00007141869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043711392,0.00014192973,0.00026058356,0.00014980798,0.00020020068,0.00009164913,0.00027475558,0.00010518018,9.996667e-7],"category_scores_gemma":[0.000037186604,0.00013830923,0.000038320555,0.000194759,0.00005715234,0.0012908671,0.00030372053,0.00006362893,0.0000020765135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019198857,0.00015698066,0.007936319,0.0015974261,0.00022621691,0.000022947315,0.0011209145,0.00012959524,0.2175872,0.0017022361,0.00014427392,0.7691839],"study_design_scores_gemma":[0.0010848003,0.00016971903,0.0046603284,0.00024153631,0.000040040915,0.0008085194,0.00019830374,0.98592937,0.006419554,0.000060890925,0.00015031594,0.00023660701],"about_ca_topic_score_codex":0.00040249192,"about_ca_topic_score_gemma":0.0003868429,"teacher_disagreement_score":0.9857998,"about_ca_system_score_codex":0.00004930764,"about_ca_system_score_gemma":0.000045390876,"threshold_uncertainty_score":0.5640087},"labels":[],"label_agreement":null},{"id":"W2169924896","doi":"10.1145/1461912.1461922","title":"Dynamic gain estimation in ambient media services","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Computer science; Context (archaeology); Reputation; Estimation; Measure (data warehouse); Service (business); Ambient intelligence; Multimedia; Human–computer interaction; Data mining; Engineering; Systems engineering; Business","score_opus":0.01981453143431108,"score_gpt":0.24874769573698158,"score_spread":0.2289331643026705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169924896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4563891,0.000029857341,0.5403514,0.0005322597,0.0002643187,0.00014608384,0.0000011485819,0.00019987827,0.0020859586],"genre_scores_gemma":[0.9875263,0.00000637984,0.011999835,0.00025358275,0.000007346029,0.000020453406,0.0000055614105,0.000004309233,0.00017625908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906176,0.0000699624,0.00020884372,0.0002553253,0.00024473976,0.0001593694],"domain_scores_gemma":[0.999408,0.00013952158,0.00006172167,0.0002870676,0.000050096918,0.000053590487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020092835,0.00008552809,0.00012367508,0.0001670087,0.00005057234,0.000051597144,0.00035500815,0.000041059433,0.000031042797],"category_scores_gemma":[0.000019010984,0.000081113096,0.000028895276,0.00037603965,0.000017144954,0.0007712797,0.000103929866,0.00006889919,0.00036315032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031289703,0.0010908477,0.052183345,0.0002581978,0.0000620743,0.0005252784,0.0486487,0.004154664,0.008303759,0.009896289,0.0014679022,0.8733777],"study_design_scores_gemma":[0.00030709876,0.000016813146,0.05056814,0.0000370702,8.030778e-7,0.00008084676,0.0000992045,0.94698155,0.00078912836,0.00079112634,0.00018870216,0.00013952139],"about_ca_topic_score_codex":0.00045220295,"about_ca_topic_score_gemma":0.0019872463,"teacher_disagreement_score":0.94282687,"about_ca_system_score_codex":0.00007434358,"about_ca_system_score_gemma":0.000038994083,"threshold_uncertainty_score":0.46676835},"labels":[],"label_agreement":null},{"id":"W2169971398","doi":"","title":"A Robotic World Model Framework Designed to Facilitate Human-robot Communication","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Robot; Computer science; Situated; Human–computer interaction; Human–robot interaction; Terminology; Artificial intelligence","score_opus":0.25848875843867364,"score_gpt":0.3103021019331644,"score_spread":0.05181334349449074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169971398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025628323,0.00003175196,0.96663594,0.00064633536,0.00010308175,0.0004462172,0.0000012564899,0.00047263093,0.029099954],"genre_scores_gemma":[0.66713494,9.886156e-7,0.32741642,0.0006635673,0.00000863507,0.000082426865,0.0000013228195,0.000009457227,0.004682226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983781,0.00023179839,0.00036339118,0.00044394072,0.00026812358,0.00031464547],"domain_scores_gemma":[0.99767363,0.0002638138,0.00010051535,0.0015819368,0.00018972726,0.00019035538],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00050578476,0.00018303399,0.00025302308,0.0002734384,0.00022554926,0.00016737291,0.0014656506,0.00007213994,0.00015163995],"category_scores_gemma":[0.00007162598,0.00018042853,0.000086093285,0.00067515165,0.000039988645,0.0007925997,0.00048275053,0.00020400227,0.0009999747],"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":[0.00007976252,0.0012704212,0.0021739262,0.000097577446,0.00023545662,0.00002190228,0.07592059,0.015226435,0.014102521,0.7181036,0.01389276,0.15887506],"study_design_scores_gemma":[0.0012064311,0.0004769382,0.008454074,0.00060762395,0.000049132483,0.000035905356,0.0006177001,0.65973264,0.012085625,0.3129464,0.0015528476,0.002234703],"about_ca_topic_score_codex":0.0004680821,"about_ca_topic_score_gemma":0.0008278576,"teacher_disagreement_score":0.6645721,"about_ca_system_score_codex":0.00008148309,"about_ca_system_score_gemma":0.000048867998,"threshold_uncertainty_score":0.99977785},"labels":[],"label_agreement":null},{"id":"W2171615064","doi":"10.1109/congress.2009.24","title":"Opportunities and Challenges in Creating Mobile Work Support for Community Nurses","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Work (physics); Nursing; Health care; Business; Healthcare delivery; Medicine; Public relations; Political science","score_opus":0.2235811650159424,"score_gpt":0.3242764671580864,"score_spread":0.10069530214214398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171615064","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6805665,0.0038891928,0.08445057,0.011769154,0.0007856395,0.0028854655,0.000021120955,0.0011801467,0.2144522],"genre_scores_gemma":[0.99541265,0.00032226654,0.0029908058,0.00027155192,0.0000283871,0.00010197017,0.00000340417,0.0000049849364,0.00086399715],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991073,0.00017758805,0.0002174433,0.00018676475,0.00010774508,0.00020315182],"domain_scores_gemma":[0.9987084,0.0007294879,0.00007003744,0.00034713378,0.00007467294,0.00007021346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007079346,0.000112848,0.00021078071,0.00011393823,0.00015687503,0.00012783665,0.00031430557,0.00005280918,0.00001534706],"category_scores_gemma":[0.000060033846,0.00010852908,0.000034718287,0.00011017313,0.000033006436,0.00058893865,0.00008205121,0.0001227545,0.0000039927954],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007360029,0.0001342633,0.0005535713,0.00003101175,0.000005743156,0.0000041544326,0.006431328,0.000001409057,0.000027675349,0.007880232,0.00023620385,0.98468703],"study_design_scores_gemma":[0.009570474,0.009384821,0.24716881,0.0021170059,0.00006572133,0.00067488395,0.23395713,0.01636155,0.0069300504,0.049692586,0.41949674,0.0045802216],"about_ca_topic_score_codex":0.000078711775,"about_ca_topic_score_gemma":0.00020517506,"teacher_disagreement_score":0.98010683,"about_ca_system_score_codex":0.000024509109,"about_ca_system_score_gemma":0.00004012242,"threshold_uncertainty_score":0.44256875},"labels":[],"label_agreement":null},{"id":"W2171804395","doi":"10.1145/1823854.1823890","title":"Towards designing better map interfaces for the mobile","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Nokia (Canada)","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.029598787980962788,"score_gpt":0.2786515366487545,"score_spread":0.24905274866779173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171804395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022407174,0.000035520945,0.97022474,0.0032712047,0.0015979818,0.0005241528,0.0000022381478,0.00019034756,0.0017466428],"genre_scores_gemma":[0.9558529,5.740781e-7,0.041673344,0.00082608586,0.000186889,0.00033133075,5.254651e-7,0.000007918704,0.0011204116],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99921805,0.00003484619,0.00015247684,0.000252602,0.00014808011,0.00019395295],"domain_scores_gemma":[0.9987335,0.00059938576,0.0000588824,0.0004545505,0.00011072839,0.00004294414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005062551,0.0000980961,0.00010813805,0.00004265186,0.00014919286,0.0003405214,0.00081288227,0.00005018291,0.00013574811],"category_scores_gemma":[0.000042491345,0.000061482744,0.00006866872,0.00009483344,0.000037551734,0.0004785694,0.00018948982,0.00015232952,0.00018153654],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004248392,0.000029905845,0.00023565299,0.000013590884,0.000034862638,9.958567e-7,0.0010286933,0.0000038974736,0.034602027,0.0019623744,0.013224292,0.94885945],"study_design_scores_gemma":[0.00077142165,0.00024382885,0.0008426297,0.000035814046,0.000020584557,0.00006177579,0.0006011188,0.06432451,0.4436699,0.0048734886,0.4840054,0.00054955005],"about_ca_topic_score_codex":0.00006992595,"about_ca_topic_score_gemma":0.0001249699,"teacher_disagreement_score":0.9483099,"about_ca_system_score_codex":0.00001032044,"about_ca_system_score_gemma":0.000038225106,"threshold_uncertainty_score":0.32836556},"labels":[],"label_agreement":null},{"id":"W217537231","doi":"10.5821/dissertation-2117-95604","title":"Unifying interaction across distributed controls in a smart environment using anthropology-based computing to make human-computer interaction \"Calm\"","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Canadian Space Agency","funders":"European Regional Development Fund; Kärntner Wirtschaftsförderungsfonds; Università degli Studi di Genova","keywords":"Human–computer interaction; Wizard of oz; Computer science; Wizard; State (computer science); Data science; World Wide Web","score_opus":0.045126757558261066,"score_gpt":0.3580555319792209,"score_spread":0.31292877442095984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W217537231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36079115,0.000011101192,0.63503456,0.00016270916,0.0027747804,0.000796222,0.000023029477,0.00020969649,0.00019672696],"genre_scores_gemma":[0.98969907,0.0000013712424,0.008350235,0.0004406102,0.00033351404,0.00007309458,0.00090763386,0.00005963717,0.00013486297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99525183,0.00062853715,0.0013404709,0.001435818,0.0005546342,0.0007886952],"domain_scores_gemma":[0.99722236,0.0005958477,0.0009493331,0.00081793836,0.00021052378,0.00020397957],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00095910626,0.0006736228,0.0010318006,0.00067703077,0.00046860395,0.00071925623,0.0007783245,0.0004814511,0.00010029184],"category_scores_gemma":[0.000071555005,0.0007412721,0.00027869642,0.0005047445,0.000055715416,0.0006557053,0.00027520154,0.00089242327,0.00016311494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010058329,0.0023109363,0.010681027,0.0012643427,0.0006663491,0.00029357566,0.014905374,0.08068059,0.060185604,0.0009544113,0.0008461299,0.82620585],"study_design_scores_gemma":[0.002900108,0.00047155394,0.013930835,0.0021592497,0.000055076536,0.00008503993,0.0027480389,0.9603949,0.008161028,0.00007663305,0.007398803,0.0016187431],"about_ca_topic_score_codex":0.002183914,"about_ca_topic_score_gemma":0.0045971307,"teacher_disagreement_score":0.8797143,"about_ca_system_score_codex":0.0012390112,"about_ca_system_score_gemma":0.00010540039,"threshold_uncertainty_score":0.99950385},"labels":[],"label_agreement":null},{"id":"W2180450130","doi":"10.5339/qfarc.2014.hbpp0599","title":"A Guideline Compliant Clinical Decision Support System In Mobile And Smart Environments For Diagnosing Medical Conditions","year":2014,"lang":"en","type":"article","venue":"Qatar Foundation Annual Research Conference Proceedings Volume 2014 Issue 1","topic":"Context-Aware Activity Recognition 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":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Clinical decision support system; Guideline; Decision support system; Sleep apnea; Computer science; Wearable computer; Medicine; Medical emergency; Artificial intelligence; Embedded system","score_opus":0.07733971870987556,"score_gpt":0.4208919161779879,"score_spread":0.34355219746811233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180450130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10186016,0.000064962136,0.88855106,0.0038275819,0.00085531204,0.002923375,0.00010194184,0.00022580926,0.0015898169],"genre_scores_gemma":[0.99006164,0.00011276489,0.0077412887,0.00022570054,0.0003113319,0.000779287,0.00012979869,0.000026187427,0.0006119721],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99483186,0.00031045935,0.0013074564,0.0011004563,0.0017456829,0.00070409063],"domain_scores_gemma":[0.9950643,0.0020393787,0.00031791354,0.00040525052,0.0015702479,0.0006028914],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01308212,0.00025011727,0.0005462774,0.00052239234,0.00045767444,0.0007827201,0.0010442466,0.00027326785,0.00051254284],"category_scores_gemma":[0.0053790403,0.00025375895,0.00008338482,0.00041076768,0.00042672237,0.0017703413,0.0007023596,0.000592431,0.0012347475],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014024487,0.0005894803,0.0365666,0.00030713715,0.00004554663,0.00001443252,0.00262733,0.0000032434732,0.00034768766,0.017219895,0.11681727,0.82532114],"study_design_scores_gemma":[0.0017592675,0.00083219755,0.00876086,0.00049701234,0.000007922663,0.00005870318,0.0018203852,0.28890115,0.00007168359,0.0013686202,0.6955931,0.00032909977],"about_ca_topic_score_codex":0.00022448217,"about_ca_topic_score_gemma":0.00015734276,"teacher_disagreement_score":0.88820153,"about_ca_system_score_codex":0.0002583693,"about_ca_system_score_gemma":0.0004063645,"threshold_uncertainty_score":0.9999915},"labels":[],"label_agreement":null},{"id":"W2182624225","doi":"","title":"Ambulatory Assessment of Lifestyle Factors for Alzheimer's Disease and Related Dementias","year":2011,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Disease; Dementia; Ambulatory; Medicine; Physical medicine and rehabilitation; Gerontology; Physical therapy; Psychological intervention; Sleep (system call); Alzheimer's disease; Computer science; Psychiatry; Internal medicine","score_opus":0.24796266547656462,"score_gpt":0.3706317929553854,"score_spread":0.12266912747882078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182624225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14368072,0.00011552557,0.8423753,0.00063291687,0.00094104244,0.0011998287,0.00012415752,0.00017805406,0.010752496],"genre_scores_gemma":[0.99660033,0.000007849597,0.0031796442,0.000072789044,0.000024635,0.000061559025,0.0000130823555,0.000008242975,0.000031841297],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99829334,0.00010358998,0.00048543717,0.00042345634,0.0005146598,0.00017953616],"domain_scores_gemma":[0.99809164,0.00044448065,0.00026591812,0.00022625805,0.0008167588,0.00015496339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052905537,0.00015726236,0.00019386048,0.00019818012,0.00012630373,0.00007696765,0.0003931486,0.00006882219,0.00016723215],"category_scores_gemma":[0.0002832709,0.00015483,0.0000805439,0.00021315283,0.00012368792,0.00043670268,0.00009160722,0.00011667467,0.000023323757],"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":[0.00003115084,0.0002543967,0.0019655116,0.000013306959,0.000088490546,0.0000017822204,0.0007822733,0.000030222112,0.00060633157,0.9555863,0.000032800894,0.040607426],"study_design_scores_gemma":[0.000187719,0.00056023715,0.13391629,0.00017326797,0.00009581876,0.00000486368,0.0006797788,0.3007433,0.04887268,0.5138631,0.00020515482,0.00069779204],"about_ca_topic_score_codex":0.000035444395,"about_ca_topic_score_gemma":0.000021730768,"teacher_disagreement_score":0.85291964,"about_ca_system_score_codex":0.00004085403,"about_ca_system_score_gemma":0.00038343988,"threshold_uncertainty_score":0.6313785},"labels":[],"label_agreement":null},{"id":"W2183096090","doi":"10.11575/prism/30655","title":"Building Flexible Displays for Awareness and Interaction","year":2004,"lang":"en","type":"article","venue":"PRISM (University of Calgary)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Set (abstract data type); Computer science; Human–computer interaction; Simple (philosophy); Internet privacy; Multimedia","score_opus":0.024944425810380692,"score_gpt":0.24919974297676759,"score_spread":0.22425531716638689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2183096090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16807713,0.000032135882,0.8304903,0.0004576967,0.00016879891,0.0001640347,4.054178e-7,0.00007330263,0.00053622737],"genre_scores_gemma":[0.8387459,0.000010256874,0.16098458,0.000027591506,0.000010688342,9.597039e-7,0.00000208085,0.0000046992545,0.0002132539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994124,0.000023438899,0.000079566766,0.00024413725,0.00011468126,0.00012578427],"domain_scores_gemma":[0.9994698,0.00010194869,0.00010583561,0.00018057718,0.00007631533,0.00006548173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013299195,0.00007588576,0.0001499795,0.00013427991,0.00018390955,0.00003549665,0.00027390104,0.000053047395,0.0000047459507],"category_scores_gemma":[0.000018426977,0.00009618908,0.000061571795,0.00012701326,0.000043264823,0.0009387218,0.00017233175,0.000066809065,0.0000059652857],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007599755,0.00012646112,0.0011762637,0.00013734362,0.000065320375,0.000017210472,0.002868402,0.000013076808,0.008320836,0.04427648,0.00025931728,0.9426633],"study_design_scores_gemma":[0.01645949,0.0012576783,0.06757787,0.001327138,0.00023549059,0.00035049787,0.0012307931,0.627373,0.06121052,0.0946704,0.12601966,0.0022874684],"about_ca_topic_score_codex":0.00085671135,"about_ca_topic_score_gemma":0.000028702725,"teacher_disagreement_score":0.9403758,"about_ca_system_score_codex":0.0000671,"about_ca_system_score_gemma":0.000059918206,"threshold_uncertainty_score":0.39224765},"labels":[],"label_agreement":null},{"id":"W2188159832","doi":"10.22215/etd/2011-08936","title":"A smart home networking simulation for energy saving","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Computer science; Telecommunications; Humanities; Art","score_opus":0.04983900354268563,"score_gpt":0.2811181473141012,"score_spread":0.23127914377141554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188159832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015542777,0.00023408087,0.9692233,0.000013752513,0.0047913436,0.00046065013,0.000003612729,0.00040018244,0.023318803],"genre_scores_gemma":[0.9721447,0.000011486456,0.004110058,0.00014024816,0.0006435043,0.00027329774,0.00032027508,0.000047082183,0.022309301],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831516,0.000071862654,0.00043987273,0.0006030654,0.00025664578,0.00031342678],"domain_scores_gemma":[0.9981474,0.00055046147,0.00041531856,0.0004693877,0.0003387836,0.0000785913],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028169737,0.00027365112,0.00036078587,0.0002595842,0.00016972357,0.0002712265,0.0005674386,0.0002891623,0.000035413632],"category_scores_gemma":[0.000036262587,0.00028544132,0.00021874372,0.00026565453,0.000006205599,0.00061960344,0.000057318186,0.000120619705,0.00003889483],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000661967,0.000051081875,0.000114114606,0.00012686693,0.00009933033,0.000003033927,0.0011811214,0.00016386234,0.00014718018,0.009227333,0.0007589813,0.9880609],"study_design_scores_gemma":[0.0021724368,0.00026778475,0.0028220483,0.0010816834,0.00012101503,0.000016078713,0.00035293432,0.7873935,0.0016374161,0.021274233,0.18058765,0.002273224],"about_ca_topic_score_codex":0.00037329018,"about_ca_topic_score_gemma":0.0024978684,"teacher_disagreement_score":0.9857877,"about_ca_system_score_codex":0.00006630895,"about_ca_system_score_gemma":0.0001172059,"threshold_uncertainty_score":0.99995977},"labels":[],"label_agreement":null},{"id":"W2188689982","doi":"10.1109/i-span.2009.146","title":"WISH 2009 Preface","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wish; Successor cardinal; Computer science; Sociology; Mathematics","score_opus":0.01793957253309937,"score_gpt":0.24495826448832483,"score_spread":0.22701869195522545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188689982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012751823,0.00009052049,0.8459026,0.0045804833,0.00032085585,0.00018677543,0.0000012520501,0.00069304585,0.13547261],"genre_scores_gemma":[0.98536146,0.0000023482369,0.006588722,0.0017476433,0.00006038159,0.0000038654357,5.4094426e-7,0.0000025920535,0.006232468],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99916846,0.000043596378,0.00013840676,0.00026681746,0.00019780012,0.00018495285],"domain_scores_gemma":[0.9993184,0.000050690356,0.00004409963,0.00043427377,0.00007159806,0.00008091119],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015996942,0.00008475508,0.00011032155,0.000058281115,0.00005168708,0.0001683545,0.00048859336,0.00003899027,0.000088158966],"category_scores_gemma":[0.000024161294,0.00007633912,0.000046807607,0.0002504661,0.000008837259,0.00075389585,0.000056851084,0.0000700888,0.0007950139],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024802052,0.000113250324,0.00028667704,0.0000029235541,0.000009436112,0.000013768518,0.00039601777,0.000004683001,0.0024270779,0.017047042,0.044259965,0.93543667],"study_design_scores_gemma":[0.002800105,0.0011504492,0.12968017,0.00015429535,0.000019740412,0.0006009323,0.00015637443,0.052022386,0.060066685,0.045697536,0.70542413,0.0022272049],"about_ca_topic_score_codex":0.00003154827,"about_ca_topic_score_gemma":0.00001882137,"teacher_disagreement_score":0.97260964,"about_ca_system_score_codex":0.000022192267,"about_ca_system_score_gemma":0.00003326324,"threshold_uncertainty_score":0.99998295},"labels":[],"label_agreement":null},{"id":"W2191657759","doi":"10.1016/j.procs.2015.05.075","title":"Ubiquitous Tele-health System for Elderly Patients with Alzheimer's","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; Acadia University","keywords":"Wearable computer; Internet of Things; Computer science; Radio-frequency identification; Health care; Elderly people; Wireless; Ultra high frequency; Identification (biology); Wireless sensor network; Wearable technology; Telecommunications; The Internet; Ubiquitous computing; Computer security; Medical emergency; Embedded system; Medicine; World Wide Web; Human–computer interaction; Gerontology; Computer network","score_opus":0.04049713077978299,"score_gpt":0.2648527621645033,"score_spread":0.22435563138472034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2191657759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02473619,0.00014509307,0.9696233,0.00072264037,0.002345358,0.0015223165,0.000011508416,0.0006477154,0.0002458556],"genre_scores_gemma":[0.87078965,4.8053386e-7,0.12830919,0.00042679283,0.000275371,0.000167137,0.000003937566,0.000017978677,0.000009477257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99621993,0.00007459568,0.00044753755,0.0011500812,0.0012732719,0.0008345643],"domain_scores_gemma":[0.9961741,0.00017076188,0.00039813787,0.0008035193,0.0017621054,0.00069135276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015522,0.00028760935,0.00039896276,0.00033630303,0.00043658976,0.00078885496,0.0022341434,0.00005592872,4.3186316e-7],"category_scores_gemma":[0.00007155471,0.00023665714,0.00006021168,0.0014746155,0.00021190468,0.0024188806,0.00048783107,0.00013807829,0.00006156343],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004799594,0.00030166827,0.0069483933,0.00018964312,0.000053416778,0.0000066536613,0.0039918167,0.00013552984,0.000019705,0.0040932465,0.0038319118,0.98038],"study_design_scores_gemma":[0.01651487,0.01711806,0.026194848,0.0016496365,0.00009879219,0.00071564893,0.000620589,0.8876807,0.005792676,0.0018778836,0.03740642,0.0043298905],"about_ca_topic_score_codex":0.000052964522,"about_ca_topic_score_gemma":0.000023048002,"teacher_disagreement_score":0.97605014,"about_ca_system_score_codex":0.00031435344,"about_ca_system_score_gemma":0.0019509998,"threshold_uncertainty_score":0.9650598},"labels":[],"label_agreement":null},{"id":"W2193431540","doi":"10.1017/s0714980815000392","title":"Perception et réceptivité des proches-aidants à l’égard de la vidéosurveillance intelligente pour la détection des chutes des aînés à domicile","year":2015,"lang":"en","type":"article","venue":"Canadian Journal on Aging / La Revue canadienne du vieillissement","topic":"Context-Aware Activity Recognition Systems","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":"Hôpital Maisonneuve-Rosemont; Centre de Santé et de Services Sociaux Cavendish; Quebec Rehabilitation Research Network; Université de Montréal; Centre de réadaptation Lethbridge-Layton-Mackay; Computer Research Institute of Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Worry; Perception; Psychology; Population; Applied psychology; Humanities; Social psychology; Medicine; Psychiatry; Art; Environmental health","score_opus":0.033661836992593924,"score_gpt":0.26016626494389605,"score_spread":0.22650442795130213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2193431540","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89599377,0.00046933527,0.09779725,0.0012514893,0.00053809397,0.0003101068,0.000028272034,0.00010459592,0.0035070754],"genre_scores_gemma":[0.9924644,0.00022269106,0.005799678,0.0004826327,0.00033724064,0.000055841014,0.000007782984,0.000056819765,0.0005728752],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99597704,0.001604631,0.00054013846,0.00058007246,0.000227126,0.0010709695],"domain_scores_gemma":[0.9956412,0.0005220301,0.00033217773,0.0004175061,0.00065507897,0.002432036],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0049880045,0.00041063165,0.00042512044,0.0007061592,0.00075883564,0.0011945225,0.00075629837,0.00016721337,0.00006792402],"category_scores_gemma":[0.0011477028,0.0004273904,0.00018915038,0.0005871837,0.00035035406,0.00093011546,0.000067313464,0.00066898525,0.000064961736],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006914383,0.00022029938,0.1423725,0.00028865636,0.00030685376,0.0020806745,0.102200165,0.0023036909,0.0058541573,0.0024483528,0.006330004,0.7355255],"study_design_scores_gemma":[0.004718229,0.0013837767,0.47160512,0.008740424,0.00014685921,0.033417087,0.020538645,0.02064308,0.0037919336,0.02141486,0.41011354,0.003486451],"about_ca_topic_score_codex":0.03169476,"about_ca_topic_score_gemma":0.3898553,"teacher_disagreement_score":0.73203903,"about_ca_system_score_codex":0.004162205,"about_ca_system_score_gemma":0.0018377221,"threshold_uncertainty_score":0.99984235},"labels":[],"label_agreement":null},{"id":"W2201009198","doi":"10.3791/53004","title":"Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment","year":2015,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stairs; Activities of daily living; Sitting; Accelerometer; Set (abstract data type); Computer science; Physical medicine and rehabilitation; Artificial intelligence; Simulation; Medicine; Physical therapy; Engineering","score_opus":0.16904817819097315,"score_gpt":0.4301458472445212,"score_spread":0.26109766905354803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2201009198","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92192113,0.0002453271,0.07654617,0.000023916326,0.00045655874,0.00037909183,0.0000025331233,0.000023213901,0.00040203522],"genre_scores_gemma":[0.99756604,0.0000020662924,0.002277992,0.000018500312,0.00006691606,0.000046522106,0.0000013953503,0.000013640385,0.000006905545],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9948597,0.0017818093,0.0008735088,0.00025146475,0.0020301815,0.00020332394],"domain_scores_gemma":[0.9973168,0.00017309692,0.0013565957,0.0003184513,0.0006677837,0.00016730683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070651853,0.0001764299,0.000489384,0.0005016502,0.00004903977,0.000082007755,0.00038195882,0.00009016968,0.000029629287],"category_scores_gemma":[0.0002623263,0.00017352613,0.00014655346,0.00028069204,0.00003071883,0.0010086567,0.00010237092,0.00016216314,0.00002276603],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000566004,0.005568636,0.0074220384,0.00020431032,0.00042888292,0.00006790047,0.019283364,0.0008184182,0.7179415,0.00006263521,0.00037406955,0.2472622],"study_design_scores_gemma":[0.021055762,0.0020818769,0.017739275,0.0035206657,0.00021045956,0.00019017751,0.004163166,0.11173313,0.8379897,0.0003702775,0.00020092214,0.00074458093],"about_ca_topic_score_codex":0.00014722775,"about_ca_topic_score_gemma":0.000010722278,"teacher_disagreement_score":0.24651761,"about_ca_system_score_codex":0.0013020571,"about_ca_system_score_gemma":0.00041304296,"threshold_uncertainty_score":0.7076191},"labels":[],"label_agreement":null},{"id":"W2201168328","doi":"10.1609/aaai.v28i2.19033","title":"A Smart Range Helping Cognitively-Impaired Persons Cooking","year":2014,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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 du Québec à Chicoutimi","funders":"","keywords":"Dignity; Autonomy; Cognitive impairment; Human–computer interaction; Cognition; Computer science; Psychology; Internet privacy; Psychiatry","score_opus":0.09773372397844114,"score_gpt":0.28594682856922443,"score_spread":0.18821310459078328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2201168328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45900413,0.00004707614,0.45129955,0.011177814,0.0019437204,0.0018022619,0.000025492625,0.00073298335,0.07396698],"genre_scores_gemma":[0.9981044,0.0000057065445,0.0009111787,0.0004583373,0.00015148478,0.000061743136,4.888902e-7,0.000019943265,0.00028672587],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99744016,0.00007861445,0.0006218472,0.00068742613,0.00068235036,0.0004896103],"domain_scores_gemma":[0.99755245,0.0003856306,0.00057562254,0.00036018543,0.0009802171,0.00014589023],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011523085,0.0003109096,0.00042769668,0.00022192753,0.00037699068,0.00046698152,0.0018855917,0.00012076857,0.000064367145],"category_scores_gemma":[0.00087844854,0.0002586425,0.00022034114,0.00078777317,0.0002877846,0.00076999323,0.00045155705,0.00037543254,0.00023203394],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111266374,0.00031050225,0.001684698,0.0001288305,0.00006706928,0.0000013596236,0.006601499,0.000006792626,0.07153617,0.4598858,0.00047706394,0.45918894],"study_design_scores_gemma":[0.00021456098,0.00058736844,0.0015588982,0.0018437089,0.000055372646,0.00005843647,0.0028634379,0.16568704,0.75146824,0.0730245,0.0016340477,0.0010043676],"about_ca_topic_score_codex":0.00010581528,"about_ca_topic_score_gemma":0.000033303226,"teacher_disagreement_score":0.6799321,"about_ca_system_score_codex":0.0000729503,"about_ca_system_score_gemma":0.00010778329,"threshold_uncertainty_score":0.9999866},"labels":[],"label_agreement":null},{"id":"W2207822899","doi":"10.1007/978-3-319-25591-0_13","title":"An Analysis Tool for the Contextual Information from Field Experiments on Driving Fatigue","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; Université du Québec à Montréal","funders":"","keywords":"Computer science; Context (archaeology); Field (mathematics); Visualization; Human–computer interaction; Cognition; Data visualization; Cloud computing; Data science; Artificial intelligence","score_opus":0.054969139084020874,"score_gpt":0.31003004826779107,"score_spread":0.2550609091837702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2207822899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045557163,0.0000959337,0.99587655,0.0005700463,0.0015750164,0.00077172025,0.0000318144,0.00012209921,0.0005012588],"genre_scores_gemma":[0.9541929,0.0000038854364,0.04280371,0.0023993915,0.00042207388,0.00006933958,0.000042489213,0.000014863525,0.00005136913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699765,0.00006499256,0.0005825465,0.0008834352,0.0010797553,0.00039163057],"domain_scores_gemma":[0.99472153,0.002616102,0.0004420982,0.0015803422,0.0005067777,0.00013314623],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010825334,0.00040288406,0.0005241007,0.0007565198,0.00031897755,0.001276744,0.002850231,0.00025945506,0.000029406448],"category_scores_gemma":[0.00028662273,0.00030966583,0.00021056272,0.00054848584,0.00017107875,0.0020980984,0.00046559973,0.00042976043,0.000032437983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015961175,0.000025525416,0.00017960943,0.000005560281,0.00010367756,0.0000031490379,0.0028847493,0.01729824,0.00003910721,0.002365345,0.000102805614,0.9769763],"study_design_scores_gemma":[0.00044010358,0.00037382,0.00032058166,0.00015856295,0.000055455046,0.000004237787,0.0000047460494,0.98155814,0.0015315375,0.011879238,0.0031166,0.00055699016],"about_ca_topic_score_codex":0.00021587519,"about_ca_topic_score_gemma":0.000357056,"teacher_disagreement_score":0.97641927,"about_ca_system_score_codex":0.0003043129,"about_ca_system_score_gemma":0.00031871753,"threshold_uncertainty_score":0.99993557},"labels":[],"label_agreement":null},{"id":"W2208601773","doi":"10.1109/cit/iucc/dasc/picom.2015.351","title":"Assist-Me - A Volunteer Mobile Emergency System to Assist Elderly People","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 de Sherbrooke","funders":"","keywords":"Intervention (counseling); Elderly people; Autonomy; Computer science; Medical emergency; Internet privacy; Computer security; Medicine; Nursing; Gerontology","score_opus":0.030403251455791674,"score_gpt":0.27094848021864776,"score_spread":0.24054522876285608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2208601773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11085322,0.00014961114,0.82857245,0.00084282627,0.0036840383,0.0009137481,0.000015262822,0.0011692862,0.053799566],"genre_scores_gemma":[0.985249,7.0768874e-7,0.003433791,0.00013724789,0.00021513818,0.00043561237,0.000004672146,0.000023626888,0.010500214],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99722564,0.00021195463,0.0005603043,0.0007462764,0.0007688107,0.0004870237],"domain_scores_gemma":[0.9974093,0.00008803351,0.00016324245,0.0011056822,0.0006346513,0.000599134],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00079527416,0.00026936198,0.00042370136,0.0002327075,0.00013825225,0.00031521003,0.0011205828,0.00011142661,0.0001832415],"category_scores_gemma":[0.000108578504,0.00024657417,0.00016013376,0.0010284937,0.0000129596,0.0009179611,0.00043983202,0.00014263166,0.0044810763],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007199366,0.0011111789,0.0956193,0.00041656804,0.00035424271,0.00010421946,0.015864544,0.00021280376,0.0018097046,0.031366684,0.49487734,0.35819143],"study_design_scores_gemma":[0.0049046758,0.004281367,0.080459364,0.00064867997,0.00014071786,0.0013449317,0.019384315,0.13057153,0.003875654,0.0010339565,0.7480086,0.0053462177],"about_ca_topic_score_codex":0.0015466646,"about_ca_topic_score_gemma":0.0020088889,"teacher_disagreement_score":0.8743958,"about_ca_system_score_codex":0.00030829545,"about_ca_system_score_gemma":0.00026769895,"threshold_uncertainty_score":0.9999986},"labels":[],"label_agreement":null},{"id":"W2211054351","doi":"10.5555/2762722.2762732","title":"Introducing Context-Awareness to MOOC Systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Context (archaeology); Variety (cybernetics); Scope (computer science); Service (business); Class (philosophy); Quality (philosophy); Computer science; Higher education; Public relations; Knowledge management; Multimedia; World Wide Web; Business; Marketing; Political science","score_opus":0.023710449722530173,"score_gpt":0.25572052311075055,"score_spread":0.23201007338822038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2211054351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023841979,0.000043321816,0.96063703,0.001909484,0.0023547683,0.0004091957,0.0000020969799,0.00063903397,0.010163108],"genre_scores_gemma":[0.9928993,6.7578634e-7,0.0030992853,0.0012305249,0.00044486483,0.00009410814,0.0000017078834,0.000017248807,0.0022122837],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99777204,0.00028252692,0.00040012348,0.0007383711,0.00040511487,0.0004018306],"domain_scores_gemma":[0.9978151,0.00040294515,0.00011216914,0.0011085949,0.00029951715,0.00026170557],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010073192,0.00020932902,0.00038141772,0.0002290857,0.00016535334,0.0005624838,0.0010054584,0.00007748588,0.000034569806],"category_scores_gemma":[0.00025732152,0.00019042677,0.00007573841,0.00052056904,0.000020329164,0.0007140088,0.00036434078,0.00012197803,0.0016138194],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019326912,0.00016478027,0.0060155666,0.00013576107,0.000094313116,0.000013473505,0.0025883927,0.0014047956,0.006360281,0.18279144,0.031629514,0.7687824],"study_design_scores_gemma":[0.0017668935,0.00047550412,0.004092989,0.00042931928,0.000021061995,0.0002989626,0.0010245723,0.4266294,0.014437863,0.0010575438,0.54792947,0.0018364462],"about_ca_topic_score_codex":0.0011668473,"about_ca_topic_score_gemma":0.00023135007,"teacher_disagreement_score":0.9690573,"about_ca_system_score_codex":0.00008857611,"about_ca_system_score_gemma":0.00007036231,"threshold_uncertainty_score":0.99916357},"labels":[],"label_agreement":null},{"id":"W2211552693","doi":"10.1109/cit/iucc/dasc/picom.2015.321","title":"Prevent Cooking Risks in Kitchen of Elderly People: Adaptable Reasoning Engine Based on Fuzzy Logic for Smart Oven","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 de Sherbrooke","funders":"","keywords":"Fuzzy logic; Elderly people; Work (physics); Cooking oil; Computer science; Risk analysis (engineering); Engineering; Artificial intelligence; Business; Medicine; Gerontology","score_opus":0.09208989425350425,"score_gpt":0.3055976043088605,"score_spread":0.21350771005535626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2211552693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07211861,0.000091808375,0.91558987,0.00062155974,0.00034383388,0.00090031297,0.000009118526,0.00015775592,0.010167116],"genre_scores_gemma":[0.9789492,0.0000010455757,0.020429024,0.00015944679,0.000037756035,0.00011281495,0.0000057216566,0.00001214961,0.000292831],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984868,0.00013646587,0.00036313376,0.00038088765,0.00033735612,0.00029536162],"domain_scores_gemma":[0.99854946,0.0005072371,0.0001807078,0.00045700266,0.00020945564,0.000096137264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012895305,0.00015472267,0.00033745624,0.00020029554,0.00003588252,0.000068742695,0.0004139793,0.00008122903,0.000014930534],"category_scores_gemma":[0.0003479772,0.00014503556,0.0000825441,0.00040668002,0.000011195764,0.00037253392,0.000092133654,0.0001212838,0.000014151601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058813154,0.0029198243,0.090258114,0.0005650193,0.0001810596,0.000042603744,0.007560838,0.029353216,0.0032011077,0.02370623,0.00788073,0.83374316],"study_design_scores_gemma":[0.0042510536,0.001590254,0.009938169,0.00062232814,0.000020749228,0.000017164026,0.00055458763,0.96467906,0.010519538,0.003345413,0.003851726,0.00060992996],"about_ca_topic_score_codex":0.0013943333,"about_ca_topic_score_gemma":0.00091956754,"teacher_disagreement_score":0.93532586,"about_ca_system_score_codex":0.00013430466,"about_ca_system_score_gemma":0.00022006228,"threshold_uncertainty_score":0.5914379},"labels":[],"label_agreement":null},{"id":"W2223588359","doi":"10.21307/ijssis-2017-752","title":"Survey of Semantic Similarity Measures in Pervasive Computing","year":2015,"lang":"en","type":"article","venue":"International Journal on Smart Sensing and Intelligent Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Similarity (geometry); Semantic similarity; Relevance (law); Context (archaeology); Ubiquitous computing; Information retrieval; Rank (graph theory); Semantic matching; Matching (statistics); Domain (mathematical analysis); Data science; Focus (optics); World Wide Web; Artificial intelligence; Human–computer interaction; Mathematics; Geography","score_opus":0.13508713358994018,"score_gpt":0.3236010739796728,"score_spread":0.18851394038973263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2223588359","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58630973,0.00053744705,0.40571108,0.0005814601,0.00600635,0.00017619082,0.000008454802,0.000043084503,0.0006261721],"genre_scores_gemma":[0.9993187,0.00003983169,0.00025596307,0.00012857311,0.00019423383,3.9085052e-7,0.000003463929,0.000009985975,0.000048831906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99694085,0.00080817746,0.00080963643,0.00028399346,0.00095467956,0.00020267184],"domain_scores_gemma":[0.9967276,0.00065039395,0.0004932248,0.00019422336,0.0017604779,0.00017408494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034984073,0.00016829847,0.0003845256,0.0004713039,0.0000643118,0.00041625224,0.0004390687,0.0000769046,0.000001112891],"category_scores_gemma":[0.0008752867,0.00015199193,0.00007353864,0.00022995114,0.00004623068,0.00033637273,0.0001437334,0.00032883527,0.000018635226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043757053,0.00077368045,0.47681725,0.0001151202,0.000944681,0.0010779112,0.015429714,0.013294675,0.0010821839,0.0025871142,0.0033980755,0.48404202],"study_design_scores_gemma":[0.0017533972,0.0004917119,0.1004474,0.003021385,0.00001638383,0.004446572,0.00188583,0.87883466,0.0027082562,0.0006125091,0.005110496,0.0006713935],"about_ca_topic_score_codex":0.0039012358,"about_ca_topic_score_gemma":0.00058465515,"teacher_disagreement_score":0.86553997,"about_ca_system_score_codex":0.00022768011,"about_ca_system_score_gemma":0.00018019676,"threshold_uncertainty_score":0.6198051},"labels":[],"label_agreement":null},{"id":"W2225276278","doi":"10.1155/2016/9467181","title":"Problem Detection in Real-Time Systems by Trace Analysis","year":2016,"lang":"en","type":"article","venue":"Advances in Computer Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Commissariat à l'Énergie Atomique et aux Énergies Alternatives; Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Computer science; Tracing; TRACE (psycholinguistics); Scheduling (production processes); Data mining; Execution time; Real-time computing; Distributed computing; Mathematical optimization; Operating system","score_opus":0.005992216566666351,"score_gpt":0.21827412554989276,"score_spread":0.2122819089832264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2225276278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02698038,0.0006215176,0.9712102,0.00005199258,0.00049526675,0.00023122758,0.0000036117171,0.0003056122,0.00010018759],"genre_scores_gemma":[0.98440844,0.00013549865,0.015218509,0.000005592577,0.000079574376,0.00008413344,0.0000011844053,0.00001429132,0.000052802843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841785,0.00008407572,0.00043425776,0.0005101031,0.00022003267,0.00033365717],"domain_scores_gemma":[0.9990073,0.00038977308,0.000109065746,0.0003832871,0.00004527477,0.000065325774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003894006,0.00018859313,0.00036794832,0.0006065685,0.000021645452,0.00010016875,0.00043859734,0.000069546666,0.0000026029516],"category_scores_gemma":[0.0000141203245,0.00016516556,0.00007405167,0.0014674156,0.000010721307,0.0017521571,0.00010672841,0.00010782114,0.00002963864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006079033,0.0000715744,0.0030964518,0.00008498527,0.00007352302,0.000030121691,0.00028168378,0.2900677,0.018531721,0.00020846922,0.000026239339,0.68752146],"study_design_scores_gemma":[0.00045584867,0.000045257264,0.0014826551,0.00023827748,0.000008197727,0.000018379063,0.0000031758204,0.9912714,0.0019077158,0.000044306256,0.004214519,0.00031028438],"about_ca_topic_score_codex":0.000104233775,"about_ca_topic_score_gemma":0.000106375795,"teacher_disagreement_score":0.95742804,"about_ca_system_score_codex":0.00023534919,"about_ca_system_score_gemma":0.000012347555,"threshold_uncertainty_score":0.67352563},"labels":[],"label_agreement":null},{"id":"W2226565836","doi":"10.1007/978-3-642-37419-7_6","title":"The Smart-CondoTMInfrastructure and Experience","year":2013,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Home automation; Middleware (distributed applications); Computer science; Variety (cybernetics); Automation; Feature (linguistics); Architecture; Actuator; Building automation; Competition (biology); Smart environment; Human–computer interaction; Real-time computing; Artificial intelligence; Embedded system; Telecommunications; Internet of Things; Engineering; Distributed computing; Geography","score_opus":0.032110957036939745,"score_gpt":0.2723175666407962,"score_spread":0.24020660960385645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2226565836","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010668307,0.0027143864,0.48158535,0.00570993,0.001530971,0.0017982038,0.000021243946,0.00028481765,0.50528824],"genre_scores_gemma":[0.8578488,0.012264428,0.1136591,0.0041841757,0.00014713705,0.0003776974,0.000040394334,0.000033503224,0.011444716],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984148,0.00004155892,0.0005914792,0.0002944745,0.0004208543,0.00023683932],"domain_scores_gemma":[0.9963987,0.00055560516,0.00035758398,0.002138048,0.0004308623,0.00011919921],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007779915,0.00021588929,0.00021230827,0.00041856852,0.0010181061,0.0020067676,0.0032371092,0.00011091991,0.000010553257],"category_scores_gemma":[0.00006559681,0.00016964445,0.000031878382,0.00028866614,0.0015426796,0.008774803,0.003006587,0.0004167361,0.00008154883],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.2325194e-7,0.000002406475,0.000051228737,0.000008412292,0.000003287847,1.3225781e-7,0.00268287,0.0000026470643,0.0000031329491,0.33378264,0.0003327451,0.66312987],"study_design_scores_gemma":[0.00032693482,0.000046244313,0.004428484,0.00020015553,0.000003987273,0.00014066977,0.00014803014,0.20056868,0.000019029008,0.019964287,0.7736707,0.00048280213],"about_ca_topic_score_codex":0.000026659807,"about_ca_topic_score_gemma":0.000022426198,"teacher_disagreement_score":0.856782,"about_ca_system_score_codex":0.00008280544,"about_ca_system_score_gemma":0.00018851383,"threshold_uncertainty_score":0.9990292},"labels":[],"label_agreement":null},{"id":"W2228066155","doi":"","title":"Context management and self-adaptivity for situation-aware smart software systems","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Context (archaeology); Computer science; Human–computer interaction; Process management; Engineering; Geography","score_opus":0.020565864840017545,"score_gpt":0.24647275438080046,"score_spread":0.22590688954078292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2228066155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011942213,0.0009704626,0.97057575,0.00012602152,0.0039682295,0.005959928,0.00007690532,0.0012994321,0.005081033],"genre_scores_gemma":[0.90534407,0.00010201286,0.014475679,0.00019365434,0.00022590064,0.0036579615,0.00072411814,0.000089610934,0.07518697],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997132,0.00015232028,0.0006266065,0.0010955401,0.0005668685,0.00042664565],"domain_scores_gemma":[0.9971756,0.00057996763,0.0004980908,0.00079555914,0.0007643165,0.00018651497],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047148918,0.0005159843,0.00069284893,0.00034413746,0.0003095105,0.0010025201,0.00068992627,0.00036502272,0.000033663146],"category_scores_gemma":[0.000049559843,0.00050562393,0.00017987128,0.00024940498,0.000017214265,0.0011012106,0.00015037128,0.00020354442,0.00022677254],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089315006,0.0008418593,0.0016955415,0.009796989,0.0020283484,0.000034286695,0.006788495,0.000013499965,0.000069546826,0.05035344,0.08263848,0.8456502],"study_design_scores_gemma":[0.016488988,0.0022555909,0.07319056,0.009106936,0.0020200913,0.0003833771,0.044229172,0.32343704,0.0027977163,0.010716394,0.50069135,0.014682793],"about_ca_topic_score_codex":0.00065096247,"about_ca_topic_score_gemma":0.0010472897,"teacher_disagreement_score":0.9561001,"about_ca_system_score_codex":0.00017484187,"about_ca_system_score_gemma":0.00013155203,"threshold_uncertainty_score":0.9997395},"labels":[],"label_agreement":null},{"id":"W2230180702","doi":"","title":"A service oriented architecture for context-aware systems","year":2007,"lang":"en","type":"article","venue":"Research in computing science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Architecture; Context (archaeology); Service (business); Computer architecture; Process management; Business; History","score_opus":0.13071404591401314,"score_gpt":0.417642970586661,"score_spread":0.28692892467264786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2230180702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14529632,0.00015148839,0.84921956,0.0016843335,0.0011277515,0.001401393,0.000006290625,0.00024336649,0.000869519],"genre_scores_gemma":[0.9926143,0.0000012385333,0.0067744665,0.00025261074,0.00023682366,0.00004517171,0.0000018584353,0.000016984723,0.00005653865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935598,0.00040605216,0.00064824603,0.001304751,0.0021844106,0.0018967743],"domain_scores_gemma":[0.99167204,0.0043083355,0.00017921087,0.00103074,0.0023778123,0.00043185888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025241107,0.00022502166,0.00035802208,0.0017571744,0.0009857389,0.0008693029,0.0032361366,0.00011371659,0.0000016419982],"category_scores_gemma":[0.0010762973,0.00021499491,0.00007078357,0.008820788,0.0004920362,0.0008407533,0.0011916307,0.0007333734,0.000058468995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014319233,0.0004984138,0.008195753,0.0007487344,0.000030311086,0.00020226992,0.010852582,0.0032040589,0.015026345,0.06522324,0.0009576274,0.8949175],"study_design_scores_gemma":[0.0019630198,0.00038913186,0.007145842,0.0013791822,0.0000023842774,0.0003121715,0.004014434,0.95425695,0.004458039,0.0022313022,0.023105385,0.00074213464],"about_ca_topic_score_codex":0.0013665028,"about_ca_topic_score_gemma":0.0012451847,"teacher_disagreement_score":0.9510529,"about_ca_system_score_codex":0.00061811326,"about_ca_system_score_gemma":0.0010074745,"threshold_uncertainty_score":0.8767238},"labels":[],"label_agreement":null},{"id":"W2247325373","doi":"10.1109/icsens.2015.7370271","title":"Optimizing pressure sensor array data for a smart-shoe fall monitoring system","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Falling (accident); Pressure sensor; Computer science; Foot pressure; Safety monitoring; Trajectory; Real-time computing; Engineering; Mechanical engineering; Medicine","score_opus":0.1589504030863628,"score_gpt":0.3129102946131226,"score_spread":0.1539598915267598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2247325373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017933145,0.0003460092,0.98855484,0.00038484414,0.0023039,0.000734932,0.00006274505,0.000801241,0.005018202],"genre_scores_gemma":[0.82321715,0.0000015554214,0.17445774,0.000037421858,0.0005492287,0.00009692109,0.000015485024,0.000023700844,0.0016007898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997943,0.00013829896,0.0003499669,0.0007667996,0.00041767908,0.00038425752],"domain_scores_gemma":[0.9969852,0.00033821777,0.00016140225,0.0018831516,0.00038051308,0.00025151565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010800881,0.0002027642,0.00032417433,0.000105819396,0.00012787235,0.000513246,0.0017363689,0.00009585653,0.000001772511],"category_scores_gemma":[0.00018569286,0.00018539776,0.00006689015,0.0002186617,0.000016835695,0.0017373284,0.00060244015,0.00011859019,0.00009652034],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006653539,0.001763518,0.049521476,0.0064735976,0.004379492,0.0003816534,0.040841017,0.005498803,0.06234732,0.031295538,0.16154377,0.6352885],"study_design_scores_gemma":[0.0041387402,0.00031740984,0.00024639803,0.0010078148,0.00016724251,0.00045463795,0.0075689373,0.599427,0.023601223,0.00013317731,0.36127642,0.001661021],"about_ca_topic_score_codex":0.0003011342,"about_ca_topic_score_gemma":0.000043244236,"teacher_disagreement_score":0.8214238,"about_ca_system_score_codex":0.000080113154,"about_ca_system_score_gemma":0.00013715752,"threshold_uncertainty_score":0.75603014},"labels":[],"label_agreement":null},{"id":"W2250728044","doi":"10.1016/j.compbiomed.2015.11.009","title":"SME2EM: Smart mobile end-to-end monitoring architecture for life-long diseases","year":2015,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Scalability; Architecture; Correctness; Context (archaeology); Adaptation (eye); Cloud computing; Resource (disambiguation); Distributed computing; Embedded system; Computer network; Database; Operating system","score_opus":0.045565075214431344,"score_gpt":0.3331767058620009,"score_spread":0.28761163064756956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250728044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16129193,0.0024915917,0.82811445,0.0033592756,0.0038213353,0.00064925913,0.000010322502,0.00012800128,0.0001338278],"genre_scores_gemma":[0.9898036,0.000025166039,0.008253655,0.00089510577,0.00081946986,0.0001485299,0.0000145820795,0.000007853651,0.00003203645],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987281,0.0001420199,0.00026093062,0.00046777044,0.00010555969,0.00029560347],"domain_scores_gemma":[0.99839324,0.0007695257,0.00007440265,0.0002958559,0.0000887762,0.0003781889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004517869,0.00016708527,0.0003802658,0.00025959482,0.00007221559,0.000022767223,0.00039325847,0.00009415621,0.0000028929437],"category_scores_gemma":[0.00026008967,0.00013322262,0.00003493751,0.00024336272,0.00014773852,0.0001114834,0.000246134,0.00014437667,0.0000050089757],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094408344,0.00008410206,0.1307209,0.00006594477,0.0000576144,0.000029597875,0.0035450829,0.000076675155,0.0004157167,0.0010128492,0.0034584133,0.8604387],"study_design_scores_gemma":[0.03279051,0.016313113,0.4506178,0.0042172736,0.00023434949,0.0010172861,0.003068672,0.034988217,0.0019815366,0.059358396,0.39180937,0.003603479],"about_ca_topic_score_codex":0.000074153824,"about_ca_topic_score_gemma":0.0000308061,"teacher_disagreement_score":0.8568352,"about_ca_system_score_codex":0.00004293779,"about_ca_system_score_gemma":0.000076974444,"threshold_uncertainty_score":0.5432661},"labels":[],"label_agreement":null},{"id":"W2258956855","doi":"10.1007/978-3-642-22688-5_30","title":"Trace of Objects to Retrieve Prediction Patterns of Activities in Smart Homes","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"TRACE (psycholinguistics); Computer science; Tracing; Realization (probability); Home automation; Smart environment; Human–computer interaction; Activities of daily living; Artificial intelligence; Data science; Computer security; Telecommunications; Internet of Things; Psychology; Programming language","score_opus":0.026092604880343526,"score_gpt":0.23683380467225917,"score_spread":0.21074119979191563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2258956855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017193643,0.00017375508,0.9786516,0.000111825866,0.0015544669,0.0006362556,0.000048136055,0.00007582546,0.0015544868],"genre_scores_gemma":[0.98388183,0.000026608992,0.01562511,0.00012511753,0.000114856004,0.000015465197,0.000002652081,0.000023855117,0.00018452678],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99647456,0.000086298416,0.0008323011,0.0011511635,0.0009986989,0.00045698052],"domain_scores_gemma":[0.99712825,0.00071610766,0.0005805641,0.0011394288,0.00030370784,0.00013193721],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010362901,0.00042182585,0.00085567345,0.001772432,0.000056350065,0.000107418986,0.0019736309,0.00031977464,0.000020837317],"category_scores_gemma":[0.00013945952,0.0004229038,0.00014739078,0.0008780585,0.0002896815,0.00091250264,0.0008350026,0.00055124715,0.000008048443],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052633884,0.00015340316,0.009638717,0.00033576714,0.000034949375,0.000047800306,0.013073149,0.0013097265,0.0021833505,0.0018142904,0.000013350824,0.97134286],"study_design_scores_gemma":[0.005181367,0.0075570783,0.13481347,0.027031612,0.00011302824,0.000683507,0.000060767852,0.1273964,0.53986585,0.14778824,0.0025870304,0.006921633],"about_ca_topic_score_codex":0.00034003117,"about_ca_topic_score_gemma":0.0010643351,"teacher_disagreement_score":0.96668816,"about_ca_system_score_codex":0.00026142073,"about_ca_system_score_gemma":0.00048526886,"threshold_uncertainty_score":0.99982226},"labels":[],"label_agreement":null},{"id":"W2259496760","doi":"10.1177/0008417415580431","title":"Vidéosurveillance intelligente et détection des chutes : perception des professionnels et des gestionnaires","year":2015,"lang":"fr","type":"article","venue":"Canadian Journal of Occupational Therapy","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.46078568725201,"score_gpt":0.43252489325680765,"score_spread":0.02826079399520237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2259496760","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68966424,0.0109899435,0.2921978,0.0032604784,0.0031950567,0.0002681826,0.00005874428,0.000029868326,0.0003357108],"genre_scores_gemma":[0.9865483,0.0015097635,0.009539647,0.000785321,0.00064964243,0.00001694177,0.000015193347,0.000032880638,0.00090228225],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9962308,0.0015983324,0.0007727476,0.00032062168,0.00058861077,0.00048889086],"domain_scores_gemma":[0.99386007,0.0004051656,0.00066066266,0.0002451514,0.003807092,0.0010218732],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031821942,0.00031274228,0.00037183013,0.00062338787,0.0005488973,0.00064483995,0.00053194666,0.0002033377,0.00029817972],"category_scores_gemma":[0.0010719633,0.0003029704,0.00022062275,0.000741759,0.00068779493,0.003351614,0.000030536525,0.0004948674,0.00010088514],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034608858,0.00018676986,0.2554667,0.00007395486,0.00019008401,0.00007928578,0.045815893,0.0014365695,0.0008142501,0.00192079,0.0058784573,0.68779117],"study_design_scores_gemma":[0.0013658913,0.0009889753,0.9316595,0.0019587877,0.00001744796,0.0020525302,0.0025309846,0.0025797987,0.00048808943,0.03048092,0.025374942,0.0005021171],"about_ca_topic_score_codex":0.0133466115,"about_ca_topic_score_gemma":0.10051745,"teacher_disagreement_score":0.68728906,"about_ca_system_score_codex":0.0012437407,"about_ca_system_score_gemma":0.0051891403,"threshold_uncertainty_score":0.99994224},"labels":[],"label_agreement":null},{"id":"W2266277915","doi":"","title":"Spatiotemporal Knowledge Representation and Reasoning under Uncertainty for Action Recognition in Smart Homes","year":2011,"lang":"en","type":"article","venue":"Constellation (Université du Québec à Chicoutimi)","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Event (particle physics); Context (archaeology); Activity recognition; Fuzzy logic; Artificial intelligence; Representation (politics); Machine learning; Process (computing); Frame (networking); Action (physics); Knowledge representation and reasoning; Realization (probability); Data mining; Mathematics","score_opus":0.06522055210237254,"score_gpt":0.24885074012585964,"score_spread":0.1836301880234871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266277915","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6372961,0.00016100248,0.34150416,0.0007062832,0.00049337593,0.0007699313,0.000011384015,0.00024193576,0.018815836],"genre_scores_gemma":[0.9974855,0.000032888667,0.0021417106,0.000056345958,0.000048559814,0.000009371613,0.00004209223,0.0000122925585,0.0001712179],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987263,0.00018175988,0.0002699795,0.0004919548,0.0001362183,0.00019376757],"domain_scores_gemma":[0.998756,0.00038614316,0.0002571482,0.0002518498,0.00025870066,0.00009016597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037616098,0.00016565963,0.00022232975,0.00040168216,0.00024446638,0.0000858549,0.00017508822,0.00012545934,0.00048860186],"category_scores_gemma":[0.00007219135,0.00019765043,0.00007622185,0.0004436744,0.00009144725,0.0013789238,0.000096342264,0.000120692835,0.000020787496],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033168966,0.00026558476,0.113844655,0.00012246949,0.000119896074,0.000012959298,0.023744646,0.0002472467,0.0005777393,0.004387273,0.002553262,0.85379255],"study_design_scores_gemma":[0.006213163,0.0003253939,0.5459429,0.000410013,0.00011245717,0.00019961591,0.009860043,0.42329693,0.002665359,0.0062648715,0.0035090225,0.0012002538],"about_ca_topic_score_codex":0.006357031,"about_ca_topic_score_gemma":0.01888114,"teacher_disagreement_score":0.85259235,"about_ca_system_score_codex":0.00030547002,"about_ca_system_score_gemma":0.0002869358,"threshold_uncertainty_score":0.9990217},"labels":[],"label_agreement":null},{"id":"W2266492292","doi":"","title":"Engineering cyber physical systems","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Context-Aware Activity Recognition Systems","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":"York University; University of Toronto; University of Victoria","funders":"","keywords":"Cyber-physical system; Transformative learning; Computer science; Context (archaeology); Wearable computer; Wearable technology; Systems engineering; Data science; Engineering; Embedded system","score_opus":0.021321704531738424,"score_gpt":0.21976045056311075,"score_spread":0.19843874603137232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266492292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08805965,0.00017527821,0.9086654,0.000055198176,0.002112041,0.00015039877,0.0000011853373,0.0007524311,0.00002838115],"genre_scores_gemma":[0.93981373,0.0000017890401,0.059617743,0.00005397664,0.00044923008,0.000024944533,5.8771315e-7,0.00001566758,0.000022301541],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979481,0.000022610888,0.0002048827,0.0006102564,0.000736681,0.00047746047],"domain_scores_gemma":[0.99834704,0.00024359966,0.00005200629,0.00052483124,0.00038378645,0.00044872725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010615689,0.00022210609,0.00027452633,0.00032342577,0.00011549797,0.0008569551,0.00090991764,0.000047634952,3.3520192e-7],"category_scores_gemma":[0.00040046373,0.00021706472,0.000042768326,0.0010142205,0.000066134475,0.0019068334,0.00063147565,0.0001798143,0.000048720696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008850673,0.0003227654,0.0059967577,0.00049996655,0.0001439824,0.00034170106,0.016796289,0.36984694,0.006034521,0.042325817,0.0042554885,0.5534269],"study_design_scores_gemma":[0.00020547413,0.000046456338,0.0011472038,0.00007665892,0.0000029583725,0.00016648501,0.000011544495,0.9929894,0.00032933528,0.000023299497,0.0046977354,0.0003034418],"about_ca_topic_score_codex":0.000022540404,"about_ca_topic_score_gemma":3.4079787e-7,"teacher_disagreement_score":0.8517541,"about_ca_system_score_codex":0.00015246181,"about_ca_system_score_gemma":0.00018796202,"threshold_uncertainty_score":0.88516426},"labels":[],"label_agreement":null},{"id":"W2271015133","doi":"","title":"An Automated Machine Learning Approach Applied to Robotic Stroke Rehabilitation","year":2012,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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":"Machine learning; Artificial intelligence; Computer science; Autoencoder; Population; Health care; Deep learning; Medicine","score_opus":0.11718683833947711,"score_gpt":0.3568039385338846,"score_spread":0.23961710019440746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2271015133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013663583,0.000010123292,0.97136724,0.0008764298,0.0004192518,0.00056780514,0.000012764852,0.0008709121,0.012211902],"genre_scores_gemma":[0.9682165,9.433057e-7,0.03098847,0.00032361876,0.00016756916,0.00014725936,0.000043566848,0.000016006828,0.00009607151],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971118,0.00033171193,0.00051741855,0.00059239316,0.0010169031,0.00042974736],"domain_scores_gemma":[0.9978633,0.000547385,0.00018381329,0.00035744376,0.0007436053,0.00030444178],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014758377,0.00023636539,0.00026193904,0.00043677253,0.00027323217,0.0003611919,0.0007217227,0.000118072,0.00009782397],"category_scores_gemma":[0.0006870156,0.00024523356,0.00006476895,0.000736438,0.000063677064,0.0010133964,0.00010032782,0.00033943757,0.0011832818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005456935,0.0007319564,0.00037020553,0.000015979047,0.000019614357,4.926131e-7,0.0028969378,0.04244515,0.01288431,0.77587163,0.000079373734,0.16462977],"study_design_scores_gemma":[0.000041613952,0.00027999506,0.0018891096,0.000022931266,0.000004501331,0.0000067667715,0.00045686675,0.98174477,0.007505956,0.007406951,0.000287531,0.00035298592],"about_ca_topic_score_codex":0.000052562278,"about_ca_topic_score_gemma":0.00003218752,"teacher_disagreement_score":0.9545529,"about_ca_system_score_codex":0.00021246595,"about_ca_system_score_gemma":0.00016811902,"threshold_uncertainty_score":1},"labels":[],"label_agreement":null},{"id":"W2276552708","doi":"","title":"An Ontological Representation Model to Tailor Ambient Assisted Interventions for Wandering","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Psychological intervention; Context (archaeology); Flexibility (engineering); Computer science; Intervention (counseling); Assisted living; Dementia; Ontology; Human–computer interaction; Context model; Psychology; Artificial intelligence; Medicine; Gerontology","score_opus":0.30874778455986374,"score_gpt":0.4220521765500449,"score_spread":0.11330439199018116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2276552708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08860486,0.0000111472345,0.9086487,0.0006595879,0.00032862893,0.00049068726,0.000004398096,0.00024941433,0.0010025195],"genre_scores_gemma":[0.9169378,2.5153187e-7,0.08191904,0.00023061826,0.00006314146,0.0002709029,0.000007831081,0.0000058027335,0.0005645943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989091,0.00008263096,0.00027086493,0.00030504662,0.00016703703,0.00026532877],"domain_scores_gemma":[0.99908286,0.00010351169,0.00007128604,0.0003977345,0.00013644758,0.00020813162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004211137,0.00009278259,0.00015611362,0.000112616246,0.000116123716,0.00016783537,0.00033073447,0.000047959034,0.000029169883],"category_scores_gemma":[0.00009773869,0.00008278882,0.00014940466,0.00018865216,0.000009844829,0.0013036093,0.00012384965,0.00004973529,0.00005601505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011103753,0.0031164398,0.01401816,0.000164952,0.00014378107,0.0000033223296,0.0067785317,0.0035173863,0.06439479,0.053657185,0.0075908033,0.8465036],"study_design_scores_gemma":[0.00093436654,0.00043614212,0.033834875,0.00010434017,0.000024923194,0.000048790687,0.00059831684,0.94543844,0.01515271,0.0016936847,0.0012379567,0.0004954275],"about_ca_topic_score_codex":0.00006437448,"about_ca_topic_score_gemma":0.00020144804,"teacher_disagreement_score":0.94192106,"about_ca_system_score_codex":0.000071311326,"about_ca_system_score_gemma":0.00001651441,"threshold_uncertainty_score":0.337603},"labels":[],"label_agreement":null},{"id":"W2282424829","doi":"10.1109/icdmw.2015.126","title":"Unsupervised Spatial Data Mining for Smart Homes","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Scalability; Exploit; Cluster analysis; Activity recognition; Ranging; Artificial intelligence; Machine learning; Data mining; Home automation; Unsupervised learning; Spatial analysis; Pattern recognition (psychology); Database","score_opus":0.22398143567348638,"score_gpt":0.3246966894524289,"score_spread":0.10071525377894253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282424829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008879523,0.00004667769,0.9830942,0.001386493,0.00093398057,0.0003013219,0.000029561019,0.00026538892,0.0050628176],"genre_scores_gemma":[0.9337548,8.1771634e-7,0.06427814,0.00039733746,0.00022227589,0.000035305562,0.00005807993,0.000010473358,0.0012427858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988405,0.00006066852,0.00019465944,0.0004467234,0.00024623965,0.00021122243],"domain_scores_gemma":[0.998243,0.00028471815,0.00005803282,0.0010856137,0.00018249314,0.0001461511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065036124,0.00010313,0.00017173235,0.0000747271,0.000058339818,0.00023092848,0.001328952,0.00004389759,0.000022271282],"category_scores_gemma":[0.00024299591,0.000092761504,0.00003488426,0.00015668047,0.000017147511,0.0011314381,0.00063443725,0.000039286984,0.00011824929],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002201597,0.00008899478,0.0046235514,0.000024895897,0.000047501217,0.0000065537392,0.0012504628,0.0000020626294,0.00022052634,0.0010005223,0.0854291,0.9072838],"study_design_scores_gemma":[0.0037610868,0.0003230594,0.0022180905,0.000058830883,0.000021712061,0.00006925044,0.001006482,0.67614186,0.0022941139,0.0013249075,0.31206396,0.00071664975],"about_ca_topic_score_codex":0.0004047289,"about_ca_topic_score_gemma":0.000549025,"teacher_disagreement_score":0.92487526,"about_ca_system_score_codex":0.000026727716,"about_ca_system_score_gemma":0.0001695483,"threshold_uncertainty_score":0.37827042},"labels":[],"label_agreement":null},{"id":"W2284853689","doi":"10.1007/978-3-642-21535-3_47","title":"Qualitative Spatial Activity Recognition Using a Complete Platform Based on Passive RFID Tags: Experimentations and Results","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Activity recognition; Process (computing); Context (archaeology); Spatial contextual awareness; Artificial intelligence; Machine learning; Facial recognition system; Ubiquitous computing; Spatial analysis; Human–computer interaction; Data mining; Pattern recognition (psychology); Remote sensing","score_opus":0.15278753937335707,"score_gpt":0.3429613825762379,"score_spread":0.19017384320288086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284853689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025151682,0.000029079165,0.9928064,0.0003289674,0.001141544,0.0008712288,0.00019563279,0.00014911492,0.0019629053],"genre_scores_gemma":[0.86948496,0.000006233658,0.12937088,0.00075342803,0.00024742112,0.000038601873,0.000034234105,0.000036094312,0.00002811814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957477,0.00021930228,0.00071511423,0.0017808577,0.0009922326,0.0005447817],"domain_scores_gemma":[0.9952141,0.0022478644,0.00087948353,0.0009584589,0.0004793743,0.00022073615],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011432588,0.00062188954,0.00066002924,0.0011258087,0.000490414,0.00053394516,0.0010285184,0.0003157942,0.000024374413],"category_scores_gemma":[0.00027552666,0.0006173152,0.0001350463,0.0004880947,0.0007290218,0.0012688168,0.00054847385,0.0007499481,0.000038179784],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118413955,0.00009814809,0.0000054533907,0.000038839087,0.00002638787,0.000048271915,0.014951938,0.0009256419,0.0008884689,0.0005103322,0.000008385192,0.98237973],"study_design_scores_gemma":[0.0021254087,0.00092982047,0.00017851332,0.0015991103,0.000028797915,0.00009286801,0.000017634993,0.93717396,0.011266809,0.04476669,0.00028454146,0.0015358375],"about_ca_topic_score_codex":0.0006474336,"about_ca_topic_score_gemma":0.000344343,"teacher_disagreement_score":0.9808439,"about_ca_system_score_codex":0.00054964045,"about_ca_system_score_gemma":0.00058064196,"threshold_uncertainty_score":0.9996278},"labels":[],"label_agreement":null},{"id":"W2291096917","doi":"10.1109/iscc.2015.7405604","title":"User activity recognition for energy saving in smart home environment","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Electricity; Home automation; Computer science; Ranking (information retrieval); Energy consumption; Energy (signal processing); Human–computer interaction; Engineering; Artificial intelligence; Telecommunications","score_opus":0.068277502093079,"score_gpt":0.25024792854460787,"score_spread":0.18197042645152886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291096917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14351937,0.000021989541,0.8527131,0.00059467996,0.00040041277,0.00026416985,0.000006891366,0.00012942764,0.0023499294],"genre_scores_gemma":[0.9892259,0.0000054660695,0.009274896,0.00023957652,0.0000712548,0.00020003325,0.0000074594595,0.000011838624,0.00096353545],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99869287,0.00013965993,0.00021024415,0.00043258368,0.00025451704,0.00027012796],"domain_scores_gemma":[0.9991036,0.00025481905,0.00009445008,0.00035296145,0.00005906937,0.00013512152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006020375,0.0001379456,0.0002004433,0.00016381554,0.000044283086,0.00011533184,0.0002879876,0.00008066663,0.000026043883],"category_scores_gemma":[0.000057610436,0.00013906528,0.00006655486,0.00017200394,0.00001818404,0.0011191205,0.00016886745,0.00007385511,0.00015202574],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052387448,0.0003355719,0.0041128187,0.000016513917,0.000023220404,0.00000915289,0.0004959396,0.00002788618,0.0030625137,0.001299549,0.0020830687,0.9884814],"study_design_scores_gemma":[0.017935233,0.0018362283,0.10186535,0.00042701836,0.00005867613,0.00027179785,0.0010576387,0.19464605,0.13423063,0.0707459,0.47232324,0.004602252],"about_ca_topic_score_codex":0.00057272,"about_ca_topic_score_gemma":0.0006748386,"teacher_disagreement_score":0.98387915,"about_ca_system_score_codex":0.00018681756,"about_ca_system_score_gemma":0.000065501736,"threshold_uncertainty_score":0.5670917},"labels":[],"label_agreement":null},{"id":"W2291907067","doi":"10.2316/j.2010.216.680-0129","title":"An Electro Pneumatic Anaesthesia Ventilator for Controlled Ventilation: Design and Development","year":2010,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ventilation (architecture); Anesthesia; Medicine; Engineering; Mechanical engineering","score_opus":0.01622353696466258,"score_gpt":0.2406368513766075,"score_spread":0.22441331441194493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291907067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087383434,0.0012428149,0.9078086,0.00014065589,0.000592915,0.0026650534,0.0000026576604,0.00015128973,0.000012593282],"genre_scores_gemma":[0.9916781,0.000006956697,0.0067247506,0.00006538079,0.00013834723,0.0012883865,0.0000032357777,0.000021950684,0.00007286125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979576,0.00024821793,0.000560791,0.0005441817,0.00026654446,0.00042270438],"domain_scores_gemma":[0.998525,0.0003276596,0.00032284687,0.00044828182,0.00017374277,0.00020246481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018908348,0.00025677166,0.00066249806,0.00015027469,0.0003254719,0.00057113305,0.00032571654,0.00013707315,0.0000050411422],"category_scores_gemma":[0.00004722045,0.00022678489,0.000076400465,0.00009506863,0.000028611528,0.00058921706,0.000028783923,0.00015161479,0.000008813597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020718882,0.00072201726,0.0031528394,0.00079760526,0.0014308004,0.000034626977,0.0037251862,0.00029771207,0.14240769,0.12332868,0.00032661317,0.72170436],"study_design_scores_gemma":[0.023082046,0.0011026545,0.0022976524,0.00015886241,0.00013829689,0.0003519386,0.00025150698,0.9487443,0.0030973714,0.0012766679,0.018557448,0.0009412618],"about_ca_topic_score_codex":0.000022844864,"about_ca_topic_score_gemma":0.000026688762,"teacher_disagreement_score":0.9484466,"about_ca_system_score_codex":0.000049706843,"about_ca_system_score_gemma":0.00024824275,"threshold_uncertainty_score":0.92480195},"labels":[],"label_agreement":null},{"id":"W2292909616","doi":"10.1016/j.jss.2016.02.023","title":"Context-awareness in the software domain—A semantic web enabled modeling approach","year":2016,"lang":"en","type":"article","venue":"Journal of Systems and Software","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Concordia University","funders":"","keywords":"Computer science; Semantic Web; Domain (mathematical analysis); World Wide Web; Context (archaeology); Software engineering; Software; Programming language; Biology","score_opus":0.03540588600884567,"score_gpt":0.2457662015245157,"score_spread":0.21036031551567003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292909616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27077365,0.0019491223,0.7258396,0.0004236653,0.00059776497,0.00030645286,0.0000076017855,0.000052093674,0.00005005184],"genre_scores_gemma":[0.9960326,0.00008277383,0.0033564284,0.00013088208,0.00026619635,0.0000306759,5.679085e-7,0.000019088107,0.000080787315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969511,0.00058568874,0.0009982876,0.00035726526,0.00072570465,0.0003819503],"domain_scores_gemma":[0.9973941,0.0008329436,0.00062373385,0.00051342073,0.0004885885,0.00014723011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026415444,0.0002596519,0.00068567635,0.00033294226,0.00020713166,0.0004331989,0.0010055504,0.0001518347,0.0000024861827],"category_scores_gemma":[0.00030415776,0.00013774684,0.00017031281,0.00038133154,0.000056861503,0.0013024954,0.00014542612,0.00029180493,0.000008481036],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000524592,0.0020298718,0.38729316,0.004170724,0.0013011904,0.0024850632,0.05433693,0.005239562,0.003931608,0.018638872,0.005595404,0.514453],"study_design_scores_gemma":[0.081438296,0.0051877135,0.03922234,0.056752454,0.00079805637,0.10944193,0.1188503,0.44528514,0.0005777362,0.060788665,0.06963391,0.012023471],"about_ca_topic_score_codex":0.00015204672,"about_ca_topic_score_gemma":0.000052386353,"teacher_disagreement_score":0.72525895,"about_ca_system_score_codex":0.00011072069,"about_ca_system_score_gemma":0.00027824639,"threshold_uncertainty_score":0.5617153},"labels":[],"label_agreement":null},{"id":"W2293438220","doi":"10.5220/0005485100050016","title":"Assistive Technology for Risks Affecting Elderly People in Outdoor Environment","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Assistive technology; Human–computer interaction; Risk analysis (engineering); Business","score_opus":0.07873117909428559,"score_gpt":0.3030694070972064,"score_spread":0.22433822800292083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293438220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17463139,0.000057280606,0.8208333,0.0015756924,0.00027040648,0.0005737964,0.000002997842,0.00021573038,0.0018393743],"genre_scores_gemma":[0.980124,0.0000013047721,0.019260593,0.000055795157,0.000033934055,0.00020608258,0.0000013633716,0.000008451449,0.00030846367],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99885494,0.00007247302,0.00021437001,0.00041415766,0.00017176164,0.00027227646],"domain_scores_gemma":[0.99910545,0.0003017767,0.000103739396,0.00035328715,0.000056690245,0.000079047815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005963544,0.000120607656,0.00022442976,0.000241799,0.00005157622,0.00006986653,0.00038361325,0.000107734224,0.000009754664],"category_scores_gemma":[0.0002096104,0.000115180104,0.000048317663,0.0003030949,0.000021824337,0.00033679075,0.00021433384,0.00012393642,0.00015279667],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017913953,0.0002490235,0.077331014,0.000018524293,0.00003016794,0.000010290989,0.0014242564,0.00006244549,0.0006038141,0.0021583426,0.0009969772,0.9170972],"study_design_scores_gemma":[0.02723726,0.009369065,0.3497137,0.0005295625,0.000103403574,0.00066207064,0.026610969,0.31279135,0.069392934,0.043138966,0.15498649,0.0054642255],"about_ca_topic_score_codex":0.0002888363,"about_ca_topic_score_gemma":0.00072278106,"teacher_disagreement_score":0.911633,"about_ca_system_score_codex":0.00018821069,"about_ca_system_score_gemma":0.00006199647,"threshold_uncertainty_score":0.46969083},"labels":[],"label_agreement":null},{"id":"W2294346212","doi":"10.1109/embc.2015.7318409","title":"Fitness activity classification by using multiclass support vector machines on head-worn sensors","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Accelerometer; Support vector machine; Multiclass classification; Computer science; Stairs; Head (geology); Wearable computer; Artificial intelligence; Pressure sensor; Activity recognition; Global Positioning System; Computer vision; Pattern recognition (psychology); Engineering; Embedded system; Telecommunications","score_opus":0.1461313565761273,"score_gpt":0.3413219706166594,"score_spread":0.19519061404053212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294346212","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71057415,0.0000068460567,0.28367633,0.0013494234,0.0008699574,0.00033464815,0.000019633017,0.00040604206,0.0027629482],"genre_scores_gemma":[0.99590564,6.8837767e-7,0.0022003625,0.00022396426,0.000111337635,0.00002172067,0.000009632494,0.000020394413,0.0015062734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789786,0.00032183775,0.00027595216,0.0006217571,0.0005474294,0.0003351888],"domain_scores_gemma":[0.9982993,0.00027399603,0.00019898749,0.0007001693,0.0002636313,0.00026388862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005748663,0.00023890882,0.00028790502,0.00014036335,0.00012855261,0.0002571383,0.00046868157,0.000119072494,0.000040316383],"category_scores_gemma":[0.00015242415,0.00021641926,0.000082723134,0.0003994006,0.000050511127,0.0010654791,0.00013533355,0.000190702,0.0003653224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003871805,0.0023253968,0.019114345,0.00011165408,0.00019126682,0.00007136252,0.0036616772,0.00067451305,0.2430351,0.0027093813,0.03799711,0.68972105],"study_design_scores_gemma":[0.0013993926,0.00023524273,0.01174438,0.00004434413,0.000014400442,0.000089808724,0.00016050793,0.937756,0.03791773,0.00009466626,0.009877252,0.0006662705],"about_ca_topic_score_codex":0.000658683,"about_ca_topic_score_gemma":0.00011570872,"teacher_disagreement_score":0.9370815,"about_ca_system_score_codex":0.00028488718,"about_ca_system_score_gemma":0.00016800598,"threshold_uncertainty_score":0.8825321},"labels":[],"label_agreement":null},{"id":"W2296428810","doi":"10.1080/0144929x.2015.1046927","title":"StencilMaps and EphemeralMaps: spatially stable interfaces that highlight command subsets","year":2015,"lang":"en","type":"article","venue":"Behaviour and Information Technology","topic":"Context-Aware Activity Recognition Systems","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":"University of Manitoba; University of Saskatchewan","funders":"","keywords":"Human–computer interaction; Computer science","score_opus":0.029342162932422988,"score_gpt":0.23395354108047495,"score_spread":0.20461137814805197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296428810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8707303,0.0004089568,0.12103453,0.0037537715,0.0003724286,0.00039101453,0.00003163207,0.0005569913,0.0027203972],"genre_scores_gemma":[0.9975234,0.000034844652,0.0020473269,0.00016070559,0.000009252871,0.000037695194,0.000024935,0.0000042974343,0.00015751387],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990689,0.000033068904,0.000310608,0.00017398801,0.0002079502,0.00020545365],"domain_scores_gemma":[0.9991663,0.000028041948,0.00020104174,0.00029996474,0.00021708323,0.00008758565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028467813,0.00014536957,0.00020821947,0.0003808859,0.000108956396,0.00026718687,0.00029525856,0.00017130004,0.000008282212],"category_scores_gemma":[0.000033524368,0.00013342951,0.000016129878,0.00025685586,0.00010893414,0.004278572,0.00035845768,0.00014636513,0.000057906647],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039812337,0.00015546239,0.29218602,0.0001048541,0.000052972904,0.000015842821,0.004800592,0.0000072321404,0.00061915716,0.04058946,0.02604163,0.63538694],"study_design_scores_gemma":[0.013675134,0.0024542904,0.15391773,0.00044488526,0.00014076545,0.0024515847,0.010399163,0.014922776,0.21477742,0.018370125,0.5649711,0.0034750171],"about_ca_topic_score_codex":0.00033667125,"about_ca_topic_score_gemma":0.00016238379,"teacher_disagreement_score":0.63191193,"about_ca_system_score_codex":0.00004187661,"about_ca_system_score_gemma":0.000059730904,"threshold_uncertainty_score":0.5441097},"labels":[],"label_agreement":null},{"id":"W2296625668","doi":"10.1109/embc.2015.7319557","title":"Estimating in-home walking speed distributions for unobtrusive detection of mild cognitive impairment in older adults","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Institute on Aging","keywords":"Dynamic time warping; Cognitive impairment; Cognition; Recall; Preferred walking speed; Physical medicine and rehabilitation; Computer science; Audiology; Medicine; Psychology; Artificial intelligence; Cognitive psychology; Psychiatry","score_opus":0.02917247775578944,"score_gpt":0.2863714918580288,"score_spread":0.25719901410223933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296625668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5148911,0.000015129687,0.48394224,0.00006966857,0.00023497766,0.0006882007,0.000018958019,0.00004401939,0.00009571676],"genre_scores_gemma":[0.9934463,3.572278e-7,0.0063335965,0.000026984291,0.00003773869,0.00011880605,0.000013639208,0.0000071808718,0.00001542802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986292,0.00010550502,0.0004433162,0.00034530697,0.00021791362,0.00025873026],"domain_scores_gemma":[0.99867535,0.00046686782,0.00019882536,0.00016892924,0.00040863783,0.00008139964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059426273,0.00013028207,0.00024935664,0.0002643689,0.000044427194,0.000049333834,0.00019138477,0.000066064735,0.0000039733022],"category_scores_gemma":[0.00032813093,0.00013096438,0.00006512047,0.00062316156,0.000027706725,0.00059281103,0.00012214419,0.00010552596,0.000008565845],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087207125,0.0026900151,0.075682364,0.0005988307,0.00014606911,0.00004575696,0.047071032,0.00077255693,0.0042589954,0.0016192027,0.0002297112,0.8660134],"study_design_scores_gemma":[0.012295443,0.00068742374,0.16106346,0.0019661833,0.000018617504,0.000049586055,0.0062498967,0.7633834,0.05062349,0.00302609,0.0000182509,0.00061817694],"about_ca_topic_score_codex":0.00067986007,"about_ca_topic_score_gemma":0.0010707668,"teacher_disagreement_score":0.8653952,"about_ca_system_score_codex":0.00026532676,"about_ca_system_score_gemma":0.00010170169,"threshold_uncertainty_score":0.53405726},"labels":[],"label_agreement":null},{"id":"W2297086838","doi":"10.1186/s12984-016-0114-0","title":"Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants","year":2016,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ottawa Hospital Research Institute; University of Ottawa","keywords":"Wearable computer; Activity recognition; Population; Physical medicine and rehabilitation; Accelerometer; Medicine; Decision tree; Classifier (UML); Artificial intelligence; Computer science; Machine learning","score_opus":0.037569734498659864,"score_gpt":0.27432953379944247,"score_spread":0.23675979930078261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2297086838","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7786069,0.000037296595,0.22065656,0.0004336987,0.00006410094,0.00016703439,0.000002096501,0.000015359312,0.000016946133],"genre_scores_gemma":[0.99762374,0.0000149605485,0.002294177,0.0000038184853,0.0000319753,0.00001930616,2.465648e-7,0.0000065300783,0.0000052527826],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998791,0.00020564257,0.00030091955,0.00015825886,0.00044196768,0.00010222689],"domain_scores_gemma":[0.9982149,0.0005244233,0.00036235066,0.00013495672,0.00069460366,0.00006873548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013927817,0.00008612851,0.0001832149,0.00020785755,0.000043200365,0.000029790517,0.000065520624,0.000029996498,0.0000012195435],"category_scores_gemma":[0.00039002183,0.000059795268,0.000032652682,0.00013352903,0.000044467408,0.0007115415,0.000019027842,0.000069612484,5.9520613e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006566749,0.00009080335,0.0034145638,0.00008246354,0.000041212927,9.208072e-7,0.0007469591,0.00020437008,0.3884812,0.000039853683,0.000007284147,0.6068247],"study_design_scores_gemma":[0.0076063992,0.0048986934,0.8440712,0.0012978632,0.00033142706,0.0002525262,0.00026527917,0.09623773,0.041838426,0.002629101,0.00013720474,0.00043415016],"about_ca_topic_score_codex":0.000015161184,"about_ca_topic_score_gemma":0.000014115329,"teacher_disagreement_score":0.84065664,"about_ca_system_score_codex":0.00005302756,"about_ca_system_score_gemma":0.00004217379,"threshold_uncertainty_score":0.24383801},"labels":[],"label_agreement":null},{"id":"W2300436456","doi":"","title":"Design and Validation of a Bike Helmet with Bone Conduction Music Playback","year":2016,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Bone conduction; Active listening; Transducer; Computer science; Acoustics; Perception; Engineering; Psychology; Physics; Communication","score_opus":0.042234090048184396,"score_gpt":0.21559131624136718,"score_spread":0.17335722619318278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300436456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22559449,0.00002219996,0.77356744,0.00031200476,0.00016778737,0.00019006526,0.000020065003,0.00002809602,0.00009782361],"genre_scores_gemma":[0.9889662,0.000006664808,0.010657846,0.00010118794,0.000040469135,0.000010342115,0.0000020189711,0.000009064325,0.00020617916],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922854,0.00007314428,0.00015320643,0.00021904084,0.00014165943,0.00018443094],"domain_scores_gemma":[0.9990402,0.00018229852,0.000109727276,0.00025812644,0.00019629054,0.00021334286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021880829,0.00009274313,0.00014183758,0.00021441029,0.00005990027,0.00005470821,0.00013383599,0.00005857391,0.00002463387],"category_scores_gemma":[0.00005870121,0.0000710065,0.000013187164,0.00023802297,0.00006856233,0.00038543914,0.000020163168,0.000042649663,0.000020862637],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046682624,0.00007532927,0.0018338107,0.00013192096,0.00013873617,0.00012615851,0.0021259184,0.00047646332,0.6298149,0.0018221382,0.011529487,0.35187846],"study_design_scores_gemma":[0.02338553,0.007907764,0.06782946,0.004416386,0.0009604026,0.0069703865,0.0029093383,0.24234925,0.5900647,0.014661612,0.030530829,0.008014362],"about_ca_topic_score_codex":0.0027717487,"about_ca_topic_score_gemma":0.002227712,"teacher_disagreement_score":0.76337177,"about_ca_system_score_codex":0.00011971865,"about_ca_system_score_gemma":0.000413239,"threshold_uncertainty_score":0.41900736},"labels":[],"label_agreement":null},{"id":"W2301566833","doi":"10.4018/978-1-61692-857-5.ch016","title":"State-of-the-Art Assistive Technology for People with Dementia","year":2011,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Mental health; Dementia; Scope (computer science); Work (physics); Independent living; Health care; Plan (archaeology); Computer science; Telecare; Home automation; Applied psychology; Human–computer interaction; Psychology; Knowledge management; Engineering; Telemedicine; Gerontology; Medicine; Psychiatry; Telecommunications","score_opus":0.023077765776221722,"score_gpt":0.2574042031393364,"score_spread":0.23432643736311465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301566833","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000477372,0.005611283,0.9834504,0.00044726688,0.00032593153,0.0005480933,0.00003229725,0.000041920193,0.009537996],"genre_scores_gemma":[0.19546604,0.012041635,0.66279817,0.0006544866,0.00015166693,0.00045089205,0.000106313564,0.00017591848,0.1281549],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986432,0.00001812403,0.00046184618,0.00044440659,0.00024888432,0.00018354904],"domain_scores_gemma":[0.9983656,0.00038864987,0.0004648777,0.0002674438,0.0004782466,0.00003519756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011373426,0.00026046345,0.0004157295,0.00023343612,0.00012591812,0.00004248218,0.00050025707,0.00011151948,0.000010139362],"category_scores_gemma":[0.000023346178,0.00020918543,0.0000680233,0.00013254564,0.00040620187,0.0009826503,0.00024582652,0.00020781282,0.000008607429],"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":[0.000058616853,0.00003868535,0.0009248586,0.00020338719,0.00013778023,0.0000051676143,0.00033410641,0.100885294,9.388683e-7,0.8022604,0.000053053176,0.09509769],"study_design_scores_gemma":[0.00017701891,0.0005517265,0.00032355532,0.0010137113,0.000056625788,0.000106341424,0.0000482038,0.009640496,0.00042896345,0.9247641,0.062328063,0.0005612274],"about_ca_topic_score_codex":0.0000034832626,"about_ca_topic_score_gemma":0.0004539236,"teacher_disagreement_score":0.32065228,"about_ca_system_score_codex":0.000038685357,"about_ca_system_score_gemma":0.00017814286,"threshold_uncertainty_score":0.8530334},"labels":[],"label_agreement":null},{"id":"W2316255109","doi":"10.1541/ieejias.134.332","title":"Infant Monitoring System using Activity Recognition","year":2014,"lang":"en","type":"article","venue":"IEEJ Transactions on Industry Applications","topic":"Context-Aware Activity Recognition Systems","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":"Alpha Technologies (Canada)","funders":"","keywords":"Activity recognition; Feature (linguistics); Computer science; Medical care; Acceleration; Physical activity; Physical medicine and rehabilitation; Artificial intelligence; Medical emergency; Pattern recognition (psychology); Medicine; Family medicine","score_opus":0.056767985278031535,"score_gpt":0.28302443151057316,"score_spread":0.22625644623254162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316255109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062380694,0.0000072553476,0.933099,0.00030612896,0.0005952148,0.00071053964,0.00004496104,0.0007923645,0.0020638935],"genre_scores_gemma":[0.98872113,0.0000025071265,0.00972332,0.000040821047,0.0003511523,0.0010146361,0.0000036370752,0.000028024553,0.00011475875],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99796015,0.00024091547,0.00038188143,0.0006737674,0.000389509,0.0003537872],"domain_scores_gemma":[0.9979652,0.00032336442,0.00023490396,0.0010242289,0.00023160643,0.00022065168],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004247449,0.00026260182,0.00027331564,0.00033310044,0.0007505683,0.00025157715,0.00055482046,0.00043141263,0.000024732119],"category_scores_gemma":[0.000012824997,0.00029902149,0.00014241131,0.0010416164,0.000054737273,0.00095944124,0.000014633338,0.00094531255,0.0003365192],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016663535,0.00047092818,0.00034288518,0.00009328918,0.00009499216,0.0000021848898,0.0002204579,0.002844258,0.012898596,0.001247632,0.00003775071,0.98173034],"study_design_scores_gemma":[0.0038566676,0.00051689777,0.009453799,0.0018721671,0.00043043707,0.0011109373,0.0017075189,0.43706664,0.4983347,0.0021664363,0.039675903,0.003807871],"about_ca_topic_score_codex":0.00019171089,"about_ca_topic_score_gemma":0.000011536868,"teacher_disagreement_score":0.9779225,"about_ca_system_score_codex":0.0003692909,"about_ca_system_score_gemma":0.00011213263,"threshold_uncertainty_score":0.9999462},"labels":[],"label_agreement":null},{"id":"W2320346031","doi":"10.1097/01258363-201209000-00183","title":"Wireless communication device implementation in environments with unique needs: results from an adult intensive care unit in Canada","year":2012,"lang":"en","type":"article","venue":"International Journal of Evidence-Based Healthcare","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Kingston General Hospital; Queen's University","funders":"","keywords":"Wireless; Unit (ring theory); Intensive care unit; Computer science; Telecommunications; Medicine; Psychology; Intensive care medicine","score_opus":0.1237282049717777,"score_gpt":0.3769977011970607,"score_spread":0.25326949622528305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320346031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97168696,0.0007217147,0.0059884475,0.020725107,0.0004508008,0.000310725,0.00009815073,0.00000856342,0.000009520141],"genre_scores_gemma":[0.9945029,0.00017578682,0.0012636662,0.0038118886,0.00010460672,0.000022325328,0.000104956576,0.00001202039,0.0000018614933],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99660665,0.00092545076,0.0009974104,0.00020920456,0.0009866896,0.00027460125],"domain_scores_gemma":[0.9952135,0.0009004038,0.0010173578,0.00039462745,0.002257861,0.00021624813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073711853,0.00016993118,0.00027615752,0.0005068217,0.000053062915,0.00008593121,0.0011365104,0.00006453752,0.000009781503],"category_scores_gemma":[0.0002446231,0.00014834208,0.00003837197,0.00039819395,0.000028408022,0.0029360903,0.000098783006,0.00045820334,0.000002914763],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005315464,0.00006457894,0.91431546,0.000023251763,0.000037250662,0.00007308406,0.0152641,0.00047945726,0.00016870488,0.00017509516,0.00003891699,0.06882857],"study_design_scores_gemma":[0.0046309233,0.00036872883,0.88970464,0.0056120134,0.00001440077,0.00008627447,0.0908601,0.0033216253,0.0042361817,0.000116744704,0.00065618544,0.00039216303],"about_ca_topic_score_codex":0.83816224,"about_ca_topic_score_gemma":0.95422804,"teacher_disagreement_score":0.11606579,"about_ca_system_score_codex":0.0027460468,"about_ca_system_score_gemma":0.0027521,"threshold_uncertainty_score":0.71808165},"labels":[],"label_agreement":null},{"id":"W2336226252","doi":"10.1007/s11517-016-1504-y","title":"A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials","year":2016,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":208,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Simon Fraser University; Canada Research Chairs","keywords":"Accelerometer; Waist; Human physiology; Set (abstract data type); Algorithm; Computer science; Artificial intelligence; Machine learning; Medicine","score_opus":0.10818570569023832,"score_gpt":0.3500614618249476,"score_spread":0.24187575613470924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336226252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5160458,0.00015384326,0.4832352,0.000040122784,0.00019261723,0.00023459837,0.00002605421,0.00006961519,0.000002115523],"genre_scores_gemma":[0.989909,0.000013156628,0.009836941,0.000038199752,0.000160947,0.0000076578135,0.000015350568,0.000017911741,8.3304843e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99582887,0.00060833717,0.0018168377,0.0006345749,0.00070196425,0.00040941275],"domain_scores_gemma":[0.9889373,0.008985814,0.0011873975,0.0003177747,0.0003331747,0.00023854362],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016830869,0.00036688236,0.0018708894,0.00029878062,0.00007146296,0.00004208399,0.0006641333,0.00037787575,0.000042044932],"category_scores_gemma":[0.0043046614,0.00024970953,0.00029409502,0.00049218326,0.00020520251,0.00017063342,0.0004973914,0.00044894314,0.0000033867789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024714944,0.00025400444,0.033385377,0.00019002157,0.00025100197,0.000013238312,0.0004651584,0.0054177805,0.75888294,0.00000927516,0.000004071843,0.20087998],"study_design_scores_gemma":[0.002546851,0.00067910505,0.019809289,0.00088427064,0.000033188524,0.000011907949,0.000028559381,0.91583073,0.059777886,0.000017273158,0.000090186746,0.00029077858],"about_ca_topic_score_codex":0.00065583666,"about_ca_topic_score_gemma":0.000011552418,"teacher_disagreement_score":0.9104129,"about_ca_system_score_codex":0.0000660146,"about_ca_system_score_gemma":0.00008364373,"threshold_uncertainty_score":0.9999955},"labels":[],"label_agreement":null},{"id":"W2339270675","doi":"10.1016/j.ijmedinf.2016.04.007","title":"Smart homes and home health monitoring technologies for older adults: A systematic review","year":2016,"lang":"en","type":"review","venue":"International Journal of Medical Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":715,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; University of Alberta","funders":"","keywords":"Gerontology; Mental health; Medicine; Independent living; Activities of daily living; Home automation; Quality of life (healthcare); Nursing; Physical therapy; Psychiatry; Computer science; Telecommunications","score_opus":0.042673752770343916,"score_gpt":0.3768082760954251,"score_spread":0.3341345233250812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339270675","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.877717e-7,0.836153,0.15779904,0.0028563512,0.0020257076,0.0010574167,0.00003144819,0.00006553561,0.000010965726],"genre_scores_gemma":[0.0000193671,0.99544054,0.003774708,0.0003295648,0.00025830467,0.00013729726,0.000005230727,0.000015404423,0.000019554507],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99377555,0.00017161608,0.0034610846,0.00014248438,0.0021947876,0.00025448995],"domain_scores_gemma":[0.9924179,0.0016884231,0.0044725137,0.00034779886,0.00085442286,0.0002189478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036129595,0.0003163866,0.0025246302,0.00060225016,0.000054068933,0.00026857795,0.0029653758,0.00028667465,0.0000074869045],"category_scores_gemma":[0.0025440948,0.00018695736,0.00050821,0.00018319163,0.00007370812,0.0010220404,0.00052543794,0.00053119456,0.000022677834],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.556344e-7,0.000016231268,5.5965563e-7,0.35901412,0.00025427327,0.000008774568,0.00014569296,3.0246992e-9,8.4297713e-10,0.00015675121,0.0007786945,0.63962394],"study_design_scores_gemma":[0.0004468035,0.00007156147,2.8841157e-7,0.84820175,0.00011193072,0.0025263396,0.00020739569,0.000096198884,2.1390828e-7,0.00009732535,0.14806846,0.00017173028],"about_ca_topic_score_codex":0.000002026742,"about_ca_topic_score_gemma":5.3021387e-7,"teacher_disagreement_score":0.6394522,"about_ca_system_score_codex":0.00032605493,"about_ca_system_score_gemma":0.0009113538,"threshold_uncertainty_score":0.76238996},"labels":[],"label_agreement":null},{"id":"W2339455727","doi":"10.1016/j.cels.2016.04.013","title":"Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations","year":2016,"lang":"en","type":"article","venue":"Cell Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Computer science","score_opus":0.06802583675140303,"score_gpt":0.28242271271471825,"score_spread":0.21439687596331522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339455727","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6897483,0.000658634,0.3013796,0.00037337944,0.0019558577,0.00081455975,0.000008244881,0.00044432533,0.004617133],"genre_scores_gemma":[0.9976465,0.000003749133,0.0003068524,0.000023917119,0.0001916436,0.00009612129,0.0000023553862,0.000022565344,0.00170626],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977692,0.00032259987,0.00057770516,0.00055997533,0.0003454186,0.00042512955],"domain_scores_gemma":[0.99820644,0.00020685131,0.00024443807,0.0010986126,0.00011328636,0.0001303771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005789247,0.00019074322,0.00030823852,0.00030302483,0.00017119352,0.00030851364,0.0011910393,0.00010835923,0.00002301386],"category_scores_gemma":[0.00008093087,0.00015475777,0.000089445915,0.0004018448,0.000020508201,0.00082352717,0.00050355657,0.00010258959,0.00024582658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022906543,0.00095239514,0.20495543,0.0011350615,0.00012819462,0.0005814654,0.007708188,0.002242441,0.6764422,0.015490365,0.0057407822,0.084600575],"study_design_scores_gemma":[0.026373524,0.0014103425,0.0793536,0.01612531,0.00014063159,0.0024820499,0.0028489653,0.26844227,0.23717105,0.02217363,0.33051628,0.012962333],"about_ca_topic_score_codex":0.0008270828,"about_ca_topic_score_gemma":0.00026465472,"teacher_disagreement_score":0.43927115,"about_ca_system_score_codex":0.00021663329,"about_ca_system_score_gemma":0.00004660581,"threshold_uncertainty_score":0.63108385},"labels":[],"label_agreement":null},{"id":"W2345442986","doi":"10.1155/2016/6156914","title":"Energy Optimization for Outdoor Activity Recognition","year":2016,"lang":"en","type":"article","venue":"Journal of Sensors","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke; Université TÉLUQ; Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Global Positioning System; Mobile phone; Context (archaeology); Mobile device; Activity recognition; Battery (electricity); Service (business); Phone; Human–computer interaction; Energy consumption; Big data; World Wide Web; Artificial intelligence; Data mining; Engineering; Telecommunications; Business","score_opus":0.04082453074836273,"score_gpt":0.25695644037997883,"score_spread":0.2161319096316161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345442986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07357157,0.0000174968,0.92332226,0.001978691,0.00078008394,0.000082868326,0.000009557394,0.00003400629,0.00020348543],"genre_scores_gemma":[0.9727599,0.00004115969,0.026398262,0.000108449916,0.00035727254,0.0000060031075,5.202915e-7,0.000011503368,0.0003169053],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989334,0.00013294654,0.00034285383,0.00016228638,0.00026488848,0.00016362224],"domain_scores_gemma":[0.99791366,0.0005095019,0.0006279999,0.00017132766,0.0006769314,0.000100574915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048370517,0.00010392818,0.00022335724,0.00021639693,0.000063835534,0.000077998775,0.0002495984,0.00007215706,0.0000338562],"category_scores_gemma":[0.00024107401,0.00007207629,0.0001643687,0.0001616384,0.000021029477,0.0014108042,0.000032626544,0.000057983125,0.000014505712],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010894073,0.00007852515,0.000057569476,0.000011877547,0.000060834172,0.00001162531,0.0001231315,0.00030310117,0.016456842,0.0001380579,0.001646469,0.98100305],"study_design_scores_gemma":[0.01508511,0.003473982,0.0020491586,0.0017587463,0.00021378018,0.0032915638,0.00028435717,0.2965,0.5803676,0.014547751,0.08041642,0.0020115115],"about_ca_topic_score_codex":0.000008330802,"about_ca_topic_score_gemma":0.0000047448925,"teacher_disagreement_score":0.9789915,"about_ca_system_score_codex":0.00013863723,"about_ca_system_score_gemma":0.0000952811,"threshold_uncertainty_score":0.29391858},"labels":[],"label_agreement":null},{"id":"W2345651684","doi":"10.1007/s12652-016-0374-3","title":"Exploring the ambient assisted living domain: a systematic review","year":2016,"lang":"en","type":"review","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":275,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"AGE-WELL; World Health Organization","keywords":"Assisted living; Pace; Computer science; Domain (mathematical analysis); Bridging (networking); Phase (matter); Ambient intelligence; Systematic review; Health care; Data science; Knowledge management; Human–computer interaction; Computer security; MEDLINE; Medicine; Nursing; Political science","score_opus":0.22296321798923932,"score_gpt":0.3455469539698055,"score_spread":0.12258373598056618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345651684","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017965755,0.79537266,0.2016983,0.0001078192,0.001427945,0.0012613289,0.0000017120117,0.000056688503,0.00005554282],"genre_scores_gemma":[0.0010378932,0.99699664,0.0011028718,0.00023508293,0.0004568834,0.00006198433,5.9630634e-7,0.00004373352,0.000064337364],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9922851,0.001731561,0.0037531331,0.0006045975,0.0010814879,0.00054415193],"domain_scores_gemma":[0.988324,0.0041702967,0.0055012666,0.0010147673,0.0007624016,0.00022729576],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0064143753,0.00066102925,0.003909333,0.0005577359,0.00055672554,0.00083797384,0.002622745,0.00011141383,0.000017057193],"category_scores_gemma":[0.0009297749,0.00036108427,0.0012290808,0.00079500297,0.00010667852,0.0010349926,0.0010153556,0.000910651,0.00009328284],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012113152,0.00006224695,3.0577223e-7,0.28197116,0.00037737683,0.00006480898,0.0010056962,3.514331e-7,0.0000013934659,0.0011762098,0.000076103104,0.7152631],"study_design_scores_gemma":[0.00006600808,0.00014814206,0.0000015956291,0.945231,0.00073610427,0.0035630022,0.00021992352,0.00008525187,0.0000039286083,0.00015293382,0.04933671,0.00045538164],"about_ca_topic_score_codex":0.0000065408485,"about_ca_topic_score_gemma":0.0000028091347,"teacher_disagreement_score":0.71480775,"about_ca_system_score_codex":0.00034379828,"about_ca_system_score_gemma":0.0002869634,"threshold_uncertainty_score":0.9998841},"labels":[],"label_agreement":null},{"id":"W2345690959","doi":"10.14569/ijacsa.2016.070454","title":"Spatiotemporal Context Modelling in Pervasive Context-Aware Computing Environment: A Logic Perspective","year":2016,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Context-Aware Activity Recognition 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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Context (archaeology); Perspective (graphical); Ubiquitous computing; Context model; Representation (politics); Human–computer interaction; Task (project management); Context awareness; Data science; Artificial intelligence; Cognitive science; Object (grammar); Systems engineering","score_opus":0.02459000223109405,"score_gpt":0.2789716502976982,"score_spread":0.2543816480666042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345690959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024559347,0.000222835,0.9684074,0.006080539,0.00033412816,0.00026867862,0.000007751698,0.000031197353,0.00008815329],"genre_scores_gemma":[0.9684493,0.000099055746,0.030601624,0.0005658343,0.00024473498,0.00001849865,4.890433e-7,0.0000066916105,0.000013782663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977601,0.00005886397,0.0005981278,0.00050109945,0.00082883507,0.0002529281],"domain_scores_gemma":[0.9971227,0.00034847562,0.0005976855,0.0002656108,0.0015131293,0.00015236625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000721424,0.0001660677,0.0002576556,0.0005186075,0.00016642096,0.0002526241,0.0014630802,0.000042287567,0.0000051504517],"category_scores_gemma":[0.00004724785,0.00012986058,0.000078037345,0.0004029091,0.00033443896,0.002240439,0.00039424546,0.00017198225,0.000016812743],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018758174,0.0001267228,0.0007235656,0.0000028487048,0.000029054385,0.000024460896,0.0013707232,0.0038205502,0.0013381573,0.05308507,0.000017788467,0.9394423],"study_design_scores_gemma":[0.01055545,0.0010676984,0.012961598,0.0017144012,0.000033362827,0.0025385905,0.0058477228,0.7737557,0.011131131,0.15494908,0.023618756,0.0018264836],"about_ca_topic_score_codex":0.000036127956,"about_ca_topic_score_gemma":0.000014596902,"teacher_disagreement_score":0.9438899,"about_ca_system_score_codex":0.0005340449,"about_ca_system_score_gemma":0.00030875424,"threshold_uncertainty_score":0.5295561},"labels":[],"label_agreement":null},{"id":"W2345785510","doi":"10.1007/978-3-319-27695-3_3","title":"Ambient Technology to Support Elderly People in Outdoor Risk Situations","year":2015,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Elderly people; Assistive technology; Quality of life (healthcare); Activities of daily living; Older people; Independent living; Disabled people; Gerontology; Business; Internet privacy; Computer science; Environmental health; Medicine; Physical therapy; Nursing; Human–computer interaction; Life style","score_opus":0.056655608034804406,"score_gpt":0.3219722669084196,"score_spread":0.2653166588736152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345785510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013314798,0.0002576828,0.8659056,0.007038855,0.000759196,0.001654713,0.00007611121,0.0003173613,0.12265905],"genre_scores_gemma":[0.8558488,0.0011133912,0.1390746,0.0012200432,0.000055446668,0.00033557968,0.00010898061,0.000024474879,0.0022186842],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99757224,0.00006548484,0.0009789459,0.0004354776,0.0006195416,0.00032831234],"domain_scores_gemma":[0.9955276,0.00024133133,0.00046519708,0.0026635022,0.00091133144,0.0001910199],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002107727,0.00025650652,0.00039391086,0.0035920106,0.00034953063,0.0005927351,0.0038392146,0.00019998838,0.000012252356],"category_scores_gemma":[0.00021016864,0.0002810592,0.000045704073,0.0017259757,0.0003838745,0.0059705856,0.0033672466,0.00062410155,0.00044926652],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023643468,0.000044500346,0.0006266161,0.000013749702,0.0000055074897,7.9079325e-7,0.00590402,0.00026140246,0.0000034781099,0.14986823,0.0010022703,0.84226704],"study_design_scores_gemma":[0.0013535371,0.0006798836,0.0074125556,0.0005316641,0.000015137071,0.0001383764,0.00036365996,0.24340531,0.000024425008,0.045509886,0.69930005,0.001265514],"about_ca_topic_score_codex":0.00010622726,"about_ca_topic_score_gemma":0.0009932924,"teacher_disagreement_score":0.85451734,"about_ca_system_score_codex":0.00042790815,"about_ca_system_score_gemma":0.00073548086,"threshold_uncertainty_score":0.9999642},"labels":[],"label_agreement":null},{"id":"W2346144301","doi":"10.1007/978-3-319-29236-6_3","title":"A Context-Aware Healthcare Architecture for the Elderly","year":2016,"lang":"en","type":"article","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Health care; Context (archaeology); Healthcare service; Automation; Architecture; Service (business); Computer science; Order (exchange); Population; Business; Knowledge management; Computer security; Process management; Medicine; Engineering; Marketing; Political science; Geography","score_opus":0.03281255244668656,"score_gpt":0.2642895145476088,"score_spread":0.23147696210092222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346144301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019042228,0.00027439249,0.9728982,0.023427546,0.0005555079,0.00079608767,0.000062230494,0.00007280494,0.0000090502],"genre_scores_gemma":[0.6172408,0.000017910455,0.38217944,0.00035956298,0.00008772209,0.000104732004,0.0000025407785,0.0000055999553,0.0000017069516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989776,0.000031869615,0.00041378546,0.00013697366,0.0001878541,0.00025192017],"domain_scores_gemma":[0.99726725,0.0016194027,0.00027145815,0.00057062873,0.00022753146,0.00004375844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005040171,0.00015890908,0.00021804047,0.00010055906,0.0011755424,0.00020772376,0.0019495451,0.00008096669,2.7070772e-7],"category_scores_gemma":[0.00015056062,0.00008548871,0.00016377137,0.00042359147,0.0002894174,0.00045165565,0.0004417365,0.00015344577,3.76384e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004322748,0.000020995338,0.00003884601,0.00015328814,0.00007568151,2.9563552e-8,0.0037904999,0.07580712,0.00008743688,0.015284574,0.00015693344,0.9045803],"study_design_scores_gemma":[0.0010483532,0.00032457995,0.0004816554,0.000379604,0.0000361801,0.00004454536,0.00007285358,0.9249896,0.0011920505,0.002048281,0.06894879,0.00043351113],"about_ca_topic_score_codex":0.00008871455,"about_ca_topic_score_gemma":0.00044633707,"teacher_disagreement_score":0.9041468,"about_ca_system_score_codex":0.000045521567,"about_ca_system_score_gemma":0.00015001086,"threshold_uncertainty_score":0.90414405},"labels":[],"label_agreement":null},{"id":"W2397289891","doi":"","title":"Context-aware mobile apps using iBeacons: towards smarter interactions","year":2015,"lang":"en","type":"article","venue":"SOURCE Sheridan's Institutional Repository (Sheridan College)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Sheridan College","funders":"","keywords":"Bluetooth; Computer science; Context (archaeology); Asset (computer security); Mobile apps; Relevance (law); World Wide Web; Work (physics); Mobile device; Context awareness; Computer security; Engineering; Telecommunications","score_opus":0.044000095845795846,"score_gpt":0.2704430817544293,"score_spread":0.2264429859086335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397289891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.363864,0.0007439617,0.58631843,0.0005260298,0.010191296,0.0017272551,0.000112830625,0.0012924122,0.035223812],"genre_scores_gemma":[0.9857928,0.0000037287762,0.00567552,0.00056550076,0.0011178906,0.00042755782,0.000023076163,0.00006441722,0.006329465],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.994845,0.0005562905,0.0011441901,0.0013194488,0.0013420675,0.0007930245],"domain_scores_gemma":[0.99595517,0.00025130884,0.00052686216,0.0013166785,0.001167807,0.0007821473],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007541506,0.00068278663,0.0007539534,0.00041613105,0.0013902868,0.00074575667,0.0014068163,0.00029516398,0.00007471005],"category_scores_gemma":[0.00018378835,0.0007147698,0.00045313247,0.0010543681,0.0005939597,0.002981615,0.0006804422,0.0007195067,0.00027438163],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003110207,0.010597969,0.038912825,0.0013419176,0.006577367,0.012062725,0.04506898,0.08259995,0.09438075,0.08951452,0.13155165,0.48428115],"study_design_scores_gemma":[0.003595201,0.0004972114,0.0008892596,0.0006641104,0.00013050482,0.009071507,0.0069462406,0.10429077,0.008863108,0.00072678865,0.86219424,0.0021310286],"about_ca_topic_score_codex":0.0009857259,"about_ca_topic_score_gemma":0.00015365382,"teacher_disagreement_score":0.7306426,"about_ca_system_score_codex":0.0016859819,"about_ca_system_score_gemma":0.0029793894,"threshold_uncertainty_score":0.99990976},"labels":[],"label_agreement":null},{"id":"W2400916137","doi":"","title":"Activity Recognition with Time-Delay Emobeddings","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"McGill University","funders":"","keywords":"Accelerometer; Computer science; Embedding; Activity recognition; Computation; Phone; Artificial intelligence; Machine learning; Real-time computing; Speech recognition; Algorithm","score_opus":0.048386058644132,"score_gpt":0.22234263925423428,"score_spread":0.17395658061010227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400916137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22865734,0.0000049422497,0.6358178,0.00017049996,0.00016052712,0.00029023702,0.0000037371735,0.00073878793,0.1341561],"genre_scores_gemma":[0.97609675,0.0000012942804,0.021428276,0.00021785722,0.000042782114,0.000038280472,0.0000020143925,0.000014510955,0.0021582376],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987425,0.00010188284,0.00015264642,0.00046193931,0.00027364807,0.00026734825],"domain_scores_gemma":[0.998988,0.0000998246,0.00012897966,0.0004690038,0.00019055362,0.00012366359],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030109828,0.00016836222,0.00019506807,0.00013397359,0.000104348095,0.000117240736,0.0004205934,0.000072905124,0.0007096955],"category_scores_gemma":[0.000022037246,0.00013607823,0.000059393522,0.000379427,0.000041327265,0.0017938155,0.00012972634,0.00013724712,0.002799916],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097818025,0.00040690176,0.0013589117,0.000021424385,0.00009587091,0.00006734702,0.0020530203,3.1593189e-7,0.0062170294,0.00073857495,0.0025981022,0.9863447],"study_design_scores_gemma":[0.0082653295,0.0047866395,0.059756633,0.0007912262,0.00019476144,0.004229186,0.00061428704,0.0697471,0.80219644,0.01972686,0.023292249,0.006399302],"about_ca_topic_score_codex":0.00029407325,"about_ca_topic_score_gemma":0.00006454707,"teacher_disagreement_score":0.97994536,"about_ca_system_score_codex":0.000046289682,"about_ca_system_score_gemma":0.000057765836,"threshold_uncertainty_score":0.99797654},"labels":[],"label_agreement":null},{"id":"W2401658486","doi":"","title":"Optimization of Activities in Smart Homes.","year":2012,"lang":"en","type":"article","venue":"Industrial Conference on Data Mining","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Université du Québec","funders":"","keywords":"Computer science; Home automation; Telecommunications","score_opus":0.32777442915781274,"score_gpt":0.333861045850029,"score_spread":0.0060866166922162734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401658486","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5310962,0.00012358835,0.43760207,0.0012755308,0.004668334,0.001026774,0.0004032161,0.0002898147,0.023514442],"genre_scores_gemma":[0.9955537,0.0000072774733,0.0040534963,0.000035325018,0.00018723278,0.000013174051,0.000079804544,0.000007922674,0.00006206765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843526,0.00023760577,0.0003703771,0.00034183788,0.00031828336,0.00029666058],"domain_scores_gemma":[0.99824154,0.00041394844,0.00026243398,0.0009390546,0.00005825099,0.00008479257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010051826,0.00014075404,0.000289669,0.00027497034,0.000042618118,0.00013297144,0.0011298651,0.00014981598,0.00008717988],"category_scores_gemma":[0.0004694362,0.00014549808,0.00002303463,0.0004324626,0.000047342794,0.0029352654,0.0005392684,0.0002364197,0.000014776219],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010774003,0.00043667262,0.044642664,0.00002719265,0.000058176684,0.000011419264,0.0036758266,0.00034211672,0.0007581253,0.0057494296,0.0029761828,0.94121444],"study_design_scores_gemma":[0.01627145,0.0015578031,0.014500698,0.0049286736,0.000111616595,0.0001750129,0.01576925,0.8884525,0.027512433,0.00032294082,0.026381588,0.0040160487],"about_ca_topic_score_codex":0.00017567478,"about_ca_topic_score_gemma":0.000046924786,"teacher_disagreement_score":0.9371984,"about_ca_system_score_codex":0.000051054292,"about_ca_system_score_gemma":0.00023646856,"threshold_uncertainty_score":0.59332395},"labels":[],"label_agreement":null},{"id":"W2401728283","doi":"","title":"State sequence analysis in hidden Markov models","year":2015,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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":"Ottawa Hospital","funders":"","keywords":"Hidden Markov model; Viterbi algorithm; Sequence (biology); Inference; Forward algorithm; Computer science; State (computer science); Markov chain; Markov model; Hidden semi-Markov model; Sequence labeling; Artificial intelligence; Algorithm; Machine learning; Markov property; Variable-order Markov model","score_opus":0.16143884102844683,"score_gpt":0.3358911258933971,"score_spread":0.17445228486495026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401728283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16812515,0.00009904057,0.8276244,0.00089348137,0.000444124,0.00043768215,0.000018409939,0.00016340385,0.002194311],"genre_scores_gemma":[0.99439603,0.000018250503,0.0051080566,0.00020865652,0.00004090423,0.00007252171,0.000009379162,0.000011908967,0.00013431396],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963802,0.00046060092,0.0009976295,0.00087104045,0.0006617194,0.0006288056],"domain_scores_gemma":[0.9978887,0.00043501472,0.0002180255,0.0008611952,0.00034513907,0.00025194365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020316807,0.0002769792,0.0005361511,0.0011579939,0.000054505428,0.00030426178,0.001346501,0.00011589038,0.000036361307],"category_scores_gemma":[0.0003630797,0.00028952403,0.00014810974,0.0046881563,0.00012810109,0.0013255823,0.00031467588,0.00036797777,0.0002534621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008123909,0.00025981647,0.003417168,0.0000123988475,0.00007090239,0.00019921831,0.009891306,0.36562017,0.00028153218,0.016937887,0.000066720684,0.60316163],"study_design_scores_gemma":[0.000057428475,0.00004694526,0.0001672291,0.00004060019,0.000010600589,0.0000072920193,0.00080554193,0.8543711,0.001247954,0.14285123,0.00007639819,0.0003177252],"about_ca_topic_score_codex":0.009547822,"about_ca_topic_score_gemma":0.02854497,"teacher_disagreement_score":0.8262709,"about_ca_system_score_codex":0.0005958209,"about_ca_system_score_gemma":0.00035733957,"threshold_uncertainty_score":0.9999557},"labels":[],"label_agreement":null},{"id":"W2402000776","doi":"","title":"Possibilistic behavior recognition in smart homes for cognitive assistance","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi; Université de Sherbrooke","funders":"","keywords":"Home automation; Activity recognition; Cognition; Computer science; Field (mathematics); Moment (physics); Order (exchange); Internet of Things; Cognitive computing; Smart environment; Human–computer interaction; Artificial intelligence; Computer security; Psychology; Telecommunications; Business; Mathematics","score_opus":0.1833253256588282,"score_gpt":0.37657687288200564,"score_spread":0.19325154722317744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402000776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25481105,0.000013917813,0.7319326,0.0014105247,0.0019843727,0.0016792284,0.00031487655,0.00022064065,0.0076328395],"genre_scores_gemma":[0.99447334,0.000003930281,0.0040838574,0.0003095281,0.00015646189,0.0007557343,0.00007078353,0.000014728931,0.00013162223],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973991,0.00012443255,0.0006384604,0.000782751,0.0006943958,0.00036088517],"domain_scores_gemma":[0.9959191,0.0015294286,0.00025278993,0.0002599882,0.0019139503,0.00012473561],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010526017,0.0002421407,0.00027744478,0.00042867503,0.00018905498,0.00039580485,0.0005945969,0.00018355984,0.00021945861],"category_scores_gemma":[0.0022918673,0.00026152402,0.000107775224,0.00060193165,0.00015481062,0.00076179503,0.00006600647,0.00048231936,0.00046518765],"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":[0.00013115688,0.0006269488,0.00066258525,0.000019472234,0.0000121336525,0.000010555081,0.0003766886,0.000006273781,0.007483955,0.25135845,0.000051969324,0.73925984],"study_design_scores_gemma":[0.00053134095,0.00065449503,0.039084945,0.0004982074,0.000033059663,0.00005652337,0.00086057605,0.1174426,0.10576581,0.73307765,0.0005015227,0.0014932847],"about_ca_topic_score_codex":0.000062191044,"about_ca_topic_score_gemma":0.0021656498,"teacher_disagreement_score":0.7396623,"about_ca_system_score_codex":0.000121952806,"about_ca_system_score_gemma":0.0004806838,"threshold_uncertainty_score":0.99998367},"labels":[],"label_agreement":null},{"id":"W2403669625","doi":"10.1007/978-3-319-18615-3_23","title":"An Event-Driven Operating System for Servomotor Control","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Servomotor; Firmware; Microcontroller; Computer science; Control engineering; Humanoid robot; Polling; Event (particle physics); Servo; DC motor; Motor controller; Embedded system; Robot; Control system; Computer hardware; Engineering; Artificial intelligence; Operating system; Electrical engineering","score_opus":0.036114407392563816,"score_gpt":0.28297436819850696,"score_spread":0.24685996080594313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403669625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001262315,0.00023831977,0.99273485,0.0003798261,0.0035277577,0.0018064787,0.000060903232,0.00043193903,0.00069368986],"genre_scores_gemma":[0.86944735,0.0000013463969,0.1284053,0.0005807529,0.0011928478,0.000110868896,0.000014038369,0.000058699457,0.00018878485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99503726,0.00014219248,0.00083638605,0.0019901805,0.0012051086,0.0007888858],"domain_scores_gemma":[0.99527794,0.00075885677,0.0005450462,0.0017839686,0.0012264897,0.0004076999],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00220207,0.0006587892,0.0009556109,0.00067827664,0.0003955429,0.0012845098,0.0039215903,0.00041711677,0.0000074948334],"category_scores_gemma":[0.00014255343,0.00062026375,0.0002121936,0.00040705112,0.00026445734,0.0016454787,0.000575727,0.0005798554,0.000060948423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030921212,0.00010278234,0.00014670432,0.0003750401,0.00007650251,0.00013182395,0.0020107902,0.10979837,0.0020978556,0.014457801,0.00012115299,0.87065023],"study_design_scores_gemma":[0.0010546066,0.00046906597,0.000017288417,0.00079685205,0.000016359638,0.00017320318,0.0000014925204,0.9896554,0.0006079589,0.0047336943,0.00168966,0.0007844185],"about_ca_topic_score_codex":0.000046935485,"about_ca_topic_score_gemma":0.00015401808,"teacher_disagreement_score":0.879857,"about_ca_system_score_codex":0.0008731073,"about_ca_system_score_gemma":0.0011983612,"threshold_uncertainty_score":0.9997522},"labels":[],"label_agreement":null},{"id":"W2404566166","doi":"10.1007/978-3-319-24717-5","title":"Security and Privacy for Mobile Healthcare Networks","year":2015,"lang":"en","type":"book","venue":"Wireless networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Health care; Computer security; Architecture; Internet privacy; State (computer science); Mobile device; World Wide Web; Political science","score_opus":0.026555825482104396,"score_gpt":0.26973047318185867,"score_spread":0.24317464769975428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404566166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017269442,0.01974826,0.9575702,0.0005330926,0.0041751666,0.0041436646,0.000111736575,0.0008037535,0.012741443],"genre_scores_gemma":[0.81946075,0.0028361033,0.0021284644,0.0024764342,0.012811407,0.0036360193,0.0012549426,0.00050998485,0.15488592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964186,0.00023515319,0.000710181,0.0013112682,0.0004914156,0.00083341025],"domain_scores_gemma":[0.9960812,0.0007515767,0.00066218147,0.0013094497,0.00066783896,0.00052779913],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009804836,0.0006517352,0.0011393753,0.00016293378,0.00027330327,0.0004718631,0.001282471,0.0011414986,0.000007362582],"category_scores_gemma":[0.00002659894,0.0006904666,0.000247803,0.00022438742,0.00012846585,0.00050499325,0.0008928666,0.0010719391,0.000016679045],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006795557,0.000088017136,0.00009301607,0.000547988,0.00020428956,0.00004028202,0.0012664654,0.0017429843,2.5928736e-7,0.0096937865,0.3660313,0.62022364],"study_design_scores_gemma":[0.0008770873,0.00027401425,0.000011108478,0.00071090925,0.000038509002,0.00008255326,0.000026449186,0.5486674,7.967993e-7,0.008000114,0.44046104,0.000850065],"about_ca_topic_score_codex":0.0000544756,"about_ca_topic_score_gemma":0.00020457865,"teacher_disagreement_score":0.9554417,"about_ca_system_score_codex":0.00041567208,"about_ca_system_score_gemma":0.00070526794,"threshold_uncertainty_score":0.99955463},"labels":[],"label_agreement":null},{"id":"W2406539874","doi":"10.5555/2034396.2034454","title":"Smart walkers!: enhancing the mobility of the elderly","year":2011,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Classifier (UML); Activity recognition; Discriminant; Sigmoid function; Intelligent sensor; Linear discriminant analysis; Artificial intelligence; Machine learning; Human–computer interaction; Random forest; Data mining; Wireless sensor network; Artificial neural network; Computer network","score_opus":0.1021268820539132,"score_gpt":0.26767368446236034,"score_spread":0.16554680240844716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406539874","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.770035,0.0006887199,0.21910213,0.00014009482,0.0035428475,0.0027106719,0.00004631692,0.00012784825,0.003606314],"genre_scores_gemma":[0.9982302,0.000019748315,0.000403037,0.00009918848,0.000046949237,0.0000984774,8.4247165e-7,0.000015378737,0.0010861771],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9974694,0.0006017905,0.00060168444,0.0005370192,0.00044972994,0.0003403804],"domain_scores_gemma":[0.9979431,0.000202289,0.0004625594,0.0010148039,0.00026279446,0.0001144639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010060404,0.000252117,0.00035864435,0.00006281816,0.00033253306,0.00010083726,0.0010165059,0.000092893984,0.000022242488],"category_scores_gemma":[0.00007959804,0.00014632684,0.00016113327,0.00031037163,0.0001768399,0.00044719654,0.00050889206,0.00019132961,0.00004137057],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004134399,0.007693349,0.30461916,0.0017607464,0.0048032356,0.00015877408,0.22184773,0.00017413398,0.031143278,0.026512839,0.0104496535,0.39042366],"study_design_scores_gemma":[0.005031208,0.0011765172,0.80705255,0.0014083277,0.00025066687,0.00024594893,0.014667059,0.12757052,0.01858737,0.00040627774,0.021935606,0.0016679087],"about_ca_topic_score_codex":0.0044041513,"about_ca_topic_score_gemma":0.00034665153,"teacher_disagreement_score":0.5024334,"about_ca_system_score_codex":0.000057909478,"about_ca_system_score_gemma":0.00006796909,"threshold_uncertainty_score":0.6657789},"labels":[],"label_agreement":null},{"id":"W2408734204","doi":"","title":"Smart Home, The Next Generation: Closing the Gap between Users and Technology","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bridge (graph theory); Computer science; Home automation; Closing (real estate); Human–computer interaction; Work (physics); End user; Smart environment; Internet privacy; World Wide Web; Engineering; Telecommunications; Internet of Things","score_opus":0.14285196049038443,"score_gpt":0.28289227347302837,"score_spread":0.14004031298264394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408734204","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64045787,0.0010992101,0.30853996,0.043262362,0.001068476,0.0005101467,0.0000026614362,0.00039652747,0.0046627745],"genre_scores_gemma":[0.9974858,0.0000096705,0.0012109057,0.00054121745,0.00039071587,0.000025681878,9.760266e-7,0.0000056636936,0.00032937835],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913764,0.00012768606,0.00015511061,0.00018421432,0.00015893813,0.00023640384],"domain_scores_gemma":[0.99902177,0.00028489283,0.00006831743,0.0005081482,0.000067609086,0.000049245216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005870815,0.00009498719,0.00011184441,0.00007510096,0.00035877922,0.00030936496,0.0004830056,0.00006940489,0.000014888431],"category_scores_gemma":[0.000036740694,0.00005201651,0.000026320855,0.0004403634,0.00012220236,0.00085719745,0.00028475368,0.00014625388,0.00009693609],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014777223,0.000045715668,0.2200035,0.000010699682,0.000110755966,0.0000016717196,0.002701587,0.0000010711624,0.0077992533,0.06585215,0.012740224,0.6907319],"study_design_scores_gemma":[0.0021832061,0.00035717632,0.16608833,0.00011857749,0.00018605197,0.0011784406,0.0054914965,0.040284697,0.06632547,0.012412176,0.7031178,0.0022565755],"about_ca_topic_score_codex":0.000044797795,"about_ca_topic_score_gemma":0.000049118597,"teacher_disagreement_score":0.6903776,"about_ca_system_score_codex":0.00002069819,"about_ca_system_score_gemma":0.000025217556,"threshold_uncertainty_score":0.29832134},"labels":[],"label_agreement":null},{"id":"W2413445513","doi":"10.3233/978-1-58603-979-0-447","title":"Improving the Efficiency and Accuracy of a Tablet PC Interface for Computerized Provider Order Entry Through Usability Evaluation and Provision of Data Entry Strategies","year":2009,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Context-Aware Activity Recognition 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 Victoria","funders":"","keywords":"Usability; Order entry; Data entry; Interface (matter); Computer science; Order (exchange); Human–computer interaction; Database; Business; Medicine; Medical emergency; Operating system","score_opus":0.11168010426819316,"score_gpt":0.42163269902807043,"score_spread":0.30995259475987724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2413445513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38890034,0.006742404,0.59796304,0.0035925263,0.00015082386,0.0025288886,0.000024438104,0.000056031087,0.000041529507],"genre_scores_gemma":[0.97026867,0.000928021,0.02863074,0.00012370737,0.000005512442,0.00003749315,0.0000030657409,0.0000018226217,9.488752e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985463,0.00009570421,0.0008169185,0.00019954455,0.00015299297,0.00018851404],"domain_scores_gemma":[0.9978087,0.00074870477,0.0006388975,0.0004856487,0.00030289823,0.000015176798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020219164,0.00011592303,0.0003674802,0.00012373117,0.00017494845,0.000039620987,0.00037079657,0.000083901905,2.5410927e-7],"category_scores_gemma":[0.001429245,0.00008136211,0.000009303194,0.00039562353,0.0004523677,0.001489895,0.00074079353,0.00015476106,6.439724e-8],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005164731,0.000105007086,0.0012339277,0.0034149548,0.000034553952,7.877137e-8,0.025536897,0.000026936015,0.000096990836,0.006625044,0.000121614496,0.96275234],"study_design_scores_gemma":[0.0034425897,0.0019649481,0.0019427493,0.001204989,0.000031381223,0.00005275614,0.06402698,0.8955534,0.0009962576,0.029593602,0.0009076943,0.0002826532],"about_ca_topic_score_codex":0.000037837664,"about_ca_topic_score_gemma":0.000027181137,"teacher_disagreement_score":0.9624697,"about_ca_system_score_codex":0.00003434659,"about_ca_system_score_gemma":0.00022005958,"threshold_uncertainty_score":0.33178505},"labels":[],"label_agreement":null},{"id":"W2413869421","doi":"10.20381/ruor-12780","title":"An infrastructure for managing context information in pervasive computing environments","year":2004,"lang":"en","type":"article","venue":"uO Research (University of Ottawa)","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Computer science; Ubiquitous computing; Context (archaeology); Adaptation (eye); Context awareness; Context management; Adaptability; Popularity; Process (computing); Mobile computing; Mobile device; Context-aware pervasive systems; Knowledge management; Human–computer interaction; World Wide Web; Data science; Computer security","score_opus":0.03306352215373998,"score_gpt":0.28372450396512083,"score_spread":0.25066098181138086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2413869421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31926158,0.0000059690938,0.677498,0.0010806015,0.00006527872,0.0005506307,0.00001511332,0.000039723822,0.0014831201],"genre_scores_gemma":[0.9903559,0.000006086805,0.009495923,0.00005010552,0.000014962699,8.833722e-7,0.000016618515,0.0000040346795,0.000055473953],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99863684,0.00015469633,0.00015284344,0.0002464481,0.00048654445,0.00032263264],"domain_scores_gemma":[0.99909794,0.00017835478,0.0001206285,0.0003353697,0.00016095022,0.00010674311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000908097,0.000086788794,0.00016990285,0.00059653923,0.000280207,0.000085975764,0.0008909345,0.00007594005,0.000013325978],"category_scores_gemma":[0.00006328442,0.00011229521,0.000054709937,0.00044202927,0.00012097195,0.002835361,0.0002817037,0.00024004909,0.000035274174],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002619298,0.00023310426,0.029334461,0.00022155378,0.000064920794,0.00005010229,0.031945296,0.006712496,0.0024819633,0.067472495,0.0005585037,0.8608989],"study_design_scores_gemma":[0.014116224,0.0015608007,0.5260718,0.0005896048,0.000016131184,0.000060593007,0.06899687,0.280653,0.003673492,0.024987942,0.07822494,0.0010485909],"about_ca_topic_score_codex":0.00034148392,"about_ca_topic_score_gemma":0.000557932,"teacher_disagreement_score":0.85985035,"about_ca_system_score_codex":0.00032788157,"about_ca_system_score_gemma":0.00013061275,"threshold_uncertainty_score":0.45792657},"labels":[],"label_agreement":null},{"id":"W2414900880","doi":"10.1016/j.medengphy.2016.10.014","title":"Review of fall detection techniques: A data availability perspective","year":2016,"lang":"en","type":"review","venue":"Medical Engineering & Physics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":206,"is_retracted":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":"Taxonomy (biology); Computer science; Perspective (graphical); Machine learning; Feature selection; Field (mathematics); Artificial intelligence; Data extraction; Data science; Data mining; MEDLINE; Ecology; Biology; Mathematics","score_opus":0.06362460603868626,"score_gpt":0.34296856628979194,"score_spread":0.2793439602511057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2414900880","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.4495836e-8,0.54472494,0.45407882,0.00005636993,0.00026979754,0.00046212593,0.00004711803,0.00025573335,0.00010511007],"genre_scores_gemma":[0.000007786003,0.9978159,0.0013138948,0.00007702585,0.0005229911,0.00016139806,0.000027851358,0.000041171403,0.000031978445],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968306,0.0002334442,0.0008980544,0.000902351,0.00084664655,0.00028894452],"domain_scores_gemma":[0.995642,0.0008211607,0.00048430046,0.0025418561,0.0003088221,0.00020185826],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001671577,0.00042141826,0.0018114721,0.000103872444,0.000027912238,0.000030596824,0.0026798085,0.00030077258,0.000027944585],"category_scores_gemma":[0.0025897166,0.00032022587,0.00039205447,0.00074738514,0.000072584044,0.000486059,0.0011401797,0.0006699971,0.000099467645],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.1652436e-7,0.000045184257,4.088612e-8,0.05773485,0.000075926044,0.0000036761312,0.000013922477,2.2570653e-8,0.0000010456569,0.00042100818,0.00041302168,0.9412911],"study_design_scores_gemma":[0.000046977468,0.000027727265,1.03647196e-7,0.20189852,0.000088893765,0.000044270553,7.814362e-7,0.00078914844,0.000014061289,0.00007677791,0.79673547,0.00027725493],"about_ca_topic_score_codex":0.000065708184,"about_ca_topic_score_gemma":0.0000038502108,"teacher_disagreement_score":0.9410138,"about_ca_system_score_codex":0.0003636841,"about_ca_system_score_gemma":0.00055392185,"threshold_uncertainty_score":0.99992496},"labels":[],"label_agreement":null},{"id":"W2417822074","doi":"10.1007/978-3-319-27695-3_4","title":"Toward Context-Aware Smart Oven to Prevent Cooking Risks in Kitchen of Elderly People","year":2015,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Context (archaeology); Microcontroller; Elderly people; Computer science; Ubiquitous computing; Assistive technology; Architecture; Context awareness; Life style; Population; Unit (ring theory); Focus (optics); Embedded system; Human–computer interaction; Medicine; Psychology; Gerontology; Environmental health; Geography","score_opus":0.13744608249980403,"score_gpt":0.3363749517815694,"score_spread":0.19892886928176537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2417822074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039238944,0.0011819744,0.8626371,0.0036903014,0.0009952807,0.0028178426,0.000095405565,0.00020447199,0.12445372],"genre_scores_gemma":[0.9885724,0.00025911586,0.010322845,0.00042304848,0.000019984334,0.00006935218,0.00002857273,0.000009673587,0.00029501715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740744,0.0000963017,0.0011481533,0.00036806715,0.00070238486,0.00027767255],"domain_scores_gemma":[0.9959496,0.00037371478,0.0005888391,0.002003648,0.0009044153,0.00017973986],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022444157,0.00025971633,0.00051947724,0.001510125,0.00015850789,0.0003779397,0.0034804025,0.0001583203,0.000008535904],"category_scores_gemma":[0.000112719485,0.00028098022,0.00006439057,0.0007949176,0.00030392257,0.0052380743,0.0029178252,0.00045163208,0.00006153485],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000077516415,0.000075916534,0.0007497394,0.0000905271,0.000013380412,8.8403357e-7,0.019640991,0.00029090297,0.0000059896265,0.0759376,0.00047482547,0.9027115],"study_design_scores_gemma":[0.002955207,0.00092612376,0.024121355,0.0051663094,0.00003404222,0.00018857964,0.0013373585,0.50638974,0.00021943847,0.022734618,0.43340072,0.002526529],"about_ca_topic_score_codex":0.0004750335,"about_ca_topic_score_gemma":0.00065132894,"teacher_disagreement_score":0.9846485,"about_ca_system_score_codex":0.00034333678,"about_ca_system_score_gemma":0.0006529633,"threshold_uncertainty_score":0.99996424},"labels":[],"label_agreement":null},{"id":"W2419341522","doi":"10.21307/ijssis-2017-588","title":"Fall Detection and Prevention for the Elderly: A Review of Trends and Challenges","year":2013,"lang":"en","type":"review","venue":"International Journal on Smart Sensing and Intelligent Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":106,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Child, Adolescent and Family Mental Health; Athabasca University","funders":"Mitacs","keywords":"Fall prevention; Falling (accident); Surprise; Fall of man; Elderly people; Gerontology; Order (exchange); Healthy aging; Medicine; Psychology; Medical emergency; Suicide prevention; Poison control; Environmental health; Political science; Business; Social psychology","score_opus":0.15781231626618641,"score_gpt":0.3606772504110675,"score_spread":0.2028649341448811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2419341522","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008719824,0.9027434,0.09312539,0.00074330164,0.0025827906,0.00064915064,0.000008846353,0.000020047035,0.00011837829],"genre_scores_gemma":[0.0011500289,0.99780613,0.00015866593,0.000043002332,0.00046617573,0.00003482227,0.0000050327662,0.000019720263,0.00031640203],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975968,0.0004457867,0.0009761265,0.00037335063,0.00045597213,0.00015197118],"domain_scores_gemma":[0.9969877,0.00091200875,0.0012063839,0.00023001737,0.00056854764,0.000095338844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014304997,0.00029160865,0.0009071169,0.00042849442,0.0001456593,0.00046050575,0.00030954112,0.00014045306,0.0000023043935],"category_scores_gemma":[0.00013925905,0.00018863595,0.00028276438,0.00011149178,0.000051225936,0.00028634488,0.000112647605,0.00030250108,0.0000047170533],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041688268,0.000014406957,3.6191142e-7,0.0072196624,0.0003526889,0.0000028819657,0.00008027875,2.9738675e-7,9.3367356e-7,0.00025301112,0.00016882655,0.9919025],"study_design_scores_gemma":[0.00012614479,0.0003248668,0.000003306465,0.08160545,0.00017748971,0.003487489,0.000057429945,0.0020991701,0.0000041102217,0.000090946894,0.91184396,0.00017965103],"about_ca_topic_score_codex":0.00008448366,"about_ca_topic_score_gemma":0.000029679215,"teacher_disagreement_score":0.9917228,"about_ca_system_score_codex":0.00009427251,"about_ca_system_score_gemma":0.000057015804,"threshold_uncertainty_score":0.7692351},"labels":[],"label_agreement":null},{"id":"W2440290529","doi":"10.4108/eai.16-5-2016.2263275","title":"dtFall -- Decision-Theoretic Framework to Report Unseen Falls","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Training set","score_opus":0.01747878120212053,"score_gpt":0.2791876423359248,"score_spread":0.26170886113380426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2440290529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03250786,0.000013898531,0.94700056,0.0039975154,0.0009206965,0.0002975067,0.0000027799838,0.00042239515,0.014836776],"genre_scores_gemma":[0.90408236,0.0000039367164,0.089483656,0.0014265416,0.00013571848,0.00004202124,3.6316996e-7,0.000015296615,0.0048101344],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977982,0.00009994169,0.00046511472,0.00071821886,0.00055338047,0.00036515706],"domain_scores_gemma":[0.9958827,0.0019273313,0.00012592734,0.0015099597,0.0002501434,0.0003039315],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008070214,0.00018117973,0.0002594968,0.00018288166,0.00008180556,0.00020713697,0.0010818788,0.00012273263,0.00044966323],"category_scores_gemma":[0.0013468454,0.00011608128,0.000112392154,0.00050375826,0.000042279797,0.00055993884,0.0004747954,0.000097593016,0.004645352],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017946108,0.00008262005,0.0035314267,0.000005935151,0.000033518256,0.00034527382,0.0005388902,0.0000014204444,0.0014470471,0.14467773,0.011030883,0.8382873],"study_design_scores_gemma":[0.0014000736,0.00052605104,0.019634753,0.0015522018,0.000024152285,0.0027737329,0.00014743628,0.00065096683,0.017155226,0.604818,0.34926313,0.0020542971],"about_ca_topic_score_codex":0.00003939099,"about_ca_topic_score_gemma":0.000040506082,"teacher_disagreement_score":0.87157446,"about_ca_system_score_codex":0.000085247215,"about_ca_system_score_gemma":0.00009581477,"threshold_uncertainty_score":0.99612963},"labels":[],"label_agreement":null},{"id":"W2463062818","doi":"10.1007/978-3-319-14227-2_4","title":"Storing and Managing Context and Context History","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Context (archaeology); Computer science; History; Archaeology","score_opus":0.031974618291975586,"score_gpt":0.22496692986723782,"score_spread":0.19299231157526223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2463062818","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031530636,0.0030149564,0.99288684,0.0008695986,0.00075263885,0.00043421084,0.000014761711,0.0000742944,0.0016373671],"genre_scores_gemma":[0.3951879,0.00043440075,0.6031026,0.0007003198,0.00023143731,0.000038258204,0.000012282219,0.00003052956,0.00026222283],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988852,0.000017774904,0.0005004305,0.00020070247,0.00020401133,0.00019189449],"domain_scores_gemma":[0.9983396,0.0005120241,0.00046063666,0.00048174956,0.00014295487,0.00006301843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047566014,0.0002637847,0.00044932106,0.00022244181,0.00059562863,0.00022327226,0.0010408403,0.00015836071,6.163719e-7],"category_scores_gemma":[0.000053427444,0.00023323126,0.00010351227,0.000082308645,0.00046445662,0.00045000835,0.0009301757,0.00034080184,4.4695267e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025030722,0.000009572233,0.000016924021,0.00059879315,0.0001528672,2.1385964e-7,0.0077722166,0.018392794,0.000015811454,0.21176201,0.00021621623,0.76106006],"study_design_scores_gemma":[0.00042031906,0.00008520482,0.00006293349,0.00062684354,0.000055237113,0.00005343377,0.000025193993,0.7057927,0.00003358586,0.0013648516,0.2909582,0.00052146026],"about_ca_topic_score_codex":0.00008342484,"about_ca_topic_score_gemma":0.00017222026,"teacher_disagreement_score":0.76053864,"about_ca_system_score_codex":0.00012130352,"about_ca_system_score_gemma":0.00010648672,"threshold_uncertainty_score":0.95108944},"labels":[],"label_agreement":null},{"id":"W2464060858","doi":"","title":"A Context-aware Healthcare Architecture For The&#13;\\nElderly","year":2015,"lang":"en","type":"book-chapter","venue":"Spectrum Research Repository (Concordia University)","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Health care; Healthcare service; Context (archaeology); Automation; Architecture; Service (business); Computer science; Order (exchange); Population; Knowledge management; Process management; Business; Medicine; Engineering; Political science; Marketing; Geography","score_opus":0.08755742059366062,"score_gpt":0.2967106485889397,"score_spread":0.20915322799527908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2464060858","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00072763336,0.006129991,0.21055633,0.04049103,0.0058813267,0.009571863,0.0005110011,0.0014560227,0.7246748],"genre_scores_gemma":[0.47090662,0.000114479924,0.000056147794,0.000111102265,0.0011046485,0.0000329936,0.000028575765,0.000104996245,0.52754045],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.994068,0.00067642174,0.0005346563,0.0016852086,0.0017776013,0.0012581112],"domain_scores_gemma":[0.99195987,0.0025439903,0.00049213413,0.0023914948,0.0018148021,0.0007976796],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0016200823,0.0006805012,0.00091279997,0.0014377172,0.0015931034,0.00065392815,0.004045574,0.00074758276,0.000042018826],"category_scores_gemma":[0.00013265174,0.0006330318,0.0006805151,0.00055650814,0.000724369,0.0006398252,0.0013277715,0.0025542267,0.00017072218],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013734358,0.00019612805,0.00089897425,0.0009801767,0.0021518858,0.004051397,0.003256407,0.000044656623,0.0001983443,0.5870322,0.12183167,0.27798474],"study_design_scores_gemma":[0.0011804235,0.0009744672,0.00017410802,0.00037456147,0.00006614127,0.00030183842,0.000509408,0.00043937474,0.00022356866,0.021663677,0.97333086,0.00076157576],"about_ca_topic_score_codex":0.011651872,"about_ca_topic_score_gemma":0.047105305,"teacher_disagreement_score":0.8514992,"about_ca_system_score_codex":0.0016635064,"about_ca_system_score_gemma":0.003457052,"threshold_uncertainty_score":0.9997469},"labels":[],"label_agreement":null},{"id":"W2472204361","doi":"","title":"Implementation of Local Transport Protocol Library (LTPlib) into Real-time Operating System (RTOS)","year":2016,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Context-Aware Activity Recognition Systems","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 Windsor","funders":"","keywords":"Real-time operating system; Protocol (science); Computer science; Operating system; Embedded system; Real-time computing","score_opus":0.014473702887267029,"score_gpt":0.24187934506160907,"score_spread":0.22740564217434206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2472204361","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90859836,0.0000055504097,0.0753855,0.00065044814,0.00011315621,0.011972596,0.00007711906,0.00042284266,0.002774406],"genre_scores_gemma":[0.9935423,0.0000016278988,0.00525125,0.000020765385,0.000042309584,0.0001833308,0.000013300067,0.000028546645,0.00091656065],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9972692,0.0004423043,0.00053803134,0.0006806242,0.000688611,0.00038123986],"domain_scores_gemma":[0.9979365,0.00020226346,0.0006050904,0.00076991366,0.00025892016,0.00022730698],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008268953,0.00027156723,0.0005315739,0.0003614519,0.00034079351,0.00005084819,0.0013464971,0.00020600334,0.00051106],"category_scores_gemma":[0.000012415805,0.00026726094,0.00024859761,0.0006453656,0.00019238252,0.0049924864,0.000457384,0.00017815473,0.00020895482],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049970084,0.00036329715,0.3625275,0.0014705354,0.00038019518,0.00018254487,0.0091065215,0.000016567024,0.40674123,0.005108879,0.00050566025,0.21309738],"study_design_scores_gemma":[0.013185257,0.0011673643,0.5983617,0.002653739,0.00014760194,0.0001498405,0.012084604,0.00092158036,0.3648884,0.00046750705,0.0043361536,0.0016362662],"about_ca_topic_score_codex":0.00048080998,"about_ca_topic_score_gemma":0.0002190832,"teacher_disagreement_score":0.23583418,"about_ca_system_score_codex":0.0002382599,"about_ca_system_score_gemma":0.0003478012,"threshold_uncertainty_score":0.99997795},"labels":[],"label_agreement":null},{"id":"W2475568343","doi":"10.4018/978-1-61520-655-1.ch034","title":"Context-Aware Systems","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Context (archaeology); Context model; Focus (optics); Contextual design; Context awareness; Architecture; Data science; World Wide Web; Knowledge management; Human–computer interaction; Artificial intelligence; Geography","score_opus":0.0377505816030845,"score_gpt":0.24920493143765343,"score_spread":0.21145434983456893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2475568343","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001379022,0.003432104,0.06567737,0.00008890074,0.0045193653,0.0009640348,0.00027287443,0.0008837999,0.9241478],"genre_scores_gemma":[0.83343554,0.0000051394018,0.00012611377,0.0005217772,0.0010682282,0.00008331872,0.000008018466,0.00007463158,0.16467726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962745,0.00010756978,0.00083215523,0.0010477016,0.00096890045,0.00076917605],"domain_scores_gemma":[0.996435,0.00016621736,0.00071615103,0.0017329673,0.0004461444,0.00050352473],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038977148,0.0008265619,0.0010875309,0.00016142863,0.00019329385,0.00060489436,0.0017216793,0.000825257,0.000056188746],"category_scores_gemma":[0.000020820835,0.00084512704,0.00046388488,0.000044844037,0.0001205383,0.00044116421,0.00071562745,0.0005457034,0.0040270863],"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":[0.000007207085,0.00001346994,0.000025259838,0.00008276316,0.00016732684,0.000079938465,0.00009131415,4.6054413e-7,0.0000074353156,0.9094546,0.0071033826,0.082966834],"study_design_scores_gemma":[0.0009289345,0.00015266203,0.000044362514,0.0013745106,0.0001430284,0.0013074653,0.000054921376,0.00047358085,0.00005022108,0.071030825,0.92234427,0.0020952486],"about_ca_topic_score_codex":0.00035546868,"about_ca_topic_score_gemma":0.0001284048,"teacher_disagreement_score":0.9152409,"about_ca_system_score_codex":0.00064892694,"about_ca_system_score_gemma":0.0003861559,"threshold_uncertainty_score":0.99939996},"labels":[],"label_agreement":null},{"id":"W2480044500","doi":"10.4018/978-1-59904-840-6.ch002","title":"Context-Aware Mobile Learning on the Semantic Web","year":2008,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Lakehead University","funders":"","keywords":"Computer science; Context (archaeology); Context awareness; Mobile device; Ubiquitous computing; Mobile technology; World Wide Web; Mobile computing; Adaptability; Discoverability; Human–computer interaction; Multimedia; Knowledge management; Telecommunications","score_opus":0.030906074588609635,"score_gpt":0.2416017652760389,"score_spread":0.21069569068742927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2480044500","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004592146,0.00043205326,0.005445946,0.00028945968,0.001071006,0.0011028714,0.000065096545,0.0007102493,0.9904241],"genre_scores_gemma":[0.87397146,0.000013813626,0.000027707592,0.0012299567,0.00030070622,0.000104232764,0.000003120184,0.00005189577,0.12429709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99682426,0.00017397606,0.0005628597,0.0010017353,0.0009135169,0.0005236719],"domain_scores_gemma":[0.997209,0.0005017892,0.0005233574,0.0012942938,0.0002780076,0.00019354153],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030692277,0.0006743616,0.0007225315,0.00012230892,0.0004911107,0.00034578698,0.0015699421,0.00046071294,0.00008302221],"category_scores_gemma":[0.000051314204,0.0005412454,0.0004688663,0.00005631618,0.00019109318,0.00015143352,0.0005373643,0.0010010874,0.0029991642],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002135886,0.00002754659,0.000032355452,0.000039171086,0.00022096527,0.00035992538,0.0004571823,0.000013051532,0.00002180341,0.8685209,0.020134948,0.11015078],"study_design_scores_gemma":[0.0008059294,0.0005684512,0.0000307102,0.0012645058,0.000063324565,0.0009905393,0.000116372794,0.0024279275,0.000110238376,0.02700769,0.9651398,0.0014744987],"about_ca_topic_score_codex":0.00009827596,"about_ca_topic_score_gemma":0.00014417512,"teacher_disagreement_score":0.9450049,"about_ca_system_score_codex":0.00035063332,"about_ca_system_score_gemma":0.00042741105,"threshold_uncertainty_score":0.9997039},"labels":[],"label_agreement":null},{"id":"W2480756099","doi":"10.4018/978-1-61692-857-5.ch017","title":"Challenging Issues of Ambient Activity Recognition for Cognitive Assistance","year":2011,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Université du Québec à Chicoutimi; Université de Sherbrooke","funders":"","keywords":"Ambient intelligence; Activities of daily living; Activity recognition; Assisted living; Cognition; Order (exchange); Computer science; Field (mathematics); Home automation; Human–computer interaction; Psychology; Business; Artificial intelligence; Medicine; Telecommunications; Gerontology","score_opus":0.0740701988053709,"score_gpt":0.31142152770677595,"score_spread":0.23735132890140503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2480756099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000018685758,0.0139709925,0.9690621,0.0002652835,0.00048271028,0.00068069436,0.0001120031,0.00006357754,0.015343948],"genre_scores_gemma":[0.64299554,0.09214019,0.21206155,0.00048995065,0.0005278231,0.0004313693,0.0003417625,0.00020236956,0.050809428],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980776,0.00004481826,0.00062085036,0.0006725137,0.00034192007,0.00024234664],"domain_scores_gemma":[0.9972617,0.0010838839,0.0006390091,0.00020246375,0.00074457034,0.00006835986],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027055046,0.00037099782,0.00060594484,0.00030468026,0.00013808116,0.00006564343,0.00031039512,0.00019734596,0.00002251235],"category_scores_gemma":[0.00007997284,0.00040488635,0.00013022262,0.00008199382,0.0003264802,0.0023827113,0.00016498263,0.00026255954,0.000013961203],"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":[0.00019507887,0.000117503834,0.000035477737,0.0006572589,0.00011648895,0.000014703559,0.0012876779,0.014983611,0.0000070429405,0.5348175,0.000019485626,0.44774818],"study_design_scores_gemma":[0.00028809704,0.00060802756,0.00011659964,0.0034037014,0.00005682945,0.000058420417,0.00019902195,0.019629246,0.0021272092,0.9606489,0.011927295,0.0009366757],"about_ca_topic_score_codex":0.000010535686,"about_ca_topic_score_gemma":0.000099781704,"teacher_disagreement_score":0.75700057,"about_ca_system_score_codex":0.000057652014,"about_ca_system_score_gemma":0.00010350621,"threshold_uncertainty_score":0.9998403},"labels":[],"label_agreement":null},{"id":"W2480923458","doi":"10.4018/978-1-4666-0203-8.ch016","title":"Smart Rooms","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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 New Brunswick","funders":"","keywords":"Computer science; Sensor web; Wireless sensor network; Task (project management); Semantic Web; Building automation; State (computer science); Point (geometry); World Wide Web; Wireless network; Wireless; Engineering; Systems engineering; Telecommunications; Computer network; Key distribution in wireless sensor networks","score_opus":0.033580126037586734,"score_gpt":0.24785960677631164,"score_spread":0.2142794807387249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2480923458","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000018755034,0.00074339024,0.042127904,0.00010655087,0.0021462694,0.00040616211,0.00007703306,0.00053911185,0.95383483],"genre_scores_gemma":[0.6228945,0.0000044922845,0.0017471641,0.0012915345,0.0013381129,0.000057009125,0.0000071787035,0.000083970735,0.37257603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976006,0.00004543779,0.0004627416,0.00071789516,0.000647573,0.00052575814],"domain_scores_gemma":[0.9977794,0.0000823147,0.00035338808,0.0012686531,0.0001883992,0.00032784362],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025076384,0.0005150409,0.0005861604,0.00010059032,0.00011619729,0.00028500345,0.0012209141,0.00049335486,0.00009064878],"category_scores_gemma":[0.000015786029,0.00053199055,0.0003443932,0.000028790564,0.000073189054,0.0002966307,0.00063138834,0.0003667663,0.0045317123],"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":[0.0000037503337,0.00000917265,0.000030465575,0.000019396726,0.000075350676,0.000037726786,0.00005351909,8.592545e-8,0.000011566328,0.8285983,0.0041798684,0.1669808],"study_design_scores_gemma":[0.00036688882,0.00007232423,0.00010571742,0.0002784176,0.0000547986,0.00041031657,0.0000036071726,0.00007563042,0.00008055831,0.25478926,0.7427982,0.00096425915],"about_ca_topic_score_codex":0.00010653953,"about_ca_topic_score_gemma":0.00012143429,"teacher_disagreement_score":0.7386183,"about_ca_system_score_codex":0.0003385008,"about_ca_system_score_gemma":0.00022488735,"threshold_uncertainty_score":0.9997132},"labels":[],"label_agreement":null},{"id":"W2483647133","doi":"","title":"The Implementation of a Planning and Scheduling Architecture for Multiple Robots Assisting Multiple Users in a Retirement Home Setting","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition 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 Toronto","funders":"","keywords":"Robot; Scheduling (production processes); Architecture; Computer science; Schedule; Plan (archaeology); Human–computer interaction; Artificial intelligence; Engineering; Operations management; Operating system","score_opus":0.22448834479337995,"score_gpt":0.39706406352138524,"score_spread":0.17257571872800528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2483647133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20398434,0.00004438397,0.7932817,0.0017021574,0.00022302174,0.0005925833,0.000022248325,0.0000382602,0.00011134786],"genre_scores_gemma":[0.98254347,0.0000016601233,0.017203566,0.000067465844,0.000051696406,0.0001112626,0.00001002137,0.0000068178524,0.0000040559953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981974,0.00014521762,0.0005324514,0.00035239736,0.0005387481,0.00023379592],"domain_scores_gemma":[0.9968871,0.0018409872,0.00030764384,0.00014058144,0.0007518503,0.00007186031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001683971,0.0001306149,0.00016183664,0.00018324204,0.0002041358,0.00023622366,0.0003250223,0.000051886702,0.0000018558245],"category_scores_gemma":[0.0017892245,0.0001156087,0.000038755625,0.00028416264,0.00007006136,0.00029671894,0.00009927711,0.00016446644,0.0000027555677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020537258,0.000101233345,0.02240389,0.000051414656,0.00003556206,0.0000028048275,0.010258274,0.030757187,0.0068475963,0.08755426,0.000027562466,0.84175485],"study_design_scores_gemma":[0.00028574732,0.0001719169,0.0061145737,0.00022892524,0.0000037605605,0.000004503484,0.006804527,0.9206029,0.021759363,0.043719206,0.00007645055,0.00022812503],"about_ca_topic_score_codex":0.00019461337,"about_ca_topic_score_gemma":0.0016572269,"teacher_disagreement_score":0.8898457,"about_ca_system_score_codex":0.00012875338,"about_ca_system_score_gemma":0.00030117374,"threshold_uncertainty_score":0.47143862},"labels":[],"label_agreement":null},{"id":"W2486559400","doi":"10.4018/978-1-60566-108-7.ch023","title":"Channel Management Schemes to Support Services in Ubiquitous Communications Infrastructures for Future Transportation Technologies","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Becton Dickinson (Canada)","funders":"","keywords":"The Internet; Intelligent transportation system; Telecommunications; Cyberspace; Emerging technologies; Engineering; Computer science; World Wide Web; Transport engineering","score_opus":0.020591897388197826,"score_gpt":0.26862589637817735,"score_spread":0.24803399898997952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2486559400","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00054224214,0.0012354915,0.119478285,0.004419501,0.0015839697,0.0071539753,0.001473787,0.00270156,0.8614112],"genre_scores_gemma":[0.9580349,0.000069872,0.031622916,0.0015535244,0.00022126603,0.00069482106,0.0001540997,0.000054909713,0.007593708],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99806714,0.00001724282,0.00054720696,0.0006589334,0.00036149242,0.0003479741],"domain_scores_gemma":[0.9979252,0.000036474346,0.00030225347,0.001466992,0.00019265147,0.00007640161],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014049954,0.00042639676,0.00048545212,0.0003351978,0.00013190685,0.00019031967,0.002270527,0.00044642415,0.0000024816222],"category_scores_gemma":[0.0000030231336,0.00045303698,0.00016909874,0.000096127,0.000050149272,0.00021356656,0.00026311347,0.00025785135,0.000028062981],"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":[0.00001695071,0.000014435487,0.000010724336,0.00013439675,0.0000606571,0.000017801878,0.00044770134,0.0000065308136,0.000007677799,0.6279925,0.0005273087,0.37076333],"study_design_scores_gemma":[0.00092257455,0.00037753154,0.0011706551,0.00084833015,0.00008356416,0.00003564747,0.0008190566,0.0002609946,0.00027238415,0.6542832,0.3398371,0.0010889147],"about_ca_topic_score_codex":0.000049370195,"about_ca_topic_score_gemma":0.002584018,"teacher_disagreement_score":0.95749265,"about_ca_system_score_codex":0.00022794955,"about_ca_system_score_gemma":0.00008111643,"threshold_uncertainty_score":0.99979216},"labels":[],"label_agreement":null},{"id":"W2489483268","doi":"10.4018/978-1-61520-655-1.ch005","title":"Modeling Context-Aware Distributed Event-Based Systems","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Event (particle physics); Context (archaeology); Component (thermodynamics); Metamodeling; Adaptation (eye); Context model; Context awareness; Situation awareness; Context management; Data science; Distributed computing; Human–computer interaction; Ubiquitous computing; Software engineering; Artificial intelligence; Engineering","score_opus":0.04012483504166461,"score_gpt":0.25596916249472246,"score_spread":0.21584432745305784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2489483268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000041520616,0.0018470427,0.82144207,0.00009146477,0.0025463267,0.0010548839,0.0011999152,0.0008664733,0.17091028],"genre_scores_gemma":[0.9893018,0.00000173382,0.00012547376,0.00032484057,0.00056745217,0.000109181434,0.00007249142,0.00007610301,0.009420908],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995584,0.00015036733,0.0010985236,0.0011748483,0.0011093068,0.00088297104],"domain_scores_gemma":[0.9962931,0.00014917446,0.00062948436,0.0016982909,0.00067601097,0.00055389287],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00048156385,0.0009486815,0.0011916035,0.00017283658,0.00026067338,0.0006248514,0.0016087968,0.00085903035,0.00002709021],"category_scores_gemma":[0.000032641445,0.000991558,0.00059110153,0.00006772335,0.00008767067,0.00040287912,0.00047760643,0.0006084988,0.0009424334],"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":[0.000040632312,0.000057019974,0.0000293166,0.00025857135,0.00034644542,0.00014222605,0.00007983669,0.0019052911,0.00002216113,0.9627372,0.0018833125,0.032498013],"study_design_scores_gemma":[0.003026091,0.00028134196,0.000011518866,0.00425175,0.00038577418,0.0006293738,0.000106960746,0.8372448,0.00013457701,0.038650133,0.11094813,0.004329567],"about_ca_topic_score_codex":0.0005811609,"about_ca_topic_score_gemma":0.00018192467,"teacher_disagreement_score":0.9892603,"about_ca_system_score_codex":0.0009881422,"about_ca_system_score_gemma":0.0006801336,"threshold_uncertainty_score":0.99983543},"labels":[],"label_agreement":null},{"id":"W2489530272","doi":"10.1007/978-3-319-41354-9_10","title":"The Wisdom of Being Wise: A Brief Introduction to Computational Wisdom","year":2016,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Sociotechnical system; Exposition (narrative); Work (physics); Computer science; Computational model; Epistemology; Management science; Artificial intelligence; Engineering; Philosophy","score_opus":0.007128943138225956,"score_gpt":0.24326829441169023,"score_spread":0.23613935127346428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2489530272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022119824,0.006689335,0.7830177,0.04701414,0.00075359683,0.0015107483,0.00003727546,0.0005120224,0.160244],"genre_scores_gemma":[0.8935584,0.029419608,0.05329967,0.001673929,0.0003056975,0.0008270494,0.0002195071,0.0000761586,0.020619975],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983533,0.000060255345,0.0009074592,0.00022523482,0.00028732073,0.00016644718],"domain_scores_gemma":[0.9969919,0.00047778853,0.0008378277,0.0011493602,0.0005032946,0.00003985373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005549368,0.00019938967,0.0003091032,0.00078389264,0.0002387271,0.000099085744,0.001188042,0.00025024835,0.000011382555],"category_scores_gemma":[0.00019879462,0.00016717013,0.000046011344,0.0002793853,0.00044446535,0.0020927424,0.000626181,0.00035770258,0.00006140433],"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":[0.000008650081,0.0000039404454,0.000012805201,0.000014466969,0.000007831336,7.321088e-8,0.00026947437,0.000038967228,0.000003800511,0.5452908,0.00014263277,0.4542065],"study_design_scores_gemma":[0.00030686453,0.000050291805,0.00002531412,0.00018320039,0.0000037686227,0.000028250071,0.00013649506,0.0006482066,0.000076218086,0.32924473,0.6691352,0.00016142655],"about_ca_topic_score_codex":0.000005242161,"about_ca_topic_score_gemma":0.00007142583,"teacher_disagreement_score":0.8933372,"about_ca_system_score_codex":0.0001272401,"about_ca_system_score_gemma":0.00008731654,"threshold_uncertainty_score":0.68170005},"labels":[],"label_agreement":null},{"id":"W2493979870","doi":"10.4018/978-1-4666-6339-8.ch078","title":"Ubiquitous Multimedia Data Access in Electronic Health Care Systems","year":2014,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Computer science; Provisioning; Multimedia; Information and Communications Technology; Health care; Mobile device; Wireless; Mobile technology; Field (mathematics); Multitude; Information transfer; Software; Telecommunications; World Wide Web","score_opus":0.04969278606844849,"score_gpt":0.3141744127023694,"score_spread":0.2644816266339209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2493979870","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004666425,0.0093543595,0.09668346,0.00035421166,0.004352768,0.0028804322,0.0010044449,0.0008458983,0.88447773],"genre_scores_gemma":[0.983451,0.000030641106,0.00036224388,0.001038778,0.0010596538,0.00010821079,0.00016378154,0.00010074694,0.013684956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951279,0.00022987387,0.0009894171,0.0017528808,0.0008880091,0.0010118793],"domain_scores_gemma":[0.9949231,0.00018702856,0.00082205015,0.003485072,0.0002505473,0.0003321964],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0006943395,0.0006831541,0.0012155266,0.00026066124,0.00013002899,0.000806845,0.0057219123,0.00052217004,0.000006617264],"category_scores_gemma":[0.000044542423,0.0007300278,0.00015275922,0.000080072554,0.000071757175,0.00044968174,0.0024831824,0.0007614093,0.0003542103],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021256523,0.0000186854,0.000061647974,0.0005192857,0.0001296198,0.00010280185,0.0004076634,0.000007646285,0.000002431876,0.72491556,0.0098213805,0.263992],"study_design_scores_gemma":[0.0030974378,0.0006796396,0.00013152808,0.0043300064,0.00008607439,0.0007927132,0.00012622797,0.017471146,0.000017345998,0.028855974,0.94096994,0.003441971],"about_ca_topic_score_codex":0.0027757508,"about_ca_topic_score_gemma":0.009229868,"teacher_disagreement_score":0.98340434,"about_ca_system_score_codex":0.0019457185,"about_ca_system_score_gemma":0.0021523123,"threshold_uncertainty_score":0.99965763},"labels":[],"label_agreement":null},{"id":"W2497653424","doi":"10.4018/978-1-61520-777-0.ch015","title":"Privacy-Based Multiagent Brokering Architecture for Ubiquitous Healthcare Systems","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition 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":"Western University","funders":"","keywords":"Computer science; Architecture; Context (archaeology); Health care; JADE (particle detector); Protocol (science); Internet privacy; Set (abstract data type); Ubiquitous computing; Information privacy; Computer security; World Wide Web; Knowledge management; Human–computer interaction","score_opus":0.0348645961401032,"score_gpt":0.27207531427221215,"score_spread":0.23721071813210895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2497653424","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010591965,0.00082522974,0.7816483,0.0007873186,0.006330114,0.004288642,0.00091594213,0.0011415023,0.20395705],"genre_scores_gemma":[0.942827,0.0000020954635,0.025124773,0.0018390198,0.002373235,0.0008951469,0.000047557856,0.00024039781,0.02665078],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.996169,0.00008201986,0.00083388155,0.0013574407,0.00079830945,0.00075937214],"domain_scores_gemma":[0.9961353,0.00028813144,0.0007296697,0.001861463,0.00052636414,0.00045907559],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036188247,0.00081465906,0.0010023462,0.000206286,0.00031021956,0.0005709257,0.0018170358,0.00096884917,0.0000045139404],"category_scores_gemma":[0.000064564956,0.0008281711,0.0005753622,0.00004052076,0.00009243016,0.000110231915,0.00049953104,0.00086848636,0.00011588898],"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":[0.0001071444,0.00004891378,0.000019746823,0.0018812006,0.0002859995,0.000184973,0.0004598061,0.00008681721,0.00094990537,0.81380343,0.0016367441,0.18053535],"study_design_scores_gemma":[0.0029288032,0.00073634565,0.000028807533,0.0034634776,0.0001407927,0.00073997345,0.000023775416,0.0074863704,0.0019874813,0.09067723,0.8887626,0.0030243567],"about_ca_topic_score_codex":0.00047232295,"about_ca_topic_score_gemma":0.0006909295,"teacher_disagreement_score":0.94272107,"about_ca_system_score_codex":0.0005345828,"about_ca_system_score_gemma":0.0008259986,"threshold_uncertainty_score":0.9994169},"labels":[],"label_agreement":null},{"id":"W2498094302","doi":"10.4018/978-1-60960-549-0.ch025","title":"Deepkøver An Adaptive Intelligent Assistance System for Monitoring Impaired People in Smart Homes","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Université de Moncton; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Realisation; Modular design; Activities of daily living; Computer science; Key (lock); Disabled people; Home automation; Psychology; Human–computer interaction; Computer security; Applied psychology; Operating system","score_opus":0.055055251681885284,"score_gpt":0.26296250555741274,"score_spread":0.20790725387552744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2498094302","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023432493,0.0007935556,0.3759251,0.000020507063,0.00474322,0.0025854807,0.00027850026,0.00083677814,0.6124736],"genre_scores_gemma":[0.9885559,0.0000026448708,0.0027063226,0.000026440079,0.00033662564,0.00027867517,0.000003903758,0.00006003483,0.008029474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99681276,0.00009589416,0.00078474585,0.0011779009,0.00053263985,0.0005960804],"domain_scores_gemma":[0.9974732,0.0001650755,0.00056414155,0.0011119884,0.0004031784,0.00028242936],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045000328,0.00064093363,0.0009648913,0.00021877623,0.00013713361,0.0002701466,0.0012620143,0.00047434279,0.000004914486],"category_scores_gemma":[0.000021840968,0.0006857887,0.00035631246,0.000068296446,0.000055360273,0.0004405534,0.00034491258,0.00030909452,0.00011290034],"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":[0.00021265235,0.00006212768,0.0009901222,0.00029121546,0.00021565075,0.0001058021,0.0018966052,0.0000037121063,0.000026618376,0.91270995,0.00015944373,0.0833261],"study_design_scores_gemma":[0.012259732,0.00851013,0.024823748,0.03406933,0.0010290517,0.0020753378,0.0070182043,0.015955871,0.010160554,0.7978982,0.06640616,0.01979372],"about_ca_topic_score_codex":0.0006500513,"about_ca_topic_score_gemma":0.0026630056,"teacher_disagreement_score":0.9862126,"about_ca_system_score_codex":0.0014629326,"about_ca_system_score_gemma":0.00034024802,"threshold_uncertainty_score":0.99955934},"labels":[],"label_agreement":null},{"id":"W2501302686","doi":"10.4018/978-1-60566-290-9.ch005","title":"An Adaptable Context Management Framework for Pervasive Computing","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Context management; Ubiquitous computing; Computer science; Context-aware pervasive systems; Context (archaeology); Context awareness; Autonomic computing; Human–computer interaction; World Wide Web; Knowledge management; Distributed computing; Data science; Cloud computing","score_opus":0.038025007897537835,"score_gpt":0.28212883131019345,"score_spread":0.24410382341265563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2501302686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010618094,0.00023783342,0.50877166,0.000092397146,0.0007031932,0.0010369059,0.00007536913,0.00035717685,0.48871484],"genre_scores_gemma":[0.7619333,0.000007475564,0.148711,0.005050942,0.0013972294,0.00011931184,0.000025088377,0.00012958769,0.08262605],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99679947,0.000056962148,0.00063829287,0.0012573156,0.00061127497,0.00063669705],"domain_scores_gemma":[0.99715674,0.00022516807,0.0005527943,0.0013973538,0.0003769134,0.00029104477],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033365536,0.0006469855,0.0008164987,0.00013481287,0.00030117144,0.0006235804,0.0016296027,0.00053536793,0.000011302796],"category_scores_gemma":[0.000020545012,0.00071850943,0.00041394713,0.000044408156,0.00006443076,0.00028776051,0.0003615228,0.0003860664,0.0001345044],"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":[0.000014050272,0.000017323533,0.0000017923516,0.000042035805,0.00010794454,0.000042926575,0.000117460455,0.0000047753265,0.0000015370947,0.6165779,0.00079849287,0.38227373],"study_design_scores_gemma":[0.0006963714,0.00040763043,0.000030492249,0.0011804539,0.00009418187,0.000092539136,0.000102810445,0.0028472433,0.000031391814,0.85279477,0.14071895,0.0010031575],"about_ca_topic_score_codex":0.000058169684,"about_ca_topic_score_gemma":0.00008608851,"teacher_disagreement_score":0.7619227,"about_ca_system_score_codex":0.00043994005,"about_ca_system_score_gemma":0.00016254462,"threshold_uncertainty_score":0.9995266},"labels":[],"label_agreement":null},{"id":"W2504088316","doi":"10.4018/978-1-61520-843-2.ch010","title":"Disability vs. Smart Environments","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Rotation formalisms in three dimensions; Computer science; Architecture; Smart environment; Scalability; Human–computer interaction; Semantic Web; Software engineering; Matching (statistics); Home automation; World Wide Web; Internet of Things; Database; Telecommunications","score_opus":0.02144063312703609,"score_gpt":0.23754976651576043,"score_spread":0.21610913338872434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504088316","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039239164,0.000049247326,0.018254949,0.00020552555,0.0019758856,0.0006388486,0.00016706898,0.0003010755,0.978015],"genre_scores_gemma":[0.8713528,0.0000016190678,0.0016335961,0.0007300041,0.00045655333,0.00005754834,0.0000083508785,0.000053171,0.12570634],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971263,0.000056871377,0.0005332269,0.0011091649,0.00074734626,0.00042712962],"domain_scores_gemma":[0.9972989,0.000105975276,0.00035412164,0.0018978274,0.000047350597,0.00029582495],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027430177,0.0005504164,0.000618146,0.000038835544,0.00015445841,0.00025387184,0.0013983429,0.00066994224,0.00011735291],"category_scores_gemma":[0.000027104568,0.0005700705,0.00036011336,0.000018483832,0.0003169434,0.0002214724,0.00084005244,0.00065444695,0.0021893107],"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":[0.000021418817,0.00007513402,0.0007243273,0.00004142656,0.00013469755,0.0000659163,0.00008051047,1.3616402e-7,0.00025182837,0.87755,0.0009960948,0.12005849],"study_design_scores_gemma":[0.0005830409,0.00013580019,0.0019183446,0.0001674907,0.000055765744,0.0001809813,0.0000026197313,0.000058394282,0.0005232371,0.35809746,0.6370875,0.0011893362],"about_ca_topic_score_codex":0.00014550638,"about_ca_topic_score_gemma":0.0003125782,"teacher_disagreement_score":0.8709604,"about_ca_system_score_codex":0.00045412834,"about_ca_system_score_gemma":0.00013196185,"threshold_uncertainty_score":0.9996751},"labels":[],"label_agreement":null},{"id":"W2504481075","doi":"10.4018/978-1-60960-469-1.ch013","title":"The Smart Condo Project","year":2011,"lang":"en","type":"book-chapter","venue":"Advances in medical technologies and clinical practice book series","topic":"Context-Aware Activity Recognition Systems","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":"Key (lock); Architecture; Computer science; Wireless sensor network; Health care; Internet privacy; Computer security; Geography; Computer network","score_opus":0.05106154262471719,"score_gpt":0.3699422947311263,"score_spread":0.3188807521064091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504481075","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000550401,0.1238553,0.007615678,0.031814203,0.002143775,0.0011034688,0.000009541461,0.001372408,0.8320801],"genre_scores_gemma":[0.0011985897,0.8948765,0.0058649527,0.0025060978,0.00020154753,0.00018966317,0.0000042369666,0.000053792417,0.09510465],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99614006,0.0002269571,0.0013817423,0.0009563895,0.0008569934,0.00043787298],"domain_scores_gemma":[0.9867426,0.010772678,0.0009650521,0.0011911842,0.00022522087,0.00010325334],"candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0030905302,0.00042203942,0.00080827565,0.00012940881,0.0002915829,0.0002083886,0.0020004725,0.0011849427,0.000046957583],"category_scores_gemma":[0.018732611,0.00028380268,0.0001818419,0.00011345227,0.0027931016,0.003883615,0.0017592923,0.0026328028,0.00007798057],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062620726,0.000031935917,0.000025535343,0.000024493409,0.000050157687,0.00017232882,0.000024480578,5.431583e-9,4.67759e-8,0.35284665,0.0010621441,0.6456996],"study_design_scores_gemma":[0.0003016899,0.00044137577,0.000008903846,0.0003453094,0.000028683711,0.00030137462,0.00048291875,0.000016906894,0.0000045239603,0.13490532,0.86284935,0.00031365664],"about_ca_topic_score_codex":0.000026236188,"about_ca_topic_score_gemma":0.00046538896,"teacher_disagreement_score":0.8617872,"about_ca_system_score_codex":0.000050312898,"about_ca_system_score_gemma":0.00040319285,"threshold_uncertainty_score":0.99996144},"labels":[],"label_agreement":null},{"id":"W2505749384","doi":"10.2196/rehab.4340","title":"Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy","year":2016,"lang":"en","type":"article","venue":"JMIR Rehabilitation and Assistive Technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Duchenne muscular dystrophy; Energy expenditure; Physical medicine and rehabilitation; Estimation; Muscular dystrophy; Physical therapy; Medicine; Computer science; Economics; Internal medicine","score_opus":0.011944816014743323,"score_gpt":0.2578355042220772,"score_spread":0.24589068820733387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505749384","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92702585,0.000051199953,0.06911043,0.0020006115,0.000028197082,0.0012475154,0.000009561545,0.00050569134,0.000020943167],"genre_scores_gemma":[0.9927633,0.0000036421818,0.005493869,0.000011925561,0.00000577351,0.0016727143,0.0000029467021,0.000011314573,0.000034512334],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99829435,0.00023278872,0.0003235391,0.0006908795,0.00022802182,0.00023042955],"domain_scores_gemma":[0.9985908,0.00075893267,0.00012446335,0.00039961858,0.00008907981,0.000037076785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034309766,0.00021207785,0.0002951898,0.000551206,0.000076322656,0.00009310945,0.0002897567,0.00006736506,0.000003205563],"category_scores_gemma":[0.0012184406,0.00014344076,0.000025737709,0.00062775775,0.00016147511,0.0006998768,0.00022085861,0.00016858621,0.00000524298],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087456,0.00062420534,0.7048238,0.000016865548,0.000023652958,0.0000027208819,0.0019129164,0.000041352698,0.0014216547,0.0013710696,0.000007891498,0.2896664],"study_design_scores_gemma":[0.0019464473,0.0061171553,0.9795396,0.00018884122,0.0000040062746,0.000007090426,0.00856851,0.001135608,0.00027666986,0.0018033824,0.000098524186,0.00031416718],"about_ca_topic_score_codex":0.00045473163,"about_ca_topic_score_gemma":0.00080991763,"teacher_disagreement_score":0.28935227,"about_ca_system_score_codex":0.00022804237,"about_ca_system_score_gemma":0.000028895985,"threshold_uncertainty_score":0.5849344},"labels":[],"label_agreement":null},{"id":"W2505822593","doi":"","title":"A Multiagent Approach to Personalization and Assistance to Multiple Persons in a Smart Home","year":2014,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Personalization; Key (lock); Computer science; Human–computer interaction; Home automation; Residence; Process (computing); Internet privacy; Computer security; World Wide Web; Telecommunications","score_opus":0.017425103810626483,"score_gpt":0.2144331485454998,"score_spread":0.19700804473487332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505822593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14072445,0.00007381848,0.8452971,0.005089673,0.00007354622,0.0004604081,0.000008975942,0.00014095588,0.008131097],"genre_scores_gemma":[0.89671516,0.0000064381716,0.10124633,0.00034873103,0.000009413302,0.00013452712,0.000016945405,0.000015773234,0.0015066859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99583966,0.0024979757,0.00029593866,0.0007230514,0.0003407978,0.00030259875],"domain_scores_gemma":[0.9970118,0.0010123505,0.00012449136,0.00097251515,0.00060806813,0.0002707862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034499248,0.00018101935,0.00023930648,0.0002889819,0.00021525097,0.00040013433,0.00073691556,0.0000723181,0.0000068204727],"category_scores_gemma":[0.0014088645,0.00020006862,0.00005882791,0.00088766555,0.00004893888,0.00036014357,0.00043148696,0.0001424011,0.00005146762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005986578,0.0026501769,0.12713648,0.0003987529,0.000079959806,0.000007202854,0.22008446,0.00019140803,0.030430958,0.14087479,0.0028110014,0.47527492],"study_design_scores_gemma":[0.00242915,0.0000041596386,0.2150253,0.0013846161,0.000014689214,0.000047160607,0.0010838048,0.7197588,0.011790251,0.00051976746,0.04682583,0.0011164368],"about_ca_topic_score_codex":0.00084404566,"about_ca_topic_score_gemma":0.0036198117,"teacher_disagreement_score":0.7559907,"about_ca_system_score_codex":0.000114712326,"about_ca_system_score_gemma":0.00005927291,"threshold_uncertainty_score":0.81585616},"labels":[],"label_agreement":null},{"id":"W2507517175","doi":"10.1109/tvcg.2016.2598608","title":"Embedded Data Representations","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":252,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Data visualization; Visualization; Computer graphics (images); Artificial intelligence","score_opus":0.06576328209612098,"score_gpt":0.32368396845097214,"score_spread":0.25792068635485116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507517175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020872275,0.000017421924,0.9957974,0.00037440797,0.0009200729,0.00023010225,0.000055033448,0.00041624164,0.00010210602],"genre_scores_gemma":[0.9970539,0.00014864566,0.0016233717,0.00081028446,0.00007795008,0.000029522604,0.000012119434,0.000017615183,0.00022656684],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827003,0.00020606648,0.00032361125,0.00068032136,0.0003250245,0.00019491585],"domain_scores_gemma":[0.9981119,0.00035032403,0.000111746645,0.001109475,0.00018031104,0.00013623948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025879993,0.0001752855,0.00017605153,0.00036755655,0.00029303716,0.00024245138,0.00064594613,0.0000887712,0.000029631701],"category_scores_gemma":[0.000007769603,0.0001423802,0.000059488088,0.0006986211,0.00008224767,0.0013799021,0.000020603364,0.00009276641,0.000051835512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028285473,0.0006121641,0.00027953883,0.000039590628,0.00019714369,0.000012747176,0.0014496277,0.00004279287,0.0003668609,0.5651158,0.0042001624,0.4276553],"study_design_scores_gemma":[0.0015502428,0.00020879244,0.00086901325,0.00015046984,0.000036084268,0.00009557493,0.00004854128,0.9828126,0.003735005,0.0022013194,0.007765812,0.00052654615],"about_ca_topic_score_codex":0.00001899363,"about_ca_topic_score_gemma":0.000049756585,"teacher_disagreement_score":0.9949667,"about_ca_system_score_codex":0.0000198006,"about_ca_system_score_gemma":0.000051212177,"threshold_uncertainty_score":0.58060956},"labels":[],"label_agreement":null},{"id":"W2515339857","doi":"10.1080/10447318.2016.1224527","title":"Uncertainty Visualization for Mobile and Wearable Devices Based Activity Recognition Systems","year":2016,"lang":"en","type":"article","venue":"International Journal of Human-Computer Interaction","topic":"Context-Aware Activity Recognition Systems","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":"Simon Fraser University","funders":"Science and Technology Department of Zhejiang Province; China Knowledge Centre for Engineering Sciences and Technology; National Natural Science Foundation of China","keywords":"Visualization; Wearable computer; Computer science; Human–computer interaction; Activity recognition; Process (computing); Wearable technology; Mobile device; Artificial intelligence; Data science; World Wide Web; Embedded system","score_opus":0.04689561399769857,"score_gpt":0.34035789072776246,"score_spread":0.29346227673006386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515339857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2682247,0.000052018968,0.72700274,0.0004096328,0.0038976164,0.0002841882,0.000017127106,0.000054448345,0.000057531273],"genre_scores_gemma":[0.99625975,0.000021765709,0.0023513194,0.00014712724,0.0010703197,0.000058380967,0.000008257736,0.000016666369,0.000066425186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980739,0.00022629426,0.0006449789,0.00033547063,0.0005404772,0.00017892508],"domain_scores_gemma":[0.9960694,0.0007700274,0.0010958,0.0001732861,0.0017952876,0.00009619802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007578854,0.00019014218,0.00029384735,0.0005472047,0.00013329776,0.000588361,0.00052329316,0.00009044801,0.000028520266],"category_scores_gemma":[0.00007502073,0.0001481448,0.00016574834,0.00011450184,0.000035061843,0.003241505,0.00008830551,0.00012570107,0.000017677336],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039587272,0.00046772443,0.00095559115,0.000090529276,0.00039981707,0.000026032943,0.00046948076,0.001235555,0.04207017,0.000805279,0.0017563492,0.9513276],"study_design_scores_gemma":[0.014335908,0.005219989,0.008603441,0.00754247,0.00017892322,0.0025731993,0.00042881962,0.81342965,0.08062833,0.00458259,0.060735222,0.0017414687],"about_ca_topic_score_codex":0.000069739195,"about_ca_topic_score_gemma":0.00003136568,"teacher_disagreement_score":0.94958615,"about_ca_system_score_codex":0.00035079237,"about_ca_system_score_gemma":0.000083067556,"threshold_uncertainty_score":0.6041169},"labels":[],"label_agreement":null},{"id":"W2516618771","doi":"10.1109/memea.2016.7533815","title":"An architecture for health data collection using off-the-shelf health sensors","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Ontario Tech University","funders":"","keywords":"Wireless sensor network; Wearable computer; Computer science; Health care; Architecture; Data collection; Wireless; Body area network; Wearable technology; Data science; Computer security; Computer network; Telecommunications; Embedded system","score_opus":0.1179467755628865,"score_gpt":0.36960410891324286,"score_spread":0.25165733335035634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516618771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056691533,0.0000781892,0.9464407,0.04602686,0.00052073185,0.00088052097,0.00007390493,0.0002470971,0.00006285877],"genre_scores_gemma":[0.8931015,0.000037046997,0.098627225,0.006693608,0.00045253566,0.000049835828,0.00003247122,0.000036645983,0.0009691008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978839,0.00045569643,0.0003540201,0.0006380346,0.000258569,0.0004097671],"domain_scores_gemma":[0.99745053,0.0004653989,0.00026228602,0.0015207255,0.00010934777,0.0001917079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015261187,0.00014374783,0.0002515904,0.00012890209,0.0005463086,0.00017042561,0.0010193767,0.0000443332,0.000010905324],"category_scores_gemma":[0.00008817589,0.000085441854,0.000049658178,0.00037185117,0.000038039678,0.0008022377,0.00021306566,0.00008393014,0.000013971636],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021911625,0.000072489514,0.00014067437,0.000038367656,0.000023706336,4.9956367e-7,0.0009311435,0.000025703212,0.00091687235,0.00062202633,0.014914817,0.9822918],"study_design_scores_gemma":[0.003150905,0.0017744265,0.0018380517,0.00036906195,0.000013234909,0.00065459963,0.0004767815,0.6033201,0.0021730985,0.003310095,0.38198796,0.00093170366],"about_ca_topic_score_codex":0.0010137912,"about_ca_topic_score_gemma":0.0021913347,"teacher_disagreement_score":0.9813601,"about_ca_system_score_codex":0.00022082384,"about_ca_system_score_gemma":0.00065661786,"threshold_uncertainty_score":0.42018193},"labels":[],"label_agreement":null},{"id":"W2518028538","doi":"10.1109/chase.2016.80","title":"Ubiquitous Computing and Context-aware Applications: Survey and Contributions","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Ubiquitous computing; Context (archaeology); Data science; Human–computer interaction","score_opus":0.024329693678649764,"score_gpt":0.2826074600949646,"score_spread":0.25827776641631484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518028538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022603516,0.00038307128,0.97397083,0.0017739984,0.00010080459,0.000453188,0.00008296574,0.00028088698,0.00035071402],"genre_scores_gemma":[0.9985292,0.000044416323,0.0007039897,0.0003336729,0.000055165347,0.00004911265,0.000004657874,0.00000721181,0.00027259198],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99865746,0.00021974974,0.00026556762,0.00046629366,0.00014810075,0.00024279409],"domain_scores_gemma":[0.9973913,0.0016465151,0.00011127973,0.00036345047,0.0003303798,0.0001570764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000722716,0.00013882625,0.00022403713,0.00008676676,0.0002474195,0.00020349075,0.00026383527,0.000072210794,0.000012779779],"category_scores_gemma":[0.00015401878,0.00010108603,0.000026453428,0.00021507769,0.00011684445,0.0005099243,0.00031027282,0.00006751714,0.00006535196],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048955444,0.000042312204,0.033920046,0.000012918662,0.000032781732,0.0000023237715,0.00013061395,8.811262e-8,0.0008868274,0.022618294,0.00097097433,0.94137794],"study_design_scores_gemma":[0.010054043,0.0004878878,0.8145779,0.00056757487,0.000049447808,0.0012395036,0.0007416263,0.021893108,0.009975143,0.01590969,0.12168912,0.0028149646],"about_ca_topic_score_codex":0.00029248514,"about_ca_topic_score_gemma":0.00051519554,"teacher_disagreement_score":0.9759257,"about_ca_system_score_codex":0.000043097058,"about_ca_system_score_gemma":0.000053896565,"threshold_uncertainty_score":0.41221687},"labels":[],"label_agreement":null},{"id":"W2519002880","doi":"10.1109/cscwd.2016.7565959","title":"Incremental clustering for human activity detection based on phone sensor data","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Western University","funders":"","keywords":"Cluster analysis; Computer science; Scheme (mathematics); Data stream clustering; Phone; Data mining; Cluster (spacecraft); Artificial intelligence; Correlation clustering; CURE data clustering algorithm; Mathematics","score_opus":0.10770907549409281,"score_gpt":0.31525586051252363,"score_spread":0.20754678501843082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519002880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07609963,6.9584974e-7,0.9211845,0.0005970237,0.00038675327,0.00040070477,0.0000438428,0.0002757841,0.0010110792],"genre_scores_gemma":[0.9947987,1.6653476e-7,0.004472398,0.00019471832,0.00013313805,0.000049259153,0.000004732522,0.000012168351,0.00033467953],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869686,0.00010453736,0.0001617568,0.00057382433,0.00024232447,0.00022070894],"domain_scores_gemma":[0.9982927,0.00040780273,0.00010378811,0.0010635001,0.00006224569,0.00007000037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005191248,0.00013288083,0.00014889287,0.00011851387,0.00019209781,0.00012162321,0.0006253198,0.00005258819,0.000049131653],"category_scores_gemma":[0.00007872821,0.000099125864,0.0000501017,0.00012356188,0.00002022113,0.0010378889,0.0002716633,0.000053332813,0.00009976865],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039273797,0.00008231014,0.00008736353,0.000012733863,0.000011525785,0.0000012238745,0.000016906259,0.0000023165571,0.50188404,0.0000231804,0.00023434983,0.4976048],"study_design_scores_gemma":[0.0026479075,0.00035178283,0.002522342,0.00009821001,0.000008824104,0.000015854115,0.00002412636,0.3390076,0.65117544,0.00007707424,0.0036777828,0.00039304091],"about_ca_topic_score_codex":0.00023436535,"about_ca_topic_score_gemma":0.0011709832,"teacher_disagreement_score":0.9186991,"about_ca_system_score_codex":0.00013666376,"about_ca_system_score_gemma":0.000028141214,"threshold_uncertainty_score":0.40422353},"labels":[],"label_agreement":null},{"id":"W2521687400","doi":"","title":"Cognitive context elicitation and modeling","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Context (archaeology); Computer science; Human–computer interaction; Task (project management); Context model; Situational ethics; Situation awareness; Adaptation (eye); Context awareness; Ubiquitous computing; Cognition; Perspective (graphical); Data science; Artificial intelligence; Psychology; Social psychology; Engineering; Systems engineering","score_opus":0.07353179194712521,"score_gpt":0.30021580916581714,"score_spread":0.2266840172186919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2521687400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15892538,0.00081106124,0.7463229,0.000042551055,0.001200993,0.0007356309,0.000012162773,0.00039641175,0.09155293],"genre_scores_gemma":[0.9915247,0.00004989706,0.00095108896,0.00019355024,0.000060869217,0.00009183342,0.00015717698,0.000024412318,0.0069464995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984953,0.000092893424,0.0003496657,0.00059601763,0.0002646662,0.0002014901],"domain_scores_gemma":[0.99865526,0.0002077616,0.00021942813,0.00023150165,0.0005883783,0.00009767083],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021483205,0.0002635778,0.00033220917,0.0002607947,0.00012356498,0.00023088191,0.00028048875,0.00024029771,0.00005882837],"category_scores_gemma":[0.00010196055,0.00026565118,0.00007752275,0.00017145538,0.0000147574865,0.00083285506,0.000052754032,0.00022815721,0.00020257889],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043202057,0.000052521373,0.000051062147,0.00011527499,0.00010268946,0.0000075113026,0.009882966,8.43565e-7,0.00018822076,0.009955368,0.00013569139,0.97946465],"study_design_scores_gemma":[0.005637325,0.00081889407,0.0038467525,0.0049837157,0.0004764096,0.00021847719,0.059129685,0.8489226,0.015374247,0.052962802,0.0020691324,0.005559948],"about_ca_topic_score_codex":0.00072962535,"about_ca_topic_score_gemma":0.0017802734,"teacher_disagreement_score":0.9739047,"about_ca_system_score_codex":0.000024327082,"about_ca_system_score_gemma":0.00011117203,"threshold_uncertainty_score":0.99997956},"labels":[],"label_agreement":null},{"id":"W2522009427","doi":"10.1016/j.future.2016.06.034","title":"A survey: Cyber-physical-social systems and their system-level design methodology","year":2016,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":209,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Cyber-physical system; Computer science; Exploit; Systems design; Design methods; Physical system; Data science; Systems engineering; Risk analysis (engineering); Computer security; Software engineering","score_opus":0.1968430304943336,"score_gpt":0.295077526629146,"score_spread":0.09823449613481239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522009427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006139676,0.0010241565,0.9370119,0.00057332503,0.053317856,0.0011479029,0.00014775849,0.0005803571,0.000057058678],"genre_scores_gemma":[0.91881984,0.000011620992,0.011516236,0.00011849759,0.06866265,0.00037372703,0.00003520553,0.000060194558,0.0004019952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.989429,0.0073622777,0.0008665463,0.0012402418,0.000517859,0.00058409665],"domain_scores_gemma":[0.9956789,0.0017960988,0.00062157825,0.0009021605,0.0007595784,0.0002416879],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0036457048,0.00057241024,0.0011162831,0.00032051333,0.0005200824,0.0010190585,0.00088713865,0.00034761566,0.0000016092639],"category_scores_gemma":[0.000055578446,0.00038112423,0.00016347397,0.0005583777,0.000077693774,0.0009654062,0.0003557078,0.0001937622,0.00010945587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011443365,0.00041517624,0.0013504259,0.00096916384,0.0014194452,0.000099000725,0.015407313,0.0012200255,0.042141516,0.11891268,0.391226,0.42672482],"study_design_scores_gemma":[0.0030991705,0.00060483447,0.0059360983,0.0005166466,0.000053086384,0.0014429524,0.00073928345,0.91108465,0.002333984,0.000030314763,0.07211555,0.0020434237],"about_ca_topic_score_codex":0.00035790063,"about_ca_topic_score_gemma":0.000076200085,"teacher_disagreement_score":0.9254957,"about_ca_system_score_codex":0.00027050005,"about_ca_system_score_gemma":0.00017298217,"threshold_uncertainty_score":0.99986404},"labels":[],"label_agreement":null},{"id":"W2525851376","doi":"10.1145/2973750.2973777","title":"LEO","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Resource (disambiguation); Distributed computing; Visibility; Resource management (computing); Inference; Real-time computing; Computer network; Artificial intelligence","score_opus":0.021202633853949,"score_gpt":0.22628207586975502,"score_spread":0.20507944201580602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525851376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004889776,0.000008555621,0.9309152,0.0041055325,0.00027193376,0.000046934423,4.9082723e-7,0.00027711072,0.05948449],"genre_scores_gemma":[0.9867993,0.0000013164976,0.0025951779,0.00032666384,0.000043101816,0.0000070303886,4.1048832e-8,0.0000022733016,0.010225125],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995088,0.000028657481,0.00007782348,0.00016473008,0.00011109313,0.00010890548],"domain_scores_gemma":[0.9994548,0.0001264584,0.000022799797,0.00030630047,0.000043821317,0.00004580438],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011373648,0.000043256176,0.00005686562,0.000037839025,0.000025098996,0.000048722166,0.000316747,0.000018813942,0.00020592423],"category_scores_gemma":[0.000027447322,0.000024653082,0.000028767396,0.00009578463,0.000011102739,0.00054618064,0.00009652489,0.000015449152,0.0025179698],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.9441265e-7,0.0000141486225,0.0010142524,0.0000010685221,0.0000050294834,0.0000035080175,0.00004456745,1.2065377e-8,0.0077575077,0.035294425,0.0075237863,0.94834113],"study_design_scores_gemma":[0.0017650662,0.00015609532,0.017859885,0.00010659295,0.0000038540447,0.00020210377,0.000039792412,0.0012265528,0.12208662,0.02283771,0.83295375,0.0007619665],"about_ca_topic_score_codex":0.000015784604,"about_ca_topic_score_gemma":0.000017990837,"teacher_disagreement_score":0.9819095,"about_ca_system_score_codex":0.000017165257,"about_ca_system_score_gemma":0.000020325304,"threshold_uncertainty_score":0.9982587},"labels":[],"label_agreement":null},{"id":"W2531582813","doi":"10.3390/computers5040022","title":"Ambient Technology to Assist Elderly People in Indoor Risks","year":2016,"lang":"en","type":"article","venue":"Computers","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Elderly people; Psychological intervention; Assistive technology; Independent living; Risk analysis (engineering); Computer science; Medicine; Gerontology; Human–computer interaction; Nursing","score_opus":0.03096403546703172,"score_gpt":0.29025075210198076,"score_spread":0.25928671663494907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531582813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36339018,0.0000206741,0.626833,0.008211141,0.0008508982,0.00024365856,0.0000026124042,0.0002658817,0.00018194896],"genre_scores_gemma":[0.990934,0.0000022811325,0.008399837,0.0004353735,0.0000545342,0.00007256767,4.7697415e-7,0.000010860728,0.000090077905],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99838257,0.00009760949,0.00030519467,0.0005883295,0.00023114703,0.00039517914],"domain_scores_gemma":[0.9987159,0.0002445492,0.00009708856,0.0007089496,0.00009462651,0.00013891284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002684771,0.0001653246,0.00028087676,0.00065299403,0.000061763225,0.00011553421,0.001092063,0.00010482974,0.000011237074],"category_scores_gemma":[0.000074725875,0.00013466009,0.00006227768,0.0011290452,0.000037645957,0.00037820463,0.00058928895,0.00012265157,0.00062229676],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057879442,0.00008449468,0.03689674,0.0000061962737,0.000012195572,0.000028674958,0.00040540742,0.000006549801,0.0019387986,0.0015775571,0.0019295163,0.9571081],"study_design_scores_gemma":[0.0069529563,0.001967122,0.821502,0.0014606912,0.000015256255,0.00043005796,0.00030002903,0.0112572545,0.023979764,0.00806707,0.12165607,0.0024117562],"about_ca_topic_score_codex":0.00014559385,"about_ca_topic_score_gemma":0.00049864047,"teacher_disagreement_score":0.9546963,"about_ca_system_score_codex":0.00018776067,"about_ca_system_score_gemma":0.00006908271,"threshold_uncertainty_score":0.7998573},"labels":[],"label_agreement":null},{"id":"W2532623943","doi":"10.1109/mms.2009.5409768","title":"Location-based services provisioning using WSN","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Provisioning; Computer science; Context (archaeology); Ubiquitous computing; Location-based service; Tourism; Mobile computing; World Wide Web; Mobile device; Context awareness; Service (business); Adaptation (eye); Process (computing); Human–computer interaction; Telecommunications; Business","score_opus":0.02458977902329222,"score_gpt":0.27331621782085,"score_spread":0.2487264387975578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2532623943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046784617,0.00005023925,0.9487958,0.0009897369,0.00015425033,0.00016748633,2.4917185e-7,0.00033630137,0.0027212973],"genre_scores_gemma":[0.9671446,1.6134051e-7,0.031076321,0.0016081053,0.000049187092,0.0000033429658,0.0000012366787,0.000003752784,0.0001132668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990762,0.000053076954,0.00018159048,0.00028627412,0.00023497667,0.00016791027],"domain_scores_gemma":[0.99917424,0.000088233144,0.00009045402,0.00037716527,0.0002053177,0.00006459368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020670415,0.00009555366,0.0001103922,0.00011335161,0.0001328596,0.00029207714,0.0004185926,0.00003900564,0.000021377886],"category_scores_gemma":[0.00001411194,0.000087157416,0.000035225366,0.0004739058,0.000008026644,0.0008327723,0.000039797058,0.000058380458,0.00009937997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014243156,0.00040141182,0.0057637068,0.00018059318,0.00002766434,0.000024925957,0.0016642597,0.0064338436,0.021206692,0.01125107,0.0002748888,0.9527567],"study_design_scores_gemma":[0.00026298792,0.00005290268,0.0031233083,0.00014657478,0.000003124429,0.000013126266,0.000060269664,0.9877141,0.0070742806,0.00060061406,0.00076023856,0.00018844874],"about_ca_topic_score_codex":0.0001847628,"about_ca_topic_score_gemma":0.000037836304,"teacher_disagreement_score":0.98128027,"about_ca_system_score_codex":0.000047448677,"about_ca_system_score_gemma":0.00012399441,"threshold_uncertainty_score":0.35541764},"labels":[],"label_agreement":null},{"id":"W2534359532","doi":"10.1016/j.jalz.2016.06.1598","title":"P2‐387: Daily Routine Monitoring in Older Adults through a Lightweight Sensor, Non‐Intrusive Infrastructure","year":2016,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","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":"Université de Montréal","funders":"","keywords":"Activities of daily living; Shower; Gerontology; Bathing; Medicine; Psychology; Physical medicine and rehabilitation; Physical therapy; Engineering","score_opus":0.015068157353529657,"score_gpt":0.2495882842933476,"score_spread":0.23452012693981794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2534359532","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6846909,0.020597838,0.27511165,0.00485556,0.007197739,0.002419276,0.000049652543,0.00081607356,0.0042613265],"genre_scores_gemma":[0.98962843,0.00006529057,0.009480231,0.00022343025,0.0004261617,0.00009972351,0.0000040423483,0.000036219793,0.000036450343],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99701804,0.00018364673,0.0006375487,0.0009501817,0.00052770966,0.00068285404],"domain_scores_gemma":[0.99812317,0.00021289896,0.00031570415,0.00095506484,0.00024379583,0.00014938702],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026983584,0.0004042701,0.00042735034,0.00019972026,0.00014615615,0.00019482405,0.0009378859,0.00018650225,0.00017586278],"category_scores_gemma":[0.000042403648,0.00031307546,0.00013991867,0.0005751168,0.00006356887,0.0018890563,0.0005297964,0.00024112003,0.00043386145],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010634889,0.0003399526,0.15116662,0.000028445089,0.0049723005,0.0002466693,0.013135931,0.0000080423515,0.0106196115,0.00094351266,0.0054464717,0.8129861],"study_design_scores_gemma":[0.011974994,0.00033463162,0.5897713,0.0020565463,0.0020287181,0.0002451387,0.0009020585,0.0013657523,0.35447723,0.0023569986,0.031739492,0.0027471655],"about_ca_topic_score_codex":0.00029292336,"about_ca_topic_score_gemma":0.000078070145,"teacher_disagreement_score":0.8102389,"about_ca_system_score_codex":0.000040871957,"about_ca_system_score_gemma":0.000097825294,"threshold_uncertainty_score":0.9999321},"labels":[],"label_agreement":null},{"id":"W2540047426","doi":"10.1109/ie.2016.52","title":"Validation of a Smart Stove for Traumatic Brain Injury Patients in a Cooking Task","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Stove; Task (project management); Traumatic brain injury; Reliability (semiconductor); Psychology; Activities of daily living; Physical medicine and rehabilitation; Medicine; Applied psychology; Psychiatry; Engineering","score_opus":0.03822611459717459,"score_gpt":0.282252537733072,"score_spread":0.24402642313589742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2540047426","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5001302,0.0000012332398,0.49870145,0.00034728332,0.000110660905,0.00038512415,0.000011452787,0.00003290595,0.0002797424],"genre_scores_gemma":[0.99808115,3.4591523e-7,0.0015036319,0.0001220893,0.000010527185,0.00007369628,0.000001998478,0.000005797561,0.00020076544],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990211,0.00009707961,0.00033599272,0.00021701102,0.00018419603,0.00014460259],"domain_scores_gemma":[0.9987809,0.00064063526,0.00016689241,0.00023828945,0.00014085232,0.000032448483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044580663,0.000078743986,0.00016978354,0.00017387637,0.000021074942,0.000030718533,0.00023621364,0.000038691003,0.000014544978],"category_scores_gemma":[0.00026235584,0.00005849068,0.00005156389,0.00021056157,0.000015066596,0.00064857875,0.00005842446,0.000024766314,0.000018840869],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056097517,0.00035997166,0.035102453,0.00010166584,0.000027085915,4.9049675e-7,0.0026527976,0.0000013926556,0.018495688,0.0029851284,0.0017714179,0.9384458],"study_design_scores_gemma":[0.026390675,0.00450956,0.21598278,0.0039484072,0.000045483244,0.000016484848,0.0007943787,0.03300203,0.66906565,0.032127235,0.011615771,0.002501562],"about_ca_topic_score_codex":0.00008068412,"about_ca_topic_score_gemma":0.00011668149,"teacher_disagreement_score":0.93594426,"about_ca_system_score_codex":0.00006664154,"about_ca_system_score_gemma":0.000039268456,"threshold_uncertainty_score":0.23851807},"labels":[],"label_agreement":null},{"id":"W2540447421","doi":"10.1109/ie.2016.24","title":"Real-Time Activity Prediction and Recognition in Smart Homes by Formal Concept Analysis","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Activity recognition; Computer science; Inference; Relevance (law); Activities of daily living; Formal concept analysis; Artificial intelligence; Machine learning; Home automation; Data mining","score_opus":0.014449134843971539,"score_gpt":0.22876606692002818,"score_spread":0.21431693207605665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2540447421","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6825232,0.000012349233,0.3139918,0.0004914725,0.00008837115,0.00019267033,0.00007600696,0.00021534246,0.0024087937],"genre_scores_gemma":[0.9980367,0.00004855977,0.0006518371,0.00003452721,0.000027402557,0.00003904148,0.000017614006,0.000005895039,0.0011384445],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99859464,0.00019975599,0.00024864124,0.0004578931,0.0002327975,0.00026629947],"domain_scores_gemma":[0.9990587,0.00033845365,0.00012497409,0.00028821043,0.000089954025,0.00009965522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004789703,0.00013870889,0.00027956552,0.00033209726,0.00007090327,0.00011151298,0.00016923531,0.00009535079,0.00016392102],"category_scores_gemma":[0.00004846584,0.000104963074,0.00007765694,0.0007037407,0.00005228442,0.0028860653,0.00012219843,0.000064904365,0.00008693543],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003223629,0.00011130161,0.02228412,0.0000073679926,0.00015785595,0.0000033536671,0.00036198174,5.691785e-7,0.048497293,0.000051624833,0.0016667669,0.9268255],"study_design_scores_gemma":[0.0076539037,0.0008803083,0.7834384,0.00027486487,0.00034791883,0.00010186428,0.0002505693,0.08656533,0.11372425,0.0025940824,0.0024378656,0.0017306685],"about_ca_topic_score_codex":0.00073805306,"about_ca_topic_score_gemma":0.00028818604,"teacher_disagreement_score":0.92509484,"about_ca_system_score_codex":0.0000965538,"about_ca_system_score_gemma":0.000030876454,"threshold_uncertainty_score":0.42802697},"labels":[],"label_agreement":null},{"id":"W2544644677","doi":"10.2196/medinform.5012","title":"Evaluating the Economic Impact of Smart Care Platforms: Qualitative and Quantitative Results of a Case Study","year":2016,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiteit Gent; iMinds; Agentschap voor Innovatie door Wetenschap en Technologie","keywords":"Business; Interoperability; Integrated care; Service provider; Software deployment; Provisioning; Information and Communications Technology; Focus group; Quality (philosophy); Process management; Service (business); Marketing; Knowledge management; Health care; Computer science; Telecommunications; Economic growth","score_opus":0.14095809515099375,"score_gpt":0.46917600478055543,"score_spread":0.32821790962956165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2544644677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98708284,0.000027278365,0.011637559,0.00007184355,0.00008245238,0.00072164764,0.00008695454,0.000024171477,0.00026523878],"genre_scores_gemma":[0.99834806,0.0000037162367,0.00154888,0.000021734248,0.0000135753335,0.000049503717,0.0000013574477,0.0000043929194,0.000008772024],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9977768,0.00024895347,0.0011033455,0.00012428738,0.0005738071,0.00017278684],"domain_scores_gemma":[0.9945609,0.0038201194,0.00078290055,0.00042251474,0.00028332093,0.00013023058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027587058,0.00013687763,0.0003575371,0.00011527132,0.0000771482,0.00003580456,0.00040299093,0.00007021186,0.000013002523],"category_scores_gemma":[0.0009818238,0.000067250054,0.00009137845,0.00015646039,0.00023916432,0.0009014482,0.00028441416,0.00013251182,0.00001394083],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118937314,0.00005978379,0.00058836065,0.00010045767,0.00018076866,0.000020007004,0.8732085,0.00000898737,0.000017821118,0.00056351424,0.00014725252,0.124985576],"study_design_scores_gemma":[0.006321466,0.007752522,0.0013427022,0.00062024454,0.00003770004,0.0006943637,0.8870059,0.095051095,0.00022354965,0.00062365667,0.00003020754,0.00029658526],"about_ca_topic_score_codex":0.00078284927,"about_ca_topic_score_gemma":0.00040764254,"teacher_disagreement_score":0.12468899,"about_ca_system_score_codex":0.0001767105,"about_ca_system_score_gemma":0.0006570017,"threshold_uncertainty_score":0.27423775},"labels":[],"label_agreement":null},{"id":"W2549747513","doi":"10.1007/978-3-319-47075-7_35","title":"Automatically Quantitative Analysis and Code Generator for Sensor Systems: The Example of Great Lakes Water Quality Monitoring","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Executable; Correctness; Liveness; Computer science; Code (set theory); Code generation; Power consumption; Embedded system; Resource (disambiguation); Microcontroller; Real-time computing; Reliability engineering; Distributed computing; Power (physics); Programming language; Engineering; Operating system","score_opus":0.06847144322963174,"score_gpt":0.29282645166531174,"score_spread":0.22435500843568001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2549747513","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030437785,0.00028775138,0.99451315,0.0005543039,0.0003926992,0.00074693345,0.00026288946,0.00004590904,0.00015260663],"genre_scores_gemma":[0.13849036,0.000059564634,0.86106503,0.000036683003,0.00013887498,0.00009358213,0.000018660216,0.000016407925,0.000080847385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984885,0.000044395456,0.00081534643,0.00018333517,0.00026660057,0.00020186092],"domain_scores_gemma":[0.9966964,0.0016333032,0.000585714,0.000633535,0.00041022905,0.00004081682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009693435,0.00023656528,0.00056806754,0.00022800965,0.0007057301,0.00026754904,0.001220177,0.00014360026,3.4913077e-7],"category_scores_gemma":[0.00010065622,0.00013356483,0.0002424896,0.00019893495,0.00041526207,0.0003799888,0.00051716785,0.00015930402,2.673966e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001805096,0.000053945638,0.00021133873,0.0026106616,0.003557814,1.8672101e-7,0.022318546,0.45940447,0.001276397,0.4602898,0.00004670114,0.05021209],"study_design_scores_gemma":[0.0003552705,0.00010347923,0.0001959747,0.00043212847,0.00031769992,0.000008745415,0.00003178823,0.98663586,0.0012207995,0.0011013207,0.00918091,0.00041601146],"about_ca_topic_score_codex":0.00009302008,"about_ca_topic_score_gemma":0.0001411319,"teacher_disagreement_score":0.5272314,"about_ca_system_score_codex":0.000046872043,"about_ca_system_score_gemma":0.00008688772,"threshold_uncertainty_score":0.5446616},"labels":[],"label_agreement":null},{"id":"W2550248751","doi":"10.1145/2910674.2935840","title":"Real-Time Constraints for Activities of Daily Living Recognition","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi; Université de Sherbrooke","funders":"","keywords":"Activity recognition; Computer science; Activities of daily living; Home automation; Assisted living; Identification (biology); Face (sociological concept); Facial recognition system; Radio-frequency identification; Artificial intelligence; Computer security; Telecommunications; Feature extraction","score_opus":0.036331418914011857,"score_gpt":0.2584164518577189,"score_spread":0.22208503294370702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550248751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13250908,0.000004409349,0.84117275,0.00040547605,0.00027901764,0.00041167974,0.000068835434,0.00028996012,0.024858803],"genre_scores_gemma":[0.98110443,0.00000972808,0.016312381,0.000040259398,0.00006373809,0.000063618005,0.0000018364141,0.000010441869,0.0023935654],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890125,0.000099993464,0.0002863372,0.00030997177,0.0001846239,0.00021782459],"domain_scores_gemma":[0.99693686,0.0022814455,0.00019809432,0.00031747197,0.00020286322,0.0000632729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000504359,0.00011781077,0.00023304898,0.000133683,0.000058352136,0.000056701956,0.00029920394,0.00006502842,0.0003615389],"category_scores_gemma":[0.0002568212,0.00008768148,0.00010477955,0.00012289044,0.00010830645,0.001073316,0.00009914319,0.000028482802,0.00014864732],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009654749,0.000059055776,0.00019368016,0.000030652056,0.000033921002,7.7486493e-7,0.0003982791,3.9759076e-8,0.22208925,0.0012675779,0.0012858905,0.77463126],"study_design_scores_gemma":[0.0040994775,0.001166726,0.0073120147,0.0028813884,0.000065861364,0.00022431578,0.001401276,0.004305999,0.9544899,0.019058188,0.003209823,0.0017850563],"about_ca_topic_score_codex":0.00004080005,"about_ca_topic_score_gemma":0.000016226228,"teacher_disagreement_score":0.8485953,"about_ca_system_score_codex":0.000045053483,"about_ca_system_score_gemma":0.00009401224,"threshold_uncertainty_score":0.39585975},"labels":[],"label_agreement":null},{"id":"W2550615967","doi":"10.1145/2910674.2910707","title":"Towards User Activity Recognition Through Energy Usage Analysis And Complex Event Processing","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Complex event processing; Event (particle physics); Energy (signal processing); Human–computer interaction; Artificial intelligence; Programming language","score_opus":0.06398244635940045,"score_gpt":0.2938401323543601,"score_spread":0.22985768599495962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550615967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07407366,0.000024988607,0.919512,0.001520749,0.00009972417,0.00010055878,0.000008889697,0.00023369628,0.0044257417],"genre_scores_gemma":[0.9867125,0.000025065414,0.011946615,0.00035632285,0.00005850989,0.000037069112,0.000004222507,0.000009617061,0.0008500595],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982439,0.0002288893,0.00025942913,0.0006270832,0.00035887855,0.00028181763],"domain_scores_gemma":[0.998907,0.00018890915,0.00019322555,0.0003965371,0.00020709555,0.0001072058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034646367,0.00019224657,0.00033693417,0.000217715,0.00018505701,0.0003314344,0.00030237396,0.000079849844,0.00020731971],"category_scores_gemma":[0.000046690224,0.0001338482,0.00013380781,0.0009141688,0.000065046195,0.002963294,0.0002484097,0.000055879787,0.000030484345],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060941506,0.00006948506,0.0011556296,0.00001075766,0.00012164967,0.000003849563,0.00019538593,5.684798e-7,0.006415096,0.00027932037,0.0001845974,0.99155754],"study_design_scores_gemma":[0.0065459628,0.0006376051,0.436201,0.00072947756,0.0011704041,0.00035665158,0.0005217692,0.15853256,0.28265792,0.032211427,0.07580557,0.004629669],"about_ca_topic_score_codex":0.00089293264,"about_ca_topic_score_gemma":0.0007551622,"teacher_disagreement_score":0.9869279,"about_ca_system_score_codex":0.00008633438,"about_ca_system_score_gemma":0.00007639065,"threshold_uncertainty_score":0.5458171},"labels":[],"label_agreement":null},{"id":"W2554549216","doi":"10.1109/syseng.2016.7753184","title":"Linking relational concept analysis and variability model within context modeling of context-aware applications","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Montréal","funders":"","keywords":"Context model; Context (archaeology); Semantic reasoner; Computer science; Ontology; Process (computing); Context management; Artificial intelligence; Data science; Programming language; Ubiquitous computing; Human–computer interaction","score_opus":0.04012646711870449,"score_gpt":0.2609184154822486,"score_spread":0.2207919483635441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554549216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011012074,0.00006131784,0.9867493,0.00072280987,0.00004663394,0.00039268678,0.000055212633,0.00015339033,0.0008065876],"genre_scores_gemma":[0.97814745,0.0000037845468,0.02130348,0.00015761021,0.00002821015,0.000090829526,0.000009103349,0.000008851003,0.00025067464],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789333,0.00018415379,0.0007034669,0.0006539949,0.0003697149,0.00019535203],"domain_scores_gemma":[0.99739516,0.0008474845,0.00031894495,0.00069170323,0.0006204562,0.00012627231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009343776,0.00017917238,0.00043939403,0.00024932463,0.0001504726,0.00006460742,0.000406049,0.00012729462,0.00003899649],"category_scores_gemma":[0.000106990374,0.00013670573,0.0001577169,0.00065846933,0.00017117627,0.00091037,0.00022431856,0.00011478365,0.000011780069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028015505,0.00021580848,0.04541556,0.000050653558,0.0008223062,9.938678e-7,0.0034358392,0.042081576,0.0015546213,0.5269386,0.000025436313,0.3794306],"study_design_scores_gemma":[0.00046073506,0.000014906002,0.0003984663,0.000042072013,0.00007766262,0.0000043733944,0.00012591419,0.9860637,0.00041721863,0.012166734,0.00003737258,0.00019082734],"about_ca_topic_score_codex":0.00015189777,"about_ca_topic_score_gemma":0.00020422626,"teacher_disagreement_score":0.96713537,"about_ca_system_score_codex":0.00007678251,"about_ca_system_score_gemma":0.00016863112,"threshold_uncertainty_score":0.5574697},"labels":[],"label_agreement":null},{"id":"W2555061653","doi":"10.1145/2992154.2996880","title":"ERWear","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Calgary","funders":"","keywords":"Wearable computer; Usability; Computer science; Human–computer interaction; Wearable technology; Internet of Things; Face (sociological concept); Embedded system","score_opus":0.0185738029249652,"score_gpt":0.22163066312828159,"score_spread":0.2030568602033164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555061653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042070067,0.000008312438,0.9442074,0.005541914,0.00026583922,0.000051534866,6.0052685e-7,0.0002986257,0.045418825],"genre_scores_gemma":[0.9862464,0.0000015634747,0.0035147169,0.00044655823,0.000044849363,0.0000069087396,4.155944e-8,0.0000026690861,0.009736278],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994842,0.00003145127,0.00008013159,0.00017071272,0.0001196057,0.000113893075],"domain_scores_gemma":[0.9994362,0.00012668384,0.000023154169,0.00031897178,0.000044924902,0.00005008498],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000121606696,0.000044899607,0.00005900729,0.00003990696,0.000027639602,0.000049926206,0.00032443897,0.000020085114,0.00023739843],"category_scores_gemma":[0.000029682207,0.000025731913,0.0000302298,0.000100798,0.000012096993,0.00053626567,0.00010418318,0.000016743788,0.0031143506],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.2989305e-7,0.000018175057,0.0007225391,0.0000013986452,0.0000067068436,0.0000047982735,0.000063327934,1.7010862e-8,0.010640319,0.038712602,0.012630882,0.9371984],"study_design_scores_gemma":[0.0013413029,0.00012750295,0.0085694175,0.00008693737,0.0000030488616,0.00014864025,0.00003296035,0.0010703058,0.08491889,0.016940484,0.88618034,0.00058016734],"about_ca_topic_score_codex":0.000019491075,"about_ca_topic_score_gemma":0.00001668927,"teacher_disagreement_score":0.9820394,"about_ca_system_score_codex":0.000017736038,"about_ca_system_score_gemma":0.000020647694,"threshold_uncertainty_score":0.9976618},"labels":[],"label_agreement":null},{"id":"W2557661846","doi":"10.1109/iemcon.2016.7746287","title":"Recursive Bayesian tracking for smart elderly living","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Computer science; Robustness (evolution); Home automation; Artificial intelligence; Recursive Bayesian estimation; Dynamic Bayesian network; Wearable computer; Set (abstract data type); Bayesian probability; Cloud computing; Machine learning; Software deployment; Real-time computing; Embedded system","score_opus":0.033020558188245404,"score_gpt":0.26513138192176505,"score_spread":0.23211082373351966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557661846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035543065,0.000022524056,0.9861186,0.002945194,0.00059304456,0.00033047947,0.0000039456645,0.00028924362,0.006142666],"genre_scores_gemma":[0.9788271,0.0000023089062,0.01737061,0.0002593341,0.0001323706,0.0000717379,2.954587e-7,0.000012153108,0.0033240505],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988244,0.00007717869,0.00021831409,0.00040624157,0.000172427,0.0003014489],"domain_scores_gemma":[0.9977252,0.0014776544,0.00010163957,0.00040032042,0.00019744912,0.00009775765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044083496,0.000120821875,0.00016495501,0.00010183069,0.00012004238,0.00018370277,0.0004848668,0.00006123218,0.00008744689],"category_scores_gemma":[0.0003202708,0.00008565876,0.00010866131,0.0001536528,0.000020911857,0.0011629084,0.000093700124,0.000043711047,0.00013601134],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002915172,0.000029309776,0.00090676436,0.000009925695,0.000018400286,0.000002245076,0.0004565654,7.200136e-8,0.002690731,0.0097817825,0.0024532727,0.983648],"study_design_scores_gemma":[0.009929862,0.0049581523,0.041952163,0.0071953763,0.00013352906,0.0009687345,0.0024588446,0.024934154,0.19950284,0.1615545,0.53932154,0.007090288],"about_ca_topic_score_codex":0.000033453027,"about_ca_topic_score_gemma":0.00014216699,"teacher_disagreement_score":0.97655773,"about_ca_system_score_codex":0.000061045546,"about_ca_system_score_gemma":0.000059715007,"threshold_uncertainty_score":0.3493063},"labels":[],"label_agreement":null},{"id":"W2558455691","doi":"10.1109/iemcon.2016.7746307","title":"A Context-aware Recommendation System using smartphone sensors","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Global Positioning System; Mobile device; Android (operating system); Geospatial analysis; Context (archaeology); Recommender system; Mobile computing; Ubiquitous computing; World Wide Web; Context awareness; Human–computer interaction; Multimedia; Telecommunications; Remote sensing","score_opus":0.04512569050612207,"score_gpt":0.2556580467710841,"score_spread":0.21053235626496203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558455691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07967133,0.000010277897,0.91275394,0.0019940697,0.0010894971,0.00028435534,0.00001319157,0.0007536237,0.0034297162],"genre_scores_gemma":[0.99593383,0.0000018101218,0.0026050182,0.00022794557,0.00012523796,0.000021802482,0.0000022691288,0.000016884209,0.0010652305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981951,0.00028321188,0.00041343377,0.00052638724,0.00025363374,0.0003282491],"domain_scores_gemma":[0.99846196,0.0003827642,0.00021686971,0.00053798145,0.00025823977,0.00014218285],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005164686,0.00019471785,0.00028134475,0.00018692325,0.00016243626,0.00017130411,0.00039975997,0.00009422963,0.00017924828],"category_scores_gemma":[0.000054807042,0.0001369527,0.000101398255,0.00034311647,0.000032724463,0.0012993142,0.00019126956,0.000070318194,0.0008411111],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022879945,0.000085273714,0.0030484318,0.00007122765,0.000094213916,0.000029987388,0.00051322416,0.0000030201427,0.017223379,0.00812806,0.0035225283,0.9672578],"study_design_scores_gemma":[0.017996915,0.00083542074,0.0115614915,0.004901285,0.00015686564,0.005354968,0.009488911,0.52074933,0.245641,0.0013379293,0.17560345,0.006372442],"about_ca_topic_score_codex":0.0002540001,"about_ca_topic_score_gemma":0.00008340801,"teacher_disagreement_score":0.96088535,"about_ca_system_score_codex":0.00034233218,"about_ca_system_score_gemma":0.00008337401,"threshold_uncertainty_score":0.9999369},"labels":[],"label_agreement":null},{"id":"W2562582799","doi":"","title":"Proposal of an adaptive service providing system for a multi-user smart home","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Computer science; Context (archaeology); Service (business); Order (exchange); Home automation; Ubiquitous computing; Human–computer interaction; Computer security; Multimedia; Operating system; Business","score_opus":0.03750670590273212,"score_gpt":0.25536093311473035,"score_spread":0.21785422721199824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562582799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03502992,0.00020071896,0.95586926,0.0031681093,0.0006499186,0.002232179,0.00026444523,0.0006072191,0.0019782067],"genre_scores_gemma":[0.77512145,0.000010990375,0.22213106,0.00004219,0.000042392996,0.0005884719,0.00010639676,0.00005889136,0.0018981795],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99126214,0.0055046985,0.0008665332,0.0013152219,0.00058512273,0.00046630227],"domain_scores_gemma":[0.9870962,0.0016575311,0.0013163359,0.0031540436,0.006525386,0.00025053497],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006515111,0.00045056533,0.00071540195,0.00033222657,0.00031261233,0.00051483256,0.0031218042,0.00036937438,0.0000069792095],"category_scores_gemma":[0.0005814029,0.00042492224,0.00028793732,0.00046528075,0.00012540695,0.00079981005,0.0023286021,0.00039669956,0.0000373629],"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":[0.0002055616,0.002857343,0.0014542616,0.005694468,0.00070310046,0.000018420207,0.04046896,0.000045078767,0.042062085,0.64023834,0.00042196937,0.2658304],"study_design_scores_gemma":[0.005136753,0.000011825694,0.0020591659,0.023976607,0.00017736215,0.000112836344,0.0012292514,0.44757798,0.502634,0.00601575,0.008644857,0.0024235803],"about_ca_topic_score_codex":0.0011883847,"about_ca_topic_score_gemma":0.0028024598,"teacher_disagreement_score":0.7400915,"about_ca_system_score_codex":0.00030468442,"about_ca_system_score_gemma":0.000959757,"threshold_uncertainty_score":0.99982023},"labels":[],"label_agreement":null},{"id":"W2562745053","doi":"10.1609/aaai.v30i1.9822","title":"Productive Aging through Intelligent Personalized Crowdsourcing","year":2016,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Research Foundation","keywords":"Crowdsourcing; Productivity; Delegation; Task (project management); Knowledge management; Business; Computer science; World Wide Web; Economic growth; Economics; Management","score_opus":0.1129454421895436,"score_gpt":0.3095647963690773,"score_spread":0.1966193541795337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562745053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22892168,0.00010772539,0.72364587,0.024949377,0.0018566372,0.0015312986,0.000015590736,0.00047578893,0.018496035],"genre_scores_gemma":[0.9960629,0.000042655745,0.002293762,0.00024690392,0.00017387782,0.00006689973,1.6596717e-7,0.000021744521,0.0010910954],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99704486,0.00004704429,0.00072634895,0.0009018254,0.0007705789,0.0005093235],"domain_scores_gemma":[0.9971674,0.00031299787,0.00065004535,0.0005084132,0.001256093,0.00010501459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084179494,0.00033895051,0.00041568553,0.00015982438,0.00028570407,0.00029037546,0.0022554395,0.000100243335,0.00013970802],"category_scores_gemma":[0.00096576376,0.00021084529,0.00023083232,0.00080375874,0.0004878902,0.0012966538,0.00061307574,0.00027772936,0.00029683983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054967375,0.00013791925,0.00027707216,0.000041308453,0.000041088322,8.24234e-7,0.005735147,0.0000027175136,0.13080591,0.63014716,0.00017541356,0.23258047],"study_design_scores_gemma":[0.000053083306,0.00012714897,0.00007430364,0.00080957625,0.000013369642,0.000019944886,0.0011996813,0.0015706376,0.8371018,0.1578346,0.00086974003,0.0003261047],"about_ca_topic_score_codex":0.00006727963,"about_ca_topic_score_gemma":0.000011135969,"teacher_disagreement_score":0.7671412,"about_ca_system_score_codex":0.00015862846,"about_ca_system_score_gemma":0.00015233459,"threshold_uncertainty_score":0.8598021},"labels":[],"label_agreement":null},{"id":"W2564294371","doi":"10.2196/iproc.6105","title":"Philips Lifeline CareSage Analytics Engine: Retrospective Evaluation on Patients of Partners Healthcare at Home","year":2016,"lang":"en","type":"article","venue":"Iproceedings","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medical emergency; Health care; Service (business); Analytics; Emergency department; Healthcare service; Population; Medicine; Ambulance service; Health services; Computer science; Business; Nursing; Data science; Environmental health; Marketing","score_opus":0.044960184874668574,"score_gpt":0.3036925947039952,"score_spread":0.2587324098293266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564294371","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9896237,0.00006200165,0.0047354945,0.0031669652,0.0006432939,0.0006304111,0.000062302024,0.00016752779,0.0009082688],"genre_scores_gemma":[0.99921286,0.0000145668655,0.00017693748,0.0001754982,0.00016756826,0.000044420183,0.000008137125,0.000016633503,0.00018339959],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976258,0.000053490552,0.00041635893,0.00054949935,0.0010640185,0.0002908193],"domain_scores_gemma":[0.9971211,0.00015432297,0.00037876662,0.00032996267,0.0018691544,0.00014671711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069369527,0.00019153365,0.00031656784,0.00024472826,0.00010873572,0.000044818767,0.00039738853,0.00010224718,0.000027343156],"category_scores_gemma":[0.00061141944,0.00014856661,0.000096677795,0.0005614172,0.000049329832,0.0006102187,0.0001643347,0.00010613264,0.00007285327],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024937384,0.0005558351,0.56254953,0.00027871213,0.0002089682,0.000005500193,0.0076700035,0.000016708113,0.011487898,0.0058850045,0.013181792,0.39791065],"study_design_scores_gemma":[0.011056928,0.003927512,0.87821287,0.002022005,0.00015363382,0.000025080557,0.00055606884,0.023811953,0.06442848,0.0072121858,0.0066608037,0.0019324921],"about_ca_topic_score_codex":0.000037814127,"about_ca_topic_score_gemma":0.000030185203,"teacher_disagreement_score":0.39597815,"about_ca_system_score_codex":0.00074438215,"about_ca_system_score_gemma":0.00009689466,"threshold_uncertainty_score":0.60583705},"labels":[],"label_agreement":null},{"id":"W2565279987","doi":"10.1109/mcc.2016.130","title":"A Tensor-Based Big Service Framework for Enhanced Living Environments","year":2016,"lang":"en","type":"article","venue":"IEEE Cloud Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Cloud computing; Computer science; Upload; Quality of service; Tensor (intrinsic definition); Plane (geometry); Service (business); Loose coupling; Human–computer interaction; Data science; Distributed computing; Computer security; World Wide Web; Telecommunications; Operating system","score_opus":0.041137127969094966,"score_gpt":0.269523913706768,"score_spread":0.22838678573767301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565279987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21481979,0.00002016587,0.7811655,0.0008914174,0.0023298091,0.00037360235,0.000006177189,0.00028704287,0.000106504536],"genre_scores_gemma":[0.9444646,9.695098e-7,0.052933585,0.0014510105,0.0009640737,0.00004542048,6.33201e-7,0.000033326694,0.0001063584],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99764013,0.00018291012,0.0004444802,0.0007795329,0.00036422707,0.00058872567],"domain_scores_gemma":[0.9945396,0.0040512807,0.00034983412,0.0007895505,0.00011872253,0.00015097721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000631986,0.00027046856,0.0003364572,0.0001162143,0.00028895392,0.00017271227,0.0009742482,0.00015288469,0.000011198562],"category_scores_gemma":[0.00031538,0.0002282679,0.00015263523,0.00036985893,0.000035296183,0.0002242393,0.00022388667,0.00015469399,0.00025456335],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027045788,0.00025184968,0.0014973361,0.00012643337,0.00009475929,0.000009399754,0.0016356641,0.00062202406,0.13637584,0.0009838938,0.00028804486,0.8580877],"study_design_scores_gemma":[0.0048483647,0.0007027097,0.007832979,0.008866719,0.00007556575,0.00007249091,0.00029486668,0.35256708,0.59552795,0.011519365,0.014426024,0.0032659033],"about_ca_topic_score_codex":0.000019884868,"about_ca_topic_score_gemma":0.000010855859,"teacher_disagreement_score":0.8548218,"about_ca_system_score_codex":0.00015525815,"about_ca_system_score_gemma":0.00008396201,"threshold_uncertainty_score":0.9308495},"labels":[],"label_agreement":null},{"id":"W2566986216","doi":"","title":"Survey of health care context models and prototyping of healthcare context framework","year":2016,"lang":"en","type":"article","venue":"Summer Computer Simulation Conference","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Health care; Computer science; Ontology; Context (archaeology); Ubiquitous computing; Android (operating system); Process (computing); Context model; Context awareness; Knowledge management; Process management; Data science; Human–computer interaction; Engineering; Artificial intelligence","score_opus":0.15572456809962168,"score_gpt":0.3480405964054352,"score_spread":0.19231602830581354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566986216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027459571,0.0017422509,0.9678123,0.0013499612,0.0003643439,0.0010749178,0.00008313499,0.00008661255,0.000026899808],"genre_scores_gemma":[0.99306643,0.000036400397,0.0063854116,0.0004089879,0.000036403144,0.000032150932,0.000010388713,0.000015946905,0.000007866301],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967099,0.0009285904,0.00097505737,0.000632774,0.0004317249,0.00032194486],"domain_scores_gemma":[0.9946334,0.0017273093,0.0008106767,0.00072749075,0.0019476363,0.00015350997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008311503,0.00025002557,0.0007228112,0.00020709967,0.00011173951,0.00007703227,0.00053927064,0.00014158734,0.000012199726],"category_scores_gemma":[0.00012156946,0.00020875776,0.000076671524,0.0003936543,0.0001382483,0.000870073,0.00029246503,0.00014441393,0.0000062609993],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003050666,0.000037386068,0.013669742,0.00022329927,0.000033540913,5.0272695e-7,0.005361524,0.00036242267,0.000011032318,0.01887521,0.000018630117,0.9613762],"study_design_scores_gemma":[0.0024331985,0.00084025995,0.09860198,0.0036624114,0.00000950432,0.000006126766,0.00052102836,0.88520366,0.0007417823,0.006227793,0.0009923158,0.00075992505],"about_ca_topic_score_codex":0.0025340163,"about_ca_topic_score_gemma":0.0010716873,"teacher_disagreement_score":0.96560687,"about_ca_system_score_codex":0.00007697426,"about_ca_system_score_gemma":0.00047481016,"threshold_uncertainty_score":0.8512894},"labels":[],"label_agreement":null},{"id":"W2572086656","doi":"","title":"Activity Recognition Through Complex Event Processing: First Findings","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Complex event processing; Leverage (statistics); Computer science; Abstraction; Activity recognition; Event (particle physics); Process (computing); Data stream mining; Task (project management); Representation (politics); Real-time computing; Human–computer interaction; Artificial intelligence; Data mining; Programming language; Engineering","score_opus":0.32075994596115737,"score_gpt":0.37643140796227037,"score_spread":0.055671462001113003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2572086656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018735081,0.000007183457,0.9559235,0.013186312,0.00042277237,0.00038825223,0.000054299664,0.0002829361,0.010999656],"genre_scores_gemma":[0.9964685,0.000017675919,0.0024687562,0.00050597754,0.00017391068,0.00011348443,0.000009816005,0.000014529748,0.00022736998],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997002,0.00016068872,0.0005138695,0.0008008485,0.0011341746,0.00038843942],"domain_scores_gemma":[0.9972651,0.00065738923,0.0002748585,0.00032102212,0.0013582265,0.00012342868],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006544952,0.00027601182,0.00026980607,0.00021553486,0.0004036786,0.00038499077,0.00080702425,0.00014176802,0.0006649151],"category_scores_gemma":[0.0006679818,0.00022483365,0.00011695298,0.0005818898,0.00016935608,0.0017957825,0.00015130204,0.00020899155,0.0019694266],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000637488,0.00031117533,0.00005390825,0.00001771983,0.00001529862,0.0000044725552,0.00041394413,0.00002052118,0.008964181,0.123099625,0.00046458284,0.86657083],"study_design_scores_gemma":[0.00026768108,0.0005018964,0.0036198508,0.00086869457,0.0000132759105,0.000056758883,0.00016965879,0.12288584,0.2839505,0.5789932,0.007405006,0.0012676108],"about_ca_topic_score_codex":0.000042890588,"about_ca_topic_score_gemma":0.00018814906,"teacher_disagreement_score":0.9777334,"about_ca_system_score_codex":0.00033948346,"about_ca_system_score_gemma":0.00039189562,"threshold_uncertainty_score":0.99880767},"labels":[],"label_agreement":null},{"id":"W2572898367","doi":"10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0036","title":"Dynamic Domain Model for Micro Context-Aware Adaptation of Applications","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Computer science; Ubiquitous computing; Adaptation (eye); Context (archaeology); Domain (mathematical analysis); Context awareness; Context model; Context management; Negotiation; Human–computer interaction; Artificial intelligence","score_opus":0.03483645186119474,"score_gpt":0.26970510234019124,"score_spread":0.2348686504789965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2572898367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003900172,0.000034764085,0.9933067,0.0013065042,0.00006567004,0.00084091246,0.000080406506,0.00014358724,0.00032125926],"genre_scores_gemma":[0.9150961,0.0000037331497,0.08259586,0.00011397315,0.000012159997,0.0004950432,0.000005825691,0.000009864195,0.001667406],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990026,0.000036710255,0.00031290023,0.00032949354,0.00014930789,0.00016899504],"domain_scores_gemma":[0.9986177,0.00036635206,0.0001806628,0.00045549852,0.00032145905,0.00005831397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023062227,0.00010822365,0.00018035149,0.00010888051,0.0000720128,0.00003080072,0.00044026604,0.00006118238,0.000010963581],"category_scores_gemma":[0.000019159808,0.000081039085,0.00009973676,0.00015910763,0.00004411772,0.00049651356,0.00007215557,0.000027171303,0.00004695684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020166774,0.00013177817,0.000120898156,0.00004961671,0.00004385006,2.460779e-7,0.0008736778,0.00013614476,0.04285743,0.066347346,0.0006341032,0.88878477],"study_design_scores_gemma":[0.0012611861,0.000058626294,0.00010946807,0.00005803867,0.000008549814,0.000009481592,0.00030095616,0.9634504,0.0056167413,0.024676766,0.0042116097,0.00023822318],"about_ca_topic_score_codex":0.000031029726,"about_ca_topic_score_gemma":0.00034136538,"teacher_disagreement_score":0.96331424,"about_ca_system_score_codex":0.00006799756,"about_ca_system_score_gemma":0.00010709587,"threshold_uncertainty_score":0.3304678},"labels":[],"label_agreement":null},{"id":"W2573003069","doi":"10.3390/s17010130","title":"Wearable Sensors for Remote Health Monitoring","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1332,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Life expectancy; Health care; Risk analysis (engineering); Wearable technology; Computer science; SAFER; Business; Computer security; Medicine; Embedded system; Environmental health","score_opus":0.27639891504552916,"score_gpt":0.43359654423533045,"score_spread":0.1571976291898013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573003069","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000061152914,0.9818224,0.010394306,0.00028021008,0.0038599346,0.002027524,0.0000581215,0.00039411613,0.00115728],"genre_scores_gemma":[0.000024736772,0.9834314,0.008005021,0.00002418573,0.0010505719,0.0000590569,0.000010927305,0.00008812193,0.007305958],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99625003,0.00048647035,0.0009051724,0.0011138003,0.00044146497,0.0008030696],"domain_scores_gemma":[0.9951179,0.0007545003,0.00146804,0.002156499,0.00020455375,0.00029849447],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012827489,0.000587829,0.002403088,0.0003576653,0.00064225256,0.000631956,0.0016162469,0.00034994117,0.0000041245803],"category_scores_gemma":[0.0003543146,0.0005496364,0.00086972397,0.00026341187,0.00005103362,0.00038506923,0.00029517914,0.00050092913,0.00048477133],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000107441,0.000011602257,5.780674e-7,0.0054339403,0.000081242,0.000012819217,0.00011937695,0.0000025449694,9.717625e-8,0.00003688395,0.0005851461,0.9937147],"study_design_scores_gemma":[0.00016869346,0.000059548427,0.0000013632434,0.0171408,0.00005177427,0.00017333296,0.00001814668,0.00087429123,0.000004381916,0.00008064454,0.9809268,0.00050022686],"about_ca_topic_score_codex":0.0003031227,"about_ca_topic_score_gemma":0.000018013603,"teacher_disagreement_score":0.9932145,"about_ca_system_score_codex":0.00038961053,"about_ca_system_score_gemma":0.00074079644,"threshold_uncertainty_score":0.99969554},"labels":[],"label_agreement":null},{"id":"W2574625309","doi":"","title":"Exploiting Environmental Sounds for Activity Recognition in Smart Homes.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Speech recognition","score_opus":0.34439884933354287,"score_gpt":0.3580037930964423,"score_spread":0.013604943762899446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574625309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14515087,0.000015958172,0.8448114,0.0018845103,0.0007523901,0.00069515296,0.000087411776,0.00014538445,0.0064569716],"genre_scores_gemma":[0.9965976,0.0000060686643,0.0026198688,0.0002417533,0.00014684469,0.0002444124,0.000039283612,0.000012400365,0.00009176377],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976715,0.0001731965,0.0004399045,0.00060039747,0.00079196633,0.000323044],"domain_scores_gemma":[0.9984657,0.0005813694,0.00020722975,0.00021778034,0.00037722985,0.0001506531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012836304,0.00020221877,0.00023229605,0.00034431345,0.0001256903,0.00028117283,0.00048251037,0.00011963856,0.000063979205],"category_scores_gemma":[0.0007296218,0.00022788194,0.00008330613,0.00035532666,0.00008080543,0.0010952691,0.00010191377,0.00023914034,0.00054070837],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014162196,0.0004893079,0.00031816086,0.0000114559,0.000013892937,0.0000051759516,0.00095222244,0.0001573345,0.0046188827,0.06417401,0.00014555728,0.92897236],"study_design_scores_gemma":[0.00034200595,0.00050091447,0.0018940018,0.00015364542,0.000006116889,0.000024518693,0.001413688,0.34458935,0.08391259,0.56530064,0.0010735616,0.0007889929],"about_ca_topic_score_codex":0.0000709693,"about_ca_topic_score_gemma":0.00023484154,"teacher_disagreement_score":0.9281834,"about_ca_system_score_codex":0.00045705115,"about_ca_system_score_gemma":0.0003508596,"threshold_uncertainty_score":0.9292756},"labels":[],"label_agreement":null},{"id":"W2576407273","doi":"","title":"Extracting Generalizable Spatial Features from Smart Phones Datasets.","year":2016,"lang":"en","type":"article","venue":"Constellation (Université du Québec à Chicoutimi)","topic":"Context-Aware Activity Recognition Systems","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 de Sherbrooke","funders":"","keywords":"Computer science; Wearable computer; Home automation; Focus (optics); Global Positioning System; Smart phone; Human–computer interaction; Scale (ratio); Smart environment; Wearable technology; Activity recognition; Smartwatch; Internet of Things; Data science; Artificial intelligence; Computer security; Telecommunications; Embedded system; Geography","score_opus":0.015068453014876878,"score_gpt":0.19718851914839755,"score_spread":0.18212006613352066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576407273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08804293,0.00041163375,0.87734514,0.00713016,0.0014157505,0.00038420333,0.00035891842,0.00062745763,0.024283826],"genre_scores_gemma":[0.9968675,0.000031125324,0.0016421694,0.00027326157,0.00022638074,0.0000017110265,0.00008849277,0.000017456263,0.00085190166],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982263,0.0001897443,0.00027253342,0.00065012777,0.00036078977,0.00030049385],"domain_scores_gemma":[0.99782324,0.00081482896,0.0003143409,0.000723082,0.0001531498,0.0001713351],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022646127,0.00024498306,0.00028237878,0.00020453832,0.00042089535,0.00019837066,0.0006957043,0.00013556605,0.0068407757],"category_scores_gemma":[0.00008084063,0.00021614593,0.00011900473,0.0002760996,0.000121277015,0.0016946592,0.0003533768,0.00013320729,0.00030071413],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102235674,0.0001580631,0.04818641,0.000020522233,0.00025970023,0.0001192861,0.0029366638,0.00013149098,0.023463434,0.0016030497,0.17170659,0.75131255],"study_design_scores_gemma":[0.00932338,0.00021229818,0.2298284,0.00065828825,0.00026532554,0.0005809134,0.0013708533,0.055622626,0.03033058,0.0012078652,0.6673499,0.0032495647],"about_ca_topic_score_codex":0.027017891,"about_ca_topic_score_gemma":0.015891574,"teacher_disagreement_score":0.90882456,"about_ca_system_score_codex":0.00024573764,"about_ca_system_score_gemma":0.00039623116,"threshold_uncertainty_score":0.99406713},"labels":[],"label_agreement":null},{"id":"W2577566608","doi":"10.1109/iceccs.2016.042","title":"Detecting Inconsistencies in Rule-Based Reasoning for Ambient Intelligence","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 de Sherbrooke","funders":"","keywords":"Semantic reasoner; Computer science; Consistency (knowledge bases); Context (archaeology); Set (abstract data type); Rule-based system; Ambient intelligence; Intelligence analysis; Artificial intelligence; Context awareness; Data mining; Machine learning; Computer security; Programming language","score_opus":0.04432847874285207,"score_gpt":0.27360371292288116,"score_spread":0.2292752341800291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577566608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08156244,0.000021137046,0.9158761,0.0009314124,0.00026228005,0.00029916997,0.0000019953409,0.00019460806,0.0008508493],"genre_scores_gemma":[0.95682096,9.595764e-7,0.04256995,0.0002228628,0.00002890872,0.00012398748,2.2913106e-7,0.00000779261,0.00022433339],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869746,0.000087284,0.00032118292,0.00041048645,0.00018181704,0.0003017585],"domain_scores_gemma":[0.99792325,0.0013643914,0.00011542947,0.0003727771,0.00015681967,0.00006730888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079176883,0.00012143949,0.00017665136,0.00020167581,0.00008809729,0.000113337584,0.000447292,0.000051221727,0.000017721217],"category_scores_gemma":[0.0007030707,0.00008781056,0.00007859555,0.0003281938,0.000036790236,0.00053193746,0.00012749992,0.00005675902,0.000056213223],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016020173,0.000049536306,0.0077201575,0.000024844074,0.000007746201,0.000005732525,0.00036389168,0.000016793967,0.0140757095,0.004937478,0.00003959274,0.9727425],"study_design_scores_gemma":[0.0018249728,0.00036896707,0.0077436017,0.0014937818,0.000008808092,0.00006534596,0.00096368324,0.1528424,0.81975603,0.009545654,0.004316709,0.0010700745],"about_ca_topic_score_codex":0.0001595287,"about_ca_topic_score_gemma":0.00064290117,"teacher_disagreement_score":0.9716724,"about_ca_system_score_codex":0.00014315703,"about_ca_system_score_gemma":0.000111717774,"threshold_uncertainty_score":0.35808107},"labels":[],"label_agreement":null},{"id":"W2579644497","doi":"10.1109/icpads.2016.0022","title":"Physical Object Model for Smart Environments with Temporal Capability","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Component (thermodynamics); Object (grammar); Server; Point (geometry); Hierarchy; Database; Space (punctuation); Home automation; Operating system; Embedded system","score_opus":0.03012914829272468,"score_gpt":0.2478858658744811,"score_spread":0.21775671758175644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579644497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14974765,0.0000013553109,0.84825104,0.0006303093,0.000051699433,0.0003747873,0.000012614972,0.000104041435,0.00082650443],"genre_scores_gemma":[0.98452294,2.412172e-7,0.011805848,0.00011350758,0.000041878575,0.00013706472,0.0000011518333,0.000009404863,0.0033679574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886835,0.00004758825,0.00013834289,0.00046635309,0.0002457088,0.0002336823],"domain_scores_gemma":[0.99904346,0.00022433652,0.00006430071,0.0005418627,0.000032497825,0.000093558854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019916853,0.0001330403,0.00018357481,0.000031847892,0.000066669025,0.000047857324,0.00034490167,0.000033729844,0.000009862103],"category_scores_gemma":[0.00002230625,0.00007567996,0.0000786955,0.00006757945,0.00006358811,0.0006950399,0.000104088904,0.000036836085,0.00009581782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003853496,0.0028800112,0.07767336,0.000115824405,0.00032833507,0.000012821586,0.004652242,0.00022010751,0.07960215,0.02020512,0.0055868984,0.80833775],"study_design_scores_gemma":[0.0033341611,0.0005994187,0.0055411304,0.000062245614,0.000019801826,0.00002541619,0.00004300434,0.9367074,0.034791533,0.01351559,0.0045786286,0.00078168703],"about_ca_topic_score_codex":0.000046816425,"about_ca_topic_score_gemma":0.00011536982,"teacher_disagreement_score":0.93648726,"about_ca_system_score_codex":0.00009301579,"about_ca_system_score_gemma":0.0000631231,"threshold_uncertainty_score":0.30861396},"labels":[],"label_agreement":null},{"id":"W2581428794","doi":"10.1109/access.2017.2653079","title":"SITE: The Simple Internet of Things Enabler for Smart Homes","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Enabling; Computer science; Usability; Simple (philosophy); Smart objects; The Internet; User interface; Human–computer interaction; Home automation; Control (management); World Wide Web; Computer security; Telecommunications; Artificial intelligence","score_opus":0.0744808304534464,"score_gpt":0.336981431479584,"score_spread":0.2625006010261376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581428794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37279356,0.000058721314,0.6188382,0.0019083341,0.0017847035,0.0006056863,0.000015019645,0.000100331825,0.0038954664],"genre_scores_gemma":[0.998039,0.0000031426823,0.00032988715,0.00044979743,0.00011439387,0.00006830855,0.000001842935,0.000009250661,0.0009843431],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905,0.000048450318,0.00022007893,0.00027426906,0.0002065396,0.00020066848],"domain_scores_gemma":[0.99787676,0.00040467829,0.0003716199,0.0010960252,0.00020898793,0.000041906766],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00049350935,0.00010790738,0.00020326502,0.000054740416,0.00024254771,0.0010739614,0.003114383,0.000049581013,0.00002464587],"category_scores_gemma":[0.000167021,0.00007609922,0.00010335956,0.00007200826,0.00007858447,0.0027317693,0.0005145725,0.00008528332,0.00003216389],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091788985,0.00025833058,0.07514671,0.00037842046,0.00030193542,0.000015454701,0.010012445,0.000015415162,0.008178887,0.0046981215,0.14885537,0.7520471],"study_design_scores_gemma":[0.0037038254,0.00038103943,0.110270865,0.00043088637,0.00009008239,0.00007757148,0.00022956904,0.080123015,0.47341356,0.03858357,0.29134887,0.0013471262],"about_ca_topic_score_codex":0.001393119,"about_ca_topic_score_gemma":0.00018921115,"teacher_disagreement_score":0.7507,"about_ca_system_score_codex":0.00001862009,"about_ca_system_score_gemma":0.000044521028,"threshold_uncertainty_score":0.99996305},"labels":[],"label_agreement":null},{"id":"W2582827436","doi":"10.1016/j.asoc.2017.01.034","title":"Detecting falls with X-Factor Hidden Markov Models","year":2017,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Computer science; Outlier; Identification (biology); Machine learning; Artificial intelligence; Activity recognition; Wearable computer; Training (meteorology); Activities of daily living; Pattern recognition (psychology); Psychology","score_opus":0.03580944863826014,"score_gpt":0.25183867430133927,"score_spread":0.21602922566307914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2582827436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22714406,0.000018375926,0.7575912,0.00012372508,0.0002732858,0.00033956004,0.0000018081658,0.000507562,0.014000458],"genre_scores_gemma":[0.9325293,6.5599255e-7,0.06697099,0.00017259101,0.00019939858,0.000017746923,0.0000010503161,0.000033795626,0.0000745001],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977513,0.000056625286,0.00035727595,0.0007905858,0.00047764435,0.0005665547],"domain_scores_gemma":[0.99716604,0.00048277134,0.00059456535,0.0014477887,0.00014587845,0.00016295025],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00050098274,0.00029935123,0.00039067317,0.000111878,0.0013073193,0.0013342527,0.0018368409,0.00010692122,0.00000659955],"category_scores_gemma":[0.00006715275,0.0002809777,0.00008048897,0.00014680008,0.000079888094,0.0009357324,0.0009611602,0.00034201876,0.00009747756],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000145319955,0.000036109588,0.0014771135,0.00003561058,0.00006287779,0.000023397239,0.0019668473,0.00022554335,0.001821148,0.0024741713,0.0000368956,0.99182576],"study_design_scores_gemma":[0.0029432862,0.00015264237,0.012666366,0.0005001524,0.000034479457,0.00024344776,0.00063545466,0.96508473,0.008618626,0.0064471634,0.00074107904,0.0019325792],"about_ca_topic_score_codex":0.00012343793,"about_ca_topic_score_gemma":0.000055202087,"teacher_disagreement_score":0.9898932,"about_ca_system_score_codex":0.00008382932,"about_ca_system_score_gemma":0.00009741759,"threshold_uncertainty_score":0.99999285},"labels":[],"label_agreement":null},{"id":"W2584306374","doi":"10.1145/3036699.3036710","title":"SIDEWINDER","year":2017,"lang":"en","type":"article","venue":"GetMobile Mobile Computing and Communications","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"","keywords":"Computer science; Software portability; Interface (matter); Embedded system; Software deployment; Participatory sensing; Mobile device; Efficient energy use; Real-time computing; Operating system; Engineering","score_opus":0.04802664794405237,"score_gpt":0.33074411183043717,"score_spread":0.2827174638863848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584306374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24637741,0.0046877377,0.67885363,0.0072913556,0.0013028189,0.0016876042,0.000018266142,0.0014956605,0.058285534],"genre_scores_gemma":[0.98736775,0.00011520228,0.0118602235,0.00018613118,0.0000654793,0.00011687134,0.00000482477,0.000012128836,0.00027139977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987096,0.00018030626,0.00029454182,0.0003887205,0.00016301322,0.00026379636],"domain_scores_gemma":[0.99370444,0.0004622008,0.0003033627,0.005237929,0.00017026607,0.0001218237],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005219805,0.00015719014,0.0002269785,0.00007418321,0.0024545107,0.0010410714,0.0035148468,0.00007208341,0.0000095297455],"category_scores_gemma":[0.00011158639,0.00016318882,0.00007520159,0.00009494917,0.00026661807,0.00064868276,0.0034185792,0.0002607264,0.00009102373],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015170406,0.00018608259,0.009256803,0.000020569272,0.00004840968,0.0000034876366,0.0016600998,0.000043420412,0.00034765116,0.017487817,0.0008216343,0.9701225],"study_design_scores_gemma":[0.002492239,0.00040190158,0.16260181,0.00056644046,0.00006364095,0.0005256429,0.001603304,0.42066687,0.0010824717,0.01456485,0.39344767,0.001983171],"about_ca_topic_score_codex":0.000164309,"about_ca_topic_score_gemma":0.000059185197,"teacher_disagreement_score":0.96813935,"about_ca_system_score_codex":0.000031272415,"about_ca_system_score_gemma":0.00006152062,"threshold_uncertainty_score":0.99999595},"labels":[],"label_agreement":null},{"id":"W2586857106","doi":"10.1109/intech.2016.7845015","title":"Combining variability, RCA and feature model for context-awareness","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Context (archaeology); Context model; Adaptation (eye); Feature (linguistics); Ontology; Artificial intelligence; Context awareness; Machine learning; Theoretical computer science","score_opus":0.04673107237117263,"score_gpt":0.27855041355261884,"score_spread":0.2318193411814462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586857106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01238487,0.00002702484,0.9798384,0.0052585583,0.00028911245,0.000434761,0.000022939148,0.00027387714,0.0014704634],"genre_scores_gemma":[0.9833046,0.0000031685759,0.011969586,0.0005507949,0.00004111778,0.000107328946,0.0000013462728,0.000012159401,0.0040099053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986838,0.00009810027,0.00020325795,0.00056174485,0.00017729435,0.0002757824],"domain_scores_gemma":[0.9978278,0.0012112088,0.000089745394,0.0005068469,0.00023800823,0.0001263828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071888,0.00016633068,0.00026929268,0.00007284742,0.00015364031,0.00017234965,0.00044199187,0.00011494102,0.00001064732],"category_scores_gemma":[0.00020318708,0.00011239998,0.00007120051,0.00011652983,0.000060883416,0.00088115834,0.00028388307,0.00006488947,0.000015212956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005128005,0.00014504362,0.009023167,0.00009015139,0.00007519701,0.0000029796277,0.001417772,0.000010331077,0.0063109426,0.23450735,0.006715145,0.74165064],"study_design_scores_gemma":[0.0039652875,0.00017510202,0.0015764722,0.00020902774,0.000020910507,0.00008121307,0.00009583736,0.9028774,0.005434392,0.07413707,0.010658082,0.0007692479],"about_ca_topic_score_codex":0.000027159631,"about_ca_topic_score_gemma":0.00009339109,"teacher_disagreement_score":0.9709197,"about_ca_system_score_codex":0.00004733077,"about_ca_system_score_gemma":0.00011879211,"threshold_uncertainty_score":0.45835385},"labels":[],"label_agreement":null},{"id":"W2587155432","doi":"10.1109/smc.2016.7844783","title":"Mitigating sensor differences for phone-based human activity recognition","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Western University","funders":"","keywords":"Activity recognition; Computer science; Smart phone; Phone; Preprocessor; Feature extraction; Outlier; Artificial intelligence; Embedded system; Real-time computing; Data mining; Machine learning; Telecommunications","score_opus":0.08850053298651599,"score_gpt":0.2901538781393611,"score_spread":0.2016533451528451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587155432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41824627,0.000002111817,0.5786904,0.0012067698,0.00021299682,0.0003039755,0.000022866154,0.00029419662,0.001020406],"genre_scores_gemma":[0.98651123,5.224651e-7,0.012189486,0.00021406969,0.00013590066,0.0001827403,0.0000035405055,0.000012538609,0.00074995804],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985047,0.0001647185,0.00024230182,0.00052065006,0.00025134257,0.0003163076],"domain_scores_gemma":[0.9978632,0.0012171698,0.00019656391,0.0003787612,0.00023682864,0.000107503496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038727108,0.00017699716,0.00023936354,0.00012272093,0.00025623944,0.00018139184,0.00035850683,0.00008326241,0.000110406756],"category_scores_gemma":[0.00018216368,0.00012296639,0.00013705826,0.00017494212,0.000054742304,0.0008381877,0.00006768575,0.00006228746,0.00016878876],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014246588,0.00010489248,0.001953354,0.000033178487,0.000021441008,0.0000018012378,0.00009014444,9.440952e-8,0.2715194,0.00031842449,0.00042829238,0.72551477],"study_design_scores_gemma":[0.0037357765,0.0004141199,0.01681828,0.00047376135,0.000021723568,0.000020078973,0.000090643574,0.009148677,0.95764107,0.009511294,0.0011650363,0.0009595387],"about_ca_topic_score_codex":0.000102371516,"about_ca_topic_score_gemma":0.00015314504,"teacher_disagreement_score":0.7245552,"about_ca_system_score_codex":0.00007148727,"about_ca_system_score_gemma":0.00006324633,"threshold_uncertainty_score":0.5014424},"labels":[],"label_agreement":null},{"id":"W2587329666","doi":"10.1109/tsc.2017.2662941","title":"Adaptable Context-Aware Cooking-Safe System","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Context (archaeology); Microcontroller; Ubiquitous computing; Context awareness; Fuzzy logic; Human–computer interaction; Embedded system; Computer security; Artificial intelligence","score_opus":0.02767501729170752,"score_gpt":0.2565122919103955,"score_spread":0.22883727461868797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587329666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033501744,0.00004589392,0.95666534,0.00040018093,0.0035894534,0.00043758756,0.00002379869,0.00096565916,0.0043703234],"genre_scores_gemma":[0.9973749,0.0000037400403,0.0015655584,0.00040288034,0.00019447185,0.000029055946,0.0000018367206,0.000039477065,0.00038806343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711424,0.00019866685,0.0006003083,0.00089388905,0.0005843758,0.0006085294],"domain_scores_gemma":[0.9964696,0.00035297987,0.0006488681,0.001955999,0.00034221454,0.00023034269],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00059421477,0.00039949996,0.00053738465,0.0002484291,0.0027069205,0.0015747705,0.0024304113,0.00018131199,0.000017840452],"category_scores_gemma":[0.0000038163457,0.00042284955,0.0002459419,0.00025085185,0.00006907578,0.0017726497,0.000038666836,0.0004681327,0.0003731302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008408314,0.00047559754,0.0006959577,0.0010841954,0.00045277167,0.00017678016,0.0044913986,0.014159686,0.0012304452,0.0026160788,0.00016262093,0.97437036],"study_design_scores_gemma":[0.0015387575,0.00014709454,0.0009005504,0.0015705988,0.000051047457,0.00024159328,0.0016773645,0.9786414,0.010799674,0.000048229023,0.0035784442,0.000805239],"about_ca_topic_score_codex":0.0015874729,"about_ca_topic_score_gemma":0.0012584269,"teacher_disagreement_score":0.97356516,"about_ca_system_score_codex":0.00020988173,"about_ca_system_score_gemma":0.00010020376,"threshold_uncertainty_score":0.9998223},"labels":[],"label_agreement":null},{"id":"W2588020636","doi":"10.1145/3011141.3011186","title":"Ubiquitous reminders to manage timetable in a smart home","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke","funders":"","keywords":"Computer science; Home automation; Population; Activities of daily living; Multimedia; Internet privacy; Psychology; Human–computer interaction; Medicine; Telecommunications; Physical therapy","score_opus":0.01842984607359719,"score_gpt":0.2316800340619305,"score_spread":0.2132501879883333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588020636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08746256,0.000023049468,0.8501822,0.0061154696,0.00060597516,0.00042169646,0.0000024364272,0.0003358163,0.054850817],"genre_scores_gemma":[0.9761736,0.0000020034086,0.004259194,0.00082675903,0.00003426749,0.00004325748,2.0492449e-7,0.000008776121,0.018651946],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986976,0.000076436976,0.00021895212,0.00043859836,0.00023788237,0.00033054396],"domain_scores_gemma":[0.99898875,0.00020250917,0.00004203072,0.0005876482,0.000050413073,0.00012863016],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044889573,0.000115610965,0.00019218907,0.0003015173,0.000032227297,0.0001081263,0.0006136741,0.00005040124,0.00015416737],"category_scores_gemma":[0.000056144887,0.00008293836,0.0000477464,0.0005789496,0.000014474111,0.00073878595,0.00029683183,0.00005340295,0.0030564498],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020563362,0.00013900688,0.006877668,0.000019912351,0.000033111814,0.00011411027,0.0011391855,0.0000030412614,0.015037899,0.0062072254,0.015559809,0.95484847],"study_design_scores_gemma":[0.008190426,0.0008617292,0.11134055,0.001113559,0.00002597724,0.0004312265,0.0006573352,0.006610121,0.07918736,0.020855276,0.76690483,0.0038215823],"about_ca_topic_score_codex":0.00031510481,"about_ca_topic_score_gemma":0.0007056948,"teacher_disagreement_score":0.95102686,"about_ca_system_score_codex":0.000103483915,"about_ca_system_score_gemma":0.00003104205,"threshold_uncertainty_score":0.99771976},"labels":[],"label_agreement":null},{"id":"W2588035946","doi":"10.1145/3011141.3011171","title":"An ambient assisted living nighttime wandering system for elderly","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Home automation; Robustness (evolution); Activities of daily living; Assisted living; Ambient intelligence; Human–computer interaction; Dementia; Assistive technology; Independent living; Living room; Psychology; Medicine; Engineering; Telecommunications; Gerontology; Architectural engineering","score_opus":0.03294034837788155,"score_gpt":0.2609384552799136,"score_spread":0.22799810690203207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588035946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05460754,0.000013121956,0.94098175,0.0003505657,0.00067152584,0.00037934713,0.0000048093107,0.0008426101,0.002148749],"genre_scores_gemma":[0.9899658,4.5038811e-7,0.00830112,0.00005582434,0.00012185199,0.000115729934,5.895724e-7,0.000015837408,0.0014227879],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998451,0.00012898598,0.0003093572,0.00051865133,0.0002551138,0.00033692294],"domain_scores_gemma":[0.99827135,0.0005903142,0.00011589202,0.0006638003,0.0001974203,0.00016120369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058251526,0.0001551635,0.00023021108,0.0001233263,0.00017177491,0.0003133751,0.00061871216,0.00007063423,0.000029504496],"category_scores_gemma":[0.000057540823,0.00010505424,0.00010345022,0.00016344567,0.000017012602,0.0012599735,0.00011414355,0.00003915842,0.00014582807],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009963519,0.00015348433,0.0011434449,0.00010908126,0.00006146603,0.0000094319485,0.0005771974,0.0000015089022,0.14485984,0.0111184865,0.0007087467,0.8412474],"study_design_scores_gemma":[0.011275423,0.0060299993,0.0662124,0.0097146565,0.00015544686,0.0019315192,0.003799921,0.3381219,0.48081478,0.001465,0.07380104,0.006677916],"about_ca_topic_score_codex":0.000053947497,"about_ca_topic_score_gemma":0.00006210573,"teacher_disagreement_score":0.9353583,"about_ca_system_score_codex":0.00016129047,"about_ca_system_score_gemma":0.000060682967,"threshold_uncertainty_score":0.42839876},"labels":[],"label_agreement":null},{"id":"W2589397338","doi":"","title":"A Comparison of Feature Extraction and Selection Techniques for Activity Recognition using Low-cost Sensors on a Smartphone","year":2012,"lang":"en","type":"article","venue":"Proceedings of the 25th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2012)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Activity recognition; Computer science; Mobile device; Feature selection; Feature extraction; Classifier (UML); Artificial intelligence; Computation; Inertial measurement unit; Pattern recognition (psychology); Machine learning; Real-time computing; Human–computer interaction","score_opus":0.05856838575700279,"score_gpt":0.3497465625487271,"score_spread":0.2911781767917243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589397338","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98897827,0.00005430137,0.0075960997,0.00067835185,0.0010630635,0.0011812155,0.000048251306,0.00004829454,0.00035217017],"genre_scores_gemma":[0.9869463,0.000020838344,0.012846774,0.000014483524,0.000089280744,0.000030353165,0.000003673122,0.0000150239875,0.0000333034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977098,0.00008831696,0.00084478443,0.00028522947,0.00089440984,0.00017747396],"domain_scores_gemma":[0.9950388,0.00026977944,0.0030501892,0.00024401178,0.0013509641,0.000046237477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016459568,0.00020847366,0.00043242783,0.00021109315,0.00018164609,0.000032631826,0.00092563505,0.00021171583,0.0000017105517],"category_scores_gemma":[0.0011500089,0.00013837886,0.00032327796,0.0006383001,0.00029498394,0.0011701381,0.00045482357,0.00034487527,3.7456195e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024869345,0.00049532164,0.01388331,0.00039911908,0.000059833143,1.1788585e-8,0.00027849432,0.00013830313,0.9340483,0.001626783,0.00009013898,0.04873172],"study_design_scores_gemma":[0.00035056612,0.00009181914,0.034627676,0.0034705575,0.000053819724,0.000018789158,0.00005237232,0.008760047,0.9510922,0.000981153,0.00036840345,0.00013262496],"about_ca_topic_score_codex":0.000066013534,"about_ca_topic_score_gemma":0.000004898111,"teacher_disagreement_score":0.048599094,"about_ca_system_score_codex":0.00014584827,"about_ca_system_score_gemma":0.000045330395,"threshold_uncertainty_score":0.5642926},"labels":[],"label_agreement":null},{"id":"W2591527860","doi":"10.1016/j.jsams.2017.01.184","title":"Increased objectively measured physical activity is associated with improved levels of energy and fatigue in a web-based physical activity RCT","year":2017,"lang":"en","type":"article","venue":"Journal of science and medicine in sport","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of British Columbia","funders":"","keywords":"Physical activity; Randomized controlled trial; Quality of life (healthcare); Physical therapy; Intervention (counseling); Medicine; Physical medicine and rehabilitation; Psychology; Internal medicine; Psychiatry; Nursing","score_opus":0.05170776003166082,"score_gpt":0.31606912476297055,"score_spread":0.2643613647313097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591527860","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996057,0.000012351204,0.0025857175,0.0008856252,0.000094759955,0.00014008513,0.0000045786937,0.000007933132,0.0002120015],"genre_scores_gemma":[0.9997434,0.0000050764525,0.000088551526,0.0000646151,0.00007777058,0.0000051358124,8.604574e-8,0.000005643562,0.000009697988],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99779165,0.00008914285,0.00027368948,0.00038658953,0.0011758037,0.00028314278],"domain_scores_gemma":[0.9972718,0.00029825477,0.001140805,0.00033712087,0.00073250645,0.0002195221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027319703,0.00018590291,0.0008069382,0.00048340502,0.00017810296,0.00007869849,0.00061468116,0.00005709578,0.0000010717847],"category_scores_gemma":[0.0010168527,0.00013011161,0.000051164454,0.0006994994,0.0014186171,0.0019789576,0.00011609104,0.00029670892,5.569133e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006216619,0.002291497,0.20931049,0.000053435906,0.000068105765,0.00024024639,0.0040959744,0.000007845659,0.5503788,0.00011328978,0.000031058607,0.2327876],"study_design_scores_gemma":[0.003709012,0.0008088596,0.9128776,0.00064669707,0.000021100259,0.000056540666,0.0001364334,0.03516223,0.04627709,0.0001409743,0.000009386621,0.00015408437],"about_ca_topic_score_codex":0.0014181704,"about_ca_topic_score_gemma":0.00063979946,"teacher_disagreement_score":0.7035671,"about_ca_system_score_codex":0.00016417545,"about_ca_system_score_gemma":0.0013699542,"threshold_uncertainty_score":0.5305798},"labels":[],"label_agreement":null},{"id":"W2591989069","doi":"","title":"Formulating Dynamic Agents' Operational State via Situation Awareness Assessment.","year":2014,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Autonomy; Computer science; Task (project management); State (computer science); Risk analysis (engineering); Distributed computing; Block (permutation group theory); Computer security; Process management; Systems engineering; Engineering; Business; Algorithm","score_opus":0.019176725573839648,"score_gpt":0.27591779498545754,"score_spread":0.2567410694116179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591989069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16947325,0.000005742021,0.8256676,0.00009378763,0.000516571,0.00036868604,0.000011468952,0.00031009462,0.003552762],"genre_scores_gemma":[0.9830817,0.000002542911,0.016103914,0.00033191417,0.00004981672,0.00010991348,0.0002457642,0.0000117166155,0.00006270708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978385,0.00020837307,0.0008012402,0.00023833977,0.0005861991,0.00032733445],"domain_scores_gemma":[0.99816513,0.00020052827,0.00052346423,0.0004157579,0.0005948493,0.00010025868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010727112,0.00022134975,0.00024628537,0.00033276965,0.00056253996,0.0010147785,0.00045049845,0.00009141082,0.000032988344],"category_scores_gemma":[0.00019887665,0.00022934665,0.00008013894,0.00048677807,0.000044931006,0.012024184,0.0001712366,0.00014960862,0.00022109896],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008639882,0.000042858428,0.005758318,0.00020716293,0.00004954595,0.0000010139692,0.00656283,0.0064016692,0.00096711866,0.0067509105,0.00014554459,0.9731044],"study_design_scores_gemma":[0.0004993659,0.0000667789,0.03847093,0.000099389705,0.0000061663836,0.00003948944,0.00012514928,0.95131314,0.00041937336,0.0071986,0.001469635,0.0002919577],"about_ca_topic_score_codex":0.00016061876,"about_ca_topic_score_gemma":0.00007302308,"teacher_disagreement_score":0.9728124,"about_ca_system_score_codex":0.0005004402,"about_ca_system_score_gemma":0.00018698191,"threshold_uncertainty_score":0.9785533},"labels":[],"label_agreement":null},{"id":"W2593796416","doi":"10.3390/s17030529","title":"A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":214,"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":"Wearable computer; Activity recognition; Accelerometer; Computer science; Artificial intelligence; Machine learning; Support vector machine; Principal component analysis; Feature (linguistics); Motion (physics); Wearable technology; Dimension (graph theory); Pattern recognition (psychology); Human–computer interaction; Data mining","score_opus":0.08897369318164437,"score_gpt":0.3240306732050064,"score_spread":0.235056980023362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593796416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899264,0.0000062394233,0.0030459764,0.0006302355,0.00026719074,0.00036681845,0.000013242592,0.00015335731,0.005590554],"genre_scores_gemma":[0.99887836,0.000007528982,0.00032243197,0.00011173936,0.00008529381,0.000028060955,0.000010463163,0.000013795221,0.0005423326],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978244,0.0004019898,0.00030706916,0.0007155451,0.00040400206,0.00034701428],"domain_scores_gemma":[0.9977868,0.00023133824,0.0004174742,0.0012486828,0.00022761477,0.00008808104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036451404,0.00023416964,0.00045374277,0.0006472318,0.000796567,0.00096622604,0.00056651636,0.0001397466,0.00004511347],"category_scores_gemma":[0.00013011639,0.00025090206,0.00020034902,0.0005478416,0.0000867166,0.0010985251,0.00014940239,0.0003059922,0.00029885763],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003649482,0.0019216428,0.052937333,0.0001786205,0.0017458629,0.00058288214,0.0077027204,0.013778453,0.10810361,0.0010154141,0.0007486945,0.8109198],"study_design_scores_gemma":[0.0023009935,0.0003172951,0.73265404,0.0002363267,0.00014531985,0.000031131225,0.00029446383,0.19186027,0.0678506,0.0013546048,0.0018111537,0.0011437956],"about_ca_topic_score_codex":0.0019918464,"about_ca_topic_score_gemma":0.0016168777,"teacher_disagreement_score":0.809776,"about_ca_system_score_codex":0.0001566186,"about_ca_system_score_gemma":0.000040969535,"threshold_uncertainty_score":0.99999434},"labels":[],"label_agreement":null},{"id":"W2605896830","doi":"10.1109/icin.2017.7899413","title":"An extended SNMP based IoT context-aware model for dynamic adaptability of embedded systems software","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Nova Chemicals (Canada)","funders":"","keywords":"Computer science; Adaptability; Context (archaeology); Distributed computing; Software; Internet of Things; The Internet; Embedded system; Software engineering; Operating system","score_opus":0.05588071233690373,"score_gpt":0.3261425719114946,"score_spread":0.27026185957459087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605896830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053766962,0.000048425336,0.94289345,0.00023098326,0.00065141404,0.0015277688,0.00023702704,0.00046553888,0.00017844247],"genre_scores_gemma":[0.9677918,5.5602857e-7,0.031211494,0.00009780404,0.00004019085,0.00031820356,0.00001945351,0.000034903358,0.00048558362],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968318,0.00030940524,0.00078107766,0.0010243799,0.0005504747,0.0005028867],"domain_scores_gemma":[0.99357957,0.000698784,0.00079635106,0.003600459,0.0010531597,0.00027165536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015853894,0.00036529498,0.00078053516,0.00016378616,0.00054484577,0.0005503932,0.002509119,0.00022373728,0.000022928167],"category_scores_gemma":[0.0005896362,0.0003425847,0.00030548527,0.000109251414,0.0001850472,0.0010073535,0.00024253648,0.0001641849,0.000016123282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085791387,0.0041127736,0.010751162,0.0031131161,0.0005546054,0.000033319913,0.0041481536,0.03648498,0.013582818,0.02371314,0.0016788851,0.90096915],"study_design_scores_gemma":[0.0014651521,0.00019587489,0.0023910864,0.00011771307,0.000020424235,0.000008816855,0.0002584646,0.9928362,0.0011518083,0.0010937677,0.000055425025,0.00040527523],"about_ca_topic_score_codex":0.00059565244,"about_ca_topic_score_gemma":0.001236303,"teacher_disagreement_score":0.9563512,"about_ca_system_score_codex":0.00015706448,"about_ca_system_score_gemma":0.00049756805,"threshold_uncertainty_score":0.9999026},"labels":[],"label_agreement":null},{"id":"W2606874317","doi":"10.1017/s1049023x17006033","title":"New Triage System: Using Digitized Information Entered via a Digital Pen","year":2017,"lang":"en","type":"article","venue":"Prehospital and Disaster Medicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Triage; Computer science; Medical emergency; Medicine","score_opus":0.026461984989225522,"score_gpt":0.25820864707854957,"score_spread":0.23174666208932404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606874317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33943394,0.00011584156,0.64340633,0.0009318616,0.0011043197,0.00052083447,0.000014100815,0.00019942579,0.014273363],"genre_scores_gemma":[0.9987807,0.0000020401196,0.0004592734,0.00009088044,0.00028956291,0.000011395828,0.000013209202,0.0000071744676,0.0003457432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871504,0.000028896045,0.00041884105,0.00026508383,0.00034472183,0.0002273882],"domain_scores_gemma":[0.9985861,0.00007460069,0.0003722385,0.00063293084,0.00008486449,0.000249264],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00018810366,0.0001938152,0.00035086804,0.00010183459,0.00025558937,0.0012116333,0.00055846584,0.000055456694,0.000016923092],"category_scores_gemma":[0.00016389742,0.00014803026,0.000057794987,0.0000635923,0.00009350053,0.0071637817,0.00045794155,0.000087231834,0.000055587174],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012585559,0.000057092,0.0112084765,0.0003399275,0.00010607541,0.000035905847,0.01920397,6.628488e-7,0.00043060858,0.0016194435,0.001226205,0.9656458],"study_design_scores_gemma":[0.13783713,0.008766615,0.33390066,0.02058245,0.00091311464,0.007015472,0.056275215,0.337985,0.001991212,0.030537272,0.055101346,0.00909453],"about_ca_topic_score_codex":0.00031080312,"about_ca_topic_score_gemma":0.000006049501,"teacher_disagreement_score":0.95655125,"about_ca_system_score_codex":0.00004460214,"about_ca_system_score_gemma":0.000040408282,"threshold_uncertainty_score":0.9998252},"labels":[],"label_agreement":null},{"id":"W2607058759","doi":"10.1109/sas.2017.7894057","title":"Sensor modality shifting in IoT deployment: Measuring non-temperature data using temperature sensors","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Flexibility (engineering); Software deployment; Computer science; Modality (human–computer interaction); Real-time computing; Internet of Things; Temperature measurement; Wireless sensor network; Intelligent sensor; Building automation; Wearable computer; Measure (data warehouse); Doors; Embedded system; Artificial intelligence; Computer network; Data mining","score_opus":0.13222466099357316,"score_gpt":0.3240246653231425,"score_spread":0.19180000432956934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607058759","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98960483,0.000052038602,0.0059396513,0.0015533508,0.000794993,0.0004898204,0.000036673013,0.0002567612,0.0012718522],"genre_scores_gemma":[0.9881835,0.0000038644043,0.010972213,0.00020522228,0.0002921512,0.000008508346,0.0000097730735,0.000033251126,0.00029150822],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99662703,0.0002951726,0.0005375117,0.0012995111,0.00063613174,0.00060461665],"domain_scores_gemma":[0.9948782,0.00017456421,0.00035445593,0.0041972767,0.00020697498,0.00018855622],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0016781974,0.00036385705,0.00049896975,0.00020252317,0.00087159907,0.0021514045,0.0030850468,0.00026270188,0.000016142909],"category_scores_gemma":[0.00047964003,0.0003417014,0.00008828016,0.00029635127,0.00006793626,0.0026882237,0.002101467,0.00065753673,0.000044543576],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081898936,0.00060063635,0.1505775,0.00047124588,0.0003208806,0.001124398,0.0054073627,0.0016986793,0.80305964,0.0008452206,0.00076471444,0.035047796],"study_design_scores_gemma":[0.0041526575,0.00006933228,0.1328741,0.0018374154,0.000056153396,0.0007006861,0.0010959038,0.71719754,0.13769521,0.00042580126,0.0011128666,0.0027823665],"about_ca_topic_score_codex":0.0017785121,"about_ca_topic_score_gemma":0.0012534987,"teacher_disagreement_score":0.7154988,"about_ca_system_score_codex":0.00017639232,"about_ca_system_score_gemma":0.00018978088,"threshold_uncertainty_score":0.9999035},"labels":[],"label_agreement":null},{"id":"W2609208504","doi":"10.13140/rg.2.2.21409.20323","title":"Spaced Learning: The Design, Feasibility and Optimisation of SMART Spaces","year":2017,"lang":"en","type":"article","venue":"Research Portal (Queen's University Belfast)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Education and Early Childhood Development","funders":"Queen's University Belfast; Education Endowment Foundation; Queen's University; Wellcome Trust","keywords":"Computer science","score_opus":0.08928529249161991,"score_gpt":0.31783136218432817,"score_spread":0.22854606969270824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609208504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89613044,0.0000317872,0.07902933,0.012617302,0.00016069671,0.0013933361,0.0000120704,0.00015109286,0.010473947],"genre_scores_gemma":[0.9949315,0.00006842927,0.0010569145,0.000005927955,0.000026284868,0.0000017348116,0.0000018140564,0.000006953774,0.0039004001],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99711,0.0011186524,0.00015266903,0.0004777336,0.0007720751,0.00036883523],"domain_scores_gemma":[0.99717844,0.0007557677,0.0002721802,0.0009973,0.00061616756,0.00018012707],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002873067,0.00013266096,0.00022698521,0.00022266807,0.0015014843,0.00039169897,0.0012486551,0.00009706665,0.00004043944],"category_scores_gemma":[0.0008281447,0.00011655796,0.00007962895,0.00027900425,0.00064317696,0.0013322127,0.0010764574,0.0004899962,0.000020617212],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001400397,0.0012892585,0.7670924,0.0005649793,0.0007941117,0.0011541691,0.016293686,0.0005313513,0.0077924877,0.07969136,0.013882122,0.10951366],"study_design_scores_gemma":[0.0052952636,0.0025630007,0.8823512,0.00045254547,0.00008133277,0.00009531161,0.017312398,0.017615259,0.020343373,0.0035214783,0.049058802,0.0013099905],"about_ca_topic_score_codex":0.002413176,"about_ca_topic_score_gemma":0.00013958235,"teacher_disagreement_score":0.11525883,"about_ca_system_score_codex":0.00010110067,"about_ca_system_score_gemma":0.00028237986,"threshold_uncertainty_score":0.9997984},"labels":[],"label_agreement":null},{"id":"W2611590119","doi":"10.29011/2577-2260.100001","title":"Smartphone-Enabled Biotelemetric System for A Smart Contact Lens","year":2018,"lang":"en","type":"article","venue":"Biosensors and Bioelectronics Open Access","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Contact lens; Lens (geology); Computer science; Human–computer interaction; Embedded system; Optometry; Optics; Medicine; Ophthalmology; Physics","score_opus":0.07294473899450768,"score_gpt":0.3487635390069626,"score_spread":0.27581880001245496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611590119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8084339,0.0013916319,0.16609524,0.0037392261,0.0029117968,0.0064104493,0.00017807982,0.00078833057,0.010051357],"genre_scores_gemma":[0.9971225,0.00007767012,0.0014252828,0.00041473264,0.00024756638,0.0001391466,0.000012843382,0.000029171099,0.00053105986],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99729794,0.00016852941,0.00052486564,0.00094751455,0.0002771178,0.000784019],"domain_scores_gemma":[0.99789345,0.00031943354,0.0003664713,0.00072838104,0.0005128977,0.00017935093],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001125006,0.00032381242,0.0005625158,0.00041889749,0.0005703788,0.0041081836,0.0027951738,0.00017664803,0.000016199856],"category_scores_gemma":[0.00011367424,0.0002783745,0.000119626195,0.001413567,0.000078531244,0.0028717367,0.0013952669,0.00015991053,0.00006470666],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018673247,0.0012352149,0.01347381,0.0012876895,0.001494426,0.0000839913,0.0013130555,0.0000014439928,0.12748457,0.21748568,0.0502299,0.5840429],"study_design_scores_gemma":[0.011909108,0.0077011716,0.009764555,0.00060596725,0.00020292054,0.0006717461,0.0004899579,0.04412779,0.4181675,0.0019473899,0.5009479,0.0034639777],"about_ca_topic_score_codex":0.0009243539,"about_ca_topic_score_gemma":0.000765331,"teacher_disagreement_score":0.5805789,"about_ca_system_score_codex":0.00022402222,"about_ca_system_score_gemma":0.0003347427,"threshold_uncertainty_score":0.99996686},"labels":[],"label_agreement":null},{"id":"W2611615090","doi":"10.1002/cpe.4136","title":"A reflection on the origins, evolution, and future of PRAGMA","year":2017,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Centro Nacional de Investigaciones Cardiovasculares; Global Lake Ecological Observatory Network; National Institute of Advanced Industrial Science and Technology; National Science Foundation","keywords":"Reflection (computer programming); Context (archaeology); Set (abstract data type); Engineering ethics; Critical reflection; Computer science; Sociology; Political science; Data science; Epistemology; Engineering; History; Pedagogy; Philosophy; Archaeology","score_opus":0.047920238820822685,"score_gpt":0.352661256295408,"score_spread":0.3047410174745853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611615090","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64548033,0.0028799346,0.32751688,0.017676448,0.0014832659,0.00048133082,0.0000048221987,0.0000822423,0.0043947403],"genre_scores_gemma":[0.998587,0.0004018072,0.00062832434,0.00023449876,0.0000978821,0.000028373452,6.3691806e-7,0.0000026640523,0.00001877472],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99908614,0.0001532023,0.00018113885,0.0002997389,0.00017982727,0.000099948826],"domain_scores_gemma":[0.9985668,0.00050587114,0.0003890798,0.0002718412,0.00021345168,0.000052959094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036377975,0.00010044856,0.000119006065,0.00004648586,0.0007521141,0.0003878712,0.00019908184,0.000045308352,0.0000027948556],"category_scores_gemma":[0.00035101763,0.00007662642,0.000015344958,0.00009562007,0.00018967513,0.0020607684,0.00011441192,0.00012623888,0.0000029861585],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043016746,0.000072057235,0.0016192856,0.00003553129,0.000020740825,0.0000040276655,0.015531184,0.0000021081755,0.00041816733,0.27101666,0.0002745386,0.71096265],"study_design_scores_gemma":[0.0050585736,0.0028455278,0.17415135,0.0008987645,0.0001508901,0.0020319454,0.057076074,0.13417849,0.004882633,0.04632433,0.57036066,0.0020407913],"about_ca_topic_score_codex":0.00009842263,"about_ca_topic_score_gemma":0.000007096172,"teacher_disagreement_score":0.7089219,"about_ca_system_score_codex":0.000016522374,"about_ca_system_score_gemma":0.000051624662,"threshold_uncertainty_score":0.578473},"labels":[],"label_agreement":null},{"id":"W2611970523","doi":"","title":"SIMACT : a 3D open source smart home simulator for activity recognition with open database and visual editor","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Java; Field (mathematics); Set (abstract data type); Open source; Home automation; Human–computer interaction; Database; Software; Operating system","score_opus":0.061739937977830346,"score_gpt":0.32796509696409776,"score_spread":0.2662251589862674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611970523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18401583,0.00001973795,0.80999696,0.00039385032,0.0013315127,0.002767526,0.00011079013,0.00018972637,0.0011740637],"genre_scores_gemma":[0.9788464,0.0000019943147,0.01822813,0.00044824654,0.0012897175,0.00041161536,0.00004431575,0.000034348082,0.00069528434],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803853,0.00021258373,0.00024109222,0.0006848526,0.00031510912,0.00050780893],"domain_scores_gemma":[0.9977345,0.00092929427,0.00022034191,0.00051818456,0.00023337208,0.00036434067],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013329154,0.00026825618,0.00041624127,0.00011791412,0.00035657204,0.0015114242,0.0010861335,0.00009081696,0.00007849075],"category_scores_gemma":[0.00013820591,0.00022258818,0.00004428754,0.00029739816,0.00005969839,0.009685181,0.00206549,0.0001543992,0.00010750017],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009588103,0.0014910852,0.010652201,0.00016252254,0.00022318026,0.0000050179465,0.00090262585,0.000003980466,0.0032568276,0.0001424551,0.01590437,0.9662969],"study_design_scores_gemma":[0.026278747,0.004251707,0.027324643,0.0009375408,0.000332216,0.000457582,0.0010744821,0.33037457,0.03675316,0.0006435596,0.5662145,0.005357292],"about_ca_topic_score_codex":0.0007791585,"about_ca_topic_score_gemma":0.00030447522,"teacher_disagreement_score":0.96093965,"about_ca_system_score_codex":0.00007521211,"about_ca_system_score_gemma":0.00015620346,"threshold_uncertainty_score":0.9995251},"labels":[],"label_agreement":null},{"id":"W2612162343","doi":"","title":"Projeto de um ambiente 3D de visualização e reprodução de eventos capturados e interpretados a partir de ambientes físicos cientes de contexto para aplicações de preparação para emergência","year":2006,"lang":"pt","type":"dissertation","venue":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Computer science; Philosophy","score_opus":0.10049889973543674,"score_gpt":0.4300397752400259,"score_spread":0.32954087550458916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612162343","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7982965,0.0048281895,0.18544935,0.0014118501,0.0004729644,0.0071728253,0.0006545795,0.00080711785,0.0009066174],"genre_scores_gemma":[0.97682184,0.0006821739,0.006112879,0.00018317426,0.0001914239,0.008346221,0.00048078367,0.00013298287,0.0070484965],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98383045,0.001258407,0.0019576943,0.003981836,0.0035459304,0.005425695],"domain_scores_gemma":[0.9895763,0.0017455562,0.001400898,0.0014067043,0.004850706,0.001019834],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.02333467,0.0011703075,0.0012573812,0.006233837,0.003440103,0.0016545036,0.0042437795,0.0013603277,0.000033926728],"category_scores_gemma":[0.013861675,0.0012645284,0.00047279705,0.00848414,0.003091615,0.0019761766,0.0010950419,0.0022748017,0.000028116761],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0017181048,0.0054902537,0.15854488,0.0028382356,0.0009995535,0.0004712512,0.036085844,0.000074069605,0.46699682,0.013790169,0.0154520385,0.2975388],"study_design_scores_gemma":[0.004810469,0.005703667,0.023680482,0.005036379,0.00037214218,0.0037163543,0.01239983,0.59520936,0.28262094,0.051256157,0.01070393,0.004490275],"about_ca_topic_score_codex":0.003110971,"about_ca_topic_score_gemma":0.0009342848,"teacher_disagreement_score":0.59513533,"about_ca_system_score_codex":0.005140866,"about_ca_system_score_gemma":0.017534941,"threshold_uncertainty_score":0.9999361},"labels":[],"label_agreement":null},{"id":"W2613828349","doi":"10.1007/978-3-319-58466-9_26","title":"Enhance the Use of Medical Wearables Through Meaningful Data Analytics","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Wearable computer; Analytics; Data science; Human–computer interaction; Embedded system","score_opus":0.16660033206744937,"score_gpt":0.3363034971850855,"score_spread":0.16970316511763614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613828349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000044474593,0.0005354386,0.99180186,0.0039253505,0.0019734455,0.00036567342,0.00003664207,0.00007181273,0.0012452856],"genre_scores_gemma":[0.59481156,0.0009664945,0.39542085,0.004863703,0.0019256589,0.000018773851,0.000033924764,0.00010943098,0.0018496277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936984,0.00012047006,0.0007840184,0.001788656,0.0030456109,0.00056280947],"domain_scores_gemma":[0.9891703,0.0025189542,0.00096031424,0.0067343726,0.00046204246,0.00015398144],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0026948163,0.00048485567,0.0007808705,0.00031313978,0.00044966635,0.0013626282,0.017349994,0.00043248176,0.000042168336],"category_scores_gemma":[0.0015072619,0.00035455966,0.00013683693,0.00033450796,0.0018693353,0.0020753166,0.0074361945,0.0011339871,0.00004280245],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053942044,0.000042568063,0.000060088714,0.000064351494,0.000056340763,0.00010157359,0.0011443417,0.0017028162,0.00004049561,0.008608498,0.00047421645,0.98769933],"study_design_scores_gemma":[0.00023918426,0.00009964099,0.00006687154,0.0019564752,0.000028356673,0.00020266845,5.0938297e-7,0.8947369,0.0010100346,0.03773663,0.063209936,0.0007128159],"about_ca_topic_score_codex":0.00039622094,"about_ca_topic_score_gemma":0.0013913774,"teacher_disagreement_score":0.9869865,"about_ca_system_score_codex":0.00012064941,"about_ca_system_score_gemma":0.0013169657,"threshold_uncertainty_score":0.9998906},"labels":[],"label_agreement":null},{"id":"W2618554223","doi":"10.1007/s40860-017-0041-2","title":"Complex behavioral pattern mining in non-intrusive sensor-based smart homes using an intelligent activity inference engine","year":2017,"lang":"en","type":"article","venue":"Journal of Reliable Intelligent Environments","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Inference engine; Home automation; Robustness (evolution); Inference; Smart environment; Reusability; Ambient intelligence; Activity recognition; Behavioral pattern; Human–computer interaction; Enhanced Data Rates for GSM Evolution; Software; Human behavior; Embedded system; Artificial intelligence; Software engineering; Telecommunications","score_opus":0.09841432985682014,"score_gpt":0.33512192740512264,"score_spread":0.2367075975483025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618554223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56948596,0.000022441378,0.42960116,0.00010326149,0.00055659335,0.00019123718,0.000006078893,0.0000103100865,0.000022964807],"genre_scores_gemma":[0.9887499,0.00007983334,0.010853994,0.00010555917,0.00011517404,0.000007970365,0.000004101731,0.000031262916,0.000052185173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969479,0.0002055083,0.00094144663,0.00051391375,0.0008569091,0.00053434796],"domain_scores_gemma":[0.99664825,0.00019123995,0.0016287368,0.0011002365,0.00010931867,0.00032220897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00093535736,0.00038568059,0.00069500174,0.00045849898,0.00029243296,0.0005180791,0.0016239034,0.00016026547,0.000101643716],"category_scores_gemma":[0.00011041541,0.000379782,0.00021763529,0.00013634424,0.00014088601,0.0020479718,0.00044766025,0.00055266026,0.000061154344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014910377,0.0028469749,0.45588145,0.000070230424,0.00014513133,0.000731558,0.002192861,0.03205704,0.036199473,0.0000042525294,0.00006691608,0.469655],"study_design_scores_gemma":[0.0024633976,0.0017936757,0.20105277,0.0014816992,0.00011872222,0.0003286559,0.0011320141,0.6336248,0.15302995,0.00011884325,0.003464748,0.0013907533],"about_ca_topic_score_codex":0.000714098,"about_ca_topic_score_gemma":0.00008004637,"teacher_disagreement_score":0.60156775,"about_ca_system_score_codex":0.00067316426,"about_ca_system_score_gemma":0.0001352973,"threshold_uncertainty_score":0.9998654},"labels":[],"label_agreement":null},{"id":"W2618842445","doi":"","title":"Proceedings of the 1st ACM workshop on Vision networks for behavior analysis","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Scope (computer science); Event (particle physics); Field (mathematics); Focus (optics); Pleasure; Virtual reality; Multimedia; Data science; Human–computer interaction","score_opus":0.051424693871077266,"score_gpt":0.29307929045703707,"score_spread":0.2416545965859598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618842445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58373445,0.00002309577,0.41089666,0.0011656949,0.0003712371,0.00091369613,0.0000039087563,0.00015330818,0.002737977],"genre_scores_gemma":[0.99515224,0.000005007376,0.0030369025,0.00018717443,0.000058280784,0.00009857596,0.0000010920743,0.0000067058495,0.0014540465],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989029,0.000016610493,0.00025967523,0.0003365691,0.0003023998,0.00018185444],"domain_scores_gemma":[0.9986532,0.0003164626,0.00018728276,0.00050370756,0.00028754227,0.000051828243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002590332,0.0001132284,0.00023931781,0.00015299501,0.00017617819,0.000071125134,0.0010502014,0.000077520446,0.000017849103],"category_scores_gemma":[0.000112353846,0.00007525586,0.00029555397,0.0013721753,0.000045616616,0.00035359827,0.00028706578,0.00009973151,0.000006351492],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016364269,0.0021280001,0.25686613,0.00007089447,0.0010463874,0.000007629904,0.0036894765,0.0015403163,0.0050763185,0.0117131565,0.061865848,0.65583223],"study_design_scores_gemma":[0.0019903944,0.0005696686,0.6108379,0.00026834535,0.00070776464,0.000077143355,0.00047040248,0.36026788,0.015754193,0.0005321559,0.007500211,0.0010239878],"about_ca_topic_score_codex":0.000023233457,"about_ca_topic_score_gemma":0.000042983014,"teacher_disagreement_score":0.6548082,"about_ca_system_score_codex":0.00003296889,"about_ca_system_score_gemma":0.000022524258,"threshold_uncertainty_score":0.3068845},"labels":[],"label_agreement":null},{"id":"W2621464581","doi":"10.3390/s17061287","title":"Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Inertial measurement unit; Activity recognition; Classifier (UML); Wearable computer; Pattern recognition (psychology); Support vector machine; Machine learning; Data mining","score_opus":0.2074829021052562,"score_gpt":0.38087394462685153,"score_spread":0.17339104252159535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621464581","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5118898,0.0000028940083,0.48301417,0.0013208187,0.00049250637,0.00072012073,0.00010607581,0.00031431846,0.0021392745],"genre_scores_gemma":[0.98461276,0.0000015882711,0.014327332,0.00012982766,0.0002932961,0.000060829734,0.00015009275,0.0000270669,0.00039718382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976431,0.00050306536,0.0002304119,0.0009718628,0.00033292908,0.00031859084],"domain_scores_gemma":[0.9957978,0.0007302715,0.00051884516,0.0025430124,0.0002820651,0.00012797306],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009985717,0.00021792793,0.00026357872,0.00014842105,0.0015707025,0.0011318462,0.0011662393,0.00017915777,0.000022323402],"category_scores_gemma":[0.0012306153,0.00022830829,0.00007372867,0.00010997669,0.000076611104,0.0019533022,0.00016349494,0.0004106391,0.00013544499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026061162,0.0007224958,0.0025442461,0.0001050889,0.00008420595,0.00001891117,0.0018896741,0.0008456381,0.022177996,0.0014773216,0.00038394038,0.9694899],"study_design_scores_gemma":[0.00073290925,0.0004231328,0.014077531,0.0002836787,0.00001878858,0.0000060857346,0.00018759079,0.9714066,0.007891347,0.002081297,0.0024472866,0.00044378068],"about_ca_topic_score_codex":0.00014462076,"about_ca_topic_score_gemma":0.00010467173,"teacher_disagreement_score":0.9705609,"about_ca_system_score_codex":0.00008593362,"about_ca_system_score_gemma":0.000065184904,"threshold_uncertainty_score":0.99990505},"labels":[],"label_agreement":null},{"id":"W2622156485","doi":"10.1152/japplphysiol.00299.2017","title":"Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models","year":2017,"lang":"en","type":"article","venue":"Journal of Applied Physiology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Institute for Aging; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministério da Ciência, Tecnologia e Inovação; Canada Research Chairs; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; AGE-WELL","keywords":"Wearable computer; Dynamics (music); Activities of daily living; Aerobic exercise; Computer science; Physical activity; Artificial intelligence; Machine learning; Physical medicine and rehabilitation; Psychology; Medicine; Physical therapy","score_opus":0.037393975276114245,"score_gpt":0.2484498809612511,"score_spread":0.21105590568513685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622156485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9241749,0.00008056963,0.07379005,0.000020850794,0.00044356278,0.00013457838,0.0000027148874,0.00004905105,0.0013037197],"genre_scores_gemma":[0.9930666,0.000021612565,0.0066154934,0.00000770827,0.00021827935,0.0000035352805,5.180902e-7,0.00002823856,0.000037985996],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979905,0.00021304075,0.00077180756,0.0003195686,0.00032959488,0.0003755056],"domain_scores_gemma":[0.9959143,0.0005439722,0.0026285443,0.00057961803,0.00023388614,0.00009970592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080879393,0.00024137301,0.0008235556,0.00026835853,0.0006625065,0.00020612158,0.0009775378,0.00014075654,0.000007378478],"category_scores_gemma":[0.00007758039,0.00023125773,0.00020125443,0.00011473653,0.00009485569,0.0014980282,0.00042650412,0.0006518508,0.0000032986889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016810218,0.000095185,0.00037853367,0.00038706645,0.00020693545,0.000027301172,0.0020708481,0.060448628,0.92911255,0.0015348182,0.0000011816053,0.005568834],"study_design_scores_gemma":[0.00091065356,0.0000927572,0.0031201285,0.0009182293,0.000047012905,0.00054813985,0.003028572,0.96810126,0.022548998,0.00036401782,0.0000067272736,0.0003135211],"about_ca_topic_score_codex":0.00014021211,"about_ca_topic_score_gemma":0.000014975859,"teacher_disagreement_score":0.9076526,"about_ca_system_score_codex":0.00024699754,"about_ca_system_score_gemma":0.00013580492,"threshold_uncertainty_score":0.9430417},"labels":[],"label_agreement":null},{"id":"W2622706983","doi":"10.3390/s17071486","title":"Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université du Québec à Chicoutimi","funders":"","keywords":"Bayesian network; Context (archaeology); Computer science; Dependency (UML); Domain (mathematical analysis); Process (computing); Machine learning; COPD; Data mining; Artificial intelligence; Discretization; Medicine; Mathematics","score_opus":0.05152361374359497,"score_gpt":0.2846989978691281,"score_spread":0.23317538412553312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622706983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10396787,0.00004919965,0.89237934,0.00076782337,0.0011998994,0.00062149076,0.0000411833,0.0001959778,0.0007772215],"genre_scores_gemma":[0.9594065,0.0000050791004,0.03916422,0.00019448463,0.00046041037,0.00003280068,0.0000045927172,0.000024538873,0.00070738187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835885,0.00009574623,0.00034019543,0.00050927117,0.0002678144,0.00042811918],"domain_scores_gemma":[0.9980782,0.00017317102,0.0003690536,0.0010539443,0.00019189922,0.00013371944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060647127,0.00018782531,0.00028682963,0.00006574484,0.0009782468,0.000442712,0.0006489486,0.00012611985,0.0000038742837],"category_scores_gemma":[0.00013424939,0.0001922537,0.00014139496,0.000075939846,0.000078281744,0.00069720304,0.00017971847,0.00013375038,0.000025444106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005047254,0.0005563231,0.014262311,0.00038478087,0.0007144294,0.00013401713,0.01575668,0.24638382,0.009498924,0.07209943,0.036518067,0.6031865],"study_design_scores_gemma":[0.00068888493,0.00004735316,0.00049531454,0.00010686698,0.000012963254,0.00004020936,0.000057302015,0.9877678,0.00019853325,0.0066767875,0.0037019413,0.00020605027],"about_ca_topic_score_codex":0.00012547098,"about_ca_topic_score_gemma":0.00022580849,"teacher_disagreement_score":0.85543865,"about_ca_system_score_codex":0.0001112391,"about_ca_system_score_gemma":0.00009872354,"threshold_uncertainty_score":0.7839879},"labels":[],"label_agreement":null},{"id":"W2623117841","doi":"10.1007/978-3-319-67585-5_45","title":"Hierarchical Task Recognition and Planning in Smart Homes with Partial Observability","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Observability; Computer science; Task (project management); Artificial intelligence; Machine learning; Simple (philosophy); Engineering","score_opus":0.05513953499689488,"score_gpt":0.26950721369370406,"score_spread":0.21436767869680917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623117841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014040537,0.00022094142,0.98237747,0.0006692353,0.00095642323,0.00056775665,0.000015292888,0.00012248334,0.0010298681],"genre_scores_gemma":[0.94389826,0.000016851052,0.05514946,0.00042827887,0.00036475444,0.000033039418,0.000012952227,0.000027789038,0.00006860646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961616,0.00013585904,0.0005066344,0.001787181,0.00080920913,0.00059956237],"domain_scores_gemma":[0.99688137,0.0010609998,0.00037341946,0.0012570182,0.00022367579,0.00020349956],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001675744,0.00048485614,0.0006862329,0.0006373562,0.00031505784,0.0009708837,0.0016654594,0.0003226773,0.000008134425],"category_scores_gemma":[0.00031451962,0.00042716676,0.00007249674,0.00023777923,0.00100742,0.0013378264,0.0009889209,0.0010543433,0.000021442718],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034806675,0.000038668008,0.006980143,0.0000675871,0.000011476858,0.00022219674,0.0015830568,0.0003821401,0.00004679977,0.00018785083,0.000004952578,0.9904403],"study_design_scores_gemma":[0.0037779135,0.0014315043,0.05558061,0.008853383,0.000046969646,0.0012660219,0.0000040834766,0.54932934,0.0017958087,0.36896905,0.0045125373,0.0044327765],"about_ca_topic_score_codex":0.00014265577,"about_ca_topic_score_gemma":0.00070094934,"teacher_disagreement_score":0.9860076,"about_ca_system_score_codex":0.00022221929,"about_ca_system_score_gemma":0.0005048068,"threshold_uncertainty_score":0.999818},"labels":[],"label_agreement":null},{"id":"W2627049180","doi":"10.2196/mhealth.5773","title":"Understanding Smart Home Sensor Data for Ageing in Place Through Everyday Household Routines: A Mixed Method Case Study","year":2017,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Aging in place; Computer science; Everyday life; Home automation; Human–computer interaction; Internet privacy; Psychology; Data science; Gerontology; Medicine; Telecommunications","score_opus":0.5018237286609528,"score_gpt":0.46058213804905995,"score_spread":0.041241590611892864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2627049180","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5208797,0.00014494157,0.47291657,0.0024431995,0.0008820602,0.0023232626,0.00014866219,0.00016316198,0.00009842689],"genre_scores_gemma":[0.98396003,0.00004576429,0.015059587,0.000475352,0.00017623673,0.000162127,0.000013196917,0.000031817854,0.000075881035],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962996,0.0006858549,0.00070508843,0.0011747818,0.0003351966,0.0007994738],"domain_scores_gemma":[0.995393,0.0012625821,0.00066007726,0.0022712687,0.00006559405,0.00034751993],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0041200966,0.0002910392,0.00066249765,0.00020685056,0.0014547462,0.0006098097,0.0010676065,0.0001235953,0.0000011100863],"category_scores_gemma":[0.00027884744,0.0002810818,0.00004791819,0.00024764743,0.000054162414,0.0019557634,0.00073407055,0.00035843425,0.0000032755092],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020262476,0.0064438363,0.39179677,0.015354029,0.0005488493,0.017880704,0.1905643,0.00015194128,0.0000881681,0.012157092,0.02074932,0.34223872],"study_design_scores_gemma":[0.052028105,0.005004405,0.35596076,0.0023186852,0.0004144875,0.01552903,0.13968286,0.3963233,0.000038696584,0.008814847,0.018936943,0.0049479124],"about_ca_topic_score_codex":0.008563708,"about_ca_topic_score_gemma":0.0118873045,"teacher_disagreement_score":0.46308032,"about_ca_system_score_codex":0.00036334916,"about_ca_system_score_gemma":0.000524319,"threshold_uncertainty_score":0.9999641},"labels":[],"label_agreement":null},{"id":"W272750617","doi":"10.1007/978-3-319-17957-5_14","title":"PHEN: Parkinson Helper Emergency Notification System Using Bayesian Belief Network","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in business information processing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Ontology; Context (archaeology); Bayesian network; Computer science; Parkinson's disease; Representation (politics); Disease; Artificial intelligence; Medicine","score_opus":0.03974464002549206,"score_gpt":0.2567701191237949,"score_spread":0.21702547909830283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W272750617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000036234538,0.0016834709,0.96798277,0.00036122158,0.0023630157,0.00078591984,0.000016638636,0.0005082788,0.026262432],"genre_scores_gemma":[0.96410364,0.0000794087,0.032312244,0.0003899224,0.001731773,0.00010669467,0.00043994488,0.00013101015,0.0007053417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960019,0.00008209831,0.0017078569,0.0006191808,0.0010373866,0.0005515787],"domain_scores_gemma":[0.99476826,0.00009977036,0.0019759075,0.00082934776,0.0021867864,0.00013990018],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000976817,0.0007000558,0.00077116303,0.0008009558,0.00039844288,0.0007719197,0.0009144649,0.00081076554,0.000046337464],"category_scores_gemma":[0.00023317638,0.00071379193,0.00012233343,0.001060158,0.00005920102,0.005622375,0.0002718781,0.00064333045,0.00016039702],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003905202,0.00002843498,0.00021224642,0.0028283158,0.000050969542,0.000011726552,0.0030371982,0.063148215,0.000023723169,0.007071142,0.0002831646,0.9232658],"study_design_scores_gemma":[0.0009854974,0.000037877347,0.00024195561,0.007885892,0.000105890635,0.00026612674,0.00007911845,0.81039834,0.00011445026,0.013723844,0.16382799,0.002333037],"about_ca_topic_score_codex":0.000052779676,"about_ca_topic_score_gemma":0.0000833438,"teacher_disagreement_score":0.9640674,"about_ca_system_score_codex":0.0009276083,"about_ca_system_score_gemma":0.00075612584,"threshold_uncertainty_score":0.9995313},"labels":[],"label_agreement":null},{"id":"W2728181887","doi":"10.1093/geroni/igx004.1062","title":"OLDER WOMEN’S PERCEPTION REGARDING A VIDEOMONITORING SYSTEM AT HOME","year":2017,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Context-Aware Activity Recognition Systems","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":"Centre for Interdisciplinary Research in Rehabilitation; Université de Montréal; Institut de Readaptation Gingras Lindsay de Montreal; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Toilet; Perception; Psychology; Applied psychology; Engineering","score_opus":0.03594170460323735,"score_gpt":0.29274178746559787,"score_spread":0.2568000828623605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2728181887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8653669,0.000008897909,0.12929335,0.00056190946,0.0019455885,0.00019745297,0.0000010125663,0.00027110873,0.0023538296],"genre_scores_gemma":[0.9980218,9.112015e-7,0.001037857,0.000073211144,0.00032583435,0.00014478258,0.0000021976016,0.000014975581,0.0003784556],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982337,0.0000839722,0.0005373559,0.00044865458,0.00035805083,0.0003382671],"domain_scores_gemma":[0.9982589,0.00007667717,0.0005179574,0.0008251718,0.00028230975,0.000039018556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014617548,0.00014720342,0.00022057991,0.0006204813,0.0006181053,0.0008346252,0.0006700137,0.00007480711,0.00001280801],"category_scores_gemma":[0.00014990712,0.00016423964,0.00003143949,0.0005895555,0.00003002055,0.0023430607,0.00043143128,0.00018260628,0.00012043479],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024102394,0.00008234487,0.4790775,0.00054182333,0.000060205613,0.000123176,0.038891494,0.00007883898,0.059026167,0.021601073,0.000504466,0.39998883],"study_design_scores_gemma":[0.0037521403,0.000055286917,0.9228419,0.0036035988,0.0000072108623,0.00032023,0.009967176,0.04518651,0.007528188,0.0009820152,0.004386516,0.0013692469],"about_ca_topic_score_codex":0.00013398001,"about_ca_topic_score_gemma":0.000019601805,"teacher_disagreement_score":0.4437644,"about_ca_system_score_codex":0.0018837361,"about_ca_system_score_gemma":0.000045154287,"threshold_uncertainty_score":0.804831},"labels":[],"label_agreement":null},{"id":"W2729651793","doi":"10.1093/geroni/igx004.1488","title":"LUDO: AN INTERVENTION SYSTEM TO DETER PERSONS WITH MILD DEMENTIA FROM INACTIVITY AND RESTLESSNESS","year":2017,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Montréal; Simon Fraser University; University of Alberta; Toronto Rehabilitation Institute","funders":"","keywords":"Intervention (counseling); Dementia; Psychology; Psychological intervention; Wearable computer; Human–computer interaction; Cognitive psychology; Applied psychology; Developmental psychology; Computer science; Medicine; Psychiatry","score_opus":0.05060158178808391,"score_gpt":0.3108844135976165,"score_spread":0.2602828318095326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729651793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6674511,0.0000089171135,0.33144554,0.00038766666,0.00022815602,0.00016594416,0.000004307473,0.00006868399,0.00023968056],"genre_scores_gemma":[0.9958797,2.3409436e-7,0.0038828182,0.000080927275,0.00006115932,0.000059802438,0.0000061155265,0.000009844783,0.000019410627],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987799,0.000119026925,0.00030054714,0.0004315126,0.00020805348,0.0001609599],"domain_scores_gemma":[0.9987959,0.000054635966,0.00032023707,0.00060171646,0.00018567758,0.000041791744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005518251,0.00011962882,0.00017606406,0.00035720156,0.00027729518,0.00079522794,0.000412516,0.000045128338,0.0000031677146],"category_scores_gemma":[0.00005361858,0.00011956482,0.000014635112,0.0003818615,0.000029218345,0.0022159477,0.00022180339,0.00012657254,0.00000825024],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004622881,0.00012207688,0.7358769,0.00011604929,0.00006508721,0.000033333083,0.006181975,0.000017216125,0.005900963,0.0052623283,0.00004681519,0.24633102],"study_design_scores_gemma":[0.001169706,0.00008276847,0.97406787,0.0011986687,0.000011339164,0.00001909761,0.0017835788,0.016348293,0.004484953,0.00022592006,0.0002754498,0.00033236295],"about_ca_topic_score_codex":0.0026734108,"about_ca_topic_score_gemma":0.0030059929,"teacher_disagreement_score":0.3284286,"about_ca_system_score_codex":0.00011268279,"about_ca_system_score_gemma":0.000024869161,"threshold_uncertainty_score":0.7668401},"labels":[],"label_agreement":null},{"id":"W2730170243","doi":"","title":"A modification of Wu and Palmer Semantic Similarity Measure","year":2016,"lang":"en","type":"article","venue":"Ubiquitous Computing Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Semantic similarity; Measure (data warehouse); Computer science; Natural language processing; Similarity (geometry); Information retrieval; Artificial intelligence; Data mining","score_opus":0.039027362019887206,"score_gpt":0.26030944484797613,"score_spread":0.22128208282808892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2730170243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2714405,0.0004090476,0.7255164,0.00043970128,0.00094181276,0.00045243208,0.000007092931,0.00027982882,0.0005132177],"genre_scores_gemma":[0.9988501,0.00000619758,0.0007647111,0.000035400757,0.0001964849,0.000012797545,8.953053e-7,0.000016722548,0.00011668418],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975754,0.00041072702,0.0006160394,0.00058509986,0.00049244956,0.0003202762],"domain_scores_gemma":[0.99764115,0.0006239779,0.0004819308,0.0007476539,0.00038824952,0.00011705493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012525888,0.00020875329,0.00045347464,0.00018114565,0.0001434502,0.0001530751,0.0005521546,0.00012659225,0.0000016585959],"category_scores_gemma":[0.00017142216,0.00015865143,0.00007966184,0.00030988647,0.00007687879,0.0003644092,0.0002481992,0.00010194805,0.000031319676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055225308,0.0005161976,0.053664528,0.0018567377,0.00044534256,0.00005082395,0.009600951,0.0003251464,0.19602442,0.03834857,0.0028467588,0.6962653],"study_design_scores_gemma":[0.0073295226,0.0009469009,0.06700331,0.010505557,0.00017303092,0.0019532398,0.0012115128,0.856937,0.038071234,0.0032097825,0.009301606,0.003357334],"about_ca_topic_score_codex":0.00026414485,"about_ca_topic_score_gemma":0.00001851658,"teacher_disagreement_score":0.85661185,"about_ca_system_score_codex":0.000080054146,"about_ca_system_score_gemma":0.000075487646,"threshold_uncertainty_score":0.64696175},"labels":[],"label_agreement":null},{"id":"W2732650755","doi":"10.1080/17483107.2017.1344887","title":"Integration of the social environment in a mobility ontology for people with motor disabilities","year":2017,"lang":"en","type":"article","venue":"Disability and Rehabilitation Assistive Technology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation; Université Laval","funders":"Canadian Institutes of Health Research","keywords":"Ontology; Perspective (graphical); Process (computing); Computer science; Geospatial analysis; Social relation; Human–computer interaction; Relation (database); Data science; Knowledge management; Psychology; Artificial intelligence; Social psychology; Epistemology","score_opus":0.018416270778490583,"score_gpt":0.2800521088109693,"score_spread":0.26163583803247875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732650755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9391561,0.0000141991295,0.041429263,0.017750291,0.00009934095,0.0013543913,0.00004311191,0.000049534825,0.00010375522],"genre_scores_gemma":[0.9941685,0.000001327472,0.0049437652,0.000013149394,0.000011320706,0.00083398697,0.0000022041907,0.0000053698705,0.000020369482],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984774,0.00024047462,0.00039745597,0.00054315425,0.00015612874,0.0001853773],"domain_scores_gemma":[0.99718267,0.0013953428,0.0003510057,0.00090497895,0.00014179994,0.000024187473],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006971159,0.000150354,0.0003892471,0.00007739213,0.00045006615,0.000046897057,0.0005502539,0.0001871348,0.0000053319886],"category_scores_gemma":[0.002393359,0.00010529827,0.000103950275,0.00014572754,0.0031878916,0.0003867736,0.00025304905,0.00016767204,8.7286264e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001144872,0.00052222086,0.93058324,0.00013222243,0.000016315174,5.1023292e-8,0.0027961365,0.0000022711683,0.0010632221,0.020253092,0.000006593588,0.04451015],"study_design_scores_gemma":[0.0006581186,0.00041469812,0.9786075,0.00003867756,0.000008793866,0.000002334289,0.0037904964,0.0008947462,0.00036033653,0.015043608,0.000066169276,0.00011454408],"about_ca_topic_score_codex":0.00030835829,"about_ca_topic_score_gemma":0.0033797282,"teacher_disagreement_score":0.055012394,"about_ca_system_score_codex":0.00022673361,"about_ca_system_score_gemma":0.000051551695,"threshold_uncertainty_score":0.99952483},"labels":[],"label_agreement":null},{"id":"W2733439154","doi":"10.1371/journal.pone.0180318","title":"Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Simon Fraser University; Mitacs; Canada Research Chairs","keywords":"Accelerometer; Poison control; Fall prevention; Medicine; Injury prevention; Physical medicine and rehabilitation; Support vector machine; Falling (accident); Artificial intelligence; Waist; Accidental fall; Young adult; Suicide prevention; Wearable computer; Machine learning; Computer science; Medical emergency; Gerontology; Body mass index; Surgery; Internal medicine","score_opus":0.10187431875685612,"score_gpt":0.2945495375058662,"score_spread":0.19267521874901009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2733439154","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8974633,0.000013441844,0.10140901,0.000050788123,0.00012011749,0.00044715757,0.00012868228,0.00006622605,0.00030126647],"genre_scores_gemma":[0.99437755,0.000005080314,0.0054717874,0.000010835313,0.000057771576,0.000017407021,0.000030782532,0.000012703705,0.00001607391],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984016,0.00014291002,0.00045291503,0.0003752872,0.0004575823,0.0001697159],"domain_scores_gemma":[0.99717766,0.0002947739,0.0010135905,0.0011445286,0.0002929169,0.000076545446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005573463,0.00014661366,0.00040180684,0.00023340287,0.00019762128,0.0001946688,0.0005791816,0.000069035086,0.0000014574744],"category_scores_gemma":[0.00024430282,0.00015630956,0.000049882172,0.00016005765,0.00006308691,0.0012385843,0.00024097535,0.00010079026,0.000009051783],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000878878,0.0009822557,0.06552957,0.0026470667,0.00029510376,0.00001248556,0.00048164086,0.00005544881,0.911712,0.00011716584,0.000029133102,0.018050265],"study_design_scores_gemma":[0.0010422055,0.00012975735,0.033733632,0.0017092638,0.00012194726,0.000006610185,0.000039037055,0.14393626,0.81896925,0.000033461132,0.000021371845,0.0002572109],"about_ca_topic_score_codex":0.006986666,"about_ca_topic_score_gemma":0.0021291329,"teacher_disagreement_score":0.14388081,"about_ca_system_score_codex":0.000092498754,"about_ca_system_score_gemma":0.00008273607,"threshold_uncertainty_score":0.9996259},"labels":[],"label_agreement":null},{"id":"W2733477507","doi":"","title":"Context Aware Mobile Personal Navigation Services Using Multi-Level Sensor Fusion","year":2011,"lang":"en","type":"article","venue":"Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Computer science; Accelerometer; Sensor fusion; Mobile device; Gyroscope; Artificial intelligence; Context awareness; Global Positioning System; Navigation system; Real-time computing; Computer vision; Human–computer interaction; Engineering; World Wide Web; Telecommunications","score_opus":0.06886158684359545,"score_gpt":0.2885912259329348,"score_spread":0.21972963908933935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2733477507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922928,0.000114671886,0.0035001936,0.00046103148,0.001989071,0.00094672234,0.00008888611,0.00006548594,0.0005411215],"genre_scores_gemma":[0.9890924,0.000022309172,0.0105976565,0.000050999213,0.000059754857,0.000021049143,0.0000068785484,0.000021353693,0.00012760963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996351,0.00009718593,0.001361897,0.00046931827,0.0015023658,0.00021825562],"domain_scores_gemma":[0.9938019,0.00016607558,0.003250413,0.00050920225,0.0022073155,0.00006507367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014330986,0.0002909295,0.00046253423,0.00017596598,0.00026960275,0.000051841474,0.003319431,0.00021209127,0.000015526857],"category_scores_gemma":[0.00033323432,0.00018380811,0.0005333963,0.0005794387,0.0005161557,0.00106679,0.0017572697,0.00037572428,0.000005188791],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021875158,0.0008186402,0.042258613,0.0009805105,0.00019496804,5.5732926e-7,0.004244114,0.0002767844,0.9071135,0.0055628796,0.000047202357,0.0382835],"study_design_scores_gemma":[0.0010535737,0.00013578597,0.06020762,0.0087512275,0.00008601252,0.00006528331,0.00070335134,0.023163082,0.90221655,0.0026509238,0.0006151684,0.00035142957],"about_ca_topic_score_codex":0.00053366023,"about_ca_topic_score_gemma":0.000013619153,"teacher_disagreement_score":0.03793207,"about_ca_system_score_codex":0.0001447653,"about_ca_system_score_gemma":0.0000949279,"threshold_uncertainty_score":0.7495477},"labels":[],"label_agreement":null},{"id":"W2738730522","doi":"10.5815/ijmecs.2017.07.01","title":"A Classification Framework for Context-aware Mobile Learning Systems","year":2017,"lang":"en","type":"article","venue":"International Journal of Modern Education and Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Context (archaeology); Field (mathematics); Mobile device; Context awareness; Architecture; Mobile computing; Layer (electronics); Multimedia; Human–computer interaction; Ubiquitous computing; Data science; Artificial intelligence; World Wide Web; Telecommunications","score_opus":0.046611180291229716,"score_gpt":0.35192444014556445,"score_spread":0.3053132598543347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738730522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04204557,0.0003095765,0.9478845,0.0017884183,0.007607165,0.00021645467,0.000002315728,0.000027928312,0.00011808239],"genre_scores_gemma":[0.9759356,0.00003735376,0.022821177,0.00024628377,0.000810795,0.000039957704,0.000001118778,0.0000067907313,0.0001009646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982852,0.00007102527,0.000437648,0.00034623212,0.00068737747,0.00017253222],"domain_scores_gemma":[0.9955541,0.00028042664,0.0011469318,0.00039765323,0.0024586497,0.00016223146],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001317348,0.000121493635,0.0001927994,0.0003304543,0.00058084144,0.0026828477,0.002404625,0.00005786004,0.0000021854019],"category_scores_gemma":[0.00034237606,0.00011320556,0.00008120399,0.00009204287,0.00019327745,0.002714003,0.0002738256,0.00020284986,0.000005275479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001290862,0.0001356948,0.0024831377,0.000010450042,0.000029486593,0.0000017319336,0.0015981735,0.00014936135,0.00031598654,0.056622516,0.00016661664,0.93847394],"study_design_scores_gemma":[0.00064604013,0.000255354,0.020155145,0.0004956253,0.000011598164,0.00061819167,0.00061042677,0.9417397,0.00030609025,0.021087026,0.013798075,0.00027674585],"about_ca_topic_score_codex":0.00002183393,"about_ca_topic_score_gemma":0.0000025118427,"teacher_disagreement_score":0.9415903,"about_ca_system_score_codex":0.00015475352,"about_ca_system_score_gemma":0.00078047137,"threshold_uncertainty_score":0.99835247},"labels":[],"label_agreement":null},{"id":"W2738942185","doi":"10.1109/memea.2017.7985846","title":"Home automation serving a healthier lifestyle","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Ottawa","funders":"","keywords":"Overweight; Physical activity; Obesity; Sedentary lifestyle; Computer science; Medicine; Physical therapy; Gerontology; Applied psychology; Psychology","score_opus":0.03704808797892694,"score_gpt":0.2973666534196182,"score_spread":0.26031856544069126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738942185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12759343,0.000030299529,0.8205479,0.012503895,0.0014521439,0.00028925814,0.0000018061829,0.0008577431,0.036723554],"genre_scores_gemma":[0.9899879,0.000002387043,0.007367129,0.0005973566,0.00011742603,0.000018269959,7.6396424e-7,0.000005932486,0.0019028134],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990782,0.000065220695,0.00017574697,0.00026758178,0.00021908121,0.00019417169],"domain_scores_gemma":[0.99846506,0.00008258375,0.00019286454,0.0010571682,0.000104034865,0.000098302815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004482358,0.00008419255,0.00013131586,0.00007036,0.00050791074,0.00080363976,0.0009298934,0.00005173818,0.00005243821],"category_scores_gemma":[0.000101704034,0.00007877702,0.000043970944,0.000066967084,0.000019905938,0.001999165,0.0003533115,0.00008246258,0.00072554324],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053244785,0.00013418491,0.03421385,0.000118641474,0.000043280626,0.00003042904,0.0022639125,0.000005822341,0.0011223736,0.03554864,0.009651871,0.91686165],"study_design_scores_gemma":[0.0014766108,0.00014760141,0.60464454,0.00023942141,0.000008513931,0.000202009,0.00012578916,0.3403968,0.0020559123,0.008992858,0.040852822,0.00085714634],"about_ca_topic_score_codex":0.000501482,"about_ca_topic_score_gemma":0.00023913183,"teacher_disagreement_score":0.91600454,"about_ca_system_score_codex":0.00004495474,"about_ca_system_score_gemma":0.0000756877,"threshold_uncertainty_score":0.93256325},"labels":[],"label_agreement":null},{"id":"W2739198354","doi":"","title":"iAid: Ultrasonic and GPS controlled Joystick Mobility Device","year":2017,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; McGill University","funders":"","keywords":"Joystick; Global Positioning System; Ultrasonic sensor; Computer science; Acoustics; Simulation; Telecommunications; Physics","score_opus":0.0646815357712738,"score_gpt":0.3548465732018542,"score_spread":0.2901650374305804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739198354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99137235,0.00011510259,0.0008676097,0.0013905129,0.0007368258,0.00078469753,0.000006645202,0.00012125069,0.0046049883],"genre_scores_gemma":[0.9968043,0.0000033128872,0.0024616884,0.00029885056,0.00007531542,0.00013208132,7.745265e-7,0.000010808722,0.00021288353],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.996102,0.0005024163,0.0005098292,0.0014374066,0.0006775883,0.00077077525],"domain_scores_gemma":[0.9956561,0.0011672475,0.000691392,0.0012923056,0.00060277304,0.0005901872],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.0031640513,0.00034946649,0.00069957116,0.00021131027,0.0021445148,0.0011069685,0.0027966136,0.00015207607,0.000042852014],"category_scores_gemma":[0.0049806694,0.00030576685,0.00009727542,0.0005049558,0.0078172665,0.0031688644,0.0012169415,0.00037121517,0.00014462072],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011824853,0.00049330445,0.20438501,0.0000617365,0.00011432156,0.00042635543,0.0031622492,0.0000074975715,0.77322674,0.006582485,0.00007597071,0.011346063],"study_design_scores_gemma":[0.004064017,0.00095080986,0.81474376,0.00015692035,0.000039193405,0.001812667,0.00031269543,0.015875202,0.13334481,0.027633496,0.00013590175,0.00093054026],"about_ca_topic_score_codex":0.00048235638,"about_ca_topic_score_gemma":0.000027907938,"teacher_disagreement_score":0.63988197,"about_ca_system_score_codex":0.00015876952,"about_ca_system_score_gemma":0.0007795835,"threshold_uncertainty_score":0.99993944},"labels":[],"label_agreement":null},{"id":"W2741203640","doi":"10.1109/icc.2017.7996993","title":"Wireless noise prevention for mobile agents in smart home","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Toronto; University of Guelph","funders":"","keywords":"Wireless sensor network; Computer science; Wireless; Home automation; Computer network; Wi-Fi array; Key distribution in wireless sensor networks; Noise (video); Wireless network; Interference (communication); Fixed wireless; Real-time computing; Embedded system; Telecommunications; Artificial intelligence; Channel (broadcasting)","score_opus":0.0750133790096574,"score_gpt":0.3381149378692012,"score_spread":0.2631015588595438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741203640","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.579354,0.0000109881785,0.41548747,0.0002394838,0.0007082447,0.0008430099,0.0000041564454,0.00009358538,0.0032590695],"genre_scores_gemma":[0.9937187,0.0000030690499,0.0015807963,0.00005679491,0.00004569445,0.00037751914,0.0000027865158,0.0000068036898,0.0042078774],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991103,0.0000446415,0.00019421222,0.00031336746,0.00014695109,0.0001905725],"domain_scores_gemma":[0.99894804,0.00007314461,0.00014765438,0.0007019061,0.0000752457,0.00005399936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038378828,0.00008849388,0.00015812305,0.0000843024,0.00016538514,0.00034711492,0.00075752527,0.000052066625,0.000024482351],"category_scores_gemma":[0.000032958334,0.000086533895,0.00007756045,0.000057963818,0.000019837275,0.0011402164,0.00021061773,0.000050903196,0.00012936423],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021048509,0.00037986552,0.03884823,0.00007389325,0.000026987818,0.000010242865,0.00055210106,0.0000065854897,0.0014946343,0.0027892515,0.0024460237,0.95335114],"study_design_scores_gemma":[0.009417624,0.00085414376,0.65009713,0.00059047865,0.000023884017,0.000053450243,0.00027519878,0.24462222,0.020630347,0.017433532,0.05439106,0.0016109196],"about_ca_topic_score_codex":0.00019136039,"about_ca_topic_score_gemma":0.000612214,"teacher_disagreement_score":0.9517402,"about_ca_system_score_codex":0.000047735266,"about_ca_system_score_gemma":0.000042329477,"threshold_uncertainty_score":0.35287496},"labels":[],"label_agreement":null},{"id":"W2742051770","doi":"10.3390/app7080771","title":"A Biomechanical Study for Developing Wearable-Sensor System to Prevent Hip Fractures among Seniors","year":2017,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Shanxi Scholarship Council of China","keywords":"Wearable computer; Physical medicine and rehabilitation; Medicine; Balance (ability); Physical therapy; Computer science; Embedded system","score_opus":0.05534445412563707,"score_gpt":0.3275364579127513,"score_spread":0.2721920037871143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742051770","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6323953,0.000011158589,0.36228156,0.0007178401,0.0007866281,0.0022271278,0.0000047136696,0.00022525367,0.0013504251],"genre_scores_gemma":[0.9808625,3.7836676e-7,0.018150296,0.00013882497,0.00012980518,0.0005924836,2.7780092e-7,0.000010298845,0.00011516338],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99736875,0.00008768312,0.00038342312,0.00097614276,0.00067608804,0.0005078846],"domain_scores_gemma":[0.99809843,0.00036701208,0.0003510305,0.0008909533,0.00012071734,0.00017186604],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00216454,0.00022134869,0.00035929278,0.00022548404,0.0018261783,0.0015919474,0.0025517195,0.00007600005,0.0000030572],"category_scores_gemma":[0.00017250888,0.00018044794,0.000076378914,0.0003859933,0.00014297833,0.00065661303,0.0006349154,0.00010034397,0.00007730373],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022961799,0.0024678148,0.020122001,0.00078024156,0.0006484203,0.00013616026,0.033993743,0.0006735206,0.082730524,0.14467911,0.0041711894,0.70936763],"study_design_scores_gemma":[0.010234088,0.0054082805,0.19116938,0.003334168,0.00028545794,0.00029323917,0.07961383,0.15785882,0.5104519,0.014211156,0.017506763,0.009632935],"about_ca_topic_score_codex":0.0002493859,"about_ca_topic_score_gemma":0.00023988748,"teacher_disagreement_score":0.6997347,"about_ca_system_score_codex":0.00010879716,"about_ca_system_score_gemma":0.00015933727,"threshold_uncertainty_score":0.99947333},"labels":[],"label_agreement":null},{"id":"W2742186290","doi":"10.1007/978-3-319-62072-5_8","title":"A Security Threat Analysis of Smart Home Network with Vulnerable Dynamic Agents","year":2017,"lang":"en","type":"book-chapter","venue":"International series on computer entertainment and media technology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Computer security; Computer science","score_opus":0.01187068298804616,"score_gpt":0.23614206207238214,"score_spread":0.22427137908433598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742186290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10474366,0.0025900356,0.6417195,0.028182367,0.024225768,0.0048108497,0.0013915636,0.0026547096,0.18968152],"genre_scores_gemma":[0.97681254,0.00069525494,0.0027261935,0.00030405252,0.00023061229,0.000079578844,0.00023160205,0.0000437468,0.01887639],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99784166,0.000034879165,0.0004679686,0.0007804573,0.00058883394,0.0002862301],"domain_scores_gemma":[0.9978764,0.00018588407,0.00069723837,0.0009078861,0.00024335466,0.00008923611],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021421023,0.00042174544,0.00084794965,0.0010542019,0.000154768,0.00015482609,0.001396374,0.00034314007,0.000076194156],"category_scores_gemma":[0.000013060792,0.00037448463,0.00019291328,0.000110918496,0.00030408223,0.0003373331,0.00082190067,0.00045771745,0.000017471273],"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":[0.00082035444,0.00053075043,0.018172706,0.00017687054,0.024320079,0.0008526591,0.002200915,0.00047085132,0.000028322485,0.5723863,0.003711148,0.37632903],"study_design_scores_gemma":[0.0068941787,0.0058609615,0.010998784,0.0046165935,0.0027085533,0.0008262753,0.00015917343,0.14273372,0.000425485,0.20705217,0.61347336,0.0042507225],"about_ca_topic_score_codex":0.000026736961,"about_ca_topic_score_gemma":0.00088433636,"teacher_disagreement_score":0.8720689,"about_ca_system_score_codex":0.0001585166,"about_ca_system_score_gemma":0.00004638297,"threshold_uncertainty_score":0.9998707},"labels":[],"label_agreement":null},{"id":"W2743199055","doi":"10.2196/mhealth.7521","title":"Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation","year":2017,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Convolutional neural network; Support vector machine; Feature (linguistics); Field (mathematics); Digital signal processing; Feature extraction; Random forest","score_opus":0.17706444959119177,"score_gpt":0.45740876716087486,"score_spread":0.2803443175696831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743199055","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6255189,0.00032397016,0.35778055,0.013332721,0.00020855205,0.0019532326,0.00008142367,0.00016181717,0.0006388189],"genre_scores_gemma":[0.8970415,0.0000648787,0.10256321,0.000110588066,0.000070837654,0.000065988286,0.000036202327,0.000010589209,0.00003618116],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99785894,0.00044223233,0.00027723936,0.0006699922,0.00050051097,0.00025110663],"domain_scores_gemma":[0.99670774,0.00018184066,0.0008222044,0.0018262404,0.00024013183,0.000221817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003214833,0.00016982797,0.00032584326,0.00015372873,0.0005876463,0.00017540611,0.0007422039,0.00011723229,0.0000015383224],"category_scores_gemma":[0.00019339318,0.00013733326,0.000012255201,0.00012829987,0.00008224982,0.0012813341,0.0005194454,0.00023254033,0.0000011428291],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012409924,0.000080408085,0.0040184744,0.00039591364,0.000010102912,8.5160843e-7,0.00044930459,1.0320672e-7,0.001170891,0.0002062993,0.00046238414,0.99308115],"study_design_scores_gemma":[0.002239104,0.00051963824,0.88712543,0.000310185,0.000049389793,0.00010017804,0.00007317968,0.095328346,0.003343312,0.00018313788,0.01037179,0.00035628898],"about_ca_topic_score_codex":0.00043469074,"about_ca_topic_score_gemma":0.0008888789,"teacher_disagreement_score":0.9927249,"about_ca_system_score_codex":0.000091795584,"about_ca_system_score_gemma":0.00065926043,"threshold_uncertainty_score":0.5600288},"labels":[],"label_agreement":null},{"id":"W2743750036","doi":"10.1007/978-3-319-66188-9_9","title":"Activity Model for Interactive Micro Context-Aware Well-Being Applications Based on ContextAA","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Ontic; Computer science; Semantics (computer science); Context (archaeology); Adaptation (eye); Representation (politics); Human–computer interaction; Smart environment; Ubiquitous computing; Ambient intelligence; Programming language; World Wide Web; Internet of Things","score_opus":0.03145519169797074,"score_gpt":0.28741631524644895,"score_spread":0.2559611235484782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743750036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000029853087,0.000053538755,0.988307,0.0015228178,0.0011450967,0.0026483112,0.00008397963,0.00027758794,0.005931787],"genre_scores_gemma":[0.9370964,0.0000062271133,0.057843372,0.0026763224,0.00045554494,0.0004417457,0.00001963204,0.00007920235,0.0013815454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947722,0.000081036545,0.0006464353,0.002666641,0.0009752362,0.0008584555],"domain_scores_gemma":[0.9921504,0.0027307293,0.0010501881,0.0028913654,0.0008932208,0.00028405088],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001116437,0.0009044791,0.0010591195,0.0010810723,0.0010006552,0.0013574054,0.0046268012,0.00052658294,0.000013333809],"category_scores_gemma":[0.00016210003,0.0009096,0.0004325237,0.00022805603,0.0007602674,0.0014585853,0.0009663309,0.0012043088,0.000101594764],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067389185,0.00011880116,0.000017012339,0.00008722702,0.000033368415,0.000016511216,0.0006296415,0.015561354,0.00031233748,0.003845099,0.0000672389,0.979244],"study_design_scores_gemma":[0.00083559105,0.00017639423,0.000026055914,0.000836492,0.000018867422,0.000024867644,5.2662574e-7,0.9488023,0.003953601,0.04049513,0.003914277,0.0009158991],"about_ca_topic_score_codex":0.00005823042,"about_ca_topic_score_gemma":0.00035964145,"teacher_disagreement_score":0.9783281,"about_ca_system_score_codex":0.00084172765,"about_ca_system_score_gemma":0.0012238674,"threshold_uncertainty_score":0.99967927},"labels":[],"label_agreement":null},{"id":"W2746124659","doi":"10.1108/ijpcc-11-2016-0056","title":"Contextual case-based reasoning applied to a mobile device","year":2017,"lang":"en","type":"article","venue":"International Journal of Pervasive Computing and Communications","topic":"Context-Aware Activity Recognition Systems","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Flexibility (engineering); Case-based reasoning; Relevance (law); Mobile device; Context (archaeology); Ontology; Human–computer interaction; Artificial intelligence; Similarity (geometry); Information retrieval; Data mining; World Wide Web","score_opus":0.05929733671765941,"score_gpt":0.3637534363556548,"score_spread":0.3044560996379954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2746124659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30477515,0.00043258184,0.683015,0.007462906,0.00079475803,0.00032971808,0.00001388529,0.000060148202,0.0031158403],"genre_scores_gemma":[0.95273125,0.000019033292,0.046404295,0.00064837706,0.00015171328,0.000017283273,0.0000019815368,0.0000080069685,0.000018036675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876714,0.00013403916,0.00045382814,0.00018157261,0.0003270962,0.00013630057],"domain_scores_gemma":[0.9958435,0.00077374105,0.0007943498,0.0010491317,0.0013937266,0.00014553576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072758563,0.000121125064,0.0002265328,0.0002069828,0.0007525073,0.0008720312,0.003308583,0.000042032596,0.0000045554943],"category_scores_gemma":[0.00044285247,0.00012092256,0.000095758725,0.00007570709,0.00010685711,0.0004326956,0.0011853882,0.00027009498,0.0000146787515],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057784277,0.00031629985,0.0044607525,0.00001330922,0.00034233055,0.00042800032,0.005910574,0.0007588266,0.0016516844,0.018096508,0.00065458886,0.96730936],"study_design_scores_gemma":[0.014706411,0.001997107,0.05080954,0.005129675,0.00029040387,0.05286467,0.01734916,0.60939735,0.005846911,0.00394296,0.23470248,0.0029633278],"about_ca_topic_score_codex":0.0001511903,"about_ca_topic_score_gemma":0.00013662464,"teacher_disagreement_score":0.964346,"about_ca_system_score_codex":0.000086586675,"about_ca_system_score_gemma":0.00016406772,"threshold_uncertainty_score":0.8409017},"labels":[],"label_agreement":null},{"id":"W2748814859","doi":"10.1109/chase.2017.87","title":"Human Activity Recognition from Sensor-Based Large-Scale Continuous Monitoring of Parkinson’s Disease Patients","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal General Hospital","funders":"","keywords":"Sitting; Physical medicine and rehabilitation; Medicine; Smartphone app; Activity recognition; Parkinson's disease; Activity monitor; Gait; Profiling (computer programming); Computer science; Artificial intelligence; Physical therapy; Disease; Physical activity; Internal medicine; Human–computer interaction; Pathology","score_opus":0.041406361028431385,"score_gpt":0.2807145049670168,"score_spread":0.2393081439385854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748814859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9282358,0.0000072107155,0.069117166,0.0001513463,0.00077314995,0.0003515419,0.00020954912,0.00018336331,0.00097084243],"genre_scores_gemma":[0.99653727,0.000001247602,0.0030169475,0.00004251525,0.00016198936,0.00004401082,0.000024010707,0.000016812346,0.00015519102],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998109,0.00019024665,0.0003194943,0.0005904067,0.00047894634,0.0003119155],"domain_scores_gemma":[0.99730486,0.00015570049,0.00060003565,0.0013350573,0.0003762235,0.00022809904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027390427,0.00021055019,0.00035345822,0.00010374945,0.00051221653,0.0003622785,0.0007852803,0.00008817476,0.000057296238],"category_scores_gemma":[0.00016974463,0.00021414195,0.00016495251,0.00007204276,0.000061872044,0.0012510521,0.0002701577,0.00014788922,0.00008361042],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035564033,0.0005776822,0.8990918,0.00002371498,0.000027064456,0.0000082810975,0.00017988273,0.0000012133746,0.0021907776,0.000005681626,0.000112936286,0.09774541],"study_design_scores_gemma":[0.001635085,0.00006851747,0.97622514,0.00015629272,0.000021710153,2.2241227e-7,0.000057862817,0.0022353595,0.018388923,0.0002651263,0.0006542932,0.0002914694],"about_ca_topic_score_codex":0.0013087769,"about_ca_topic_score_gemma":0.00012227653,"teacher_disagreement_score":0.097453944,"about_ca_system_score_codex":0.000072084295,"about_ca_system_score_gemma":0.000058920163,"threshold_uncertainty_score":0.87324554},"labels":[],"label_agreement":null},{"id":"W2750895665","doi":"","title":"Mitigating sensor differences for phone-based human activity recognition","year":2016,"lang":"en","type":"article","venue":"IEEE Conference Proceedings","topic":"Context-Aware Activity Recognition Systems","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":"Western University","funders":"","keywords":"Activity recognition; Computer science; Phone; Preprocessor; Smart phone; Outlier; Artificial intelligence; Machine learning; Telecommunications","score_opus":0.09632250980745156,"score_gpt":0.2883333155480427,"score_spread":0.19201080574059115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750895665","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60704815,0.0000033129509,0.3895521,0.0013816098,0.00035671497,0.00051538186,0.000034210763,0.00038798992,0.0007205293],"genre_scores_gemma":[0.9934288,0.0000026454109,0.005295998,0.00017310846,0.00024302182,0.00047236265,0.0000026759749,0.000024399733,0.00035697865],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977077,0.000045663666,0.00038807673,0.0008834309,0.00042275086,0.00055233017],"domain_scores_gemma":[0.9974124,0.0005410025,0.00045566613,0.00024660357,0.0011490326,0.00019528234],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005319465,0.00033193888,0.0004151642,0.0002069272,0.00037888892,0.000556255,0.0007344924,0.00016858304,0.00005309981],"category_scores_gemma":[0.0003203228,0.0002598975,0.00015992073,0.00030427554,0.00014029068,0.0017441424,0.0000776609,0.00015765644,0.00012163221],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030186899,0.00009750268,0.00354294,0.000116740375,0.00002544875,0.0000013905036,0.0005466343,3.221588e-8,0.6787969,0.0005973828,0.0004404801,0.3158044],"study_design_scores_gemma":[0.0028116796,0.0004832575,0.007388147,0.0012975684,0.000035304794,0.00002930566,0.00023697995,0.00938821,0.962695,0.013823697,0.0006871585,0.0011236811],"about_ca_topic_score_codex":0.000054643115,"about_ca_topic_score_gemma":0.000030558433,"teacher_disagreement_score":0.38638064,"about_ca_system_score_codex":0.00011838711,"about_ca_system_score_gemma":0.00016735635,"threshold_uncertainty_score":0.99998534},"labels":[],"label_agreement":null},{"id":"W2755293182","doi":"10.1109/ichi.2017.84","title":"Multivariate Hidden Markov Models for Personal Smartphone Sensor Data: Time Series Analysis","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Saskatchewan","funders":"","keywords":"Hidden Markov model; Computer science; Phone; Ground truth; Artificial intelligence; Multivariate statistics; Speech recognition; Time series; Mobile phone; Markov model; Machine learning; Pattern recognition (psychology); Markov chain","score_opus":0.08132152768090864,"score_gpt":0.3053535560081582,"score_spread":0.22403202832724958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755293182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0080205025,0.000019798179,0.98369026,0.002684967,0.0003377248,0.00041302142,0.0003973181,0.0002400069,0.0041963947],"genre_scores_gemma":[0.86409277,0.000003502346,0.11068031,0.00014634572,0.00020490616,0.000055588665,0.0001154468,0.000017401346,0.024683736],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805146,0.00009912629,0.00029810297,0.00086260133,0.0003404506,0.00034825294],"domain_scores_gemma":[0.99685454,0.00028768746,0.0002920109,0.0021567594,0.00027124802,0.0001377656],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007384486,0.00021316823,0.00046397236,0.00018352807,0.0006296205,0.0011638405,0.0020056267,0.00008858307,0.00014567816],"category_scores_gemma":[0.00016727459,0.0001913585,0.00020824131,0.00020205308,0.00007495458,0.004935847,0.001154016,0.00008873684,0.0002129242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005136454,0.0007596968,0.0068344725,0.00019971757,0.009436023,0.000111591195,0.008160271,0.00008880162,0.010568248,0.0035331794,0.046580613,0.91321373],"study_design_scores_gemma":[0.00063702493,0.000034367247,0.0040069935,0.000011591464,0.00015748842,0.000018148854,0.00006557982,0.9918166,0.0004023417,0.00049672084,0.0020385997,0.00031457286],"about_ca_topic_score_codex":0.0007912338,"about_ca_topic_score_gemma":0.00044919227,"teacher_disagreement_score":0.99172777,"about_ca_system_score_codex":0.0000419531,"about_ca_system_score_gemma":0.00008951068,"threshold_uncertainty_score":0.99987304},"labels":[],"label_agreement":null},{"id":"W2758445693","doi":"10.1177/1541931213601663","title":"Gait-Guided Adaptive Interfaces: Managing Cognitive Load In Older Users","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"","keywords":"Distraction; Gait; Task (project management); Cognitive load; Usability; Cognition; Computer science; Human–computer interaction; Wearable computer; Fear of falling; Physical medicine and rehabilitation; Psychology; Poison control; Human factors and ergonomics; Engineering; Cognitive psychology; Medicine; Embedded system","score_opus":0.03889750571144475,"score_gpt":0.26919286879410487,"score_spread":0.23029536308266013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758445693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99641705,0.000051163635,0.000252968,0.00019335453,0.00021416371,0.0003272718,0.000014795472,0.000048290025,0.0024809346],"genre_scores_gemma":[0.9991757,0.000024248815,0.0005225812,0.00007570598,0.00005285293,0.000013857815,4.6890798e-7,0.000017415456,0.000117138006],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99850774,0.000017075397,0.0004106749,0.0005012223,0.00022172833,0.0003415585],"domain_scores_gemma":[0.99839604,0.00014864208,0.0008090575,0.00019490978,0.00037696335,0.00007438075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007999654,0.00024815684,0.00036502932,0.000049687784,0.0008572966,0.00053018384,0.0011044483,0.000111073205,0.0000019337422],"category_scores_gemma":[0.00020836003,0.00020629771,0.00018858789,0.000094733594,0.00027076702,0.0018003631,0.0012539038,0.00031447582,0.0000014823212],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009048606,0.0002464529,0.712399,0.00049942645,0.00045704455,0.000001285596,0.25709328,0.000021705722,0.008286453,0.0051820558,0.0018273408,0.013895493],"study_design_scores_gemma":[0.0039110794,0.0002453747,0.7493245,0.0050540213,0.000112794994,0.000020359728,0.170402,0.01713453,0.04725953,0.004651891,0.00023925414,0.0016446346],"about_ca_topic_score_codex":0.00074878143,"about_ca_topic_score_gemma":0.000094431525,"teacher_disagreement_score":0.08669127,"about_ca_system_score_codex":0.00017901983,"about_ca_system_score_gemma":0.000045395387,"threshold_uncertainty_score":0.8412577},"labels":[],"label_agreement":null},{"id":"W2762224430","doi":"10.1109/ihtc.2017.8058201","title":"Benefits of automatic human action recognition in an assistive system for people with dementia","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Toronto","funders":"","keywords":"Action recognition; Computer science; Task (project management); Action (physics); Human–computer interaction; Context (archaeology); Activity recognition; Recall; Artificial intelligence; Task analysis; Machine learning; Psychology; Cognitive psychology; Engineering","score_opus":0.08222569584836634,"score_gpt":0.3089264386000673,"score_spread":0.22670074275170096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762224430","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8028518,0.0000057571997,0.19518007,0.000052683783,0.0001706758,0.0005713897,0.000012603795,0.00012593373,0.0010290592],"genre_scores_gemma":[0.9933946,3.8880356e-7,0.0063145296,0.0000074502873,0.00003482705,0.00019300752,0.000014548214,0.000009420281,0.000031194744],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989289,0.00008155924,0.00029418076,0.00032767572,0.0002041102,0.00016358763],"domain_scores_gemma":[0.9985003,0.000093573406,0.00048664305,0.00057260535,0.00029499966,0.00005187943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042340352,0.00011545046,0.0002653323,0.00015658785,0.00028038182,0.00026184993,0.00043425255,0.000057275174,0.000009690275],"category_scores_gemma":[0.000032440308,0.0001030602,0.00004222101,0.000100640995,0.000021831425,0.0018085003,0.00006819102,0.000051738843,0.000010888523],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079932724,0.00065342017,0.13806042,0.0014684653,0.00022036214,0.0000035503465,0.0020766892,0.000042785643,0.0052965065,0.0056708227,0.00009867482,0.8463284],"study_design_scores_gemma":[0.0018386391,0.0005904764,0.93710995,0.0008535804,0.00004922607,0.00002580906,0.0007112355,0.038700696,0.019547485,0.00026280986,0.000009790718,0.0003002866],"about_ca_topic_score_codex":0.0006678089,"about_ca_topic_score_gemma":0.013742413,"teacher_disagreement_score":0.8460281,"about_ca_system_score_codex":0.00008088057,"about_ca_system_score_gemma":0.000043639182,"threshold_uncertainty_score":0.76685894},"labels":[],"label_agreement":null},{"id":"W2764058170","doi":"10.1007/s11042-017-5246-0","title":"CASP: context-aware stress prediction system","year":2017,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"CASP; Computer science; Context (archaeology); Stress (linguistics); Protein structure prediction","score_opus":0.041294954687101655,"score_gpt":0.2680840172572439,"score_spread":0.22678906257014225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2764058170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011686218,0.00021969793,0.9780077,0.0012287638,0.00057682523,0.001655365,0.0008840786,0.00066744577,0.005073949],"genre_scores_gemma":[0.9968072,0.000019102767,0.0014288706,0.000045107474,0.00033766215,0.0010309616,0.000045296118,0.000011438985,0.00027434187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988008,0.000043127577,0.00026604743,0.0004756429,0.00020477214,0.00020959257],"domain_scores_gemma":[0.99809176,0.0001956272,0.0002586363,0.0011161176,0.00016938696,0.00016847823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018537241,0.00015278996,0.00020868423,0.000058181628,0.00096301665,0.0009087479,0.0007191521,0.00009770128,0.000007452597],"category_scores_gemma":[0.00004543618,0.0001477266,0.000054968255,0.00007270107,0.00010496573,0.001055312,0.0002517028,0.00013046997,0.00014682171],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028365068,0.00007442986,0.009856787,0.00007671862,0.000034022156,0.0000048458846,0.00038920023,0.0000027994492,0.00033894923,0.008016645,0.000576966,0.9806258],"study_design_scores_gemma":[0.004162579,0.00015265711,0.3811086,0.00069418625,0.000118380805,0.00031800338,0.0028611962,0.40794346,0.007271327,0.000650932,0.193257,0.0014616596],"about_ca_topic_score_codex":0.00023680752,"about_ca_topic_score_gemma":0.00012141253,"teacher_disagreement_score":0.985121,"about_ca_system_score_codex":0.000053121716,"about_ca_system_score_gemma":0.000044605793,"threshold_uncertainty_score":0.87630767},"labels":[],"label_agreement":null},{"id":"W2764209689","doi":"10.1007/978-3-319-69179-4_58","title":"Location-Aware Human Activity Recognition","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Activity recognition; Computer science; Wearable computer; Machine learning; Artificial intelligence; Accelerometer; Focus (optics); Human–computer interaction","score_opus":0.05371711191074713,"score_gpt":0.2899555645043942,"score_spread":0.23623845259364706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2764209689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029803364,0.00012910803,0.9876043,0.0007317137,0.002420897,0.0006763498,0.000014982821,0.00033209057,0.0077924835],"genre_scores_gemma":[0.97962826,0.000015580885,0.017778473,0.0004941477,0.00086007337,0.0000391891,0.000018291696,0.00005116059,0.0011148427],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952984,0.000091730435,0.00055272976,0.0021111981,0.0012532974,0.0006926454],"domain_scores_gemma":[0.99468875,0.00052253937,0.00087824906,0.0027759294,0.00089348096,0.0002410453],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012542328,0.00066886673,0.0007365172,0.0010165396,0.0009863564,0.0016212774,0.0042413566,0.00051004044,0.000052968993],"category_scores_gemma":[0.00019124745,0.0006949713,0.00019341405,0.00034495932,0.0007626991,0.002272148,0.0014459899,0.0010880285,0.00037964838],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038901585,0.00004326537,0.00004508246,0.00007373742,0.000015795382,0.00005473739,0.0003122082,0.0002378181,0.00027222544,0.00064372085,0.00003739068,0.99826014],"study_design_scores_gemma":[0.002236641,0.0009218993,0.0038057673,0.0074799336,0.0000765549,0.0007677738,9.460124e-7,0.38433856,0.018417468,0.5678695,0.008068208,0.006016786],"about_ca_topic_score_codex":0.00022094979,"about_ca_topic_score_gemma":0.00092404505,"teacher_disagreement_score":0.99224335,"about_ca_system_score_codex":0.00058812095,"about_ca_system_score_gemma":0.00085078384,"threshold_uncertainty_score":0.99955016},"labels":[],"label_agreement":null},{"id":"W2765275233","doi":"10.3390/s17112496","title":"Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges","year":2017,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":607,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Health care; Life expectancy; Wearable computer; Population ageing; Wearable technology; Business; Expectancy theory; Population; Internet privacy; Risk analysis (engineering); Engineering; Medicine; Computer science; Psychology; Environmental health; Economic growth","score_opus":0.4694568834817912,"score_gpt":0.4936928425368305,"score_spread":0.02423595905503928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765275233","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000034721463,0.99261975,0.00028542458,0.002881053,0.0010885977,0.0018804534,0.000052300107,0.00012222989,0.0010666926],"genre_scores_gemma":[0.000034632958,0.99767816,0.0005961876,0.000015093697,0.00035227108,0.0004098442,0.000012086383,0.000040830444,0.0008608873],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962718,0.0009106387,0.00051290897,0.001082774,0.0005690357,0.0006528705],"domain_scores_gemma":[0.9954414,0.0019968615,0.00046338426,0.0013103125,0.0005546508,0.00023338858],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021764238,0.00037858254,0.001541658,0.00045596497,0.0005481542,0.00046853302,0.0011807424,0.00031344825,0.0000033396052],"category_scores_gemma":[0.00052267045,0.0003194297,0.00025799754,0.00020190048,0.00015972144,0.00044925127,0.0003881718,0.00053495006,0.00008549635],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021647454,0.000017834074,5.3787363e-7,0.014476195,0.00004563081,0.000008932106,0.0002963836,1.8334964e-8,1.5099054e-8,0.000616841,0.00025424888,0.9842812],"study_design_scores_gemma":[0.00014073624,0.0002239533,0.0000027132533,0.006133725,0.000023118046,0.000094601084,0.000113175316,0.000023680735,7.426548e-7,0.0008382527,0.99208647,0.0003188436],"about_ca_topic_score_codex":0.000045248533,"about_ca_topic_score_gemma":0.00035978953,"teacher_disagreement_score":0.9918322,"about_ca_system_score_codex":0.00013082485,"about_ca_system_score_gemma":0.0004390026,"threshold_uncertainty_score":0.9999258},"labels":[],"label_agreement":null},{"id":"W2766253199","doi":"10.3844/jcssp.2017.460.469","title":"Using MVCA to Improve Architecture Modularity of Smart Spaces","year":2017,"lang":"en","type":"article","venue":"Journal of Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"École de technologie supérieure","keywords":"Computer science; Architecture; Reference architecture; Applications architecture; Modular design; Maintainability; Space-based architecture; Reusability; Software engineering; Adapter (computing); Solution architecture; Human–computer interaction; Software architecture; Distributed computing; Embedded system; Operating system; Software","score_opus":0.04902678096218353,"score_gpt":0.31388428318696554,"score_spread":0.264857502224782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766253199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35103372,0.000018313229,0.64597225,0.0009861508,0.0017954097,0.00008889223,0.0000013037306,0.000010167455,0.00009378283],"genre_scores_gemma":[0.77815133,8.8729087e-7,0.2214537,0.00013427553,0.00024487457,4.587218e-7,1.14067795e-8,0.0000038751464,0.0000106034995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772596,0.00007989213,0.0005013679,0.00035243854,0.0010228015,0.00031753158],"domain_scores_gemma":[0.9963543,0.000105314444,0.0011871695,0.0010725264,0.000991854,0.00028884373],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0020543428,0.00014734046,0.00039234094,0.00045935306,0.00045043076,0.0011309554,0.004056588,0.000044322787,0.0000021816147],"category_scores_gemma":[0.00023835161,0.00012122566,0.00015084188,0.0004150063,0.00031649315,0.0024590474,0.0011939262,0.00025048215,0.0000048882134],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050053477,0.0002281846,0.0070251646,0.00006427392,0.00006212468,0.000105709776,0.0032311894,0.0038906815,0.19493374,0.0017535246,0.00023247852,0.7884229],"study_design_scores_gemma":[0.002072591,0.0025393462,0.11105548,0.0011970046,0.000044110602,0.0026603672,0.00006100949,0.6302547,0.23484384,0.010231384,0.00396623,0.0010739244],"about_ca_topic_score_codex":0.000075070115,"about_ca_topic_score_gemma":0.00001664036,"teacher_disagreement_score":0.7873489,"about_ca_system_score_codex":0.00008425352,"about_ca_system_score_gemma":0.0005200112,"threshold_uncertainty_score":0.99990594},"labels":[],"label_agreement":null},{"id":"W2768006888","doi":"10.3390/s17112562","title":"The Virtual Environment for Rapid Prototyping of the Intelligent Environment","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Rapid prototyping; Virtual prototyping; Systems engineering; Human–computer interaction; Engineering; Computer science; Virtual machine; Software engineering; Embedded system; Operating system; Simulation; Mechanical engineering","score_opus":0.035854595668966475,"score_gpt":0.24904690150520306,"score_spread":0.21319230583623658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768006888","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6340303,0.000846002,0.32819122,0.018499902,0.0035470969,0.012057456,0.000054486834,0.0001396855,0.002633888],"genre_scores_gemma":[0.9979364,0.000110925095,0.00056488026,0.00004936352,0.00007730713,0.00021649592,3.5212628e-7,0.000010123652,0.0010341215],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988334,0.00012081093,0.00025752018,0.00027325435,0.00030095637,0.00021401249],"domain_scores_gemma":[0.9978375,0.00029357476,0.0003563663,0.0014506446,0.000018367875,0.000043591863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058062334,0.0001179577,0.00013711238,0.000018434272,0.0007881433,0.00015363187,0.0012754375,0.00004120913,0.000013415374],"category_scores_gemma":[0.00010883357,0.00007026486,0.00015349513,0.000020489166,0.0002130457,0.00013539141,0.0004707878,0.00009328725,0.000059768172],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022104206,0.00007011353,0.000459829,0.000014737509,0.000063431435,8.131587e-7,0.0012156412,0.00018448508,0.001392732,0.0033444134,0.0001866062,0.9930451],"study_design_scores_gemma":[0.0006125673,0.00028201757,0.013976777,0.000092619885,0.000021449485,0.000015876562,0.0004937492,0.017483456,0.10950611,0.0010341323,0.8561537,0.00032752176],"about_ca_topic_score_codex":0.000022481145,"about_ca_topic_score_gemma":0.0000085193315,"teacher_disagreement_score":0.99271756,"about_ca_system_score_codex":0.000066995955,"about_ca_system_score_gemma":0.000023807681,"threshold_uncertainty_score":0.60618407},"labels":[],"label_agreement":null},{"id":"W2768878649","doi":"10.1007/978-3-319-05939-6_37","title":"Erratum to: Context-Aware Systems and Applications","year":2014,"lang":"en","type":"erratum","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Context (archaeology); History; Archaeology","score_opus":0.022697152925029246,"score_gpt":0.2516921580697752,"score_spread":0.22899500514474594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768878649","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005016377,0.0008160982,0.99091583,0.0014509709,0.0049074083,0.0012716884,0.00009447765,0.00011815192,0.00037517733],"genre_scores_gemma":[0.157111,0.00019551882,0.838769,0.0010947145,0.0015229788,0.00089806406,0.00015471881,0.000050785482,0.0002031793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982943,0.00005275312,0.0007351844,0.00027678628,0.00032679358,0.00031418217],"domain_scores_gemma":[0.99758667,0.0004952475,0.00055706716,0.000914703,0.00033803043,0.00010831398],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006074197,0.00032950894,0.0005598968,0.00031080437,0.0011389849,0.00071556726,0.0025163523,0.00029548374,1.8551431e-7],"category_scores_gemma":[0.00010445778,0.00027665228,0.00014706506,0.0008005574,0.0002934405,0.000497089,0.001239967,0.00055964635,0.0000014631606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042796864,0.00010905093,0.00003875202,0.003088263,0.00040639614,2.146914e-7,0.008163841,0.6401491,0.000025801746,0.03516454,0.06304578,0.24980399],"study_design_scores_gemma":[0.00015472971,0.000055719313,0.000060658032,0.00034960717,0.000029517214,0.000023716782,0.000021285727,0.63481736,0.000014303641,0.00011812187,0.36402854,0.00032645004],"about_ca_topic_score_codex":0.00016620224,"about_ca_topic_score_gemma":0.00021304227,"teacher_disagreement_score":0.30098277,"about_ca_system_score_codex":0.00007680801,"about_ca_system_score_gemma":0.00025991685,"threshold_uncertainty_score":0.9999686},"labels":[],"label_agreement":null},{"id":"W2770200973","doi":"10.21307/ijssis-2017-775","title":"Cyber-Awareness Improvement Using Artificial Intelligence Techniques","year":2015,"lang":"en","type":"article","venue":"International Journal on Smart Sensing and Intelligent Systems","topic":"Context-Aware Activity Recognition Systems","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":"Algonquin College","funders":"","keywords":"Human multitasking; Computer science; Variety (cybernetics); Process (computing); Software; Artificial intelligence; Human–computer interaction; Software engineering; Psychology; Operating system; Cognitive psychology","score_opus":0.1290713088984891,"score_gpt":0.3423601868109557,"score_spread":0.2132888779124666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770200973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072888665,0.00018926995,0.9148439,0.00083980587,0.009971412,0.00027230656,0.00000638692,0.00017078362,0.0008174675],"genre_scores_gemma":[0.9941054,0.000055327426,0.004030153,0.00034647022,0.0013208914,0.0000042904094,0.0000035103901,0.000024391586,0.00010955975],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966918,0.00027399635,0.0009886103,0.0004927974,0.0012072893,0.00034548363],"domain_scores_gemma":[0.99709845,0.000201228,0.0005579106,0.00034080335,0.0014399082,0.00036168253],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0016419055,0.0003002256,0.00036934187,0.00057139446,0.00022865285,0.0016477812,0.00069268886,0.00012374457,0.00000503762],"category_scores_gemma":[0.00020396609,0.00026866462,0.00013109879,0.00022904314,0.000070896596,0.000752065,0.0002596046,0.00039522647,0.00007416061],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011560413,0.00024101627,0.00040354245,0.00002013763,0.00029020995,0.00037987964,0.0014469248,0.001309775,0.0055378205,0.017337363,0.0005247074,0.97239304],"study_design_scores_gemma":[0.00037389234,0.0012032501,0.000043645716,0.0021276765,0.000046110457,0.0117115835,0.0036106955,0.7610616,0.14947863,0.015179544,0.053821716,0.0013416753],"about_ca_topic_score_codex":0.00084035547,"about_ca_topic_score_gemma":0.000028646848,"teacher_disagreement_score":0.97105134,"about_ca_system_score_codex":0.0005943639,"about_ca_system_score_gemma":0.00025342582,"threshold_uncertainty_score":0.9999766},"labels":[],"label_agreement":null},{"id":"W2770265759","doi":"10.1016/j.future.2017.11.029","title":"A robust human activity recognition system using smartphone sensors and deep learning","year":2017,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":653,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Deanship of Scientific Research, King Saud University","keywords":"Activity recognition; Computer science; Artificial intelligence; Inertial measurement unit; Support vector machine; Wearable computer; Machine learning; Pattern recognition (psychology); Linear discriminant analysis; Artificial neural network; Deep belief network; Autoregressive model; Embedded system","score_opus":0.09103652507543153,"score_gpt":0.2634169325374095,"score_spread":0.17238040746197797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770265759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34399787,0.00022635623,0.630538,0.00012799725,0.024089871,0.00046135436,0.0000061009564,0.00036577156,0.00018668268],"genre_scores_gemma":[0.9524378,0.0000095122095,0.009746864,0.00002923096,0.037538216,0.000055674256,0.000027995706,0.000036822752,0.00011784895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99676037,0.0008316246,0.00053211267,0.00097141805,0.0005014303,0.0004030224],"domain_scores_gemma":[0.9972556,0.00007443553,0.00093805604,0.0010692691,0.00045377336,0.00020883034],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009503808,0.00038991479,0.00056132453,0.0002482555,0.0025745044,0.0034665086,0.0006172391,0.0002662693,0.0000034605187],"category_scores_gemma":[0.000023885379,0.0003996556,0.00011788988,0.0001621176,0.00005852925,0.002053504,0.00040209276,0.0003617423,0.000051277766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006109587,0.00038255574,0.009770659,0.001919584,0.00079019926,0.0003815844,0.007778323,0.027833063,0.08081298,0.0041448283,0.009499371,0.85662574],"study_design_scores_gemma":[0.0008437221,0.00011330347,0.0038806773,0.00026939876,0.000029432687,0.00074962224,0.00019985237,0.98759115,0.0013879974,0.0000023056816,0.0043533365,0.00057918753],"about_ca_topic_score_codex":0.0005775954,"about_ca_topic_score_gemma":0.00019878555,"teacher_disagreement_score":0.9597581,"about_ca_system_score_codex":0.00023997351,"about_ca_system_score_gemma":0.00005420146,"threshold_uncertainty_score":0.9998455},"labels":[],"label_agreement":null},{"id":"W2772780151","doi":"10.1109/icdmw.2017.97","title":"Towards Automatic Feature Extraction for Activity Recognition from Wearable Sensors: A Deep Learning Approach","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Softmax function; Feature extraction; Classifier (UML); Activity recognition; Dimensionality reduction; Pattern recognition (psychology); Machine learning; Wearable computer; Generalization; Matrix decomposition; Deep learning","score_opus":0.0569353017406226,"score_gpt":0.2971040498722523,"score_spread":0.2401687481316297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772780151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16511865,0.000034413184,0.820522,0.000857394,0.00058253267,0.0006886733,0.000012637049,0.000612124,0.011571532],"genre_scores_gemma":[0.89960986,0.0000100663,0.09809199,0.000048241924,0.0002777611,0.00021708688,0.00002286445,0.000022910835,0.0016992412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980897,0.00023578116,0.00021673087,0.00072705594,0.00036490016,0.0003658834],"domain_scores_gemma":[0.9978493,0.0003677239,0.0005089285,0.0008874352,0.00025829673,0.00012834258],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00067133526,0.0002471036,0.00036013345,0.0001235956,0.0010032103,0.0014306429,0.00068893045,0.00023683214,0.00006240538],"category_scores_gemma":[0.00057600904,0.00023795798,0.00018767893,0.00011390416,0.000042458043,0.0033456353,0.00018579995,0.000393197,0.00016868963],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024272686,0.00013244108,0.00016908183,0.000053717566,0.000062937055,0.000003325459,0.00045009126,0.000026119153,0.0043773954,0.000045230074,0.00034446863,0.9943109],"study_design_scores_gemma":[0.001227913,0.00012798943,0.017226709,0.00014426574,0.000043385597,0.00006916733,0.00025567706,0.9595376,0.015314245,0.0024618907,0.0030443512,0.0005467729],"about_ca_topic_score_codex":0.0009333747,"about_ca_topic_score_gemma":0.00013740337,"teacher_disagreement_score":0.99376416,"about_ca_system_score_codex":0.00013021198,"about_ca_system_score_gemma":0.00007406336,"threshold_uncertainty_score":0.99960595},"labels":[],"label_agreement":null},{"id":"W2775840793","doi":"10.4018/978-1-5225-5222-2.ch005","title":"Smart Senior Residences","year":2017,"lang":"en","type":"book-chapter","venue":"Advances in healthcare information systems and administration book series","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Exploit; Economic shortage; Work (physics); Context (archaeology); Business; Assisted living; Population ageing; Population; Public relations; Operations management; Marketing; Computer science; Engineering; Nursing; Medicine; Political science; Computer security; Geography","score_opus":0.02700446500465981,"score_gpt":0.296307552096813,"score_spread":0.26930308709215317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775840793","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000076590026,0.06190647,0.018526403,0.015899746,0.008037422,0.0046768063,0.0005246789,0.00080273167,0.88954914],"genre_scores_gemma":[0.6190927,0.04912683,0.0017477524,0.001767091,0.00090702384,0.00074706896,0.000488752,0.00007799155,0.32604477],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971275,0.00008559952,0.0014261251,0.0004384822,0.00062339,0.0002989471],"domain_scores_gemma":[0.99661183,0.00019143228,0.0017337056,0.0007898798,0.00048791402,0.00018522546],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.00062277785,0.0004184226,0.00065230817,0.0003697586,0.00047950723,0.0012674535,0.00051923987,0.0003888123,0.000011781903],"category_scores_gemma":[0.000096505035,0.00041819646,0.00007876169,0.0000405238,0.00017361701,0.02411438,0.00013530333,0.0003891524,0.000100162135],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050604747,0.0000061160827,0.00039999353,0.0015627188,0.000017987122,0.000028627403,0.00091414386,0.000006025508,6.660048e-7,0.9080963,0.00043141475,0.08848536],"study_design_scores_gemma":[0.00022965713,0.00027660554,0.00020259617,0.0014946914,0.000005118244,0.0003572027,0.00024151392,0.00018169053,0.000017913626,0.0050024292,0.9915241,0.00046645504],"about_ca_topic_score_codex":0.00024262916,"about_ca_topic_score_gemma":0.001971652,"teacher_disagreement_score":0.99109274,"about_ca_system_score_codex":0.00014013043,"about_ca_system_score_gemma":0.0006671532,"threshold_uncertainty_score":0.99982697},"labels":[],"label_agreement":null},{"id":"W2779949088","doi":"10.1016/j.ijmedinf.2017.12.015","title":"The state of knowledge on technologies and their use for fall detection: A scoping review","year":2017,"lang":"en","type":"review","venue":"International Journal of Medical Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Fall prevention; Wearable computer; Wearable technology; Computer science; Emerging technologies; Poison control; Human factors and ergonomics; Applied psychology; Medicine; Psychology; Artificial intelligence; Medical emergency","score_opus":0.1914557929076299,"score_gpt":0.43574723906203866,"score_spread":0.24429144615440876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779949088","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000030550711,0.8490058,0.1485713,0.00048346646,0.0013262017,0.000522995,0.000018653162,0.00001946078,0.000049033115],"genre_scores_gemma":[0.000080528145,0.9990784,0.00058383396,0.000082213366,0.00010869305,0.000035031426,0.0000015406204,0.0000072371777,0.000022506512],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970227,0.00010606038,0.001793509,0.000080731756,0.00086709834,0.00012989293],"domain_scores_gemma":[0.9917335,0.003416807,0.0034190968,0.0003553,0.0009994989,0.000075812415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027894077,0.00019444934,0.000995508,0.0002647083,0.000107018546,0.00037375503,0.0028186175,0.00015197675,0.0000010439597],"category_scores_gemma":[0.0043734238,0.00010254423,0.0003702462,0.000090447254,0.00016846637,0.0007585905,0.0005088037,0.00053277635,0.000004530367],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003230276,0.00001661913,1.2248496e-7,0.010548841,0.00019884278,0.0000025185707,0.00014958563,1.0261224e-7,1.4501783e-8,0.0001145848,0.00025874586,0.98870677],"study_design_scores_gemma":[0.00018703884,0.00010065722,1.7570736e-7,0.33193144,0.0000348585,0.00062073034,0.00003653625,0.00087461,0.0000051646502,0.00022083889,0.6659006,0.000087346925],"about_ca_topic_score_codex":0.0000028317804,"about_ca_topic_score_gemma":0.000037411843,"teacher_disagreement_score":0.98861945,"about_ca_system_score_codex":0.000088057066,"about_ca_system_score_gemma":0.00074408116,"threshold_uncertainty_score":0.52377397},"labels":[],"label_agreement":null},{"id":"W2781769479","doi":"10.1007/978-3-319-73450-7_30","title":"ECOPPA: Extensible Context Ontology for Persuasive Physical-Activity Applications","year":2018,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Ontology; Context (archaeology); Computer science; Extensibility; Physical activity; Ubiquitous computing; Context awareness; Human–computer interaction; World Wide Web; Medicine","score_opus":0.041796746029851846,"score_gpt":0.3157935894932558,"score_spread":0.27399684346340397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781769479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000629662,0.009641428,0.951355,0.0000744831,0.0016775767,0.0024475795,0.000042826614,0.00017671408,0.03395475],"genre_scores_gemma":[0.97144693,0.00074979017,0.001953871,0.00012201773,0.0015665973,0.0005255486,0.000019514699,0.00008427081,0.02353146],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99734515,0.00008213368,0.00061571697,0.0012268708,0.0002695344,0.0004605783],"domain_scores_gemma":[0.99626905,0.0013367382,0.0010767977,0.0007009431,0.0004875977,0.00012888409],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004172898,0.00050982594,0.0010670412,0.0002844255,0.0002760251,0.00021127195,0.0006323952,0.00025865372,0.000008394426],"category_scores_gemma":[0.000053858243,0.0005196574,0.00021325237,0.000103709055,0.00020366858,0.0005238972,0.00035307356,0.0003557418,0.00009383504],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024434925,0.000093092916,0.000046467103,0.0005329369,0.00009690234,0.000008714843,0.0013916048,0.00012342545,0.000023827946,0.2534046,0.00024162838,0.74401236],"study_design_scores_gemma":[0.0006417397,0.0004995329,0.000022047809,0.0019745964,0.000053539476,0.00020730903,0.0008225088,0.12745029,0.00027694734,0.03195683,0.8347997,0.0012949818],"about_ca_topic_score_codex":0.000093501585,"about_ca_topic_score_gemma":0.00022752924,"teacher_disagreement_score":0.97081727,"about_ca_system_score_codex":0.00022459564,"about_ca_system_score_gemma":0.00010149392,"threshold_uncertainty_score":0.9997255},"labels":[],"label_agreement":null},{"id":"W2782020705","doi":"10.18438/b87q2x","title":"A Mixed Methods Approach to Iterative Service Design of an In-Person Reference Service Point","year":2017,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Council of University Libraries; University of Toronto","funders":"","keywords":"Computer science; Service (business); Point (geometry); Service design; Service delivery framework; Information retrieval; World Wide Web; Mathematics","score_opus":0.12476285447042791,"score_gpt":0.3402629511179945,"score_spread":0.21550009664756659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782020705","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035748612,0.000051587576,0.9517482,0.03863859,0.00013970824,0.00073523284,0.000009355434,0.0000991642,0.0050033233],"genre_scores_gemma":[0.35023138,0.000023840621,0.61314654,0.036433123,0.00002167432,0.00010686296,0.000014924208,0.0000069668263,0.000014672891],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99700934,0.001625878,0.00050917536,0.0003324553,0.000320584,0.00020259529],"domain_scores_gemma":[0.99517566,0.002446349,0.0008721333,0.0009885556,0.000337057,0.00018024535],"candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0024841588,0.00018930057,0.00027220586,0.000314119,0.00025936658,0.0014678731,0.0011753188,0.000099337085,0.000016828322],"category_scores_gemma":[0.0019920343,0.00018154483,0.00002334764,0.00067616836,0.000027060723,0.35862955,0.00032662266,0.00025103643,0.000039105136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002838714,0.0008718177,0.00039840615,0.0019242305,0.000110562716,0.000019364363,0.041923374,0.0066755847,0.00812301,0.1864359,0.0010975664,0.74958146],"study_design_scores_gemma":[0.0012539744,0.00060027273,0.016738834,0.0012695495,0.000029349741,0.000109996065,0.0056165233,0.9214142,0.029439233,0.0003371162,0.022516752,0.00067420205],"about_ca_topic_score_codex":0.00016860265,"about_ca_topic_score_gemma":0.0000014240939,"teacher_disagreement_score":0.9147386,"about_ca_system_score_codex":0.00001973802,"about_ca_system_score_gemma":0.00029109043,"threshold_uncertainty_score":0.9995687},"labels":[],"label_agreement":null},{"id":"W2782575185","doi":"10.4018/978-1-61520-611-7.ch103","title":"Building Context-Aware E-Commerce Systems","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Computer science; Context (archaeology); Purchasing; Domain (mathematical analysis); Context model; Context awareness; World Wide Web; Knowledge management; Data science; Artificial intelligence; Business; Marketing","score_opus":0.030317784814319848,"score_gpt":0.26172372916942316,"score_spread":0.2314059443551033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782575185","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007865724,0.000594665,0.1951899,0.00018328331,0.0054249545,0.0009961383,0.00023019411,0.00096560916,0.7963366],"genre_scores_gemma":[0.8822543,0.0000029567352,0.0018300411,0.001064984,0.0012183634,0.00011151476,0.0000072749694,0.00013009402,0.11338047],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956673,0.00010996945,0.0009799224,0.0014511939,0.0010371874,0.00075447734],"domain_scores_gemma":[0.9957294,0.00028214214,0.0008530683,0.002108324,0.00055739953,0.00046965908],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00048230457,0.0009604733,0.0012536565,0.00022145762,0.0003309058,0.0010031211,0.0025059565,0.0012407682,0.000037639224],"category_scores_gemma":[0.00004700919,0.001011008,0.0005281241,0.000060721424,0.00017953085,0.0003944105,0.00096400175,0.0012730076,0.001242071],"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":[0.000008613944,0.000012248692,0.000013340937,0.000083219464,0.000146494,0.00013525638,0.00006803679,7.803839e-7,0.00018131196,0.940474,0.0021735562,0.056703165],"study_design_scores_gemma":[0.0011300563,0.00017501408,0.00003397269,0.001863489,0.00012649737,0.001639927,0.000042047464,0.0010043163,0.00033043118,0.15528555,0.8359427,0.0024260085],"about_ca_topic_score_codex":0.0006219846,"about_ca_topic_score_gemma":0.00032602798,"teacher_disagreement_score":0.8821756,"about_ca_system_score_codex":0.00050708686,"about_ca_system_score_gemma":0.0004601187,"threshold_uncertainty_score":0.99953556},"labels":[],"label_agreement":null},{"id":"W2782968733","doi":"10.1109/icmla.2017.000-5","title":"Model Guided Deep Learning Approach Towards Prediction of Physical System Behavior","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Impact","funders":"","keywords":"Computer science; Model predictive control; Physical system; Process (computing); Artificial intelligence; Cyber-physical system; Machine learning; Deep learning; Control system; Control (management); Engineering","score_opus":0.09372289724306691,"score_gpt":0.29916397317319304,"score_spread":0.20544107593012612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782968733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14891975,0.0000058751657,0.81227094,0.000028473047,0.00017710587,0.00028389838,0.0000046134855,0.00032002715,0.037989307],"genre_scores_gemma":[0.98913926,5.1523966e-7,0.009922468,0.000005641885,0.00009893484,0.00010151952,0.0000036302847,0.000011073318,0.00071696565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985817,0.00009358111,0.000288818,0.00040151723,0.0004388324,0.00019552377],"domain_scores_gemma":[0.99839324,0.000030437886,0.00033373127,0.0009104366,0.00024687068,0.000085303385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038177412,0.00014128051,0.0003024949,0.00008235839,0.00030659663,0.00025069114,0.0009277183,0.00007634037,0.0000022664258],"category_scores_gemma":[0.00006362745,0.00012543374,0.00013910998,0.00007387094,0.00005440091,0.0010678985,0.00036865103,0.0001555365,0.00002432858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029267689,0.0016170008,0.023140099,0.00073410757,0.00021622692,0.00002429861,0.009161201,0.016158726,0.03292595,0.066824354,0.00060911075,0.8485597],"study_design_scores_gemma":[0.00031871579,0.00005515559,0.005563098,0.000034206794,0.000020208076,0.00003558017,0.00013510784,0.99092525,0.002727634,0.00003823961,0.000036048576,0.00011077587],"about_ca_topic_score_codex":0.00017812364,"about_ca_topic_score_gemma":0.000005708704,"teacher_disagreement_score":0.9747665,"about_ca_system_score_codex":0.00007795151,"about_ca_system_score_gemma":0.000060588543,"threshold_uncertainty_score":0.511504},"labels":[],"label_agreement":null},{"id":"W2783971628","doi":"","title":"Procedimiento para la gestión M2M empleando LWM2M","year":2017,"lang":"es","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Context-Aware Activity Recognition Systems","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":"Lakehead University","funders":"","keywords":"Political science; Computer science","score_opus":0.43563863544674875,"score_gpt":0.5962371800250791,"score_spread":0.16059854457833034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783971628","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89076936,0.023851296,0.016216462,0.0031832508,0.0064627877,0.0031602401,0.00031617912,0.00032137142,0.055719033],"genre_scores_gemma":[0.9907032,0.005785758,0.00046934737,0.00036956472,0.00083977217,0.00014018675,0.000009517562,0.00011291063,0.0015697421],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99213487,0.0011719192,0.0019196594,0.0015886602,0.0020913621,0.0010935126],"domain_scores_gemma":[0.9884708,0.0014292758,0.004636624,0.0030946326,0.0014695092,0.0008991875],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.004470813,0.00092094997,0.0020016853,0.0012701049,0.0019238308,0.02945736,0.017165745,0.00041564702,0.0034151396],"category_scores_gemma":[0.0023805364,0.0008965285,0.00061534194,0.0009769221,0.0006701975,0.015503977,0.007075694,0.0011771746,0.00034541634],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025556618,0.00083069544,0.63195217,0.00051070325,0.0005787017,0.000306612,0.0008553995,0.000023516708,0.014804276,0.00077615224,0.05103434,0.2980719],"study_design_scores_gemma":[0.0017288685,0.000039965074,0.8762305,0.0032340048,0.00017464282,0.00024846877,0.000072012816,0.00087378605,0.012223033,0.003547114,0.10035349,0.0012741109],"about_ca_topic_score_codex":0.0034081952,"about_ca_topic_score_gemma":0.00020769468,"teacher_disagreement_score":0.29679778,"about_ca_system_score_codex":0.00022565403,"about_ca_system_score_gemma":0.00082266633,"threshold_uncertainty_score":0.9993755},"labels":[],"label_agreement":null},{"id":"W2786078236","doi":"10.1109/lsc.2017.8268169","title":"Smartphone-based continuous mobility monitoring of Parkinsons disease patients reveals impacts of ambulatory bout length on gait features","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Ambulatory; Gait; Medicine; Physical medicine and rehabilitation; Gait analysis; Physical therapy; Internal medicine","score_opus":0.024073952789472004,"score_gpt":0.27939817064622025,"score_spread":0.25532421785674825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786078236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953792,0.00006162409,0.0021500613,0.00012916065,0.0007901432,0.0005335887,0.00007666315,0.0000941272,0.00078541314],"genre_scores_gemma":[0.9991612,0.0000033753113,0.00054119807,0.000057371784,0.000062970495,0.000029922146,0.0000036850295,0.000013878756,0.00012640208],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978696,0.0002043785,0.0004910298,0.00050384394,0.000615871,0.00031529384],"domain_scores_gemma":[0.99608505,0.00039484203,0.0008002122,0.0019752155,0.00044734016,0.00029736053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054459076,0.00022999448,0.0004938683,0.00013682799,0.000215369,0.00016026665,0.0010097085,0.0000865537,0.000011649364],"category_scores_gemma":[0.0008377163,0.00020306771,0.00021550468,0.00009666322,0.00014013374,0.00062810915,0.00023058469,0.00015381747,0.000013535287],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011223405,0.0006435172,0.9813159,0.00012334212,0.000036872636,0.0000041898707,0.00016064306,0.00000957037,0.0013374531,0.00010382288,0.00046696153,0.015685476],"study_design_scores_gemma":[0.0010294995,0.00018107121,0.9718756,0.00032459554,0.000014855924,3.6693942e-7,0.000023882261,0.00016091613,0.025974343,0.00011318471,0.00010682078,0.00019486215],"about_ca_topic_score_codex":0.0005338149,"about_ca_topic_score_gemma":0.000031432817,"teacher_disagreement_score":0.02463689,"about_ca_system_score_codex":0.000086977845,"about_ca_system_score_gemma":0.00017655588,"threshold_uncertainty_score":0.8280861},"labels":[],"label_agreement":null},{"id":"W2786374877","doi":"10.1109/iscmi.2017.8279623","title":"Analysis and comparison of two task models in a partially observable Markov decision process based assistive system","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"","keywords":"Task (project management); Computer science; Process (computing); Partially observable Markov decision process; Human–computer interaction; Activities of daily living; Task analysis; Field (mathematics); Dementia; Independence (probability theory); Cognition; Assistive technology; Artificial intelligence; Markov model; Psychology; Markov chain; Machine learning; Engineering","score_opus":0.06425904136245943,"score_gpt":0.3381007410648859,"score_spread":0.2738416997024265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786374877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32194498,0.00002693499,0.67615616,0.000066813576,0.00006144492,0.00021311472,0.000007128929,0.000047385143,0.0014760239],"genre_scores_gemma":[0.9877232,6.6368233e-7,0.012150542,0.000019415093,0.000010140767,0.00004660616,0.0000024055319,0.0000060704415,0.000040983465],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980748,0.00016189212,0.00055206264,0.0005396435,0.00045193598,0.00021964604],"domain_scores_gemma":[0.9976735,0.00041385155,0.00053565134,0.00094461115,0.00032899412,0.00010336628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086531567,0.00015933171,0.00067062484,0.00032956354,0.00018612362,0.00040183007,0.00083815644,0.00006772891,0.0000065839417],"category_scores_gemma":[0.00010838833,0.0001408989,0.00010344863,0.0005190042,0.000054904907,0.0012865631,0.00023521708,0.00009678957,0.000004644384],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086734115,0.0002659402,0.90692884,0.00017264832,0.00019458462,0.000015692209,0.00065125135,0.010074791,0.00027890716,0.0008976054,0.000037276313,0.080395706],"study_design_scores_gemma":[0.00076892896,0.000037122434,0.118444145,0.00018069948,0.00005915824,0.0000015971958,0.00013825443,0.87863994,0.0013863074,0.00019503027,0.0000052794803,0.00014354386],"about_ca_topic_score_codex":0.0018960555,"about_ca_topic_score_gemma":0.009001469,"teacher_disagreement_score":0.86856514,"about_ca_system_score_codex":0.00007029432,"about_ca_system_score_gemma":0.00012929171,"threshold_uncertainty_score":0.57456905},"labels":[],"label_agreement":null},{"id":"W2787391877","doi":"10.3390/s18010268","title":"A More Efficient Transportable and Scalable System for Real-Time Activities and Exercises Recognition","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scalability; Wasting; Computer science; Myotonic dystrophy; Population; Human–computer interaction; Multimedia; Embedded system; Operating system; Medicine","score_opus":0.01879660456042717,"score_gpt":0.2375070556333404,"score_spread":0.21871045107291323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787391877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98460704,0.0000546877,0.01237338,0.00013455606,0.00024444514,0.0006322416,0.00007093858,0.00031368528,0.001569004],"genre_scores_gemma":[0.9956208,0.00001584319,0.0033788865,0.000027886445,0.00011466793,0.0000728736,0.000008121013,0.000018154085,0.0007428067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998748,0.00006572062,0.00022469158,0.00048264142,0.00018559475,0.00029336356],"domain_scores_gemma":[0.9990935,0.00026932496,0.00011307176,0.00026171407,0.00016363717,0.000098763274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003467058,0.0001705784,0.00029715383,0.00013914758,0.00032169445,0.00024848542,0.000121341116,0.00008327433,0.000008497838],"category_scores_gemma":[0.000025339164,0.00016455459,0.00004892652,0.00018829448,0.00017445628,0.00032488775,0.00004124267,0.00005219165,0.000032908938],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011410274,0.00072520727,0.003490372,0.0043899463,0.00047301,0.00011189566,0.052000843,0.00017521738,0.21416414,0.0023732006,0.0040883985,0.71686673],"study_design_scores_gemma":[0.0069404566,0.002193723,0.007883438,0.0029158029,0.00036605142,0.0011866749,0.012952041,0.56810397,0.382569,0.00080563984,0.011116312,0.0029668962],"about_ca_topic_score_codex":0.00022499956,"about_ca_topic_score_gemma":0.000037140875,"teacher_disagreement_score":0.71389985,"about_ca_system_score_codex":0.000046120935,"about_ca_system_score_gemma":0.000042689575,"threshold_uncertainty_score":0.67103416},"labels":[],"label_agreement":null},{"id":"W2791609610","doi":"10.1016/j.jbiomech.2018.01.034","title":"Classifying running speed conditions using a single wearable sensor: Optimal segmentation and feature extraction methods","year":2018,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Running Injury Clinic; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Accelerometer; Feature (linguistics); Pattern recognition (psychology); Feature extraction; Computer science; Artificial intelligence; STRIDE; Gait","score_opus":0.08698380811361471,"score_gpt":0.38340314046771823,"score_spread":0.2964193323541035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791609610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18303595,0.00028371465,0.81521547,0.00025680338,0.0009961452,0.00009519514,0.0000028793627,0.000033755652,0.00008011527],"genre_scores_gemma":[0.57100123,0.000016206803,0.42856738,0.00007171512,0.00028086806,5.106912e-7,7.720541e-7,0.000010415848,0.000050895596],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987104,0.00021301623,0.00037635455,0.0002034997,0.00029923546,0.00019752284],"domain_scores_gemma":[0.9983044,0.00016311245,0.00073767826,0.00015836055,0.00051997736,0.00011643819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010564479,0.00013184149,0.00024114703,0.0004204176,0.00027829883,0.00036006235,0.00017323266,0.000119569726,0.00001592133],"category_scores_gemma":[0.000085919295,0.00012849914,0.000085764455,0.00050434616,0.000036884117,0.0016578037,0.000079193094,0.00024461487,0.00000600687],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014346705,0.00004224048,0.000006604227,0.000010266704,0.000043783308,0.000014902911,0.0004598079,0.0000148504,0.88938737,0.0000543824,0.00009353118,0.10985793],"study_design_scores_gemma":[0.0006513307,0.00043161542,0.00003018011,0.00024400474,0.00005670115,0.0034961952,0.0011273838,0.5337344,0.4579483,0.0008831876,0.0011997379,0.00019700038],"about_ca_topic_score_codex":0.000010538884,"about_ca_topic_score_gemma":0.0000023025905,"teacher_disagreement_score":0.5337195,"about_ca_system_score_codex":0.00021662847,"about_ca_system_score_gemma":0.000098570476,"threshold_uncertainty_score":0.52400434},"labels":[],"label_agreement":null},{"id":"W2795615756","doi":"10.1109/jiot.2018.2823084","title":"Locomotion Activity Recognition Using Stacked Denoising Autoencoders","year":2018,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Accelerometer; Gyroscope; Artificial intelligence; Activity recognition; Deep learning; Machine learning; Noise (video); Pattern recognition (psychology); Noise reduction; Feature extraction; Computer vision; Engineering","score_opus":0.07348675670862373,"score_gpt":0.3005656204199559,"score_spread":0.22707886371133215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795615756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4633462,0.000010872178,0.5343824,0.00013525313,0.0016640552,0.000076556025,9.899509e-7,0.000059380804,0.00032427334],"genre_scores_gemma":[0.95958114,0.000003979003,0.03972243,0.00015239333,0.00043928082,0.0000015521681,5.502329e-7,0.00001785928,0.00008082241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788684,0.00033644497,0.0005418026,0.0003343031,0.0005700775,0.0003305305],"domain_scores_gemma":[0.99767,0.0001406104,0.0009595485,0.00028817504,0.0007921419,0.00014950399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013040024,0.00019820515,0.0003137153,0.0003875795,0.00018120755,0.0004762331,0.00072282506,0.00012164,0.000100029945],"category_scores_gemma":[0.0001506913,0.00019851209,0.00018608804,0.00031579065,0.00013431127,0.0038769431,0.00013220179,0.00046791285,0.00009412644],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018984241,0.0003947918,0.0008577962,0.00008579935,0.0003049832,0.000077632365,0.013934682,0.000085739,0.26405427,0.00005675323,0.0027373885,0.7172203],"study_design_scores_gemma":[0.0012667696,0.00072061055,0.0008406463,0.0014419378,0.000058046586,0.0034966203,0.00023101487,0.5114356,0.47442406,0.004929461,0.0005745412,0.0005806508],"about_ca_topic_score_codex":0.00032972067,"about_ca_topic_score_gemma":0.000022848713,"teacher_disagreement_score":0.7166397,"about_ca_system_score_codex":0.0003053264,"about_ca_system_score_gemma":0.00014709447,"threshold_uncertainty_score":0.8095088},"labels":[],"label_agreement":null},{"id":"W2796184738","doi":"10.1145/3173574.3173693","title":"Understanding Older Users' Acceptance of Wearable Interfaces for Sensor-based Fall Risk Assessment","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Government of Canada; AGE-WELL","keywords":"Wearable computer; Computer science; USable; Interface (matter); Wearable technology; Human–computer interaction; Software deployment; Field (mathematics); Multimedia; Embedded system; Software engineering","score_opus":0.1153299718038535,"score_gpt":0.3280639400659092,"score_spread":0.2127339682620557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796184738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06529347,0.000014427202,0.9274071,0.00048693456,0.00049110793,0.0004539558,0.00001335048,0.0001465524,0.0056931167],"genre_scores_gemma":[0.9387662,0.0000032202634,0.060454816,0.0001716755,0.000056084795,0.000030592044,8.198827e-7,0.000011011851,0.0005055709],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986249,0.000120479395,0.00030845936,0.00040720004,0.00027263796,0.00026630037],"domain_scores_gemma":[0.9983286,0.0005634375,0.0002644678,0.00048173868,0.00029535,0.00006639816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057486194,0.0001443409,0.0002478723,0.00013367155,0.00015581567,0.00018884543,0.00049900584,0.00006387176,0.00010103448],"category_scores_gemma":[0.00006219682,0.00012992651,0.00009351134,0.00027213446,0.00008864718,0.0006865088,0.00010587067,0.00008909314,0.000030736228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006202632,0.0029996657,0.45582172,0.0015919481,0.0018460175,0.000019162952,0.015402405,0.0012414521,0.081443086,0.10105512,0.07110778,0.26685137],"study_design_scores_gemma":[0.005845847,0.0016732449,0.014426246,0.00082023954,0.00007757278,0.000013734209,0.0058602635,0.72532827,0.2301654,0.008093483,0.0064317025,0.001264013],"about_ca_topic_score_codex":0.00019874815,"about_ca_topic_score_gemma":0.0003386143,"teacher_disagreement_score":0.87347275,"about_ca_system_score_codex":0.00017598979,"about_ca_system_score_gemma":0.000120327524,"threshold_uncertainty_score":0.529825},"labels":[],"label_agreement":null},{"id":"W2797884605","doi":"10.1155/2018/8164376","title":"A Platform for e-Health Control and Location Services for Wandering Patients","year":2018,"lang":"en","type":"article","venue":"Mobile Information Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional; Ministerio de Economía y Competitividad; Consejo Nacional de Ciencia y Tecnología; University of Victoria","keywords":"Computer science; Mobile device; Smart device; Control (management); Bluetooth; Web service; Transmission (telecommunications); Telecommunications; Wireless; World Wide Web; Human–computer interaction; Artificial intelligence","score_opus":0.014681890340180654,"score_gpt":0.25955737440695276,"score_spread":0.2448754840667721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797884605","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028442204,0.00008241244,0.9657479,0.00010559062,0.0010621856,0.0040961625,0.00009415411,0.000178354,0.00019104671],"genre_scores_gemma":[0.9963738,0.000002282661,0.000836862,0.00042803006,0.00015833972,0.0021015087,0.00006402604,0.0000074791656,0.000027688413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869263,0.00002854084,0.0006269156,0.0001768497,0.00022577627,0.0002492804],"domain_scores_gemma":[0.9976887,0.00018735464,0.0005368496,0.0002557803,0.001235293,0.0000959939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007068902,0.00013327316,0.0002457414,0.00016759137,0.0003216587,0.0005278893,0.00023239365,0.0000712491,6.6784565e-7],"category_scores_gemma":[0.000040105842,0.00012508873,0.000035791152,0.00021000208,0.000021330728,0.00388365,0.000043908825,0.000034509616,0.000037386937],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023759106,0.00012876498,0.006203066,0.0065264073,0.00015369609,6.423916e-8,0.035830803,0.00038446952,0.000064660235,0.008001308,0.004363654,0.9381055],"study_design_scores_gemma":[0.0052528596,0.0011850864,0.0025135416,0.00037602527,0.000009251321,0.0000133147605,0.0018868615,0.6760129,0.00017588086,0.00010659083,0.31211692,0.00035080832],"about_ca_topic_score_codex":0.00014457073,"about_ca_topic_score_gemma":0.0000452949,"teacher_disagreement_score":0.96793157,"about_ca_system_score_codex":0.00011716801,"about_ca_system_score_gemma":0.0000910049,"threshold_uncertainty_score":0.5100971},"labels":[],"label_agreement":null},{"id":"W2800116075","doi":"10.2196/mhealth.8516","title":"A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute for Health and Care Research; Waseda University; NIHR Leicester Biomedical Research Centre; Loughborough University; Medical Research Council; University Hospitals of Leicester NHS Trust","keywords":"Accelerometer; Context (archaeology); Computer science; Beacon; Intervention (counseling); Focus (optics); Human–computer interaction; Wireless; Movement (music); Behavior change; Artificial intelligence; Psychology; Algorithm; Machine learning; Telecommunications; Social psychology","score_opus":0.06376294288401482,"score_gpt":0.37039875951124074,"score_spread":0.30663581662722594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800116075","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9040756,0.000112672955,0.09418435,0.00036968078,0.00016114743,0.0009619241,0.000105068844,0.000024484862,0.000005075422],"genre_scores_gemma":[0.9881714,0.000046681485,0.010337158,0.001113069,0.00005369594,0.00022100404,0.000031640462,0.000011030605,0.000014321185],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99857926,0.000061844126,0.00049019343,0.00034765777,0.00019216345,0.00032885696],"domain_scores_gemma":[0.9988629,0.00026318396,0.00039028915,0.00013430425,0.00016867653,0.00018064216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000641569,0.00015951734,0.00029300555,0.000088006025,0.0004367687,0.00012614048,0.00013509416,0.00005816089,0.0000015426224],"category_scores_gemma":[0.000011199052,0.0001296759,0.00001746217,0.000097255645,0.00011304108,0.00025000467,0.00013257521,0.0001031745,2.5808717e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014569881,0.000065306296,0.007127998,0.00016248302,0.000013424592,1.562205e-7,0.0060719233,8.211549e-10,0.0005365222,0.0001310174,0.00020122784,0.9856754],"study_design_scores_gemma":[0.0051223803,0.0017463918,0.9802368,0.00021668278,0.000053970925,0.000045650977,0.0035660104,0.0028328972,0.0032772808,0.00006287616,0.002391303,0.00044774928],"about_ca_topic_score_codex":0.0001504867,"about_ca_topic_score_gemma":0.000117214906,"teacher_disagreement_score":0.98522764,"about_ca_system_score_codex":0.000050575014,"about_ca_system_score_gemma":0.00017824431,"threshold_uncertainty_score":0.528803},"labels":[],"label_agreement":null},{"id":"W2800342439","doi":"10.1109/iscas.2018.8351648","title":"Video-based Human Fall Detection in Smart Homes Using Deep Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Telus (Canada); University of British Columbia","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Task (project management); Machine learning; Artificial neural network; Long short term memory; Deep neural networks; Task analysis; Activity recognition; Recurrent neural network; Computer vision; Real-time computing; Engineering","score_opus":0.03859909935968636,"score_gpt":0.28170587847641143,"score_spread":0.24310677911672507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800342439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45863244,0.000008324802,0.5392341,0.000039765633,0.00020392513,0.00009112076,7.1910364e-8,0.0001895677,0.0016006773],"genre_scores_gemma":[0.9962389,2.3891963e-7,0.003309537,0.0001424483,0.00010535587,0.000010624103,6.957058e-7,0.0000104461815,0.00018176556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871147,0.00021146433,0.00024143631,0.00037208133,0.00021278548,0.00025075173],"domain_scores_gemma":[0.99930537,0.00012339438,0.00009858207,0.00028086282,0.0001325203,0.00005927146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047690765,0.00012027274,0.0001621346,0.0003169131,0.00022771877,0.00020636209,0.000287765,0.00007083031,0.00004885342],"category_scores_gemma":[0.0000673509,0.00012341412,0.000053660326,0.00051115867,0.00004460873,0.00065464596,0.000103817256,0.00016117796,0.00014196937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002330547,0.00018094097,0.13298221,0.00004823285,0.00003115606,0.000027494889,0.0020783993,0.0003442883,0.14699793,0.0005068174,0.000035103294,0.7167441],"study_design_scores_gemma":[0.0011826156,0.0003519099,0.04841968,0.000115888215,0.0000066536413,0.000040195522,0.00021311603,0.8585487,0.08661901,0.0004503296,0.0035274045,0.00052451884],"about_ca_topic_score_codex":0.0017760344,"about_ca_topic_score_gemma":0.011807318,"teacher_disagreement_score":0.85820436,"about_ca_system_score_codex":0.00013456514,"about_ca_system_score_gemma":0.0000368047,"threshold_uncertainty_score":0.65887606},"labels":[],"label_agreement":null},{"id":"W2801877906","doi":"10.1016/j.medengphy.2018.04.008","title":"Detection of daily postures and walking modalities using a single chest-mounted tri-axial accelerometer","year":2018,"lang":"en","type":"article","venue":"Medical Engineering & Physics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Accelerometer; Sitting; Modalities; Computer science; Trunk; Sensitivity (control systems); Artificial intelligence; Computer vision; Physical medicine and rehabilitation; Simulation; Medicine; Engineering","score_opus":0.02739599047529057,"score_gpt":0.25020384124593875,"score_spread":0.22280785077064819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801877906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47593716,0.000036774374,0.5234722,0.000028936329,0.00034505836,0.00005656284,0.0000013665792,0.00009518415,0.000026758982],"genre_scores_gemma":[0.99614257,0.0000019762958,0.00322196,0.000044851462,0.00055878115,0.000006078691,0.0000010338367,0.000016029691,0.0000067017795],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875563,0.00004152682,0.00025755048,0.00025238594,0.0004705502,0.00022233947],"domain_scores_gemma":[0.9992343,0.00020481764,0.000094096795,0.00022708697,0.00012694854,0.000112710426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026840568,0.00014818425,0.0002530967,0.00009571519,0.000062139516,0.00008474491,0.00025958472,0.00009903024,0.0000066449843],"category_scores_gemma":[0.00026141157,0.00014574916,0.000055775658,0.00034473627,0.00008825683,0.00045751146,0.00017415275,0.00017132288,0.0000028504394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020958396,0.00013503147,0.00012464683,0.00021071981,0.00010587908,0.000009679219,0.003476754,0.00015743975,0.5531869,0.0005911118,0.0000113811175,0.4419695],"study_design_scores_gemma":[0.0006731775,0.00023627894,0.0021073576,0.00030130392,0.000021166763,0.00006842413,0.00006307279,0.81308043,0.18224238,0.00037537335,0.00052143075,0.00030960565],"about_ca_topic_score_codex":0.000112240974,"about_ca_topic_score_gemma":0.000008596869,"teacher_disagreement_score":0.812923,"about_ca_system_score_codex":0.00005608647,"about_ca_system_score_gemma":0.00004698561,"threshold_uncertainty_score":0.59434783},"labels":[],"label_agreement":null},{"id":"W2804355829","doi":"10.1016/j.ijmedinf.2018.05.001","title":"Erratum to ‘The state of knowledge on technologies and their use for fall detection: A scoping review’ [Int. J. Med. Inform. 111 (2018) 58–71]","year":2018,"lang":"en","type":"erratum","venue":"International Journal of Medical Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"State (computer science); INT; Data science; Computer science; Knowledge management; Medicine; Algorithm","score_opus":0.055055339784069124,"score_gpt":0.33909240601147733,"score_spread":0.2840370662274082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804355829","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006146879,0.009932541,0.9144323,0.01412566,0.05665397,0.0019482176,0.00017461865,0.00014773544,0.0019702988],"genre_scores_gemma":[0.1164713,0.6328918,0.0798219,0.07823346,0.035583198,0.0020210573,0.0005072314,0.0006671805,0.05380292],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99520177,0.00010929404,0.0022383186,0.00015934886,0.0020151194,0.0002761769],"domain_scores_gemma":[0.99263203,0.0014623392,0.0024049026,0.00048347298,0.00281317,0.00020410347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036868993,0.00033033715,0.0008077882,0.00075182057,0.000097279924,0.00036330443,0.0032150103,0.0003674275,0.000012647236],"category_scores_gemma":[0.0062278598,0.00020745053,0.00027518295,0.00034394345,0.00022504636,0.0012564895,0.00093919015,0.0012210001,0.000027111413],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004867956,0.00004560185,0.0000014410414,0.0013242483,0.00024963333,0.0000042189317,0.0019165956,0.0000023473779,0.0000012462149,0.000060637773,0.5489242,0.44742113],"study_design_scores_gemma":[0.0007250847,0.0009910404,0.0000125903525,0.0883126,0.000037075373,0.0007955267,0.00047821857,0.011382184,0.0002817633,0.0006228449,0.89603287,0.00032817238],"about_ca_topic_score_codex":0.000016236734,"about_ca_topic_score_gemma":0.00032638916,"teacher_disagreement_score":0.83461034,"about_ca_system_score_codex":0.00020173322,"about_ca_system_score_gemma":0.0011113274,"threshold_uncertainty_score":0.8459587},"labels":[],"label_agreement":null},{"id":"W2805082063","doi":"10.20381/ruor-21833","title":"Data storage of human participant data - the REB perspective","year":2018,"lang":"fr","type":"article","venue":"uO Research (University of Ottawa)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Computer science; Artificial intelligence","score_opus":0.6330026384911824,"score_gpt":0.4557374227282219,"score_spread":0.17726521576296056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805082063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34656823,0.006193148,0.1778362,0.19013426,0.004779494,0.005931167,0.018551005,0.0003649582,0.24964155],"genre_scores_gemma":[0.9813686,0.000087675464,0.0010548884,0.00002485779,0.00033031203,4.406167e-7,0.000077063465,0.00001547197,0.017040689],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99371076,0.002391632,0.000309126,0.0011849089,0.0016344852,0.00076908787],"domain_scores_gemma":[0.988564,0.0012893887,0.00037615697,0.0068538547,0.0026487221,0.0002678498],"candidate_categories":["sts","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.008437134,0.00018357237,0.0004597101,0.00039573686,0.0011840208,0.00015775095,0.011727272,0.00015520478,0.0009665474],"category_scores_gemma":[0.0011370765,0.00019249703,0.00008975357,0.0016233788,0.004508572,0.0031761664,0.012560557,0.0007462184,0.00042808143],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005895236,0.0019650536,0.0059755975,0.0004999681,0.0014664796,0.00045271163,0.06183175,0.0000046252208,0.006840616,0.3942958,0.45976382,0.06684461],"study_design_scores_gemma":[0.0017439405,0.0018534644,0.031608593,0.0007088392,0.00025727222,0.00006840793,0.08354477,0.08566116,0.000804841,0.003728984,0.78943926,0.00058045407],"about_ca_topic_score_codex":0.012954878,"about_ca_topic_score_gemma":0.032186117,"teacher_disagreement_score":0.6348004,"about_ca_system_score_codex":0.00020475143,"about_ca_system_score_gemma":0.0008876314,"threshold_uncertainty_score":0.9999467},"labels":[],"label_agreement":null},{"id":"W2807971989","doi":"10.1007/s00607-018-0631-8","title":"On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition","year":2018,"lang":"en","type":"article","venue":"Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Provisioning; Personalization; Cloud computing; Context (archaeology); World Wide Web; Service provider; Service (business); Service delivery framework; Mobile computing; Mobile QoS; Computer network; Business","score_opus":0.055359060092074815,"score_gpt":0.30339243517674586,"score_spread":0.24803337508467105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807971989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4733688,0.000058202317,0.52487427,0.00022743871,0.0005024208,0.0006990316,0.000009830741,0.0001953215,0.000064707325],"genre_scores_gemma":[0.9803963,6.0707765e-7,0.016522724,0.0025539836,0.0004445282,0.000029769864,0.000010376156,0.000030391846,0.000011362247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979647,0.00021136503,0.00036540406,0.0007463039,0.0002965977,0.0004156211],"domain_scores_gemma":[0.99730784,0.00093938835,0.0003262808,0.00036401252,0.00092136743,0.000141088],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005551847,0.0002761185,0.00036916937,0.00014988089,0.00083799724,0.00035099773,0.00045291494,0.000109627246,0.000007249365],"category_scores_gemma":[0.000036701305,0.00029087334,0.00007780462,0.0005287128,0.00006916512,0.0005709131,0.0003744403,0.00017889537,0.00003539028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014592325,0.00082054356,0.0020813604,0.001411393,0.0006786009,0.00004867861,0.099571586,0.0055206507,0.041547254,0.027750598,0.0016166912,0.8174934],"study_design_scores_gemma":[0.0015531894,0.0003967172,0.00021071789,0.0007587915,0.000021889782,0.000078380814,0.0015042212,0.99201375,0.0020598934,0.0003686732,0.0006515848,0.00038219918],"about_ca_topic_score_codex":0.00023930584,"about_ca_topic_score_gemma":0.00008453157,"teacher_disagreement_score":0.9864931,"about_ca_system_score_codex":0.00015401644,"about_ca_system_score_gemma":0.00010577981,"threshold_uncertainty_score":0.99995434},"labels":[],"label_agreement":null},{"id":"W2808891699","doi":"10.1007/978-3-319-94523-1_5","title":"Technological Approach for Early and Unobtrusive Detection of Possible Health Changes Toward More Effective Treatment","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Cognition; Process (computing); Cognitive decline; Elderly people; Human–computer interaction; Artificial intelligence; Gerontology; Psychology; Dementia; Medicine; Psychiatry","score_opus":0.03594056717654879,"score_gpt":0.2785596417475892,"score_spread":0.2426190745710404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808891699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024099725,0.0005647005,0.99350476,0.0005635674,0.00041887118,0.0022286666,0.000025473857,0.00015549267,0.0001284893],"genre_scores_gemma":[0.8661411,0.000048094764,0.13307631,0.00022270491,0.00022924693,0.00019578301,0.0000045445167,0.00002286006,0.000059361613],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99722177,0.000067405024,0.00036392538,0.001407855,0.00046814402,0.00047088324],"domain_scores_gemma":[0.99773926,0.0006139507,0.0004830975,0.0006892258,0.00034686472,0.00012761244],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076059625,0.0004428468,0.0007647671,0.00081683754,0.0002220621,0.00020514196,0.00092416647,0.0003588708,0.0000017531561],"category_scores_gemma":[0.00008453241,0.0003589325,0.0001148307,0.0004805803,0.0012466474,0.00030781186,0.0005392958,0.0002804861,0.0000024965689],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019635845,0.000045593104,0.000035167446,0.000098356824,0.000026025675,0.0000030443882,0.0022039441,0.00006439531,0.00046319925,0.0005363514,9.891689e-7,0.9965033],"study_design_scores_gemma":[0.0047792904,0.028712101,0.003974038,0.0023769888,0.000098113065,0.0007868899,0.00002564151,0.66703516,0.16020563,0.12617217,0.002428192,0.0034057868],"about_ca_topic_score_codex":0.00008533877,"about_ca_topic_score_gemma":0.000101958874,"teacher_disagreement_score":0.9930975,"about_ca_system_score_codex":0.0005206981,"about_ca_system_score_gemma":0.00026273975,"threshold_uncertainty_score":0.9998863},"labels":[],"label_agreement":null},{"id":"W2809763015","doi":"10.1109/uic-atc.2017.8397534","title":"A 3D simulator for intelligent environment experiments","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Domain (mathematical analysis); Human–computer interaction; Set (abstract data type); Interface (matter); Virtual machine; Scratch; Virtual reality; Virtual Laboratory; Simulation; Multimedia; Operating system","score_opus":0.09118343824958178,"score_gpt":0.3281632756206935,"score_spread":0.2369798373711117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809763015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009788092,0.000036310303,0.98508376,0.00044714528,0.0005121842,0.00049780705,0.0000032422784,0.00008744814,0.0035440028],"genre_scores_gemma":[0.97996265,0.000003579071,0.017059196,0.00019252549,0.00007475475,0.00013875811,9.3152437e-7,0.0000079962265,0.0025596388],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990723,0.000020745978,0.00017343258,0.00034781045,0.00018859946,0.00019711633],"domain_scores_gemma":[0.9986008,0.00009238829,0.00013206647,0.0010515434,0.000028847537,0.00009437467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018351965,0.0001094742,0.00013945415,0.00003568962,0.00030165777,0.00038041934,0.0008852447,0.000039332004,0.00011594694],"category_scores_gemma":[0.000045038607,0.000098222656,0.00008080483,0.000012192772,0.000031173007,0.00057055376,0.00038839038,0.000035772617,0.00043570672],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002657018,0.00045191307,0.002316957,0.000028291162,0.00012164379,0.00000930081,0.0016786925,0.000091316775,0.0055517955,0.008531985,0.003093897,0.9780976],"study_design_scores_gemma":[0.002066419,0.00038520945,0.0026840968,0.00006163167,0.000015124106,0.000017339411,0.00020393592,0.25902972,0.18227309,0.0022530458,0.55013037,0.0008800373],"about_ca_topic_score_codex":0.000040117815,"about_ca_topic_score_gemma":0.0000051086217,"teacher_disagreement_score":0.9772176,"about_ca_system_score_codex":0.000065870285,"about_ca_system_score_gemma":0.00001993085,"threshold_uncertainty_score":0.5600274},"labels":[],"label_agreement":null},{"id":"W2810303044","doi":"10.1145/3197768.3201522","title":"Modeling Human Activities Using Behaviour Trees in Smart Homes","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Home automation; Computer science; Probabilistic logic; Process (computing); Human–computer interaction; Activities of daily living; Population; Data science; Artificial intelligence","score_opus":0.08066133232424025,"score_gpt":0.3144272615547672,"score_spread":0.23376592923052694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810303044","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6596253,0.0000176676,0.3378818,0.00005501389,0.0002024658,0.00008906081,9.063461e-7,0.0001474051,0.0019803965],"genre_scores_gemma":[0.99487823,7.7365695e-7,0.004582398,0.00007689801,0.00012028546,0.00001129835,8.9166747e-7,0.0000113677725,0.00031785967],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987519,0.00009618427,0.00025806724,0.00037322546,0.00023529095,0.00028538122],"domain_scores_gemma":[0.99936956,0.00004259879,0.000055361903,0.00039362218,0.000079883896,0.000058957623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003072623,0.00014291046,0.00021493227,0.00029631873,0.00017345021,0.00025266546,0.00042979943,0.00006522676,0.000054759912],"category_scores_gemma":[0.0000132125515,0.00014074949,0.00005986069,0.00030512735,0.00005168174,0.0013431683,0.00022167839,0.000108716806,0.000034502365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052980096,0.0014540973,0.32902056,0.000084460626,0.00017239102,0.00015234048,0.027638745,0.0018528061,0.37673298,0.020305129,0.000665868,0.24186766],"study_design_scores_gemma":[0.0005765977,0.00011478737,0.0057944395,0.00011940367,0.0000072555804,0.00005091619,0.0010608229,0.96348053,0.027127752,0.0011342738,0.00006576675,0.00046746258],"about_ca_topic_score_codex":0.0027031163,"about_ca_topic_score_gemma":0.0077403034,"teacher_disagreement_score":0.9616277,"about_ca_system_score_codex":0.00009494292,"about_ca_system_score_gemma":0.00005767771,"threshold_uncertainty_score":0.5739597},"labels":[],"label_agreement":null},{"id":"W2810573466","doi":"10.1109/uic-atc.2017.8397511","title":"A position-independent method for soil types recognition using inertial data from a wearable device","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Université du Québec à Chicoutimi","funders":"","keywords":"Wearable computer; Gyroscope; Accelerometer; Computer science; Random forest; Position (finance); Independence (probability theory); Wearable technology; Artificial intelligence; SIGNAL (programming language); Pattern recognition (psychology); Inertial measurement unit; Embedded system; Engineering; Mathematics; Statistics","score_opus":0.21217323521685222,"score_gpt":0.38564144836547026,"score_spread":0.17346821314861804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810573466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027697017,0.00006423055,0.9678852,0.0010100671,0.00079210126,0.00042500452,0.00023860861,0.00015016049,0.0017376215],"genre_scores_gemma":[0.5909745,0.0000053596013,0.40769896,0.00044170435,0.00044599528,0.000046682922,0.00013019222,0.000018983765,0.00023763184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824584,0.00015639226,0.0002946649,0.000749021,0.00029119136,0.00026288175],"domain_scores_gemma":[0.9970152,0.00042922204,0.00031529344,0.0018480585,0.0002918371,0.00010040693],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00084896805,0.00016286851,0.0002568347,0.00008677357,0.0006154743,0.0011470543,0.0017617311,0.00010911877,0.00010521051],"category_scores_gemma":[0.00033088453,0.00015863637,0.000072530114,0.000075828684,0.000022633643,0.0030408897,0.0008624603,0.000114109855,0.00020798492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012687522,0.0002739119,0.0005607753,0.00005972246,0.00030373872,0.00002312584,0.00044327855,0.00004335024,0.0630377,0.00028180346,0.0025895108,0.9322562],"study_design_scores_gemma":[0.001326354,0.000059519327,0.00082132634,0.00019209796,0.000081427606,0.000062161715,0.00006841196,0.963348,0.026236303,0.0050314134,0.0023494873,0.0004234975],"about_ca_topic_score_codex":0.008649899,"about_ca_topic_score_gemma":0.0036533356,"teacher_disagreement_score":0.96330464,"about_ca_system_score_codex":0.00006859949,"about_ca_system_score_gemma":0.0001729681,"threshold_uncertainty_score":0.99988985},"labels":[],"label_agreement":null},{"id":"W2811401409","doi":"10.1109/uic-atc.2017.8397512","title":"Transportable and scalable system for activities and exercises recognition in real-time","year":2017,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Scalability; Wearable computer; Window (computing); Human–computer interaction; Embedded system; Operating system","score_opus":0.038287690453397374,"score_gpt":0.2600825899801478,"score_spread":0.22179489952675047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811401409","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94095933,0.00007819339,0.039696366,0.00032358992,0.00023375974,0.0008959763,0.00004380936,0.00026027396,0.017508669],"genre_scores_gemma":[0.9941036,0.000064670225,0.0042649745,0.000013755796,0.0000334649,0.00015078305,0.0000045932848,0.000010085699,0.0013540766],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99901676,0.00003933743,0.00021080281,0.0003947066,0.000114762894,0.00022363976],"domain_scores_gemma":[0.9991162,0.0002324464,0.00013810355,0.00037600205,0.00006106376,0.00007622915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046845883,0.00012983882,0.00029646125,0.00011555494,0.0003172468,0.00058175216,0.00023077503,0.00007520157,0.000009893703],"category_scores_gemma":[0.000031140353,0.00012498634,0.000031778298,0.00004988091,0.00006117082,0.0020965235,0.00006643021,0.000050322556,0.000011084835],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020839725,0.00020662266,0.027951598,0.0016160145,0.000070658345,0.00004115525,0.0025594307,0.0000019359195,0.02587902,0.0036060186,0.0010259835,0.93683314],"study_design_scores_gemma":[0.02711068,0.0018884388,0.36438388,0.008469423,0.00034070647,0.0011456751,0.010070757,0.20282339,0.34778318,0.020410318,0.00925232,0.006321252],"about_ca_topic_score_codex":0.0012700579,"about_ca_topic_score_gemma":0.00060542853,"teacher_disagreement_score":0.9305119,"about_ca_system_score_codex":0.00003620677,"about_ca_system_score_gemma":0.000035512112,"threshold_uncertainty_score":0.56098497},"labels":[],"label_agreement":null},{"id":"W2834138627","doi":"10.1109/i2mtc.2018.8409727","title":"Preliminary results for measurement and classification of overnight wandering by dementia patient using multi-sensors","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Dementia; Residence; Spouse; Medicine; Sleep (system call); Computer science; Computer security","score_opus":0.1286678745681576,"score_gpt":0.2908782534480659,"score_spread":0.1622103788799083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2834138627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30225846,0.00008873281,0.69666624,0.0001055117,0.00017222344,0.00040068626,0.000013247997,0.00004208193,0.00025278336],"genre_scores_gemma":[0.9724458,0.0000019598297,0.027445074,0.000025044119,0.000021705206,0.000019348992,0.0000025591735,0.000006198525,0.00003233797],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879676,0.00006348955,0.00035364588,0.000333195,0.00030740508,0.00014547819],"domain_scores_gemma":[0.9989299,0.000076823264,0.00023923654,0.00027204363,0.0004298463,0.00005211497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044217161,0.00009664674,0.00012926637,0.0000626266,0.000111268564,0.000056080353,0.00013804532,0.000042056647,0.0000022613629],"category_scores_gemma":[0.000076252574,0.000089519075,0.000034220724,0.00009407664,0.000042653628,0.00033102074,0.0000997999,0.000028798051,0.0000029404232],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043924392,0.0005135218,0.0023890224,0.00015481465,0.000265911,8.853443e-7,0.0050490173,0.000016924743,0.8313696,0.0002844801,0.0042762924,0.15524031],"study_design_scores_gemma":[0.0026519622,0.0012501745,0.012858038,0.00019625259,0.000055397802,0.000013980131,0.00046207503,0.5794059,0.39767787,0.00005508903,0.005011301,0.0003619283],"about_ca_topic_score_codex":0.00006389799,"about_ca_topic_score_gemma":0.000045293727,"teacher_disagreement_score":0.6701873,"about_ca_system_score_codex":0.00006503371,"about_ca_system_score_gemma":0.000034252218,"threshold_uncertainty_score":0.3650482},"labels":[],"label_agreement":null},{"id":"W2885310130","doi":"10.3390/s18082474","title":"Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Northern Ireland Community Relations Council","keywords":"Wearable computer; Loneliness; Random forest; Artificial intelligence; Sitting; Computer science; Machine learning; Classifier (UML); Human–computer interaction; Psychology; Medicine; Embedded system","score_opus":0.058981283372181975,"score_gpt":0.3097974550540443,"score_spread":0.2508161716818623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885310130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99102706,0.00008704677,0.007982318,0.000013639087,0.00014159977,0.00053164107,0.0000019112217,0.000104877196,0.00010988837],"genre_scores_gemma":[0.9994506,6.70556e-7,0.00043451836,0.00002180351,0.000047488775,0.000005992951,1.6716216e-7,0.0000148441795,0.000023917017],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974533,0.0010053556,0.00042703055,0.0005401325,0.00031834625,0.00025588248],"domain_scores_gemma":[0.9987807,0.00033723254,0.0002661304,0.00034110583,0.00020348845,0.00007128926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015915115,0.00016846553,0.0003211142,0.00037726716,0.00019238084,0.000082591425,0.00016097869,0.000076101256,0.000012153907],"category_scores_gemma":[0.00033039742,0.00017593602,0.00005179243,0.00071049755,0.00010912674,0.00030356835,0.00011331543,0.000247688,0.000008773463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019426405,0.000636832,0.5301104,0.00011643497,0.00005323482,0.000056110173,0.0073515987,0.003401947,0.40594676,0.0000046975297,8.780762e-7,0.0521268],"study_design_scores_gemma":[0.002037875,0.0010996122,0.055871397,0.00024203131,0.000019316798,0.00002547246,0.0020839833,0.8179921,0.120025195,0.000053894957,0.00014638994,0.0004027599],"about_ca_topic_score_codex":0.0011877354,"about_ca_topic_score_gemma":0.001588126,"teacher_disagreement_score":0.81459016,"about_ca_system_score_codex":0.00012578435,"about_ca_system_score_gemma":0.00006821994,"threshold_uncertainty_score":0.71744627},"labels":[],"label_agreement":null},{"id":"W2887221062","doi":"10.1109/jbhi.2018.2864686","title":"Monitoring Health Status in Long Term Care Through the Use of Ambient Technologies and Serious Games","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"AGE-WELL","keywords":"Long-term care; Dementia; Health care; Minimum Data Set; Set (abstract data type); Term (time); Emergency department; Assisted living; Activities of daily living; Gerontology; Medicine; Cognition; Medical emergency; Health data; Anxiety; Computer science; Nursing; Nursing homes; Psychiatry","score_opus":0.07872864018102277,"score_gpt":0.3483670499409431,"score_spread":0.2696384097599203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887221062","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86454606,0.004070994,0.12058894,0.0091335755,0.0012801533,0.00032480995,0.000011987528,0.0000365859,0.000006908538],"genre_scores_gemma":[0.98494875,0.004776257,0.009689353,0.00048151502,0.000097014265,0.0000020614953,3.548546e-7,0.0000030216625,0.0000016596078],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99807143,0.00006478709,0.00111539,0.000064140935,0.0003946782,0.00028955302],"domain_scores_gemma":[0.99838877,0.0001382097,0.000978139,0.00016639344,0.0001812345,0.00014722695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006804287,0.00009447834,0.0003451385,0.00019805605,0.00013551473,0.00009628511,0.00023941397,0.00006708605,4.9130847e-7],"category_scores_gemma":[0.00006305655,0.00005875043,0.000027885008,0.00030042918,0.0003124828,0.00080982177,0.0001274133,0.0002753897,6.5223577e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009293772,0.000031649295,0.011599262,0.00058588514,0.000012753584,0.0000030300469,0.034018178,0.0000012411899,0.000008026329,0.000020247491,0.00041199176,0.95329845],"study_design_scores_gemma":[0.008063966,0.015092454,0.717527,0.01696574,0.000033561282,0.0028484918,0.10702023,0.009352112,0.0034121461,0.0011895907,0.11753364,0.0009610843],"about_ca_topic_score_codex":0.000271779,"about_ca_topic_score_gemma":0.00010636914,"teacher_disagreement_score":0.9523374,"about_ca_system_score_codex":0.00011660264,"about_ca_system_score_gemma":0.00040640924,"threshold_uncertainty_score":0.23957731},"labels":[],"label_agreement":null},{"id":"W2888460178","doi":"10.3390/s18082725","title":"Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quaternion; Wearable computer; Orientation (vector space); Computer science; Artificial intelligence; Computer vision; Frame (networking); Units of measurement; Wireless sensor network; Mathematics; Embedded system","score_opus":0.06201171240997967,"score_gpt":0.3019740769788967,"score_spread":0.23996236456891706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888460178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5576863,0.0000014447725,0.44018936,0.0006402747,0.00041629048,0.0003886962,0.00009566453,0.00014653022,0.00043541106],"genre_scores_gemma":[0.9935449,0.0000026068838,0.005592467,0.00013287691,0.0001957611,0.000044334876,0.000080354985,0.000019372239,0.00038736896],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978146,0.00042052192,0.0003799197,0.00053373753,0.00049666315,0.00035453326],"domain_scores_gemma":[0.99845797,0.00020518001,0.00018131045,0.0004151945,0.0005379665,0.00020236315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027844403,0.00020743822,0.00021842043,0.0003130866,0.00049322884,0.00030128635,0.00026212703,0.00010057294,0.00017188356],"category_scores_gemma":[0.00014141836,0.00022307123,0.00008905328,0.0008436071,0.00006430228,0.0012068885,0.000071971794,0.00016460128,0.00061235344],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049898337,0.0012848788,0.0021959972,0.00010154356,0.00040997835,0.000027171862,0.020208651,0.0018666711,0.7681729,0.0009948788,0.0037227261,0.20051563],"study_design_scores_gemma":[0.0022981383,0.00039513034,0.010942791,0.00026882542,0.000077639794,0.00019649955,0.0010106716,0.7447801,0.23500665,0.0010692999,0.0028501167,0.0011040893],"about_ca_topic_score_codex":0.001040294,"about_ca_topic_score_gemma":0.00027998406,"teacher_disagreement_score":0.7429135,"about_ca_system_score_codex":0.00012129458,"about_ca_system_score_gemma":0.0000973534,"threshold_uncertainty_score":0.9096581},"labels":[],"label_agreement":null},{"id":"W2888789276","doi":"10.1016/j.neucom.2018.08.033","title":"Recognizing multi-resident activities in non-intrusive sensor-based smart homes by formal concept analysis","year":2018,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Formal concept analysis; Activity recognition; Home automation; Artificial intelligence; Telecommunications; Algorithm","score_opus":0.021295774177347463,"score_gpt":0.2688916674304313,"score_spread":0.24759589325308384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888789276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4841922,0.000022671182,0.5144896,0.0001973608,0.0003959929,0.00028342928,0.000008005779,0.000192686,0.0002180437],"genre_scores_gemma":[0.98968637,0.0000012563941,0.009149877,0.0008137481,0.00019949125,0.000024329973,0.000009971597,0.000025115112,0.0000898352],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969213,0.00038403028,0.00060906773,0.000887313,0.0004508272,0.0007474722],"domain_scores_gemma":[0.99776447,0.00091671705,0.00041203512,0.00054999546,0.00021114467,0.00014561987],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057915173,0.00032958115,0.0005839595,0.0007113034,0.00036127015,0.00043165198,0.00070374657,0.00011099016,0.000025682399],"category_scores_gemma":[0.00014289308,0.00035664346,0.00023688175,0.0017032148,0.000120970144,0.0010967623,0.00042951613,0.00034622292,0.000058582777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011899033,0.0007029026,0.22293615,0.00016353435,0.0007928878,0.00032197495,0.022501625,0.005110412,0.037563212,0.000075683485,0.0025754229,0.7071372],"study_design_scores_gemma":[0.001820569,0.00020827948,0.038347222,0.00014380814,0.000064923806,0.000026134363,0.00092893216,0.9142894,0.042662628,0.0000127232,0.00083015685,0.00066518225],"about_ca_topic_score_codex":0.0006849917,"about_ca_topic_score_gemma":0.00035031527,"teacher_disagreement_score":0.90917903,"about_ca_system_score_codex":0.00013260444,"about_ca_system_score_gemma":0.000098142074,"threshold_uncertainty_score":0.99988854},"labels":[],"label_agreement":null},{"id":"W2888935455","doi":"10.2196/11883","title":"A Protocol-Driven, Digital Conversational Agent at the Hospital Bedside to Support Nurse Teams and to Mitigate Delirium and Falls Risk","year":2018,"lang":"en","type":"article","venue":"Iproceedings","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health","keywords":"Delirium; Activity-based costing; Protocol (science); Medical emergency; Medicine; Nursing; Intensive care medicine; Business; Alternative medicine","score_opus":0.012996923310113455,"score_gpt":0.264111573811392,"score_spread":0.25111465050127857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888935455","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96604806,0.000002437095,0.008414398,0.0053435145,0.00023845461,0.018146515,0.00005529933,0.00014053112,0.0016107783],"genre_scores_gemma":[0.98928344,3.8803597e-7,0.0007060207,0.0014500263,0.00021398035,0.0076518687,0.0000024688063,0.000015077651,0.00067675224],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984037,0.000015060237,0.00026238023,0.00060787407,0.00039177036,0.000319193],"domain_scores_gemma":[0.9986455,0.00009384032,0.00014834435,0.00020163928,0.0005843889,0.00032630586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027265694,0.00020067998,0.00018301726,0.00009858569,0.0003193082,0.0007450309,0.00037428728,0.00005863147,0.000025579744],"category_scores_gemma":[0.00025827324,0.00015750687,0.000045556495,0.00026612403,0.00017734198,0.0010054386,0.00050271046,0.000106225954,0.0003891889],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002543139,0.00034862233,0.31514353,0.00014080023,0.00022965355,0.000042132637,0.098988615,0.000003455536,0.008559545,0.0014476654,0.18997946,0.38486218],"study_design_scores_gemma":[0.006174542,0.008320897,0.2337858,0.00054046314,0.00008902428,0.00086176256,0.008581858,0.008052439,0.028787931,0.0024625266,0.69959176,0.0027509835],"about_ca_topic_score_codex":0.00006605758,"about_ca_topic_score_gemma":0.000046972793,"teacher_disagreement_score":0.5096123,"about_ca_system_score_codex":0.000097172415,"about_ca_system_score_gemma":0.00007656035,"threshold_uncertainty_score":0.718435},"labels":[],"label_agreement":null},{"id":"W2890646019","doi":"","title":"Proceedings of the 2nd ACM workshop on middleware for context-aware applications in the IoT : In conjunction with ACM/IFIP/USENIX ACM international middleware conference : December 8 2015 : Vancouver, Canada","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Middleware (distributed applications); Computer science; Context (archaeology); Conjunction (astronomy); World Wide Web; Software engineering; Database; Geography; Archaeology; Physics","score_opus":0.03921827161001111,"score_gpt":0.2537443481174112,"score_spread":0.21452607650740008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890646019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5508002,0.0013923453,0.27076933,0.13131449,0.0049159965,0.016923834,0.0015826414,0.000542489,0.021758702],"genre_scores_gemma":[0.99082464,0.00003564939,0.005049435,0.00027486417,0.000034228415,0.0012764885,0.000173279,0.000038080194,0.0022933064],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99529886,0.0012455978,0.00084298954,0.0010998129,0.0010922229,0.00042049278],"domain_scores_gemma":[0.9846313,0.0042305007,0.0011910716,0.0038146863,0.006000651,0.00013177993],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0037846386,0.00046990375,0.00055807474,0.00028812094,0.0002683869,0.00048192163,0.008337054,0.00030148457,0.000019694106],"category_scores_gemma":[0.004386112,0.00035597096,0.00016164422,0.0009627143,0.00023736188,0.00032075672,0.0029430075,0.00096150086,0.0000030296962],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009260937,0.0066688904,0.13413385,0.0024142868,0.0010380243,0.00002386731,0.15918483,0.0018173654,0.0009891153,0.099016495,0.22351414,0.37027305],"study_design_scores_gemma":[0.016820822,0.000035561952,0.070943855,0.044464964,0.0003961634,0.00015206065,0.060927577,0.10119665,0.030445267,0.07786689,0.5905673,0.0061829044],"about_ca_topic_score_codex":0.05508904,"about_ca_topic_score_gemma":0.8580053,"teacher_disagreement_score":0.8029163,"about_ca_system_score_codex":0.00073160016,"about_ca_system_score_gemma":0.0020611698,"threshold_uncertainty_score":0.99988925},"labels":[],"label_agreement":null},{"id":"W2895271991","doi":"10.1007/s12652-018-1070-2","title":"Clustering of human activities from emerging movements","year":2018,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Cluster analysis; Exploit; Activity recognition; Artificial intelligence; Machine learning; Flocking (texture); Field (mathematics); Computational intelligence; Data mining; Pattern recognition (psychology)","score_opus":0.054233860057499104,"score_gpt":0.312012035779129,"score_spread":0.2577781757216299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895271991","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54205877,0.00009701485,0.45701423,0.000021823103,0.00050269224,0.00005297503,8.2417745e-7,0.000017263363,0.00023439266],"genre_scores_gemma":[0.9903456,0.000018725998,0.009079404,0.00009481761,0.00042156896,4.7521448e-7,4.656461e-7,0.0000098688015,0.000029079338],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99811035,0.00010571953,0.00088380696,0.00023973951,0.00043035127,0.00023004223],"domain_scores_gemma":[0.9979627,0.00023274137,0.001075783,0.00024082893,0.00040087826,0.00008705936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007099005,0.00016104127,0.0004191931,0.00030082784,0.00028658507,0.00020645026,0.00064075005,0.000046170197,0.000052635965],"category_scores_gemma":[0.000043926426,0.00015351937,0.00011971762,0.00020040524,0.000115372735,0.0007396992,0.00044068476,0.00020740801,0.0000049810296],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088501525,0.00036507295,0.0062540295,0.00011792966,0.00045383215,0.000049342,0.045980763,0.00019989442,0.13846517,0.0035672395,0.00013748946,0.80432075],"study_design_scores_gemma":[0.002141665,0.0037369626,0.010191765,0.0042944127,0.00010929367,0.00032217582,0.015463485,0.33284146,0.6106444,0.017546853,0.0014366569,0.0012708758],"about_ca_topic_score_codex":0.00013773926,"about_ca_topic_score_gemma":0.000040015104,"teacher_disagreement_score":0.80304986,"about_ca_system_score_codex":0.000052618838,"about_ca_system_score_gemma":0.000038292714,"threshold_uncertainty_score":0.62603384},"labels":[],"label_agreement":null},{"id":"W2895837758","doi":"10.1016/j.jalz.2018.06.2033","title":"TD‐P‐017: LONGITUDINAL, VISION‐BASED MONITORING OF CHANGES OF GAIT IN DEMENTIA: A PILOT STUDY","year":2018,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute","funders":"","keywords":"Gait; Balance (ability); Physical medicine and rehabilitation; Dementia; STRIDE; Rehabilitation; Longitudinal study; Medicine; Gait analysis; Physical therapy; Psychology; Disease","score_opus":0.09909935500063173,"score_gpt":0.3275872110597788,"score_spread":0.22848785605914707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895837758","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94899017,0.014222799,0.032751434,0.00029085577,0.0016449705,0.0014302476,0.000010624337,0.00013573353,0.0005231823],"genre_scores_gemma":[0.9956448,0.000008479061,0.0040837633,0.000029153538,0.000121654186,0.0000888127,0.0000017750665,0.000019189085,0.0000023585476],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974975,0.00025398072,0.0006497176,0.00058891065,0.0006404599,0.00036945814],"domain_scores_gemma":[0.998049,0.00016674813,0.0004567035,0.0008522184,0.00039046316,0.00008486276],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008859826,0.00023899034,0.0004497921,0.0004632084,0.00009718847,0.000076469645,0.0009034071,0.000045177658,0.00008682221],"category_scores_gemma":[0.000031673717,0.00024601386,0.000070580514,0.00084247004,0.00010956431,0.0005359651,0.0004514384,0.00011744937,0.00004863316],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012423091,0.0042103003,0.8763277,0.00003950796,0.008820121,0.00002940497,0.0019008083,0.000011838064,0.01915043,0.00015217236,0.00040273002,0.08883077],"study_design_scores_gemma":[0.003450722,0.007611511,0.7481954,0.00036269677,0.0039099683,0.000006781781,0.0005004585,0.002210131,0.23223287,0.00011630799,0.0007574581,0.0006456713],"about_ca_topic_score_codex":0.0007427334,"about_ca_topic_score_gemma":0.0009885292,"teacher_disagreement_score":0.21308243,"about_ca_system_score_codex":0.000009836091,"about_ca_system_score_gemma":0.00007919325,"threshold_uncertainty_score":0.9999992},"labels":[],"label_agreement":null},{"id":"W2897101290","doi":"10.1109/msmc.2018.2806565","title":"An Emerging Wearable World: New Gadgetry Produces a Rising Tide of Changes and Challenges","year":2018,"lang":"en","type":"article","venue":"IEEE Systems Man and Cybernetics Magazine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Jiangxi Provincial Department of Science and Technology; Natural Science Foundation of Beijing Municipality; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Wearable computer; Wearable technology; Computer science; Computer security; Human–computer interaction; Embedded system","score_opus":0.050730584522862,"score_gpt":0.27825742005344417,"score_spread":0.22752683553058217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897101290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8979243,0.042492203,0.03147217,0.003606664,0.0031173914,0.0012692506,0.000015154063,0.0004711542,0.019631688],"genre_scores_gemma":[0.99262565,0.0011848309,0.0011049499,0.00004177661,0.0007397752,0.00001118204,9.77419e-7,0.000023930372,0.004266948],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982147,0.00017769322,0.0003540224,0.0006150759,0.0003164003,0.00032209232],"domain_scores_gemma":[0.998637,0.00009046035,0.0002815744,0.0005749498,0.00020115735,0.00021480901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005969621,0.00023337404,0.00041845138,0.0003307425,0.00012324502,0.00025346613,0.00033932412,0.00007093468,0.0000065835275],"category_scores_gemma":[0.000020575953,0.00022372628,0.00002750996,0.0003328295,0.00012995726,0.00041201728,0.00009526855,0.00010983939,0.00003271761],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000568241,0.00027289538,0.0034746502,0.0018702084,0.00018369537,0.00003795456,0.016245928,0.00002957043,0.057541598,0.006332049,0.0045287497,0.90942585],"study_design_scores_gemma":[0.0081801815,0.006733962,0.05669527,0.011791399,0.00035048486,0.0020737483,0.0060343347,0.18142462,0.10220044,0.0046840743,0.61427987,0.005551599],"about_ca_topic_score_codex":0.00043559953,"about_ca_topic_score_gemma":0.0025548139,"teacher_disagreement_score":0.9038743,"about_ca_system_score_codex":0.000019468192,"about_ca_system_score_gemma":0.00005211196,"threshold_uncertainty_score":0.9123293},"labels":[],"label_agreement":null},{"id":"W2898572688","doi":"10.1109/globecom48099.2022.10001500","title":"Real-time Context-aware learning System for IoT Applications","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Context-Aware Activity Recognition 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":"Seneca Polytechnic; Université de Moncton","funders":"","keywords":"Computer science; Context (archaeology); Mobile device; Machine learning; Artificial intelligence; Mobile computing; Context switch; Context awareness; Server; Real-time computing; Context model; Internet of Things; Distributed computing; Embedded system; Computer network; Operating system","score_opus":0.04302834273524246,"score_gpt":0.2936228094687547,"score_spread":0.2505944667335122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898572688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052733985,0.0015947181,0.9353439,0.007125546,0.0018071922,0.006204044,0.0033463489,0.0034009549,0.03590386],"genre_scores_gemma":[0.98102343,0.00013719255,0.008156231,0.00024319948,0.00008928838,0.008252801,0.00055733963,0.000040048417,0.001500475],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99470896,0.0016427939,0.0009847529,0.0011007943,0.0008324657,0.00073024875],"domain_scores_gemma":[0.99278677,0.0009992105,0.0007828774,0.0043694694,0.0007732518,0.0002884158],"candidate_categories":["metaepi_narrow","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.0014651819,0.00045972195,0.00068018964,0.00022855419,0.0035851728,0.0006279669,0.0074872025,0.00013167344,0.00033470907],"category_scores_gemma":[0.00009599644,0.00058262184,0.00034318233,0.0020104195,0.00021007871,0.0006122152,0.0035692535,0.0008362727,0.0004356506],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114604256,0.0012396874,0.0015122261,0.00027229253,0.00055081735,0.000018562221,0.0019724413,0.0015977874,0.004587792,0.59604937,0.029857282,0.36222717],"study_design_scores_gemma":[0.0022655185,0.00051685324,0.0005602202,0.0001301149,0.00014446198,0.00046730027,0.009658171,0.37881947,0.00028367605,0.0048813755,0.60056466,0.0017082071],"about_ca_topic_score_codex":0.0010614668,"about_ca_topic_score_gemma":0.00043175978,"teacher_disagreement_score":0.97575,"about_ca_system_score_codex":0.0016681162,"about_ca_system_score_gemma":0.00103692,"threshold_uncertainty_score":0.9996625},"labels":[],"label_agreement":null},{"id":"W2900035673","doi":"10.1108/jet-04-2018-0022","title":"An intelligent video-monitoring system to detect falls: a proof of concept","year":2018,"lang":"en","type":"article","venue":"Journal of Enabling Technologies","topic":"Context-Aware Activity Recognition Systems","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":"Centre intégré universitaire de santé et de services sociaux de l'Est-de-l'Île-de-Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université de Montréal","funders":"","keywords":"Logbook; Apartment; Context (archaeology); Computer science; Test (biology); Originality; Proof of concept; Simulation; Artificial intelligence; Computer security; Psychology; Engineering; Geography; Social psychology","score_opus":0.047796828388824826,"score_gpt":0.3064017659684371,"score_spread":0.2586049375796123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900035673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29587966,0.0008188839,0.70119697,0.00027635635,0.0010712843,0.00021567779,0.0000014477041,0.00047988753,0.000059835955],"genre_scores_gemma":[0.951929,0.00001288789,0.047794655,0.000009593446,0.00022615703,0.0000093724,4.553645e-8,0.000011304878,0.0000070095193],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998125,0.00009417945,0.00077930425,0.00026224356,0.00046486425,0.0002744182],"domain_scores_gemma":[0.99713176,0.00020866553,0.0008059656,0.0006754835,0.0011010394,0.000077052486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010406827,0.00016389949,0.0004483281,0.0007834914,0.00009463175,0.00016354496,0.0018512395,0.00014799177,0.000001363518],"category_scores_gemma":[0.0008706021,0.00013394139,0.00012020874,0.0008543712,0.00012224625,0.0008305984,0.00035760307,0.00028472434,0.0000070051497],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004031974,0.00007216578,0.0005771374,0.00008476829,0.0000913335,0.000049061207,0.002115987,0.00014106387,0.053455666,0.00069697876,0.00010278717,0.9425727],"study_design_scores_gemma":[0.00020157879,0.0016538006,0.00007330936,0.0010589055,0.000010704104,0.00026839838,0.00597703,0.00092452485,0.9872182,0.00062185334,0.0018257806,0.00016589485],"about_ca_topic_score_codex":0.0000188868,"about_ca_topic_score_gemma":0.000007583346,"teacher_disagreement_score":0.94240683,"about_ca_system_score_codex":0.00018735394,"about_ca_system_score_gemma":0.000114097704,"threshold_uncertainty_score":0.5461971},"labels":[],"label_agreement":null},{"id":"W2900475759","doi":"10.3390/jsan7040049","title":"Special Issue: Wireless Sensor and Actuator Networks for Smart Cities","year":2018,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Wireless sensor network; Wireless; Wireless network; Key distribution in wireless sensor networks; Telecommunications; Actuator; Computer network; Artificial intelligence","score_opus":0.01755360926345899,"score_gpt":0.24652043059719791,"score_spread":0.2289668213337389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900475759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47753373,0.0006198544,0.51019186,0.0016963362,0.008158992,0.0006305492,0.000018981427,0.00009604513,0.0010536392],"genre_scores_gemma":[0.954347,0.0003732831,0.0057788882,0.00078318233,0.0380608,0.0000075110806,0.000002012376,0.00004007591,0.0006072177],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977503,0.00015418434,0.00074872724,0.00041419358,0.00038383773,0.0005487689],"domain_scores_gemma":[0.9969948,0.0009013217,0.0007133702,0.0003109823,0.00068532204,0.0003942021],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00080527284,0.0003455994,0.00071937667,0.00020247357,0.00038696686,0.0006516048,0.00040489825,0.00025819452,0.000066980945],"category_scores_gemma":[0.00011176,0.00029411653,0.00019829666,0.00024857448,0.0002591547,0.0010015754,0.00016516616,0.00043784291,0.0000082061215],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007606125,0.00017305335,0.0028539826,0.00009974555,0.00053348823,0.00013366461,0.0033574908,0.00008579144,0.0005661747,0.0005301814,0.13268375,0.85822207],"study_design_scores_gemma":[0.0072020623,0.0029201664,0.009174034,0.0009765696,0.00033092574,0.0042233076,0.0024425678,0.33362392,0.0012849543,0.0009429813,0.63495505,0.001923473],"about_ca_topic_score_codex":0.000010990674,"about_ca_topic_score_gemma":0.00002731007,"teacher_disagreement_score":0.85629857,"about_ca_system_score_codex":0.000058186703,"about_ca_system_score_gemma":0.00007898777,"threshold_uncertainty_score":0.9999511},"labels":[],"label_agreement":null},{"id":"W2900784907","doi":"10.1049/iet-wss.2018.5032","title":"Hybrid user action prediction system for automated home using association rules and ontology","year":2018,"lang":"en","type":"article","venue":"IET Wireless Sensor Systems","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Ontology; Computer science; Home automation; The Internet; Automation; Graph; Internet of Things; Artificial intelligence; Machine learning; World Wide Web; Theoretical computer science; Telecommunications; Engineering","score_opus":0.0380131483343573,"score_gpt":0.2785626743064144,"score_spread":0.2405495259720571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900784907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69431746,0.00006946353,0.29689032,0.00007530725,0.0058620865,0.00092444837,0.00013885598,0.0016155941,0.00010649678],"genre_scores_gemma":[0.9972382,0.000004963109,0.0012091361,0.000020703208,0.0010421284,0.00013196262,0.000025750434,0.000036798687,0.0002903215],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718463,0.0005277001,0.00066076586,0.0007014543,0.00044438834,0.00048107415],"domain_scores_gemma":[0.99739933,0.00043719605,0.0007614345,0.00046268373,0.0008069707,0.00013241419],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00092549226,0.0002764155,0.00056911516,0.00026998582,0.00043604462,0.00052091706,0.00027247748,0.0002684229,0.0000013372321],"category_scores_gemma":[0.00007790954,0.000285036,0.00009781649,0.00022293828,0.00005223154,0.00105921,0.00008001835,0.00012707464,0.00006142008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011206737,0.0012114567,0.16972661,0.013279532,0.004821207,0.00024911863,0.013234602,0.002237084,0.6396213,0.031039622,0.0360368,0.08742199],"study_design_scores_gemma":[0.0010812554,0.0001868709,0.0036263661,0.00039182408,0.000057531637,0.00078937114,0.0004632306,0.9854487,0.0046097143,0.000016643797,0.0029749598,0.00035352432],"about_ca_topic_score_codex":0.0006796145,"about_ca_topic_score_gemma":0.000085923166,"teacher_disagreement_score":0.98321164,"about_ca_system_score_codex":0.00093246164,"about_ca_system_score_gemma":0.000087475644,"threshold_uncertainty_score":0.9999602},"labels":[],"label_agreement":null},{"id":"W2901081366","doi":"10.2196/11201","title":"Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Environmental Health Sciences","keywords":"Wearable computer; Computer science; Multivariate statistics; Artificial intelligence; Segmentation; Pattern recognition (psychology); Computer vision; Machine learning","score_opus":0.25754440802378,"score_gpt":0.4747668898643175,"score_spread":0.2172224818405375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901081366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43168572,0.00006297104,0.5641721,0.0012252625,0.000603273,0.0014983935,0.00013434052,0.00024968712,0.00036826206],"genre_scores_gemma":[0.82664454,0.000029407034,0.17082117,0.0014520392,0.0005598734,0.0003086366,0.00009626756,0.000021921433,0.00006613894],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99650437,0.0010696895,0.000456767,0.0010905084,0.00032248718,0.0005562069],"domain_scores_gemma":[0.9973849,0.00045167483,0.00036035926,0.0010609266,0.00018075606,0.000561362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020768726,0.00022928046,0.0003805174,0.00022178091,0.00077146845,0.00025306907,0.0005482356,0.00012401876,0.000040400788],"category_scores_gemma":[0.0001083252,0.00023708816,0.000028995171,0.00050410157,0.000048881095,0.0013472557,0.00047134582,0.0002742922,0.0002066002],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054623357,0.000100863195,0.0003561306,0.00012057295,0.000015529433,0.0000016943567,0.0020446808,3.9465135e-7,0.012054868,0.000036498815,0.00073473784,0.9844794],"study_design_scores_gemma":[0.014564425,0.0051467028,0.46310377,0.0023448195,0.00036336549,0.00022585916,0.0037534246,0.31091228,0.08031142,0.019717881,0.094325796,0.005230271],"about_ca_topic_score_codex":0.006604075,"about_ca_topic_score_gemma":0.0007681273,"teacher_disagreement_score":0.9792491,"about_ca_system_score_codex":0.00016781788,"about_ca_system_score_gemma":0.00021483713,"threshold_uncertainty_score":0.998343},"labels":[],"label_agreement":null},{"id":"W2901118369","doi":"10.1109/healthcom.2018.8531150","title":"Deep Convolutional Neural Network Learning for Activity Recognition using real-life sensor’s data in smart devices","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Activity recognition; Computer science; Convolutional neural network; Wearable computer; Deep learning; Smartwatch; Wearable technology; Machine learning; Artificial intelligence; Human–computer interaction; Smart device; Phone; Embedded system","score_opus":0.1582504485854699,"score_gpt":0.33388356458151863,"score_spread":0.17563311599604872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901118369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3663463,0.000022281878,0.6314671,0.0002797181,0.0006491077,0.00035681165,0.000009697542,0.00017825796,0.00069072365],"genre_scores_gemma":[0.97041595,0.0000042818797,0.028279701,0.00032064086,0.00080251106,0.000025860243,0.00007333229,0.000017108807,0.00006064027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99762446,0.00042697272,0.0003667215,0.00078323844,0.00030527424,0.00049335626],"domain_scores_gemma":[0.99775976,0.0008840605,0.00026546896,0.0006208412,0.00033837795,0.00013150547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012937891,0.00019654002,0.0002968953,0.00014756834,0.00036332678,0.00025083355,0.00067814643,0.00011733497,0.00005868457],"category_scores_gemma":[0.0003808505,0.00020707691,0.00006030094,0.00052557094,0.00008364905,0.0023430332,0.0005303717,0.0002184481,0.00007517187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054691854,0.0005199499,0.25962532,0.00022985478,0.00026784578,0.000028247608,0.0011951074,0.005222271,0.0046371208,0.001086335,0.0027884585,0.7238526],"study_design_scores_gemma":[0.0005642349,0.00008519587,0.032502975,0.00005959049,0.000011422661,0.00003790124,0.00007543886,0.9648159,0.00012498435,0.0002278804,0.0012196655,0.00027479167],"about_ca_topic_score_codex":0.001431355,"about_ca_topic_score_gemma":0.0056421133,"teacher_disagreement_score":0.95959365,"about_ca_system_score_codex":0.00011512335,"about_ca_system_score_gemma":0.0001795595,"threshold_uncertainty_score":0.8444351},"labels":[],"label_agreement":null},{"id":"W2901750619","doi":"10.1109/iemcon.2018.8614822","title":"Detecting Irregular Patterns in IoT Streaming Data for Fall Detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Analytics; Cloud computing; Wearable computer; IBM; Accelerometer; Data stream mining; Machine learning; Real-time computing; Artificial intelligence; Data mining; Embedded system; Operating system","score_opus":0.07543558258928121,"score_gpt":0.3039385975527931,"score_spread":0.2285030149635119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901750619","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3459305,0.0000056386402,0.65299386,0.000080125,0.00038729655,0.00024249824,0.000009714201,0.00013927853,0.0002111071],"genre_scores_gemma":[0.9908865,4.487379e-7,0.0086117545,0.0000795656,0.0002536863,0.000036008678,0.000005602829,0.000011458483,0.00011496294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986533,0.00007648862,0.00023194904,0.000592868,0.00016742594,0.00027797918],"domain_scores_gemma":[0.998443,0.00028048555,0.00010221729,0.0010063511,0.000116162606,0.000051787847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065981265,0.00011867654,0.00015112717,0.00017453104,0.00012824226,0.00019069726,0.0009013093,0.000065841785,0.00000999979],"category_scores_gemma":[0.00018770841,0.000118965705,0.00003390653,0.0002808773,0.000017942328,0.00077025284,0.000492211,0.00008728533,0.00003926952],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005120768,0.000031525935,0.0031488282,0.000018769826,0.00000943358,0.0000019444847,0.00026417407,0.0000012192467,0.0045265993,0.00004469135,0.000030007888,0.99191767],"study_design_scores_gemma":[0.0012978521,0.00031500624,0.020793632,0.0001772016,0.000010689367,0.00007996741,0.0006768421,0.8773004,0.08948074,0.00055474095,0.008741306,0.00057163223],"about_ca_topic_score_codex":0.0015314382,"about_ca_topic_score_gemma":0.026855484,"teacher_disagreement_score":0.99134606,"about_ca_system_score_codex":0.00007906073,"about_ca_system_score_gemma":0.00003234652,"threshold_uncertainty_score":0.9909019},"labels":[],"label_agreement":null},{"id":"W2904246200","doi":"10.1109/bigdata.2018.8622515","title":"An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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 Waterloo","funders":"","keywords":"Computer science; Control reconfiguration; Embodied cognition; Context (archaeology); Situated; Analytics; Internet of Things; Human–computer interaction; Artificial intelligence; Software; Embodied agent; Distributed computing; Machine learning; Data science; Embedded system","score_opus":0.05347070491772776,"score_gpt":0.29167947861787946,"score_spread":0.2382087737001517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904246200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011367575,0.0000088955585,0.96844476,0.000705237,0.00028521856,0.000247985,0.000010959099,0.00064259797,0.01828676],"genre_scores_gemma":[0.98903257,8.793022e-7,0.0063504823,0.002703149,0.00013853847,0.000017727816,0.000014384751,0.000025485466,0.0017167826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773264,0.00026263477,0.0003591652,0.0007119057,0.0005332668,0.00040036562],"domain_scores_gemma":[0.9980569,0.00020039853,0.00017017857,0.00094833114,0.00035942614,0.00026478883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005813696,0.0002721376,0.00032797857,0.00027237096,0.00031640782,0.00031082839,0.0008480362,0.00010916872,0.00021857757],"category_scores_gemma":[0.00007836183,0.00024469962,0.0001239716,0.00039057314,0.000077688586,0.00039022704,0.00013695254,0.00028396957,0.0005241103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029458388,0.0020359876,0.012450802,0.00009190058,0.0002466254,0.000114816925,0.004174211,0.11182691,0.0034696169,0.02988322,0.0065208506,0.8288905],"study_design_scores_gemma":[0.0006507056,0.0005106758,0.0002082763,0.00003463403,0.0000096013855,0.000006326285,0.0000622883,0.993833,0.0018118896,0.00025130122,0.0023030168,0.00031826276],"about_ca_topic_score_codex":0.00019469,"about_ca_topic_score_gemma":0.0006432861,"teacher_disagreement_score":0.977665,"about_ca_system_score_codex":0.00012087479,"about_ca_system_score_gemma":0.0001449183,"threshold_uncertainty_score":0.99785614},"labels":[],"label_agreement":null},{"id":"W2904287387","doi":"10.1109/smartworld.2018.00140","title":"A Novel and Distributed Approach for Activity Recognition Inside Smart Homes","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Life expectancy; Architecture; Home automation; Independence (probability theory); Computer science; Point (geometry); Dementia; Activity recognition; Computer security; Risk analysis (engineering); Disease; Artificial intelligence; Business; Telecommunications; Medicine","score_opus":0.06808969810080003,"score_gpt":0.2695192932640971,"score_spread":0.20142959516329711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904287387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07421964,0.000009344296,0.9219214,0.00036932016,0.00019641337,0.0005117729,0.00009018313,0.00025223778,0.0024297459],"genre_scores_gemma":[0.9256335,0.0000018328564,0.07373614,0.00017634664,0.00013357052,0.00013001547,0.000040239753,0.00000918606,0.00013917938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886554,0.00005382288,0.00016682861,0.000503682,0.00016220554,0.00024791266],"domain_scores_gemma":[0.9989041,0.00026635354,0.00010533284,0.00032251782,0.00029584684,0.00010584456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037733154,0.00014892207,0.0002131945,0.000103331724,0.00018619222,0.00023929482,0.00022811104,0.00008558778,0.000010460724],"category_scores_gemma":[0.00014770342,0.0001368379,0.00005969748,0.00029574917,0.00008664067,0.0009779247,0.00017422877,0.000075643155,0.000025346064],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088974804,0.0005175962,0.0013418742,0.000099053694,0.000094778785,0.0000010455076,0.00050410314,4.923854e-7,0.025357861,0.0011660028,0.0030397463,0.96778846],"study_design_scores_gemma":[0.0106555335,0.0020601957,0.103218466,0.00020307025,0.000114278715,0.00083126867,0.0007210864,0.6597646,0.17801523,0.010616611,0.030893637,0.0029060238],"about_ca_topic_score_codex":0.00016096112,"about_ca_topic_score_gemma":0.00011922251,"teacher_disagreement_score":0.96488243,"about_ca_system_score_codex":0.000042047635,"about_ca_system_score_gemma":0.00005138505,"threshold_uncertainty_score":0.55800873},"labels":[],"label_agreement":null},{"id":"W2904800513","doi":"10.1109/icce-berlin.2018.8576191","title":"Privacy-Aware and Acceptable Lifelogging services for older and frail people: the PAAL project","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Lifelog; Internet privacy; Context (archaeology); Variety (cybernetics); Order (exchange); Assisted living; Business; Information privacy; Independence (probability theory); Computer science; Gerontology; Medicine","score_opus":0.02543821314807446,"score_gpt":0.28307497069278614,"score_spread":0.25763675754471166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904800513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3869299,0.00030402525,0.6017815,0.0052075777,0.0008434756,0.0018930471,0.000011854113,0.0004294337,0.0025992075],"genre_scores_gemma":[0.993862,0.000009684597,0.0039931266,0.0009213513,0.00026514067,0.000120323035,0.0000018017032,0.000010669414,0.0008159139],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989259,0.000061425555,0.00016602346,0.0004254808,0.00015892086,0.00026224743],"domain_scores_gemma":[0.9989283,0.00035915582,0.000086540254,0.00039697002,0.00017193353,0.00005707906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003856149,0.00013966484,0.00017294109,0.00006560076,0.00037791877,0.000621187,0.00048250164,0.000056842084,0.000029182369],"category_scores_gemma":[0.000029007726,0.000092063594,0.000030806863,0.0002303119,0.0000582528,0.00094980246,0.0005341945,0.00006999243,0.000013126421],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039210823,0.0001243003,0.05023966,0.0005876898,0.00015496077,0.0000026565492,0.049299296,3.8653397e-7,0.00064798986,0.0029446546,0.01789887,0.87806034],"study_design_scores_gemma":[0.0053001964,0.00095621945,0.27047184,0.00050428364,0.000104600156,0.00055393897,0.013265077,0.3205145,0.005621819,0.0045062406,0.37623772,0.0019635817],"about_ca_topic_score_codex":0.0004846092,"about_ca_topic_score_gemma":0.0011321901,"teacher_disagreement_score":0.8760967,"about_ca_system_score_codex":0.000014190232,"about_ca_system_score_gemma":0.00006292444,"threshold_uncertainty_score":0.599012},"labels":[],"label_agreement":null},{"id":"W2908077777","doi":"10.1109/icsai.2018.8599300","title":"Resource-aware Service-oriented Approach for Elderly Healthcare","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Dependability; Adaptability; Reusability; Scalability; Computer science; Resource (disambiguation); Health care; Service (business); Quality of service; Process management; Knowledge management; Business; Software engineering; Computer network; Database; Software","score_opus":0.04851929230607316,"score_gpt":0.28636365930651286,"score_spread":0.2378443670004397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908077777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003622674,0.000029717123,0.97853225,0.005927493,0.00038050604,0.0008439019,0.000022007076,0.00063703983,0.010004389],"genre_scores_gemma":[0.9446552,6.106751e-7,0.04866863,0.0040449277,0.00050074904,0.00022774868,0.00004063751,0.00002503128,0.0018364796],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980761,0.00013091977,0.00032675389,0.00068764616,0.00032893097,0.00044967644],"domain_scores_gemma":[0.9978397,0.00016119535,0.00013261111,0.0008535863,0.0008122022,0.00020068698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043199668,0.00019255695,0.00025728357,0.00014235244,0.00030858637,0.00017612863,0.0007924885,0.00012562389,0.000033277927],"category_scores_gemma":[0.000037511658,0.00017614961,0.00009875782,0.0007150318,0.000040364488,0.00052188255,0.00023122039,0.00011801394,0.00016020115],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015925683,0.0005553779,0.0017912916,0.00070206437,0.00016433612,0.0000067085825,0.009811218,0.0000067982837,0.0006600706,0.04970288,0.079524204,0.8569158],"study_design_scores_gemma":[0.0025788175,0.0016352525,0.00077333784,0.00012219328,0.000018495777,0.00014207287,0.0026330398,0.2735638,0.004782879,0.0014060019,0.71128786,0.0010562468],"about_ca_topic_score_codex":0.0003645008,"about_ca_topic_score_gemma":0.00041927674,"teacher_disagreement_score":0.9410325,"about_ca_system_score_codex":0.0000710168,"about_ca_system_score_gemma":0.0001210695,"threshold_uncertainty_score":0.7183173},"labels":[],"label_agreement":null},{"id":"W2909166797","doi":"10.5539/cis.v12n1p12","title":"Developing Mobile Tracking Applications for Patient Treatment","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Mobile technology; Quality (philosophy); Mobile device; TRACE (psycholinguistics); Health technology; Health care; Multimedia; World Wide Web","score_opus":0.027846873435313904,"score_gpt":0.2768842897200369,"score_spread":0.24903741628472303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909166797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038709342,0.000018680837,0.9587046,0.00013295257,0.00034274778,0.0011616242,0.000004461735,0.00009427242,0.0008313074],"genre_scores_gemma":[0.93566483,0.000010430268,0.06332813,0.0005707689,0.000031845473,0.00037526118,0.000005416442,0.0000020056248,0.000011308154],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991076,0.000010709608,0.00026016586,0.00021762747,0.00021845782,0.00018546933],"domain_scores_gemma":[0.9991022,0.00010517317,0.00013007203,0.00027223802,0.00031986702,0.00007045901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000265162,0.00009060919,0.00011175295,0.00021871304,0.00026500487,0.0006397659,0.00034765643,0.00002194926,0.0000023817238],"category_scores_gemma":[0.00000647319,0.00007799085,0.000031277305,0.00048487657,0.000048572463,0.008106573,0.0001535058,0.000026249236,0.000095496835],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010369773,0.000009945976,0.00019032114,0.000016620459,0.0000024776925,2.8000226e-8,0.0019996606,0.00011123142,0.00008760092,0.020591225,0.000020980036,0.9769689],"study_design_scores_gemma":[0.0008606549,0.0005369798,0.0040683253,0.00005219019,0.0000033837798,0.000050254443,0.00033071922,0.47292504,0.0064764037,0.00077417115,0.5135302,0.00039167353],"about_ca_topic_score_codex":0.000004387918,"about_ca_topic_score_gemma":5.1225464e-7,"teacher_disagreement_score":0.9765772,"about_ca_system_score_codex":0.0001258103,"about_ca_system_score_gemma":0.00017736979,"threshold_uncertainty_score":0.6169277},"labels":[],"label_agreement":null},{"id":"W2909346415","doi":"10.2196/12425","title":"An Ecological Approach to Smart Homes for Health Care Services: Conceptual Framework of a Smart Servicescape Wheel","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education; National Research Foundation","keywords":"Servicescape; Social ecological model; Conceptual framework; Health care; Psychology; Computer science; Business; Architectural engineering; Process management; Sociology; Medicine; Marketing; Engineering; Service (business); Political science","score_opus":0.04398244802239519,"score_gpt":0.35004774825867313,"score_spread":0.3060653002362779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909346415","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93455935,0.001273233,0.053628605,0.004204476,0.0007894624,0.0045109545,0.00018803788,0.0002964199,0.000549466],"genre_scores_gemma":[0.9632315,0.000057369023,0.022229224,0.013783373,0.00016556814,0.0004069634,0.000067480156,0.000028036628,0.000030458867],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9962778,0.0004524406,0.0007677338,0.0010883645,0.00045326992,0.0009603572],"domain_scores_gemma":[0.99661714,0.00043069743,0.00055806694,0.00093390857,0.0003218303,0.0011383364],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010388207,0.0003335941,0.0009548762,0.00023847897,0.00032451912,0.0001312382,0.00087654544,0.00028050237,0.000014548698],"category_scores_gemma":[0.000017134213,0.00030449382,0.00010432815,0.00059972156,0.000057969188,0.0005818085,0.0002014824,0.00035062927,0.00003233272],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018087224,0.006805321,0.20994397,0.082176074,0.00019662418,0.0000054948305,0.2949714,0.00015078815,0.00009318432,0.09557926,0.0049672443,0.30330193],"study_design_scores_gemma":[0.012462034,0.035476927,0.68870586,0.0025275857,0.00009938915,0.0001738832,0.0994073,0.05452768,0.00013899383,0.0029895382,0.1000925,0.0033982974],"about_ca_topic_score_codex":0.0006720322,"about_ca_topic_score_gemma":0.0006530139,"teacher_disagreement_score":0.4787619,"about_ca_system_score_codex":0.00020728275,"about_ca_system_score_gemma":0.0009535148,"threshold_uncertainty_score":0.9999407},"labels":[],"label_agreement":null},{"id":"W2909706188","doi":"10.1109/iemcon.2018.8615065","title":"Physical activity classification and assessment using smartphone","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Bishop's University","funders":"","keywords":"Accelerometer; Computer science; Physical activity; Activity recognition; Smart phone; Domain (mathematical analysis); Psychological intervention; Activities of daily living; Phone; Global Positioning System; Human–computer interaction; Machine learning; Physical medicine and rehabilitation; Physical therapy; Medicine; Telecommunications","score_opus":0.11057552193674493,"score_gpt":0.364012407437417,"score_spread":0.25343688550067206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909706188","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5222743,0.0000020887405,0.47358614,0.00030636706,0.00013521507,0.000071645714,4.8940984e-7,0.00007909799,0.0035446293],"genre_scores_gemma":[0.9833795,0.0000012619513,0.016257338,0.00006588952,0.00018454663,0.000007834315,3.4118239e-7,0.0000045508186,0.000098778604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991821,0.000094707684,0.00007492346,0.00032247172,0.00018994958,0.00013581749],"domain_scores_gemma":[0.9993299,0.00009988697,0.00007003149,0.00032034353,0.00011401663,0.00006578918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018653272,0.000085816384,0.000121238525,0.000055064083,0.00013441544,0.00017801973,0.0001630622,0.00003179854,0.000012539275],"category_scores_gemma":[0.000008667058,0.000077452925,0.000026647363,0.0002042069,0.00007053991,0.00079621165,0.00015001193,0.0000801642,0.000050206836],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047317412,0.00022542608,0.00322527,0.0000075021085,0.000016855163,0.0000012746552,0.00038900375,1.6715093e-7,0.25396514,0.009634387,0.00030273374,0.7322275],"study_design_scores_gemma":[0.00042230068,0.00012304728,0.3015888,0.000019155012,0.000006239616,0.00003955277,0.00005866989,0.6660262,0.027229328,0.0010026178,0.0032233866,0.00026075848],"about_ca_topic_score_codex":0.000081516,"about_ca_topic_score_gemma":0.00002055687,"teacher_disagreement_score":0.73196673,"about_ca_system_score_codex":0.00006457257,"about_ca_system_score_gemma":0.00005639988,"threshold_uncertainty_score":0.31584385},"labels":[],"label_agreement":null},{"id":"W2911540882","doi":"10.4149/cai_2018_6_1411","title":"Using Probabilistic Temporal Logic PCTL and Model Checking for Context Prediction","year":2018,"lang":"en","type":"article","venue":"Computing and Informatics","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University of Edmonton; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Probabilistic logic; Probabilistic CTL; Computer science; Model checking; Temporal logic; Context (archaeology); Probabilistic argumentation; Linear temporal logic; Theoretical computer science; Task (project management); Probabilistic relevance model; Artificial intelligence; Property (philosophy); Machine learning; Probabilistic analysis of algorithms","score_opus":0.10386896674593563,"score_gpt":0.3104117797728971,"score_spread":0.20654281302696145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911540882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21312758,0.000039593066,0.7857875,0.00003510303,0.00025745516,0.00028442,0.0000055768155,0.00011865468,0.00034410265],"genre_scores_gemma":[0.89341307,0.0000017996402,0.106196575,0.00022316465,0.00014037098,0.0000038876997,0.0000031544666,0.0000054974353,0.000012461259],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990853,0.000023234108,0.00041337303,0.00015115754,0.00012211686,0.0002047867],"domain_scores_gemma":[0.9991378,0.0001499815,0.00023660752,0.00018401003,0.000218935,0.000072668496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005426912,0.00012389125,0.0001884691,0.00008574306,0.0003561816,0.00029918412,0.00014949936,0.00006581327,2.9601125e-7],"category_scores_gemma":[0.00012278592,0.000115346804,0.000027927734,0.00010712943,0.000100538586,0.00058201194,0.00020771912,0.00008132382,0.0000019595836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003447434,0.000093773735,0.00645125,0.0011992442,0.000078928824,7.828982e-7,0.03967643,0.0050526205,0.0003064694,0.020116428,0.0006822373,0.9263074],"study_design_scores_gemma":[0.00039348265,0.0001373747,0.00014053959,0.00014468755,0.000008816299,0.000058915804,0.00034561864,0.9952132,0.000063764506,0.0029467384,0.00041783394,0.00012903073],"about_ca_topic_score_codex":0.0000127073245,"about_ca_topic_score_gemma":0.0000048083866,"teacher_disagreement_score":0.9901606,"about_ca_system_score_codex":0.000036347283,"about_ca_system_score_gemma":0.00006150259,"threshold_uncertainty_score":0.4703706},"labels":[],"label_agreement":null},{"id":"W2911586749","doi":"10.1007/978-3-642-16599-3_5","title":"Smart Interactions","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Toronto; IBM (Canada)","funders":"","keywords":"Computer science; Smart environment; Human–computer interaction; Internet of Things; World Wide Web","score_opus":0.026379802284357153,"score_gpt":0.26508003165713534,"score_spread":0.23870022937277818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911586749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008422696,0.000095325195,0.97385895,0.0014397281,0.0074323197,0.00040705968,0.0000080061745,0.00032113903,0.016353272],"genre_scores_gemma":[0.5993377,0.000023705472,0.39120415,0.0032459034,0.0018135069,0.00005036573,0.000012627158,0.000091825015,0.0042202356],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99612695,0.000046132674,0.0005876955,0.001655407,0.0009641037,0.0006197387],"domain_scores_gemma":[0.99616003,0.00088750926,0.0003779496,0.0019092096,0.00042612784,0.0002391933],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00084090524,0.00053879555,0.0005699605,0.0011882145,0.00034967533,0.0009385872,0.003428348,0.00037473248,0.00013665423],"category_scores_gemma":[0.00015143612,0.0005264401,0.0002057338,0.00060943054,0.00059617625,0.0013341932,0.0014909337,0.0018807994,0.00050957414],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002899377,0.000037265985,0.000057446137,0.000020176421,0.000014998448,0.000073867945,0.0005467746,0.00019512924,0.0012330312,0.005521969,0.000048426642,0.992248],"study_design_scores_gemma":[0.0011213213,0.0004236616,0.0006677442,0.0018154867,0.00003920359,0.0020549274,7.515105e-7,0.33655292,0.018822128,0.39474565,0.23978712,0.003969103],"about_ca_topic_score_codex":0.00006737221,"about_ca_topic_score_gemma":0.0010253624,"teacher_disagreement_score":0.9882789,"about_ca_system_score_codex":0.00030183973,"about_ca_system_score_gemma":0.00064919255,"threshold_uncertainty_score":0.9997187},"labels":[],"label_agreement":null},{"id":"W2912129383","doi":"","title":"Proceedings of the 2008 Ambi-Sys workshop on Software Organisation and MonIToring of Ambient Systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Computer science; Context (archaeology); Variety (cybernetics); Set (abstract data type); Cloud computing; Anticipation (artificial intelligence); Software; Architecture; Ubiquitous computing; Software engineering; Human–computer interaction; Artificial intelligence","score_opus":0.038775405056111364,"score_gpt":0.2361109647642621,"score_spread":0.19733555970815075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912129383","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98076653,0.00007675403,0.01748879,0.00011351243,0.0005591418,0.00037506435,0.0000019491522,0.00009625298,0.00052200095],"genre_scores_gemma":[0.9982631,0.000020467962,0.0011274435,0.000015634983,0.0000600529,0.000017237651,1.6512243e-7,0.000008307397,0.0004875931],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99881834,0.000027492417,0.00031966236,0.00025695248,0.00044141,0.00013612692],"domain_scores_gemma":[0.99901146,0.00013418651,0.00027977303,0.00023140489,0.0002952434,0.00004795647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002147658,0.00011318263,0.00021425328,0.00009102321,0.000118103504,0.000050526014,0.00035309204,0.000062532825,0.000003244579],"category_scores_gemma":[0.00010041394,0.0000822463,0.00004713277,0.0003743112,0.0000642174,0.00040211345,0.00016322662,0.000094241914,0.0000065499753],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003769776,0.00046891265,0.87468135,0.00061920064,0.0001318264,0.0000052239334,0.016730834,0.00011155073,0.064865075,0.0050402717,0.003767323,0.03354075],"study_design_scores_gemma":[0.0013369218,0.0003193571,0.59239393,0.0018362944,0.000028349392,0.0003622691,0.0044609816,0.0052296254,0.39232966,0.00019026453,0.00089034706,0.000621969],"about_ca_topic_score_codex":0.000107370215,"about_ca_topic_score_gemma":0.000002944154,"teacher_disagreement_score":0.3274646,"about_ca_system_score_codex":0.000050853836,"about_ca_system_score_gemma":0.000043232903,"threshold_uncertainty_score":0.3353907},"labels":[],"label_agreement":null},{"id":"W2914692564","doi":"","title":"Proceedings of the SIGCHI Conference on Human Factors in Computing Systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Vision; Globe; Theme (computing); Diversity (politics); Multidisciplinary approach; Event (particle physics); Everyday life; Sociology; Public relations; Engineering ethics; Library science; Computer science; Political science; Psychology; World Wide Web; Engineering; Social science","score_opus":0.07411143428466084,"score_gpt":0.2812493969877744,"score_spread":0.20713796270311358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914692564","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93856055,0.0000020564883,0.0202429,0.00017880677,0.0003253248,0.0003003455,6.7488367e-7,0.00009793296,0.040291388],"genre_scores_gemma":[0.9995395,1.0178319e-7,0.00008343818,0.00006188205,0.00003170943,0.000004775843,2.1441687e-7,0.0000054717493,0.0002729354],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99882734,0.00006835475,0.00032421414,0.0002885527,0.0003103546,0.00018120733],"domain_scores_gemma":[0.99912685,0.00020203348,0.00023258904,0.00023802709,0.00015987513,0.000040648338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050182786,0.00012151214,0.00023642051,0.00010798407,0.000088002125,0.00016339698,0.0008090905,0.000052699772,0.000004631761],"category_scores_gemma":[0.00007036616,0.000080510385,0.00005333907,0.0003257073,0.00003249522,0.00025546525,0.00021121546,0.00014813531,0.000011465388],"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":[0.000002483195,0.00013969332,0.13643496,0.00012807116,0.000015135066,3.207879e-7,0.0036757293,0.00005127336,0.021928377,0.8327687,0.00044111002,0.004414158],"study_design_scores_gemma":[0.0022293131,0.00070387434,0.45293954,0.0026497839,0.000013105583,0.00003368347,0.003600777,0.43576288,0.09226452,0.0065517747,0.0019765557,0.001274169],"about_ca_topic_score_codex":0.0004395788,"about_ca_topic_score_gemma":0.00007145232,"teacher_disagreement_score":0.82621694,"about_ca_system_score_codex":0.00004572649,"about_ca_system_score_gemma":0.000022041608,"threshold_uncertainty_score":0.3283118},"labels":[],"label_agreement":null},{"id":"W2914896135","doi":"","title":"Proceedings of the 21st International Conference on Intelligent User Interfaces","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 British Columbia","funders":"","keywords":"Computer science; The arts; Pleasure; Library science; World Wide Web; Psychology","score_opus":0.05856014646093815,"score_gpt":0.2751076364145793,"score_spread":0.21654748995364115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914896135","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41081122,0.000010936634,0.16940576,0.02758439,0.0029763659,0.0005968451,0.000014113612,0.00026582973,0.38833454],"genre_scores_gemma":[0.99378073,0.000010433219,0.00036736965,0.00019203124,0.000034900484,0.000013564247,6.73945e-8,0.0000038213825,0.005597084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911433,0.000015219866,0.00021010615,0.00023864646,0.00031391837,0.000107752254],"domain_scores_gemma":[0.9991926,0.000094946336,0.0001441405,0.00019110992,0.00034349304,0.000033708577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017522249,0.00008913978,0.00010230301,0.00006766246,0.00002926859,0.000099415316,0.0011754116,0.000032245876,0.0002898429],"category_scores_gemma":[0.00007200763,0.00004332844,0.0000540612,0.00012777602,0.000063754145,0.00049874,0.00037653447,0.000056918627,0.00016982916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015716585,0.00012715206,0.0060981484,0.0000107230835,0.000051834464,2.92901e-7,0.0007876626,2.154159e-7,0.06803435,0.6664166,0.0036321892,0.25482512],"study_design_scores_gemma":[0.0004230077,0.00014213276,0.004873308,0.0005500639,0.0000040002815,0.000022856644,0.00068589963,0.0009159702,0.9603743,0.004879133,0.026886724,0.00024258584],"about_ca_topic_score_codex":0.00002003075,"about_ca_topic_score_gemma":0.00002013846,"teacher_disagreement_score":0.89233994,"about_ca_system_score_codex":0.000048777478,"about_ca_system_score_gemma":0.00003156133,"threshold_uncertainty_score":0.31735763},"labels":[],"label_agreement":null},{"id":"W2915037867","doi":"10.1007/978-3-030-11890-7_72","title":"Standardizing a Shoe Insole Based on ISO/IEEE 11073 Personal Health Device (X73-PHD) Standards","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Wearable computer; Domain (mathematical analysis); Healthcare service; Architecture; Health care; Embedded system; Human–computer interaction; Service (business); Work (physics); Wearable technology; Software engineering; Engineering; Business; Mechanical engineering; Mathematics","score_opus":0.042461947901959736,"score_gpt":0.31271264960346257,"score_spread":0.27025070170150284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915037867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028005845,0.026961252,0.8904944,0.00022261831,0.0052801142,0.0017025863,0.00023731806,0.00029357892,0.074528076],"genre_scores_gemma":[0.98531866,0.001468413,0.0019290253,0.0010481139,0.00083530945,0.000038723512,0.000035101042,0.00015484076,0.009171792],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942693,0.00029238738,0.0015029545,0.0015773196,0.0015954269,0.00076260214],"domain_scores_gemma":[0.99606687,0.0010911601,0.0012765395,0.0008220172,0.00048463818,0.0002587895],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025630803,0.0008011745,0.0015447448,0.00066417793,0.0003526867,0.0005572438,0.0007904402,0.00030577325,0.000020887728],"category_scores_gemma":[0.00007000695,0.0008011117,0.0002627901,0.00021299998,0.00009989367,0.00062132516,0.00029709365,0.00095743046,0.000058179663],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014367193,0.00013196965,0.001008711,0.004083254,0.00017942977,0.00013747542,0.0037864596,0.018018782,0.000008982145,0.05776354,0.0010765322,0.9136612],"study_design_scores_gemma":[0.0008039759,0.0007496713,0.000019649791,0.015538473,0.000014620358,0.00011601072,0.0006045756,0.36563838,0.000036110203,0.00048272294,0.6147339,0.0012619131],"about_ca_topic_score_codex":0.00020097785,"about_ca_topic_score_gemma":0.00026356895,"teacher_disagreement_score":0.98503864,"about_ca_system_score_codex":0.0013046397,"about_ca_system_score_gemma":0.0009862564,"threshold_uncertainty_score":0.99944395},"labels":[],"label_agreement":null},{"id":"W2920709754","doi":"10.1049/iet-wss.2018.5209","title":"Ontology evolution for personalised and adaptive activity recognition","year":2019,"lang":"en","type":"article","venue":"IET Wireless Sensor Systems","topic":"Context-Aware Activity Recognition Systems","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":"Exfo Electro-Optical Engineering (Canada); HealthForceOntario","funders":"","keywords":"Ontology; Computer science; Heuristics; Process ontology; Representation (politics); Artificial intelligence; Machine learning; Domain knowledge","score_opus":0.03502272030909973,"score_gpt":0.24322007668937143,"score_spread":0.2081973563802717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920709754","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66905594,0.00015437095,0.326233,0.0002291437,0.0017649687,0.0015469465,0.00008389289,0.0002351214,0.00069659314],"genre_scores_gemma":[0.99795914,0.0000057841844,0.00080727565,0.000045555258,0.00022753041,0.00026271897,0.00001235075,0.000025868078,0.00065377995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976602,0.0004481722,0.00033555413,0.00080792245,0.00032543516,0.00042268238],"domain_scores_gemma":[0.9979084,0.0007088455,0.00035512281,0.0004689271,0.00042150114,0.0001371929],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055872806,0.0002689176,0.0005507346,0.00018888415,0.00015957576,0.00021493377,0.00028081282,0.00023473913,0.0000073714677],"category_scores_gemma":[0.000049557715,0.00027480366,0.00012904243,0.00024087851,0.000056231704,0.000961914,0.00008107772,0.00017201768,0.00018153057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027286762,0.001450314,0.03457815,0.0040427665,0.0015948943,0.0001421588,0.013939339,0.00030445488,0.35605487,0.060405407,0.0048140716,0.5199449],"study_design_scores_gemma":[0.0066226954,0.0014812708,0.017505558,0.00085069967,0.000088308676,0.0013224282,0.003598798,0.95635253,0.0054931706,0.0009995583,0.0038647405,0.001820245],"about_ca_topic_score_codex":0.0007320584,"about_ca_topic_score_gemma":0.00016478944,"teacher_disagreement_score":0.9560481,"about_ca_system_score_codex":0.00024734743,"about_ca_system_score_gemma":0.00009639778,"threshold_uncertainty_score":0.99997044},"labels":[],"label_agreement":null},{"id":"W2921843133","doi":"10.1155/2019/8612021","title":"Application of Modified PageRank Algorithm for Anomaly Detection in Movements of Older Adults","year":2019,"lang":"en","type":"article","venue":"International Journal of Telemedicine and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Computer science; Anomaly detection; Statistic; Graph; PageRank; Population; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science; Statistics; Medicine; Mathematics","score_opus":0.008742559003856492,"score_gpt":0.27003232511384967,"score_spread":0.26128976610999316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921843133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14098093,0.00007281104,0.85793394,0.00023923624,0.0001369108,0.00054611027,0.00002196736,0.000005198498,0.00006286714],"genre_scores_gemma":[0.990404,0.00003600648,0.009242054,0.000061124825,0.00013398592,0.000093680166,0.000007700321,0.0000045507154,0.000016909007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988179,0.000020290941,0.0006320716,0.00014391227,0.00031567307,0.00007012211],"domain_scores_gemma":[0.9981146,0.00024742688,0.0006941669,0.00014699622,0.0007605254,0.00003630703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003520216,0.000070174974,0.00020816668,0.00031941338,0.000013514807,0.000011579676,0.0003589107,0.000038288916,0.0000044245085],"category_scores_gemma":[0.00003112696,0.00006401843,0.000061024486,0.00019511896,0.00002440125,0.0002660671,0.000042038635,0.00007490828,0.0000010219946],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033821114,0.00018051754,0.005827672,0.000034209344,0.000055910088,3.620782e-7,0.00024523886,0.00003573201,0.009755332,0.0007587235,0.000010439986,0.983062],"study_design_scores_gemma":[0.018658435,0.0012595771,0.19863614,0.0010431263,0.00006969969,0.00019673884,0.0012516979,0.64902616,0.11099662,0.010249268,0.0081251,0.00048744003],"about_ca_topic_score_codex":0.00013355813,"about_ca_topic_score_gemma":0.000016869772,"teacher_disagreement_score":0.9825746,"about_ca_system_score_codex":0.000034935045,"about_ca_system_score_gemma":0.000036884747,"threshold_uncertainty_score":0.26105958},"labels":[],"label_agreement":null},{"id":"W2921850771","doi":"10.5430/air.v8n1p25","title":"Body sensor networks for monitoring performances in sports: A brief overview and some new thoughts","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wearable computer; Computer science; Focus (optics); Wireless sensor network; Point (geometry); Human–computer interaction; Multimedia; Embedded system; Computer network","score_opus":0.20290455410229685,"score_gpt":0.4145204979329958,"score_spread":0.21161594383069893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921850771","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74058694,0.0034338993,0.25004268,0.0014714414,0.0016835006,0.0021479563,0.0000032354585,0.00014115647,0.0004892176],"genre_scores_gemma":[0.9956806,0.00093711773,0.001968801,0.000034218767,0.000806652,0.00007413343,0.0000012589395,0.000016310254,0.00048090896],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972593,0.0001911383,0.00047681935,0.00066043,0.0006743842,0.0007379305],"domain_scores_gemma":[0.99799293,0.0009637062,0.00008463527,0.0005068216,0.00026098985,0.00019092289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026703821,0.00016259612,0.00029953232,0.00036074044,0.00018579332,0.00047479643,0.0006197775,0.000115715244,0.00002999026],"category_scores_gemma":[0.0001765893,0.0001565965,0.000067507026,0.0009369108,0.00007908164,0.0013955575,0.00031705736,0.00042250482,0.00014129635],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074218224,0.000091191025,0.0122680245,0.000116641844,0.000012845979,0.000018103388,0.0014916661,0.00028347797,0.0012565142,0.012230715,0.00011233277,0.9720443],"study_design_scores_gemma":[0.0003516048,0.0008468629,0.01163289,0.0015809956,0.00000882034,0.00006671504,0.00334912,0.80695915,0.068007015,0.08837107,0.0177773,0.0010484606],"about_ca_topic_score_codex":0.0004755687,"about_ca_topic_score_gemma":0.00012345109,"teacher_disagreement_score":0.9709958,"about_ca_system_score_codex":0.000098029755,"about_ca_system_score_gemma":0.00019621028,"threshold_uncertainty_score":0.63858205},"labels":[],"label_agreement":null},{"id":"W2923851841","doi":"10.1145/3290607.3299029","title":"Addressing the Challenges of Situationally-Induced Impairments and Disabilities in Mobile Interaction","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Active listening; Context (archaeology); Phone; Mobile phone; Human–computer interaction; Data science; Multimedia; Internet privacy; Psychology; Telecommunications","score_opus":0.12260688935292255,"score_gpt":0.3340819153859856,"score_spread":0.21147502603306303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923851841","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99421096,0.00014899994,0.000869045,0.00057343364,0.00018610457,0.00031974076,7.428373e-7,0.000025805311,0.0036651657],"genre_scores_gemma":[0.9996941,0.00001241703,0.00012093614,0.000032761374,0.000013753031,0.000041137875,5.84501e-7,0.0000021577152,0.000082154096],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99928993,0.00011073993,0.00017592096,0.00017975403,0.00016068474,0.00008299734],"domain_scores_gemma":[0.9990298,0.0005895137,0.00007887441,0.00023201191,0.000053345088,0.000016432326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003233431,0.000061697814,0.00011334233,0.00006366946,0.000020801877,0.0000522024,0.00016893487,0.000026666361,0.000027544562],"category_scores_gemma":[0.000040179544,0.00004357916,0.000023968769,0.000085343054,0.000019848943,0.0006897243,0.00012138624,0.00006481632,0.000008144161],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038569575,0.0005031253,0.060096923,0.00027063547,0.00006888074,0.0000016384471,0.048845172,0.00009258581,0.031445857,0.018401671,0.00007624214,0.8401587],"study_design_scores_gemma":[0.005066889,0.0017073401,0.64738214,0.0017867062,0.000021632417,0.0002315422,0.1522526,0.11738208,0.053346384,0.017770186,0.0017183166,0.0013342153],"about_ca_topic_score_codex":0.000092073344,"about_ca_topic_score_gemma":0.00018293028,"teacher_disagreement_score":0.8388245,"about_ca_system_score_codex":0.000034141984,"about_ca_system_score_gemma":0.00002366746,"threshold_uncertainty_score":0.17771065},"labels":[],"label_agreement":null},{"id":"W2925721318","doi":"","title":"Monitoring Activities Related to Medication Adherence in Ambient Assisted Living Environments.","year":2017,"lang":"en","type":"article","venue":"PubMed","topic":"Context-Aware Activity Recognition Systems","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":"Dalhousie University","funders":"","keywords":"Medication adherence; Assisted living; Psychological intervention; Self-management; Activities of daily living; Patient compliance; Health care; Medication therapy management; Medicine; Disease management; Medical emergency; Computer science; Health management system; Physical therapy; Nursing; Alternative medicine; Family medicine; Pharmacy","score_opus":0.05470727274054065,"score_gpt":0.269928175698087,"score_spread":0.21522090295754637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2925721318","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9765676,0.000061005077,0.01628294,0.0010279987,0.0019271863,0.0008844011,0.0000018333352,0.00013749383,0.0031094921],"genre_scores_gemma":[0.9968309,0.000013315135,0.00035618717,0.000031293992,0.000049827486,0.0016163174,4.6592092e-7,0.000009699727,0.0010919888],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99836785,0.00013220051,0.00028549612,0.00044452708,0.00040182055,0.0003680867],"domain_scores_gemma":[0.9984631,0.00020692042,0.00026624204,0.00088211766,0.000015748128,0.00016584925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008412975,0.00012906504,0.00018277844,0.00016847013,0.00023256827,0.00038290094,0.000980191,0.00007714367,0.0000067848196],"category_scores_gemma":[0.0005248705,0.00014020508,0.00004093966,0.00013249756,0.000043632866,0.0013662827,0.00048021926,0.00016707747,0.00006587057],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002881394,0.000077537836,0.0927188,0.000006619354,0.000014286146,0.0000083524,0.00093760504,0.000003878718,0.0011088292,0.000046959856,0.000029546658,0.9050447],"study_design_scores_gemma":[0.00018559069,0.000012193594,0.9940886,0.000109135435,0.000002760858,0.000007437415,0.00008689552,0.00028728775,0.004312535,0.000064417836,0.0006884199,0.0001547341],"about_ca_topic_score_codex":0.00024394956,"about_ca_topic_score_gemma":0.00007096893,"teacher_disagreement_score":0.90488994,"about_ca_system_score_codex":0.0002693736,"about_ca_system_score_gemma":0.000023957577,"threshold_uncertainty_score":0.57173973},"labels":[],"label_agreement":null},{"id":"W2942343679","doi":"10.2196/14090","title":"A Combination of Indoor Localization and Wearable Sensor–Based Physical Activity Recognition to Assess Older Patients Undergoing Subacute Rehabilitation: Baseline Study Results","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health; Agency for Healthcare Research and Quality; U.S. Department of Health and Human Services","keywords":"Wearable computer; Beacon; Computer science; Wearable technology; Activity tracker; Accelerometer; Activity recognition; Smartwatch; Bluetooth; Population; Artificial intelligence; Human–computer interaction; Wireless; Real-time computing; Medicine; Embedded system; Telecommunications","score_opus":0.05686620121311808,"score_gpt":0.35298189572118543,"score_spread":0.29611569450806735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942343679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95366454,0.0000085306765,0.041904368,0.0015422917,0.00019452679,0.0025122897,0.0000417614,0.00007817082,0.00005353977],"genre_scores_gemma":[0.99820614,0.000007476491,0.0012569509,0.00033029512,0.000034963778,0.0000945777,0.00003643793,0.000013962696,0.000019167355],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99724364,0.000891729,0.00046065246,0.00064493064,0.00047267927,0.00028638568],"domain_scores_gemma":[0.99696887,0.0014395037,0.00044520217,0.00035626552,0.00053009915,0.00026009313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012991655,0.00017968837,0.0003942463,0.0002821043,0.00019606114,0.00008324245,0.00011013248,0.00007571101,0.0000022471331],"category_scores_gemma":[0.0003233709,0.00018183039,0.000032689193,0.00064999436,0.000027727092,0.00072546693,0.00007336705,0.00016436855,0.000014419464],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019032808,0.012176562,0.43291023,0.004992657,0.00004837944,0.0000027602398,0.017939385,0.00017941784,0.000192213,0.00021473622,0.00059809186,0.5288423],"study_design_scores_gemma":[0.009621985,0.0065005766,0.861585,0.0005269447,0.000030802654,0.0000027164817,0.0010529871,0.11980622,0.00023102545,0.00023165002,0.00006903105,0.00034100882],"about_ca_topic_score_codex":0.00025028863,"about_ca_topic_score_gemma":0.000109732166,"teacher_disagreement_score":0.5285013,"about_ca_system_score_codex":0.00014601974,"about_ca_system_score_gemma":0.00029530644,"threshold_uncertainty_score":0.7414828},"labels":[],"label_agreement":null},{"id":"W29441421","doi":"10.1007/978-3-642-30779-9_1","title":"Challenges of User-Centred Research in the Development of Ambient Assisted Living Systems","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Assisted living; Human–computer interaction; World Wide Web; Gerontology","score_opus":0.18230999200559436,"score_gpt":0.32819950018984895,"score_spread":0.14588950818425458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W29441421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024565004,0.0058560367,0.9848885,0.0004788409,0.0016534261,0.001194969,0.000004074376,0.000052749885,0.0034149184],"genre_scores_gemma":[0.9775423,0.00007009417,0.022099316,0.000033731365,0.00015683018,0.00003569657,0.0000011380445,0.00001930075,0.00004160356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940011,0.0005343847,0.001138819,0.0010661689,0.0024878972,0.00077164534],"domain_scores_gemma":[0.9934111,0.0035836045,0.00062308315,0.001565814,0.0007039644,0.000112437156],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0073121204,0.00037851,0.00074828736,0.0015900253,0.0001772982,0.00023876308,0.004137836,0.00029016403,0.000004971679],"category_scores_gemma":[0.00020106226,0.00029468487,0.00010774145,0.0010968936,0.00056172913,0.0005465763,0.0016202014,0.0008766101,0.000017734425],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005778461,0.00023156125,0.0002978596,0.00043071326,0.000025808187,0.000017315562,0.025322678,0.0005006137,0.0005715722,0.008871065,0.0000063675843,0.96371865],"study_design_scores_gemma":[0.006448463,0.0034219578,0.24036504,0.1373996,0.00014747302,0.0021076966,0.0012164828,0.47533444,0.035942394,0.04858827,0.036584713,0.012443479],"about_ca_topic_score_codex":0.00009402775,"about_ca_topic_score_gemma":0.00070304755,"teacher_disagreement_score":0.9750858,"about_ca_system_score_codex":0.00043123664,"about_ca_system_score_gemma":0.0009103708,"threshold_uncertainty_score":0.9999505},"labels":[],"label_agreement":null},{"id":"W2946689049","doi":"10.1007/s10044-020-00901-9","title":"Spatio-temporal adversarial learning for detecting unseen falls","year":2020,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute; University Health Network","funders":"","keywords":"Autoencoder; Adversarial system; Classifier (UML); Deep learning; Rendering (computer graphics); Modalities; Class (philosophy); Adversarial machine learning; Feature learning","score_opus":0.03176615309929862,"score_gpt":0.2679380683893751,"score_spread":0.2361719152900765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946689049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010350581,0.000023486347,0.98778456,0.0012657179,0.00001576143,0.00032872744,0.000012522029,0.0001167792,0.000101848455],"genre_scores_gemma":[0.99588674,0.0000036878218,0.0032157137,0.00033583835,0.00020281323,0.00025596947,0.000055108587,0.0000068151953,0.0000373379],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990476,0.00005576361,0.00023115479,0.0004054078,0.00012468592,0.00013541465],"domain_scores_gemma":[0.99923956,0.00019244726,0.0001710645,0.00019362858,0.00009343476,0.00010984341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001746721,0.000096652046,0.00021514294,0.00010698012,0.00024649865,0.00019356258,0.00023889959,0.000036485297,0.000015696003],"category_scores_gemma":[0.000033847442,0.00009992331,0.00014771412,0.00074953516,0.000014620934,0.0001889793,0.00009375659,0.000090145106,0.000027451455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004453317,0.00002855445,0.12818995,0.000028592125,0.00032408966,4.937931e-7,0.0011181226,0.00023409746,0.0005080523,0.00025008846,0.000042490912,0.869271],"study_design_scores_gemma":[0.00078232726,0.00008915424,0.0121007925,0.000010459052,0.000526776,0.0000028096556,0.00047633055,0.9279351,0.0016130446,0.00031813062,0.055693798,0.00045122742],"about_ca_topic_score_codex":0.0002351621,"about_ca_topic_score_gemma":0.000314986,"teacher_disagreement_score":0.98553616,"about_ca_system_score_codex":0.000013712446,"about_ca_system_score_gemma":0.000017029517,"threshold_uncertainty_score":0.40747544},"labels":[],"label_agreement":null},{"id":"W2947016638","doi":"10.1186/s12938-019-0677-7","title":"Would a thermal sensor improve arm motion classification accuracy of a single wrist-mounted inertial device?","year":2019,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canada Research Chairs; Simon Fraser University; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Inertial measurement unit; Wearable computer; Computer science; Artificial intelligence; Accelerometer; Computer vision; Simulation; Interface (matter); Bluetooth; Embedded system","score_opus":0.02674653744611099,"score_gpt":0.2672587992853611,"score_spread":0.24051226183925012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947016638","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6420355,0.000042300744,0.35534942,0.00096262293,0.0009481651,0.0003045981,0.00004317528,0.00027302947,0.000041201536],"genre_scores_gemma":[0.9916923,0.0000023308949,0.0077308523,0.00008461968,0.00030794792,0.000016167363,0.00008426948,0.000020381274,0.0000611013],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998296,0.000046131896,0.0005118004,0.00039193433,0.00048150058,0.00027260632],"domain_scores_gemma":[0.99868256,0.0002665279,0.0002296129,0.00048763052,0.00018343484,0.0001502448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002687346,0.00018859601,0.00030095095,0.0002468938,0.000021776468,0.00005178352,0.0004410963,0.00026211079,0.000042280208],"category_scores_gemma":[0.00044829774,0.00017417186,0.00009886658,0.00058787107,0.00003843574,0.000388617,0.00013478659,0.00032170027,0.000067826455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001480046,0.0005197459,0.00011699378,0.00011347788,0.00003332411,0.0000038621465,0.00013795462,0.00010544385,0.9036301,0.0001660813,0.000023224651,0.09513499],"study_design_scores_gemma":[0.0011919312,0.0003287534,0.010460479,0.00017889017,0.00001511223,0.000027417978,0.000053266223,0.9581497,0.01994086,0.0000070811257,0.009331022,0.0003155186],"about_ca_topic_score_codex":0.000053116564,"about_ca_topic_score_gemma":0.0000029957246,"teacher_disagreement_score":0.95804423,"about_ca_system_score_codex":0.000113195805,"about_ca_system_score_gemma":0.00008257319,"threshold_uncertainty_score":0.7102522},"labels":[],"label_agreement":null},{"id":"W2947550009","doi":"10.1016/j.asoc.2019.105532","title":"Situation-aware model for multi-objective decision making in ambient intelligence","year":2019,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Ambient intelligence; Decision support system; Process (computing); Action (physics); Control (management); Fuzzy logic; Work (physics); Artificial intelligence; Risk analysis (engineering); Operations research","score_opus":0.05148594655210161,"score_gpt":0.31632914222902064,"score_spread":0.26484319567691905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947550009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10511406,0.00001772295,0.8928322,0.000028760756,0.00037057075,0.0012113997,0.0000029882326,0.00020501502,0.00021727385],"genre_scores_gemma":[0.7976001,5.279663e-7,0.20205209,0.0002144774,0.000033577282,0.000055800465,0.0000033728898,0.0000191061,0.000020963973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997793,0.000044920467,0.0005488558,0.00082923064,0.00034913066,0.00043485695],"domain_scores_gemma":[0.9975318,0.0014292892,0.00027234363,0.0005029318,0.0002027582,0.000060928152],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008528756,0.00022960066,0.00034508828,0.00027277452,0.00016554641,0.00019882043,0.0007237927,0.00010977656,0.0000039440347],"category_scores_gemma":[0.0001071608,0.00025219226,0.00009777941,0.0005337597,0.000020618265,0.00035616843,0.00048521004,0.0002250785,0.0001418837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003884322,0.00014108948,0.0018030895,0.000055320535,0.000018255161,0.0000020843238,0.009008203,0.23324685,0.0010087154,0.0066505745,0.000025217052,0.74800175],"study_design_scores_gemma":[0.0005269833,0.00002463983,0.0012126826,0.00021583233,0.0000028645156,0.000004965808,0.00042310715,0.9912107,0.0007350351,0.0053342436,0.000016771328,0.00029218275],"about_ca_topic_score_codex":0.000020586116,"about_ca_topic_score_gemma":0.000081854145,"teacher_disagreement_score":0.75796384,"about_ca_system_score_codex":0.00022942839,"about_ca_system_score_gemma":0.000112714406,"threshold_uncertainty_score":0.999993},"labels":[],"label_agreement":null},{"id":"W2947600942","doi":"","title":"Dynamic Action Selection in OpenAI Using Spiking Neural Networks.","year":2019,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Waterloo","funders":"","keywords":"Action selection; Computer science; Selection (genetic algorithm); Action (physics); Spiking neural network; Artificial intelligence; Artificial neural network; Machine learning; Neuroscience; Biology","score_opus":0.11839329323463736,"score_gpt":0.40499668800023225,"score_spread":0.2866033947655949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947600942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8237919,0.0000910865,0.17237836,0.0015493117,0.0010318321,0.0008343613,8.315903e-7,0.0001266929,0.00019557006],"genre_scores_gemma":[0.99860305,0.00003097938,0.0005162989,0.0002191199,0.00024207299,0.000041971583,0.0000019086704,0.00001853507,0.0003260548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692905,0.00079171645,0.0002567424,0.00050270115,0.0008221002,0.0006976633],"domain_scores_gemma":[0.9985476,0.0005277834,0.000085283566,0.0004678416,0.0002958335,0.000075665914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039910963,0.0001469038,0.00020168802,0.00012492109,0.00047074145,0.00056944083,0.0009330335,0.000121966514,0.000032381555],"category_scores_gemma":[0.000061819184,0.000120685516,0.00015060202,0.0019930417,0.00008029838,0.0015535913,0.0005675526,0.0012576518,0.000085496904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029317202,0.00050738425,0.07602872,0.0004911201,0.00042780698,0.00004061285,0.022850858,0.28443682,0.14832892,0.0027181238,0.011904134,0.45197234],"study_design_scores_gemma":[0.00033517653,0.000050642033,0.0062246877,0.0000541089,0.000002043204,0.000035416917,0.00044423775,0.9915794,0.00029059406,0.00025547802,0.0005924565,0.0001357472],"about_ca_topic_score_codex":0.0010244742,"about_ca_topic_score_gemma":0.00031506052,"teacher_disagreement_score":0.7071426,"about_ca_system_score_codex":0.00078122405,"about_ca_system_score_gemma":0.00017205498,"threshold_uncertainty_score":0.54911315},"labels":[],"label_agreement":null},{"id":"W2948471521","doi":"10.3390/s19112629","title":"Monitoring Methods of Human Body Joints: State-of-the-Art and Research Challenges","year":2019,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":183,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Joseph’s Healthcare Hamilton; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL; McMaster University","keywords":"Joint (building); Continuous monitoring; Population; Risk analysis (engineering); Computer science; Engineering; Medicine; Physical medicine and rehabilitation; Operations management; Environmental health; Civil engineering","score_opus":0.37819852086805045,"score_gpt":0.4958291659095268,"score_spread":0.11763064504147636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948471521","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046145858,0.9957056,0.00041087894,0.000049378363,0.0008206114,0.0008631445,0.00001060303,0.000036614554,0.0016417076],"genre_scores_gemma":[0.0007618421,0.9955201,0.002162291,9.813085e-7,0.00009311505,0.000031380943,7.6112644e-7,0.000032711283,0.0013968071],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99455905,0.0031748805,0.0007024948,0.0005889654,0.0006514321,0.00032315278],"domain_scores_gemma":[0.9957799,0.0018938187,0.00058172236,0.0012843751,0.00038467252,0.000075539356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003677151,0.00025925937,0.0014403221,0.0004335758,0.00009954742,0.00006514387,0.0010212299,0.00017076555,0.0000035008713],"category_scores_gemma":[0.00030376637,0.00019399328,0.00030608464,0.0005476363,0.00015825893,0.000167518,0.0008692059,0.00061152066,0.00004218305],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.254818e-7,0.000020798458,0.0000061330193,0.008614263,0.00008170818,0.0000018624227,0.0007041018,1.4038575e-7,0.000046908022,0.0001320308,0.000028878583,0.99036264],"study_design_scores_gemma":[0.00019833539,0.00018572126,0.00012913896,0.03150359,0.000108884844,0.00007078469,0.0002405781,0.000065650514,0.002238679,0.00069195137,0.9641094,0.00045728358],"about_ca_topic_score_codex":0.0000441848,"about_ca_topic_score_gemma":0.0000067960577,"teacher_disagreement_score":0.98990536,"about_ca_system_score_codex":0.00007898305,"about_ca_system_score_gemma":0.00016934794,"threshold_uncertainty_score":0.79108167},"labels":[],"label_agreement":null},{"id":"W2949479937","doi":"10.1109/percomw.2019.8730742","title":"The Impact of Data Reduction on Wearable-Based Human Activity Recognition","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Generalizability theory; Computer science; Feature selection; Reduction (mathematics); Feature extraction; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Machine learning; Feature (linguistics); Dimensionality reduction; Data mining; Activity recognition; Data reduction; Mathematics; Statistics","score_opus":0.11908896319411508,"score_gpt":0.34970356787782064,"score_spread":0.23061460468370557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949479937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9366106,0.000013134405,0.04979722,0.0004784845,0.00044152534,0.00059215253,0.000033559092,0.0001749878,0.011858348],"genre_scores_gemma":[0.9990154,0.0000032242174,0.0003508472,0.000017791086,0.000058186768,0.000011804361,0.000020563055,0.000008801941,0.0005133914],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99849164,0.00027687705,0.00022133905,0.00047065853,0.0003339327,0.00020552265],"domain_scores_gemma":[0.99735814,0.00037777808,0.00024923202,0.0017954524,0.0001647253,0.00005465615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009275562,0.00013513869,0.0001834504,0.00011007347,0.00017090743,0.00017316156,0.0010045072,0.000063303356,0.00011059822],"category_scores_gemma":[0.000048993712,0.000090579975,0.000104263934,0.00034271958,0.000041112755,0.0013760143,0.00018518227,0.00016683579,0.0003842557],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012089238,0.00036760853,0.0007333676,0.000022177532,0.00008309639,8.6702875e-7,0.00007812835,0.00008593482,0.08425692,0.00027224427,0.002726596,0.9112522],"study_design_scores_gemma":[0.0070084957,0.005486975,0.10227524,0.00097334,0.000062016254,0.00012801374,0.00031817728,0.43399352,0.43689734,0.007820728,0.0031102626,0.0019258797],"about_ca_topic_score_codex":0.0011465251,"about_ca_topic_score_gemma":0.000063419204,"teacher_disagreement_score":0.9093263,"about_ca_system_score_codex":0.00010577987,"about_ca_system_score_gemma":0.00014280113,"threshold_uncertainty_score":0.4938958},"labels":[],"label_agreement":null},{"id":"W2949605588","doi":"10.82308/34165","title":"Automated off-line cardiorespiratory event detection and validation","year":2006,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cardiorespiratory fitness; Artifact (error); Asynchrony (computer programming); Apnea; Computer science; Sleep apnea; Event (particle physics); Heartbeat; Breathing; Visual inspection; Artificial intelligence; Medicine; Physical therapy; Anesthesia","score_opus":0.01924883404961013,"score_gpt":0.2380068761316636,"score_spread":0.21875804208205346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949605588","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98960876,0.00021706556,0.0014098656,0.00004489769,0.00086171954,0.00056043273,0.00010762654,0.0019531632,0.005236489],"genre_scores_gemma":[0.998657,0.000014517756,0.00071162253,0.00015999477,0.00007765206,0.00008746642,0.000016549724,0.000043586388,0.00023162081],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99670595,0.00062958786,0.00066996814,0.0008850369,0.00065036886,0.00045911473],"domain_scores_gemma":[0.99814147,0.00022880758,0.00034707226,0.0007082172,0.0003754542,0.00019900997],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001275895,0.00036162086,0.00041521588,0.00036115482,0.00074792583,0.00023944922,0.00045151194,0.00024447413,0.00001691512],"category_scores_gemma":[0.00022246226,0.00039325646,0.00016692663,0.0007475897,0.00005493715,0.0021376712,0.0003052555,0.00039709234,0.00028928943],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023468903,0.00014811521,0.00023665799,0.000050979044,0.00006985835,0.0000426637,0.000003935885,0.00024650962,0.18570457,0.008153547,0.0000115029725,0.80530816],"study_design_scores_gemma":[0.0028763562,0.00037710575,0.016967094,0.00021226221,0.00011428862,0.00052577973,0.000036013862,0.035352904,0.8035615,0.013070896,0.12520243,0.0017033807],"about_ca_topic_score_codex":0.00045344752,"about_ca_topic_score_gemma":0.00038675338,"teacher_disagreement_score":0.8036048,"about_ca_system_score_codex":0.00046573236,"about_ca_system_score_gemma":0.000031631294,"threshold_uncertainty_score":0.99985194},"labels":[],"label_agreement":null},{"id":"W2950853153","doi":"10.13140/2.1.1100.1284","title":"Application of Sensor Networks in a Smart Apartment","year":2010,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Apartment; Computer science; Architectural engineering; Computer security; Engineering; Civil engineering","score_opus":0.011560175408287724,"score_gpt":0.24010381124177305,"score_spread":0.22854363583348533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950853153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09760063,0.000005472449,0.8974123,0.00033169935,0.00019378625,0.00025612535,5.026205e-7,0.0000647346,0.0041347677],"genre_scores_gemma":[0.99211764,7.2800265e-7,0.007563857,0.000098323755,0.00003244668,0.00005455086,0.0000012676228,0.0000029105495,0.00012825838],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935675,0.000033809425,0.00019240838,0.00019164344,0.00011529989,0.00011011516],"domain_scores_gemma":[0.99934864,0.00010144068,0.000075006734,0.00038018852,0.000057960562,0.000036781574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002719661,0.000056652487,0.000109935114,0.000071093455,0.000014610347,0.000024553943,0.00023480767,0.00004832675,0.000017141505],"category_scores_gemma":[0.000013752451,0.00005238235,0.000028563054,0.0002453297,0.000016340266,0.00017703448,0.00007209084,0.000102167505,0.000047442067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011947788,0.00044183165,0.07948779,0.000024473191,0.000021495656,0.0000045372244,0.00057304266,0.00027986395,0.046569977,0.034077775,0.00072999723,0.83777726],"study_design_scores_gemma":[0.00072095415,0.0000584232,0.064745724,0.00001734931,0.0000032041978,0.000032720156,0.000048269652,0.9009589,0.016093021,0.00084435,0.016218355,0.00025875677],"about_ca_topic_score_codex":0.0002394361,"about_ca_topic_score_gemma":0.0008705538,"teacher_disagreement_score":0.900679,"about_ca_system_score_codex":0.000012308208,"about_ca_system_score_gemma":0.000018289522,"threshold_uncertainty_score":0.21360902},"labels":[],"label_agreement":null},{"id":"W2952273598","doi":"10.1145/3307772.3328280","title":"ODToolkit","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Occupancy; Computer science; Benchmark (surveying); Suite; Data mining; Domain (mathematical analysis); Sensor fusion; Documentation; Code (set theory); Set (abstract data type); Source code; Machine learning; Artificial intelligence; Programming language; Engineering","score_opus":0.009384275221235994,"score_gpt":0.20658227681876815,"score_spread":0.19719800159753215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952273598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08529021,0.000020763242,0.56652254,0.0010993215,0.00092127913,0.00020014941,5.352443e-7,0.00045289024,0.3454923],"genre_scores_gemma":[0.9808865,5.0836206e-7,0.002874999,0.0005502192,0.00002842596,0.0000050535236,3.3395267e-7,0.0000031104544,0.015650854],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999414,0.00002768354,0.00008968615,0.00020303774,0.00014609704,0.00011944427],"domain_scores_gemma":[0.9993905,0.000078476776,0.000027858785,0.00041699316,0.000044841672,0.000041355182],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012807701,0.000052789805,0.00008250343,0.000047324753,0.00001862587,0.000106134765,0.0003895924,0.000024925228,0.00052822666],"category_scores_gemma":[0.000009417482,0.000045720768,0.000037609447,0.00015924414,0.0000047793337,0.00055842416,0.00012650406,0.000045642628,0.011676256],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044312396,0.000112230904,0.024322875,0.000029195995,0.00004148029,0.000013420698,0.00064430444,0.0000063708385,0.009351833,0.19082107,0.021357043,0.7532957],"study_design_scores_gemma":[0.001994531,0.00029475254,0.038429055,0.00007426116,0.000005852202,0.00023961638,0.0002189066,0.06365076,0.02972033,0.009751142,0.8544405,0.0011803139],"about_ca_topic_score_codex":0.000032105017,"about_ca_topic_score_gemma":0.000008756202,"teacher_disagreement_score":0.89559627,"about_ca_system_score_codex":0.000015785181,"about_ca_system_score_gemma":0.000024313265,"threshold_uncertainty_score":0.98909324},"labels":[],"label_agreement":null},{"id":"W2952548149","doi":"10.48550/arxiv.1812.06791","title":"An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; University of Waterloo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Control reconfiguration; Embodied cognition; Situated; Context (archaeology); Internet of Things; Analytics; Software; Human–computer interaction; Artificial intelligence; Data science; Distributed computing; Embedded system","score_opus":0.12085114971466522,"score_gpt":0.22271672661159017,"score_spread":0.10186557689692495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952548149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08185539,0.000016303853,0.91238594,0.00011928311,0.00059141684,0.00048556525,0.00008340672,0.00068377866,0.0037789305],"genre_scores_gemma":[0.9967547,0.000014953295,0.00096620806,0.00048029763,0.00013466769,0.0000032385826,0.00009428378,0.000052084462,0.0014995467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960923,0.00063260214,0.00039719156,0.0020082477,0.00030660597,0.0005630449],"domain_scores_gemma":[0.99590516,0.00026474372,0.0006063354,0.0022360806,0.0005542365,0.00043342012],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006481929,0.0006330374,0.00071856665,0.00070208515,0.0003933992,0.00034479774,0.002465438,0.00048369137,0.00008596234],"category_scores_gemma":[0.00007784508,0.0007437336,0.00040659512,0.00062311644,0.00015715831,0.0004056029,0.0011648333,0.0012141962,0.00029876802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097795564,0.0003346964,0.0031356758,0.000078376936,0.00013211109,0.00024585373,0.00030159383,0.98611265,0.000022337574,0.0055041057,0.00024468813,0.003790114],"study_design_scores_gemma":[0.0008851295,0.00026838225,0.00019915849,0.00021001091,0.00008809985,0.000003887363,0.00006877713,0.99398565,0.00012293026,0.0029157053,0.00048567087,0.0007665929],"about_ca_topic_score_codex":0.0003284082,"about_ca_topic_score_gemma":0.0004026728,"teacher_disagreement_score":0.91489935,"about_ca_system_score_codex":0.00057134766,"about_ca_system_score_gemma":0.00048588624,"threshold_uncertainty_score":0.99950135},"labels":[],"label_agreement":null},{"id":"W2954595942","doi":"10.1007/978-3-030-16450-8_2","title":"A Survey on Accessible Context-Aware Systems","year":2019,"lang":"en","type":"book-chapter","venue":"EAI/Springer Innovations in Communication and Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Personalization; Computer science; Adaptation (eye); Context (archaeology); Ambient intelligence; Ubiquitous computing; Population; Service (business); Inclusion (mineral); Human–computer interaction; World Wide Web; Data science; Internet privacy; Psychology; Medicine; Business","score_opus":0.09355974212937823,"score_gpt":0.30634096348911477,"score_spread":0.21278122135973654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954595942","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008994902,0.0065447316,0.28274062,0.002935963,0.0039960807,0.0043440275,0.00018252953,0.0014442949,0.68881685],"genre_scores_gemma":[0.9687012,0.00013722484,0.0007022237,0.0005797119,0.0000781976,0.00002867231,0.00017302173,0.00006182983,0.029537959],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99671257,0.00038699084,0.0012424993,0.00082788384,0.0004943467,0.00033572697],"domain_scores_gemma":[0.99377817,0.0018312288,0.0010548395,0.002451077,0.0008118005,0.00007290854],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022054764,0.0004789846,0.0007375215,0.0010881032,0.00040620635,0.00084237556,0.001949791,0.00039689892,0.000024879733],"category_scores_gemma":[0.00015886393,0.0005429128,0.00008363723,0.00062302925,0.00010741265,0.00055144064,0.0015019404,0.0012397332,0.00021772091],"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":[0.000015406982,0.00013618483,0.0074573485,0.00017112748,0.00013953134,0.000006060557,0.0009247646,0.00034474646,0.000010212527,0.7531882,0.0028418647,0.23476455],"study_design_scores_gemma":[0.0059058326,0.00041809335,0.15791513,0.017839633,0.00006535804,0.00012853311,0.000692479,0.39042503,0.00007947372,0.010070382,0.41067293,0.0057871384],"about_ca_topic_score_codex":0.0005588749,"about_ca_topic_score_gemma":0.0005983408,"teacher_disagreement_score":0.95970625,"about_ca_system_score_codex":0.00027450113,"about_ca_system_score_gemma":0.00028895185,"threshold_uncertainty_score":0.9997022},"labels":[],"label_agreement":null},{"id":"W2955931876","doi":"10.1080/17538157.2019.1624968","title":"Interconnected living in a quarter for persons with dementia","year":2019,"lang":"en","type":"article","venue":"Informatics for Health and Social Care","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Dementia; Psychological intervention; Work (physics); Activities of daily living; Gerontology; Perception; Medicine; Assisted living; Psychology; Nursing; Psychiatry; Disease; Engineering; Geography","score_opus":0.018005369918079495,"score_gpt":0.2807642604733174,"score_spread":0.2627588905552379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955931876","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6400482,0.00017464654,0.35455716,0.0016285587,0.00035067217,0.0025026032,0.00008479173,0.0000989546,0.00055443234],"genre_scores_gemma":[0.99474424,0.000001351877,0.0037560898,0.0012986738,0.000030149386,0.00013285928,0.000018455683,0.000005429641,0.000012736741],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99926037,0.000019388097,0.00027455753,0.000091875525,0.00009945848,0.00025435898],"domain_scores_gemma":[0.99936146,0.00020212862,0.00014979203,0.000089165704,0.00012955909,0.0000679044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022442563,0.00008295997,0.00019739677,0.00009219242,0.00016272864,0.00011662778,0.00012375753,0.00004930555,0.0000016434083],"category_scores_gemma":[0.000021058822,0.00007624281,0.000041764895,0.00009188657,0.000011261311,0.0005134144,0.000034406516,0.00006739435,0.0000032267915],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058108853,0.000027493912,0.015891246,0.003690868,0.00004521681,1.9337031e-7,0.8884998,0.0000019803704,0.000002329122,0.003092816,0.0012685121,0.08742142],"study_design_scores_gemma":[0.009138359,0.0047339876,0.012913809,0.0016952511,0.00003412004,0.00003194532,0.81493783,0.10396734,0.00002839154,0.0005482189,0.05076976,0.0012010008],"about_ca_topic_score_codex":0.000057751582,"about_ca_topic_score_gemma":0.0007074244,"teacher_disagreement_score":0.35469607,"about_ca_system_score_codex":0.00007960963,"about_ca_system_score_gemma":0.0002266662,"threshold_uncertainty_score":0.31090915},"labels":[],"label_agreement":null},{"id":"W2958890564","doi":"10.1007/978-3-030-24322-7_28","title":"Novel Algorithm on Human Body Fall Detection","year":2019,"lang":"en","type":"book-chapter","venue":"Learning and analytics in intelligent systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; sort; Trigonometry; Machine learning; Algorithm; Wearable computer; Artificial neural network; Computer vision; Embedded system; Mathematics","score_opus":0.05506791561949759,"score_gpt":0.29199525878729166,"score_spread":0.23692734316779407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958890564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021187651,0.00056885736,0.83176416,0.000032776705,0.0019787622,0.00060516794,0.000014173531,0.00025288924,0.16457134],"genre_scores_gemma":[0.53064495,0.00018911755,0.00019547062,0.000039524628,0.0004389373,0.000015044589,0.000028700775,0.00008466473,0.4683636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972476,0.00010544001,0.0007916673,0.0009312147,0.0005882981,0.0003357989],"domain_scores_gemma":[0.9981668,0.00035912028,0.0006230921,0.00054908596,0.00017831923,0.00012356759],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00078915386,0.0004734245,0.0007647759,0.0008085601,0.00018799592,0.00049099204,0.00046793537,0.00048687716,0.000016320397],"category_scores_gemma":[0.000052436048,0.0004873978,0.00018827757,0.0001200967,0.000051750485,0.00015958033,0.00020775294,0.0012917615,0.00046743965],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030535106,0.00033975055,0.001248925,0.001358442,0.0010291989,0.00020938182,0.0019525627,0.0075868964,0.0011932234,0.25886363,0.0003797036,0.7258077],"study_design_scores_gemma":[0.0006249308,0.0008705256,0.00007111443,0.004458743,0.00007942922,0.0002796247,0.00028955823,0.5931579,0.00028688236,0.0013016508,0.39701626,0.001563407],"about_ca_topic_score_codex":0.00047740387,"about_ca_topic_score_gemma":0.000098270146,"teacher_disagreement_score":0.83156866,"about_ca_system_score_codex":0.0003189262,"about_ca_system_score_gemma":0.000052579806,"threshold_uncertainty_score":0.99975777},"labels":[],"label_agreement":null},{"id":"W2959606730","doi":"10.1109/icc.2019.8761355","title":"Power Conservation in Cloud-Assisted Real-Time Context Learning System","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Moncton","funders":"","keywords":"Cloud computing; Computer science; Real-time computing; Mobile device; Context (archaeology); Process (computing); Mobile computing; Computer network; Operating system","score_opus":0.015175670266746764,"score_gpt":0.2280919509464334,"score_spread":0.21291628067968663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959606730","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81580174,0.00002165315,0.040828116,0.00072740496,0.00093306124,0.0006693696,0.0000015254157,0.0008637193,0.14015344],"genre_scores_gemma":[0.9903319,0.0000011943662,0.00048681098,0.00021527703,0.000027278984,0.000025552175,0.000004926978,0.000013750949,0.008893343],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980888,0.0003912724,0.0004430107,0.000476647,0.00031923776,0.0002810429],"domain_scores_gemma":[0.99857646,0.00045550632,0.00021091648,0.00048458047,0.00019612034,0.00007640077],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007803615,0.00016274114,0.00034467326,0.00020451928,0.00006009346,0.00020220135,0.00041541757,0.000111590045,0.00025407493],"category_scores_gemma":[0.00006314477,0.00015844178,0.00007411759,0.0005261322,0.000017363414,0.00078403956,0.00014930173,0.0001879216,0.0041377335],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024830556,0.00068469706,0.48942795,0.0006595333,0.00028632008,0.00025970527,0.010282922,0.00024138406,0.15706742,0.17688939,0.016646095,0.1473063],"study_design_scores_gemma":[0.010121943,0.0010372561,0.33502442,0.0022449086,0.000022718052,0.00072881434,0.009602825,0.5645059,0.0075685144,0.00025263045,0.066143125,0.0027469557],"about_ca_topic_score_codex":0.0011333553,"about_ca_topic_score_gemma":0.000149419,"teacher_disagreement_score":0.5642645,"about_ca_system_score_codex":0.00022202295,"about_ca_system_score_gemma":0.000100397694,"threshold_uncertainty_score":0.99663764},"labels":[],"label_agreement":null},{"id":"W2961852463","doi":"10.2196/14245","title":"Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial","year":2019,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Regional Development Fund; European Commission","keywords":"Activities of daily living; Medicine; Independent living; Cognition; Prospective cohort study; Protocol (science); Gerontology; Prospective memory; Psychology; Physical therapy; Psychiatry","score_opus":0.31359336234743845,"score_gpt":0.5445373507571283,"score_spread":0.23094398840968983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961852463","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15011305,7.192528e-8,0.015213842,0.000026820759,0.000022657623,0.8344349,0.000011690436,0.00007309277,0.000103833474],"genre_scores_gemma":[0.35844517,1.351203e-8,0.00042091028,9.725157e-7,0.00009487272,0.6409548,5.566109e-7,0.0000200842,0.00006264383],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99475527,0.0014295529,0.001049246,0.0007696715,0.0014223699,0.00057388307],"domain_scores_gemma":[0.9953431,0.00086646364,0.0006621769,0.0011905496,0.001806625,0.0001310905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004203214,0.0002447534,0.0006048111,0.00043270152,0.00029755576,0.00028235643,0.0011562717,0.00013860119,0.000068521025],"category_scores_gemma":[0.00024601916,0.00019243894,0.00020870508,0.001500656,0.00020833219,0.0008345573,0.00054564327,0.00038526658,0.000039668852],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.1546027,0.034685265,0.01873663,0.004430966,0.00032148827,0.000019607614,0.045317024,0.00012099834,0.6232772,0.0065018004,0.00020675447,0.11177957],"study_design_scores_gemma":[0.06977383,0.087586515,0.01902841,0.004373005,0.000036850193,0.000037922342,0.01082337,0.06481221,0.7336245,0.0073355343,0.000899166,0.0016686377],"about_ca_topic_score_codex":0.0011083628,"about_ca_topic_score_gemma":0.00033173646,"teacher_disagreement_score":0.20833214,"about_ca_system_score_codex":0.0002258387,"about_ca_system_score_gemma":0.0008419946,"threshold_uncertainty_score":0.7847432},"labels":[],"label_agreement":null},{"id":"W2963563124","doi":"10.3390/s19143213","title":"Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":254,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Fundação de Amparo à Pesquisa do Estado do Amazonas","keywords":"Activity recognition; Software portability; Accelerometer; Context (archaeology); Computer science; Inertial measurement unit; Perspective (graphical); Variety (cybernetics); Human–computer interaction; Ubiquitous computing; Data science; Artificial intelligence","score_opus":0.11253697159660356,"score_gpt":0.33036939376693997,"score_spread":0.2178324221703364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963563124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99699694,0.000032818905,0.00059060735,0.00011999805,0.00066901004,0.0005076584,0.0000108681725,0.00022997957,0.0008421277],"genre_scores_gemma":[0.99859416,0.0000062960744,0.0009347346,0.00013108694,0.00013457383,0.000010710513,0.00000795367,0.000027447853,0.0001530073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973768,0.00067012716,0.00037627088,0.0007356732,0.0004021624,0.00043894644],"domain_scores_gemma":[0.9986406,0.000117059084,0.00022559256,0.00074310106,0.00013001203,0.00014362764],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006203826,0.00026033138,0.00041687445,0.00036451037,0.00010840152,0.00018890585,0.00037770803,0.00016246823,0.00011114391],"category_scores_gemma":[0.000052381176,0.0002829657,0.00011935429,0.00072594284,0.00003581045,0.0013368373,0.000159866,0.00031748117,0.00051146536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000160771,0.0014482315,0.03584587,0.00042531185,0.00013097668,0.00030045462,0.0072490764,0.0026172346,0.46860924,0.00046079527,0.00015107366,0.482601],"study_design_scores_gemma":[0.0071455734,0.00092589733,0.25661054,0.0013449476,0.00006156888,0.0007253587,0.0010138466,0.64833105,0.07581458,0.002138677,0.0023955556,0.0034923775],"about_ca_topic_score_codex":0.0020116575,"about_ca_topic_score_gemma":0.00089168514,"teacher_disagreement_score":0.64571387,"about_ca_system_score_codex":0.00020776872,"about_ca_system_score_gemma":0.000080666025,"threshold_uncertainty_score":0.9999623},"labels":[],"label_agreement":null},{"id":"W2963993350","doi":"10.1155/2018/7316954","title":"Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors","year":2018,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":346,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Residual; Computer science; Deep learning; Artificial intelligence; Domain (mathematical analysis); Connection (principal bundle); State (computer science); Wearable computer; Pattern recognition (psychology); Long short term memory; Machine learning; Recurrent neural network; Artificial neural network; Algorithm; Engineering; Embedded system; Mathematics","score_opus":0.06830659696497564,"score_gpt":0.28702418718026473,"score_spread":0.21871759021528908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963993350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15699528,0.000018210823,0.8414046,0.000059472084,0.00017926832,0.0006000685,0.0000028055424,0.00026140435,0.00047886],"genre_scores_gemma":[0.92362696,9.3479423e-7,0.07598553,0.0000131962515,0.0001804285,0.000116879004,0.0000015625225,0.000034013712,0.000040491108],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983657,0.000060723964,0.0004120393,0.0004364305,0.000256405,0.0004687041],"domain_scores_gemma":[0.99887496,0.00040164712,0.0001094996,0.00038692026,0.00013032262,0.00009666828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089003344,0.0002157352,0.0003402681,0.0002628461,0.0001285871,0.0001658685,0.0003211267,0.0001321766,0.000027958118],"category_scores_gemma":[0.00030408462,0.00022716561,0.00007695926,0.00041306793,0.000043288714,0.0006625248,0.00013988442,0.00020762935,0.00007325399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007442063,0.002283711,0.00043889237,0.009230546,0.000411287,0.00006178539,0.014275117,0.09564122,0.64348096,0.020298196,0.00035850273,0.21344535],"study_design_scores_gemma":[0.00049030373,0.00010220935,0.000048268543,0.000663799,0.000009940301,0.000046984285,0.000022884778,0.9597497,0.017055605,0.021379424,0.00008884846,0.00034205455],"about_ca_topic_score_codex":0.00003799034,"about_ca_topic_score_gemma":0.00002793658,"teacher_disagreement_score":0.86410844,"about_ca_system_score_codex":0.00015220698,"about_ca_system_score_gemma":0.000025468295,"threshold_uncertainty_score":0.92635447},"labels":[],"label_agreement":null},{"id":"W2964143491","doi":"10.1109/wf-iot.2019.8767259","title":"Sensor-enabled Functional-Mobility Assessment: An Exploratory Investigation","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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 Alberta","funders":"","keywords":"Computer science","score_opus":0.050194925024971464,"score_gpt":0.2665501932503768,"score_spread":0.21635526822540535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964143491","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6720319,0.00000717782,0.3147412,0.0005932277,0.00088620506,0.00043988012,0.0000031280742,0.00054397085,0.010753345],"genre_scores_gemma":[0.99172235,5.312517e-7,0.0053427313,0.00071611156,0.000086329186,0.000057295852,0.000022008398,0.000010119689,0.0020425532],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99807656,0.00038481408,0.0002840116,0.000601484,0.00043898934,0.00021414527],"domain_scores_gemma":[0.99832916,0.00019783001,0.00012646522,0.00087993714,0.000300671,0.00016590912],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00089471985,0.00014924629,0.00019162527,0.00012616134,0.00010366638,0.0002562475,0.00034056563,0.000079899095,0.0003956521],"category_scores_gemma":[0.00003113556,0.00014166393,0.000061137594,0.00039104774,0.00003223286,0.003352064,0.0001413892,0.00015810404,0.0015066153],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055340795,0.0014198355,0.42577285,0.00031100292,0.0002408866,0.000025216794,0.0050271396,0.0007514976,0.34972915,0.1178114,0.0113577945,0.08749791],"study_design_scores_gemma":[0.004051998,0.00083424576,0.578948,0.00012545464,0.00002975047,0.00010250113,0.0038398672,0.3258606,0.028875785,0.024783287,0.030483235,0.0020652579],"about_ca_topic_score_codex":0.000053096428,"about_ca_topic_score_gemma":0.00006424772,"teacher_disagreement_score":0.3251091,"about_ca_system_score_codex":0.00013497347,"about_ca_system_score_gemma":0.00025118163,"threshold_uncertainty_score":0.99927086},"labels":[],"label_agreement":null},{"id":"W2964762837","doi":"10.24963/ijcai.2019/779","title":"A Novel Distribution-Embedded Neural Network for Sensor-Based Activity Recognition","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Research (Canada)","funders":"Ministry of Education, India; Nanyang Technological University","keywords":"Computer science; Feature engineering; Artificial intelligence; Deep learning; Feature (linguistics); Machine learning; Activity recognition; Pattern recognition (psychology); Feature extraction; Artificial neural network; Feature learning; Statistical model","score_opus":0.04526841301805444,"score_gpt":0.2669027697229508,"score_spread":0.22163435670489634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964762837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18472241,0.000004843949,0.8111357,0.0009658286,0.0010003246,0.0009549986,0.00015404235,0.00039819133,0.00066366],"genre_scores_gemma":[0.97738796,3.1340187e-7,0.020976577,0.0006295549,0.00022134506,0.000146578,0.00015819822,0.000016157479,0.0004633002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828595,0.00010654716,0.00026083682,0.00060381624,0.0002823521,0.00046052778],"domain_scores_gemma":[0.997948,0.0008423212,0.00019572608,0.0005705803,0.0003221101,0.00012125118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046754887,0.00021322184,0.00029650523,0.000056488443,0.00015211513,0.00023117245,0.00037074336,0.000119765005,0.0000848482],"category_scores_gemma":[0.00009660233,0.00020767009,0.00020533212,0.0004341537,0.000024194369,0.00086527166,0.00008723644,0.00014890295,0.0003342477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006559133,0.0015423793,0.008718355,0.00034604262,0.00024390088,0.000009743204,0.00028189793,0.005130811,0.06310075,0.004947439,0.025206119,0.88981664],"study_design_scores_gemma":[0.0029306908,0.00030064638,0.006722038,0.00007472246,0.000020045383,0.000041768446,0.000037113135,0.96986294,0.010740627,0.0007476753,0.007872606,0.00064912555],"about_ca_topic_score_codex":0.00009989926,"about_ca_topic_score_gemma":0.00003989066,"teacher_disagreement_score":0.9647321,"about_ca_system_score_codex":0.0001069482,"about_ca_system_score_gemma":0.00011009428,"threshold_uncertainty_score":0.84685403},"labels":[],"label_agreement":null},{"id":"W2967395846","doi":"10.1109/cec.2019.8790249","title":"User and Task Identification of Smartwatch Data with an Ensemble of Nonlinear Symbolic Models","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Nonlinear system; Machine learning; Identification (biology); Symbolic regression; Genetic programming; Artificial intelligence; Model selection; Linear model; Classifier (UML); Ensemble forecasting; Kinematics; Task (project management); Data modeling; Nonlinear model; System identification; Engineering","score_opus":0.04156034355154438,"score_gpt":0.2696198477618605,"score_spread":0.22805950421031612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967395846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.604743,0.000012901778,0.39450097,0.000070020666,0.000044090953,0.00018366851,0.000023091518,0.000028996847,0.00039323955],"genre_scores_gemma":[0.98828566,0.0000049991577,0.011173808,0.000023962219,0.000011653501,0.0000034906275,0.0000250702,0.000006624952,0.00046475642],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998929,0.0000619851,0.0002777638,0.00037898324,0.00023999876,0.00011225735],"domain_scores_gemma":[0.9980909,0.00007631149,0.00018940592,0.001387193,0.00020278772,0.000053402517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044518508,0.00007866425,0.0001979593,0.00007946661,0.000017531916,0.00005816822,0.0006392937,0.000036357997,0.00000814471],"category_scores_gemma":[0.0000102946005,0.0000643438,0.000012883973,0.00020161891,0.000029890576,0.0020008064,0.00026121968,0.000042723634,0.00001701058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022971531,0.0017711887,0.09638591,0.0009833173,0.0004220313,0.00000705195,0.009812712,0.0008869581,0.5807788,0.042249326,0.0010669108,0.26540607],"study_design_scores_gemma":[0.00065929483,0.00020483644,0.009913658,0.00006220887,0.000016727254,0.00002917284,0.00021392423,0.947311,0.0402936,0.00080831465,0.00028610908,0.00020118833],"about_ca_topic_score_codex":0.0005147429,"about_ca_topic_score_gemma":0.0002339297,"teacher_disagreement_score":0.946424,"about_ca_system_score_codex":0.0000066829707,"about_ca_system_score_gemma":0.000061840285,"threshold_uncertainty_score":0.2623864},"labels":[],"label_agreement":null},{"id":"W2967611518","doi":"10.1145/3340496.3342760","title":"A recommendation system for emergency mobile applications using context attributes: REMAC","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Calgary","funders":"","keywords":"Computer science; Context (archaeology); Recommender system; Mobile computing; World Wide Web; Telecommunications","score_opus":0.06202352660829485,"score_gpt":0.3058432957261746,"score_spread":0.24381976911787973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967611518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008532043,0.00004052376,0.98367155,0.00031813816,0.00096256915,0.0031151336,0.000043053235,0.00041210253,0.0029048936],"genre_scores_gemma":[0.98778325,0.0000031901438,0.009692687,0.00008004819,0.000117326665,0.0013042692,0.00004162387,0.00001634502,0.0009612463],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99851894,0.000085533866,0.00046661176,0.0005192064,0.00015532863,0.00025438512],"domain_scores_gemma":[0.99842626,0.00022109976,0.00025679538,0.0006042983,0.00040851018,0.000083010185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040454578,0.00014946854,0.000255241,0.000128127,0.00016145522,0.00010793567,0.00044366263,0.00007691995,0.00028464274],"category_scores_gemma":[0.0000181349,0.00014867185,0.0001297491,0.00042843207,0.000008033975,0.00079185027,0.00012216587,0.00006679106,0.0005791376],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039056067,0.0004498117,0.0061551905,0.0012043408,0.0002522148,0.0000010143859,0.0012016018,0.00013808871,0.03707281,0.094017014,0.0082224235,0.8512464],"study_design_scores_gemma":[0.0019058331,0.00028053657,0.00018586754,0.00014503855,0.00004215285,0.00009391924,0.0028288502,0.46700472,0.011347633,0.00071558845,0.5145288,0.00092106615],"about_ca_topic_score_codex":0.00007286407,"about_ca_topic_score_gemma":0.000039608894,"teacher_disagreement_score":0.9792512,"about_ca_system_score_codex":0.00020211193,"about_ca_system_score_gemma":0.0000715666,"threshold_uncertainty_score":0.7443835},"labels":[],"label_agreement":null},{"id":"W2968036265","doi":"10.1145/3342428.3342665","title":"Real-time Gait Speed Evaluation at Home","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Gait; Task (project management); Cognition; Computer science; Preferred walking speed; Gait analysis; Population; Scale (ratio); Physical medicine and rehabilitation; Human–computer interaction; Simulation; Psychology; Engineering; Medicine; Geography","score_opus":0.03335145779835802,"score_gpt":0.27495721076194296,"score_spread":0.24160575296358494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968036265","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8005751,0.000011410513,0.0065728966,0.0005806567,0.0006592336,0.00064130273,0.00000214383,0.00037845946,0.1905788],"genre_scores_gemma":[0.9661337,0.0000025434608,0.0012523155,0.0001312633,0.00006203779,0.000010761754,0.000008612733,0.000009002311,0.03238973],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839157,0.00017315465,0.00020658046,0.0004123618,0.0006113922,0.00020493988],"domain_scores_gemma":[0.99874145,0.00017926068,0.00010169034,0.00066740456,0.00023234608,0.000077853314],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00084179104,0.00011212208,0.00017835812,0.000107094784,0.000053246808,0.00013012599,0.00042004744,0.0000588269,0.0036956496],"category_scores_gemma":[0.00003022415,0.00010288415,0.00006892583,0.00026789965,0.000011329954,0.0007279846,0.0002437669,0.000051832365,0.029705308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058032245,0.00029979993,0.021973241,0.00007379319,0.00015786887,0.0000162252,0.0019606943,0.00015319929,0.574901,0.00742623,0.05111526,0.34186465],"study_design_scores_gemma":[0.003241739,0.000296846,0.06615756,0.00008803762,0.000031391817,0.00014779039,0.00007947546,0.8835546,0.028218055,0.0032013247,0.013950982,0.001032218],"about_ca_topic_score_codex":0.00013208289,"about_ca_topic_score_gemma":0.000024070354,"teacher_disagreement_score":0.8834014,"about_ca_system_score_codex":0.0002034566,"about_ca_system_score_gemma":0.00008094378,"threshold_uncertainty_score":0.9972151},"labels":[],"label_agreement":null},{"id":"W2969346625","doi":"10.21105/joss.01587","title":"studentlife: Tidy Handling and Navigation of a Valuable Mobile-Health Dataset","year":2019,"lang":"en","type":"article","venue":"The Journal of Open Source Software","topic":"Context-Aware Activity Recognition Systems","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":"Université de Montréal","funders":"","keywords":"Computer science; Geography; Cartography","score_opus":0.032685172207266626,"score_gpt":0.3346448732321521,"score_spread":0.30195970102488545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969346625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75420666,0.0011585555,0.24281521,0.00033718254,0.00032642935,0.0010275139,0.000079758014,0.00002060801,0.000028056893],"genre_scores_gemma":[0.9953796,0.000052042076,0.004055245,0.00025141277,0.000051148127,0.000007050557,0.000015583073,0.000012319756,0.0001755454],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981898,0.00044270355,0.0005420355,0.00014984341,0.0005127488,0.0001628611],"domain_scores_gemma":[0.9975549,0.0005526323,0.0010502802,0.00048853096,0.0002559578,0.0000976812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037372604,0.0001075649,0.00038972427,0.00008250633,0.00014421451,0.0002814099,0.0016268503,0.000032979486,0.000027244289],"category_scores_gemma":[0.00008500541,0.000075312724,0.000045356224,0.00023350706,0.000038252634,0.0012689988,0.000799404,0.0002311253,0.000023961078],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051994156,0.0008804809,0.10402099,0.00093819795,0.000814442,0.00002788868,0.059194088,0.0063983547,0.0034437012,0.00017580151,0.03185864,0.7917275],"study_design_scores_gemma":[0.046697203,0.02842842,0.0780778,0.02417949,0.0010442946,0.01811761,0.074096136,0.047839627,0.01844069,0.008981848,0.64921814,0.0048787533],"about_ca_topic_score_codex":0.00025359713,"about_ca_topic_score_gemma":0.000010617065,"teacher_disagreement_score":0.7868487,"about_ca_system_score_codex":0.000050463113,"about_ca_system_score_gemma":0.0002248528,"threshold_uncertainty_score":0.3071164},"labels":[],"label_agreement":null},{"id":"W2969466393","doi":"10.3390/app9173459","title":"Energy Management for Smart Homes—State of the Art","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Home automation; Computer science; Ambient intelligence; Building automation; Process (computing); Context (archaeology); Computer security; Smart environment; Human–computer interaction; Internet of Things; Telecommunications","score_opus":0.017802340229060704,"score_gpt":0.2281143411421185,"score_spread":0.2103120009130578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969466393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10633295,0.000067470355,0.6759825,0.0017472358,0.0020163204,0.0014158944,0.000008963676,0.0001428036,0.21228586],"genre_scores_gemma":[0.99295866,0.0000032209114,0.003060901,0.00036043138,0.000013478986,0.000084691994,3.9990158e-7,0.0000032961805,0.003514925],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99881494,0.000026551259,0.00018512312,0.00035937765,0.000399107,0.00021488409],"domain_scores_gemma":[0.9991899,0.00016352827,0.0001471931,0.00042680313,0.0000415245,0.000031043564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006043803,0.00008582009,0.00013957956,0.00008598576,0.000135583,0.00010637179,0.0012606824,0.00001662065,0.000011292947],"category_scores_gemma":[0.00000385114,0.000057826437,0.00006985389,0.0005978005,0.00012937517,0.0002221165,0.0003039127,0.000030183677,0.00008092796],"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":[0.000011497173,0.00009155159,0.0017180628,0.0000810849,0.000048572696,5.419061e-7,0.0005669903,0.00017434118,0.009076923,0.4374224,0.005811896,0.54499614],"study_design_scores_gemma":[0.0027075883,0.00044771857,0.021995896,0.00023061465,0.000035866404,0.00002458998,0.0008291053,0.04984166,0.18143271,0.1631214,0.5780665,0.0012663495],"about_ca_topic_score_codex":0.000013455123,"about_ca_topic_score_gemma":0.000028978944,"teacher_disagreement_score":0.8866257,"about_ca_system_score_codex":0.000016148722,"about_ca_system_score_gemma":0.000046381887,"threshold_uncertainty_score":0.23580937},"labels":[],"label_agreement":null},{"id":"W2972515751","doi":"10.1145/3341162.3354058","title":"Designing to support uncomfortable breathing exercises","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 New Brunswick","funders":"","keywords":"Breathing; Computer science; Breathing exercises; Human–computer interaction; Risk analysis (engineering); Psychology; Medicine; Physical therapy","score_opus":0.029313371706122273,"score_gpt":0.25942122129458406,"score_spread":0.23010784958846178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972515751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063035555,0.000008736401,0.87317675,0.00027652396,0.0006280797,0.00042178846,0.0000011278307,0.00043290696,0.06201852],"genre_scores_gemma":[0.9446128,4.756992e-7,0.042943157,0.0008041956,0.00003781542,0.000028202028,0.0000012753949,0.000011226227,0.011560862],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99867517,0.000046229783,0.00022380285,0.00043007615,0.00030161743,0.00032310974],"domain_scores_gemma":[0.9989494,0.00017793318,0.00005863247,0.0005750692,0.00008743638,0.00015152115],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046951976,0.00013116506,0.0002236924,0.00015140645,0.00007871203,0.0003356271,0.0006265363,0.000046046123,0.00049732655],"category_scores_gemma":[0.000026244656,0.00012193356,0.00006300785,0.00038860444,0.000005755875,0.0012222121,0.00029416892,0.000084930725,0.003910613],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003606355,0.00027244145,0.09092066,0.00016685633,0.00010656819,0.0001442059,0.008303862,0.0006557814,0.087086506,0.014023938,0.04261093,0.7556722],"study_design_scores_gemma":[0.0044014095,0.002453233,0.01151149,0.0009159156,0.00006514472,0.0013946822,0.0032592372,0.04275989,0.6614918,0.004530614,0.2623505,0.004866078],"about_ca_topic_score_codex":0.00021662035,"about_ca_topic_score_gemma":0.000045875695,"teacher_disagreement_score":0.88157725,"about_ca_system_score_codex":0.000059977538,"about_ca_system_score_gemma":0.00009489993,"threshold_uncertainty_score":0.996865},"labels":[],"label_agreement":null},{"id":"W2972579581","doi":"10.1145/3341162.3343807","title":"Semantic human activity annotation tool using skeletonized surveillance videos","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Autodesk (Canada)","funders":"","keywords":"Computer science; Activity recognition; Crowd sourcing; Context (archaeology); Annotation; Semantic annotation; Activity detection; World Wide Web; Information retrieval; Data science; Artificial intelligence","score_opus":0.02930198308358839,"score_gpt":0.2837869546064481,"score_spread":0.2544849715228597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972579581","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63748723,0.000006284601,0.35970193,0.00014262352,0.00035353284,0.0003398119,0.0000016328797,0.00024327612,0.0017237063],"genre_scores_gemma":[0.9962308,7.2007504e-7,0.0024956514,0.0001356191,0.000064908374,0.000011224292,0.0000027734256,0.000014085726,0.0010442298],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823445,0.0002638026,0.00027050998,0.0005452334,0.00038246805,0.00030354699],"domain_scores_gemma":[0.99856603,0.00025519368,0.00020350263,0.00071675313,0.00019776367,0.000060785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005824134,0.00017976646,0.0003147338,0.00014493756,0.00014490937,0.0003019529,0.0004491757,0.000078077246,0.00013766557],"category_scores_gemma":[0.000049933467,0.00017787321,0.00010121946,0.0004572618,0.000020834656,0.0014945837,0.00018174724,0.00013438161,0.0005229384],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022926928,0.0002010975,0.06424937,0.00012697172,0.00007744906,0.000015460242,0.00055347313,0.0001366532,0.8392474,0.0032799612,0.00030388366,0.09178537],"study_design_scores_gemma":[0.0040967264,0.00029431906,0.23504148,0.0001990592,0.000017143826,0.00018033726,0.000112107424,0.67268103,0.081043,0.0018854502,0.0026216859,0.0018276373],"about_ca_topic_score_codex":0.0006748917,"about_ca_topic_score_gemma":0.00020187817,"teacher_disagreement_score":0.7582044,"about_ca_system_score_codex":0.000115823714,"about_ca_system_score_gemma":0.00008217138,"threshold_uncertainty_score":0.7253459},"labels":[],"label_agreement":null},{"id":"W2973438844","doi":"10.1177/2327857919081032","title":"Comparison of Gait Speed Estimation of Multiple Sensor-Based Technologies","year":2019,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care","topic":"Context-Aware Activity Recognition Systems","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; University of Waterloo; Research Institute for Aging; University Health Network","funders":"","keywords":"eHealth; Gait; Physical medicine and rehabilitation; Computer science; Population; Population ageing; Psychological intervention; Quality of life (healthcare); Independence (probability theory); Health care; Applied psychology; Medicine; Psychology; Nursing; Environmental health","score_opus":0.04199582741962697,"score_gpt":0.317890057203641,"score_spread":0.275894229784014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973438844","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99824405,0.00003749754,0.00010863073,0.00060937024,0.00030421966,0.00035493314,0.000028009583,0.00003123515,0.00028205765],"genre_scores_gemma":[0.9993183,0.0000058774813,0.0006127984,0.000029764196,0.000007717434,0.0000044197454,0.00000496575,0.0000064190604,0.000009723609],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988328,0.00000971539,0.0005744418,0.0002283059,0.00024430465,0.00011041126],"domain_scores_gemma":[0.99857986,0.00018239355,0.0008185973,0.00013713208,0.00026058606,0.00002142512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020080945,0.00011313922,0.00031858782,0.00019529232,0.00004357257,0.00003212515,0.00061391137,0.00006777239,0.0000020544542],"category_scores_gemma":[0.00008097456,0.000092812676,0.00006796187,0.00011776117,0.000057616628,0.00018826233,0.0001749867,0.00013271646,6.8121506e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007880348,0.00011990673,0.9687346,0.0006329719,0.000019914403,2.336263e-8,0.0031118512,0.0029112992,0.01690095,0.005646837,0.000040080045,0.0018027365],"study_design_scores_gemma":[0.0019590282,0.00087969983,0.24937174,0.0019812782,0.000008353968,0.0000021099042,0.009229801,0.16407026,0.5711453,0.0006160981,0.0003573942,0.000378966],"about_ca_topic_score_codex":0.00039617726,"about_ca_topic_score_gemma":0.000036355443,"teacher_disagreement_score":0.7193629,"about_ca_system_score_codex":0.00021307424,"about_ca_system_score_gemma":0.00004858732,"threshold_uncertainty_score":0.37847912},"labels":[],"label_agreement":null},{"id":"W2974089011","doi":"10.1155/2019/9507938","title":"A Real-Time Patient Monitoring Framework for Fall Detection","year":2019,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Edge device; Wearable computer; Laptop; Enhanced Data Rates for GSM Evolution; Edge computing; Analytics; ALARM; Real-time computing; Cloud computing; Mobile device; Artificial intelligence; Computer security; Embedded system; Data mining; Operating system","score_opus":0.02336426046336846,"score_gpt":0.2837255335891126,"score_spread":0.26036127312574414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974089011","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58898264,0.00019307951,0.40938938,0.00006768112,0.0002603925,0.0006358,0.0000027652868,0.00020415944,0.00026411598],"genre_scores_gemma":[0.92856616,0.00012454484,0.071002804,0.000019005352,0.00005497588,0.00018372931,0.0000043386385,0.000016142667,0.000028275257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988254,0.00014639628,0.00032182658,0.00034339566,0.0001334424,0.00022954767],"domain_scores_gemma":[0.996846,0.0012317222,0.00022752405,0.0014261866,0.0001958643,0.00007267655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032051234,0.00014020347,0.000227306,0.000102792685,0.00043909613,0.00022875392,0.000777945,0.000089886315,0.0000015911768],"category_scores_gemma":[0.000027281134,0.00015231853,0.00007306941,0.00025351372,0.000036634086,0.00030245434,0.00083628425,0.00019243009,0.000034435012],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004217389,0.00007785338,0.0017669168,0.00003617246,0.000024166155,1.9971844e-7,0.0022730483,0.0000717944,0.011022789,0.003203225,0.000005104271,0.9815145],"study_design_scores_gemma":[0.0005947218,0.00041229633,0.0019961817,0.00061704725,0.00001539748,0.000033770153,0.0011132803,0.98000044,0.0053869816,0.0015933737,0.007711819,0.000524675],"about_ca_topic_score_codex":0.00012535563,"about_ca_topic_score_gemma":0.000007803251,"teacher_disagreement_score":0.9809898,"about_ca_system_score_codex":0.00006868756,"about_ca_system_score_gemma":0.00003228246,"threshold_uncertainty_score":0.62113696},"labels":[],"label_agreement":null},{"id":"W2977972340","doi":"10.3390/diagnostics9040135","title":"A Pervasive Healthcare System for COPD Patients","year":2019,"lang":"en","type":"article","venue":"Diagnostics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autonomy; Ubiquitous computing; Context (archaeology); Ontology; COPD; Telemedicine; Health care; Medicine; Computer science; Pulmonary disease; Healthcare system; Medical emergency; Knowledge management; Human–computer interaction; Political science","score_opus":0.016468559321059482,"score_gpt":0.24259658626150676,"score_spread":0.22612802694044729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977972340","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60086167,0.00065794715,0.37475812,0.002572892,0.011003885,0.0061046463,0.000580017,0.00094087975,0.0025199216],"genre_scores_gemma":[0.9974629,0.000011073909,0.0015514987,0.0005056932,0.00010313393,0.00018301996,0.00003617346,0.000016647748,0.000129816],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987181,0.00007961627,0.00026007363,0.00037574818,0.00027596048,0.0002905294],"domain_scores_gemma":[0.9971012,0.0015298486,0.00015887922,0.0005110932,0.0005790368,0.00011993982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016618927,0.00013898309,0.00024985668,0.00007620164,0.000089086454,0.0001220754,0.000463238,0.00008241746,0.0000049232353],"category_scores_gemma":[0.00040184872,0.0001391185,0.00009122812,0.00018279154,0.000011572765,0.00029535047,0.00013656476,0.000084853265,0.0006123187],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046111163,0.00063109986,0.70873076,0.0026775189,0.0001460117,0.000032166252,0.0057244333,0.000034599463,0.0000638055,0.07060338,0.034040816,0.17726931],"study_design_scores_gemma":[0.024811562,0.006763864,0.3997756,0.006525721,0.00019739146,0.0001728449,0.005798346,0.094661705,0.009482,0.0036920155,0.44263554,0.0054834005],"about_ca_topic_score_codex":0.0000698425,"about_ca_topic_score_gemma":0.000029996776,"teacher_disagreement_score":0.40859473,"about_ca_system_score_codex":0.0001499584,"about_ca_system_score_gemma":0.00010142306,"threshold_uncertainty_score":0.7870322},"labels":[],"label_agreement":null},{"id":"W2978760291","doi":"10.2196/13305","title":"Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Intelligence Advanced Research Projects Activity; Office of the Director of National Intelligence","keywords":"Wearable computer; Troubleshooting; Data collection; Computer science; Wearable technology; Health care; mHealth; Data science; Scale (ratio); Human–computer interaction; Nursing; Medicine; Psychological intervention; Embedded system","score_opus":0.14578120928612617,"score_gpt":0.41542301422836486,"score_spread":0.26964180494223866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978760291","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9839441,0.00041649694,0.007932556,0.0052695004,0.00014985583,0.0021708002,0.000048924652,0.000054811786,0.000012934079],"genre_scores_gemma":[0.99680483,0.00015152844,0.002569806,0.00028978867,0.00004013901,0.000055247772,0.000037218302,0.000018992601,0.000032440654],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99788415,0.0002957642,0.00050840643,0.0005995338,0.00017062484,0.0005415122],"domain_scores_gemma":[0.99900836,0.00014628898,0.00032871694,0.00024804246,0.00004349803,0.00022507277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009227796,0.00015621637,0.0003243414,0.00023198496,0.0005421433,0.0001297968,0.00011710764,0.000048721067,0.000013565798],"category_scores_gemma":[0.000018243569,0.0001711955,0.000019201876,0.00035697548,0.000015976402,0.00046205474,0.00014826795,0.00017267176,0.0000045781917],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036081175,0.0003874338,0.79632086,0.0013620985,0.000019227808,0.0000020190555,0.038319856,0.000021407604,0.000058936243,0.0005449075,0.00012420487,0.16280295],"study_design_scores_gemma":[0.010270926,0.002877143,0.81810236,0.0006591609,0.000043759126,0.00017307664,0.119775794,0.038750377,0.000043182365,0.00032207763,0.007963569,0.0010186019],"about_ca_topic_score_codex":0.0014055681,"about_ca_topic_score_gemma":0.0010706959,"teacher_disagreement_score":0.16178435,"about_ca_system_score_codex":0.00038093413,"about_ca_system_score_gemma":0.0003515902,"threshold_uncertainty_score":0.69811493},"labels":[],"label_agreement":null},{"id":"W2979569543","doi":"10.1007/978-3-030-32785-9_4","title":"Long Short Term Memory Based Model for Abnormal Behavior Prediction in Elderly Persons","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Computer science; Long short term memory; Term (time); Artificial intelligence; Recurrent neural network; Machine learning; Intervention (counseling); Home automation; Artificial neural network; Psychology; Telecommunications","score_opus":0.03539538842245469,"score_gpt":0.26415611060320715,"score_spread":0.22876072218075247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979569543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002847177,0.00009342242,0.99148357,0.00021722316,0.0021867028,0.0020095177,0.000078039804,0.00021004285,0.0008743217],"genre_scores_gemma":[0.92552674,0.0000026812877,0.07235531,0.00049897353,0.00032572923,0.00017776809,0.00003191126,0.0000556123,0.0010252885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954571,0.000051491927,0.00072064524,0.0019188661,0.0010573704,0.00079449185],"domain_scores_gemma":[0.99724656,0.00060553424,0.0002616506,0.0013872117,0.0003173133,0.00018174898],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011876071,0.0006101849,0.0007036749,0.0013934753,0.00021052574,0.0005584932,0.0024142389,0.00051198987,0.000015800382],"category_scores_gemma":[0.000052005,0.0006309403,0.00026298538,0.00046171417,0.0003081355,0.0014172929,0.0005447119,0.0008596676,0.000026569714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025362382,0.00013119867,0.0033822975,0.00014410807,0.00001351012,0.000079601195,0.0012676988,0.0949899,0.0003833669,0.0002709166,0.00004116897,0.8992709],"study_design_scores_gemma":[0.0005992884,0.0002651307,0.0017949974,0.000516877,0.000018314528,0.0000744342,4.516516e-7,0.9945549,0.00052464707,0.000968839,0.000045334175,0.00063681084],"about_ca_topic_score_codex":0.000033710687,"about_ca_topic_score_gemma":0.00095050596,"teacher_disagreement_score":0.92267954,"about_ca_system_score_codex":0.00063840253,"about_ca_system_score_gemma":0.0014021886,"threshold_uncertainty_score":0.9996142},"labels":[],"label_agreement":null},{"id":"W2980764660","doi":"10.48550/arxiv.1910.08167","title":"Context-Augmented Software Development Projects: Literature Review and Preliminary Framework","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Context (archaeology); Software development; Software; Reuse; Software engineering; Knowledge management; Software project management; Data science; Package development process; Software construction; Engineering","score_opus":0.06557561057708045,"score_gpt":0.20352943873440174,"score_spread":0.1379538281573213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980764660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018389186,0.044473693,0.93126947,0.0005077775,0.0011271434,0.0030460553,0.00006411482,0.00071881514,0.00040376352],"genre_scores_gemma":[0.9776739,0.010775847,0.007097825,0.0015171145,0.000068850175,0.000022394415,0.000086177104,0.000041805546,0.002716127],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99684644,0.00035419362,0.0004142144,0.0017718844,0.00018351534,0.00042976043],"domain_scores_gemma":[0.99682164,0.00037907,0.00053669006,0.0015646066,0.00048050162,0.00021746225],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004374364,0.0005708272,0.00083734706,0.00032613016,0.00018385371,0.00031925892,0.0015323347,0.000569524,0.000029602175],"category_scores_gemma":[0.0001955552,0.00061638065,0.00019843108,0.00082832883,0.00008560487,0.0007633575,0.0031995703,0.0011951583,0.00017400728],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092198106,0.003042694,0.061754744,0.13729374,0.0064701596,0.008709806,0.035243254,0.0035052754,0.00004893078,0.06929557,0.046422217,0.6272916],"study_design_scores_gemma":[0.010832862,0.0024452875,0.034088388,0.5132337,0.003659195,0.001829793,0.0022494604,0.099305086,0.001082516,0.04886387,0.26224437,0.02016543],"about_ca_topic_score_codex":0.00002232363,"about_ca_topic_score_gemma":0.000014181513,"teacher_disagreement_score":0.95928466,"about_ca_system_score_codex":0.00030264005,"about_ca_system_score_gemma":0.00051797595,"threshold_uncertainty_score":0.9996288},"labels":[],"label_agreement":null},{"id":"W2983735760","doi":"10.3390/s19225026","title":"A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sliding window protocol; Accelerometer; Gyroscope; Computer science; Inertial measurement unit; Artificial intelligence; Activity recognition; Inertial frame of reference; Window (computing); Pattern recognition (psychology); Engineering; Operating system","score_opus":0.09615887368840895,"score_gpt":0.34251273637233604,"score_spread":0.2463538626839271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983735760","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90225077,0.000018797879,0.09587651,0.000048183727,0.00043130686,0.0009279319,0.000022210128,0.000087031985,0.00033726325],"genre_scores_gemma":[0.99042904,0.0000016553669,0.009385079,0.000022394597,0.00008019311,0.000014699835,0.000006664284,0.000025983058,0.000034298027],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979326,0.0002270558,0.00050486595,0.0006309693,0.00033192808,0.00037259673],"domain_scores_gemma":[0.99780405,0.00082921545,0.00062359043,0.00035373794,0.00029628043,0.0000931373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057466305,0.0002619747,0.0006680489,0.00032578994,0.00023764516,0.00014372237,0.00021327821,0.00013795396,0.00000689959],"category_scores_gemma":[0.00017498176,0.0002906015,0.0001539327,0.00038549278,0.000053593587,0.0008608807,0.00015728286,0.00021046953,0.000015971758],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013935262,0.00012515373,0.007910974,0.00032512326,0.00013422978,0.0000035127293,0.005921879,0.0010151411,0.9632518,0.00046662486,0.000015451704,0.020690728],"study_design_scores_gemma":[0.0018323819,0.0004682485,0.0071675517,0.00066736504,0.000047755188,0.000033354652,0.0024166708,0.8700409,0.116322815,0.00034902347,0.00006715127,0.0005867782],"about_ca_topic_score_codex":0.0005098333,"about_ca_topic_score_gemma":0.00008871308,"teacher_disagreement_score":0.86902577,"about_ca_system_score_codex":0.00011526835,"about_ca_system_score_gemma":0.00005908194,"threshold_uncertainty_score":0.99995464},"labels":[],"label_agreement":null},{"id":"W2984750599","doi":"10.1007/978-3-030-34365-1_1","title":"Declarative Approach to Model Checking for Context-Aware Applications","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Model checking; Context (archaeology); Programming language; Cognitive science; Psychology; History","score_opus":0.05174079150478006,"score_gpt":0.26202125175290675,"score_spread":0.2102804602481267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984750599","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017966046,0.00017844167,0.99309444,0.0006330032,0.0002714053,0.0025280106,0.00019037272,0.00008626219,0.0030000696],"genre_scores_gemma":[0.03738749,0.000015924363,0.9614104,0.00041488488,0.000118043856,0.00037290913,0.00006053397,0.000023931443,0.00019590073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985431,0.00001080882,0.00064724276,0.00027600693,0.00026375518,0.0002590931],"domain_scores_gemma":[0.9977017,0.00049331767,0.00052326213,0.00079814484,0.00041533704,0.000068238434],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004271505,0.00031861308,0.0004943293,0.00026264405,0.0008619769,0.00030927255,0.0023660148,0.00023169034,2.0467954e-7],"category_scores_gemma":[0.000044475713,0.00027679428,0.00026473802,0.0002595125,0.00019347697,0.0005078436,0.00084370794,0.00032043844,0.0000014814792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017170707,0.00001984125,9.0351915e-7,0.0002595432,0.00008272429,6.29512e-9,0.0023828663,0.79746383,0.0000081290755,0.16456705,0.000104039296,0.035109345],"study_design_scores_gemma":[0.0002148012,0.000050363044,0.000003073063,0.00016468401,0.00003298899,0.0000061001588,0.00001229954,0.96261275,0.00007474561,0.0015574093,0.034944672,0.0003261021],"about_ca_topic_score_codex":0.00001365256,"about_ca_topic_score_gemma":0.00004650368,"teacher_disagreement_score":0.16514894,"about_ca_system_score_codex":0.00011117229,"about_ca_system_score_gemma":0.00035481478,"threshold_uncertainty_score":0.9999684},"labels":[],"label_agreement":null},{"id":"W2987253836","doi":"10.1016/b978-0-12-817356-5.00001-2","title":"CLOUDFALL 1.0: A Smart, Low-Cost, IoT-Based Fall-Detection Sensor Node","year":2019,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Becton Dickinson (Canada)","funders":"","keywords":"Wearable computer; Reliability (semiconductor); Computer science; Node (physics); Wireless sensor network; Embedded system; Internet of Things; Software; Computer security; Real-time computing; Engineering; Computer network","score_opus":0.02230004720915226,"score_gpt":0.2330228409437249,"score_spread":0.21072279373457264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987253836","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002611099,0.00020104875,0.03682105,0.00019061432,0.0032501542,0.0023729454,0.00010787807,0.0007948,0.9560004],"genre_scores_gemma":[0.026401175,0.0000069501652,0.0012225951,0.0013995131,0.00066281634,0.00014986597,0.000031367686,0.00018564859,0.96994007],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99571544,0.00018608934,0.00091121596,0.0015376938,0.0009742724,0.0006752698],"domain_scores_gemma":[0.9957904,0.00041524967,0.0008164945,0.0022199492,0.00045673692,0.00030112872],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006253766,0.00090921106,0.0010983245,0.0005423809,0.00021064034,0.00041239083,0.0012930662,0.0007915035,0.000092005874],"category_scores_gemma":[0.000047485224,0.00094732834,0.0007032675,0.00006435618,0.00011627851,0.00019524184,0.00038386928,0.001074499,0.0047636987],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028584402,0.00002306432,0.000015855854,0.00019531959,0.00012200814,0.000064339765,0.00014348645,0.000007379033,0.00045321495,0.0004451496,0.00012592341,0.99837565],"study_design_scores_gemma":[0.0010576677,0.00011157091,0.00004503492,0.0010364476,0.000083184175,0.000099548226,0.000005148942,0.0035701077,0.0012717678,0.000754937,0.99072343,0.0012411255],"about_ca_topic_score_codex":0.000016210772,"about_ca_topic_score_gemma":0.00027815154,"teacher_disagreement_score":0.99713457,"about_ca_system_score_codex":0.000433277,"about_ca_system_score_gemma":0.00055138505,"threshold_uncertainty_score":0.99929774},"labels":[],"label_agreement":null},{"id":"W2988296394","doi":"10.3390/electronics8111354","title":"Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks","year":2019,"lang":"en","type":"article","venue":"Electronics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China; Shenzhen University; University of British Columbia; National Natural Science Foundation of China","keywords":"Computer science; Accelerometer; Wearable computer; Artificial neural network; Convolutional neural network; Computation; Artificial intelligence; Deep learning; Wearable technology; Machine learning; Embedded system; Real-time computing","score_opus":0.019273288850714075,"score_gpt":0.23641921654940695,"score_spread":0.21714592769869287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988296394","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61546516,0.00044623518,0.38321275,0.000029545592,0.00029585726,0.00022003194,2.9959813e-7,0.00017344138,0.00015664517],"genre_scores_gemma":[0.99794376,0.000015478203,0.0015704148,0.00014476226,0.00014875432,0.000012755726,0.0000029039813,0.000026544927,0.00013463254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829495,0.0001410814,0.00020128473,0.000527311,0.00028629458,0.0005490966],"domain_scores_gemma":[0.9989309,0.00006724673,0.00014675067,0.0006271926,0.00012390187,0.00010403936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002119962,0.0002022122,0.00022288914,0.00013650091,0.0001261746,0.00035269474,0.000538956,0.00011151288,0.000017603079],"category_scores_gemma":[0.000004460187,0.00017735251,0.000062400984,0.0005344345,0.000015157723,0.0016468536,0.000081084196,0.00036603006,0.000044464763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012039542,0.00017138329,0.0051551657,0.000023555178,0.00012894765,0.000020070634,0.00044824582,0.0035798473,0.023936177,0.000471022,0.000024631538,0.96592057],"study_design_scores_gemma":[0.0006957731,0.002113349,0.0010099299,0.000021241063,0.000015505962,0.00028777187,0.000016932983,0.9808869,0.007545123,0.00018117814,0.0067579816,0.00046834577],"about_ca_topic_score_codex":0.000024331332,"about_ca_topic_score_gemma":0.0005301975,"teacher_disagreement_score":0.977307,"about_ca_system_score_codex":0.00018522282,"about_ca_system_score_gemma":0.00009084725,"threshold_uncertainty_score":0.7232226},"labels":[],"label_agreement":null},{"id":"W2989668789","doi":"10.3233/shti190346","title":"Proactively Guiding Patients Through ADL via Knowledge-Based and Context-Driven Activity Recognition","year":2019,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Context-Aware Activity Recognition Systems","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":"Dalhousie University","funders":"","keywords":"Context (archaeology); Knowledge management; Computer science; Geography","score_opus":0.10175323267095344,"score_gpt":0.34109614224957896,"score_spread":0.23934290957862553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989668789","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9520043,0.0013716131,0.04211173,0.0014750973,0.00061114813,0.0014160672,0.0000120598925,0.0002651136,0.0007328506],"genre_scores_gemma":[0.99276966,0.0004726582,0.0061666635,0.0004565178,0.000009356043,0.00010520189,0.0000031125103,0.000006738393,0.0000100811],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985836,0.00011330424,0.00058679894,0.00024216298,0.00013141135,0.0003427317],"domain_scores_gemma":[0.998609,0.00035407132,0.00045802127,0.00027295455,0.0002618914,0.000044030956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051168154,0.0001917574,0.00047769392,0.0004358496,0.00025122307,0.00003122852,0.0002093568,0.00017965444,0.0000014035079],"category_scores_gemma":[0.00023584021,0.00017977477,0.0000238331,0.0006287644,0.00024394668,0.0012310465,0.0003962122,0.0002787682,0.000030782503],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030353729,0.00015552423,0.05846432,0.0013644315,0.00006175279,7.0822057e-7,0.013217224,0.000002243539,0.000009270736,0.0016617279,0.00015528689,0.92487717],"study_design_scores_gemma":[0.041964054,0.016383072,0.24649702,0.01664144,0.00011855306,0.00035128734,0.089111015,0.41011646,0.011429371,0.087939225,0.073040254,0.006408241],"about_ca_topic_score_codex":0.000029308872,"about_ca_topic_score_gemma":0.00006654931,"teacher_disagreement_score":0.9184689,"about_ca_system_score_codex":0.00019461426,"about_ca_system_score_gemma":0.00010560783,"threshold_uncertainty_score":0.73310024},"labels":[],"label_agreement":null},{"id":"W2992764686","doi":"","title":"LINDSAY Virtual Nephron: From Physics to Physiology","year":2012,"lang":"en","type":"article","venue":"Journal of undergraduate research in Alberta","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Nephron; Reabsorption; Computer science; Human–computer interaction; Simulation; Biology; Kidney","score_opus":0.10122355466166705,"score_gpt":0.3704566483669043,"score_spread":0.26923309370523724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992764686","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4977131,0.00059647637,0.20760702,0.28645727,0.0039771725,0.0008270242,0.000010239935,0.000045784906,0.0027658925],"genre_scores_gemma":[0.9957011,0.000061738196,0.0019624915,0.00032491505,0.0015232636,0.000011436019,0.0000015904932,0.000018756451,0.0003947454],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958782,0.0012410557,0.0006237992,0.00030071387,0.001104527,0.00085166626],"domain_scores_gemma":[0.99473244,0.0033955644,0.00026409706,0.0005657603,0.00050748105,0.000534648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002362739,0.00017908723,0.00046153032,0.0005007617,0.00011106635,0.00017873885,0.0013519234,0.000111065,0.0000108879085],"category_scores_gemma":[0.0005249424,0.00015560187,0.00015152691,0.001187069,0.000087049586,0.0016373193,0.0005597849,0.0009712568,0.0005645502],"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":[0.00084823294,0.0038435538,0.0154963145,0.00008548041,0.0007177258,0.00020866025,0.00893328,0.00071623235,0.11031852,0.20317338,0.044812962,0.6108456],"study_design_scores_gemma":[0.009456147,0.005596663,0.026441693,0.001968413,0.000050539267,0.0007492966,0.0011106615,0.017351044,0.039198186,0.65342665,0.24232183,0.0023288536],"about_ca_topic_score_codex":0.0019409753,"about_ca_topic_score_gemma":0.00059083186,"teacher_disagreement_score":0.6085168,"about_ca_system_score_codex":0.00032288054,"about_ca_system_score_gemma":0.00033774742,"threshold_uncertainty_score":0.725634},"labels":[],"label_agreement":null},{"id":"W2994264650","doi":"10.1007/s12652-019-01633-3","title":"Special issue on advances in ambient intelligence and pervasive computing","year":2019,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computational intelligence; Ambient intelligence; Computer science; Ubiquitous computing; Data science; Business intelligence; Artificial intelligence; Human–computer interaction; Knowledge management","score_opus":0.033375206772494746,"score_gpt":0.29284272559393915,"score_spread":0.2594675188214444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994264650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75254947,0.0014104778,0.23937131,0.00033117124,0.003543682,0.0004995081,0.0000013766136,0.000049177983,0.0022438215],"genre_scores_gemma":[0.9932498,0.00052395265,0.0038308783,0.00035848608,0.0019560778,8.9250284e-7,5.3152786e-7,0.000015895046,0.00006350586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996875,0.00022486581,0.0012212807,0.0005855686,0.00063326804,0.00046001867],"domain_scores_gemma":[0.9972663,0.00089277496,0.00095535535,0.00030406183,0.00040169014,0.00017984475],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014313136,0.00031873057,0.0006993207,0.00058395695,0.00022213445,0.00045173513,0.00076559454,0.00009194465,0.00006530825],"category_scores_gemma":[0.00015316768,0.00029407674,0.00013538927,0.0004701626,0.000108801636,0.0010992388,0.0004688656,0.0006531517,0.00007783218],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010540024,0.00024716358,0.0060544033,0.00012389198,0.000041541916,0.00011050966,0.012895712,0.003400352,0.0003545836,0.008001465,0.00010983321,0.96855515],"study_design_scores_gemma":[0.0055782516,0.014004582,0.035139848,0.018203031,0.00014529686,0.005306966,0.046798598,0.6700942,0.056358747,0.02741704,0.11579051,0.0051629115],"about_ca_topic_score_codex":0.000030794734,"about_ca_topic_score_gemma":0.000034503064,"teacher_disagreement_score":0.96339226,"about_ca_system_score_codex":0.000144203,"about_ca_system_score_gemma":0.00007809679,"threshold_uncertainty_score":0.9999511},"labels":[],"label_agreement":null},{"id":"W2995918236","doi":"","title":"An architectural framework for developing advanced integrated environmental monitoring systems","year":2008,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Windsor; Ontario Centres of Excellence","keywords":"Computer science; Environmental resource management; Risk analysis (engineering); Environmental planning; Business; Environmental science","score_opus":0.04128907517079199,"score_gpt":0.24916046502551423,"score_spread":0.20787138985472223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995918236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6757463,0.00011714165,0.32283434,0.000115066156,0.00052129594,0.00041744355,0.000041581032,0.00017315552,0.000033694752],"genre_scores_gemma":[0.9143988,0.000013058847,0.08516255,0.000023606557,0.000096126496,0.0000047100284,0.000027548942,0.000024610774,0.00024898563],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976581,0.00028657305,0.0002906771,0.0007377158,0.00052859006,0.00049834815],"domain_scores_gemma":[0.9981509,0.00037623328,0.00035126598,0.00074609264,0.00014065654,0.0002348955],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004604988,0.0002999617,0.00045954564,0.00033368546,0.00091529044,0.00008679165,0.0014454419,0.0002533505,0.000016719358],"category_scores_gemma":[0.00009555052,0.00036849096,0.00020384787,0.0004568107,0.00017498166,0.002289245,0.00031860283,0.00046043235,0.000058527567],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001217453,0.00071805215,0.5990107,0.0004252428,0.0006768306,0.00036891014,0.03387962,0.0013200298,0.19488622,0.005546278,0.00006477353,0.1618859],"study_design_scores_gemma":[0.0061756335,0.0011823033,0.9023609,0.0015397134,0.00010418996,0.0013681797,0.012256995,0.009183985,0.055569388,0.0027013647,0.004788084,0.0027692497],"about_ca_topic_score_codex":0.000073280826,"about_ca_topic_score_gemma":0.000023112445,"teacher_disagreement_score":0.30335024,"about_ca_system_score_codex":0.00043152002,"about_ca_system_score_gemma":0.00014804944,"threshold_uncertainty_score":0.9998767},"labels":[],"label_agreement":null},{"id":"W2997151802","doi":"10.1007/s12369-019-00614-y","title":"ENRICHME: Perception and Interaction of an Assistive Robot for the Elderly at Home","year":2020,"lang":"en","type":"article","venue":"International Journal of Social Robotics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"H2020 European Research Council; Horizon 2020 Framework Programme","keywords":"Perception; Robotics; Robot; Mechatronics; Ageing society; Quality of life (healthcare); Assistive technology; Healthy aging; Cognition; Healthy ageing; Elderly people; Human–computer interaction; Computer science; Psychology; Artificial intelligence; Gerontology; Medicine; Nursing","score_opus":0.04681136895007097,"score_gpt":0.318126630894954,"score_spread":0.27131526194488303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997151802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088577874,0.000039242143,0.8927692,0.017008722,0.0013996143,0.0001244056,0.000013745377,0.000013221319,0.00005395235],"genre_scores_gemma":[0.9909695,0.000018820307,0.007923687,0.00025935462,0.0007898776,0.0000025475872,0.000002989368,0.0000065333725,0.000026686608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989285,0.000079489946,0.0003716479,0.00011767758,0.00042475783,0.00007791431],"domain_scores_gemma":[0.99793214,0.0003966127,0.0005980649,0.000054395066,0.00095710414,0.00006170667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027767775,0.00008008035,0.00017205611,0.000071755414,0.0001043584,0.00012866803,0.0004820386,0.00005091888,0.000011329954],"category_scores_gemma":[0.00015603389,0.000064865286,0.00012193314,0.00008746411,0.00005086161,0.0007641335,0.00010988904,0.00014387077,0.000002925912],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086707924,0.0003529572,0.0025456469,0.00005204768,0.0009973461,0.000030071047,0.020460645,0.007129422,0.03112244,0.00831407,0.0021924076,0.92593586],"study_design_scores_gemma":[0.009416553,0.0066568097,0.3302976,0.00035052758,0.00049051346,0.001492004,0.012348179,0.6103829,0.004043527,0.007342138,0.015999418,0.0011797894],"about_ca_topic_score_codex":0.000010396616,"about_ca_topic_score_gemma":0.000014118803,"teacher_disagreement_score":0.92475605,"about_ca_system_score_codex":0.00013698467,"about_ca_system_score_gemma":0.000060344406,"threshold_uncertainty_score":0.26451296},"labels":[],"label_agreement":null},{"id":"W2997667424","doi":"10.1109/jbhi.2019.2963388","title":"Highly Accurate Bathroom Activity Recognition Using Infrared Proximity Sensors","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Toilet; Computer science; Reliability (semiconductor); Limiting; Activity recognition; Assisted living; Artificial intelligence; Human–computer interaction; Computer vision; Medicine; Engineering; Gerontology; Pathology","score_opus":0.08199362234265468,"score_gpt":0.32319383226759013,"score_spread":0.24120020992493546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997667424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7444289,0.00004573684,0.2515528,0.0020561796,0.0014758351,0.0002700006,0.000014916516,0.000032890905,0.00012279737],"genre_scores_gemma":[0.9740591,0.0001543877,0.024321686,0.0011507968,0.00027749347,0.0000016256613,0.000002339389,0.0000075215185,0.000025052786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749047,0.00015442239,0.0012493021,0.00010520077,0.0006735377,0.00032705974],"domain_scores_gemma":[0.99712235,0.0002004766,0.0016896623,0.00020036135,0.00032928083,0.0004578798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021334726,0.00015556143,0.0004910612,0.00032783148,0.00014398103,0.00016387766,0.0002888739,0.00014233547,0.000015414207],"category_scores_gemma":[0.00008212266,0.00012305405,0.00008746233,0.0003862673,0.00007380069,0.0027029007,0.00008590712,0.0004677571,0.000042726086],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010613282,0.0003500974,0.0009170762,0.0015843713,0.00012614393,0.000022362861,0.0072152307,0.0000311872,0.0009759515,0.000053283566,0.00307912,0.985539],"study_design_scores_gemma":[0.010259996,0.0053990907,0.015114218,0.003679527,0.000080661695,0.0066200867,0.0023077833,0.89711946,0.0043520234,0.004739277,0.048807144,0.0015207559],"about_ca_topic_score_codex":0.0000387658,"about_ca_topic_score_gemma":0.000003578083,"teacher_disagreement_score":0.98401827,"about_ca_system_score_codex":0.00016796732,"about_ca_system_score_gemma":0.00067114417,"threshold_uncertainty_score":0.5017999},"labels":[],"label_agreement":null},{"id":"W2999070142","doi":"10.1109/sensors43011.2019.8956668","title":"Activity Recognition with Wearable Accelerometers using Deep Convolutional Neural Network and the Effect of Sensor Placement","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Accelerometer; Activity recognition; Computer science; Convolutional neural network; Wearable computer; Artificial intelligence; Pipeline (software); Machine learning; Wireless sensor network; Locality; Pattern recognition (psychology); Real-time computing; Embedded system; Computer network","score_opus":0.023464242841553434,"score_gpt":0.23503207118110248,"score_spread":0.21156782833954904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999070142","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8712725,0.00005330322,0.1269299,0.00013015105,0.0002199918,0.00073074485,0.0000018762837,0.000046954116,0.00061455334],"genre_scores_gemma":[0.9973912,0.0000038443536,0.0023841467,0.00006643134,0.000039728387,0.000023361214,0.0000010227232,0.0000073563497,0.000082878556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849653,0.0004928244,0.00016810649,0.00031026464,0.00030411014,0.00022814203],"domain_scores_gemma":[0.9982727,0.0011295556,0.00018991115,0.000272745,0.00008514714,0.000049915314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080410443,0.0001474066,0.00030344055,0.000058211073,0.00011162414,0.00010743546,0.0001792783,0.000043492262,0.000051753632],"category_scores_gemma":[0.000022051188,0.0000894714,0.000059101072,0.00027627734,0.00010461262,0.000633518,0.00013388599,0.00012299328,0.000020805019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011572115,0.00032162797,0.24974695,0.00088270655,0.0016629598,0.000023328474,0.0018357217,0.045502633,0.042676244,0.0008214342,0.00029586756,0.6446584],"study_design_scores_gemma":[0.0072145676,0.00096991163,0.012683405,0.00021895299,0.00006263741,0.000198594,0.000080693666,0.96710986,0.010991583,0.00009961328,0.000035347515,0.00033482056],"about_ca_topic_score_codex":0.00020306408,"about_ca_topic_score_gemma":0.000032702836,"teacher_disagreement_score":0.92160726,"about_ca_system_score_codex":0.000046913483,"about_ca_system_score_gemma":0.000028093855,"threshold_uncertainty_score":0.36485377},"labels":[],"label_agreement":null},{"id":"W2999753048","doi":"10.1109/sensors43011.2019.8956951","title":"Logistic Model Tree for Human Activity Recognition Using Smartphone-Based Inertial Sensors","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Random forest; Computer science; Activity recognition; Artificial intelligence; Inertial measurement unit; Tree (set theory); Logistic regression; Decision tree; Machine learning; Data mining; Set (abstract data type); Data set; Pattern recognition (psychology); Mathematics","score_opus":0.17015110596944505,"score_gpt":0.32743518636044905,"score_spread":0.157284080391004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999753048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46614233,0.0000013832313,0.5317633,0.00011175344,0.00026326027,0.0004649474,0.000015691057,0.00019370804,0.001043631],"genre_scores_gemma":[0.97411245,1.5120523e-7,0.024989476,0.00022864895,0.00009903386,0.000054857108,0.000018020351,0.000023384662,0.00047400614],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982535,0.00013539176,0.00029900557,0.0006382773,0.0003016611,0.00037213642],"domain_scores_gemma":[0.99847883,0.00036412294,0.00020376248,0.0005795401,0.00026502658,0.00010869958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004262579,0.00023053742,0.00033286551,0.00021759392,0.00017358725,0.00020264318,0.000368257,0.00013475961,0.00005275172],"category_scores_gemma":[0.0000802283,0.00022838336,0.00018195405,0.00026336915,0.000036753747,0.0008227546,0.000098135606,0.00013790387,0.0001617444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028664208,0.0007798627,0.001764376,0.0003022662,0.000110937275,0.0000083059485,0.00040086237,0.023237217,0.670025,0.0012453946,0.00065364735,0.30118552],"study_design_scores_gemma":[0.0013597559,0.00012743431,0.00036151856,0.000042506927,0.000016163543,0.000008904738,0.000013004803,0.9560685,0.040172983,0.0014358441,0.00005236419,0.00034102492],"about_ca_topic_score_codex":0.00038266857,"about_ca_topic_score_gemma":0.00032589879,"teacher_disagreement_score":0.9328313,"about_ca_system_score_codex":0.00016732409,"about_ca_system_score_gemma":0.00016811198,"threshold_uncertainty_score":0.9313203},"labels":[],"label_agreement":null},{"id":"W2999955388","doi":"10.3390/s22145222","title":"Personalized Activity Recognition with Deep Triplet Embeddings","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Activity recognition; Pattern recognition (psychology); Cross entropy; Machine learning; Categorical variable; Deep learning; Feature (linguistics); Generalization; Mathematics","score_opus":0.02599975266163782,"score_gpt":0.23598395365014316,"score_spread":0.20998420098850534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999955388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9402757,0.00002766974,0.05316186,0.0011588157,0.00042843935,0.0004233285,0.00003327298,0.00043727914,0.0040536025],"genre_scores_gemma":[0.99596244,0.0000023864545,0.0022635271,0.0003323584,0.000066925866,0.00012674753,0.000012827091,0.000020837506,0.0012119387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787694,0.00045957082,0.00016976727,0.00055063044,0.00062207534,0.0003210356],"domain_scores_gemma":[0.99893075,0.00023646209,0.00019607374,0.00040560437,0.00012168922,0.00010944378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048210175,0.00018190211,0.0002537453,0.00019321422,0.0004414802,0.00014190865,0.00041280157,0.000038785427,0.0004006321],"category_scores_gemma":[0.000053468473,0.00018008496,0.00010497056,0.00069685315,0.000053664662,0.0004911974,0.0002180535,0.00031627258,0.00014490452],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087987934,0.0008113213,0.0014645142,0.00008146705,0.000321309,0.00046648862,0.016052721,0.0006341169,0.01018991,0.0012458514,0.003169192,0.96468323],"study_design_scores_gemma":[0.021699782,0.0044105756,0.0127413785,0.0002474289,0.00025860054,0.008049821,0.014247881,0.5353284,0.037717428,0.005862139,0.3532996,0.0061369785],"about_ca_topic_score_codex":0.0001333721,"about_ca_topic_score_gemma":0.00005039605,"teacher_disagreement_score":0.9585462,"about_ca_system_score_codex":0.00019356253,"about_ca_system_score_gemma":0.00008169026,"threshold_uncertainty_score":0.7343651},"labels":[],"label_agreement":null},{"id":"W3002496996","doi":"10.3389/fbioe.2019.00473","title":"Sensor Data Required for Automatic Recognition of Athletic Tasks Using Deep Neural Networks","year":2020,"lang":"en","type":"article","venue":"Frontiers in Bioengineering and Biotechnology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Economic Development, Job Creation and Trade","keywords":"Torso; Computer science; Artificial intelligence; Motion capture; Convolutional neural network; Deep learning; Inertial measurement unit; Wearable computer; Hyperparameter; Computer vision; Motion (physics); Pattern recognition (psychology)","score_opus":0.052726246179606466,"score_gpt":0.2485198175170446,"score_spread":0.19579357133743813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002496996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076144926,0.00075377605,0.9206557,0.001316213,0.00053932687,0.00029211116,0.000025990148,0.00027060925,0.0000012927457],"genre_scores_gemma":[0.7639067,0.000035331217,0.23592643,0.0000491584,0.00004202911,0.000009709787,0.000019263885,0.000010859317,4.8134535e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988801,0.00004351967,0.00032028698,0.0004313347,0.00007281255,0.00025195154],"domain_scores_gemma":[0.9993063,0.00007736232,0.00011969462,0.00040925236,0.00003295509,0.000054411492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022804562,0.0001409934,0.00033175468,0.00024161908,0.000040792318,0.00003234276,0.0005225699,0.0002553594,5.1696117e-7],"category_scores_gemma":[0.00021302815,0.00014980177,0.000031630512,0.00049644936,0.00007639276,0.00025685268,0.0002836333,0.00020266739,4.196858e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026056276,0.000038915976,0.00086177455,0.0003672179,0.0000833932,0.00001793679,0.00035362807,0.0037152506,0.01738761,0.000067099136,0.00027459362,0.9768065],"study_design_scores_gemma":[0.00038947322,0.000096155825,0.0000665488,0.000059208654,0.000014332293,0.000036055608,0.00013987653,0.99682915,0.0019291972,0.00008241467,0.00019759679,0.00015996594],"about_ca_topic_score_codex":0.000013762025,"about_ca_topic_score_gemma":0.0000033973304,"teacher_disagreement_score":0.99311393,"about_ca_system_score_codex":0.000028145385,"about_ca_system_score_gemma":0.000011403808,"threshold_uncertainty_score":0.6108739},"labels":[],"label_agreement":null},{"id":"W3004671230","doi":"10.1109/bibm47256.2019.8983009","title":"Smart Phone Based Human Activity Recognition","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Activity recognition; Computer science; Benchmark (surveying); Artificial intelligence; Convolutional neural network; Feature extraction; Feature selection; Accelerometer; Field (mathematics); Machine learning; Feature (linguistics); Pattern recognition (psychology); Phone; Mobile phone; Data mining","score_opus":0.04098002224790117,"score_gpt":0.2591660217835866,"score_spread":0.21818599953568543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004671230","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59045094,0.0000043872974,0.35682097,0.0005929215,0.0006548238,0.0003907986,0.0000051921297,0.0005381798,0.050541777],"genre_scores_gemma":[0.99443513,3.52253e-7,0.00242623,0.0005436419,0.00005562509,0.000033892935,0.000009495071,0.000011834836,0.002483787],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985576,0.00017144809,0.00018728375,0.0004973137,0.00032732656,0.00025901163],"domain_scores_gemma":[0.9987701,0.0001987253,0.000119694094,0.0006654637,0.00014660631,0.00009942244],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004197037,0.00015534306,0.00021845494,0.00016198464,0.0001038663,0.00019356878,0.0004260339,0.000084481064,0.0011571704],"category_scores_gemma":[0.000024498211,0.00015273324,0.000106861975,0.00036410996,0.000019172672,0.0010854208,0.00012158963,0.00015421027,0.006241825],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003208668,0.0006144756,0.00961809,0.00008758045,0.0000566221,0.00001520848,0.00024085397,0.000005477874,0.21593258,0.0008912861,0.0048723463,0.7676334],"study_design_scores_gemma":[0.007592351,0.0009089078,0.09911992,0.0003501259,0.000035878726,0.00010854465,0.00013029417,0.12005502,0.72585267,0.005787691,0.03737428,0.0026842924],"about_ca_topic_score_codex":0.0003502883,"about_ca_topic_score_gemma":0.00016498794,"teacher_disagreement_score":0.7649491,"about_ca_system_score_codex":0.00007544866,"about_ca_system_score_gemma":0.00006766782,"threshold_uncertainty_score":0.9997559},"labels":[],"label_agreement":null},{"id":"W3005837745","doi":"10.1016/s1474-4422(20)30033-8","title":"Wearable-based mobility monitoring: the long road ahead","year":2020,"lang":"en","type":"letter","venue":"The Lancet Neurology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Western Hospital","funders":"Ipsen; University of Toronto; UCB; Dystonia Medical Research Foundation Canada; Michael J. Fox Foundation for Parkinson's Research; Cummings Foundation; Sunovion; Medtronic; AbbVie Canada; Boston Scientific Corporation; Chiesi Farmaceutici","keywords":"Wearable computer; Wearable technology; Computer science; Clinical trial; Warning system; Tracking (education); Data science; Artificial intelligence; Medicine; Psychology; Embedded system; Telecommunications","score_opus":0.08102115550176789,"score_gpt":0.2829845473969714,"score_spread":0.2019633918952035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005837745","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003370358,0.00036232415,0.01276547,0.9774776,0.003916121,0.00076010753,0.000023638004,0.0005276464,0.0007967079],"genre_scores_gemma":[0.2518547,0.000023323344,0.00005333892,0.73766303,0.009914642,0.00021741084,0.000008610926,0.000041051262,0.00022391383],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947349,0.0024392607,0.0004356873,0.0010239158,0.0005662858,0.00079994695],"domain_scores_gemma":[0.994448,0.0021547007,0.000396165,0.0028105748,0.0001250383,0.00006552963],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00077510206,0.00043288287,0.0008382387,0.000074654105,0.00033531623,0.00031043217,0.004558458,0.00046695478,0.000030392866],"category_scores_gemma":[0.0001997013,0.0002681326,0.0002502387,0.00044128066,0.00030141455,0.000174716,0.0006614425,0.0043100673,0.0005901546],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010497035,0.000045935525,0.0040558735,0.00014018512,0.000105407555,0.0010865333,0.0003232566,0.00007360387,0.000060479237,0.000035651585,0.9629542,0.031013872],"study_design_scores_gemma":[0.0006573276,0.00027616168,0.012167443,0.000045053046,0.000041990814,0.00024553656,0.0000030477577,0.0059669535,0.00021333496,0.0009359143,0.9790245,0.0004227021],"about_ca_topic_score_codex":0.00020601299,"about_ca_topic_score_gemma":0.000026639203,"teacher_disagreement_score":0.24848431,"about_ca_system_score_codex":0.00003912577,"about_ca_system_score_gemma":0.00019410941,"threshold_uncertainty_score":0.9999771},"labels":[],"label_agreement":null},{"id":"W3006195787","doi":"10.1109/siitme47687.2019.8990804","title":"Analysis of activity detection data pre-processing","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Science North","funders":"","keywords":"Computer science; Raw data; Process (computing); Data processing; MATLAB; Activity recognition; Data mining; Real-time computing; Database; Artificial intelligence; Operating system","score_opus":0.05009296384675027,"score_gpt":0.3013760072303821,"score_spread":0.25128304338363183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006195787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38721633,0.000010577663,0.61028075,0.000035231613,0.00010357996,0.00008304064,0.0000053858435,0.00008201557,0.002183073],"genre_scores_gemma":[0.9983854,7.088174e-7,0.0011924873,0.000025287442,0.000013702458,0.000002937408,0.0000050323806,0.0000035778244,0.00037082113],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998913,0.00008452701,0.00017242454,0.0004446165,0.00026683405,0.000118582226],"domain_scores_gemma":[0.99822867,0.00013090204,0.00018398016,0.0013055061,0.00011415262,0.000036778223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004339757,0.00007840583,0.0002516157,0.000308405,0.00003641448,0.00009960884,0.00085076474,0.000045113462,0.00007215663],"category_scores_gemma":[0.00003594737,0.00007155156,0.000068299814,0.0014922981,0.000012961205,0.0019269534,0.00033349695,0.00007004722,0.00004770681],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074265654,0.00005394601,0.011415829,0.000024636649,0.00020501434,3.3429635e-7,0.00017969552,0.000056998855,0.023452159,0.000028623124,0.000011549449,0.9645638],"study_design_scores_gemma":[0.00012669216,0.000024597242,0.104650006,0.000010840747,0.00009098099,0.0000026847695,0.000030446425,0.87188435,0.02271405,0.000017204833,0.0003367191,0.00011142923],"about_ca_topic_score_codex":0.00046869813,"about_ca_topic_score_gemma":0.00049181713,"teacher_disagreement_score":0.9644524,"about_ca_system_score_codex":0.000027143533,"about_ca_system_score_gemma":0.000046314253,"threshold_uncertainty_score":0.2917788},"labels":[],"label_agreement":null},{"id":"W3009202061","doi":"10.22215/etd/2013-07201","title":"Wireless network system based multi-non-invasive sensors for smart home","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University; Library and Archives Canada","funders":"","keywords":"Bluetooth; USB; Wireless; Wireless network; Computer science; Embedded system; Engineering; Telecommunications; Software; Operating system","score_opus":0.02415752193344333,"score_gpt":0.2527748688815491,"score_spread":0.22861734694810576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009202061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07342313,0.00011347508,0.90724957,0.000095200674,0.009275867,0.0046549123,0.00006675969,0.0011479784,0.003973089],"genre_scores_gemma":[0.9492545,0.0000053817403,0.019918116,0.00019048931,0.00063862145,0.0020371412,0.0007446503,0.00011607665,0.027095024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966,0.00022071759,0.00078662176,0.0011608973,0.0005304971,0.00070127705],"domain_scores_gemma":[0.9956348,0.0012380584,0.00074861286,0.0010676136,0.001067178,0.00024376925],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005075357,0.00061853236,0.0009820608,0.0002986664,0.00030728214,0.0005872152,0.0011493063,0.0005870668,0.00003159306],"category_scores_gemma":[0.000060880964,0.00059256854,0.0004635886,0.00048378308,0.000022566574,0.0005911269,0.00007162425,0.00030366395,0.00074769504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012184905,0.0028259044,0.0073682815,0.06043287,0.005761742,0.00057441654,0.017159076,0.0054829796,0.018457374,0.03964748,0.35952222,0.48154917],"study_design_scores_gemma":[0.0036307757,0.000284541,0.0035648586,0.004202128,0.00016049159,0.00004179994,0.0030828563,0.9673947,0.011668803,0.000091471935,0.0032664686,0.0026111058],"about_ca_topic_score_codex":0.0005575526,"about_ca_topic_score_gemma":0.0015573435,"teacher_disagreement_score":0.96191174,"about_ca_system_score_codex":0.00025684113,"about_ca_system_score_gemma":0.000573954,"threshold_uncertainty_score":0.99965256},"labels":[],"label_agreement":null},{"id":"W3010959684","doi":"10.1177/2055668319887864","title":"Context awareness architecture for ambient-assisted living applications: Case study of nighttime wandering","year":2020,"lang":"en","type":"article","venue":"Journal of Rehabilitation and Assistive Technologies Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Assisted living; Context (archaeology); Architecture; Computer science; Psychology; Art; Gerontology; Visual arts; History; Medicine","score_opus":0.024780098926457304,"score_gpt":0.26413982924575075,"score_spread":0.23935973031929345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010959684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40970862,0.00019947397,0.5888896,0.00063375966,0.000044971835,0.00038840433,0.000002902071,0.0001307111,0.0000015873353],"genre_scores_gemma":[0.9641369,0.0000041776457,0.03571856,0.000010500482,0.000022014718,0.00009712027,2.2729218e-7,0.000009343457,0.0000011787106],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9988957,0.000048824204,0.000530181,0.00022644342,0.00017816879,0.00012070714],"domain_scores_gemma":[0.9971424,0.0017751366,0.00042306833,0.0001810176,0.00042522975,0.00005315613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003576535,0.00013835054,0.0003685338,0.00030253315,0.00008652611,0.000063528736,0.00028117502,0.00006909263,5.1799344e-7],"category_scores_gemma":[0.0015453185,0.0001230271,0.00009201341,0.0004434554,0.000044912613,0.00032460762,0.0001298804,0.00019630553,1.908766e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000568694,0.0006000512,0.015912315,0.00084661145,0.0003213192,0.00009647425,0.0152507685,0.0030837,0.022574117,0.001126907,0.000056390054,0.9400745],"study_design_scores_gemma":[0.0122309765,0.019036204,0.087695844,0.003400028,0.00043046512,0.0064764083,0.55070454,0.28898934,0.019307448,0.0010621358,0.008275718,0.0023909293],"about_ca_topic_score_codex":0.000009090249,"about_ca_topic_score_gemma":0.000010651238,"teacher_disagreement_score":0.9376835,"about_ca_system_score_codex":0.000053701468,"about_ca_system_score_gemma":0.000039678584,"threshold_uncertainty_score":0.50169},"labels":[],"label_agreement":null},{"id":"W3011443346","doi":"10.1145/3379336.3381492","title":"Using Augmented Reality and Ontologies to Co-design Assistive Technologies in Smart Homes","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Autonomy; Home automation; Human–computer interaction; Augmented reality; Assistive technology; Assisted living; Computer science; Internet privacy; Co-design; Independent living; Activities of daily living; Disabled people; Psychology; Applied psychology; Gerontology; Medicine","score_opus":0.19664345073407982,"score_gpt":0.34173993476747977,"score_spread":0.14509648403339995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011443346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039054357,0.00006692207,0.94719374,0.0111019155,0.000059775528,0.00044015748,0.0000049305077,0.00087898114,0.0011991967],"genre_scores_gemma":[0.9622385,0.0000063236093,0.0373523,0.00034010244,0.0000070614046,0.000027143564,6.465962e-7,0.0000044317444,0.00002351482],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987262,0.00017481827,0.00023085575,0.00048053538,0.00016855219,0.00021905056],"domain_scores_gemma":[0.9992013,0.0003386456,0.00007198007,0.00025846873,0.000063913445,0.00006569042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032753783,0.00013749262,0.00028168331,0.00015069637,0.000065012595,0.00016247567,0.00039369817,0.000081057646,0.0000053603994],"category_scores_gemma":[0.0004409729,0.00012252544,0.000025148149,0.00055614504,0.00006464329,0.00045738346,0.00038301715,0.000119245364,0.000019562416],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002160538,0.0002588125,0.1521336,0.00015721766,0.00016052324,0.00022994597,0.0061098775,0.00024081934,0.031340763,0.006235827,0.010271641,0.7926449],"study_design_scores_gemma":[0.0045744083,0.0020440973,0.36749402,0.0006307163,0.00004638668,0.00021226813,0.026937962,0.3487881,0.22631755,0.008216325,0.011545874,0.003192263],"about_ca_topic_score_codex":0.00040097465,"about_ca_topic_score_gemma":0.00021677023,"teacher_disagreement_score":0.9231841,"about_ca_system_score_codex":0.000087178334,"about_ca_system_score_gemma":0.000059707043,"threshold_uncertainty_score":0.49964425},"labels":[],"label_agreement":null},{"id":"W3013748060","doi":"10.3390/s20071888","title":"Pilot Site Deployment of an IoT Solution for Older Adults’ Early Behavior Change Detection","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Horizon 2020 Framework Programme","keywords":"Software deployment; Gerontology; Population; Population ageing; Isolation (microbiology); Internet of Things; Psychology; Sedentary behavior; Physical activity; Medicine; Computer security; Computer science; Environmental health; Physical medicine and rehabilitation","score_opus":0.07084083939770626,"score_gpt":0.27133707747723884,"score_spread":0.2004962380795326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013748060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91647965,0.000022525459,0.081317455,0.00028283446,0.00036968788,0.0012811671,0.000023973698,0.00020747715,0.000015224247],"genre_scores_gemma":[0.99719214,9.5281047e-7,0.0020454372,0.00016631029,0.00020664012,0.0003457557,0.000005274973,0.000017188659,0.000020271997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988368,0.00009709457,0.0002389754,0.00038992686,0.00023334728,0.00020384694],"domain_scores_gemma":[0.9992156,0.000060696253,0.00016265026,0.00025871032,0.00017446616,0.00012785861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014237403,0.00012416005,0.00017556791,0.000083018545,0.00007821314,0.000048732803,0.00019803505,0.00004588508,0.000007202883],"category_scores_gemma":[0.00003165014,0.000132349,0.00008159941,0.0002556464,0.000019347854,0.00035319405,0.00006258108,0.00007743886,0.00003954327],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019362025,0.00035741224,0.0025817065,0.00011435995,0.000025570504,0.000008511093,0.010277668,0.0000080847,0.065120846,0.000034477263,0.0000349305,0.92124283],"study_design_scores_gemma":[0.005126211,0.006639855,0.30501878,0.00026419674,0.00010814942,0.00005957296,0.0004989256,0.37331912,0.30649492,0.00004027041,0.0014591075,0.0009708773],"about_ca_topic_score_codex":0.00063422666,"about_ca_topic_score_gemma":0.0004412924,"teacher_disagreement_score":0.92027193,"about_ca_system_score_codex":0.00004465542,"about_ca_system_score_gemma":0.000014947236,"threshold_uncertainty_score":0.53970355},"labels":[],"label_agreement":null},{"id":"W3013752062","doi":"10.5383/juspn.06.01.001","title":"Personal Mobile Grids: Ubiquitous Grid Environments For Personal Users","year":2015,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Plan for Science, Technology and Innovation","keywords":"Computer science; Distributed computing; Grid; Grid computing; Scheduling (production processes); Heuristic; Benchmark (surveying); Mobile device; Engineering; Operating system; Artificial intelligence","score_opus":0.03574278460480261,"score_gpt":0.25734008732048286,"score_spread":0.22159730271568023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013752062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38104716,0.026104435,0.5730257,0.0006230246,0.016719555,0.0019987295,0.00010222081,0.00009395861,0.00028523715],"genre_scores_gemma":[0.99270034,0.00038410042,0.00052862155,0.00024813425,0.00548427,0.00011414912,0.000007619928,0.000041004478,0.0004917669],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967034,0.00034041514,0.0009628302,0.0004960233,0.00089370116,0.0006036578],"domain_scores_gemma":[0.99690264,0.0005573627,0.0010723529,0.00025436186,0.00055782316,0.0006554267],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017752146,0.00038876312,0.0008444688,0.00022125109,0.00026389366,0.00051158783,0.0006200048,0.0002686595,0.000008955778],"category_scores_gemma":[0.000090257396,0.000333381,0.00036307756,0.00021389419,0.000108005646,0.00112786,0.00018759943,0.00046375688,0.000011168488],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027189925,0.0025472734,0.037072256,0.0015963379,0.0046510217,0.0019858547,0.118751235,0.023489624,0.0049018813,0.0008214981,0.40417582,0.39728823],"study_design_scores_gemma":[0.010448027,0.007851636,0.0021694768,0.0017983519,0.0003455042,0.014871784,0.020171696,0.5260914,0.00024254416,0.00018977774,0.41384894,0.0019708634],"about_ca_topic_score_codex":0.0001017913,"about_ca_topic_score_gemma":0.000026543472,"teacher_disagreement_score":0.6116532,"about_ca_system_score_codex":0.00030674165,"about_ca_system_score_gemma":0.0002980797,"threshold_uncertainty_score":0.99991184},"labels":[],"label_agreement":null},{"id":"W3014206001","doi":"10.4017/gt.2020.19.1.004.00","title":"An intelligent video-monitoring system to detect responsive behaviours associated with Alzheimer’s disease and related disorders","year":2020,"lang":"en","type":"article","venue":"Gerontechnology","topic":"Context-Aware Activity Recognition Systems","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":"Cegep Edouard Montpetit; Institut Universitaire de Gériatrie de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université de Montréal","funders":"","keywords":"Disease; Alzheimer's disease; Video monitoring; Disease monitoring; Psychology; Neuroscience; Medicine; Computer science; Real-time computing; Internal medicine","score_opus":0.03139842523627235,"score_gpt":0.2681110196935326,"score_spread":0.23671259445726028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014206001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86873394,0.000715347,0.12201559,0.0051276935,0.000376714,0.00090848614,0.000020328262,0.0020442088,0.000057694186],"genre_scores_gemma":[0.9985064,0.0000075217667,0.0011842168,0.00010456138,0.000023368151,0.00012967474,0.0000044401845,0.000029829342,0.000009939475],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99787164,0.0002785997,0.00033364713,0.0008353767,0.00026130027,0.00041946373],"domain_scores_gemma":[0.9984452,0.0001664798,0.00018720438,0.000616081,0.00014384251,0.00044116183],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026772366,0.000257706,0.00035994744,0.0002649273,0.00017182992,0.00011510207,0.00072541315,0.00020023138,0.0000030174808],"category_scores_gemma":[0.00027411996,0.0002514417,0.000051721847,0.0007669297,0.000112037575,0.00041971923,0.00031333038,0.0003313422,0.000040878065],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009933045,0.00028242765,0.06442824,0.000064238535,0.0010357714,0.0011273547,0.008751957,0.00029687706,0.00721871,0.0046292334,0.00031316213,0.91085875],"study_design_scores_gemma":[0.017073162,0.03337884,0.61730695,0.0060309293,0.0028074137,0.0016901356,0.0405036,0.11178835,0.13997233,0.004710846,0.011578887,0.013158535],"about_ca_topic_score_codex":0.00008879495,"about_ca_topic_score_gemma":0.0001845811,"teacher_disagreement_score":0.8977002,"about_ca_system_score_codex":0.00014524058,"about_ca_system_score_gemma":0.0000899141,"threshold_uncertainty_score":0.9999938},"labels":[],"label_agreement":null},{"id":"W3015014633","doi":"10.1109/jiot.2020.2985082","title":"Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":539,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Hunan Province","keywords":"Computer science; Deep learning; Artificial intelligence; Wearable computer; Big data; Activity recognition; Wearable technology; Machine learning; Cloud computing; Ubiquitous computing; The Internet; Mobile computing; Mobile device; Sensor fusion; Context (archaeology); Human–computer interaction; Data mining; World Wide Web; Embedded system; Computer network","score_opus":0.05812137957742441,"score_gpt":0.3010272481516337,"score_spread":0.24290586857420926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015014633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.291695,0.000059423626,0.70493686,0.0018731183,0.00078367133,0.00027396544,0.000003201279,0.00009576279,0.0002789637],"genre_scores_gemma":[0.99081904,0.000013031511,0.007877342,0.0007933309,0.0002665487,0.000018327599,0.00000451376,0.0000310928,0.00017677789],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99725014,0.0003717306,0.00094707654,0.00048037278,0.0005848113,0.0003658918],"domain_scores_gemma":[0.99625933,0.00038517127,0.0018750693,0.00022498371,0.0009803217,0.00027514802],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010917212,0.00026990366,0.00066077395,0.0002399646,0.000105451836,0.00023644432,0.0012321296,0.00017423353,0.000064419786],"category_scores_gemma":[0.0004983179,0.00027061327,0.00041193847,0.0002625497,0.00008193884,0.002417614,0.00021051918,0.0009620725,0.000024285024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007296304,0.00038511056,0.00086458365,0.0010560116,0.00051709585,0.000030423107,0.06724545,0.000043720604,0.25160575,0.0006363233,0.003125126,0.6737608],"study_design_scores_gemma":[0.0028050386,0.0038676844,0.0005278404,0.0018991681,0.00007256535,0.000390776,0.0007043128,0.13401794,0.84872866,0.0043656495,0.0018976048,0.00072277704],"about_ca_topic_score_codex":0.000494075,"about_ca_topic_score_gemma":0.000026002564,"teacher_disagreement_score":0.69912404,"about_ca_system_score_codex":0.00011273538,"about_ca_system_score_gemma":0.00009520557,"threshold_uncertainty_score":0.9999746},"labels":[],"label_agreement":null},{"id":"W3017463498","doi":"10.1109/ifetc46817.2019.9073718","title":"Fall Detection with Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Convolutional neural network; Computer science; Artificial neural network; Wearable computer; Artificial intelligence; Deep learning; Machine learning; Field (mathematics); Sensitivity (control systems); Recurrent neural network; Time delay neural network; Engineering; Embedded system","score_opus":0.011687991757315528,"score_gpt":0.2041192091590493,"score_spread":0.19243121740173377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017463498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22364397,0.000009850239,0.76816547,0.0001105576,0.00043167308,0.00016490051,1.0464024e-7,0.00025125995,0.0072221947],"genre_scores_gemma":[0.9975602,4.2609807e-7,0.0007138588,0.00029838478,0.000057432124,0.000010850818,4.148066e-7,0.0000059994495,0.0013524592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928087,0.000047234378,0.00009584243,0.00025889263,0.00015449393,0.00016268846],"domain_scores_gemma":[0.9994179,0.00007143072,0.000049348095,0.0003445526,0.00006652653,0.000050236424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000106599175,0.00008246046,0.00010069723,0.000051175834,0.000038612776,0.00014472895,0.00025498314,0.000038141483,0.000030071811],"category_scores_gemma":[0.000003411872,0.00006263535,0.000030681385,0.00024243779,0.0000087588305,0.000659265,0.00007346852,0.000097911994,0.0002797425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033964512,0.00006195514,0.03309032,0.000014692403,0.000042610827,0.000013765062,0.00024701408,0.002809065,0.001693211,0.0012536139,0.0003147914,0.960425],"study_design_scores_gemma":[0.000393999,0.00019718385,0.008007134,0.000010170332,0.0000022630518,0.00011092065,0.000029664894,0.9880031,0.00096510455,0.00004282626,0.0020589826,0.00017866438],"about_ca_topic_score_codex":0.00019098353,"about_ca_topic_score_gemma":0.00063803885,"teacher_disagreement_score":0.985194,"about_ca_system_score_codex":0.000023376402,"about_ca_system_score_gemma":0.000011420902,"threshold_uncertainty_score":0.3595617},"labels":[],"label_agreement":null},{"id":"W3017571824","doi":"10.1007/s40860-020-00104-3","title":"A smart cooking device for assisting cognitively impaired users","year":2020,"lang":"en","type":"article","venue":"Journal of Reliable Intelligent Environments","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Dignity; Computer science; Autonomy; Human–computer interaction; Cognitive impairment; Cognition; Tragedy (event); Internet privacy; Computer security; Medicine","score_opus":0.06903320538906865,"score_gpt":0.27174409193264776,"score_spread":0.2027108865435791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017571824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066417545,0.00026963753,0.93051815,0.0014823019,0.00057727785,0.00039460984,0.00000856445,0.00003502863,0.0002968836],"genre_scores_gemma":[0.98295563,0.00007913548,0.015049442,0.0014263401,0.00026621963,0.000016459171,0.00000214151,0.000027525704,0.00017708619],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99780035,0.00009971534,0.00084019086,0.00033237072,0.00058578333,0.00034159914],"domain_scores_gemma":[0.99795854,0.00044523933,0.0009664686,0.00019555709,0.00012505197,0.00030916318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073201733,0.00021448126,0.00041718583,0.00011933598,0.00014134172,0.00017353018,0.00071572606,0.00008433512,0.00003929596],"category_scores_gemma":[0.00034503255,0.00020596832,0.0002671072,0.00022634846,0.000037969538,0.0008553942,0.00018466522,0.00027288066,0.00010763083],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011526834,0.0015886427,0.08461804,0.00060160743,0.0023507604,0.00058384234,0.009656441,0.0052640787,0.09661997,0.00031932045,0.027736995,0.7695076],"study_design_scores_gemma":[0.004437977,0.002851182,0.007056406,0.0013570763,0.00032206834,0.00055632537,0.0018378539,0.092061095,0.19299044,0.0006397292,0.69453174,0.0013581332],"about_ca_topic_score_codex":0.000012474108,"about_ca_topic_score_gemma":0.0000012402596,"teacher_disagreement_score":0.9165381,"about_ca_system_score_codex":0.00021805504,"about_ca_system_score_gemma":0.00009076042,"threshold_uncertainty_score":0.83991444},"labels":[],"label_agreement":null},{"id":"W3019075546","doi":"10.3390/s20082359","title":"Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Autoencoder; Hyperparameter; Artificial intelligence; Deep learning; Computer science; Machine learning; Convolutional neural network; Activities of daily living; Independence (probability theory); Psychology","score_opus":0.07250050099421257,"score_gpt":0.26080822313372737,"score_spread":0.1883077221395148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019075546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82103455,0.000023379374,0.17762572,0.00023823028,0.00018925025,0.00023217205,0.0000053084827,0.00023951872,0.00041188073],"genre_scores_gemma":[0.9960709,0.0000012284497,0.0036238923,0.00007133022,0.00011450022,0.000015438696,0.000009705137,0.000015874886,0.00007709407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985157,0.00021103569,0.00028615075,0.0004110007,0.0003035152,0.0002725576],"domain_scores_gemma":[0.9994362,0.000056754354,0.00012623002,0.00017420668,0.000072276656,0.00013433186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019418114,0.0001459073,0.00019651456,0.00014315385,0.00012813129,0.0001405384,0.00023313223,0.00009000649,0.000024304827],"category_scores_gemma":[0.00004192792,0.00016779698,0.00007552763,0.00044750434,0.000024367539,0.0009994818,0.00009358659,0.00031791354,0.00005169273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009027127,0.0002806962,0.35259035,0.0000971211,0.00006210908,0.0004979139,0.06069112,0.3530987,0.0038797366,0.00042545627,0.00014627923,0.22814028],"study_design_scores_gemma":[0.00036552656,0.00011325735,0.004470552,0.00002601521,0.000009145433,0.000052443596,0.00078895764,0.9934376,0.00024980295,0.000022610073,0.000294629,0.00016944746],"about_ca_topic_score_codex":0.0005141311,"about_ca_topic_score_gemma":0.0002144676,"teacher_disagreement_score":0.64033896,"about_ca_system_score_codex":0.00009171407,"about_ca_system_score_gemma":0.000055667955,"threshold_uncertainty_score":0.68425626},"labels":[],"label_agreement":null},{"id":"W3020902453","doi":"10.1007/978-3-319-14227-2_31","title":"A Comprehensive View of Ubiquitous Learning Context Usage in Context-Aware Learning System","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Context (archaeology); Computer science; Set (abstract data type); Order (exchange); Data science; Human–computer interaction; Artificial intelligence; Knowledge management; Geography","score_opus":0.027971960979097422,"score_gpt":0.24064238089523732,"score_spread":0.21267041991613989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020902453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010595416,0.0008967805,0.9952833,0.00022413694,0.00052389747,0.00077143754,0.000022882397,0.00010854476,0.0011094515],"genre_scores_gemma":[0.868626,0.00008950308,0.13093644,0.00010425387,0.000090987174,0.00003890121,0.000022355347,0.000023985634,0.00006759567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794775,0.00008381848,0.0010977377,0.00023837978,0.00035161097,0.00028071555],"domain_scores_gemma":[0.99674803,0.001140653,0.0011033587,0.00055680616,0.00039634903,0.000054785454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00068739924,0.0003583139,0.0008575137,0.0003780489,0.0006243885,0.0001955568,0.0018974099,0.00026631245,7.551551e-7],"category_scores_gemma":[0.00009180059,0.00031684755,0.00026631437,0.00032559453,0.00038891126,0.00038150276,0.0009410181,0.0007911158,0.0000014081334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004691102,0.000018984674,0.000037792433,0.0018428391,0.00014797442,4.917502e-7,0.006894552,0.6733709,0.000029357663,0.047423642,0.000016500338,0.2702123],"study_design_scores_gemma":[0.0005079296,0.00015523283,0.000060631846,0.002167731,0.000036261375,0.000039989893,0.00012077118,0.931569,0.00010994351,0.00012188285,0.06467215,0.00043851606],"about_ca_topic_score_codex":0.00015617521,"about_ca_topic_score_gemma":0.0002696765,"teacher_disagreement_score":0.86756647,"about_ca_system_score_codex":0.00013099774,"about_ca_system_score_gemma":0.00018732375,"threshold_uncertainty_score":0.99992836},"labels":[],"label_agreement":null},{"id":"W3029887780","doi":"10.4018/978-1-7998-4381-8.ch007","title":"Comprehensive Ontological Model for Senior Wellness Activity Recognition in Smart Homes","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in information security, privacy, and ethics book series","topic":"Context-Aware Activity Recognition Systems","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":"Lakehead University","funders":"","keywords":"Activity recognition; Ontology; Context (archaeology); Home automation; Identification (biology); Inference; Set (abstract data type); Computer science; Field (mathematics); Human–computer interaction; Smart objects; Smart environment; Artificial intelligence; Internet privacy; Internet of Things; Telecommunications; Geography","score_opus":0.06691525385607257,"score_gpt":0.30375754900074026,"score_spread":0.2368422951446677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029887780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016090734,0.0069577117,0.84361684,0.015307068,0.0022853499,0.005608214,0.0010036008,0.00081454567,0.122797586],"genre_scores_gemma":[0.86917824,0.082046784,0.026005888,0.012611874,0.0005237597,0.0015849562,0.001449565,0.00016495641,0.0064339903],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99747145,0.00012104943,0.00093144784,0.0005725358,0.00055357255,0.00034994842],"domain_scores_gemma":[0.9969493,0.001290458,0.00069632777,0.00042451877,0.00050727226,0.000132084],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006493469,0.0005121238,0.00085330656,0.00041527697,0.00024381939,0.00035002254,0.0005812856,0.0008590428,0.000012706841],"category_scores_gemma":[0.00039856634,0.0005435223,0.00014346837,0.00012605538,0.0003138379,0.014650505,0.0006168726,0.0015960665,0.00003438889],"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":[0.0009226753,0.00011122141,0.00012370662,0.0058433698,0.000089897956,0.00002733709,0.068520434,0.0003294784,0.000013181114,0.6026204,0.0005218602,0.32087642],"study_design_scores_gemma":[0.0012594527,0.00021684958,0.000080102436,0.0007524109,0.000018254823,0.00006056676,0.00045981948,0.047808878,0.00017214066,0.52711356,0.4210997,0.0009583079],"about_ca_topic_score_codex":0.000028513101,"about_ca_topic_score_gemma":0.0007361846,"teacher_disagreement_score":0.86756915,"about_ca_system_score_codex":0.00016493586,"about_ca_system_score_gemma":0.00030339335,"threshold_uncertainty_score":0.9997016},"labels":[],"label_agreement":null},{"id":"W3031974266","doi":"10.36227/techrxiv.12046842.v1","title":"Controlling a Drone using Virtual Assistant Amazon Alexa","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Lakehead University","keywords":"Drone; Amazon rainforest; Arduino; Transmitter; Computer science; Computer security; Telecommunications; Embedded system","score_opus":0.09521145545869061,"score_gpt":0.2954936201745624,"score_spread":0.20028216471587176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3031974266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018874219,0.0001807075,0.9722721,0.0016159755,0.00208084,0.0007110152,0.000032774446,0.0008234557,0.0034089067],"genre_scores_gemma":[0.9740916,0.000008911403,0.024187984,0.0007077363,0.0004969922,0.000051873827,0.000014245826,0.000038194634,0.000402499],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996368,0.0003289391,0.0007581533,0.0013505378,0.0007423935,0.00045197422],"domain_scores_gemma":[0.99742717,0.00036464646,0.00055097963,0.001108596,0.00027139805,0.00027724155],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005926166,0.0004790645,0.0009313326,0.00022110861,0.00015335788,0.0010327582,0.0015005402,0.00034704912,0.00008503451],"category_scores_gemma":[0.00015211043,0.00048065456,0.00035778782,0.00032001836,0.00005114556,0.00048437255,0.0026001753,0.00085722306,0.00021732994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000515386,0.0015151718,0.0025648917,0.0016359119,0.004140542,0.0020199602,0.012426312,0.015963491,0.15077896,0.08454934,0.009271612,0.7146184],"study_design_scores_gemma":[0.0010766358,0.0001114786,0.00021254629,0.00047259172,0.00006334425,0.00006708907,0.00014073512,0.98812884,0.0022951434,0.0023781834,0.004088361,0.000965027],"about_ca_topic_score_codex":0.0005930813,"about_ca_topic_score_gemma":0.0000973838,"teacher_disagreement_score":0.97216535,"about_ca_system_score_codex":0.0003108818,"about_ca_system_score_gemma":0.00054641825,"threshold_uncertainty_score":0.9997645},"labels":[],"label_agreement":null},{"id":"W3035213724","doi":"10.1109/isorc49007.2020.00024","title":"A Situation-Aware Adaptation Framework for Intelligent Transportation Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Ontario Tech University","funders":"","keywords":"Computer science; Adaptation (eye); Scheduling (production processes); Process (computing); Distributed computing; Systems design; Knowledge base; Software engineering; Artificial intelligence; Engineering","score_opus":0.09867798297803738,"score_gpt":0.28805577095507123,"score_spread":0.18937778797703386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035213724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015275581,0.000062555904,0.9930913,0.003229875,0.00056636974,0.0009443916,0.000024478857,0.00041713842,0.00013637038],"genre_scores_gemma":[0.9575711,0.000004698564,0.041156575,0.0007062295,0.00019033097,0.00024569145,0.0000495038,0.000012743207,0.00006311704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986821,0.000060824288,0.0003862615,0.00040659728,0.0002925683,0.00017165182],"domain_scores_gemma":[0.99872786,0.00043674913,0.00016789068,0.00022166902,0.00031056517,0.00013529226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018168907,0.00013434284,0.00019466104,0.000064341315,0.0000962106,0.00022654873,0.00033123518,0.00009336632,0.000025021347],"category_scores_gemma":[0.00012175898,0.00013319719,0.0001000681,0.00037720136,0.000010291533,0.0007055626,0.0000125214765,0.00009187561,0.00012811499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009792905,0.00017725983,0.0007327786,0.0005522894,0.00016986091,0.000009460533,0.046073314,0.011451096,0.0006783835,0.6086513,0.0039948868,0.32741144],"study_design_scores_gemma":[0.0003637937,0.00020851675,0.00035729384,0.000093664894,0.000018473367,0.0000031020427,0.002992073,0.98061436,0.0013395661,0.0049070935,0.008786382,0.00031570226],"about_ca_topic_score_codex":0.00008500785,"about_ca_topic_score_gemma":0.000059598013,"teacher_disagreement_score":0.96916324,"about_ca_system_score_codex":0.00004459699,"about_ca_system_score_gemma":0.00007909072,"threshold_uncertainty_score":0.5431624},"labels":[],"label_agreement":null},{"id":"W3035361568","doi":"10.5539/cis.v13n3p40","title":"Bidirectional Residual LSTM-based Human Activity Recognition","year":2020,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Residual; Artificial intelligence; Machine learning; F1 score; Receiver operating characteristic; Recall; Precision and recall; Wearable computer; Area under curve; Deep learning; Pattern recognition (psychology); Algorithm; Bioinformatics","score_opus":0.053527967467241824,"score_gpt":0.26642349917711,"score_spread":0.2128955317098682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035361568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1297663,0.0000051211086,0.86417115,0.0024946728,0.00037993465,0.00019468348,0.000009995721,0.00030308208,0.0026750888],"genre_scores_gemma":[0.9867054,0.0000021621056,0.009653961,0.003452282,0.00015585563,0.00001371367,0.000009621213,0.0000026641087,0.000004291074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850494,0.000063363266,0.0002810642,0.0003239175,0.000602088,0.00022460212],"domain_scores_gemma":[0.99887645,0.000094934876,0.0001796333,0.00021984178,0.00039957682,0.00022957774],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00060713786,0.00012957444,0.00014325997,0.00030063477,0.0005306997,0.0009146453,0.000531026,0.000044375098,0.00001844957],"category_scores_gemma":[0.000076487464,0.00012824808,0.000037725196,0.0011097833,0.00019088521,0.0141375065,0.00027325205,0.00014169423,0.00018146963],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015935551,0.000041275987,0.0006539176,0.00005094261,0.000007031783,0.0000015898629,0.0018810985,0.00011488937,0.0038806512,0.0024075767,0.002025035,0.98892003],"study_design_scores_gemma":[0.0012885734,0.00043450375,0.05716131,0.00006416767,0.0000060184598,0.00005388715,0.000048049475,0.8905787,0.024913602,0.00048057508,0.02438118,0.0005894587],"about_ca_topic_score_codex":0.000020296364,"about_ca_topic_score_gemma":0.0000017055879,"teacher_disagreement_score":0.9883306,"about_ca_system_score_codex":0.000051934767,"about_ca_system_score_gemma":0.00021170254,"threshold_uncertainty_score":0.99965125},"labels":[],"label_agreement":null},{"id":"W3037420459","doi":"","title":"Discriminative Coherence: Balancing Performance and Latency Bounds in Data-Sharing Multi-Core Real-Time Systems.","year":2020,"lang":"en","type":"article","venue":"Euromicro Conference on Real-Time Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Latency (audio); Coherence (philosophical gambling strategy); Discriminative model; Core (optical fiber); Distributed computing; Real-time computing; Artificial intelligence; Telecommunications","score_opus":0.12899792998140175,"score_gpt":0.2983063424637576,"score_spread":0.16930841248235584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037420459","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9627383,0.00025743453,0.008914706,0.0007533352,0.001365224,0.0032711374,0.0004443879,0.0011683381,0.02108714],"genre_scores_gemma":[0.9963372,0.00024348435,0.000746802,0.00006706083,0.00019783371,0.00013425073,0.00012946578,0.00007531096,0.002068641],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99393916,0.0006070817,0.0013693057,0.0024167371,0.00073538086,0.0009323072],"domain_scores_gemma":[0.9959063,0.00041574464,0.0008216001,0.0019837972,0.0003850666,0.0004874743],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016308442,0.00074286526,0.0013471012,0.00035884918,0.00031643364,0.0014902897,0.0027579519,0.0002508366,0.000047657504],"category_scores_gemma":[0.00022012186,0.00072077854,0.00008264641,0.00082033506,0.00016778841,0.0020472584,0.0015799941,0.00062722655,0.0010404275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005896966,0.0011847325,0.07749826,0.006379698,0.0006812359,0.0014404465,0.04228433,0.0010407502,0.81649214,0.0119669195,0.01252595,0.02791584],"study_design_scores_gemma":[0.0018708163,0.00035820063,0.013901939,0.0024191202,0.00003263278,0.00018393502,0.0008859546,0.978043,0.0006176182,0.00001660598,0.0005795163,0.0010906433],"about_ca_topic_score_codex":0.003132882,"about_ca_topic_score_gemma":0.00006121034,"teacher_disagreement_score":0.97700226,"about_ca_system_score_codex":0.0002522256,"about_ca_system_score_gemma":0.0003843153,"threshold_uncertainty_score":0.9997374},"labels":[],"label_agreement":null},{"id":"W3037699712","doi":"10.22215/etd/2020-14078","title":"Exploiting Wi-Fi Channel State Information for Artificial Intelligence-Based Human Activity Recognition of Similar Dynamic Motions","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Activity recognition; Activities of daily living; Computer science; Sitting; Artificial intelligence; Support vector machine; Motion (physics); Random forest; Assisted living; Ambient intelligence; Measure (data warehouse); Machine learning; Pattern recognition (psychology); Data mining","score_opus":0.09051213046755253,"score_gpt":0.31362478827207624,"score_spread":0.22311265780452372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037699712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039723076,0.000007486747,0.9554452,0.0003541601,0.0009142124,0.0016714616,0.00042537937,0.0003856117,0.0010734418],"genre_scores_gemma":[0.98954624,0.000006813503,0.0050137434,0.000105589985,0.000078544355,0.0004540044,0.0046221656,0.000036795245,0.00013608023],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972314,0.00016907959,0.0011233164,0.0005625682,0.00055386586,0.00035971825],"domain_scores_gemma":[0.9965639,0.00033927284,0.0014047163,0.00043333872,0.0011250501,0.00013369578],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005344909,0.00041812845,0.00061082526,0.0006697461,0.00033423,0.0003351228,0.0005870821,0.00028599176,0.00004107738],"category_scores_gemma":[0.00032922588,0.000479402,0.00035246907,0.00068025314,0.000034277735,0.002519997,0.000058886137,0.00036402393,0.00008662287],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013352398,0.00027413736,0.0000020055436,0.001383196,0.00011933405,0.0000016530898,0.0047240923,0.0004039525,0.00810217,0.0010767721,0.00011679058,0.98366237],"study_design_scores_gemma":[0.00042630165,0.00057245564,0.00020338818,0.00081894494,0.00009738685,0.0000030003503,0.0033245855,0.7648354,0.20198421,0.026493596,0.00021285586,0.0010278721],"about_ca_topic_score_codex":0.00015951323,"about_ca_topic_score_gemma":0.0014235748,"teacher_disagreement_score":0.9826345,"about_ca_system_score_codex":0.00018466111,"about_ca_system_score_gemma":0.00043265766,"threshold_uncertainty_score":0.99976575},"labels":[],"label_agreement":null},{"id":"W3040296275","doi":"10.1109/i2mtc43012.2020.9128582","title":"Comparative Study of Time Series-based Human Activity Recognition using Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Activity recognition; Generalization; Pattern recognition (psychology); Machine learning; Deep learning; Flexibility (engineering); Artificial neural network; Time series","score_opus":0.14761895224331006,"score_gpt":0.31822365548304393,"score_spread":0.17060470323973387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040296275","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6250019,0.0000040443347,0.373789,0.00016531466,0.00008848899,0.00044498805,0.000008725824,0.00014568126,0.00035187288],"genre_scores_gemma":[0.9983658,7.0265536e-8,0.0013054308,0.00017211407,0.00009735556,0.000018823244,0.000012663806,0.000008348397,0.000019378405],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981559,0.00049055874,0.00033815365,0.00045248304,0.00035769772,0.00020523675],"domain_scores_gemma":[0.998816,0.00020431262,0.00028820246,0.0002533479,0.00031255922,0.0001255867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021639552,0.00018520285,0.00042911601,0.000081798324,0.00018798979,0.000092603,0.0003434051,0.000058803107,0.00014655937],"category_scores_gemma":[0.000021262498,0.00018919972,0.00008721582,0.0005329905,0.00006492294,0.0010075239,0.00015762051,0.00018881373,0.00003570096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035638493,0.021795273,0.07074572,0.00049474725,0.0026905537,0.0002869802,0.037039407,0.29385254,0.3915262,0.00088285666,0.0070356424,0.17008623],"study_design_scores_gemma":[0.0010985635,0.0008220246,0.004298688,0.000015392514,0.000018646355,0.000008406193,0.00023366725,0.98990107,0.003338919,0.000028895845,0.000013217033,0.00022252556],"about_ca_topic_score_codex":0.00025362853,"about_ca_topic_score_gemma":0.000111877394,"teacher_disagreement_score":0.6960485,"about_ca_system_score_codex":0.000060795966,"about_ca_system_score_gemma":0.000070234906,"threshold_uncertainty_score":0.7715341},"labels":[],"label_agreement":null},{"id":"W3040736388","doi":"10.1038/s41597-020-0563-y","title":"A database of human gait performance on irregular and uneven surfaces collected by wearable sensors","year":2020,"lang":"en","type":"article","venue":"Scientific Data","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":119,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"National Institute for Occupational Safety and Health; U.S. Department of Health and Human Services","keywords":"Wearable computer; Gait; Computer science; Database; Gait analysis; Wearable technology; Physical medicine and rehabilitation; Embedded system; Medicine","score_opus":0.07286879744542986,"score_gpt":0.2707043208546184,"score_spread":0.19783552340918853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040736388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907391,0.00025485782,0.002746034,0.001005525,0.0004308206,0.0003908121,0.003664893,0.00013620393,0.000631773],"genre_scores_gemma":[0.9965807,0.000010679062,0.0008301018,0.00008392984,0.000019119256,0.000004512389,0.0007164121,0.000008775043,0.0017457434],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978037,0.00013954511,0.00028007684,0.0010023017,0.0005326249,0.00024173968],"domain_scores_gemma":[0.9976483,0.00010619221,0.0001646131,0.0018008997,0.000120813755,0.0001591592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009209826,0.00013579565,0.00023131233,0.000105726525,0.00033923256,0.00045588578,0.0018078707,0.000035526347,0.000048041216],"category_scores_gemma":[0.00015148844,0.00013142642,0.000019152794,0.0010787094,0.00021753625,0.0014051918,0.0012530204,0.00011734878,0.00009586077],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040850056,0.00026403175,0.0013873013,0.00035728197,0.00007018499,0.000015815738,0.0021145637,0.000036995203,0.3478646,0.00019683145,0.63052124,0.017130328],"study_design_scores_gemma":[0.0022105747,0.0005910294,0.0038869493,0.0007503091,0.000046279987,0.000032717762,0.00057116675,0.5998737,0.2042643,0.0000863312,0.18667817,0.0010084622],"about_ca_topic_score_codex":0.00007786249,"about_ca_topic_score_gemma":0.000035136254,"teacher_disagreement_score":0.5998367,"about_ca_system_score_codex":0.00001555188,"about_ca_system_score_gemma":0.00009864348,"threshold_uncertainty_score":0.53594136},"labels":[],"label_agreement":null},{"id":"W3041787762","doi":"10.24963/ijcai.2020/744","title":"Context Aware Sequence Modeling","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Context model; Context (archaeology); Granularity; Feature (linguistics); Sentence; Data modeling; Sequence (biology); Artificial intelligence; Contextual design; Data science; Data mining; Machine learning; Database; Programming language","score_opus":0.1686468363097001,"score_gpt":0.2826054880780864,"score_spread":0.1139586517683863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041787762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008081016,0.00004466862,0.97748864,0.008465543,0.00017443596,0.00014922828,0.0000032551998,0.0006045126,0.0049886927],"genre_scores_gemma":[0.9907277,0.0000026186358,0.003144478,0.0058496725,0.00008937099,0.000013564017,0.0000014302266,0.000007819134,0.0001632955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885637,0.00006114608,0.00021553213,0.00041742317,0.00024615414,0.00020337827],"domain_scores_gemma":[0.9992298,0.00007491725,0.0000494621,0.00031550232,0.00014409257,0.00018623953],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012112929,0.000117526644,0.00017166542,0.00003504955,0.00007835491,0.00018879374,0.00067779166,0.000044795375,0.00008858147],"category_scores_gemma":[0.00006196326,0.00011040556,0.000068390545,0.0002984118,0.000015118371,0.0008901225,0.0002313323,0.00012234937,0.00089623115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002159689,0.00008992191,0.001060304,0.00010127748,0.000088496985,0.00015059381,0.008125812,0.0025912477,0.0105341505,0.049909163,0.009099789,0.9182277],"study_design_scores_gemma":[0.00019042715,0.00003372971,0.000005572753,0.000015334801,0.0000016701798,0.000020469728,0.00024079507,0.99401,0.0013392124,0.00037688046,0.003598994,0.00016695204],"about_ca_topic_score_codex":0.0001427629,"about_ca_topic_score_gemma":0.00003225502,"teacher_disagreement_score":0.9914187,"about_ca_system_score_codex":0.000029865241,"about_ca_system_score_gemma":0.0000824712,"threshold_uncertainty_score":0.9998817},"labels":[],"label_agreement":null},{"id":"W3044651858","doi":"10.1109/access.2020.3010715","title":"Deep Neural Networks for Human Activity Recognition With Wearable Sensors: Leave-One-Subject-Out Cross-Validation for Model Selection","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":182,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cross-validation; Artificial intelligence; Convolutional neural network; Wearable computer; Machine learning; Deep learning; Artificial neural network; Support vector machine; Subject (documents); Sliding window protocol; Pattern recognition (psychology); Window (computing)","score_opus":0.14341874853232703,"score_gpt":0.3486028257703112,"score_spread":0.20518407723798418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044651858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3256881,0.000008795566,0.6719577,0.00035236508,0.00028666513,0.0012731728,0.000016674609,0.0003247805,0.00009176539],"genre_scores_gemma":[0.9937402,0.0000029473563,0.0043240385,0.00031529667,0.0007222011,0.0007154753,0.000035403482,0.000049778184,0.00009464849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978086,0.00013507788,0.00036105304,0.0008777634,0.00032883193,0.0004886801],"domain_scores_gemma":[0.9980903,0.00031439407,0.00040587876,0.0003093893,0.0007125523,0.00016744075],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00039765853,0.0002884491,0.0003733392,0.000119323755,0.000578564,0.0013994202,0.00062658876,0.00018149667,0.0000075287667],"category_scores_gemma":[0.00008603401,0.00030220108,0.00015037578,0.00048858457,0.000045296492,0.0044876183,0.000083250074,0.00025018366,0.000012086599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016165012,0.00044405952,0.0023118847,0.00052854477,0.000259857,0.000003815595,0.0018880916,0.66003066,0.04736967,0.00010638335,0.0007894599,0.28465104],"study_design_scores_gemma":[0.0014139363,0.0003578873,0.00022265191,0.000048922655,0.000038179714,0.000009599564,0.000012580213,0.90864897,0.08795246,0.0008747414,0.000039400056,0.0003806513],"about_ca_topic_score_codex":0.00010000133,"about_ca_topic_score_gemma":0.00030704922,"teacher_disagreement_score":0.6680521,"about_ca_system_score_codex":0.00012279802,"about_ca_system_score_gemma":0.0000719248,"threshold_uncertainty_score":0.999943},"labels":[],"label_agreement":null},{"id":"W3046528107","doi":"10.1007/978-3-030-54407-2_31","title":"Human Body Fall Recognition System","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Falling (accident); Artificial intelligence; Computer vision; Pattern recognition (psychology)","score_opus":0.03764273890207143,"score_gpt":0.2533086433848125,"score_spread":0.2156659044827411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046528107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015893944,0.00016287119,0.97829264,0.00076765945,0.002601501,0.00077805715,0.000024060471,0.0007893054,0.016424933],"genre_scores_gemma":[0.9431625,0.000006952947,0.053262185,0.0016397174,0.0014033829,0.000043033957,0.000029948626,0.00008007382,0.00037217772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99449784,0.00011849637,0.0008942819,0.0023599647,0.0014314209,0.00069798727],"domain_scores_gemma":[0.9966137,0.0003974066,0.0006169641,0.0015274315,0.00049691444,0.00034760853],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001005571,0.0007322745,0.00090483873,0.0009751254,0.0004266493,0.0011063231,0.0035614925,0.0004698372,0.00002240209],"category_scores_gemma":[0.00009063636,0.0007465865,0.00025766098,0.00080252375,0.00041414605,0.0011581935,0.0015315702,0.001147883,0.0007215516],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065555487,0.00004603186,0.0000461518,0.00029877728,0.000043256245,0.00043020392,0.0008180268,0.00036318082,0.0008531557,0.012822915,0.00014600104,0.98412573],"study_design_scores_gemma":[0.0029542013,0.0018506742,0.00058557355,0.0097176405,0.00012775985,0.0021789605,0.000005657352,0.70358783,0.011461788,0.24388906,0.01604558,0.0075953025],"about_ca_topic_score_codex":0.00009268902,"about_ca_topic_score_gemma":0.00016118377,"teacher_disagreement_score":0.97653043,"about_ca_system_score_codex":0.00073485356,"about_ca_system_score_gemma":0.00049924734,"threshold_uncertainty_score":0.9999306},"labels":[],"label_agreement":null},{"id":"W3048964982","doi":"10.1139/apnm-2019-0969","title":"Examining the effects of applying ActiGraph low-frequency extension feature to analyze the sleeping behaviours of preschool-aged children","year":2020,"lang":"en","type":"article","venue":"Applied Physiology Nutrition and Metabolism","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sleep (system call); Novelty; Sleep patterns; Audiology; Polysomnography; Accelerometer; Psychology; Physical therapy; Physical medicine and rehabilitation; Medicine; Circadian rhythm; Computer science; Psychiatry; Electroencephalography; Social psychology","score_opus":0.014995051353929496,"score_gpt":0.23082451279654434,"score_spread":0.21582946144261483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048964982","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9873408,0.0015537777,0.008440946,0.0008640512,0.00018320284,0.0015034759,0.000011895906,0.00006300966,0.000038833103],"genre_scores_gemma":[0.9970637,0.00014580852,0.0011373451,0.0011633851,0.00015451797,0.00030958775,0.000012402152,0.0000114192535,0.0000018591705],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99852574,0.0002925396,0.00028394762,0.00046696092,0.00021326219,0.00021757405],"domain_scores_gemma":[0.99853665,0.00049923395,0.00028826605,0.0004727213,0.00009881886,0.00010434054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027245303,0.00019145572,0.00045418873,0.00010519505,0.00020568381,0.00003364193,0.00053705083,0.00010911278,0.0000025871161],"category_scores_gemma":[0.00009366131,0.00012981005,0.00010172262,0.00062685297,0.00012374306,0.00017348748,0.00022325765,0.0003214436,0.000002673731],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002653211,0.00008097879,0.00047513194,0.000095657124,0.00008299939,6.677564e-7,0.0013892669,0.0000044531866,0.9696624,0.0015887534,0.0003265137,0.026266644],"study_design_scores_gemma":[0.0014711061,0.000045031647,0.9087372,0.00013978378,0.00011552242,0.0000096434815,0.00025587078,0.000100265504,0.087686904,0.00095120614,0.00028332876,0.0002041347],"about_ca_topic_score_codex":0.000044269345,"about_ca_topic_score_gemma":0.0000034621826,"teacher_disagreement_score":0.9082621,"about_ca_system_score_codex":0.0000052650316,"about_ca_system_score_gemma":0.000020521173,"threshold_uncertainty_score":0.52935004},"labels":[],"label_agreement":null},{"id":"W30663798","doi":"10.1002/ajh.25413","title":"A Context-Aware Authentication Approach Based on Behavioral Definitions.","year":2010,"lang":"en","type":"article","venue":"IKE","topic":"Context-Aware Activity Recognition Systems","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":"Western University","funders":"","keywords":"Computer science; Authentication (law); Ubiquitous computing; Context (archaeology); Mobile device; Computer security; Context awareness; Mobile computing; Human–computer interaction; Distributed computing; World Wide Web; Computer network","score_opus":0.06613612421202847,"score_gpt":0.27827584501697916,"score_spread":0.2121397208049507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W30663798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14120245,0.000009987422,0.83841,0.0017948948,0.0014525136,0.00066254695,0.000050932176,0.0007326387,0.015684014],"genre_scores_gemma":[0.99157256,2.795082e-7,0.0069952565,0.0009840035,0.000080613834,0.0001735803,0.000037703445,0.000014254567,0.00014173969],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986574,0.00008955972,0.00023524332,0.0004431864,0.00034507434,0.00022952128],"domain_scores_gemma":[0.99858016,0.0001521073,0.00011673803,0.000810125,0.00022129758,0.000119546115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028583177,0.0001545994,0.00016289754,0.00017824465,0.00016674216,0.0002269102,0.00055042363,0.00011821856,0.0000636973],"category_scores_gemma":[0.000056155553,0.0001537829,0.00010209891,0.00029392636,0.000060421073,0.00039758766,0.00006648095,0.00031129495,0.00054414035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069846996,0.0068012085,0.005349923,0.00010566487,0.000059438906,0.000055034034,0.003466776,0.000043069696,0.018911008,0.15643747,0.011852205,0.79684836],"study_design_scores_gemma":[0.0071395347,0.0013158997,0.03912475,0.00027801152,0.00014157114,0.00031186358,0.0010171803,0.83124197,0.020944558,0.009829759,0.08563951,0.003015379],"about_ca_topic_score_codex":0.000058225378,"about_ca_topic_score_gemma":0.00006239189,"teacher_disagreement_score":0.8503701,"about_ca_system_score_codex":0.000035362125,"about_ca_system_score_gemma":0.00010993196,"threshold_uncertainty_score":0.6994005},"labels":[],"label_agreement":null},{"id":"W3082017561","doi":"10.1109/embc44109.2020.9175610","title":"Learning-Aided User Intent Estimation for Smart Rollators","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Reliability (semiconductor); Computer science; Encoder; Estimation; Inertial measurement unit; Population; Artificial intelligence; Machine learning; Simulation; Engineering","score_opus":0.04164809002946115,"score_gpt":0.2591887385175165,"score_spread":0.21754064848805535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082017561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022383679,0.0000071023114,0.9691629,0.0058739027,0.0002695612,0.00037581357,0.0000010397665,0.000460827,0.0014652082],"genre_scores_gemma":[0.97765416,5.67325e-7,0.02024984,0.0010492045,0.000064410415,0.000056123125,0.0000045766506,0.0000094245115,0.00091169117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990708,0.000059465237,0.0002142453,0.00031590194,0.00017831518,0.00016122677],"domain_scores_gemma":[0.99926114,0.00020572371,0.00009176372,0.0001705748,0.00015062717,0.00012017477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001965736,0.00010190411,0.00015770715,0.000050890063,0.00008597418,0.0001808088,0.00029854546,0.000043974393,0.00004678486],"category_scores_gemma":[0.00034983765,0.000093767645,0.00008765717,0.00023657712,0.000011042192,0.00060032605,0.00012087983,0.00009035572,0.00033170384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013033008,0.00014907903,0.011380024,0.00018362411,0.00017763158,0.000009093101,0.007363748,0.0031190196,0.0055727223,0.020743398,0.042156495,0.9090148],"study_design_scores_gemma":[0.00060529896,0.00026228692,0.00093610515,0.00001716602,0.0000068214454,0.0000071895106,0.000147223,0.9485544,0.005619955,0.0004277552,0.043211244,0.00020453188],"about_ca_topic_score_codex":0.000030765812,"about_ca_topic_score_gemma":0.000012531204,"teacher_disagreement_score":0.95527047,"about_ca_system_score_codex":0.000032347776,"about_ca_system_score_gemma":0.000041783653,"threshold_uncertainty_score":0.42634925},"labels":[],"label_agreement":null},{"id":"W3083411455","doi":"10.1145/3411170.3411233","title":"Classification models for data fusion in human activity recognition","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Sensor fusion; Fusion; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.4049788599100126,"score_gpt":0.35246758571101144,"score_spread":0.052511274199001146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083411455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046465077,0.000006853181,0.94564176,0.0044453777,0.0001063988,0.00064099685,0.000047806494,0.00023560772,0.002410155],"genre_scores_gemma":[0.9897349,0.000003837604,0.009400336,0.0004611662,0.00008895304,0.00008255088,0.00017383644,0.000009555821,0.000044851815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856627,0.00011918089,0.00024870806,0.00068856304,0.00020019658,0.0001771062],"domain_scores_gemma":[0.9988111,0.00017436435,0.00012926226,0.00068357063,0.00011183611,0.0000898559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046069332,0.00011295351,0.00017440067,0.0000916983,0.00009630502,0.00014813074,0.00085317635,0.000078035744,0.0000198997],"category_scores_gemma":[0.00009928603,0.00011688003,0.000037784866,0.00035691328,0.000013910395,0.0027927354,0.00037895804,0.00011398383,0.000051943305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038069476,0.00018942857,0.00020244434,0.00006114826,0.000010959416,0.0000018696521,0.00064075633,0.000022598984,0.056262307,0.0032273931,0.0034486603,0.93589437],"study_design_scores_gemma":[0.00058405055,0.000068752415,0.0008789109,0.000022866541,0.0000036956678,0.0000021603526,0.000051719762,0.98801774,0.0036783249,0.005298638,0.0012182995,0.00017485423],"about_ca_topic_score_codex":0.00016543198,"about_ca_topic_score_gemma":0.00027583455,"teacher_disagreement_score":0.98799515,"about_ca_system_score_codex":0.000052118314,"about_ca_system_score_gemma":0.00005285153,"threshold_uncertainty_score":0.47662294},"labels":[],"label_agreement":null},{"id":"W3083668903","doi":"10.1145/3411170.3411235","title":"Fuzzy Features for Quality Estimation of Activity Instances in a Dataset","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Centre for Interdisciplinary Research in Rehabilitation; Université de Sherbrooke; Université de Montréal; Université du Québec à Chicoutimi","funders":"","keywords":"Exploit; Computer science; Artificial intelligence; Machine learning; Fuzzy logic; Field (mathematics); Quality (philosophy); Estimation; Data mining; Engineering; Mathematics","score_opus":0.09845859038405635,"score_gpt":0.3576718774279298,"score_spread":0.2592132870438734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083668903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09833521,0.000026891876,0.8954432,0.0038338236,0.000100619,0.00048017397,0.00055528426,0.000082828614,0.001141945],"genre_scores_gemma":[0.98594886,0.0000011083831,0.013586727,0.00033750516,0.000018022516,0.000033676806,0.000060856524,0.000002700242,0.00001054354],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991063,0.00011723715,0.00021793376,0.00027386236,0.00017694282,0.000107735],"domain_scores_gemma":[0.99911636,0.00038876812,0.00014909457,0.00024710078,0.00005068293,0.00004797903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038862103,0.00007718689,0.00021058232,0.00005150789,0.000025338863,0.000051550433,0.00033643589,0.000039396928,0.000004268996],"category_scores_gemma":[0.00029507105,0.00007102984,0.000038103317,0.00030251517,0.00002027913,0.0009427413,0.00009925296,0.00006469189,0.000006472716],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023820072,0.00033122918,0.002332015,0.0005519291,0.000042394135,0.0000032535281,0.0030540153,0.00042753358,0.010420772,0.03396567,0.018309971,0.930323],"study_design_scores_gemma":[0.008852663,0.0011769497,0.24079445,0.00034141954,0.000032815897,0.00002283889,0.001149604,0.4743995,0.2201399,0.031095775,0.020075392,0.0019186601],"about_ca_topic_score_codex":0.00035618394,"about_ca_topic_score_gemma":0.00070413563,"teacher_disagreement_score":0.92840433,"about_ca_system_score_codex":0.00002167832,"about_ca_system_score_gemma":0.000058007543,"threshold_uncertainty_score":0.28965127},"labels":[],"label_agreement":null},{"id":"W3089154531","doi":"10.2196/21016","title":"Enabling Remote Patient Monitoring Through the Use of Smart Thermostat Data in Canada: Exploratory Study","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Research Institute for Aging; University Health Network; University of Toronto; University of Waterloo","funders":"University of Waterloo","keywords":"Data collection; Computer science; Sample (material); mHealth; Data science; Medicine; Statistics","score_opus":0.31649658678130915,"score_gpt":0.36618678093866963,"score_spread":0.04969019415736048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089154531","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98886526,0.0007303317,0.0041810377,0.004656659,0.00042463111,0.0010530596,0.000036176993,0.000039253835,0.0000135763885],"genre_scores_gemma":[0.9974236,0.00018505378,0.0005575036,0.001718128,0.00008062644,0.000019129127,0.0000032073983,0.0000112073185,0.0000015611138],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9975317,0.0005334364,0.000590712,0.00052516826,0.00044740352,0.00037155589],"domain_scores_gemma":[0.9980376,0.0004435995,0.0003228194,0.0008764336,0.00008206263,0.00023752078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047569536,0.00014672798,0.00031985377,0.000042556978,0.00017343982,0.000067523244,0.0006197297,0.000028849414,0.0000013143944],"category_scores_gemma":[0.000110446446,0.00011370918,0.000014187821,0.0004967577,0.000022528908,0.0008785369,0.00051218795,0.0002982348,0.0000016265798],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016251093,0.0002451955,0.11692328,0.0007479223,0.00003456809,0.000067377376,0.10647735,0.0000722458,0.000028362698,0.000056469948,0.0018181866,0.7733665],"study_design_scores_gemma":[0.007404762,0.004551306,0.56883717,0.0013528902,0.0000963505,0.0000679314,0.16970551,0.13871467,0.0004915759,0.00025044905,0.106629185,0.0018981993],"about_ca_topic_score_codex":0.61075425,"about_ca_topic_score_gemma":0.50459284,"teacher_disagreement_score":0.77146834,"about_ca_system_score_codex":0.00022345645,"about_ca_system_score_gemma":0.0021710764,"threshold_uncertainty_score":0.50444704},"labels":[],"label_agreement":null},{"id":"W3089240108","doi":"10.2196/20215","title":"Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University; Université du Québec à Chicoutimi; HEC Montréal; Université de Sherbrooke; Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research","keywords":"Activities of daily living; Psychosocial; Aging in place; Medicine; Gerontology; Independent living; Health care; Hygiene; Social support; Sleep hygiene; Assisted living; Nursing; Psychology; Cognition; Physical therapy; Psychiatry","score_opus":0.1164795539622652,"score_gpt":0.3398686795127876,"score_spread":0.2233891255505224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089240108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81782454,0.0000169275,0.17825781,0.0013264675,0.00050285057,0.0017691774,0.0000036761173,0.00024787083,0.000050662416],"genre_scores_gemma":[0.99086523,3.3168035e-7,0.0071962713,0.0013120145,0.00038591507,0.0002065003,0.0000014749085,0.000022151524,0.000010085561],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962201,0.0001779959,0.00096665596,0.00034478694,0.001859018,0.0004314388],"domain_scores_gemma":[0.99674535,0.0003565988,0.00034465664,0.0008312779,0.0003250943,0.0013970507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007584974,0.00027699268,0.00034328704,0.00011628096,0.00029326367,0.0005618558,0.0008596106,0.000060976334,0.000017911329],"category_scores_gemma":[0.0005244573,0.00019012678,0.00007710873,0.0009963474,0.000041617637,0.0009151464,0.0009440764,0.00035025537,0.000082533275],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019366841,0.0027801874,0.04414829,0.0004141465,0.0006490023,0.020535903,0.5298096,0.0004525745,0.00009338114,0.000010154563,0.001990229,0.39892286],"study_design_scores_gemma":[0.0055783065,0.0038323803,0.092667714,0.012196211,0.0007976013,0.024004864,0.34467503,0.5037219,0.0019159629,0.000004915059,0.0065650265,0.0040400857],"about_ca_topic_score_codex":0.00009568565,"about_ca_topic_score_gemma":0.000014667402,"teacher_disagreement_score":0.5032693,"about_ca_system_score_codex":0.0000924728,"about_ca_system_score_gemma":0.00018382733,"threshold_uncertainty_score":0.7753145},"labels":[],"label_agreement":null},{"id":"W3089391872","doi":"10.1109/thms.2020.3017784","title":"An Empirical Approach to Modeling User-System Interaction Conflicts in Smart Homes","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Empirical research; Cluster analysis; Home automation; Smart city; Class (philosophy); Sample (material); Computer security; Human–computer interaction; Internet of Things; Artificial intelligence; Telecommunications","score_opus":0.13118263230948773,"score_gpt":0.3453238863638838,"score_spread":0.21414125405439605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089391872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09465104,0.00004038385,0.8994987,0.00039227135,0.0015012496,0.001268206,0.00004900976,0.0009306396,0.0016684941],"genre_scores_gemma":[0.9978111,0.0000018899491,0.00096972444,0.00038137074,0.00022332986,0.00044790513,0.00001598661,0.00006230243,0.00008643629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955987,0.0008078163,0.0010791548,0.0012675511,0.00072448415,0.0005223217],"domain_scores_gemma":[0.99788743,0.00018438157,0.00020976788,0.0009402559,0.00020465595,0.00057348324],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007048929,0.00048444903,0.0007914402,0.00070429256,0.0003724694,0.00063196296,0.0010281802,0.00021313825,0.000009217445],"category_scores_gemma":[0.000013164484,0.0004909425,0.00021401663,0.0011221179,0.000024992336,0.0015482256,0.000011932088,0.0007464415,0.00023316793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016594041,0.0012495478,0.00043273924,0.00059872697,0.00016105291,0.0000470572,0.016000256,0.9655437,0.005634966,0.0010192933,0.00020224504,0.008944501],"study_design_scores_gemma":[0.00092262495,0.00040693194,0.00007990671,0.00024628433,0.000018984403,0.000098827026,0.0017994472,0.9941117,0.00062944356,0.0000036622334,0.0011560165,0.0005261347],"about_ca_topic_score_codex":0.0023173518,"about_ca_topic_score_gemma":0.00045260444,"teacher_disagreement_score":0.90316004,"about_ca_system_score_codex":0.0004294622,"about_ca_system_score_gemma":0.000077325865,"threshold_uncertainty_score":0.99975425},"labels":[],"label_agreement":null},{"id":"W3090602253","doi":"10.1109/icra40945.2020.9196856","title":"PARC: A Plan and Activity Recognition Component for Assistive Robots","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 du Québec à Montréal","funders":"","keywords":"Robot; Computer science; Software deployment; Mobile robot; Activity recognition; Human–computer interaction; Component (thermodynamics); Artificial intelligence; Plan (archaeology); Assisted living; Robotics; Software engineering","score_opus":0.12068420343510519,"score_gpt":0.272283677078849,"score_spread":0.1515994736437438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090602253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072153814,0.0000146753855,0.9195922,0.0059537394,0.00015037239,0.0005549932,0.00003989644,0.00025163539,0.0012886645],"genre_scores_gemma":[0.987009,0.0000034223067,0.011817714,0.0009126591,0.0000935729,0.00009972729,0.000011938573,0.000007918752,0.000044057448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893975,0.00010127202,0.00015159119,0.00045882314,0.00016218668,0.00018639234],"domain_scores_gemma":[0.99902534,0.0004319811,0.00010085759,0.00015357762,0.000108116,0.00018010172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017571336,0.0001316216,0.00021864151,0.000045831963,0.00011592041,0.00017075439,0.00019158902,0.000054470336,0.000018177312],"category_scores_gemma":[0.00009349359,0.00012512902,0.000060071812,0.00014660392,0.00002677534,0.00060480967,0.00014547507,0.00009199282,0.000060213766],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014753205,0.0001513052,0.0013313639,0.00009716174,0.000083849794,0.000010027687,0.0010177394,0.000005830862,0.010172201,0.00059000746,0.0035373478,0.9828556],"study_design_scores_gemma":[0.008147532,0.002253076,0.12102934,0.00027052735,0.00010297148,0.00021701319,0.00060601736,0.75025237,0.08360343,0.0053361226,0.025869112,0.0023124737],"about_ca_topic_score_codex":0.00006702424,"about_ca_topic_score_gemma":0.000044798264,"teacher_disagreement_score":0.98054314,"about_ca_system_score_codex":0.000029774066,"about_ca_system_score_gemma":0.00003593891,"threshold_uncertainty_score":0.5102613},"labels":[],"label_agreement":null},{"id":"W3090729831","doi":"10.1109/iscas45731.2020.9181231","title":"Using Machine Learning for Person Identification through Physical Activities","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Dalhousie University","funders":"","keywords":"Identification (biology); Computer science; Machine learning; Artificial intelligence; Artificial neural network; Focus (optics); Physical activity; Performing arts; Human–computer interaction","score_opus":0.1764975119754803,"score_gpt":0.32994523671459064,"score_spread":0.15344772473911034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090729831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07745872,0.000031533877,0.91959417,0.0015906807,0.00013928206,0.00023379781,0.0000034314799,0.0002872381,0.00066116557],"genre_scores_gemma":[0.9877712,0.000001139442,0.011396851,0.00028776866,0.00022368289,0.000023725648,0.0000053694976,0.000011110858,0.00027913673],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991146,0.00008610708,0.00012016681,0.00034215872,0.00018211773,0.00015488689],"domain_scores_gemma":[0.99942636,0.00020309881,0.000109448636,0.00014886358,0.00006314574,0.000049080954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011731724,0.00010359657,0.00016304656,0.000027429925,0.00017987055,0.00024468472,0.00025729588,0.000029506504,0.000011060835],"category_scores_gemma":[0.00008346184,0.000100721656,0.000098350996,0.00019949685,0.000019492843,0.001359918,0.000069436595,0.000107319756,0.00003475638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006492249,0.00024984093,0.0014554284,0.00026006697,0.00015462638,0.0000055040205,0.053339362,0.0009473288,0.68430716,0.026010329,0.0009450351,0.23226039],"study_design_scores_gemma":[0.00024382096,0.00006465318,0.00006873073,0.0000112043235,0.0000087771805,0.0000060625453,0.00077864254,0.9414845,0.0538264,0.0003050256,0.003050555,0.0001515935],"about_ca_topic_score_codex":0.000097988675,"about_ca_topic_score_gemma":0.000008718101,"teacher_disagreement_score":0.9405372,"about_ca_system_score_codex":0.00003786604,"about_ca_system_score_gemma":0.000030326764,"threshold_uncertainty_score":0.410731},"labels":[],"label_agreement":null},{"id":"W3092819774","doi":"10.20381/ruor-5114","title":"Development and Evaluation of a BlackBerry-based Wearable Mobility Monitoring System","year":2012,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wearable computer; Computer science; Embedded system","score_opus":0.014391250876599442,"score_gpt":0.19293260287728858,"score_spread":0.17854135200068913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092819774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94743896,0.0013193175,0.00065071526,0.00009928763,0.0008448315,0.000591322,0.000052940355,0.00002254149,0.048980094],"genre_scores_gemma":[0.99665916,0.000010015997,0.0022837818,0.000015018211,0.00003736836,0.000051872277,0.000014517187,0.000014163799,0.0009140933],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99607354,0.00017202404,0.00046843573,0.00033492528,0.0027254112,0.00022563573],"domain_scores_gemma":[0.99879074,0.00024439848,0.00048501397,0.00028454454,0.0000067512483,0.00018855558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010993104,0.00022022024,0.0003727617,0.000050450468,0.00013064352,0.00003772508,0.00026890248,0.000056266224,0.0000053082877],"category_scores_gemma":[0.00000565873,0.00022983251,0.000027334634,0.00009811243,0.000022828157,0.00053467473,0.000079185476,0.00012570946,3.4469214e-9],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078256946,0.0002968587,0.0891641,0.013228938,0.00062274095,0.00003225535,0.004258994,0.00027789257,0.056085084,0.019172942,0.00011529539,0.8159623],"study_design_scores_gemma":[0.00089420786,0.000050815954,0.36808744,0.002864387,0.00011906205,0.000006816608,0.009803046,0.024285715,0.59117675,0.000115202114,0.0019766327,0.0006199364],"about_ca_topic_score_codex":0.002453853,"about_ca_topic_score_gemma":0.020022986,"teacher_disagreement_score":0.81534237,"about_ca_system_score_codex":0.000043213022,"about_ca_system_score_gemma":0.004688563,"threshold_uncertainty_score":0.99785906},"labels":[],"label_agreement":null},{"id":"W3093643609","doi":"10.2196/21209","title":"Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University Health Network; University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Internet privacy; The Internet; Wearable computer; Event (particle physics); Anomaly detection; Public health; Ubiquitous computing; Tracking (education); Computer security; Behavioral pattern; Public health surveillance; Activity recognition; Mental health; Human–computer interaction; World Wide Web; Psychology; Data mining; Medicine; Artificial intelligence; Psychiatry","score_opus":0.49004135729622494,"score_gpt":0.3793201948349918,"score_spread":0.11072116246123315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093643609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28934652,0.00046872726,0.7044608,0.003582862,0.00021784469,0.0016651644,0.00020787136,0.000046811463,0.0000033854808],"genre_scores_gemma":[0.9920149,0.000032319604,0.006500205,0.0013020564,0.00005270095,0.000040491508,0.000029762201,0.00002345147,0.0000041406956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969536,0.00052616245,0.0010381461,0.00061218924,0.00037877396,0.00049114524],"domain_scores_gemma":[0.9966296,0.0006768494,0.0013559682,0.00080011785,0.00027116973,0.00026632022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013368594,0.00020787898,0.0006718467,0.0000953619,0.00023069319,0.00010443898,0.0007684887,0.00008828944,0.0000033240462],"category_scores_gemma":[0.00016231247,0.00016857887,0.00007320328,0.0006013088,0.00019127007,0.001704733,0.00039900327,0.00022825667,1.369942e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010877354,0.0014030227,0.111812666,0.029189905,0.00034682069,0.000007432846,0.17761923,0.00008179858,0.004111399,0.009214608,0.0014957445,0.66362965],"study_design_scores_gemma":[0.009845105,0.008038474,0.04504224,0.0010467679,0.00016286729,0.00017411077,0.015357184,0.90560657,0.004202143,0.0004955527,0.00851221,0.00151677],"about_ca_topic_score_codex":0.003802054,"about_ca_topic_score_gemma":0.00044004983,"teacher_disagreement_score":0.9055248,"about_ca_system_score_codex":0.00017847783,"about_ca_system_score_gemma":0.0010582731,"threshold_uncertainty_score":0.6874447},"labels":[],"label_agreement":null},{"id":"W3094005538","doi":"10.1155/2020/8867926","title":"Passive Observer of Activities for Aging in Place Using a Network of RGB-D Sensors","year":2020,"lang":"en","type":"article","venue":"International Journal of Telemedicine and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Computer science; Observer (physics); Artificial intelligence; Computer vision; Modalities; Visualization; Assisted living; RGB color model; Virtual reality; Contrast (vision); Human–computer interaction; Medicine","score_opus":0.04592223614086313,"score_gpt":0.31338467965998495,"score_spread":0.26746244351912185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094005538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32350636,0.00016239364,0.6702,0.0057272124,0.00011811,0.00021370756,0.000016117472,0.000005545529,0.000050564864],"genre_scores_gemma":[0.9892634,0.000039403243,0.01002726,0.0001966758,0.000449412,0.000011193793,0.0000015695563,0.0000040458917,0.0000070418696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999081,0.000029707513,0.00047449666,0.000098974626,0.00024221645,0.000073566334],"domain_scores_gemma":[0.9984271,0.00041557875,0.0006086146,0.00006871486,0.00043074763,0.00004922379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022062872,0.000061281564,0.0002214219,0.00009449249,0.000019788555,0.000014839605,0.00029889875,0.000022910968,0.000004135014],"category_scores_gemma":[0.00008558166,0.000055449236,0.000059277263,0.00019144356,0.00004193287,0.0002619249,0.00006469033,0.00008771474,1.5116646e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006535354,0.00084927795,0.09124472,0.00070633483,0.0016512207,0.00007017231,0.016436206,0.033658482,0.09825913,0.04319758,0.0064324685,0.7068409],"study_design_scores_gemma":[0.022227786,0.0023230943,0.06727417,0.0052620093,0.000425976,0.0015648409,0.013217966,0.6590406,0.09235697,0.023248581,0.11162786,0.0014301895],"about_ca_topic_score_codex":0.000028798726,"about_ca_topic_score_gemma":0.0000093193385,"teacher_disagreement_score":0.70541066,"about_ca_system_score_codex":0.000029426032,"about_ca_system_score_gemma":0.00009021104,"threshold_uncertainty_score":0.22611544},"labels":[],"label_agreement":null},{"id":"W3094510469","doi":"10.3390/s20205945","title":"Smart Sensors and Devices in Artificial Intelligence","year":2020,"lang":"en","type":"editorial","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Artificial intelligence; Engineering; Computer science; Robotics; Intelligent sensor; Embedded system; Real-time computing; Wireless sensor network; Operating system","score_opus":0.035308097347787344,"score_gpt":0.27945372257220674,"score_spread":0.2441456252244194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094510469","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015659103,0.00032092902,0.0055071847,0.0019253449,0.9738278,0.0007183505,0.000104387495,0.00046458276,0.0014723275],"genre_scores_gemma":[0.3016323,0.00017234546,0.0024695992,0.0002368606,0.6945638,0.000060376813,0.00008132614,0.00011275985,0.0006706382],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99650735,0.00039282916,0.0006919546,0.0011447584,0.000824227,0.00043891245],"domain_scores_gemma":[0.99681485,0.0018825608,0.00032329533,0.0005568022,0.00021830821,0.00020416218],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000557509,0.00042727645,0.0006951622,0.0003528264,0.00009151536,0.0004754305,0.00078466925,0.000611312,0.000016904496],"category_scores_gemma":[0.0011254802,0.0004539233,0.00011334941,0.0006775129,0.00010158171,0.000353206,0.0004199183,0.0010245371,0.00040651485],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011718628,0.000190268,0.0004867561,0.0007222868,0.00018437851,0.001093244,0.008426869,0.0001556835,0.00015529383,0.0014175865,0.6277332,0.35931724],"study_design_scores_gemma":[0.000151169,0.00013584824,0.00013901711,0.00040041225,0.000024214829,0.000027231034,0.0005126262,0.018783642,0.00063583115,0.0014903038,0.9767152,0.000984503],"about_ca_topic_score_codex":0.0004919239,"about_ca_topic_score_gemma":0.001357176,"teacher_disagreement_score":0.35833275,"about_ca_system_score_codex":0.00011087364,"about_ca_system_score_gemma":0.00025291718,"threshold_uncertainty_score":0.99979126},"labels":[],"label_agreement":null},{"id":"W3094896678","doi":"10.1136/bmjopen-2019-033758","title":"Reference architectures for ambient assisted living: a scoping review protocol","year":2020,"lang":"en","type":"review","venue":"BMJ Open","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Medicine; Protocol (science); Gerontology; Alternative medicine; Pathology","score_opus":0.4231331578135891,"score_gpt":0.5345458483535219,"score_spread":0.1114126905399328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094896678","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.3228605e-9,0.4252249,0.016515238,0.0002659224,0.00012369693,0.5558747,0.00003194299,0.00013307703,0.0018304867],"genre_scores_gemma":[1.1770555e-7,0.2295617,0.0052740164,0.0006254563,0.0001492021,0.76399523,0.00001771908,0.000046658613,0.00032990935],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947194,0.0013235064,0.0015693726,0.0013843045,0.00056272105,0.00044073755],"domain_scores_gemma":[0.9945607,0.0014296307,0.0017825196,0.0017516976,0.00023747854,0.0002379651],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0023711591,0.0006055986,0.00333304,0.00015214534,0.00018778359,0.0011231019,0.005144259,0.00020168595,0.000074821444],"category_scores_gemma":[0.0017274526,0.0004794456,0.0006145633,0.0007897964,0.00003109109,0.0002541217,0.003674743,0.00039096898,0.00024527666],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037763457,0.000034462464,1.6029317e-7,0.27353305,0.00005193037,0.000010512415,0.000027559554,2.8414515e-8,7.856002e-8,0.00004393989,0.0026576729,0.7236368],"study_design_scores_gemma":[0.000091087364,0.000088417655,5.7799315e-7,0.45870155,0.00006500684,0.00012984377,7.568025e-7,0.00002410606,5.388552e-7,0.000017536595,0.540526,0.00035461428],"about_ca_topic_score_codex":0.000036895333,"about_ca_topic_score_gemma":0.00011114687,"teacher_disagreement_score":0.7232822,"about_ca_system_score_codex":0.00016439673,"about_ca_system_score_gemma":0.0028024856,"threshold_uncertainty_score":0.9999138},"labels":[],"label_agreement":null},{"id":"W3095677657","doi":"10.1016/j.scs.2020.102572","title":"Automated cognitive health assessment in smart homes using machine learning","year":2020,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":174,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Dementia; Cognition; Applied psychology; Home automation; Activities of daily living; Psychology; AdaBoost; Quality of life (healthcare); Gerontology; Machine learning; Computer science; Clinical psychology; Medicine; Support vector machine; Psychiatry","score_opus":0.027295550175526283,"score_gpt":0.2952265449248475,"score_spread":0.26793099474932125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095677657","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7305224,0.0024147371,0.2536294,0.009608547,0.00016267478,0.0010798766,0.00002224139,0.0013008396,0.0012593173],"genre_scores_gemma":[0.9955679,0.00014173886,0.0021724142,0.0017476948,0.000035047604,0.000018191398,0.000011496358,0.000012184081,0.00029332063],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998464,0.00024542832,0.0002544498,0.0003631069,0.0002115976,0.00046142278],"domain_scores_gemma":[0.99928033,0.00018175258,0.00013775905,0.00008715937,0.00016891475,0.00014406859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005839158,0.00015324276,0.00032043882,0.0000474057,0.00042276212,0.00030786954,0.00014498536,0.000060782026,0.000011093407],"category_scores_gemma":[0.000062961204,0.00015943621,0.00008306524,0.00047176555,0.00006764327,0.00064411527,0.00031854006,0.00033128005,0.00000129879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008250974,0.0005014527,0.33616114,0.008079173,0.0007380036,0.00053239125,0.51613533,0.001751696,0.00038205285,0.038170382,0.0064167646,0.091049075],"study_design_scores_gemma":[0.00092554063,0.0001758538,0.009016854,0.000099475714,0.000005362272,0.000013818075,0.14525358,0.84160954,0.00002546947,0.0002705461,0.0023568335,0.000247146],"about_ca_topic_score_codex":0.0023103477,"about_ca_topic_score_gemma":0.000029371815,"teacher_disagreement_score":0.8398578,"about_ca_system_score_codex":0.00027601264,"about_ca_system_score_gemma":0.00048967224,"threshold_uncertainty_score":0.650162},"labels":[],"label_agreement":null},{"id":"W3096387972","doi":"10.3390/s20216145","title":"Study of Effectiveness of Prior Knowledge for Smart Home Kit Installation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Installation; Session (web analytics); Control (management); Engineering; Computer science; World Wide Web; Artificial intelligence; Operating system","score_opus":0.054461236350253094,"score_gpt":0.29327767319971626,"score_spread":0.23881643684946316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096387972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97102654,0.000022874432,0.027091151,0.00003846885,0.00020843108,0.001077746,0.000008398036,0.000060392016,0.00046602354],"genre_scores_gemma":[0.99954623,5.036284e-7,0.0003546489,0.0000070963038,0.00003108376,0.000028145634,0.0000019274491,0.000008714159,0.000021665644],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989042,0.00029614472,0.00026705238,0.00026854943,0.00016032555,0.000103672384],"domain_scores_gemma":[0.9986056,0.0006650564,0.00017047442,0.0002338472,0.00027351783,0.00005151162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038761026,0.00009300321,0.00030138425,0.00008506458,0.000034229906,0.000017056513,0.0002359299,0.00003840125,0.0000015500402],"category_scores_gemma":[0.00012658906,0.00009213958,0.00007402057,0.000379363,0.000019854595,0.00017392496,0.0001001715,0.00004751642,0.000013697145],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017153434,0.0060765045,0.15942132,0.0076345275,0.001190227,0.00003120339,0.16426261,0.0020145897,0.1617809,0.006265207,0.00020195087,0.4894056],"study_design_scores_gemma":[0.018564869,0.00992693,0.63936746,0.00092517067,0.00017424815,0.00002625092,0.00678615,0.1106625,0.20730487,0.0010593042,0.003825,0.0013772415],"about_ca_topic_score_codex":0.00003379137,"about_ca_topic_score_gemma":0.000035554465,"teacher_disagreement_score":0.48802838,"about_ca_system_score_codex":0.00002413333,"about_ca_system_score_gemma":0.00004873693,"threshold_uncertainty_score":0.3757343},"labels":[],"label_agreement":null},{"id":"W3099335657","doi":"10.2196/21964","title":"Use of an Internet-of-Things Smart Home System for Healthy Aging in Older Adults in Residential Settings: Pilot Feasibility Study","year":2020,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"School of Nursing, University of Washington; University of Washington","keywords":"Aging in place; Internet of Things; The Internet; Curiosity; Home automation; Gerontology; Bachelor; Psychology; Independent living; Applied psychology; Internet privacy; Medicine; Multimedia; Computer science; Telecommunications; World Wide Web; Social psychology","score_opus":0.06681702430417946,"score_gpt":0.3203293808059301,"score_spread":0.25351235650175064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099335657","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9595171,0.000027443572,0.036782883,0.00048099202,0.00028199202,0.0027571975,0.0000050069416,0.00013782058,0.000009549671],"genre_scores_gemma":[0.99766415,2.2307744e-7,0.0018543836,0.00021371285,0.00004867515,0.00018829863,0.0000037784966,0.000020826898,0.0000059363792],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968958,0.000558747,0.001015521,0.00079598173,0.00038767015,0.00034631608],"domain_scores_gemma":[0.9982228,0.00045715657,0.00049535633,0.00050683186,0.00019060065,0.0001272261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014963608,0.00019695394,0.000579607,0.0003504653,0.0000418785,0.00015345449,0.0006424393,0.00004952052,0.0000015971135],"category_scores_gemma":[0.00016782239,0.0002173959,0.00008340411,0.000693714,0.000026797035,0.0019060022,0.00036876908,0.00024902626,0.000002598554],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012381297,0.0028159767,0.7269802,0.0063385013,0.00006764168,0.00011269695,0.20805416,0.000058011025,0.0021023697,0.00011008234,0.00017490383,0.051947307],"study_design_scores_gemma":[0.012811327,0.0033155547,0.5988103,0.0065741665,0.00002351489,0.00003468158,0.018050728,0.35297573,0.0063719726,0.000062113395,0.000051182757,0.0009187195],"about_ca_topic_score_codex":0.0037837983,"about_ca_topic_score_gemma":0.0007903449,"teacher_disagreement_score":0.3529177,"about_ca_system_score_codex":0.00020994834,"about_ca_system_score_gemma":0.000103011094,"threshold_uncertainty_score":0.8865147},"labels":[],"label_agreement":null},{"id":"W3106359073","doi":"10.3390/s20216377","title":"Classification of Aggressive Movements Using Smartwatches","year":2020,"lang":"en","type":"letter","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Random forest; Naive Bayes classifier; Machine learning; Computer science; Smartwatch; Support vector machine; Sensitivity (control systems); Multilayer perceptron; Decision tree; Feature selection; Pattern recognition (psychology); Artificial neural network; Wearable computer; Engineering","score_opus":0.08680730585647801,"score_gpt":0.28165801964757586,"score_spread":0.19485071379109786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106359073","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.102197625,0.00026198995,0.07228474,0.8141985,0.0045633423,0.0017725694,0.0002525135,0.00070059777,0.003768146],"genre_scores_gemma":[0.5108684,0.000034758956,0.009275668,0.47176117,0.004723213,0.000058729864,0.00025814804,0.00016266483,0.0028572967],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99781966,0.00027365494,0.00046342556,0.00059854815,0.00058832054,0.0002563975],"domain_scores_gemma":[0.997116,0.00023023147,0.0016838099,0.000675254,0.00024108647,0.000053582797],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000101274876,0.0002599917,0.00045576761,0.00019292357,0.000065733046,0.00009997648,0.0007115968,0.00031351458,0.000016347543],"category_scores_gemma":[0.00015861425,0.00026832143,0.00017141964,0.00035637798,0.00006811508,0.00022072905,0.00019974781,0.0006164202,0.00011205365],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017040316,0.00009718526,0.0017380423,0.0009585731,0.0006248775,0.0013258862,0.0041319877,0.00007705253,0.015224693,0.000101130405,0.9310583,0.044645235],"study_design_scores_gemma":[0.0012102292,0.00021789334,0.0024037242,0.0019528163,0.00015389094,0.00021059862,0.00043393407,0.15649723,0.01772689,0.0019390832,0.81512994,0.0021237829],"about_ca_topic_score_codex":0.000118307165,"about_ca_topic_score_gemma":0.0000046539963,"teacher_disagreement_score":0.40867072,"about_ca_system_score_codex":0.00010698505,"about_ca_system_score_gemma":0.00012752369,"threshold_uncertainty_score":0.9999769},"labels":[],"label_agreement":null},{"id":"W3107160162","doi":"10.1108/jet-03-2020-0012","title":"Implementing an intelligent video monitoring system to detect falls of older adults at home: a multiple case study","year":2020,"lang":"en","type":"article","venue":"Journal of Enabling Technologies","topic":"Context-Aware Activity Recognition Systems","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":"Centre intégré universitaire de santé et de services sociaux de l'Est-de-l'Île-de-Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université de Montréal","funders":"","keywords":"Respondent; Originality; Falling (accident); Psychology; Usability; Perception; Applied psychology; Fear of falling; Medicine; Human factors and ergonomics; Computer science; Poison control; Medical emergency; Social psychology; Psychiatry; Human–computer interaction","score_opus":0.05471164497797975,"score_gpt":0.3040563427406309,"score_spread":0.24934469776265117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107160162","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8793128,0.00043732076,0.11866983,0.00020260781,0.0003653047,0.0005163951,0.0000026183052,0.0004901711,0.00000294686],"genre_scores_gemma":[0.98403,0.000014716953,0.01580159,0.000009926702,0.00010162188,0.000022873688,9.816976e-8,0.000017363876,0.0000017783583],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99746853,0.00013741247,0.001081868,0.00039194548,0.00052622217,0.00039403272],"domain_scores_gemma":[0.9973824,0.0003764551,0.00096622616,0.0005300035,0.0006114776,0.00013345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011902464,0.00021502162,0.00055877,0.00057203,0.00019050966,0.00017626132,0.0012563141,0.00009761583,0.000001183565],"category_scores_gemma":[0.000997079,0.00018569063,0.00014719417,0.00092321873,0.000025091515,0.00074265443,0.0013517834,0.00033940934,0.0000050965814],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018051917,0.0003364952,0.07179396,0.00046594723,0.0003355333,0.005329221,0.03508717,0.00041543,0.031054087,0.000026655318,0.000044238364,0.85493076],"study_design_scores_gemma":[0.0030695873,0.004523508,0.0018364149,0.002310729,0.00008090802,0.0068729594,0.49276215,0.010120718,0.4770521,0.0000388296,0.00059173576,0.00074038305],"about_ca_topic_score_codex":0.00014519536,"about_ca_topic_score_gemma":0.00012632183,"teacher_disagreement_score":0.85419035,"about_ca_system_score_codex":0.00022525653,"about_ca_system_score_gemma":0.000057779467,"threshold_uncertainty_score":0.7572244},"labels":[],"label_agreement":null},{"id":"W3109838872","doi":"10.1159/000512208","title":"The Collaborative Aging Research Using Technology Initiative: An Open, Sharable, Technology-Agnostic Platform for the Research Community","year":2020,"lang":"en","type":"article","venue":"Digital Biomarkers","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bruyère; University of Ottawa","funders":"National Institute on Aging","keywords":"Cart; Flexibility (engineering); Wearable computer; Scalability; Wearable technology; Computer science; Gerontology; Psychology; Medicine; Engineering; Database","score_opus":0.34901636213054993,"score_gpt":0.4550720211286374,"score_spread":0.1060556589980875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109838872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5737507,0.0062736217,0.14203833,0.20976463,0.0014463259,0.02360894,0.0011438105,0.002400061,0.03957358],"genre_scores_gemma":[0.9978326,0.000023848532,0.0010929103,0.00028512508,0.000048825503,0.00062061974,0.000015739983,0.000028310507,0.00005201886],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9965907,0.0007968596,0.0004127571,0.00060817995,0.00071786356,0.00087365886],"domain_scores_gemma":[0.9858828,0.009937704,0.00016923943,0.0013664601,0.0024716465,0.00017210732],"candidate_categories":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.005302118,0.00021862895,0.0002830258,0.0006202408,0.0048715067,0.003835685,0.0066676517,0.00021174503,0.0000029263797],"category_scores_gemma":[0.007212236,0.00015080682,0.000051589537,0.008299075,0.0017780674,0.0031541842,0.004675482,0.0011437548,0.000044374487],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037060535,0.00046733048,0.0032861058,0.00008698858,0.0005275133,0.000037295093,0.006704158,0.0000117266245,0.0046711047,0.041616205,0.012175652,0.9300453],"study_design_scores_gemma":[0.004527544,0.0058001853,0.001201488,0.0011228941,0.0000454263,0.00025569217,0.38836977,0.10353245,0.036494765,0.19256932,0.26422104,0.0018594428],"about_ca_topic_score_codex":0.00016843744,"about_ca_topic_score_gemma":0.00023791412,"teacher_disagreement_score":0.9281859,"about_ca_system_score_codex":0.00026145368,"about_ca_system_score_gemma":0.0012009899,"threshold_uncertainty_score":0.99870676},"labels":[],"label_agreement":null},{"id":"W3109948023","doi":"10.1007/978-3-030-63007-2_60","title":"Towards a Context-Aware Knowledge Model for Smart Service Systems","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Context (archaeology); Service model; Service (business); Software engineering; Human–computer interaction; World Wide Web","score_opus":0.059836687757828765,"score_gpt":0.27637144022799276,"score_spread":0.216534752470164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109948023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013337889,0.0009980289,0.9869725,0.0024635852,0.0039542955,0.0020191069,0.00010972549,0.0005387408,0.0029306542],"genre_scores_gemma":[0.9154513,0.00002180473,0.07582658,0.005638864,0.0013195929,0.00029428062,0.000034618115,0.00014901866,0.0012639781],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940141,0.00009219922,0.001045886,0.002747966,0.0011437745,0.00095611805],"domain_scores_gemma":[0.99463403,0.001002396,0.0005789537,0.0017949499,0.0015180735,0.0004715879],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001227579,0.0009791654,0.0013573017,0.000844345,0.00040571767,0.0013043131,0.0050199307,0.0006205861,0.00000739954],"category_scores_gemma":[0.0001659441,0.00096460024,0.00034759313,0.0010095082,0.00033779582,0.0011748251,0.0020272313,0.0009718473,0.00019244879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038645354,0.0000972426,0.000016122503,0.0009517035,0.000092972194,0.00006467403,0.0055477414,0.030881032,0.00015157931,0.027669357,0.00055308227,0.9339358],"study_design_scores_gemma":[0.00063824066,0.00016352152,0.00000489465,0.00095100806,0.000021654696,0.00010294934,0.0000015520643,0.96487045,0.00028899903,0.025350595,0.0065839947,0.0010221256],"about_ca_topic_score_codex":0.0001283985,"about_ca_topic_score_gemma":0.0008086028,"teacher_disagreement_score":0.9339894,"about_ca_system_score_codex":0.0006752723,"about_ca_system_score_gemma":0.002403021,"threshold_uncertainty_score":0.99973243},"labels":[],"label_agreement":null},{"id":"W3110176764","doi":"10.3390/technologies8040074","title":"Comparative Analysis of Real-Time Fall Detection Using Fuzzy Logic Web Services and Machine Learning","year":2020,"lang":"en","type":"article","venue":"Technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Nottingham Trent University","keywords":"Machine learning; Computer science; Artificial intelligence; Fuzzy logic; Wearable computer; Decision tree; Accelerometer; Service (business); Real-time computing; Embedded system; Operating system","score_opus":0.06505758466146373,"score_gpt":0.2798912708069797,"score_spread":0.21483368614551596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110176764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.960003,0.00032769568,0.03605157,0.00040546377,0.00002052184,0.00012828312,0.000007534673,0.0026490844,0.0004068022],"genre_scores_gemma":[0.99601585,0.000058721816,0.003882553,0.000021221205,0.000005195656,0.000005581644,0.0000027942813,0.0000034354675,0.000004662428],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999078,0.000081709135,0.00022020795,0.00033623708,0.00014919322,0.00013464781],"domain_scores_gemma":[0.9992765,0.00012927492,0.00025321989,0.00022265132,0.00009458246,0.000023724513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014069845,0.00012699471,0.00043622672,0.0003792365,0.00009711365,0.00007159723,0.0003801658,0.000099298675,0.0000020356229],"category_scores_gemma":[0.000048813246,0.00011548358,0.00006507969,0.0016771549,0.00008636878,0.0003474479,0.0004147749,0.00016479836,0.000010589596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003958439,0.000063532425,0.041350704,0.00016465122,0.0011450369,0.000015716381,0.0048547774,0.0024566182,0.8744492,0.0008088197,0.00000928978,0.0746421],"study_design_scores_gemma":[0.00014319069,0.00014129077,0.0024081857,0.000020725205,0.000118704644,0.0000065604245,0.0014257616,0.96676356,0.028461237,0.00027403125,0.000086077656,0.00015067897],"about_ca_topic_score_codex":0.00039783475,"about_ca_topic_score_gemma":0.00028806864,"teacher_disagreement_score":0.96430695,"about_ca_system_score_codex":0.000025502217,"about_ca_system_score_gemma":0.0000125593715,"threshold_uncertainty_score":0.47092834},"labels":[],"label_agreement":null},{"id":"W3110566950","doi":"10.18280/isi.250501","title":"Context-Aware Multi-layered Ontology for Composite Situation Model in Pervasive Computing","year":2020,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ontology; Computer science; Interoperability; Context (archaeology); Flexibility (engineering); Context management; Ubiquitous computing; Mobile computing; Mobile device; Reusability; Context awareness; World Wide Web; Human–computer interaction; Telecommunications","score_opus":0.06223945478103822,"score_gpt":0.27533241561608124,"score_spread":0.21309296083504303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110566950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0905915,0.00004156627,0.90655607,0.0009171887,0.0002188104,0.0010587104,0.000039547922,0.00034064858,0.00023599106],"genre_scores_gemma":[0.97199845,0.000002697091,0.02594745,0.0017464374,0.000050830553,0.00011610078,0.0001172459,0.000012552371,0.00000823015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799705,0.00013717017,0.00091513887,0.00031315692,0.00025464152,0.0003828562],"domain_scores_gemma":[0.99819154,0.00029469604,0.000541154,0.0002536374,0.0005849493,0.00013401725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043713144,0.00024495888,0.00043283787,0.0002630264,0.00025219002,0.00035626153,0.0005168682,0.00016803964,0.0000024043882],"category_scores_gemma":[0.0003564647,0.0002730265,0.00011843083,0.00046686197,0.00006193839,0.0047230064,0.00017725003,0.00018628284,0.00007258529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022687411,0.00015643206,0.0074335453,0.0010669685,0.000113945855,0.000008879703,0.14304154,0.059897922,0.0025918426,0.009697474,0.0005056137,0.77525896],"study_design_scores_gemma":[0.0017960563,0.00010528299,0.0024986328,0.00013485321,0.0000069180714,0.000024087203,0.0016766225,0.9915903,0.00094100705,0.0007207011,0.000214053,0.00029146578],"about_ca_topic_score_codex":0.0001357359,"about_ca_topic_score_gemma":0.00024041464,"teacher_disagreement_score":0.9316924,"about_ca_system_score_codex":0.00033491146,"about_ca_system_score_gemma":0.00019051308,"threshold_uncertainty_score":0.99997216},"labels":[],"label_agreement":null},{"id":"W3111377738","doi":"10.3390/s20247137","title":"Towards a Simulation Framework for Smart Indoor Spaces","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"AGE-WELL","keywords":"Software deployment; Computer science; Context (archaeology); Smart environment; Wireless sensor network; Process (computing); Real-time computing; Event (particle physics); Home automation; Window (computing); Building automation; Embedded system; Work (physics); Human–computer interaction; Simulation; Distributed computing; Engineering; Internet of Things; Telecommunications; Computer network; Software engineering; Operating system","score_opus":0.06935534494269459,"score_gpt":0.3098953563799878,"score_spread":0.2405400114372932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111377738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12257775,0.000022896236,0.8677102,0.008063499,0.00038860078,0.00039975645,0.000010519348,0.0002923736,0.0005344418],"genre_scores_gemma":[0.9642645,9.723665e-7,0.03418938,0.0011521732,0.0002674018,0.000022208496,0.0000025896543,0.000012021827,0.000088737295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900913,0.000054964064,0.00017950102,0.00034587554,0.00021540059,0.00019510156],"domain_scores_gemma":[0.99887097,0.0005307857,0.000104318475,0.00024274028,0.00012887293,0.00012233786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013931106,0.00011609788,0.00018168513,0.00005151891,0.00008215369,0.00018690283,0.00028847015,0.00009034653,0.000016969603],"category_scores_gemma":[0.0005382883,0.000114830145,0.00009611105,0.00032250964,0.000018177212,0.00027982105,0.00008338361,0.000112309,0.00015221995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002574226,0.00020510503,0.005793609,0.000362878,0.0002701012,0.000048827947,0.033595435,0.021480188,0.001712985,0.088958345,0.003115979,0.8441991],"study_design_scores_gemma":[0.0005510529,0.00019731562,0.0013594554,0.000055850338,0.0000118485395,0.0000056162526,0.00029663357,0.9278821,0.0033606172,0.01398581,0.05194857,0.00034515822],"about_ca_topic_score_codex":0.000022227976,"about_ca_topic_score_gemma":0.000005862426,"teacher_disagreement_score":0.9064019,"about_ca_system_score_codex":0.000025454401,"about_ca_system_score_gemma":0.000040503157,"threshold_uncertainty_score":0.46826375},"labels":[],"label_agreement":null},{"id":"W3111791014","doi":"10.1002/alz.043171","title":"A sensor technology to continuously monitor changes in cognition","year":2020,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cognition; ROWE; Activities of daily living; Physical medicine and rehabilitation; Psychology; Gerontology; Computer science; Medicine; Simulation; Physical therapy","score_opus":0.03873591272007571,"score_gpt":0.2784383624740124,"score_spread":0.23970244975393667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111791014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32441708,0.028253885,0.4107029,0.21971633,0.0032118445,0.0057023573,0.00008462283,0.003574947,0.004336019],"genre_scores_gemma":[0.9901765,0.00000822213,0.0066965595,0.0027792004,0.0001353363,0.00017803608,0.0000049005575,0.000016935996,0.0000043003774],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984404,0.00010582079,0.00028229237,0.0005661414,0.0002419026,0.00036348225],"domain_scores_gemma":[0.9991993,0.00006603054,0.00011035891,0.0003242831,0.00014198445,0.00015804553],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018118735,0.00018359273,0.0002705081,0.0003076778,0.00006446901,0.0001218044,0.0005552232,0.00010605554,0.00005361864],"category_scores_gemma":[0.00006554255,0.00020413517,0.0000454279,0.0010347558,0.000030275594,0.00032195268,0.000336457,0.00016153927,0.0008948424],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004210518,0.0001702887,0.0063731577,0.000011545831,0.002241131,0.00015432437,0.0030031342,0.000005003709,0.05416558,0.00069344725,0.0040639862,0.9290763],"study_design_scores_gemma":[0.004978037,0.0017620606,0.013662063,0.00028587636,0.003278769,0.0001699993,0.0021483104,0.007654432,0.80010873,0.0013641496,0.162101,0.0024865859],"about_ca_topic_score_codex":0.000058358946,"about_ca_topic_score_gemma":0.00023515952,"teacher_disagreement_score":0.9265897,"about_ca_system_score_codex":0.0000068972017,"about_ca_system_score_gemma":0.00003019379,"threshold_uncertainty_score":0.99988306},"labels":[],"label_agreement":null},{"id":"W3112325539","doi":"10.1177/2055668320964121","title":"Joining semantic and augmented reality to design smart homes for assistance","year":2020,"lang":"en","type":"article","venue":"Journal of Rehabilitation and Assistive Technologies Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Université de Sherbrooke","funders":"","keywords":"Process (computing); Ontology; Augmented reality; Computer science; Human–computer interaction; Assisted living; Work (physics); Focus (optics); Knowledge management; Psychology; Engineering; Medicine; Nursing","score_opus":0.034627047979173105,"score_gpt":0.2605186688284652,"score_spread":0.2258916208492921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112325539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04578828,0.0002774065,0.9369087,0.016469654,0.00009311838,0.00024888484,0.000003249786,0.00020549817,0.000005164242],"genre_scores_gemma":[0.7506285,0.000012518169,0.24926597,0.000055184155,0.00001262616,0.00001778311,1.5739154e-7,0.000005061194,0.0000022332497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991671,0.0000391877,0.00032664408,0.00020180724,0.00014029954,0.00012498721],"domain_scores_gemma":[0.9981649,0.0012343628,0.00019223013,0.00010533722,0.00023116531,0.00007199103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056130346,0.00010979664,0.00027125172,0.00019656899,0.00005748811,0.00009689882,0.00017405426,0.00005433117,3.0040434e-7],"category_scores_gemma":[0.0037627958,0.00009949023,0.00004965555,0.00031532432,0.000032562377,0.00038951368,0.0000883845,0.00013039629,3.7381687e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028659854,0.00010836172,0.020152593,0.001185694,0.00030446792,0.000025302912,0.0045350897,0.0029604146,0.07060932,0.006234968,0.003921458,0.88967574],"study_design_scores_gemma":[0.004512204,0.0077490695,0.3720092,0.0023441412,0.00011871012,0.00021274724,0.01303944,0.5517427,0.023436038,0.0033929513,0.020028561,0.0014142599],"about_ca_topic_score_codex":0.0000010221707,"about_ca_topic_score_gemma":7.276443e-7,"teacher_disagreement_score":0.8882615,"about_ca_system_score_codex":0.000057507255,"about_ca_system_score_gemma":0.000026036203,"threshold_uncertainty_score":0.450469},"labels":[],"label_agreement":null},{"id":"W3114311377","doi":"10.1142/s1793962321300016","title":"Computationally aware systems: Recommended features through a review of 188 definitions and its relevance to advanced simulation","year":2020,"lang":"en","type":"review","venue":"Advances in Complex Systems","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Relevance (law); Computer science; Consciousness; Computation; Cognition; Data science; Cognitive science; Management science; Artificial intelligence; Psychology; Neuroscience; Algorithm; Engineering","score_opus":0.16069810797265976,"score_gpt":0.40389242215600296,"score_spread":0.2431943141833432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114311377","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.1380156e-7,0.8457764,0.14666063,0.00023343321,0.001085344,0.0047377944,0.000490058,0.00021039668,0.000805632],"genre_scores_gemma":[0.0020439825,0.9911635,0.0045115217,0.0003318985,0.00018922272,0.0013348658,0.00030797042,0.00007418303,0.00004283978],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99287564,0.0015116709,0.0027467895,0.0014673857,0.000897129,0.00050139107],"domain_scores_gemma":[0.99149084,0.004338953,0.0022286687,0.0009186921,0.0008019191,0.00022091671],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00074857776,0.0008317163,0.0041180425,0.00041955314,0.00017562049,0.00021110487,0.0013317805,0.00025013674,0.0000072304992],"category_scores_gemma":[0.0009112046,0.0008060012,0.00035170425,0.0022188316,0.000049064216,0.0017926933,0.0004998164,0.000548263,0.00008002578],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001194313,0.000090688576,0.0000018737204,0.27309406,0.0001367834,0.000037402828,0.00032740168,0.008249272,0.000001243084,0.017424561,0.0009659267,0.6996589],"study_design_scores_gemma":[0.0003026476,0.00011331978,0.0000031346967,0.18857405,0.000081205515,0.0002020471,0.00006308531,0.01085414,2.3045298e-7,0.00022301695,0.79896265,0.000620486],"about_ca_topic_score_codex":0.000038840273,"about_ca_topic_score_gemma":0.00004019236,"teacher_disagreement_score":0.7979967,"about_ca_system_score_codex":0.00041107548,"about_ca_system_score_gemma":0.00037120577,"threshold_uncertainty_score":0.99943906},"labels":[],"label_agreement":null},{"id":"W3116667534","doi":"10.2196/21926","title":"Deep Learning–Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Orionin Tutkimussäätiö; Academy of Finland","keywords":"Computer science; Activity recognition; Sensor fusion; Artificial intelligence; Discriminative model; Wearable computer; Machine learning; Generalizability theory; Dimensionality reduction; Classifier (UML); Deep learning; Pattern recognition (psychology); Data mining","score_opus":0.172212174706064,"score_gpt":0.3786604843711602,"score_spread":0.20644830966509617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116667534","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9121185,0.00025653586,0.085117936,0.0009616892,0.00020454358,0.0011000185,0.000009768767,0.00021879573,0.000012245122],"genre_scores_gemma":[0.99706954,0.000023474671,0.002079592,0.00061069935,0.00014804474,0.00003837017,0.000006744623,0.00002021573,0.0000033026777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966733,0.0009287174,0.00058565376,0.0009508722,0.00033334357,0.0005281507],"domain_scores_gemma":[0.99818116,0.00028968658,0.0003040244,0.00058826356,0.00008789908,0.0005489718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011244401,0.00022725237,0.0004170465,0.00022370553,0.00052082154,0.00015726952,0.00037939235,0.00010660325,0.0000075921375],"category_scores_gemma":[0.00015175458,0.00023447922,0.00002998624,0.0008199836,0.00003361439,0.0007364426,0.0003395381,0.00060792913,0.0000128776965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042884896,0.0013632132,0.11029663,0.0019825238,0.000042979496,0.0018632645,0.032771952,0.003658335,0.00021657361,0.000017183933,0.00003712677,0.8473214],"study_design_scores_gemma":[0.001941899,0.0013011948,0.011120057,0.000055617085,0.000011373394,0.00054915814,0.004556153,0.9799374,0.000029670025,0.00001047998,0.000253684,0.00023335293],"about_ca_topic_score_codex":0.0052642976,"about_ca_topic_score_gemma":0.008148445,"teacher_disagreement_score":0.976279,"about_ca_system_score_codex":0.00015258833,"about_ca_system_score_gemma":0.0003479591,"threshold_uncertainty_score":0.9561785},"labels":[],"label_agreement":null},{"id":"W3120018734","doi":"10.1109/ssci47803.2020.9308415","title":"Towards the Development of an Adaptive System for Detecting Anomaly in Human Activities","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University","keywords":"Anomaly detection; Computer science; Component (thermodynamics); Novelty; Gesture; Anomaly (physics); Population; Artificial intelligence; Data collection; Novelty detection; Machine learning; Data mining; Psychology; Medicine","score_opus":0.10576325105970563,"score_gpt":0.2832289432924112,"score_spread":0.17746569223270559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120018734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4517249,0.0000077068025,0.54591656,0.00012728017,0.00006709834,0.00037813123,0.000002025874,0.000103686085,0.0016726649],"genre_scores_gemma":[0.9753005,3.868436e-8,0.024453677,0.00005886741,0.000044049597,0.00011501211,6.4641404e-7,0.00000773519,0.000019487727],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988358,0.00013293803,0.00035952433,0.00029163557,0.00020400507,0.0001761244],"domain_scores_gemma":[0.99926585,0.00021180457,0.0001650733,0.0002165386,0.00008501477,0.000055733835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056973356,0.000112201786,0.00022657463,0.00007138902,0.00015037022,0.000083417915,0.0005515482,0.000038587666,0.0000029430357],"category_scores_gemma":[0.00003649314,0.00008495016,0.000051619216,0.0002671142,0.00002178832,0.00054937863,0.00017161787,0.000080858445,0.0000024552787],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010120727,0.00013107869,0.0007825757,0.00039265497,0.00011881951,0.000006779086,0.077572115,0.0000727949,0.04918545,0.011995931,0.000025678848,0.8596149],"study_design_scores_gemma":[0.0013292648,0.00076326024,0.0053938217,0.00026381566,0.000010825346,0.000019469271,0.04789891,0.1301188,0.81292725,0.00018522191,0.00056473754,0.000524629],"about_ca_topic_score_codex":0.00014385345,"about_ca_topic_score_gemma":0.0010042643,"teacher_disagreement_score":0.85909027,"about_ca_system_score_codex":0.00009534373,"about_ca_system_score_gemma":0.00016084305,"threshold_uncertainty_score":0.3464167},"labels":[],"label_agreement":null},{"id":"W3123339574","doi":"10.22215/etd/2015-10941","title":"Patient Mobility Monitoring Through the Analysis of Bed Exit Centre of Pressure","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Warning system; Frame (networking); Event (particle physics); Work (physics); Computer science; Human–computer interaction; Engineering; Simulation; Telecommunications; Mechanical engineering","score_opus":0.03666621840914321,"score_gpt":0.3054108094711064,"score_spread":0.2687445910619632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123339574","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9668746,0.0017218568,0.009247551,0.00011012629,0.0024257915,0.0013838344,0.00016913952,0.00015320827,0.017913904],"genre_scores_gemma":[0.9980934,0.000017292747,0.0005789181,0.000005118453,0.000033490673,0.00002901542,0.00008588493,0.000009063758,0.0011478037],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99761826,0.0002560403,0.0006774141,0.0004537975,0.000829089,0.00016539764],"domain_scores_gemma":[0.9964177,0.00029976398,0.0008881379,0.0011313201,0.0012147473,0.00004835545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035142648,0.00021212705,0.00068889686,0.0002799177,0.000048198675,0.000051877054,0.000771124,0.00017324286,0.000058156922],"category_scores_gemma":[0.000115639465,0.00015069023,0.00036285355,0.0017030356,0.000030611343,0.00035591394,0.00012269567,0.00017191256,0.0000047919557],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037602196,0.0032271815,0.05868006,0.0028415793,0.028724572,0.000010642956,0.2907301,0.0036220343,0.0051241587,0.0027165837,0.009630327,0.5943167],"study_design_scores_gemma":[0.0030071412,0.0009028898,0.30342785,0.0027675217,0.019428233,0.0000076177785,0.13989325,0.06266769,0.42762136,0.008590729,0.027592422,0.00409329],"about_ca_topic_score_codex":0.002531262,"about_ca_topic_score_gemma":0.0009342803,"teacher_disagreement_score":0.59022343,"about_ca_system_score_codex":0.0000342468,"about_ca_system_score_gemma":0.00013091591,"threshold_uncertainty_score":0.61449695},"labels":[],"label_agreement":null},{"id":"W3126233134","doi":"10.3390/s21041070","title":"On the Impact of Biceps Muscle Fatigue in Human Activity Recognition","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biceps; Wearable computer; Muscle fatigue; Physical medicine and rehabilitation; Feed forward; Work (physics); Computer science; Muscular fatigue; Artificial neural network; Physical therapy; Electromyography; Medicine; Artificial intelligence; Engineering","score_opus":0.08800449911368347,"score_gpt":0.3233516225808209,"score_spread":0.23534712346713743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126233134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992938,0.000007209671,0.0021068729,0.00038555314,0.00011601257,0.00017549221,0.000015555948,0.000050382274,0.004204953],"genre_scores_gemma":[0.99971014,0.0000020365376,0.0000817861,0.00005506927,0.000028537666,0.000011409082,0.000004074565,0.0000081154785,0.00009880028],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985657,0.00050116127,0.00020240905,0.00030631712,0.00023345718,0.00019096634],"domain_scores_gemma":[0.99850893,0.00063218875,0.00014838115,0.00052324974,0.00014883718,0.00003840166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003912068,0.00012194532,0.00020017201,0.00013078524,0.00009460827,0.00009059319,0.00024305936,0.00006291267,0.000109159155],"category_scores_gemma":[0.00024561927,0.00009320658,0.00014931949,0.00062461296,0.000045580746,0.0002397069,0.00008332437,0.00020168972,0.000055522192],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057977275,0.0016846071,0.005541192,0.00007327637,0.00018979402,0.00020508598,0.0075720325,0.0016655752,0.38230053,0.003037671,0.0016123973,0.59605986],"study_design_scores_gemma":[0.0018106985,0.0005788717,0.5218321,0.0005887787,0.000016104359,0.00008625291,0.0007557728,0.036797244,0.42228237,0.014165857,0.00025397522,0.00083198457],"about_ca_topic_score_codex":0.0011530088,"about_ca_topic_score_gemma":0.00046177712,"teacher_disagreement_score":0.5952279,"about_ca_system_score_codex":0.000099067496,"about_ca_system_score_gemma":0.000099755205,"threshold_uncertainty_score":0.3800854},"labels":[],"label_agreement":null},{"id":"W3126482160","doi":"","title":"Adaptive Engineering of an Embedded System, Engineered for use by Search and Rescue Canines","year":2011,"lang":"en","type":"article","venue":"SOURCE Sheridan's Institutional Repository (Sheridan College)","topic":"Context-Aware Activity Recognition 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":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Computer science; Engineering","score_opus":0.02898453247642244,"score_gpt":0.212431096121689,"score_spread":0.18344656364526657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126482160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.605544,0.00014976971,0.39164653,0.000014213534,0.0007853359,0.0008454078,0.00016697572,0.0003287984,0.0005189626],"genre_scores_gemma":[0.9612964,0.0000019559693,0.03762435,0.000019179715,0.00019379496,0.00022118883,0.000015898968,0.00004367911,0.0005835933],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727494,0.00019002044,0.00068281434,0.00080031075,0.0005857363,0.00046617136],"domain_scores_gemma":[0.99795955,0.00025437408,0.00014740719,0.0006975866,0.00059939234,0.0003416758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051335857,0.0003946305,0.00054272346,0.00024568784,0.0004724639,0.00017631052,0.000683697,0.00020805678,0.0000040993846],"category_scores_gemma":[0.00010776998,0.00041772754,0.00016234955,0.0004550165,0.00023398366,0.0016389281,0.00021775467,0.00023634224,0.0000029765029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043049348,0.0030595043,0.0057373988,0.004948791,0.0035172226,0.0015353874,0.03240206,0.06508907,0.59126467,0.25754914,0.0029149926,0.027676845],"study_design_scores_gemma":[0.0030211566,0.0012452571,0.0050089257,0.0008056576,0.00010276074,0.0019143359,0.0029390266,0.8558607,0.12191264,0.000040084742,0.0056411456,0.0015082906],"about_ca_topic_score_codex":0.0014544595,"about_ca_topic_score_gemma":0.000073472016,"teacher_disagreement_score":0.79077166,"about_ca_system_score_codex":0.00031510016,"about_ca_system_score_gemma":0.00048402022,"threshold_uncertainty_score":0.99982744},"labels":[],"label_agreement":null},{"id":"W3134316714","doi":"10.1007/s12553-021-00636-5","title":"OpenTera: A microservice architecture solution for rapid prototyping of robotic solutions to COVID-19 challenges in care facilities","year":2022,"lang":"en","type":"article","venue":"Health and Technology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; AGE-WELL","keywords":"Telehealth; Personalization; Context (archaeology); Computer science; Robot; Telemedicine; Session (web analytics); Health care; World Wide Web; Artificial intelligence","score_opus":0.10081814184478907,"score_gpt":0.3227445875740803,"score_spread":0.22192644572929124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134316714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022370512,0.05416651,0.6680178,0.24702764,0.0003430421,0.0073753903,0.00018748932,0.00043296936,0.000078627316],"genre_scores_gemma":[0.99031556,0.00021093045,0.0056218547,0.0010570401,0.00001056824,0.0027519413,0.000011161004,0.0000065841396,0.000014343448],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99878293,0.0001468346,0.00027064656,0.0003631008,0.00009212194,0.000344357],"domain_scores_gemma":[0.9993149,0.000114014634,0.000112775524,0.0002703766,0.00007189577,0.00011602391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003807311,0.000092363545,0.00025972415,0.0006090737,0.00036994,0.0000131587485,0.00033769658,0.000059401405,0.0000030797994],"category_scores_gemma":[0.000094708856,0.00010367967,0.000026249067,0.0005208198,0.00004216188,0.00007363346,0.00047200825,0.00018697768,7.611944e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006610284,0.00009185783,0.00024202667,0.0024060751,0.0000120404475,0.000002616426,0.027218688,0.00013610497,0.00030986292,0.014478497,0.00013537904,0.95490074],"study_design_scores_gemma":[0.0051945643,0.006436318,0.000660471,0.00047427314,0.000008656686,0.0007477874,0.07196259,0.010983317,0.00047857367,0.026093237,0.8761322,0.0008279976],"about_ca_topic_score_codex":0.00047777095,"about_ca_topic_score_gemma":0.003143053,"teacher_disagreement_score":0.96794504,"about_ca_system_score_codex":0.00023330918,"about_ca_system_score_gemma":0.0004845226,"threshold_uncertainty_score":0.4227934},"labels":[],"label_agreement":null},{"id":"W3135131585","doi":"10.21700/ijcis.2016.110","title":"Automatic Fall Detection System using Sensing Floors","year":2016,"lang":"en","type":"article","venue":"International Journal of Computing and Information Sciences","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature selection; Computer science; Autonomy; Accelerometer; Feature (linguistics); Feature extraction; Independence (probability theory); Computer security; Real-time computing; Artificial intelligence","score_opus":0.024424476844998565,"score_gpt":0.28277114611436477,"score_spread":0.2583466692693662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135131585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40901273,0.000014485556,0.588943,0.00030090922,0.0013481898,0.000027526636,4.8428666e-7,0.00003728486,0.00031539088],"genre_scores_gemma":[0.9840685,0.0000065276477,0.015652439,0.000110228015,0.00015840084,9.9499985e-8,7.882135e-8,0.0000015880955,0.0000021395924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831814,0.00008991278,0.00064490474,0.000089914094,0.0007346062,0.00012251122],"domain_scores_gemma":[0.9978923,0.00028434716,0.00089277345,0.00007069581,0.00079665397,0.00006318533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015436596,0.00008228507,0.0001388137,0.0005495734,0.00019117522,0.00063015276,0.00048981543,0.000031138472,0.0000016250196],"category_scores_gemma":[0.00021017193,0.00005474822,0.000056681652,0.00024892658,0.000076914475,0.0065684184,0.00012239353,0.00006361303,0.000011699318],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028952995,0.000004143785,0.0004047749,0.0000096785125,0.000022601867,0.0000031389409,0.00084025814,0.0002085137,0.0013849911,0.00094222225,0.000010810878,0.996166],"study_design_scores_gemma":[0.0005410008,0.00008916114,0.002720164,0.00086046406,0.000005985824,0.0029477729,0.0007847253,0.98703784,0.0038279726,0.0002282676,0.0008223468,0.00013429146],"about_ca_topic_score_codex":0.000038173603,"about_ca_topic_score_gemma":0.0000027121305,"teacher_disagreement_score":0.9960317,"about_ca_system_score_codex":0.0001396816,"about_ca_system_score_gemma":0.00011688771,"threshold_uncertainty_score":0.6076578},"labels":[],"label_agreement":null},{"id":"W3135203625","doi":"10.1007/s00779-021-01548-x","title":"Special issue on trends &amp; advances to mine intelligence from ambient data","year":2021,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Ambient intelligence; Data science; Human–computer interaction","score_opus":0.06971065499295895,"score_gpt":0.3195852549282096,"score_spread":0.24987459993525066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135203625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40334874,0.0021863198,0.5564938,0.011958418,0.008856343,0.00029071287,0.00031206262,0.00043009222,0.016123494],"genre_scores_gemma":[0.9530114,0.000071487346,0.025071181,0.0022604014,0.017624866,0.0000049559458,0.00013800134,0.000020534048,0.0017971656],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997879,0.00011385574,0.0002864232,0.0009873343,0.00041376636,0.0003196194],"domain_scores_gemma":[0.9985605,0.00044777198,0.000098820215,0.0005851702,0.00011660808,0.00019112353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025981225,0.00020195579,0.00028885124,0.00011488767,0.0002319358,0.0003388101,0.0007475323,0.00004738673,0.00018925763],"category_scores_gemma":[0.00008829181,0.00020042826,0.000051467563,0.000478663,0.000034171,0.0003846719,0.0012489209,0.00017674592,0.00021667434],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012575141,0.00008226462,0.0003113059,0.000011519713,0.00002051464,0.000048162892,0.003112629,0.000041053187,0.00039581765,0.000110334055,0.007215417,0.9886384],"study_design_scores_gemma":[0.00025462062,0.00013998906,0.0030097854,0.00027148682,0.000013678968,0.00014410162,0.00071677665,0.051662788,0.0019408031,0.00019798064,0.94113356,0.000514442],"about_ca_topic_score_codex":0.00019224474,"about_ca_topic_score_gemma":0.0007193589,"teacher_disagreement_score":0.98812395,"about_ca_system_score_codex":0.000042528005,"about_ca_system_score_gemma":0.00005331711,"threshold_uncertainty_score":0.8173227},"labels":[],"label_agreement":null},{"id":"W3135542633","doi":"10.3390/s21051669","title":"Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Workplace Safety and Insurance Board","keywords":"Smartwatch; Artificial intelligence; Machine learning; Deep learning; Activity recognition; Computer science; Context (archaeology); Inertial measurement unit; Wearable computer","score_opus":0.03629879010880702,"score_gpt":0.26016735076695724,"score_spread":0.22386856065815022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135542633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9379916,0.000009199782,0.06060051,0.00014011697,0.00043502584,0.0001938584,0.000053168667,0.00012295553,0.00045355436],"genre_scores_gemma":[0.99927765,0.0000037817176,0.00042670986,0.0000102337635,0.00008792444,0.000010743839,0.000043744963,0.000013340564,0.00012585233],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99815774,0.00039046904,0.00036079154,0.00044323885,0.0004204688,0.000227281],"domain_scores_gemma":[0.99814427,0.00016700233,0.0003959073,0.00048239517,0.0007332455,0.00007716675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031092446,0.000176756,0.00034949367,0.00010675725,0.000111421665,0.000047153928,0.00015777309,0.00012520667,0.000018411203],"category_scores_gemma":[0.00013780277,0.00017642001,0.00012202367,0.0005919937,0.00008426713,0.00041585614,0.00008376701,0.00018904603,0.000027919647],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009691884,0.00028960154,0.001002362,0.000106937485,0.00009719175,0.000037138532,0.00092053803,0.000077007855,0.8596665,0.00005830866,0.000038559225,0.13760889],"study_design_scores_gemma":[0.00066275214,0.00026518173,0.008270159,0.00011018782,0.000030741983,0.000070692884,0.00018791934,0.0034316026,0.9863201,0.0001400652,0.00027787476,0.0002327581],"about_ca_topic_score_codex":0.00035532037,"about_ca_topic_score_gemma":0.0008755717,"teacher_disagreement_score":0.13737613,"about_ca_system_score_codex":0.00009072899,"about_ca_system_score_gemma":0.000092865215,"threshold_uncertainty_score":0.71941996},"labels":[],"label_agreement":null},{"id":"W3136176808","doi":"10.2196/23681","title":"The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study","year":2021,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Science Foundation; National Institutes of Health; National Cancer Institute; University of Florida; Division of Information and Intelligent Systems; National Institute on Aging","keywords":"Energy expenditure; Estimation; Physical activity; Computer science; Gerontology; Psychology; Physical medicine and rehabilitation; Medicine; Statistics; Mathematics; Engineering","score_opus":0.03400406620071172,"score_gpt":0.3503433090828565,"score_spread":0.31633924288214477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136176808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961257,0.00007143728,0.0024320646,0.00057658524,0.00011976638,0.00058742694,0.000010008929,0.000025652746,0.000051383788],"genre_scores_gemma":[0.9995053,0.000092212,0.00008648659,0.00006928569,0.000043517997,0.00016780903,0.000012686006,0.0000064694214,0.000016258173],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99803233,0.00088554516,0.0002202671,0.00040807296,0.0002521275,0.00020162872],"domain_scores_gemma":[0.998537,0.0008681664,0.00020844452,0.00020792597,0.000058126043,0.00012033406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006125012,0.00013880455,0.0002653049,0.000060133483,0.00018989728,0.00008205379,0.000053449905,0.000054525986,0.0000017805681],"category_scores_gemma":[0.000059192065,0.0001053351,0.000018620707,0.00018171719,0.000029426445,0.000288595,0.00006297242,0.00015547335,0.0000021965477],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039528887,0.00053378113,0.010684577,0.00053119817,0.0000128166275,0.0000061651713,0.0050431066,0.0000059078397,0.000046171004,0.00006620873,0.00005798049,0.9826168],"study_design_scores_gemma":[0.012001535,0.004589452,0.91011286,0.0009949213,0.00006401818,0.00014331212,0.0035762037,0.05966892,0.007692423,0.00044773586,0.00014818626,0.00056045846],"about_ca_topic_score_codex":0.00024651503,"about_ca_topic_score_gemma":0.00016573565,"teacher_disagreement_score":0.9820563,"about_ca_system_score_codex":0.000064599146,"about_ca_system_score_gemma":0.000072530594,"threshold_uncertainty_score":0.42954406},"labels":[],"label_agreement":null},{"id":"W3137686095","doi":"10.1109/bigdata50022.2020.9378000","title":"Increasing Prediction Accuracy for Human Activity Recognition Using Optimized Hyperparameters","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Hyperparameter; Random forest; Support vector machine; Computer science; Naive Bayes classifier; Machine learning; Artificial intelligence; Activity recognition; Hyperparameter optimization; Context (archaeology); Decision tree","score_opus":0.1726874806045976,"score_gpt":0.3198375199123713,"score_spread":0.1471500393077737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137686095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41391975,0.000004260506,0.5840883,0.0004910815,0.00018885596,0.0005441757,0.00002118452,0.0003969865,0.0003453958],"genre_scores_gemma":[0.8687185,0.0000014863331,0.13046935,0.00050271105,0.0001968635,0.0000645825,0.000019173578,0.000018550307,0.000008805921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823314,0.000289478,0.00032974535,0.00060346414,0.00025690428,0.00028726368],"domain_scores_gemma":[0.9982259,0.00082017365,0.00025192834,0.0002955008,0.00021838999,0.00018806061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005299759,0.00019761288,0.00031024372,0.00011830192,0.000324282,0.0003705646,0.0003577687,0.000104382554,0.000031689913],"category_scores_gemma":[0.0006970176,0.0002078348,0.00016754336,0.00037628945,0.000030204226,0.0022600617,0.00015529765,0.00015120284,0.000030996216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037490574,0.0002073247,0.0006272882,0.0001263787,0.00014650701,0.000006222451,0.0011364593,0.00037516566,0.39571482,0.00013533865,0.00044241422,0.6007072],"study_design_scores_gemma":[0.0024796845,0.00027407234,0.00050783093,0.00008189541,0.00005302976,0.000088191315,0.00011695576,0.9517452,0.04293807,0.00067015697,0.0005959167,0.00044897955],"about_ca_topic_score_codex":0.00036001825,"about_ca_topic_score_gemma":0.000009637212,"teacher_disagreement_score":0.95137006,"about_ca_system_score_codex":0.0001212862,"about_ca_system_score_gemma":0.00007600271,"threshold_uncertainty_score":0.8475257},"labels":[],"label_agreement":null},{"id":"W3137926756","doi":"10.1007/s11036-021-01741-5","title":"Alternative Deep Learning Architectures for Feature-Level Fusion in Human Activity Recognition","year":2021,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Concatenation (mathematics); Fuse (electrical); Deep learning; Artificial intelligence; Convolutional neural network; Domain (mathematical analysis); Convolution (computer science); Feature (linguistics); Pattern recognition (psychology); Sensor fusion; Activity recognition; Machine learning; Raw data; Feature learning; Artificial neural network","score_opus":0.03160857824323783,"score_gpt":0.28458030371021914,"score_spread":0.2529717254669813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137926756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.111977614,0.00027643677,0.8863448,0.00013221345,0.0000596807,0.00080998166,0.000015934756,0.00007494526,0.00030840375],"genre_scores_gemma":[0.99530834,0.000041684536,0.001685624,0.00007910652,0.00025043116,0.0024108074,0.00007779007,0.000011369015,0.00013485053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989499,0.000120513476,0.0001529228,0.000471138,0.00010266213,0.00020290382],"domain_scores_gemma":[0.99908805,0.00035994855,0.000119620076,0.00023263355,0.00013526529,0.00006446477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021551961,0.00012433389,0.00017940701,0.00007973962,0.00032843853,0.000143471,0.00016343777,0.00008758861,0.0000063584685],"category_scores_gemma":[0.000019558169,0.00013020665,0.000058286783,0.0003528304,0.00003005288,0.00010537056,0.00013620622,0.00026654545,0.0000028210777],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055459236,0.000111866386,0.000591077,0.000015133669,0.000012527438,0.0000017394433,0.0001468395,0.016340263,0.0018648489,0.00051061204,0.00003707369,0.9803625],"study_design_scores_gemma":[0.0020336097,0.0002509182,0.034342337,0.00018964917,0.000032285243,0.000086917644,0.00026744712,0.90412706,0.011894564,0.021992324,0.02391784,0.00086504297],"about_ca_topic_score_codex":0.0000733056,"about_ca_topic_score_gemma":0.0005312787,"teacher_disagreement_score":0.97949743,"about_ca_system_score_codex":0.000039692015,"about_ca_system_score_gemma":0.000020633743,"threshold_uncertainty_score":0.5309673},"labels":[],"label_agreement":null},{"id":"W3139565802","doi":"10.1109/access.2021.3068877","title":"Acti-DM1: Monitoring the Activity Level of People With Myotonic Dystrophy Type 1 Through Activity and Exercise Recognition","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke; Cégep de Chicoutimi; Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Myotonic dystrophy; Disease; Physical medicine and rehabilitation; Health professionals; Computer science; Physical therapy; Medicine; Medical education; Health care; Pathology","score_opus":0.16446402584627676,"score_gpt":0.33088716932471435,"score_spread":0.1664231434784376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139565802","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9048205,0.00012240965,0.093006514,0.0003587757,0.0008817161,0.00035520448,0.000023673012,0.000086805194,0.00034439162],"genre_scores_gemma":[0.99829876,0.00016938073,0.0011880324,0.00002678355,0.00013122076,0.00007049024,0.0000018655296,0.000020693857,0.00009279764],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982095,0.0002380138,0.00020291567,0.0005893971,0.00046040994,0.00029977402],"domain_scores_gemma":[0.99785286,0.00044596478,0.0003189447,0.000779166,0.0005263151,0.00007674011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026791828,0.0002332506,0.00040261328,0.00007091864,0.00025182022,0.00045031804,0.0006714953,0.00010262539,0.000016284199],"category_scores_gemma":[0.000066200904,0.00018162059,0.00007219946,0.0010041561,0.00008389671,0.0033813934,0.00034503633,0.0003139165,0.000014886791],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043088422,0.00069539744,0.02230534,0.000420321,0.00028775437,0.00007824139,0.004283364,0.000055495675,0.15269016,0.00004808729,0.00021993612,0.818485],"study_design_scores_gemma":[0.0007232523,0.00012713553,0.16043244,0.00034874407,0.000066056455,0.00012437378,0.0002030955,0.0007728484,0.83619976,0.00048746952,0.00014091695,0.0003738817],"about_ca_topic_score_codex":0.001140273,"about_ca_topic_score_gemma":0.00042956846,"teacher_disagreement_score":0.8181111,"about_ca_system_score_codex":0.00007287209,"about_ca_system_score_gemma":0.00024723733,"threshold_uncertainty_score":0.7406273},"labels":[],"label_agreement":null},{"id":"W3149228704","doi":"10.1109/compsac.2008.200","title":"Design and Implementation of Context Calculus in the GIPSY Environment","year":2008,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Context (archaeology); Computer science; Java; Programming language; Calculus (dental); Archaeology; History","score_opus":0.056576474728606954,"score_gpt":0.28953649711315455,"score_spread":0.2329600223845476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149228704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041967437,0.0003039327,0.95524585,0.00078219915,0.00012550564,0.0011740448,0.000005711467,0.000024992909,0.00037034028],"genre_scores_gemma":[0.99286044,0.00021813375,0.0064806836,0.00021246007,0.000016746464,0.00016387204,0.0000047944977,0.000005384288,0.00003748379],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828553,0.00046862813,0.00039054084,0.00039111852,0.00032094365,0.00014320869],"domain_scores_gemma":[0.9988527,0.0003322938,0.00023278732,0.00052425783,0.000027671143,0.000030286954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007221858,0.0001550264,0.00025200803,0.00011107099,0.000038961607,0.00007067161,0.000544371,0.00008786304,0.000033014607],"category_scores_gemma":[0.000007433274,0.00011730199,0.000051596442,0.000073575786,0.000052699852,0.00015433803,0.0005291212,0.00019288756,0.000014668174],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012684428,0.00017955556,0.0030016757,0.000107380794,0.00007938637,0.00003565986,0.027198534,0.00054002035,0.0006066541,0.0020887158,0.0018565208,0.9642932],"study_design_scores_gemma":[0.013986648,0.0019014627,0.34579667,0.0011110393,0.00020362267,0.0015101078,0.038058296,0.49277246,0.05749133,0.011280814,0.030757383,0.0051301518],"about_ca_topic_score_codex":0.0015425172,"about_ca_topic_score_gemma":0.00014356434,"teacher_disagreement_score":0.95916307,"about_ca_system_score_codex":0.00006744259,"about_ca_system_score_gemma":0.00008916288,"threshold_uncertainty_score":0.47834367},"labels":[],"label_agreement":null},{"id":"W3149572079","doi":"10.2196/25705","title":"A Digital Platform for Facilitating Personalized Dementia Care in Nursing Homes: Formative Evaluation Study","year":2021,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Personalization; Dementia; Context (archaeology); Formative assessment; Nursing; Psychology; Computer science; Medicine; World Wide Web","score_opus":0.13530862640191657,"score_gpt":0.45959477130328447,"score_spread":0.32428614490136787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149572079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84125406,0.0006710449,0.14262712,0.00021573974,0.00028295582,0.004884601,0.00013861562,0.0001003885,0.009825468],"genre_scores_gemma":[0.9968821,0.0000025631969,0.0008706823,0.000011899695,0.00003142251,0.001998849,0.00011019987,0.000013014175,0.00007928731],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99566567,0.0007614897,0.0005773769,0.0005011274,0.0018124571,0.0006818768],"domain_scores_gemma":[0.9950225,0.0015424443,0.000161532,0.00040130274,0.002755991,0.00011625383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030630818,0.00020154964,0.00034671417,0.0005932824,0.0005125664,0.0009170459,0.0005132192,0.000075971155,0.00003194018],"category_scores_gemma":[0.0006561287,0.00019630443,0.00013209869,0.0014408161,0.000106187006,0.005419789,0.00031532004,0.00041654083,0.00006875351],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006500867,0.00047350948,0.00080366625,0.00014076503,0.000070925395,0.000006269547,0.42167562,0.000011216918,0.000071366194,0.0004543332,0.00020223981,0.57602507],"study_design_scores_gemma":[0.0075231763,0.0015258347,0.010976767,0.0008320722,0.000015347106,0.000028835184,0.85391337,0.1186097,0.0010637803,0.0041082096,0.0009420695,0.00046081588],"about_ca_topic_score_codex":0.000013586188,"about_ca_topic_score_gemma":0.00011592614,"teacher_disagreement_score":0.57556427,"about_ca_system_score_codex":0.001097924,"about_ca_system_score_gemma":0.00064627046,"threshold_uncertainty_score":0.8843095},"labels":[],"label_agreement":null},{"id":"W3155272344","doi":"","title":"Policy-Based Personalized Context Dissemination for Location-Aware Services","year":2012,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Context (archaeology); Negotiation; Computer science; Internet privacy; Protocol (science); Architecture; Context awareness; Business; Tracking (education); World Wide Web; Knowledge management; Political science; Psychology; Medicine; Geography","score_opus":0.023827452129620993,"score_gpt":0.3085994319711172,"score_spread":0.2847719798414962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155272344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063557997,0.000115781295,0.9874509,0.0037093386,0.00034664225,0.00056089874,0.000017808256,0.00029199803,0.0011508556],"genre_scores_gemma":[0.9924056,8.493812e-7,0.003982852,0.0014487827,0.00025585102,0.00024113576,0.000042757885,0.000012794317,0.0016093915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887294,0.000075293734,0.00022171007,0.00025837243,0.00025671205,0.0003149941],"domain_scores_gemma":[0.99835825,0.00054686615,0.00013958587,0.0003524806,0.00045746233,0.00014532595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039884038,0.00014415229,0.00017208584,0.00017935238,0.00015114565,0.00017273884,0.0004223332,0.000070322654,0.000052197935],"category_scores_gemma":[0.00007368294,0.00013301005,0.00009001594,0.000435989,0.000026758285,0.0012878573,0.000052691514,0.000045974415,0.00014150234],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093201226,0.00094624085,0.017972622,0.0010199323,0.00014650344,0.0000010894826,0.0145814335,0.000021973603,0.0048253816,0.26589176,0.008471027,0.68602884],"study_design_scores_gemma":[0.0076722843,0.00044014797,0.030212902,0.00096121116,0.00009737804,0.00007945931,0.008818744,0.6689515,0.07445273,0.0038529336,0.2017328,0.002727906],"about_ca_topic_score_codex":0.00023847393,"about_ca_topic_score_gemma":0.00012470274,"teacher_disagreement_score":0.9860498,"about_ca_system_score_codex":0.00009838889,"about_ca_system_score_gemma":0.00013324858,"threshold_uncertainty_score":0.5423993},"labels":[],"label_agreement":null},{"id":"W3155692624","doi":"10.3390/s21082786","title":"From Offline to Real-Time Distributed Activity Recognition in Wireless Sensor Networks for Healthcare: A Review","year":2021,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université du Québec à Chicoutimi","funders":"","keywords":"Activity recognition; Computer science; Wireless sensor network; Context (archaeology); Field (mathematics); Wireless; Artificial intelligence; Machine learning; Feature extraction; Ambient intelligence; Real-time computing; Computer network; Telecommunications","score_opus":0.08266494054558529,"score_gpt":0.3516915973087813,"score_spread":0.26902665676319604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155692624","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013823355,0.94784397,0.038871627,0.002643462,0.0010535306,0.005974596,0.0030623747,0.00036207243,0.00005011937],"genre_scores_gemma":[0.000042916603,0.9915934,0.002500684,0.00035503964,0.0006966666,0.0010156832,0.003484998,0.00009903812,0.0002115694],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9930364,0.0022891937,0.0014978226,0.0018596353,0.00049307354,0.0008238742],"domain_scores_gemma":[0.99385566,0.0025991346,0.001030565,0.0015268625,0.0005814265,0.000406362],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001341392,0.00082771934,0.004323675,0.00033076722,0.0001231684,0.00021358543,0.0008354096,0.0006286586,0.000037400983],"category_scores_gemma":[0.0006172609,0.0008323038,0.0009350235,0.0022906563,0.00003182744,0.00029991224,0.0003717879,0.0007471156,0.00028818034],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001036254,0.00010029947,5.9916056e-7,0.01030225,0.000106635045,0.00007123488,0.000037554808,0.0000049578834,0.0000022416489,0.0000075037765,0.0033442343,0.9860121],"study_design_scores_gemma":[0.00048369577,0.000117509124,0.0000070309848,0.17180878,0.00036082676,0.0001269191,0.000012163209,0.00593038,0.000008331999,0.000049468705,0.81974375,0.0013511676],"about_ca_topic_score_codex":0.001647826,"about_ca_topic_score_gemma":0.0004731922,"teacher_disagreement_score":0.984661,"about_ca_system_score_codex":0.00071282877,"about_ca_system_score_gemma":0.0006017588,"threshold_uncertainty_score":0.9994128},"labels":[],"label_agreement":null},{"id":"W3163912335","doi":"10.1145/3448021","title":"Similarity Embedding Networks for Robust Human Activity Recognition","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Embedding; Pattern recognition (psychology); Similarity (geometry); Pairwise comparison; Activity recognition; Cluster analysis; Convolutional neural network; Deep learning; Machine learning; Generalization; Data mining; Mathematics","score_opus":0.1595380997247741,"score_gpt":0.34323993684266196,"score_spread":0.18370183711788787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163912335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0131797185,0.00019498281,0.97814023,0.00055435306,0.0019245548,0.00051500084,0.004792968,0.00031500575,0.00038316546],"genre_scores_gemma":[0.9676756,0.000060273178,0.027743699,0.0001741879,0.00044286507,0.0001868176,0.0030767343,0.000049934064,0.00058987224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684066,0.0003772203,0.00044117146,0.0015800818,0.00028465345,0.0004762025],"domain_scores_gemma":[0.9933999,0.0017545619,0.00019265235,0.004151868,0.00033689753,0.00016412343],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053577754,0.0003652805,0.00047438836,0.00016240487,0.00077469845,0.0009140321,0.002319765,0.00023993583,0.000121689234],"category_scores_gemma":[0.000237235,0.00041531547,0.00024948776,0.0006500361,0.000065242755,0.0054396032,0.00029220735,0.00052930164,0.00010161159],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106568616,0.002286644,0.000103102255,0.000092909955,0.00048734702,0.000027951059,0.0004422146,0.0016560786,0.0044210283,0.00010890332,0.0043911566,0.9858761],"study_design_scores_gemma":[0.005971317,0.0004454757,0.0034831762,0.001203792,0.00069810025,0.00009736064,0.00065202,0.88670665,0.064204454,0.013579221,0.019902363,0.0030560952],"about_ca_topic_score_codex":0.00022330646,"about_ca_topic_score_gemma":0.0031458747,"teacher_disagreement_score":0.98282,"about_ca_system_score_codex":0.00018094305,"about_ca_system_score_gemma":0.00026083627,"threshold_uncertainty_score":0.9998299},"labels":[],"label_agreement":null},{"id":"W3164609076","doi":"10.2139/ssrn.3199244","title":"Rules and Ontologies in Support of Real-Time Ubiquitous Application","year":2005,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Data science","score_opus":0.009095848905978834,"score_gpt":0.24682650012762972,"score_spread":0.23773065122165088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164609076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8311345,0.0008026351,0.16258311,0.0026166511,0.000065257394,0.00022886456,0.0000023829623,0.00008785629,0.002478715],"genre_scores_gemma":[0.99733055,0.0010214542,0.0012649241,0.000040069976,0.000076798955,0.000009364141,0.0000011609268,0.0000058266023,0.0002498391],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984847,0.00009075751,0.00031765937,0.00019338763,0.0001820848,0.0007314204],"domain_scores_gemma":[0.9993808,0.00009456717,0.00021774383,0.00019334666,0.00007306348,0.000040479696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012400391,0.000093420786,0.00019906591,0.0001665335,0.000049737344,0.000048046993,0.00035925614,0.00005808269,0.0000058981664],"category_scores_gemma":[0.000030900857,0.000088082284,0.00004105322,0.00014715514,0.000034635115,0.0005424096,0.000069702655,0.00042380733,0.00003748385],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022305067,0.00010906842,0.0045679626,0.000009359171,0.000041670235,0.0000034305356,0.0006267689,0.000014670161,0.00853604,0.048354615,0.0001433009,0.9375708],"study_design_scores_gemma":[0.008330934,0.0036221924,0.10587009,0.00021945394,0.00009697724,0.016717704,0.0029661863,0.029673481,0.019396817,0.7770593,0.033755016,0.0022918466],"about_ca_topic_score_codex":0.00016752357,"about_ca_topic_score_gemma":0.001878284,"teacher_disagreement_score":0.93527895,"about_ca_system_score_codex":0.00034849782,"about_ca_system_score_gemma":0.0006397964,"threshold_uncertainty_score":0.35918912},"labels":[],"label_agreement":null},{"id":"W3164647893","doi":"10.1590/1413-81232021265.03382021","title":"LARIISA: soluções digitais inteligentes para apoio à tomada de decisão na gestão da Estratégia de Saúde da Família","year":2021,"lang":"pt","type":"article","venue":"Ciência & Saúde Coletiva","topic":"Context-Aware Activity Recognition Systems","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":"Université de Montréal","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.058226933178463794,"score_gpt":0.2982384211136759,"score_spread":0.2400114879352121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164647893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4680167,0.006862054,0.5001095,0.0054138997,0.004338499,0.0017902383,0.00048038602,0.0010393774,0.011949338],"genre_scores_gemma":[0.98678005,0.00026190915,0.00368637,0.0024796706,0.00088678725,0.0002608061,0.00011195901,0.00017815125,0.005354295],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9896314,0.0015848556,0.0018300306,0.0026493974,0.0016160328,0.0026882759],"domain_scores_gemma":[0.99057436,0.0026721982,0.00087983505,0.002779946,0.0017136196,0.0013800269],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0016721056,0.0013708215,0.0017063246,0.00046020685,0.0009006601,0.0038749774,0.002932448,0.0008843501,0.00069410645],"category_scores_gemma":[0.002954993,0.0016126592,0.000822223,0.002546355,0.0004934915,0.0018831724,0.0020675585,0.0016009248,0.001856996],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038425467,0.0033802723,0.15455039,0.0010186306,0.0019826572,0.006981786,0.013561246,0.00018340384,0.048081163,0.0057832515,0.082216196,0.6818768],"study_design_scores_gemma":[0.0066779586,0.0015617551,0.24145088,0.0071991524,0.0010299465,0.006973858,0.007924727,0.034681506,0.19204643,0.0048641614,0.4869884,0.008601243],"about_ca_topic_score_codex":0.00096025376,"about_ca_topic_score_gemma":0.0013315773,"teacher_disagreement_score":0.67327553,"about_ca_system_score_codex":0.0016001821,"about_ca_system_score_gemma":0.0052581066,"threshold_uncertainty_score":0.9999043},"labels":[],"label_agreement":null},{"id":"W3164963437","doi":"","title":"Human activity recognition system","year":2021,"lang":"en","type":"article","venue":"International journal of advance research, ideas and innovations in technology","topic":"Context-Aware Activity Recognition Systems","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":"Horizon College and Seminary","funders":"","keywords":"Computer science; Artificial intelligence; Activity recognition; Task (project management); Search engine indexing; Recall; Analytics; Human–computer interaction; Action recognition; Precision and recall; Cognitive neuroscience of visual object recognition; Object (grammar); Machine learning; Computer vision; Data mining","score_opus":0.0626547085023447,"score_gpt":0.39490493697891504,"score_spread":0.3322502284765704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164963437","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83678496,0.0004960727,0.14689949,0.012707853,0.0008171179,0.00014451722,0.000011215121,0.000077994875,0.0020607868],"genre_scores_gemma":[0.99084646,0.00012115406,0.008768424,0.000057417128,0.00011086575,0.000018843553,0.0000025236156,0.000007196181,0.00006712388],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99811935,0.00019062679,0.0005594179,0.0002609615,0.000647744,0.00022187836],"domain_scores_gemma":[0.99300134,0.00020203604,0.0003427247,0.00025703348,0.0061543263,0.000042523607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013625479,0.00009168654,0.00022771585,0.002214869,0.00012662691,0.00016100619,0.00072021934,0.00012224725,0.00001037619],"category_scores_gemma":[0.001219183,0.000095411866,0.00004081734,0.002195905,0.00015746377,0.0012444159,0.00038853034,0.00075013284,0.00000953293],"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":[0.000017787332,0.000218821,0.0030231958,0.000026160322,0.000071106864,0.0005264218,0.00008828213,0.000009721819,0.036630362,0.18349397,0.00024958825,0.7756446],"study_design_scores_gemma":[0.0055739237,0.000856321,0.03592025,0.0029994354,0.000013837833,0.013455906,0.004658516,0.003600846,0.18465045,0.70521355,0.042218547,0.0008384109],"about_ca_topic_score_codex":0.000028202296,"about_ca_topic_score_gemma":0.00007856105,"teacher_disagreement_score":0.7748062,"about_ca_system_score_codex":0.0003757563,"about_ca_system_score_gemma":0.00023525838,"threshold_uncertainty_score":0.3890783},"labels":[],"label_agreement":null},{"id":"W3165010466","doi":"10.1145/3412382.3458788","title":"A Multi-source Unsupervised Domain Adaptation Method for Wearable Sensor based Human Activity Recognition","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"McMaster University","funders":"","keywords":"Computer science; Domain adaptation; Adaptation (eye); Wearable computer; Artificial intelligence; Domain (mathematical analysis); Activity recognition; Pattern recognition (psychology); Labeled data; Field (mathematics); Machine learning; Unsupervised learning; Speech recognition; Mathematics","score_opus":0.10855420757808701,"score_gpt":0.32598942245457674,"score_spread":0.21743521487648973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165010466","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034263726,0.00002006329,0.96229255,0.001281315,0.00023914195,0.00073493615,0.000028864959,0.0004570917,0.0006823282],"genre_scores_gemma":[0.31528127,0.000001014326,0.6821229,0.00061957486,0.00007079854,0.00021611994,0.000039240826,0.00002405846,0.0016250444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728405,0.00085117534,0.0003378077,0.0008165173,0.00033758188,0.00037289082],"domain_scores_gemma":[0.99739754,0.0010123038,0.00018265905,0.0006092264,0.0006528629,0.00014540357],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001104333,0.00023392477,0.00035002455,0.00017390416,0.00035823285,0.0003439915,0.0002908444,0.00014693112,0.00012032741],"category_scores_gemma":[0.0002372175,0.00025042112,0.00022939351,0.0005689979,0.00002160276,0.0008905826,0.00009364092,0.0001632401,0.000099239565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058385078,0.00065648387,0.000112471556,0.0001308057,0.000080544356,0.000018151051,0.0011330519,0.0003310331,0.41573706,0.00016761164,0.00024815218,0.58132625],"study_design_scores_gemma":[0.0031847358,0.00011720982,0.00085573405,0.00009678108,0.000023789435,0.000038911665,0.00069567346,0.85013205,0.13905746,0.0008574568,0.0044871876,0.0004529972],"about_ca_topic_score_codex":0.00047934533,"about_ca_topic_score_gemma":0.0006485432,"teacher_disagreement_score":0.84980106,"about_ca_system_score_codex":0.00012642563,"about_ca_system_score_gemma":0.0002175671,"threshold_uncertainty_score":0.9999948},"labels":[],"label_agreement":null},{"id":"W3167346101","doi":"10.1109/widstaif52235.2021.9430216","title":"Integrating Modalities into Context Aware eLearning System Using Cloud Computing","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Cloud computing; Context (archaeology); Multimedia; Scalability; Modalities; Variety (cybernetics); Human–computer interaction; Architecture; World Wide Web; Data science; Artificial intelligence","score_opus":0.03715831727217034,"score_gpt":0.2750307763515148,"score_spread":0.23787245907934446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167346101","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17475887,0.00023981756,0.81827235,0.00021717713,0.001287778,0.00015005525,0.0000016493937,0.0007642283,0.0043080808],"genre_scores_gemma":[0.9660079,9.665945e-7,0.03285527,0.00028126224,0.00034681184,0.0000061977557,0.0000053019294,0.000025575098,0.00047075155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710065,0.0006108266,0.0006536933,0.0007178193,0.00045931354,0.00045772418],"domain_scores_gemma":[0.99759525,0.00064207346,0.000306691,0.00062960945,0.0006796525,0.00014673846],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00078057125,0.00027986994,0.00050576415,0.00014994375,0.00057266315,0.0008946887,0.0005928353,0.00011632004,0.00003427523],"category_scores_gemma":[0.00022788228,0.00027944162,0.00017244482,0.0006484165,0.00004607401,0.000938013,0.0007086707,0.00036472577,0.00009170122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016884398,0.0001871814,0.015347287,0.0014418777,0.00044227118,0.00073676975,0.04223728,0.002283163,0.023037598,0.32626754,0.0008158853,0.5871863],"study_design_scores_gemma":[0.00039667578,0.000027694623,0.00007938786,0.0009441392,0.000012232917,0.00063829205,0.04643053,0.940352,0.009180325,0.00022810389,0.0012540132,0.0004565562],"about_ca_topic_score_codex":0.0015839166,"about_ca_topic_score_gemma":0.0003564779,"teacher_disagreement_score":0.93806887,"about_ca_system_score_codex":0.00038191775,"about_ca_system_score_gemma":0.00033896713,"threshold_uncertainty_score":0.9999658},"labels":[],"label_agreement":null},{"id":"W3168160807","doi":"10.1109/lsens.2021.3089619","title":"Fall Event Detection System Using Inception-Densenet Inspired Sparse Siamese Network","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Event (particle physics); Feature (linguistics); Shot (pellet); Computer vision; Convolutional neural network; Pattern recognition (psychology); One shot; Real-time computing; Engineering","score_opus":0.02876007418449261,"score_gpt":0.2412283753310762,"score_spread":0.21246830114658358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168160807","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6149863,0.00004042148,0.38027778,0.0007108386,0.0034096735,0.00020733225,0.000004458739,0.00027809883,0.00008507579],"genre_scores_gemma":[0.9929499,0.0000035702885,0.0044856807,0.0014204814,0.00094974134,0.000019137125,0.0000045857064,0.000033811615,0.00013309039],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697995,0.00068978156,0.00052264374,0.0007412524,0.00053093955,0.00053542113],"domain_scores_gemma":[0.9983458,0.00018921835,0.0003061907,0.00080365443,0.00018834638,0.0001667829],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041959554,0.00028602,0.00037807692,0.00015732614,0.00028748112,0.00030678246,0.00036257054,0.00013209332,0.00000572133],"category_scores_gemma":[0.000040516996,0.00032578423,0.00020667973,0.00082787866,0.000042376854,0.0004936739,0.00012408616,0.000259789,0.00021175382],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037176454,0.00011186927,0.0018408827,0.0001770476,0.00023506412,0.0013024913,0.0015732379,0.09782517,0.8674685,0.00013512583,0.004668386,0.024625067],"study_design_scores_gemma":[0.0026057798,0.00014062447,0.0055696396,0.0013622293,0.00014662615,0.004820824,0.0010251561,0.8144389,0.15174761,0.000096483695,0.01567935,0.0023668155],"about_ca_topic_score_codex":0.0003053285,"about_ca_topic_score_gemma":0.00031115775,"teacher_disagreement_score":0.7166137,"about_ca_system_score_codex":0.00048725796,"about_ca_system_score_gemma":0.00009337152,"threshold_uncertainty_score":0.9999194},"labels":[],"label_agreement":null},{"id":"W3169408724","doi":"10.1155/2021/2026895","title":"An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Artificial intelligence; Computer science; k-nearest neighbors algorithm; Kernel (algebra); Support vector machine; Artificial neural network; Pattern recognition (psychology); Radial basis function kernel; Machine learning; Standard deviation; Kernel method; Data mining; Mathematics; Statistics","score_opus":0.03501988498422588,"score_gpt":0.30121328383783896,"score_spread":0.2661933988536131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169408724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5239198,0.000024241548,0.475621,0.000116027135,0.00015847372,0.00011100514,0.00001811821,0.000018660558,0.000012610824],"genre_scores_gemma":[0.95891553,0.000016306953,0.040876955,0.00004086428,0.00007787491,0.000014573694,0.00002774741,0.000012154048,0.000018004454],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99879766,0.000066297885,0.0004281483,0.00027850535,0.00027785404,0.00015153126],"domain_scores_gemma":[0.9983122,0.00008845244,0.00049936085,0.00017629772,0.0007998153,0.00012386957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032585545,0.00012564982,0.0002555728,0.0001280839,0.00013386348,0.00008982191,0.00014210024,0.000062473606,0.0000028654501],"category_scores_gemma":[0.000030574065,0.00013130062,0.000114452596,0.00018747433,0.000020250836,0.0013843651,0.0000037288426,0.00014691536,7.1798024e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013259114,0.00034875586,0.000078548954,0.00006925796,0.000036967194,0.000023111395,0.0028034782,0.042258542,0.6582671,0.00034081217,0.000007479305,0.29563335],"study_design_scores_gemma":[0.009032522,0.0015142609,0.08585459,0.00067092484,0.00021574875,0.0003279523,0.0018000863,0.29606155,0.58281523,0.020564772,0.00016640282,0.0009759821],"about_ca_topic_score_codex":0.000004354142,"about_ca_topic_score_gemma":0.00007637087,"teacher_disagreement_score":0.43499568,"about_ca_system_score_codex":0.00005710967,"about_ca_system_score_gemma":0.00012664367,"threshold_uncertainty_score":0.53542835},"labels":[],"label_agreement":null},{"id":"W3173359823","doi":"10.3390/informatics8030041","title":"A Self-Adaptive and Efficient Context-Aware Healthcare Model for COPD Diseases","year":2021,"lang":"en","type":"article","venue":"Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Adaptation (eye); Health care; Ubiquitous computing; Computer science; Context awareness; COPD; Everyday life; Pulmonary disease; Risk analysis (engineering); Medicine; Human–computer interaction; Psychology; Political science","score_opus":0.03440086472143066,"score_gpt":0.2661284874904587,"score_spread":0.23172762276902803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173359823","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009518129,0.00044181454,0.98760825,0.00089806214,0.00023044432,0.00059062155,0.00021309964,0.00025517473,0.00024438428],"genre_scores_gemma":[0.9638141,0.000018646479,0.034406934,0.0015022364,0.000031361866,0.000108538916,0.000019408277,0.000009839026,0.000088975576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875695,0.000046566005,0.0004464548,0.00018459391,0.00028111582,0.00028435158],"domain_scores_gemma":[0.99830335,0.00033428057,0.00020096492,0.00039144317,0.0005691931,0.00020074019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016230445,0.00016410113,0.00027925184,0.00007714406,0.00019915932,0.00024941808,0.00025846646,0.00007267755,0.000001732252],"category_scores_gemma":[0.0000778943,0.00016082065,0.0000855861,0.00020576848,0.00003175605,0.0005877239,0.00025074786,0.000102168524,0.00002078189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012263878,0.0009441518,0.0014042208,0.004582293,0.0004769989,0.000054685715,0.13691774,0.0068495506,0.00001930371,0.14167151,0.010013814,0.6969431],"study_design_scores_gemma":[0.0006682632,0.000057202746,0.00008074539,0.00008853424,0.000015468748,0.00005009892,0.0020652562,0.9935838,0.00009891597,0.00073929096,0.002356421,0.00019600516],"about_ca_topic_score_codex":0.000009776924,"about_ca_topic_score_gemma":0.000044286506,"teacher_disagreement_score":0.9867343,"about_ca_system_score_codex":0.00009583099,"about_ca_system_score_gemma":0.00042794936,"threshold_uncertainty_score":0.65580755},"labels":[],"label_agreement":null},{"id":"W3174806317","doi":"10.2196/27862","title":"Implementation of Unobtrusive Sensing Systems for Older Adult Care: Scoping Review","year":2021,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Commission","keywords":"Thematic analysis; Scopus; Inclusion (mineral); Psychology; Gerontology; Population; Health care; MEDLINE; Medicine; Applied psychology; Qualitative research; Social psychology","score_opus":0.032670368104425834,"score_gpt":0.3603438106346452,"score_spread":0.32767344253021935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174806317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015479586,0.029329197,0.94838834,0.0011123015,0.0012732968,0.003767025,0.000021194397,0.00018057192,0.00044849105],"genre_scores_gemma":[0.9943713,0.00044896294,0.0042214533,0.00056485116,0.00013354977,0.00018200504,0.000025037365,0.000017653447,0.000035162702],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9985916,0.00014475941,0.0004533708,0.0003615535,0.00024108817,0.00020760497],"domain_scores_gemma":[0.9980721,0.00021609783,0.00031850563,0.00036664933,0.0009827405,0.000043944157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024527556,0.00011650129,0.00032754152,0.0000737311,0.000098962424,0.0001245178,0.00016073328,0.000029908182,0.000008426027],"category_scores_gemma":[0.00005301831,0.0001270262,0.00010961945,0.00034792308,0.000010053576,0.00045258945,0.00011686959,0.000057534544,0.000005937932],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033744143,0.000039671268,0.0018355801,0.06543265,0.00012705912,0.000047522,0.01278841,0.000029763949,0.006521553,0.0018864,0.0021571075,0.90913093],"study_design_scores_gemma":[0.0072437935,0.0002362809,0.003972956,0.6959341,0.00031164786,0.0010227838,0.04412585,0.025198283,0.213539,0.00026625165,0.0055450043,0.0026040464],"about_ca_topic_score_codex":0.00013554098,"about_ca_topic_score_gemma":0.000104170715,"teacher_disagreement_score":0.97889173,"about_ca_system_score_codex":0.000087241846,"about_ca_system_score_gemma":0.00023897651,"threshold_uncertainty_score":0.5179978},"labels":[],"label_agreement":null},{"id":"W3177026772","doi":"10.1016/j.mlwa.2021.100072","title":"Trends in human activity recognition with focus on machine learning and power requirements","year":2021,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Activity recognition; Convolutional neural network; Data science; Field (mathematics); Deep learning; Artificial intelligence; Machine learning","score_opus":0.027478327613590987,"score_gpt":0.2885644445983348,"score_spread":0.2610861169847438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177026772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48137417,0.00023782242,0.48978704,0.003485474,0.00004731967,0.0008122982,0.000027244456,0.0007485113,0.023480121],"genre_scores_gemma":[0.9952039,0.000010300741,0.002911039,0.00007336479,0.000029199333,0.00036183107,0.00010968676,0.000033529883,0.0012671598],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99794054,0.0003625509,0.00023761287,0.00079101487,0.0003600073,0.00030829938],"domain_scores_gemma":[0.99888515,0.00019808936,0.00024856426,0.0004051942,0.00014159505,0.00012139554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036465665,0.0002548624,0.0002900357,0.00038320798,0.00047829066,0.0002297844,0.00020779471,0.00007417294,0.00009475376],"category_scores_gemma":[0.00003973439,0.00022813617,0.00003973455,0.001217812,0.000056107623,0.0004368001,0.00014288873,0.00078082946,0.000036374335],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009524704,0.000565099,0.14990228,0.000031507043,0.00008303403,0.000054894834,0.00078219565,0.0003197878,0.0053462167,0.0012369746,0.000017795932,0.84156495],"study_design_scores_gemma":[0.023311792,0.008170246,0.6779026,0.0019146916,0.00029081784,0.0018823602,0.0009162747,0.13146105,0.03450847,0.0049979854,0.108751595,0.0058921343],"about_ca_topic_score_codex":0.00045938988,"about_ca_topic_score_gemma":0.001660642,"teacher_disagreement_score":0.8356728,"about_ca_system_score_codex":0.00010009172,"about_ca_system_score_gemma":0.000046715446,"threshold_uncertainty_score":0.9303123},"labels":[],"label_agreement":null},{"id":"W3178681001","doi":"10.3390/s21144713","title":"Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Centre Hospitalier de l’Université de Montréal; Institut National de la Recherche Scientifique; Université TÉLUQ","funders":"Canada Research Chairs","keywords":"Activity recognition; Wearable computer; Computer science; Artificial intelligence; Machine learning; Feature engineering; Accelerometer; Classifier (UML); Wearable technology; Feature (linguistics); Deep learning; Embedded system","score_opus":0.0546264820847625,"score_gpt":0.2832454706733922,"score_spread":0.22861898858862967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178681001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56744045,0.000013634725,0.43085235,0.000086922395,0.000048834103,0.00020160673,0.000005254997,0.00009792304,0.0012530372],"genre_scores_gemma":[0.98235804,0.00000524627,0.017277481,0.00003606032,0.000057656605,0.000035007204,0.00004053972,0.000018446544,0.00017152955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982613,0.0005742059,0.00014411606,0.0005416014,0.00024617877,0.00023264508],"domain_scores_gemma":[0.9986719,0.0006408569,0.00017764865,0.00022261206,0.0001815461,0.00010542942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000347881,0.00016350471,0.00030885308,0.00010868239,0.00017676542,0.00009146644,0.000111071764,0.00008245158,0.0000034852128],"category_scores_gemma":[0.0003037231,0.00016980078,0.00011070197,0.0002553519,0.00004498707,0.00028697128,0.00005217482,0.00030995754,0.0000043101686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023182448,0.0007884845,0.0012447111,0.00017999932,0.000057091966,0.000011299885,0.0017418498,0.01806974,0.03301674,0.00016826457,0.00000905587,0.94448096],"study_design_scores_gemma":[0.00082898786,0.000389913,0.0006266254,0.000032953867,0.000015505313,0.000018239816,0.00034893633,0.98480976,0.012308209,0.00014210417,0.0002808269,0.00019792347],"about_ca_topic_score_codex":0.000037903137,"about_ca_topic_score_gemma":0.000026269883,"teacher_disagreement_score":0.96674,"about_ca_system_score_codex":0.000035884143,"about_ca_system_score_gemma":0.000049606242,"threshold_uncertainty_score":0.69242746},"labels":[],"label_agreement":null},{"id":"W3185246004","doi":"10.2196/24402","title":"Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation","year":2021,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Feature selection; Feature (linguistics); Pattern recognition (psychology); Naive Bayes classifier; Inertial measurement unit; Feature extraction; Filter (signal processing); Computer vision; Support vector machine","score_opus":0.05454604702258784,"score_gpt":0.31858122220109564,"score_spread":0.2640351751785078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185246004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4209706,0.00047574413,0.56492746,0.010854697,0.00089069846,0.0008914322,0.00003782644,0.00038892025,0.0005626441],"genre_scores_gemma":[0.94773644,0.00012176861,0.048298776,0.0029616673,0.00026138852,0.0001400404,0.0002584998,0.000019066292,0.00020233783],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99794394,0.00034821642,0.00032728325,0.0006600056,0.00035807706,0.00036249714],"domain_scores_gemma":[0.99880564,0.00016244636,0.00020631803,0.00021757586,0.00026815623,0.00033983606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006112071,0.00019448322,0.0002454385,0.00022062867,0.00050880323,0.00020796795,0.00008481133,0.00010722411,0.000016880806],"category_scores_gemma":[0.00004459399,0.00020279484,0.000034424706,0.00043192646,0.00002001572,0.00044823892,0.000046055553,0.00028970363,0.00003176139],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019998733,0.00011946938,0.00079107145,0.0002638732,0.0000055479964,0.00000814199,0.00028166713,0.000002031426,0.000021594939,0.000047507965,0.0007006368,0.9977385],"study_design_scores_gemma":[0.0107201105,0.0031672756,0.41023204,0.001828775,0.00012546103,0.0028117218,0.0008665047,0.37752587,0.06434071,0.0034031135,0.12200467,0.0029737328],"about_ca_topic_score_codex":0.00002639804,"about_ca_topic_score_gemma":0.000029686807,"teacher_disagreement_score":0.99476475,"about_ca_system_score_codex":0.00022127661,"about_ca_system_score_gemma":0.00066540967,"threshold_uncertainty_score":0.8269733},"labels":[],"label_agreement":null},{"id":"W3186897603","doi":"10.1109/memea52024.2021.9478605","title":"Few-shot Fall Detection using Shallow Siamese Network","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Shot (pellet); Convolutional neural network; Transfer of learning; Feature extraction; Deep learning; Feature (linguistics); Falling (accident); Computer vision; Wearable computer; Pattern recognition (psychology); Machine learning; Embedded system","score_opus":0.06876412722861594,"score_gpt":0.2781579165778948,"score_spread":0.20939378934927885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186897603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.104448415,0.000112954775,0.8858924,0.0002451194,0.0011040083,0.00011094356,6.4223735e-7,0.00029816382,0.0077873883],"genre_scores_gemma":[0.98549527,0.0000034732168,0.012492196,0.0005487986,0.00031619373,0.00000932527,0.0000014583203,0.0000116284045,0.0011216609],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985413,0.00018276702,0.00023090547,0.00046354212,0.00027182206,0.0003096595],"domain_scores_gemma":[0.99893147,0.00014528062,0.00008411798,0.0005222155,0.00021351573,0.00010339312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029095743,0.00013399974,0.00019030401,0.000058406124,0.00016624057,0.00030882744,0.0003082792,0.00008294561,0.00006602556],"category_scores_gemma":[0.00005784528,0.00013803499,0.00010740319,0.00068391743,0.000015700349,0.0007155507,0.0002619077,0.00013984718,0.00017070118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001894485,0.00027595274,0.01904371,0.000063795494,0.0001940999,0.00038748552,0.0009288085,0.0033944421,0.07288308,0.004352183,0.0012524028,0.8972051],"study_design_scores_gemma":[0.00070229353,0.000075606884,0.00602153,0.000102010665,0.000021378917,0.0008535924,0.00019257517,0.9406392,0.020804316,0.0023790787,0.027503436,0.0007049996],"about_ca_topic_score_codex":0.0003359325,"about_ca_topic_score_gemma":0.0027650471,"teacher_disagreement_score":0.9372447,"about_ca_system_score_codex":0.00009288212,"about_ca_system_score_gemma":0.000114098206,"threshold_uncertainty_score":0.56289035},"labels":[],"label_agreement":null},{"id":"W3193423404","doi":"10.1145/3466696","title":"Autonomic Security Management for IoT Smart Spaces","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Internet of Things","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Blackberry (Canada); University of Alberta","funders":"","keywords":"Computer science; Computer security; Smart environment; Context (archaeology); Security management; Security information and event management; Security controls; Ontology; Cloud computing security; Internet of Things; Cloud computing; Control (management)","score_opus":0.02294470755265303,"score_gpt":0.2588315692273737,"score_spread":0.23588686167472067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193423404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026080292,0.000038358638,0.96730983,0.003021214,0.0010489632,0.00029265342,0.000014219241,0.0001603736,0.0020340732],"genre_scores_gemma":[0.9497135,0.000010079505,0.046410076,0.00044508744,0.000017716931,0.00006507229,0.000003371929,0.0000128919255,0.0033221561],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878,0.00006426076,0.0003175578,0.00043997896,0.00020261933,0.00019554391],"domain_scores_gemma":[0.9985777,0.0002960466,0.0001377399,0.00077763957,0.0001476651,0.000063200925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026289906,0.00014872642,0.00023912007,0.0001630873,0.000076624834,0.00015176917,0.00084736344,0.00006873694,0.00010878096],"category_scores_gemma":[0.000029250583,0.00016202749,0.00022968081,0.00020006363,0.000037655213,0.00044053077,0.000052839598,0.0001550628,0.000058405793],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019848681,0.001743033,0.00020940372,0.0008176152,0.0016286526,0.000060524297,0.021914043,0.00028936466,0.0038187073,0.03683331,0.0060536987,0.92643315],"study_design_scores_gemma":[0.0045754095,0.001010652,0.0008673119,0.0013623807,0.00029433265,0.00025995055,0.0026489103,0.10464649,0.6335199,0.06888802,0.18039197,0.0015347055],"about_ca_topic_score_codex":0.0001623231,"about_ca_topic_score_gemma":0.00007103612,"teacher_disagreement_score":0.92489845,"about_ca_system_score_codex":0.000094557596,"about_ca_system_score_gemma":0.000056540015,"threshold_uncertainty_score":0.66072893},"labels":[],"label_agreement":null},{"id":"W3193971657","doi":"10.1080/24751839.2021.1962105","title":"A knowledge-based model for context-aware smart service systems","year":2021,"lang":"en","type":"article","venue":"Journal of Information and Telecommunication","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Service (business); Service system; Context (archaeology); Chatbot; Context awareness; Knowledge management; Domain knowledge; Smart environment; Services computing; Context model; World Wide Web; Internet of Things; Web service; Business; Artificial intelligence","score_opus":0.04543024304129305,"score_gpt":0.28072018333089727,"score_spread":0.2352899402896042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193971657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008160323,0.0007377678,0.9869392,0.003124562,0.00024744653,0.00022385486,0.000009636855,0.000035860136,0.0005213762],"genre_scores_gemma":[0.9885693,0.00007971797,0.010237951,0.0009856155,0.000026661013,0.000026416681,0.000027543354,0.000004595993,0.00004219126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987583,0.00014622771,0.0007412692,0.00006727009,0.00017605019,0.00011086374],"domain_scores_gemma":[0.99575263,0.0003653187,0.0007922513,0.0004090228,0.002594626,0.00008616993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086922577,0.00009777273,0.00022588586,0.0002065061,0.00016353981,0.00038997878,0.00046281566,0.00007680513,0.0000029506232],"category_scores_gemma":[0.00015953265,0.000092988164,0.00008059997,0.00030834656,0.00001330356,0.002784815,0.00009523971,0.00015987863,0.000012255522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014379817,0.00044796098,0.0005805327,0.0011164597,0.00021747497,0.0000018927515,0.01430657,0.010553342,0.0006519825,0.023715539,0.0067793783,0.94148505],"study_design_scores_gemma":[0.0011578803,0.00004702738,0.0002219629,0.00016381743,0.000012658749,0.00012909292,0.00066741754,0.9665935,0.0006226782,0.00020829917,0.030070147,0.00010552445],"about_ca_topic_score_codex":0.000014235328,"about_ca_topic_score_gemma":0.00012404862,"teacher_disagreement_score":0.98040897,"about_ca_system_score_codex":0.00008446703,"about_ca_system_score_gemma":0.00050762086,"threshold_uncertainty_score":0.37919474},"labels":[],"label_agreement":null},{"id":"W3194188314","doi":"10.1109/icsgrc53186.2021.9515261","title":"A Comparison of Sequential Prediction Algorithms in IoT Enabled Smart Environments","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"","keywords":"Computer science; Algorithm; Sequence (biology); Event (particle physics); Window (computing); Artificial intelligence; Sliding window protocol; Machine learning; Internet of Things; Data mining","score_opus":0.051243252046462524,"score_gpt":0.29947131796293325,"score_spread":0.24822806591647073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194188314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2094953,0.00008079594,0.78523576,0.00020896745,0.0006505574,0.00021179118,0.000009171736,0.00007297397,0.004034678],"genre_scores_gemma":[0.9930806,0.0000038009157,0.0058248877,0.00004612145,0.00003387099,0.000019969268,0.000009694291,0.000005261106,0.0009757488],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985645,0.00017981355,0.000407818,0.0003404029,0.00033389914,0.00017360908],"domain_scores_gemma":[0.99935335,0.00008046743,0.00011558568,0.00035675237,0.000039524166,0.00005431487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025317378,0.00009562407,0.00025386142,0.00010706566,0.00003216103,0.00005030657,0.00023093984,0.000068720205,0.00014563419],"category_scores_gemma":[0.000028318274,0.00010143773,0.00005965652,0.000331256,0.000023322486,0.00028914484,0.00020031616,0.000110671805,0.000060134356],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036566653,0.0027960292,0.37723675,0.00011064264,0.00019493877,0.00012837947,0.0049383775,0.0007013495,0.2549008,0.0018109148,0.0019784777,0.35516676],"study_design_scores_gemma":[0.0025588935,0.00019909644,0.08932569,0.00013228676,0.000018114948,0.00008137849,0.0006311479,0.38914675,0.50309145,0.000469595,0.013959649,0.00038592107],"about_ca_topic_score_codex":0.00017894004,"about_ca_topic_score_gemma":0.00017603916,"teacher_disagreement_score":0.78358537,"about_ca_system_score_codex":0.0000894042,"about_ca_system_score_gemma":0.00007524996,"threshold_uncertainty_score":0.41365105},"labels":[],"label_agreement":null},{"id":"W3194968676","doi":"10.1007/s12652-021-03433-0","title":"Web-based objects detection to discover key objects in human activities","year":2021,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Key (lock); Set (abstract data type); Process (computing); Activity recognition; Computational intelligence; Object (grammar); Artificial intelligence; Data science; Computer security","score_opus":0.032299497641783234,"score_gpt":0.2847406659291103,"score_spread":0.25244116828732704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194968676","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65229976,0.00013055567,0.34651026,0.00010608084,0.0005595002,0.00011154582,5.988623e-7,0.000030766412,0.0002509423],"genre_scores_gemma":[0.99608326,0.000011929512,0.0032738382,0.00037114456,0.00019032789,0.0000027010874,5.6400245e-7,0.000013247776,0.000052993237],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977704,0.0002621874,0.00076757855,0.00037716774,0.0004889016,0.00033371642],"domain_scores_gemma":[0.9983199,0.00039954344,0.0004849847,0.00027264416,0.0003717487,0.00015114788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088638766,0.00020629789,0.00045263942,0.0005771383,0.00026188086,0.0005615825,0.00042286477,0.00007015222,0.000016851442],"category_scores_gemma":[0.00017100299,0.00020537568,0.00014563852,0.00065670063,0.00004005982,0.0007949604,0.00025801448,0.00040216293,0.000008359785],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020782587,0.0011835498,0.0068961983,0.00031252304,0.00020134138,0.0011765263,0.039645422,0.010466071,0.4450151,0.0038583204,0.0001439004,0.49089324],"study_design_scores_gemma":[0.0019910217,0.0013856076,0.007568973,0.002272205,0.000040695297,0.000896339,0.0071430593,0.07362916,0.9003,0.0028353373,0.0010028265,0.00093477184],"about_ca_topic_score_codex":0.000058365746,"about_ca_topic_score_gemma":0.00064118195,"teacher_disagreement_score":0.48995847,"about_ca_system_score_codex":0.00019209234,"about_ca_system_score_gemma":0.0002505922,"threshold_uncertainty_score":0.8374977},"labels":[],"label_agreement":null},{"id":"W3196399896","doi":"10.1145/3460421.3480417","title":"Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Activity recognition; Computer science; Accelerometer; Artificial intelligence; Leverage (statistics); Pattern recognition (psychology); Convolutional neural network; Transfer of learning; Machine learning; Convolution (computer science); Artificial neural network; Task (project management); Motion (physics); Dimension (graph theory); Mathematics","score_opus":0.03142720996502948,"score_gpt":0.2789294444342768,"score_spread":0.24750223446924732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196399896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7020115,0.000016707048,0.29121238,0.00063321734,0.00041542837,0.00027116807,0.000014810919,0.0008293134,0.0045954194],"genre_scores_gemma":[0.98841834,0.0000017514212,0.008703655,0.00041456425,0.00013211858,0.000058075137,0.0000781059,0.000018840026,0.0021745285],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99756736,0.00042238322,0.00029806202,0.0008030729,0.0005624766,0.00034667036],"domain_scores_gemma":[0.9984602,0.00020604719,0.000109396235,0.00043290283,0.00056068745,0.00023081894],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00053441216,0.00020747213,0.00024767153,0.00012542764,0.000429597,0.0005293325,0.00030768162,0.00013046348,0.0005096782],"category_scores_gemma":[0.00013869024,0.00022669668,0.000117859214,0.00050384435,0.000020160232,0.0014377654,0.00035228563,0.0002958374,0.0008343561],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030688192,0.0010516342,0.008790504,0.000096106734,0.00013867512,0.000065213564,0.0012254685,0.00013026047,0.38119742,0.00018689514,0.007327727,0.5997594],"study_design_scores_gemma":[0.0046524196,0.0008649822,0.06322861,0.0002697727,0.000058000307,0.00048779993,0.00026424765,0.26492923,0.63455194,0.00147001,0.02697323,0.00224974],"about_ca_topic_score_codex":0.00011565921,"about_ca_topic_score_gemma":0.00006194915,"teacher_disagreement_score":0.5975097,"about_ca_system_score_codex":0.00015350491,"about_ca_system_score_gemma":0.00010384129,"threshold_uncertainty_score":0.9999436},"labels":[],"label_agreement":null},{"id":"W3197817230","doi":"10.3390/electronics10182194","title":"Active Sense: Early Staging of Non-Insulin Dependent Diabetes Mellitus (NIDDM) Hinges upon Recognizing Daily Activity Pattern","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Context-Aware Activity Recognition Systems","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":"Memorial University of Newfoundland","funders":"","keywords":"Diabetes mellitus; Medicine; Risk factor; Subject (documents); Internal medicine; Artificial intelligence; Computer science; Endocrinology","score_opus":0.016137877905844845,"score_gpt":0.24306268285879412,"score_spread":0.22692480495294928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197817230","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8783446,0.0019702897,0.1180699,0.000421934,0.00040398602,0.00026532495,0.00018925397,0.00014638844,0.00018832697],"genre_scores_gemma":[0.9985408,0.00034203863,0.00074229337,0.00011239026,0.00009305971,0.00003181775,0.000047110643,0.00003007748,0.00006037219],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968247,0.00045602297,0.0004236344,0.00084030477,0.00063105766,0.0008243276],"domain_scores_gemma":[0.9974884,0.00062490255,0.00044304144,0.0008452458,0.00045479834,0.00014362227],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067553815,0.00032560393,0.0005533518,0.00018144299,0.00017915113,0.00025979424,0.0005138018,0.00015493023,0.000022184075],"category_scores_gemma":[0.00015905149,0.000366412,0.00018734107,0.00062728254,0.00003179113,0.0010735003,0.0004071049,0.0006444551,0.000042646905],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020683725,0.00019714968,0.0016997737,0.000077615645,0.00019256513,0.000055681907,0.0013898584,0.000020501342,0.1195166,0.000022945987,0.000097935175,0.8767087],"study_design_scores_gemma":[0.0008719258,0.0002503835,0.00966006,0.00018866132,0.00004571917,0.000038347232,0.00015269533,0.009157259,0.97589225,0.0003908701,0.0027836303,0.0005682237],"about_ca_topic_score_codex":0.00018695125,"about_ca_topic_score_gemma":0.00079620135,"teacher_disagreement_score":0.8761405,"about_ca_system_score_codex":0.00038757222,"about_ca_system_score_gemma":0.0005472127,"threshold_uncertainty_score":0.99987876},"labels":[],"label_agreement":null},{"id":"W3198886025","doi":"10.1080/09720529.2021.1932920","title":"Enhanced lightweight and secure session key establishment protocol for smart home inhabitants","year":2021,"lang":"en","type":"article","venue":"Journal of Discrete Mathematical Sciences and Cryptography","topic":"Context-Aware Activity Recognition 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 Toronto","funders":"","keywords":"Session (web analytics); Session key; Computer science; Security token; Computer security; Key (lock); Protocol (science); Computer network; Wireless; Session Initiation Protocol; Telecommunications; World Wide Web; Server","score_opus":0.024469637831037287,"score_gpt":0.3049949493301963,"score_spread":0.280525311499159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198886025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0718723,0.00013712491,0.91400224,0.0026434911,0.00017636022,0.009505427,0.000006017389,0.000026863316,0.001630145],"genre_scores_gemma":[0.8636543,0.000024698751,0.13124093,0.00021581481,0.00013777765,0.0046859113,4.1402225e-7,0.000011148099,0.000028998793],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99817234,0.00010574062,0.0005434052,0.00031981428,0.0005897793,0.0002689206],"domain_scores_gemma":[0.9985609,0.0003907677,0.00039226864,0.00015835819,0.00025389396,0.0002437851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010168419,0.0001603008,0.00038050202,0.00018820711,0.00027643383,0.0007455109,0.0003493542,0.00006471065,0.00002931406],"category_scores_gemma":[0.00012096366,0.00010085879,0.00016292598,0.0006819392,0.00020508906,0.0011659992,0.00018453274,0.00013089222,0.0000013107765],"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":[0.0003006674,0.0019677714,0.009470844,0.0034273856,0.0004432263,0.00016795141,0.012710823,0.000005913062,0.043178726,0.74962455,0.0025555054,0.17614663],"study_design_scores_gemma":[0.0050861416,0.0030347449,0.0050525805,0.003723061,0.000086698456,0.0013421307,0.0025338884,0.0076246266,0.037327964,0.9242175,0.008982115,0.0009884987],"about_ca_topic_score_codex":7.790624e-7,"about_ca_topic_score_gemma":0.0000030422368,"teacher_disagreement_score":0.791782,"about_ca_system_score_codex":0.000011734916,"about_ca_system_score_gemma":0.00010948784,"threshold_uncertainty_score":0.7188979},"labels":[],"label_agreement":null},{"id":"W3199057864","doi":"10.1109/iaict52856.2021.9532513","title":"IoT device for Athlete's movements recognition using inertial measurement unit (IMU)","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Moncton","funders":"","keywords":"Inertial measurement unit; Wearable computer; Units of measurement; Computer science; Data collection; Visualization; Athletes; Protocol (science); Internet of Things; Real-time computing; Simulation; Human–computer interaction; Artificial intelligence; Embedded system","score_opus":0.24984764218915495,"score_gpt":0.31950459131511344,"score_spread":0.06965694912595849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199057864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17231156,0.00010123824,0.8234811,0.00043484144,0.0009687974,0.0005403951,0.000016987591,0.00021070334,0.0019344267],"genre_scores_gemma":[0.95613736,0.0000028958214,0.041760996,0.0013644068,0.0002451771,0.00009349527,0.000022363849,0.000021711454,0.0003516169],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979642,0.00013246531,0.00039710777,0.0005426542,0.00062963774,0.0003339049],"domain_scores_gemma":[0.99773484,0.00013470171,0.00015316802,0.00043816093,0.0014201971,0.000118906115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007286255,0.00018289158,0.00023151543,0.00011550035,0.0001921317,0.0002788764,0.00033082018,0.0000807574,0.00010618868],"category_scores_gemma":[0.00018369418,0.00019187466,0.0001262767,0.00047101398,0.0000144860915,0.00050637824,0.00021277177,0.0000937564,0.00010326066],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004954141,0.00060926296,0.0009528666,0.00020338618,0.00045953682,0.00004156815,0.0005593985,0.00014952487,0.41089386,0.0005656108,0.0010295027,0.58448595],"study_design_scores_gemma":[0.0041502174,0.00022640909,0.0012530815,0.0005210103,0.00011484877,0.00010561468,0.00043729227,0.24076024,0.7155575,0.00242451,0.0332594,0.001189882],"about_ca_topic_score_codex":0.00017493381,"about_ca_topic_score_gemma":0.00044669074,"teacher_disagreement_score":0.78382576,"about_ca_system_score_codex":0.00019987837,"about_ca_system_score_gemma":0.0002964149,"threshold_uncertainty_score":0.78244215},"labels":[],"label_agreement":null},{"id":"W3200260935","doi":"10.1016/j.patcog.2021.108332","title":"Gaussian-guided feature alignment for unsupervised cross-subject adaptation","year":2021,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Metric (unit); Artificial intelligence; Computer science; Pattern recognition (psychology); Feature (linguistics); Gaussian; Generalization; Inertial measurement unit; Feature vector; Divergence (linguistics); Performance metric; Mathematics; Engineering","score_opus":0.083874387626855,"score_gpt":0.3082893037845101,"score_spread":0.22441491615765508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200260935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07885208,0.0001432347,0.91518813,0.0022787184,0.0012462523,0.00074790407,0.00017887927,0.00029897632,0.0010658441],"genre_scores_gemma":[0.9843396,0.000025805852,0.01170858,0.0016731592,0.000364385,0.00048664067,0.000717293,0.000034599223,0.000649968],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977847,0.00024133161,0.0004055201,0.0007647546,0.00042004476,0.00038369116],"domain_scores_gemma":[0.99813247,0.00031208646,0.00021757177,0.00050041307,0.0007059321,0.00013149968],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043877796,0.00025012725,0.0002829443,0.00013473044,0.00022353361,0.00057622953,0.00031476025,0.00023447203,0.0001427287],"category_scores_gemma":[0.00016077362,0.00026983407,0.0002199893,0.00037031536,0.00002428509,0.000985917,0.00010760211,0.00020836855,0.000346118],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020555746,0.00017220802,0.0019553185,0.00014556841,0.00008167416,0.0000403304,0.0009936832,0.000014847101,0.0064596874,0.000037873215,0.0029350887,0.98714316],"study_design_scores_gemma":[0.01811781,0.00094480184,0.067537546,0.0018255198,0.00029191363,0.0016906912,0.0019550803,0.24131666,0.59386903,0.02093886,0.047209144,0.004302947],"about_ca_topic_score_codex":0.000049985287,"about_ca_topic_score_gemma":0.00014090035,"teacher_disagreement_score":0.98284024,"about_ca_system_score_codex":0.00014707989,"about_ca_system_score_gemma":0.0001451898,"threshold_uncertainty_score":0.9999754},"labels":[],"label_agreement":null},{"id":"W320082670","doi":"10.4271/2006-01-0812","title":"NaviQ - A User Satisfaction Questionnaire for IVNS","year":2006,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Context-Aware Activity Recognition Systems","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 Guelph","funders":"","keywords":"User satisfaction; Computer science; Human–computer interaction","score_opus":0.014888610342271565,"score_gpt":0.2575214291352863,"score_spread":0.24263281879301474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W320082670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8005551,0.0016941644,0.0066216174,0.051343683,0.0051870015,0.013014075,0.0005200908,0.033738177,0.0873261],"genre_scores_gemma":[0.98636955,0.000045167242,0.009224245,0.0015314166,0.00041173733,0.0012684419,0.000046226214,0.00010758328,0.0009956176],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99453294,0.00027698817,0.0012796832,0.0017582492,0.0010955307,0.0010566321],"domain_scores_gemma":[0.9961788,0.0008268663,0.00041852874,0.0018969218,0.0003394412,0.00033945747],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009061687,0.0008105651,0.0009549874,0.00033129362,0.00067355484,0.0004467092,0.0014564348,0.00087113166,0.0001241704],"category_scores_gemma":[0.0006874956,0.0007575313,0.0006649585,0.0010696471,0.0005458503,0.0018928424,0.00048864086,0.00094431697,0.00025819446],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014075932,0.00036651807,0.0012739628,0.000066817076,0.00003810128,0.000028770684,0.000023315068,0.000011418583,0.88632315,0.06958957,0.010522945,0.031614687],"study_design_scores_gemma":[0.0009090926,0.0008708741,0.90462655,0.00031907432,0.00004541444,0.00022167465,0.000024526826,6.867327e-7,0.0002427425,0.009797282,0.08213503,0.0008070265],"about_ca_topic_score_codex":0.00051727967,"about_ca_topic_score_gemma":0.22513825,"teacher_disagreement_score":0.9033526,"about_ca_system_score_codex":0.00058854005,"about_ca_system_score_gemma":0.0001934352,"threshold_uncertainty_score":0.9994876},"labels":[],"label_agreement":null},{"id":"W3202451637","doi":"10.1080/07853890.2021.1897439","title":"Proximity-aware interactive displays for rehabilitation centres","year":2021,"lang":"en","type":"article","venue":"Annals of Medicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Proxemics; Context (archaeology); Computer science; Human–computer interaction; Rehabilitation; Ubiquitous computing; Multimedia; World Wide Web; Medicine","score_opus":0.0673705440147087,"score_gpt":0.3588359447208305,"score_spread":0.2914654007061218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202451637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04001362,0.0007377748,0.77536064,0.17894782,0.0013590016,0.0009400293,0.000054977612,0.00013128178,0.0024548557],"genre_scores_gemma":[0.9961328,0.000026838406,0.0020914173,0.0011182294,0.0001795504,0.000057686753,0.000027447159,0.000007475289,0.000358576],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987074,0.00015560939,0.0003502415,0.00032020142,0.00029106572,0.00017544345],"domain_scores_gemma":[0.9964174,0.0017541132,0.00021453372,0.00038717547,0.0011463983,0.00008036999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053959875,0.00010596982,0.00030447938,0.00010675807,0.00004928357,0.000018968583,0.00025021812,0.000044906385,0.00004528565],"category_scores_gemma":[0.0020032963,0.00009016676,0.00009297303,0.0002972562,0.00008630901,0.0006087566,0.0000895012,0.000079902544,0.00000852491],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029407008,0.00095877657,0.0043649357,0.0011552253,0.0003506212,0.000046835565,0.020243373,0.000015063103,0.01589275,0.035300687,0.18705614,0.73432153],"study_design_scores_gemma":[0.014411363,0.008273553,0.061259955,0.009002055,0.00017427812,0.00029798722,0.019235598,0.05335769,0.27675736,0.119948,0.43551368,0.0017684826],"about_ca_topic_score_codex":0.000046351826,"about_ca_topic_score_gemma":0.000055594002,"teacher_disagreement_score":0.9561192,"about_ca_system_score_codex":0.000015478834,"about_ca_system_score_gemma":0.00008233409,"threshold_uncertainty_score":0.36768937},"labels":[],"label_agreement":null},{"id":"W3203871918","doi":"10.1145/3472290","title":"A Survey on Deep Learning for Human Activity Recognition","year":2021,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":253,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Deep learning; Computer science; Artificial intelligence; Data science; Activity recognition; Machine learning","score_opus":0.2303350838921676,"score_gpt":0.3915422512201308,"score_spread":0.1612071673279632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203871918","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018594241,0.462945,0.53290415,0.000017290897,0.0016275963,0.0013987737,0.00008951399,0.0006234813,0.00020825973],"genre_scores_gemma":[0.049346726,0.92962074,0.008976474,0.00017105405,0.0023370618,0.000598641,0.0073026232,0.00060935173,0.0010373255],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.979427,0.016149238,0.001070299,0.0019178026,0.00061150483,0.0008241739],"domain_scores_gemma":[0.9762945,0.01928117,0.0014795681,0.0019351592,0.00080695725,0.00020260135],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.014031649,0.00087883393,0.002929665,0.000533348,0.00082350126,0.0008868159,0.002310602,0.00058390124,0.000019767538],"category_scores_gemma":[0.0068119406,0.0009186741,0.0010325175,0.0014128014,0.000051718143,0.0003743487,0.0013825566,0.0013352731,0.00025531618],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013842043,0.00013642362,0.00008050286,0.0017710357,0.00020370037,0.000014873783,0.00006616951,0.000007109614,7.651463e-7,0.000017438122,0.00024800593,0.9974526],"study_design_scores_gemma":[0.0023274636,0.0013861678,0.012701525,0.05950325,0.0006390487,0.00028241702,0.00002936288,0.011427127,0.00004500259,0.0006407649,0.9042533,0.006764592],"about_ca_topic_score_codex":0.00080608664,"about_ca_topic_score_gemma":0.00088901276,"teacher_disagreement_score":0.990688,"about_ca_system_score_codex":0.0003768828,"about_ca_system_score_gemma":0.00037833746,"threshold_uncertainty_score":0.9993264},"labels":[],"label_agreement":null},{"id":"W3205137388","doi":"10.3390/s21216997","title":"Human Activity Recognition: A Comparative Study to Assess the Contribution Level of Accelerometer, ECG, and PPG Signals","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Activity recognition; Accelerometer; Random forest; Artificial intelligence; Computer science; Photoplethysmogram; Pattern recognition (psychology); Feature selection; Classifier (UML); Curse of dimensionality; Inertial measurement unit; Support vector machine; Speech recognition; Machine learning; Computer vision","score_opus":0.39542541375855733,"score_gpt":0.3934748632998047,"score_spread":0.0019505504587526423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205137388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9656194,0.000023505683,0.032552186,0.0004635737,0.00017236178,0.0007488059,0.000060578885,0.00005489976,0.00030467872],"genre_scores_gemma":[0.99919087,0.0000012451386,0.00044683478,0.00012084655,0.000044721946,0.000045140347,0.0000042939646,0.000006580315,0.00013948497],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99756765,0.0010521155,0.000309268,0.00047148677,0.0003842693,0.00021521792],"domain_scores_gemma":[0.9977774,0.00068851054,0.00022151985,0.00046862807,0.00074482034,0.000099121884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007742057,0.00016896032,0.00042237068,0.000107929554,0.00024346438,0.00022027481,0.00029345497,0.00004491665,0.000031319523],"category_scores_gemma":[0.00016829246,0.00014122394,0.00007341609,0.0006351887,0.00005892328,0.00039055405,0.00029486106,0.00017187376,0.000034237746],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002772466,0.008119178,0.033533674,0.00016504104,0.0019964352,0.00034450486,0.064984426,0.00024752424,0.60705984,0.001159593,0.0027408968,0.27937165],"study_design_scores_gemma":[0.003342331,0.0014208445,0.40064377,0.0002519988,0.00012480459,0.00020854374,0.012328885,0.0051843906,0.5733601,0.0014285524,0.0007814411,0.00092432974],"about_ca_topic_score_codex":0.00018562879,"about_ca_topic_score_gemma":0.0007894588,"teacher_disagreement_score":0.3671101,"about_ca_system_score_codex":0.000050436374,"about_ca_system_score_gemma":0.00007233576,"threshold_uncertainty_score":0.57589453},"labels":[],"label_agreement":null},{"id":"W3205200024","doi":"10.1007/s12652-021-03528-8","title":"LE2ML: a microservices-based machine learning workbench as part of an agnostic, reliable and scalable architecture for smart homes","year":2021,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Microservices; Workbench; Computer science; Scalability; Computational intelligence; Architecture; Software engineering; Artificial intelligence; Computer architecture; Distributed computing; Operating system; Cloud computing; Visualization","score_opus":0.029430226131789916,"score_gpt":0.27704007842821377,"score_spread":0.24760985229642385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205200024","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6093987,0.002133411,0.38761294,0.0002817258,0.00032229186,0.00016234088,0.0000026141122,0.00002753772,0.00005843694],"genre_scores_gemma":[0.9636288,0.00010564358,0.03576979,0.00027289937,0.0001252211,0.0000021723313,0.000005056361,0.000014808708,0.00007558983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813783,0.00014751231,0.0007503677,0.00035675892,0.00032027313,0.00028722658],"domain_scores_gemma":[0.9973036,0.00079617166,0.00075322756,0.00022748439,0.0007637425,0.00015575309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010845453,0.00019021264,0.00051260734,0.00022148261,0.0003201834,0.00037858155,0.00038477345,0.00007120648,0.000021676768],"category_scores_gemma":[0.0002360366,0.00017616486,0.00012844075,0.00031743222,0.00006488785,0.0003963385,0.00020476889,0.00036278687,0.0000017461289],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056702964,0.0012176177,0.022178777,0.0015057985,0.0004613551,0.0003103552,0.012922896,0.026919205,0.036886558,0.0031398912,0.00022622902,0.8936643],"study_design_scores_gemma":[0.0033782884,0.0045926967,0.0025102946,0.005365461,0.00021027328,0.002460723,0.0025902765,0.6446927,0.29898977,0.011809805,0.022278583,0.001121093],"about_ca_topic_score_codex":0.00009643661,"about_ca_topic_score_gemma":0.00008897181,"teacher_disagreement_score":0.8925432,"about_ca_system_score_codex":0.000028076047,"about_ca_system_score_gemma":0.00016912214,"threshold_uncertainty_score":0.71837944},"labels":[],"label_agreement":null},{"id":"W3210766530","doi":"10.3390/s22041476","title":"Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":489,"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":"Wearable computer; Activity recognition; Deep learning; Human–computer interaction; Wearable technology; Computer science; Artificial intelligence; Embedded system","score_opus":0.07817396725530723,"score_gpt":0.32966179546578067,"score_spread":0.25148782821047344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210766530","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012809629,0.990863,0.00042627903,0.000075230644,0.0002734114,0.0018868553,0.000011016322,0.00038795517,0.0059481203],"genre_scores_gemma":[0.00014739644,0.9978709,0.00024051109,0.000097433964,0.00010524302,0.0005491613,0.000042681877,0.000080310674,0.0008663653],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99356896,0.0028820357,0.00076428277,0.0013783994,0.00081668416,0.0005896261],"domain_scores_gemma":[0.9965739,0.0011682787,0.001109247,0.000915663,0.00009472675,0.00013813261],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013223673,0.0007030525,0.0022979325,0.0006397294,0.00035595885,0.00017090337,0.00075896183,0.00020153935,0.00030470232],"category_scores_gemma":[0.00030089548,0.0006128045,0.00043274273,0.0018383409,0.00005693989,0.0006832283,0.00024220117,0.0018504588,0.00044945555],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006310783,0.0000969414,0.0000019889424,0.014225486,0.000048654736,0.00013786946,0.000087491106,0.000058809925,3.3971972e-7,0.0000069206308,0.000030284798,0.98529893],"study_design_scores_gemma":[0.00024182374,0.00031918855,0.0000028125232,0.0857676,0.00011878558,0.00030401396,0.000039031245,0.0003089269,0.000003867171,0.000029331395,0.9121137,0.000750914],"about_ca_topic_score_codex":0.000120538774,"about_ca_topic_score_gemma":0.00019359509,"teacher_disagreement_score":0.984548,"about_ca_system_score_codex":0.00054542464,"about_ca_system_score_gemma":0.00020296116,"threshold_uncertainty_score":0.99963236},"labels":[],"label_agreement":null},{"id":"W3212060125","doi":"","title":"CoAware: Designing Solutions for Being Aware of a Co-Located Partner’s Smartphone Usage Activities","year":2020,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Saskatchewan; University of British Columbia","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.10115863510743316,"score_gpt":0.31114187013760614,"score_spread":0.20998323503017297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212060125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028162502,0.00014942107,0.9685678,0.0014866855,0.00032680514,0.00055248407,0.00008004141,0.00034827736,0.0003260208],"genre_scores_gemma":[0.99630827,0.000016265616,0.0030384678,0.00034679292,0.000063215106,0.000114216746,0.000013497893,0.000027375505,0.00007188501],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979259,0.0002104247,0.00049052265,0.00054812554,0.00033614138,0.00048891996],"domain_scores_gemma":[0.99793416,0.0008478977,0.00031434116,0.00044245392,0.00029167192,0.0001694533],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005984633,0.00024905664,0.0004423824,0.0002069531,0.0003206617,0.00016804802,0.00079267647,0.00012887143,0.000014576533],"category_scores_gemma":[0.00023486104,0.0002720121,0.00020955209,0.00080491486,0.00016113037,0.00074730255,0.0003026817,0.00028371176,0.000017917253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096107874,0.0015478324,0.016643122,0.0033426632,0.002282073,0.000083805586,0.092854895,0.003407997,0.61222446,0.111862786,0.066055,0.0887343],"study_design_scores_gemma":[0.0028775246,0.0012859614,0.0006870626,0.00079106697,0.00011797523,0.00006722131,0.0032871193,0.31983626,0.64994603,0.0049046655,0.014758576,0.0014405425],"about_ca_topic_score_codex":0.00007180019,"about_ca_topic_score_gemma":0.00007065089,"teacher_disagreement_score":0.9681458,"about_ca_system_score_codex":0.000048578473,"about_ca_system_score_gemma":0.00015389487,"threshold_uncertainty_score":0.9999732},"labels":[],"label_agreement":null},{"id":"W3213348235","doi":"10.1145/3456172.3456197","title":"Near-Native Interrupt Latency in Real-Time Guests: Handler Emulation Through Memory Map Morphing","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Emulation; Computer science; Interrupt; Embedded system; Latency (audio); Operating system; Morphing; Real-time computing; Psychology; Computer graphics (images)","score_opus":0.04156357165071944,"score_gpt":0.29058841345013026,"score_spread":0.2490248417994108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213348235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40480208,0.00017320788,0.4912062,0.0042711045,0.0014690185,0.00059458805,0.000006468713,0.0007043612,0.09677297],"genre_scores_gemma":[0.9781296,0.000011310359,0.015076451,0.0003131858,0.000085064894,0.00002793117,0.000015439507,0.00001549223,0.006325517],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980747,0.00030559444,0.00042710817,0.00057984504,0.00033186845,0.00028088517],"domain_scores_gemma":[0.9987045,0.00029459197,0.00013925575,0.0005216788,0.0002790006,0.00006097995],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038445008,0.00017892825,0.00029382628,0.00009926196,0.00010404951,0.00036271985,0.00037055698,0.000098545715,0.00050593767],"category_scores_gemma":[0.00011407602,0.00017793063,0.00008463031,0.00058869726,0.000038412287,0.0021818958,0.00035255015,0.00019170025,0.0007998836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018119234,0.0030882945,0.043028206,0.0005764995,0.0007953425,0.0031656502,0.22381923,0.0037957078,0.12995401,0.04670147,0.027385809,0.51750857],"study_design_scores_gemma":[0.004972602,0.0002908104,0.05656866,0.0015578234,0.00003888393,0.00037591526,0.0037303476,0.82730424,0.06540807,0.029111251,0.008271113,0.0023702956],"about_ca_topic_score_codex":0.0010861838,"about_ca_topic_score_gemma":0.00048401725,"teacher_disagreement_score":0.8235085,"about_ca_system_score_codex":0.00017056824,"about_ca_system_score_gemma":0.00015454908,"threshold_uncertainty_score":0.9999781},"labels":[],"label_agreement":null},{"id":"W3214733928","doi":"10.3233/ais-210616","title":"Caregiver development of activity-supporting services for smart homes","year":2021,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Smart Environments","topic":"Context-Aware Activity Recognition Systems","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":"Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Wizard; Activities of daily living; Wizard of oz; Home automation; Service (business); Computer science; Assisted living; Bathing; Upload; Independent living; Internet privacy; Human–computer interaction; Gerontology; World Wide Web; Medicine; Business; Physical therapy","score_opus":0.03245618576611292,"score_gpt":0.27041258316289374,"score_spread":0.23795639739678082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214733928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6039521,0.00020255402,0.39521185,0.00015041308,0.00032489066,0.00010263513,0.000003023014,0.000004156434,0.000048365622],"genre_scores_gemma":[0.9882549,0.00014280496,0.011238626,0.00008496226,0.000035598343,0.0000067200185,0.000001936654,0.00000790009,0.00022655545],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983413,0.00007186318,0.0006641774,0.00025530945,0.00044628425,0.00022101695],"domain_scores_gemma":[0.99861664,0.00016878998,0.00076424505,0.00021607029,0.000112781825,0.00012149054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006105504,0.00014531739,0.00034663393,0.00012190638,0.00011635953,0.00007712513,0.00034312363,0.00005826798,0.00002293722],"category_scores_gemma":[0.000032556018,0.00013412024,0.0001296847,0.00012590036,0.000041766303,0.00072583434,0.0002262416,0.00011915122,0.000008485106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007820237,0.0007345382,0.041841783,0.00037590758,0.00039495193,0.00007804204,0.014261542,0.00008058701,0.0646165,0.00013601157,0.00009405674,0.8773079],"study_design_scores_gemma":[0.00052653655,0.0003589647,0.035968002,0.0004876729,0.000052391024,0.0002631482,0.0032685655,0.0018919961,0.92160994,0.00052685273,0.034681685,0.0003642492],"about_ca_topic_score_codex":0.000009344045,"about_ca_topic_score_gemma":0.000020567632,"teacher_disagreement_score":0.87694365,"about_ca_system_score_codex":0.00007344153,"about_ca_system_score_gemma":0.00011531013,"threshold_uncertainty_score":0.5469265},"labels":[],"label_agreement":null},{"id":"W3217357390","doi":"10.1109/5gwf52925.2021.00039","title":"Human Activity Recognition and People Count for a SMART Public Transportation System","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Polytechnique Montréal","funders":"","keywords":"Activity recognition; Computer science; Public transport; Artificial intelligence; Machine learning; Human–computer interaction; Engineering; Transport engineering","score_opus":0.06827436082284524,"score_gpt":0.2636426873568802,"score_spread":0.19536832653403494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217357390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48132792,0.000022112172,0.5161192,0.0007221657,0.00023322734,0.0003600265,0.00005289287,0.00026396837,0.00089848577],"genre_scores_gemma":[0.9970964,0.0000022092597,0.0022156807,0.000098490724,0.000053611937,0.00021260699,0.00008049536,0.00001075483,0.00022971058],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987422,0.000113982904,0.00023172004,0.00047861598,0.00022050705,0.00021292496],"domain_scores_gemma":[0.9988385,0.0001693503,0.00012350385,0.00028402556,0.0004804724,0.00010416585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040238933,0.00012952102,0.00023359322,0.00009293526,0.00023045113,0.00038643557,0.00013674112,0.00008024225,0.000025044232],"category_scores_gemma":[0.00003579023,0.00013641524,0.00007986708,0.0003141109,0.00001444264,0.0012145565,0.00002352541,0.00007331654,0.000026624195],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003690795,0.0006856978,0.00919131,0.0017192503,0.00024530326,0.000046651752,0.0040336894,0.0000016390586,0.055133417,0.031697996,0.0022884444,0.8949197],"study_design_scores_gemma":[0.017908359,0.0015945716,0.62051314,0.001499103,0.0004172878,0.0015609005,0.010485094,0.11888223,0.15908143,0.023834849,0.038811103,0.005411927],"about_ca_topic_score_codex":0.00031397102,"about_ca_topic_score_gemma":0.0067834915,"teacher_disagreement_score":0.8895078,"about_ca_system_score_codex":0.000088991976,"about_ca_system_score_gemma":0.000113295035,"threshold_uncertainty_score":0.5562852},"labels":[],"label_agreement":null},{"id":"W3217516827","doi":"10.2196/31970","title":"The Smarter Safer Homes Solution to Support Older People Living in Their Own Homes Through Enhanced Care Models: Protocol for a Stratified Randomized Controlled Trial","year":2021,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Commonwealth Scientific and Industrial Research Organisation; Australian Government","keywords":"SAFER; Service provider; Medicine; Telecare; Nursing; Government (linguistics); Population; Workforce; Service (business); Aging in place; Work (physics); Intervention (counseling); Health care; Business; Gerontology; Telemedicine; Marketing; Environmental health; Computer science; Computer security","score_opus":0.17641267673575417,"score_gpt":0.4771406191474691,"score_spread":0.3007279424117149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217516827","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040564482,0.000003958351,0.16139328,0.0011700224,0.000053860327,0.8353538,0.000011014675,0.00012096466,0.001487442],"genre_scores_gemma":[0.06679142,5.3891796e-7,0.0010181685,0.00007343606,0.00023375181,0.9304052,0.000006761347,0.000038252096,0.0014324737],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.98982185,0.005097831,0.0013575265,0.0011051008,0.0013944109,0.0012232944],"domain_scores_gemma":[0.98767555,0.008401846,0.0003448496,0.0012883642,0.002095299,0.00019411268],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0081501035,0.0003984366,0.0015844445,0.0002914248,0.00074065925,0.0018218908,0.0013142659,0.00022302278,0.0000948072],"category_scores_gemma":[0.002163996,0.0002530372,0.00062338135,0.0011231933,0.00014459522,0.001437308,0.0006912545,0.0006001157,0.000036126297],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.8877551,0.0014962252,0.000028050636,0.0010528234,0.00021579704,0.00001542631,0.04286876,0.000046083132,0.0024926502,0.0029399013,0.004811364,0.05627785],"study_design_scores_gemma":[0.96410257,0.0014475815,0.000054318883,0.0022683202,0.00000410646,0.000006574786,0.0026552675,0.008247237,0.00594185,0.0045040287,0.010294912,0.00047321682],"about_ca_topic_score_codex":0.00005443342,"about_ca_topic_score_gemma":0.0015611683,"teacher_disagreement_score":0.16037512,"about_ca_system_score_codex":0.0003278997,"about_ca_system_score_gemma":0.0019730714,"threshold_uncertainty_score":0.9999922},"labels":[],"label_agreement":null},{"id":"W33699643","doi":"","title":"A self-configuration middleware for smart spaces","year":2009,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke","funders":"","keywords":"Middleware (distributed applications); Autonomic computing; Software deployment; Computer science; Ubiquitous computing; Configuration Management (ITSM); Context-aware pervasive systems; Distributed computing; Component (thermodynamics); Process (computing); Embedded system; Operating system; Computer network; Cloud computing","score_opus":0.021992011345695214,"score_gpt":0.23675065537121884,"score_spread":0.21475864402552364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W33699643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006071706,0.0007686915,0.9439656,0.018928422,0.0006084455,0.001536046,0.00007187495,0.001072046,0.026977183],"genre_scores_gemma":[0.7095631,0.00020650677,0.28202754,0.00031373402,0.00009354604,0.0005125917,0.00052740023,0.000058256795,0.006697307],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9927097,0.0041850554,0.00071863504,0.0013207421,0.00058278115,0.0004831067],"domain_scores_gemma":[0.9900176,0.0021625303,0.00089805335,0.002927578,0.0037620165,0.00023224574],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005573204,0.00047762456,0.00057431165,0.0003383839,0.0004615938,0.0017953586,0.002493462,0.00043462752,0.00003266415],"category_scores_gemma":[0.0012132593,0.0005366921,0.00039136855,0.0004641417,0.00008341429,0.00060526433,0.0010866536,0.0005601105,0.000088525485],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031692674,0.0019297701,0.0008162782,0.0012263109,0.00039864169,0.000014273612,0.032920804,0.00005650224,0.0026329476,0.26956803,0.008581135,0.6818236],"study_design_scores_gemma":[0.003803458,0.0000074924897,0.004459453,0.008539077,0.00025791087,0.000121486584,0.00047250753,0.37698835,0.16595268,0.084934294,0.35064414,0.003819136],"about_ca_topic_score_codex":0.00048357743,"about_ca_topic_score_gemma":0.0013497458,"teacher_disagreement_score":0.7034914,"about_ca_system_score_codex":0.00022700873,"about_ca_system_score_gemma":0.00058241637,"threshold_uncertainty_score":0.9997085},"labels":[],"label_agreement":null},{"id":"W347628959","doi":"","title":"Proceedings of the 2009 International Conference on Intelligent User Interfaces, February 8-11, 2009, Sanibel Island, Florida, USA","year":2009,"lang":"en","type":"article","venue":"Intelligent User Interfaces","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science","score_opus":0.04596534152141394,"score_gpt":0.28534041723226355,"score_spread":0.23937507571084962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W347628959","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91076446,0.0004701715,0.056098916,0.006879117,0.0064515453,0.0015044895,0.00014764801,0.00033614464,0.017347496],"genre_scores_gemma":[0.9919206,0.0002535453,0.00063987094,0.0010539779,0.00027326023,0.00005377537,0.000006893795,0.00003611857,0.0057619573],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99526715,0.00013008055,0.001394622,0.0012000463,0.001299864,0.00070826407],"domain_scores_gemma":[0.996681,0.00023433189,0.00085395854,0.0009159758,0.0010895344,0.00022524214],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007022549,0.0007056933,0.0007083948,0.00051179767,0.00019498546,0.00081895385,0.004536567,0.00026603998,0.00096060015],"category_scores_gemma":[0.00020079513,0.00054011866,0.00034765108,0.00079707726,0.00023754356,0.0014941595,0.00087120914,0.0007095609,0.0007815409],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013273067,0.0046316525,0.07185076,0.000347845,0.0016066398,0.00006349103,0.030086681,0.00081691286,0.10588175,0.093738556,0.26691005,0.42273834],"study_design_scores_gemma":[0.0007285414,0.0009873811,0.0102828415,0.0017859689,0.00004971705,0.00015145875,0.0021867577,0.0068104067,0.9153782,0.0032386703,0.057191767,0.0012082926],"about_ca_topic_score_codex":0.00052865426,"about_ca_topic_score_gemma":0.0009217659,"teacher_disagreement_score":0.80949646,"about_ca_system_score_codex":0.00029783032,"about_ca_system_score_gemma":0.00015062593,"threshold_uncertainty_score":0.9999965},"labels":[],"label_agreement":null},{"id":"W36749043","doi":"10.1128/mbio.03384-22","title":"Contextual Reasoning based Mobile Recommender System","year":2012,"lang":"en","type":"dissertation","venue":"mBio","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; Universität des Saarlandes; Deutsche Forschungsgemeinschaft; Burroughs Wellcome Fund","keywords":"Recommender system; Computer science; Context (archaeology); Process (computing); Popularity; Mobile device; Collaborative filtering; Human–computer interaction; Context awareness; World Wide Web; Data science; Multimedia","score_opus":0.023866094959814614,"score_gpt":0.26975971947565136,"score_spread":0.24589362451583674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W36749043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11726754,0.008081394,0.47878143,0.0003987013,0.04938245,0.006833711,0.00030483425,0.00648968,0.33246025],"genre_scores_gemma":[0.9909496,0.000002461155,0.0012115407,0.0001249722,0.00037132064,0.0004875712,0.0004758149,0.000055634122,0.006321117],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99750173,0.00030671904,0.00052568567,0.0006801543,0.0004915386,0.00049419346],"domain_scores_gemma":[0.9977621,0.000295848,0.0005325332,0.00091096084,0.00030071437,0.00019784644],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000651268,0.0004024078,0.00057949615,0.00026287904,0.0002075085,0.0003262019,0.00088063744,0.00042523697,0.00014993663],"category_scores_gemma":[0.000053361025,0.00041906405,0.00023679936,0.0003204968,0.000015153665,0.00059472886,0.000074574375,0.00043307824,0.000994873],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016899151,0.0007073967,0.0019409556,0.0027402572,0.0005618436,0.00011516407,0.010712068,0.0000149296,0.0030599753,0.009482434,0.0461979,0.9242981],"study_design_scores_gemma":[0.008663353,0.0011900419,0.018879097,0.014898948,0.00066987093,0.00058293773,0.029771082,0.071824774,0.0438686,0.00017430475,0.7996033,0.009873726],"about_ca_topic_score_codex":0.00029531895,"about_ca_topic_score_gemma":0.00030995542,"teacher_disagreement_score":0.91442436,"about_ca_system_score_codex":0.000324041,"about_ca_system_score_gemma":0.000243901,"threshold_uncertainty_score":0.99982613},"labels":[],"label_agreement":null},{"id":"W4200308169","doi":"10.1109/ictc52510.2021.9620781","title":"Compressed Neural Network for Thermal Array-Based Fall Detection System on Embedded AI","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Information and Communication Technology Convergence (ICTC)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Information Technology Research Centre; Ministry of Education, Science and Technology","keywords":"Computer science; Pruning; Artificial intelligence; Artificial neural network; Deep learning; ALARM; Constant false alarm rate; False alarm; Real-time computing; Computer vision; Machine learning; Engineering","score_opus":0.02905659715942332,"score_gpt":0.26895809591469366,"score_spread":0.23990149875527034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200308169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020830156,0.00007846966,0.95440507,0.014491187,0.0014652392,0.00067843165,0.000058465594,0.00056547625,0.0074275145],"genre_scores_gemma":[0.9916947,0.00006681701,0.005722134,0.0017463297,0.000047833342,0.00041841477,0.00020533633,0.000009477579,0.000088991095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981434,0.0002028013,0.0006559377,0.0003506243,0.00038675292,0.00026049654],"domain_scores_gemma":[0.99667186,0.00026467987,0.000499057,0.0010216873,0.0014625948,0.00008010325],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038762734,0.0002431361,0.00027451766,0.0003865146,0.0004091924,0.00043487956,0.0012321853,0.00025018447,0.00010029557],"category_scores_gemma":[0.00016252886,0.00025818098,0.000100671146,0.00052360195,0.00012151058,0.0013102721,0.00021311047,0.0004274774,0.00015131097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002178509,0.00017542191,0.0008303285,0.00009110725,0.00015917081,0.00000345721,0.0007570934,0.003029279,0.0066116005,0.73937684,0.00083421014,0.24791361],"study_design_scores_gemma":[0.0014488905,0.00020518071,0.0005635279,0.00027155448,0.000013716646,0.00003449771,0.001634787,0.9511292,0.030282,0.0021237878,0.0119095715,0.00038328153],"about_ca_topic_score_codex":0.00002619948,"about_ca_topic_score_gemma":0.00006214748,"teacher_disagreement_score":0.97086453,"about_ca_system_score_codex":0.00014912439,"about_ca_system_score_gemma":0.00019708078,"threshold_uncertainty_score":0.99998707},"labels":[],"label_agreement":null},{"id":"W4205136210","doi":"10.1051/matecconf/202235502024","title":"Resident user load classification method based on improved Gaussian mixture model clustering","year":2022,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Principal component analysis; Mixture model; Computer science; Data mining; Dimensionality reduction; Correlation clustering; Gaussian; Pattern recognition (psychology); Clustering high-dimensional data; CURE data clustering algorithm; Curse of dimensionality; Artificial intelligence; Machine learning","score_opus":0.049519478068500856,"score_gpt":0.29215239894538986,"score_spread":0.242632920876889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205136210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01009009,0.000028426144,0.96493196,0.00230611,0.00038312026,0.00042205452,0.0000380932,0.00020259105,0.021597547],"genre_scores_gemma":[0.98687387,0.0000022275042,0.011955643,0.0002618364,0.000022176315,0.00015993051,0.0000071365116,0.000010710339,0.00070648664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978553,0.0003933476,0.00039019916,0.00047878068,0.0006643106,0.00021808491],"domain_scores_gemma":[0.9984801,0.00027560093,0.0003540747,0.0006262438,0.00019430165,0.00006967405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009628921,0.00017320414,0.0002860723,0.00021919145,0.00018904472,0.00017901689,0.0009069191,0.000063173706,0.00021550001],"category_scores_gemma":[0.00006244441,0.00016421506,0.00008920042,0.00031774264,0.0000332012,0.00027873274,0.00027929942,0.00022501855,0.000012021175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092528225,0.0013069735,0.0064360755,0.00080978277,0.00029094113,0.00004659168,0.00704535,0.06950687,0.36456424,0.17547557,0.008248395,0.36534393],"study_design_scores_gemma":[0.0004048311,0.00014207647,0.0011695059,0.00004494132,0.000008047014,0.000005174746,0.00029169023,0.98723096,0.005171651,0.00088043715,0.004459669,0.00019104459],"about_ca_topic_score_codex":0.00025690795,"about_ca_topic_score_gemma":0.00017273055,"teacher_disagreement_score":0.97678375,"about_ca_system_score_codex":0.00013366851,"about_ca_system_score_gemma":0.0012599544,"threshold_uncertainty_score":0.6696496},"labels":[],"label_agreement":null},{"id":"W4205157073","doi":"10.1002/alz.056085","title":"Evaluating changes in gait and activity associated with cognitive impairment using a home‐based technology platform","year":2021,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Carleton University; Bruyère; University of Ottawa","funders":"","keywords":"Clinical Dementia Rating; Gait; Activities of daily living; Physical medicine and rehabilitation; Medicine; Dementia; Rating scale; Cohort; Cognition; Cognitive impairment; Montreal Cognitive Assessment; Cognitive decline; Physical therapy; Gerontology; Psychology; Disease; Internal medicine; Developmental psychology; Psychiatry","score_opus":0.09502133782675304,"score_gpt":0.3282458471282219,"score_spread":0.23322450930146887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205157073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9652775,0.0038780225,0.029419046,0.0005656139,0.00012407512,0.0005080653,0.00001248575,0.00015137954,0.00006383853],"genre_scores_gemma":[0.99528766,0.000005288746,0.0043802545,0.00018243388,0.000015985841,0.000099196404,0.000010089054,0.000017376906,0.0000016909405],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99815226,0.0002010039,0.00021931421,0.0006210017,0.0003839864,0.0004224476],"domain_scores_gemma":[0.9987737,0.0003141816,0.00025267736,0.00028477644,0.00030021436,0.00007443303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062632764,0.00022320717,0.00033306106,0.0003253168,0.00016334321,0.00016074488,0.0001981532,0.0001245218,0.000027166372],"category_scores_gemma":[0.00009136178,0.00022560744,0.000035554076,0.0010419335,0.000077253564,0.00052865566,0.00030534423,0.00021740363,0.0000058488486],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013002928,0.0011054922,0.08454236,0.000027628437,0.008637798,0.00040051414,0.001689133,0.00007327358,0.026181374,0.0001393922,0.000018138671,0.87705487],"study_design_scores_gemma":[0.0125432145,0.0018107297,0.1387881,0.0017043282,0.006288335,0.00030620286,0.0015853837,0.37728804,0.45679584,0.00091882265,0.00007606339,0.0018949247],"about_ca_topic_score_codex":0.000113074726,"about_ca_topic_score_gemma":0.0018369475,"teacher_disagreement_score":0.8751599,"about_ca_system_score_codex":0.00004464004,"about_ca_system_score_gemma":0.00028143192,"threshold_uncertainty_score":0.92000043},"labels":[],"label_agreement":null},{"id":"W4205175281","doi":"10.3390/s22020438","title":"Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":108,"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":"Current (fluid); Computer science; Systems engineering; Engineering; Risk analysis (engineering); Medicine; Electrical engineering","score_opus":0.6249967064854097,"score_gpt":0.495899882838799,"score_spread":0.12909682364661074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205175281","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000024826272,0.9890165,0.0036939161,0.00068115455,0.0032985532,0.002846506,0.00014034082,0.00021561867,0.000104942636],"genre_scores_gemma":[0.00015208818,0.99696493,0.00018431642,0.0000052085074,0.0005484026,0.00194537,0.000028915327,0.000050648967,0.00012008945],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99399346,0.0025901212,0.00071709085,0.001040658,0.0008478333,0.0008108578],"domain_scores_gemma":[0.9949283,0.0031272478,0.00045600007,0.0009728884,0.00023579151,0.0002797978],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0041186963,0.00036795862,0.0013714561,0.0008408891,0.0008884101,0.00032116685,0.00086455245,0.00015342329,0.0000028579673],"category_scores_gemma":[0.00028310312,0.0003545879,0.00031058662,0.0007028096,0.00007817487,0.00015240318,0.00051216787,0.0009031148,0.00003134639],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013757256,0.00006758493,3.863753e-7,0.018752899,0.00003935004,0.0000029262033,0.00036353595,0.0000057120374,2.0756106e-8,0.00087763974,0.00059770735,0.97929084],"study_design_scores_gemma":[0.00017418648,0.00016673813,0.0000012995908,0.0071129072,0.000023361483,0.00004450263,0.00022953456,0.0014235725,1.5949364e-7,0.00003114083,0.9904997,0.0002929409],"about_ca_topic_score_codex":0.00010509627,"about_ca_topic_score_gemma":0.000013206087,"teacher_disagreement_score":0.98990196,"about_ca_system_score_codex":0.0007389413,"about_ca_system_score_gemma":0.0013187882,"threshold_uncertainty_score":0.9998906},"labels":[],"label_agreement":null},{"id":"W4205324336","doi":"10.1145/3488162.3488185","title":"Leveraging Simulation and Virtual Reality for a Long Term Care Facility Service Robot During COVID-19","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"JDSU (Canada); University of British Columbia; Ontario Tech University","funders":"Mitacs","keywords":"Virtual reality; Robot; Computer science; Human–computer interaction; Wired glove; Avatar; Gesture; Metaverse; Human–robot interaction; Robotics; Aether; Artificial intelligence","score_opus":0.09143323765166443,"score_gpt":0.33665646131906,"score_spread":0.24522322366739557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205324336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34412166,0.000040724193,0.6543047,0.0009055488,0.000106792395,0.00025835348,0.000023997636,0.00016778476,0.000070444454],"genre_scores_gemma":[0.9978868,0.0000012238097,0.0011112834,0.0007332376,0.000042485128,0.000032567095,0.00004183715,0.000005174057,0.00014543386],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99854255,0.00016127559,0.0002559526,0.00061476656,0.00021038872,0.00021504961],"domain_scores_gemma":[0.9983799,0.0005175307,0.000084753454,0.0004548388,0.00038218312,0.00018079764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028970116,0.00013689965,0.0002023972,0.000055580436,0.00033304855,0.0002982061,0.00019532948,0.00006683633,0.00002246567],"category_scores_gemma":[0.00022569277,0.00014733548,0.000059163907,0.00025390784,0.000015518748,0.00074728415,0.00030681575,0.000082393395,0.000007669906],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022845574,0.00033812606,0.2812948,0.006165109,0.0002788266,0.00020719005,0.081521064,0.111122996,0.021345278,0.0010496175,0.00011228326,0.49633625],"study_design_scores_gemma":[0.004630783,0.00008885911,0.524832,0.00015312059,0.000041694413,0.0002058446,0.00512703,0.44657293,0.015371583,0.0005255748,0.0013454484,0.0011051357],"about_ca_topic_score_codex":0.00038627035,"about_ca_topic_score_gemma":0.0016726071,"teacher_disagreement_score":0.6537651,"about_ca_system_score_codex":0.0002072815,"about_ca_system_score_gemma":0.00021141711,"threshold_uncertainty_score":0.60081667},"labels":[],"label_agreement":null},{"id":"W4205631737","doi":"10.2147/cia.s329668","title":"Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review","year":2022,"lang":"en","type":"article","venue":"Clinical Interventions in Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Pixel; Sensitivity (control systems); Image resolution; Resolution (logic); Infrared; Metrology","score_opus":0.13109708179851512,"score_gpt":0.4054174872882483,"score_spread":0.27432040548973313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205631737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2438806,0.005682767,0.7415293,0.0011160747,0.002140854,0.0041792556,0.000020699943,0.0006242275,0.0008262332],"genre_scores_gemma":[0.99740005,0.00002722542,0.0011397031,0.0003670624,0.000054697706,0.00045796315,0.000005025749,0.000017595994,0.0005307043],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9947797,0.0022868856,0.0017066544,0.0005637262,0.00039924544,0.000263787],"domain_scores_gemma":[0.99765,0.00044497068,0.00085707253,0.00077985105,0.00018274852,0.00008533011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029148862,0.00017309804,0.00068120396,0.000325076,0.00045509636,0.00014085807,0.00065894966,0.00005075958,0.000052635743],"category_scores_gemma":[0.0006069453,0.00016945737,0.00049880915,0.0010752976,0.00007289672,0.0005164132,0.0005810673,0.0006609912,0.000025688785],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015358695,0.011447057,0.029482253,0.87732345,0.0031411937,0.0027483208,0.0069773383,0.011599356,0.00070364383,0.007950879,0.003644608,0.044828303],"study_design_scores_gemma":[0.0041786265,0.0017266201,0.0063732527,0.7266523,0.00078315876,0.0018777692,0.0023740714,0.25132233,0.00012208421,0.0021997294,0.00057334366,0.0018166632],"about_ca_topic_score_codex":0.000103996535,"about_ca_topic_score_gemma":0.00028914466,"teacher_disagreement_score":0.7535194,"about_ca_system_score_codex":0.0003951709,"about_ca_system_score_gemma":0.000063806394,"threshold_uncertainty_score":0.6910271},"labels":[],"label_agreement":null},{"id":"W4205770184","doi":"10.4018/ijsi.289170","title":"KDA-Based WKNN-SVM Method for Activity Recognition System From Smartphone Data","year":2022,"lang":"en","type":"article","venue":"International Journal of Software Innovation","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Montréal","funders":"","keywords":"Support vector machine; Accelerometer; Artificial intelligence; Computer science; Pattern recognition (psychology); Gyroscope; Linear discriminant analysis; Kernel (algebra); Activity recognition; Process (computing); Machine learning; Data mining; Engineering; Mathematics","score_opus":0.10393650319449244,"score_gpt":0.34329715860992016,"score_spread":0.23936065541542773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205770184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052299608,0.000033689143,0.93818295,0.002337199,0.00539384,0.00030361,0.0012822506,0.00013674088,0.000030108535],"genre_scores_gemma":[0.74280345,0.0000011265658,0.25509295,0.0005921586,0.0006886426,0.00006439983,0.00071411836,0.000020877476,0.000022246182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99665153,0.0003865994,0.0009826017,0.00045092785,0.0013477524,0.0001806153],"domain_scores_gemma":[0.99278516,0.0014527701,0.0019562726,0.00055652077,0.0031966579,0.000052597592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029864006,0.0001808412,0.00032865492,0.000924868,0.00022124173,0.0002962843,0.002331632,0.000071212344,0.000071986266],"category_scores_gemma":[0.0010637034,0.00019642363,0.00012344387,0.00095125375,0.000019364941,0.0021775716,0.0005629583,0.00039331685,0.000013524076],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004533984,0.00032398562,0.0009953892,0.000032714408,0.00034261215,0.000050397175,0.00018096699,0.00044790062,0.007965966,0.00048892206,0.005315629,0.98340213],"study_design_scores_gemma":[0.023045361,0.0021631713,0.02130469,0.0016121047,0.00035739207,0.0028863086,0.0013458751,0.6083151,0.11199604,0.029435344,0.19490355,0.002635052],"about_ca_topic_score_codex":0.00017947423,"about_ca_topic_score_gemma":0.000017740334,"teacher_disagreement_score":0.9807671,"about_ca_system_score_codex":0.00074836425,"about_ca_system_score_gemma":0.0005312572,"threshold_uncertainty_score":0.8009923},"labels":[],"label_agreement":null},{"id":"W4206180796","doi":"10.22215/etd/2021-14703","title":"Knowledge Sharing Strategies for Smart Architectural Development: Facilitating Interdisciplinary Collaborative Design","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Scope (computer science); Engineering; Card sorting; Architecture; Engineering management; Computer science; Knowledge management; Systems engineering","score_opus":0.0645125924793377,"score_gpt":0.34250088284228725,"score_spread":0.27798829036294953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206180796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02253136,0.0005931879,0.9529594,0.00004653878,0.0015883467,0.0013732713,0.000022586246,0.00034812692,0.020537142],"genre_scores_gemma":[0.7053873,0.0000028234,0.2596891,0.00003408351,0.0001758009,0.0019677393,0.0011106373,0.00007449683,0.031558055],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9972473,0.00021654915,0.0007130847,0.0010936861,0.00028882618,0.00044054945],"domain_scores_gemma":[0.99676126,0.0012472345,0.00034638387,0.00044584987,0.0010881108,0.000111168665],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006024318,0.0004848405,0.00061038375,0.00035706995,0.00052159245,0.0011845378,0.0009513342,0.0002279637,0.00003789454],"category_scores_gemma":[0.00013767196,0.00047811356,0.00017174629,0.0006590551,0.000025161302,0.0009689806,0.000498322,0.0003251895,0.000060622915],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001264049,0.00014203283,0.000023044202,0.0013600021,0.0004542939,0.00002172262,0.31533113,0.00010766805,0.0020553113,0.0039719874,0.0010217326,0.6753847],"study_design_scores_gemma":[0.0037420688,0.0010026603,0.0021794653,0.007927379,0.00016117071,0.00016947271,0.7316476,0.13069135,0.07022121,0.03722415,0.0086836675,0.0063497694],"about_ca_topic_score_codex":0.000014057239,"about_ca_topic_score_gemma":0.002418524,"teacher_disagreement_score":0.6932703,"about_ca_system_score_codex":0.00021013562,"about_ca_system_score_gemma":0.0021225396,"threshold_uncertainty_score":0.9998523},"labels":[],"label_agreement":null},{"id":"W4206319684","doi":"10.3390/app12010479","title":"Context-Aware End-User Development Review","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Computer science; Personalization; Context (archaeology); End-user development; Implementation; Software deployment; Context management; Adaptation (eye); Software engineering; World Wide Web; Software; Data science; End user; Human–computer interaction; Ubiquitous computing","score_opus":0.044247884687693424,"score_gpt":0.2671449785972542,"score_spread":0.2228970939095608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206319684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04686212,0.029157555,0.61790687,0.032071173,0.00689407,0.0075175576,0.000056437,0.003364119,0.25617006],"genre_scores_gemma":[0.98911923,0.000051958734,0.002870405,0.0066331616,0.00003392061,0.0006195362,0.000004263359,0.0000073777155,0.0006601306],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972112,0.0001476818,0.0003965462,0.00073021336,0.0011097157,0.00040465486],"domain_scores_gemma":[0.9989433,0.00022362288,0.0002125748,0.00044646574,0.000061570114,0.000112447175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002094448,0.00017648673,0.0002887876,0.00015013848,0.0012727346,0.00018517987,0.002071775,0.00002225959,0.0006714904],"category_scores_gemma":[0.000023944278,0.00016247525,0.000064255255,0.0013084227,0.00014972569,0.0004215988,0.0010483947,0.00019842447,0.00037676212],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004315267,0.00015674929,0.00050260255,0.00015207728,0.00003271475,0.000019589197,0.0019628084,0.000024712903,0.000724715,0.06624972,0.031202871,0.89896715],"study_design_scores_gemma":[0.00029879983,0.00006923445,0.00051433884,0.00013084745,0.000006744508,0.00008875796,0.0008599152,0.00037463862,0.0027907416,0.0007443811,0.9936257,0.000495924],"about_ca_topic_score_codex":0.00002720827,"about_ca_topic_score_gemma":0.00003629315,"teacher_disagreement_score":0.9624228,"about_ca_system_score_codex":0.00012642781,"about_ca_system_score_gemma":0.00051368,"threshold_uncertainty_score":0.9788974},"labels":[],"label_agreement":null},{"id":"W4206353987","doi":"10.2196/29744","title":"Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality?","year":2021,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Software deployment; Older people; Population; Mythology; Population ageing; Gerontology; Computer science; Internet privacy; Medicine; Environmental health","score_opus":0.02468512674057996,"score_gpt":0.2947694804771419,"score_spread":0.2700843537365619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206353987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4136433,0.0012803001,0.58192426,0.00027135175,0.0008601815,0.0016199198,0.000013395781,0.00020960397,0.00017770991],"genre_scores_gemma":[0.9985664,0.000012042242,0.00050751294,0.000048365415,0.00009846166,0.00047790288,0.00000904079,0.0000109257535,0.00026936916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987738,0.00016142508,0.00026358734,0.00044664214,0.00015860572,0.00019589957],"domain_scores_gemma":[0.9992159,0.00014862798,0.00011214853,0.00027498693,0.00019787022,0.000050481456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002559844,0.00011013406,0.00020555705,0.00013351756,0.000092058755,0.0002657764,0.00012617187,0.000068063695,0.0000025117013],"category_scores_gemma":[0.000040621962,0.00010857895,0.000050012666,0.00034271998,0.000008990176,0.0006749281,0.00009664943,0.00009004292,0.0000037654509],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041591527,0.00007523361,0.0021762422,0.0017704129,0.000024293624,0.000044996217,0.014368716,0.000022127946,0.0017722164,0.00015526444,0.00009899992,0.9794499],"study_design_scores_gemma":[0.035583384,0.0015787153,0.30666548,0.035245482,0.000157197,0.0031405278,0.134365,0.35284606,0.042957347,0.0026709754,0.07914927,0.005640548],"about_ca_topic_score_codex":0.00019521154,"about_ca_topic_score_gemma":0.0010737955,"teacher_disagreement_score":0.97380936,"about_ca_system_score_codex":0.00012652074,"about_ca_system_score_gemma":0.00006963563,"threshold_uncertainty_score":0.4427721},"labels":[],"label_agreement":null},{"id":"W4206551694","doi":"10.7717/peerj-cs.788","title":"Ambient intelligence governance review: from service-oriented to self-service","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Context-Aware Activity Recognition Systems","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 de Sherbrooke","funders":"","keywords":"Ambient intelligence; Computer science; Interoperability; Service-oriented architecture; Service (business); Context (archaeology); Context awareness; Adaptation (eye); Architecture; Ubiquitous computing; Process (computing); Corporate governance; Knowledge management; Process management; Data science; World Wide Web; Artificial intelligence; Web service; Human–computer interaction; Engineering; Business","score_opus":0.02219663327169951,"score_gpt":0.25953249499540965,"score_spread":0.23733586172371013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206551694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021828106,0.0010617594,0.94230366,0.02767768,0.004774601,0.0010778466,0.00007523822,0.00081311323,0.0003880286],"genre_scores_gemma":[0.70969296,0.00016224723,0.18551219,0.10340988,0.0004228542,0.00057068514,0.00001986397,0.00004210396,0.00016722242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99347955,0.00030134036,0.0007058162,0.0019521349,0.0027213655,0.0008398051],"domain_scores_gemma":[0.99540097,0.00028697032,0.00040007287,0.0022217494,0.0011692232,0.00052100245],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020192594,0.0003729755,0.00050118787,0.00024047297,0.0009927923,0.00049905415,0.007012855,0.000034365075,0.00017735327],"category_scores_gemma":[0.00009494166,0.00039921617,0.00011789111,0.008345349,0.00006732844,0.0017652925,0.0071129324,0.0004259361,0.0009536089],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004849946,0.0016191511,0.0016960453,0.00063173415,0.00011979543,0.0002369212,0.022708409,0.0078471955,0.0069188075,0.01435252,0.053311814,0.8905091],"study_design_scores_gemma":[0.0003784645,0.00034639725,0.007926668,0.00069723395,0.000023578925,0.00020320725,0.000100255595,0.63561666,0.0037665654,0.0006425683,0.34906927,0.0012291203],"about_ca_topic_score_codex":0.0013284845,"about_ca_topic_score_gemma":0.00020497246,"teacher_disagreement_score":0.88927996,"about_ca_system_score_codex":0.000685066,"about_ca_system_score_gemma":0.0006139324,"threshold_uncertainty_score":0.999846},"labels":[],"label_agreement":null},{"id":"W4210290209","doi":"10.1108/978-1-80262-365-920221004","title":"The Ecology of the Augmented Mind: Designing Immersive Human+Computer Systems","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Ecology; Human–computer interaction; Computer science; Cognitive science; Psychology; Biology","score_opus":0.031736314708792894,"score_gpt":0.23490361095877768,"score_spread":0.20316729624998478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210290209","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026585793,0.0009816088,0.29306567,0.0016695699,0.01728236,0.0039600446,0.00006643873,0.0001924344,0.68251604],"genre_scores_gemma":[0.17271177,0.00004540076,0.0008466524,0.0005511036,0.0008581081,0.00027618028,0.000021367183,0.000108347,0.8245811],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99707294,0.0004050152,0.00077459903,0.00067726395,0.00071107835,0.00035909622],"domain_scores_gemma":[0.99586904,0.0012869701,0.0010880283,0.0014094423,0.00027875032,0.00006774041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087551173,0.00037970897,0.00058035715,0.00017491535,0.00081332366,0.0002007276,0.0025919022,0.00022309877,0.00035525742],"category_scores_gemma":[0.000016783151,0.00024129747,0.00039774843,0.00010960955,0.0001873336,0.00021384993,0.0018126473,0.00060284714,0.00009936719],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026339858,0.00010933574,0.00015909642,0.00017770824,0.0017965063,0.000085479885,0.0023393943,0.00028011267,0.0012890621,0.8830011,0.052446336,0.05828953],"study_design_scores_gemma":[0.0010015446,0.00054886466,0.00023533749,0.0004514285,0.00015874977,0.00039772087,0.00040175373,0.0068476032,0.0010306904,0.0030980452,0.98471934,0.001108907],"about_ca_topic_score_codex":0.00017507943,"about_ca_topic_score_gemma":0.00014535511,"teacher_disagreement_score":0.93227303,"about_ca_system_score_codex":0.00029225598,"about_ca_system_score_gemma":0.00023376213,"threshold_uncertainty_score":0.9839825},"labels":[],"label_agreement":null},{"id":"W4210317297","doi":"10.1145/3502803.3502809","title":"Searching Efficient Models for Human Activity Recognition","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Lakehead University","funders":"","keywords":"Computer science; Activity recognition; Artificial intelligence","score_opus":0.12741526327679178,"score_gpt":0.32178300760432205,"score_spread":0.19436774432753026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210317297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15248574,0.0000135691025,0.83494544,0.00056583824,0.00024358122,0.00030578088,0.000009558572,0.0002507556,0.011179716],"genre_scores_gemma":[0.9836856,0.0000010975249,0.015172201,0.00018097443,0.00008402291,0.00009516636,0.000013219601,0.000011607463,0.00075610745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985257,0.00017557404,0.00018398096,0.00052882027,0.00029022392,0.00029572417],"domain_scores_gemma":[0.99864453,0.00035509828,0.00007670599,0.0004450408,0.0003748406,0.00010377451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053262425,0.0001239078,0.00018577995,0.00010236644,0.00028741348,0.00031677997,0.00028817635,0.000059385093,0.00003949196],"category_scores_gemma":[0.00007443007,0.0001279283,0.00013625935,0.0002954588,0.000017927008,0.0006485614,0.00022285726,0.0001279728,0.00006677176],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010723937,0.00040085582,0.000015079583,0.0000720102,0.00004583672,0.000016385944,0.00075102755,0.0009924407,0.05613444,0.02679381,0.00033298045,0.91443443],"study_design_scores_gemma":[0.0008385112,0.000079792415,0.00018510586,0.00007285913,0.000009538624,0.000064573236,0.00010944781,0.80971724,0.14391063,0.04370115,0.0009382924,0.00037288645],"about_ca_topic_score_codex":0.00012517103,"about_ca_topic_score_gemma":0.00016079417,"teacher_disagreement_score":0.91406155,"about_ca_system_score_codex":0.00008105681,"about_ca_system_score_gemma":0.000116042334,"threshold_uncertainty_score":0.5216765},"labels":[],"label_agreement":null},{"id":"W4210501891","doi":"10.1109/chase52844.2021.00019","title":"Sensor-Based Human Activity Recognition for Elderly In-patients with a Luong Self-Attention Network","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Western University; McMaster University","funders":"","keywords":"Activity recognition; Pooling; Wearable computer; Inertial measurement unit; Computer science; Task (project management); Deep learning; Physical activity; Baseline (sea); Wearable technology; Sarcopenia; Physical medicine and rehabilitation; Artificial intelligence; Machine learning; Medicine; Embedded system; Engineering","score_opus":0.02377874415358329,"score_gpt":0.24408923043181022,"score_spread":0.2203104862782269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210501891","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6069089,0.0000029816533,0.39127144,0.00020539624,0.00019478696,0.00055124913,0.000006776965,0.00021237419,0.0006460915],"genre_scores_gemma":[0.97486025,3.0270337e-7,0.024459297,0.00020660242,0.00008515689,0.00013853026,0.000069790345,0.000017740998,0.00016233747],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9981992,0.00024249605,0.00026294458,0.0006074595,0.0003238378,0.00036402437],"domain_scores_gemma":[0.99848497,0.00026248713,0.0001818174,0.0004209089,0.0005672898,0.00008255542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003525493,0.00018620165,0.0002616447,0.00012790266,0.00019149779,0.00023267268,0.00018772636,0.00009563879,0.000029785855],"category_scores_gemma":[0.00003527565,0.00018213876,0.00009970559,0.00062342285,0.00001461883,0.0008759762,0.00005924121,0.00013966832,0.000032093954],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028139263,0.003173036,0.45350444,0.0002832453,0.00021302892,0.000043534055,0.00031817664,0.00020735907,0.0020639838,0.0005834858,0.001262975,0.5380653],"study_design_scores_gemma":[0.014370839,0.0017231306,0.9356126,0.0006525899,0.000080661935,0.000019715242,0.000109752604,0.034774702,0.0076260827,0.0018930862,0.0017164607,0.0014203588],"about_ca_topic_score_codex":0.00007370331,"about_ca_topic_score_gemma":0.00057612825,"teacher_disagreement_score":0.536645,"about_ca_system_score_codex":0.00011270362,"about_ca_system_score_gemma":0.0001282122,"threshold_uncertainty_score":0.7427403},"labels":[],"label_agreement":null},{"id":"W4210857196","doi":"10.1007/s12652-022-03735-x","title":"From speech acts to assistance acts for cognitive assistance in ambient assisted living: how to nudge cognitively impaired people to act independently","year":2022,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Quebec Network for Research on Aging; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Cognition; Nudge theory; Computer science; Assisted living; Ontology; Human–computer interaction; Activities of daily living; Psychology; Cognitive psychology; Social psychology; Medicine","score_opus":0.058723874050001824,"score_gpt":0.3052737586577532,"score_spread":0.24654988460775137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210857196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5841327,0.00006862013,0.41226578,0.0015107704,0.00088248774,0.0009821511,0.00005330064,0.000046840556,0.00005736229],"genre_scores_gemma":[0.9835475,0.0000050892377,0.01348401,0.0024092547,0.0003031359,0.00008256484,0.0000066782595,0.000042686625,0.000119077056],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99493647,0.0005598433,0.0012791735,0.0010461194,0.0013457253,0.00083265576],"domain_scores_gemma":[0.993809,0.0026262973,0.0011683976,0.00039060882,0.001346539,0.0006591615],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002498977,0.00046550907,0.0010257306,0.001051893,0.00067555596,0.0011603578,0.0014422707,0.00009387494,0.00004076464],"category_scores_gemma":[0.0014972988,0.00050735706,0.0002705525,0.001651839,0.00003089284,0.00088362896,0.001301234,0.00070912903,0.000020962761],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0049008024,0.005016878,0.03275015,0.00028817484,0.0010466428,0.0011163775,0.16953547,0.002697718,0.05894038,0.0012473112,0.005706162,0.71675396],"study_design_scores_gemma":[0.00891082,0.016515084,0.7029184,0.008925833,0.0004123841,0.0016989114,0.09588868,0.04489278,0.08765865,0.0024892846,0.023715103,0.005974074],"about_ca_topic_score_codex":0.00017322646,"about_ca_topic_score_gemma":0.001088856,"teacher_disagreement_score":0.71077985,"about_ca_system_score_codex":0.0007906303,"about_ca_system_score_gemma":0.00035696774,"threshold_uncertainty_score":0.99987656},"labels":[],"label_agreement":null},{"id":"W4212903060","doi":"10.3390/automation3010007","title":"A Multipurpose Wearable Sensor-Based System for Weight Training","year":2022,"lang":"en","type":"article","venue":"Automation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Mitacs","keywords":"Support vector machine; Artificial intelligence; Computer science; Linear discriminant analysis; Activity recognition; Wearable computer; Machine learning; Random forest; Inertial measurement unit; Perceptron; Decision tree; Accelerometer; Multilayer perceptron; Classifier (UML); Pattern recognition (psychology); Artificial neural network","score_opus":0.04210137497059739,"score_gpt":0.25486585630346653,"score_spread":0.21276448133286913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212903060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054649975,0.000038670176,0.94060725,0.0008316952,0.0010143638,0.00087735645,0.00002670647,0.0011397995,0.00081418274],"genre_scores_gemma":[0.98065215,1.4207538e-7,0.018117534,0.00016692707,0.00006632307,0.0007648179,0.00001878479,0.000013631457,0.00019967247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863464,0.00022819695,0.00026563607,0.0003203501,0.00033409905,0.00021710638],"domain_scores_gemma":[0.99897337,0.00033915427,0.00020169212,0.00033397932,0.0001009684,0.00005081086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070437655,0.000106075364,0.00017031503,0.00017447743,0.0004429231,0.0001315943,0.00032505245,0.000032987642,0.000021029955],"category_scores_gemma":[0.00004908448,0.00011869411,0.00009080299,0.0003540064,0.000008777627,0.00046220238,0.00007418393,0.000086945816,0.000049828188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008568649,0.0003587361,0.0005460643,0.00063287525,0.00012868068,0.000045505243,0.014739946,0.014627511,0.022791125,0.014898953,0.0044063684,0.92673856],"study_design_scores_gemma":[0.00095321384,0.0000886346,0.00026562248,0.000048008857,0.000007173056,0.0000492667,0.00076146214,0.9811843,0.0023674727,0.00009083571,0.014020045,0.00016398808],"about_ca_topic_score_codex":0.00004220101,"about_ca_topic_score_gemma":0.0000039559027,"teacher_disagreement_score":0.9665568,"about_ca_system_score_codex":0.0002574498,"about_ca_system_score_gemma":0.00013806637,"threshold_uncertainty_score":0.48402053},"labels":[],"label_agreement":null},{"id":"W4214671235","doi":"10.36227/techrxiv.19172369","title":"Deep Reinforcement Learning in Human Activity Recognition: A Survey","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Reinforcement learning; Activity recognition; Field (mathematics); Computer science; Deep learning; Artificial intelligence; Key (lock); Human–computer interaction; Data science; Machine learning; Computer security","score_opus":0.10120361642330274,"score_gpt":0.31386440901213597,"score_spread":0.21266079258883325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214671235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11900732,0.000052935855,0.84162974,0.00033022766,0.0014732188,0.0012765389,0.000009073317,0.00080086285,0.03542009],"genre_scores_gemma":[0.9953119,0.000017080212,0.0007022873,0.00014708663,0.00008417187,0.00059411867,0.00027043084,0.000029886189,0.0028430165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947617,0.002009772,0.00066780794,0.0012754461,0.00079868274,0.00048658508],"domain_scores_gemma":[0.9973551,0.0006064774,0.00056802336,0.0011146298,0.00022121632,0.00013452981],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031209225,0.00042013742,0.0006559402,0.0006037878,0.00032166636,0.0005038887,0.0014347986,0.00025640527,0.0019085038],"category_scores_gemma":[0.00025141975,0.00049530755,0.00021017763,0.000674003,0.000034420507,0.00060516834,0.0049688444,0.0021447516,0.00014570911],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013870875,0.0011517638,0.057033647,0.00067667867,0.000434997,0.00033183565,0.0060671857,0.08271603,0.0006757019,0.000764913,0.0017827959,0.8482257],"study_design_scores_gemma":[0.0034861001,0.00078529987,0.224779,0.0007155003,0.000042925116,0.00009757444,0.0005712255,0.7488993,0.0020253705,0.0059467847,0.008108188,0.004542783],"about_ca_topic_score_codex":0.011000042,"about_ca_topic_score_gemma":0.008545068,"teacher_disagreement_score":0.8763046,"about_ca_system_score_codex":0.00071939226,"about_ca_system_score_gemma":0.00032070826,"threshold_uncertainty_score":0.99974984},"labels":[],"label_agreement":null},{"id":"W4214716450","doi":"10.36227/techrxiv.19172369.v1","title":"Deep Reinforcement Learning in Human Activity Recognition: A Survey","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Reinforcement learning; Field (mathematics); Activity recognition; Computer science; Artificial intelligence; Deep learning; Key (lock); Human–computer interaction; Data science; Machine learning; Computer security","score_opus":0.10120361642330274,"score_gpt":0.31386440901213597,"score_spread":0.21266079258883325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214716450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11900732,0.000052935855,0.84162974,0.00033022766,0.0014732188,0.0012765389,0.000009073317,0.00080086285,0.03542009],"genre_scores_gemma":[0.9953119,0.000017080212,0.0007022873,0.00014708663,0.00008417187,0.00059411867,0.00027043084,0.000029886189,0.0028430165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947617,0.002009772,0.00066780794,0.0012754461,0.00079868274,0.00048658508],"domain_scores_gemma":[0.9973551,0.0006064774,0.00056802336,0.0011146298,0.00022121632,0.00013452981],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031209225,0.00042013742,0.0006559402,0.0006037878,0.00032166636,0.0005038887,0.0014347986,0.00025640527,0.0019085038],"category_scores_gemma":[0.00025141975,0.00049530755,0.00021017763,0.000674003,0.000034420507,0.00060516834,0.0049688444,0.0021447516,0.00014570911],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013870875,0.0011517638,0.057033647,0.00067667867,0.000434997,0.00033183565,0.0060671857,0.08271603,0.0006757019,0.000764913,0.0017827959,0.8482257],"study_design_scores_gemma":[0.0034861001,0.00078529987,0.224779,0.0007155003,0.000042925116,0.00009757444,0.0005712255,0.7488993,0.0020253705,0.0059467847,0.008108188,0.004542783],"about_ca_topic_score_codex":0.011000042,"about_ca_topic_score_gemma":0.008545068,"teacher_disagreement_score":0.8763046,"about_ca_system_score_codex":0.00071939226,"about_ca_system_score_gemma":0.00032070826,"threshold_uncertainty_score":0.99974984},"labels":[],"label_agreement":null},{"id":"W4220743649","doi":"10.36227/techrxiv.19172369.v2","title":"Deep Reinforcement Learning in Human Activity Recognition: A Survey","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Reinforcement learning; Field (mathematics); Activity recognition; Computer science; Artificial intelligence; Deep learning; Key (lock); Data science; Human–computer interaction; Machine learning; Computer security","score_opus":0.10120361642330274,"score_gpt":0.31386440901213597,"score_spread":0.21266079258883325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220743649","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11900732,0.000052935855,0.84162974,0.00033022766,0.0014732188,0.0012765389,0.000009073317,0.00080086285,0.03542009],"genre_scores_gemma":[0.9953119,0.000017080212,0.0007022873,0.00014708663,0.00008417187,0.00059411867,0.00027043084,0.000029886189,0.0028430165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947617,0.002009772,0.00066780794,0.0012754461,0.00079868274,0.00048658508],"domain_scores_gemma":[0.9973551,0.0006064774,0.00056802336,0.0011146298,0.00022121632,0.00013452981],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031209225,0.00042013742,0.0006559402,0.0006037878,0.00032166636,0.0005038887,0.0014347986,0.00025640527,0.0019085038],"category_scores_gemma":[0.00025141975,0.00049530755,0.00021017763,0.000674003,0.000034420507,0.00060516834,0.0049688444,0.0021447516,0.00014570911],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013870875,0.0011517638,0.057033647,0.00067667867,0.000434997,0.00033183565,0.0060671857,0.08271603,0.0006757019,0.000764913,0.0017827959,0.8482257],"study_design_scores_gemma":[0.0034861001,0.00078529987,0.224779,0.0007155003,0.000042925116,0.00009757444,0.0005712255,0.7488993,0.0020253705,0.0059467847,0.008108188,0.004542783],"about_ca_topic_score_codex":0.011000042,"about_ca_topic_score_gemma":0.008545068,"teacher_disagreement_score":0.8763046,"about_ca_system_score_codex":0.00071939226,"about_ca_system_score_gemma":0.00032070826,"threshold_uncertainty_score":0.99974984},"labels":[],"label_agreement":null},{"id":"W4223469422","doi":"10.2196/28260","title":"Detecting Anomalies in Daily Activity Routines of Older Persons in Single Resident Smart Homes: Proof-of-Concept Study","year":2022,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Activities of daily living; Standard deviation; Set (abstract data type); Computer science; Anomaly detection; Duration (music); Scale (ratio); Medicine; Gerontology; Psychology; Statistics; Data mining; Geography; Mathematics; Physical therapy; Cartography","score_opus":0.036687248840226123,"score_gpt":0.2863298733349766,"score_spread":0.2496426244947505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223469422","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99264276,0.000099460856,0.0056798905,0.00015097194,0.00020790217,0.00093593687,0.0000045952233,0.00005936419,0.00021909963],"genre_scores_gemma":[0.9993758,9.863199e-8,0.0002082519,0.000014856803,0.000020830243,0.00027488562,5.8749754e-7,0.000013099615,0.00009158274],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99774706,0.00060431956,0.00044433013,0.0004691634,0.00045020707,0.00028490502],"domain_scores_gemma":[0.99875045,0.0003790313,0.0003483435,0.0004293009,0.000059465576,0.000033432832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009877647,0.00015503458,0.00039689676,0.0005359351,0.00012355918,0.000064968095,0.00052177935,0.000027460892,0.000021085805],"category_scores_gemma":[0.00008907821,0.0001737617,0.00007433843,0.001133832,0.000036941638,0.000649087,0.00067780254,0.00025764672,4.771427e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033724453,0.0015479196,0.7041646,0.00012360382,0.000032925574,0.00007401197,0.15088494,0.00054769637,0.008409928,0.000009925655,0.000034312863,0.13413636],"study_design_scores_gemma":[0.0030273956,0.000855551,0.81691307,0.0005304226,0.000013126431,0.000058100384,0.08950744,0.014410334,0.07384668,0.0000792235,0.0001063542,0.00065227546],"about_ca_topic_score_codex":0.0016008038,"about_ca_topic_score_gemma":0.0021156517,"teacher_disagreement_score":0.13348408,"about_ca_system_score_codex":0.00022311966,"about_ca_system_score_gemma":0.00010104797,"threshold_uncertainty_score":0.7085796},"labels":[],"label_agreement":null},{"id":"W4224224459","doi":"10.1016/j.buildenv.2022.109057","title":"Transfer learning for estimating occupancy and recognizing activities in smart buildings","year":2022,"lang":"en","type":"article","venue":"Building and Environment","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Occupancy; Computer science; Transfer of learning; Domain (mathematical analysis); Reuse; Machine learning; Artificial intelligence; Work (physics); Engineering","score_opus":0.025134284194433403,"score_gpt":0.2355153246485352,"score_spread":0.2103810404541018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224224459","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6163749,0.00021808811,0.38273963,0.00027982958,0.0001100794,0.0001922186,0.0000025698948,0.000046299614,0.000036379235],"genre_scores_gemma":[0.9466148,0.000036483234,0.052913055,0.0000867086,0.000030000829,0.0002319996,0.000001754419,0.000013826973,0.00007134955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879104,0.000111223475,0.00020693571,0.00045529567,0.00017650869,0.0002589776],"domain_scores_gemma":[0.9994264,0.0003405289,0.000051919145,0.000120444936,0.0000030987412,0.0000576127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072110596,0.00013665526,0.00019744539,0.00013509704,0.00046201155,0.00011267283,0.00014158673,0.000029394818,0.000011811322],"category_scores_gemma":[0.000029942938,0.00015809883,0.0000366977,0.00008632056,0.00003051579,0.00043722638,0.00026473543,0.00021932535,6.927885e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026939631,0.000067719,0.018400913,0.00008700068,0.000024805966,0.00000847577,0.0037888514,0.0054983273,0.015272701,0.0007434021,0.00003057406,0.9560503],"study_design_scores_gemma":[0.00509957,0.0011453421,0.016331632,0.00046234345,0.00006279365,0.00044459334,0.0054439055,0.8537605,0.010184888,0.004191631,0.10096473,0.0019080775],"about_ca_topic_score_codex":0.00006920199,"about_ca_topic_score_gemma":0.0000033421434,"teacher_disagreement_score":0.9541422,"about_ca_system_score_codex":0.00010527643,"about_ca_system_score_gemma":0.000011668216,"threshold_uncertainty_score":0.64470834},"labels":[],"label_agreement":null},{"id":"W4224260518","doi":"10.3390/s22082829","title":"LIPSHOK: LIARA Portable Smart Home Kit","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Home automation; Variety (cybernetics); Modular design; Context (archaeology); Embedded system; Implementation; Computer science; Architecture; Cover (algebra); Systems engineering; Engineering; Software engineering; Telecommunications; Operating system; Artificial intelligence","score_opus":0.01962379691736156,"score_gpt":0.2225571053647789,"score_spread":0.20293330844741736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224260518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9604966,0.00017005068,0.007859178,0.00296645,0.0033204851,0.0004361955,0.000039660765,0.00079632836,0.023915047],"genre_scores_gemma":[0.98742497,0.0000032915864,0.0006856373,0.00053375633,0.00010196499,0.0000520261,0.000006986741,0.000016728207,0.0111746285],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982151,0.0002171269,0.00024764257,0.00046191123,0.00050598057,0.00035224418],"domain_scores_gemma":[0.99886817,0.0001419129,0.00012353607,0.0006766949,0.000071957504,0.00011770532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046445476,0.00014034605,0.00021503588,0.00016750759,0.00033398156,0.00014510847,0.0006788957,0.00003500231,0.00046081957],"category_scores_gemma":[0.000034648245,0.00015744234,0.00010746657,0.0006143606,0.000024350078,0.00032392525,0.00049786404,0.00025831675,0.0006831494],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001675661,0.002084362,0.06258401,0.00022940546,0.0006894635,0.0030128101,0.021126637,0.006725765,0.012161353,0.044840753,0.25413334,0.59224457],"study_design_scores_gemma":[0.00092216895,0.0002605394,0.008296454,0.000022961929,0.00001592801,0.00074438367,0.0008981996,0.032653376,0.0018881653,0.0020876871,0.9513453,0.0008648188],"about_ca_topic_score_codex":0.00022963893,"about_ca_topic_score_gemma":0.00003389681,"teacher_disagreement_score":0.697212,"about_ca_system_score_codex":0.00010626873,"about_ca_system_score_gemma":0.00009328862,"threshold_uncertainty_score":0.8780731},"labels":[],"label_agreement":null},{"id":"W4225264643","doi":"10.4018/978-1-6684-5295-0.ch026","title":"Comprehensive Ontological Model for Senior Wellness Activity Recognition in Smart Homes","year":2022,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Lakehead University","funders":"","keywords":"Ontology; Activity recognition; Context (archaeology); Home automation; Identification (biology); Inference; Set (abstract data type); Computer science; Field (mathematics); Human–computer interaction; Smart objects; Smart environment; Artificial intelligence; Internet privacy; Internet of Things; Geography; Telecommunications","score_opus":0.0747340704604796,"score_gpt":0.2826177264431696,"score_spread":0.20788365598269004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225264643","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040733614,0.0001835258,0.33090824,0.00036538867,0.0017745888,0.0029535869,0.0014660467,0.0006053952,0.6576699],"genre_scores_gemma":[0.97897196,0.000007922752,0.0033881555,0.000744356,0.00020367476,0.00077031204,0.000055626126,0.00006944654,0.01578853],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996751,0.00014118287,0.0005893518,0.0013149409,0.00064171443,0.0005617984],"domain_scores_gemma":[0.99779505,0.0004738463,0.00049354,0.0007839513,0.00027123236,0.00018237632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030765397,0.00063308806,0.0010458095,0.00022197138,0.00023366894,0.0002022856,0.00093708467,0.0005443079,0.00005442246],"category_scores_gemma":[0.000040620376,0.0006902372,0.0004712597,0.00006712728,0.0001036561,0.0003781653,0.0006897337,0.0006416863,0.00009342158],"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":[0.00033990972,0.00014311192,0.000024059094,0.00022056905,0.00017324166,0.0001866532,0.00041394657,0.00008292861,0.00010633019,0.5657391,0.0013865778,0.43118358],"study_design_scores_gemma":[0.0029294756,0.0004830867,0.00019749884,0.00042729944,0.0001034253,0.00040674652,0.000065487984,0.07915646,0.00021777935,0.85836995,0.055340976,0.0023018285],"about_ca_topic_score_codex":0.00024359631,"about_ca_topic_score_gemma":0.0008287881,"teacher_disagreement_score":0.97489864,"about_ca_system_score_codex":0.00088192575,"about_ca_system_score_gemma":0.0004093073,"threshold_uncertainty_score":0.9995549},"labels":[],"label_agreement":null},{"id":"W4225327638","doi":"10.21203/rs.3.rs-1614908/v1","title":"Attention Vision Transformers for Human Fall Detection","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Accelerometer; Transformer; Inertial measurement unit; Computer science; Artificial intelligence; Activity recognition; Machine translation; Computer vision; Speech recognition; Pattern recognition (psychology); Simulation; Engineering; Voltage","score_opus":0.12092626645475077,"score_gpt":0.4400089321862193,"score_spread":0.3190826657314685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225327638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10051296,0.0001961406,0.88922423,0.0010732465,0.0014034734,0.0045603244,0.00014893658,0.00046009946,0.002420563],"genre_scores_gemma":[0.9952047,0.000037614667,0.00040129764,0.000018055634,0.00025567715,0.0026675465,0.00021043868,0.00004359636,0.0011610911],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99471575,0.0011083466,0.00047640063,0.0012072274,0.0017984947,0.00069380424],"domain_scores_gemma":[0.99744606,0.0005307151,0.00017459794,0.00080912095,0.00084929005,0.00019020335],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0042322986,0.00026765052,0.0003677262,0.0010354322,0.0011628858,0.00073027064,0.0012113512,0.00031269377,0.00009794167],"category_scores_gemma":[0.00015194897,0.00029472116,0.0004623365,0.0006982728,0.00007301965,0.00057797064,0.0014076851,0.0016696062,0.00006515543],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011854294,0.00050656794,0.0007298673,0.0029261848,0.0001569586,0.000024651417,0.0015599008,0.00008824861,0.023216104,0.0016466342,0.0019341059,0.9670922],"study_design_scores_gemma":[0.011327751,0.014119301,0.07801366,0.0060529257,0.00016423431,0.00016517367,0.007256687,0.25574082,0.02362518,0.10528638,0.49182436,0.0064235195],"about_ca_topic_score_codex":0.0009928532,"about_ca_topic_score_gemma":0.0011795631,"teacher_disagreement_score":0.96066874,"about_ca_system_score_codex":0.00092150114,"about_ca_system_score_gemma":0.00028750647,"threshold_uncertainty_score":0.99995047},"labels":[],"label_agreement":null},{"id":"W4226018817","doi":"10.3390/electronics11071143","title":"OntoDomus: A Semantic Model for Ambient Assisted Living System Based on Smart Homes","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"AGE-WELL","keywords":"Ontology; Ambient intelligence; Multidisciplinary approach; Computer science; Context (archaeology); Terminology; Protégé; Web Ontology Language; Process (computing); Rotation formalisms in three dimensions; Semantic Web; Assisted living; Home automation; Independent living; OWL-S; Ubiquitous computing; Human–computer interaction; Adaptation (eye); Reusability; Context awareness; World Wide Web; Psychology; Software; Social Semantic Web; Phone","score_opus":0.025971869163627016,"score_gpt":0.2446003682889282,"score_spread":0.2186284991253012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226018817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019977136,0.00049235957,0.97633654,0.0006108205,0.0005478493,0.00088441675,0.000025219177,0.00055004645,0.0005756282],"genre_scores_gemma":[0.99568146,0.0000028610857,0.0022575085,0.00047031807,0.00004243928,0.0007404173,0.000012122305,0.000032461954,0.0007604249],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997539,0.00029271856,0.00033418866,0.000610123,0.00061717146,0.0006068044],"domain_scores_gemma":[0.99822855,0.0006269855,0.00021533298,0.0007230268,0.00012062342,0.00008549427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010862718,0.00021442327,0.0003252248,0.0002350162,0.00054062554,0.00019256245,0.0007833162,0.00004217944,0.0000098077635],"category_scores_gemma":[0.000081526145,0.00023472565,0.00020863386,0.0004662716,0.000012308864,0.0002230553,0.00022455705,0.00030425706,0.000019255513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082507887,0.0066654314,0.0017413594,0.0026578344,0.0010467569,0.00019742863,0.0095762,0.32852915,0.022648992,0.10863328,0.02707418,0.4904043],"study_design_scores_gemma":[0.0005315789,0.00045477523,0.000101884296,0.0000900264,0.000017568682,0.000047580903,0.00009372998,0.9927429,0.00037194477,0.00011350557,0.005165838,0.00026869957],"about_ca_topic_score_codex":0.000025575659,"about_ca_topic_score_gemma":0.00017413472,"teacher_disagreement_score":0.9757043,"about_ca_system_score_codex":0.0012876069,"about_ca_system_score_gemma":0.00058881,"threshold_uncertainty_score":0.9571834},"labels":[],"label_agreement":null},{"id":"W4226021167","doi":"","title":"Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review","year":2022,"lang":"en","type":"review","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Context-Aware Activity Recognition Systems","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":"Western University","funders":"","keywords":"Computer science; Medicine; Artificial intelligence; Digital library; Machine learning; Information retrieval; Data science; Medical physics","score_opus":0.36206844383593023,"score_gpt":0.5407743718181601,"score_spread":0.1787059279822299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226021167","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009051834,0.98380613,0.008189399,0.000017041475,0.00085608364,0.006039297,0.000065390246,0.00016360737,0.00077252596],"genre_scores_gemma":[0.0005439418,0.99721885,0.00023339358,0.0001576195,0.0001893328,0.0011374099,0.000026797119,0.00013625524,0.00035643126],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9876779,0.00479673,0.0034146502,0.0013628871,0.002095911,0.00065194565],"domain_scores_gemma":[0.9870952,0.0011294873,0.008418147,0.0022021162,0.0007734561,0.00038159225],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0036937834,0.0010673454,0.006518338,0.0020685904,0.0009777206,0.0031536866,0.007506054,0.0002908425,0.0015213536],"category_scores_gemma":[0.00077951996,0.0008784198,0.0011231994,0.004521558,0.00012595869,0.004734025,0.0028833058,0.0014570086,0.00004016804],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014969013,0.00025136975,0.00005176705,0.80402905,0.0016160847,0.00036667712,0.00014885054,0.000033853947,0.000048089078,0.000025264175,0.002566955,0.19084707],"study_design_scores_gemma":[0.0003669887,0.000042011336,0.00006076104,0.8391438,0.0027253618,0.0017947619,0.00004166429,0.00034437276,0.000026329782,0.00018082549,0.15372537,0.0015477671],"about_ca_topic_score_codex":0.00060574757,"about_ca_topic_score_gemma":0.000098684,"teacher_disagreement_score":0.1892993,"about_ca_system_score_codex":0.0012780443,"about_ca_system_score_gemma":0.0009072337,"threshold_uncertainty_score":0.9993914},"labels":[],"label_agreement":null},{"id":"W4226043156","doi":"10.1093/geroni/igab046.3648","title":"The Role of Relationship Quality for Solitude Experiences during the Pandemic","year":2021,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Context-Aware Activity Recognition Systems","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 British Columbia","funders":"","keywords":"Solitude; Psychology; Affect (linguistics); Experience sampling method; Social psychology; Developmental psychology; Communication; Psychotherapist","score_opus":0.07214593653059298,"score_gpt":0.34591256836643763,"score_spread":0.27376663183584465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226043156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8884861,0.00017655056,0.10938712,0.0010974157,0.0002336915,0.00015883647,0.000001070676,0.000038269256,0.00042095492],"genre_scores_gemma":[0.99903065,0.000002576966,0.0005814647,0.000069959606,0.000042990512,0.00016759757,0.0000016112974,0.0000030699584,0.00010010756],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99882406,0.00017887907,0.00050881994,0.00017086897,0.00018975233,0.00012761568],"domain_scores_gemma":[0.9976399,0.001419297,0.0002671008,0.00031280977,0.0003535146,0.0000073889955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016677107,0.00005349639,0.00008807483,0.000082385755,0.00028417847,0.00013058633,0.00031524326,0.000027040936,0.0000021831331],"category_scores_gemma":[0.00090457726,0.0000384056,0.00002773831,0.0012077453,0.000043441498,0.00036749872,0.000093610426,0.00010476326,0.0000014343561],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008505623,0.000034079796,0.6184977,0.00003971758,0.00001648012,6.436769e-7,0.03831612,0.00006563504,0.021431817,0.25184196,0.000019143814,0.069728136],"study_design_scores_gemma":[0.0008775492,0.000014674246,0.74987537,0.00019285922,0.0000034527188,0.000030650448,0.060480334,0.0102219535,0.08296194,0.08808701,0.0069404114,0.0003138062],"about_ca_topic_score_codex":0.000036918434,"about_ca_topic_score_gemma":0.00011668028,"teacher_disagreement_score":0.16375497,"about_ca_system_score_codex":0.00004861917,"about_ca_system_score_gemma":0.00009400243,"threshold_uncertainty_score":0.21856998},"labels":[],"label_agreement":null},{"id":"W4226079607","doi":"10.1007/s40368-022-00707-9","title":"Recognizing Overton window for child behaviour guidance","year":2022,"lang":"en","type":"letter","venue":"European Archives of Paediatric Dentistry","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Window (computing); World Wide Web","score_opus":0.02479580338932291,"score_gpt":0.24309389118632863,"score_spread":0.2182980877970057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226079607","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028877407,0.0085004885,0.6940344,0.1021193,0.025746938,0.004723018,0.004112046,0.0016090566,0.15626702],"genre_scores_gemma":[0.25865418,0.001495688,0.13958815,0.404342,0.07176343,0.0014050555,0.008035436,0.002308301,0.11240778],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99506503,0.0011387245,0.001024558,0.0012919281,0.0008083577,0.00067141326],"domain_scores_gemma":[0.99573463,0.0013289357,0.0013389669,0.0013911092,0.00008632428,0.00012000707],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007055599,0.00053196953,0.00070400647,0.00063028914,0.00035289695,0.00027737036,0.0032819144,0.00014265916,0.00008363626],"category_scores_gemma":[0.0002854328,0.00062559755,0.0007815777,0.00047103586,0.0001028691,0.0003912929,0.0017052466,0.0017201959,0.00010133807],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029719873,0.0001514716,0.0018440593,0.00092106167,0.00019018505,0.0019591756,0.0005205332,0.000006833618,0.0001773649,0.00004152125,0.869072,0.12508608],"study_design_scores_gemma":[0.0011781303,0.00024133848,0.008930769,0.00031402748,0.0001472921,0.00071533414,0.000029628494,0.00017852623,0.0001915339,0.00018216447,0.986903,0.0009882726],"about_ca_topic_score_codex":0.000018161998,"about_ca_topic_score_gemma":0.0000031709576,"teacher_disagreement_score":0.5544463,"about_ca_system_score_codex":0.00006541992,"about_ca_system_score_gemma":0.00013748712,"threshold_uncertainty_score":0.99961954},"labels":[],"label_agreement":null},{"id":"W4226247552","doi":"10.1109/access.2022.3157726","title":"Activities Recognition, Anomaly Detection and Next Activity Prediction Based on Neural Networks in Smart Homes","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Universiti Kebangsaan Malaysia","keywords":"Autoencoder; Computer science; Activity recognition; Anomaly detection; Artificial intelligence; Deep learning; Recurrent neural network; Artificial neural network; Machine learning; Long short term memory; Anomaly (physics); Pattern recognition (psychology)","score_opus":0.042883348980844165,"score_gpt":0.25937951810843707,"score_spread":0.2164961691275929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226247552","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83779657,0.000030783776,0.15909478,0.00033396928,0.0017994164,0.00042797264,0.000026226759,0.00024873842,0.00024154497],"genre_scores_gemma":[0.9988617,0.0000074037443,0.00004044834,0.00044720512,0.00015409842,0.00043469892,0.000008911706,0.000018980405,0.000026564241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977946,0.00058086024,0.00025834684,0.00063381984,0.00041425516,0.00031807064],"domain_scores_gemma":[0.9987565,0.00049722655,0.00021003792,0.0003923432,0.000059601774,0.00008425694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005603152,0.00021336929,0.0002573082,0.0005125636,0.0004370665,0.0005968705,0.00048343095,0.00007967148,0.000039107515],"category_scores_gemma":[0.000037255657,0.00024499494,0.00006961429,0.0009041445,0.000045544995,0.0033354044,0.00023004501,0.0004933212,0.0000031853233],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035812487,0.00040851533,0.031065784,0.00004385237,0.000027597762,0.000039677525,0.00042821086,0.017518686,0.002550644,0.0000048972647,0.00018801274,0.947366],"study_design_scores_gemma":[0.00089636067,0.00034358035,0.10303914,0.000029248953,0.00000878531,0.000052753567,0.000075387514,0.89090973,0.0039330325,0.000111308465,0.00031666062,0.0002840341],"about_ca_topic_score_codex":0.0006006917,"about_ca_topic_score_gemma":0.0005847102,"teacher_disagreement_score":0.947082,"about_ca_system_score_codex":0.00024497038,"about_ca_system_score_gemma":0.000056035482,"threshold_uncertainty_score":0.99906033},"labels":[],"label_agreement":null},{"id":"W4226490471","doi":"10.1109/jsen.2022.3159205","title":"Smart Indoor Space Simulation Methodologies: A Review","year":2022,"lang":"en","type":"review","venue":"IEEE Sensors Journal","topic":"Context-Aware Activity Recognition Systems","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 Alberta","funders":"","keywords":"Computer science; Taxonomy (biology); Systems engineering; Process (computing); Space (punctuation); TRACE (psycholinguistics); Software engineering; Data science; Engineering","score_opus":0.3340283501462818,"score_gpt":0.4391871316926743,"score_spread":0.10515878154639252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226490471","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.150663e-7,0.8549747,0.13903563,0.00025093547,0.0038920662,0.00094078004,0.000016951564,0.00018430267,0.0007041336],"genre_scores_gemma":[0.0000033628523,0.9891603,0.008838571,0.00034212475,0.00064424955,0.00007497388,0.00000796647,0.00006108967,0.000867359],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98952657,0.006511792,0.0015690625,0.00076640584,0.0010766988,0.0005494865],"domain_scores_gemma":[0.99035305,0.005599942,0.0024304357,0.0010829192,0.00026480033,0.00026884096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0055028554,0.00063113926,0.0027971826,0.0006379063,0.0004499842,0.00046869315,0.0018608463,0.00027128795,0.0006196038],"category_scores_gemma":[0.0020342374,0.00052749296,0.0014277081,0.0013848157,0.00004508958,0.0007532264,0.00035221595,0.0021885945,0.00041171216],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016102553,0.000021737675,3.6404984e-7,0.0071644005,0.00017449215,0.00019781206,0.00007612928,0.00008266802,2.006807e-7,0.000013288623,0.006535831,0.9857315],"study_design_scores_gemma":[0.00013964652,0.00006986357,3.9836166e-7,0.022503573,0.00035170425,0.005356937,0.000014052806,0.0005117928,7.2820876e-7,0.00010712239,0.9704214,0.0005227984],"about_ca_topic_score_codex":0.000010220987,"about_ca_topic_score_gemma":0.0000021527887,"teacher_disagreement_score":0.9852087,"about_ca_system_score_codex":0.00060147856,"about_ca_system_score_gemma":0.0007940536,"threshold_uncertainty_score":0.99971765},"labels":[],"label_agreement":null},{"id":"W4229670627","doi":"10.4018/978-1-4666-4054-2.ch011","title":"Ubiquitous Multimedia Data Access in Electronic Health Care Systems","year":2013,"lang":"en","type":"book-chapter","venue":"Advances in wireless technologies and telecommunication book series","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Provisioning; Computer science; Multimedia; Information and Communications Technology; Health care; Mobile device; Wireless; Mobile technology; Field (mathematics); Multitude; Software; Special section; Information transfer; Telecommunications; World Wide Web; Engineering","score_opus":0.028821899595017558,"score_gpt":0.3006926207738628,"score_spread":0.2718707211788452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229670627","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041545078,0.9350955,0.0075483727,0.0071193003,0.00071216066,0.0041644196,0.00017570819,0.002714766,0.04205433],"genre_scores_gemma":[0.36184886,0.62914616,0.0029077118,0.00014402573,0.00003598906,0.00062771584,0.00037017372,0.00007551371,0.004843828],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969341,0.000130261,0.0009702945,0.0010571883,0.00030304558,0.0006050701],"domain_scores_gemma":[0.9942245,0.0004167976,0.00089158415,0.0042453394,0.00017081796,0.000050973114],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.00039846904,0.000516039,0.00096618506,0.00067619013,0.00020809486,0.00043954243,0.006293969,0.00052402396,0.000008052443],"category_scores_gemma":[0.00007706268,0.00052814727,0.000045399764,0.00025026192,0.00042231384,0.0071887714,0.004853958,0.0012231605,0.00002118812],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009549291,0.000025264222,0.00013050885,0.0003680444,0.000022505918,0.0000055604523,0.00032708715,0.000009906425,0.000004151275,0.13705924,0.00024845198,0.8617897],"study_design_scores_gemma":[0.0005585988,0.0002276263,0.000076975695,0.0017584754,0.000008175697,0.0000784202,0.0022205887,0.0030756446,0.00010929232,0.010251381,0.98066205,0.0009727902],"about_ca_topic_score_codex":0.00041002998,"about_ca_topic_score_gemma":0.0110695185,"teacher_disagreement_score":0.98041356,"about_ca_system_score_codex":0.0005563695,"about_ca_system_score_gemma":0.0003443363,"threshold_uncertainty_score":0.999717},"labels":[],"label_agreement":null},{"id":"W4230438372","doi":"10.1145/2954679.2872398","title":"Sidewinder","year":2016,"lang":"en","type":"article","venue":"ACM SIGPLAN Notices","topic":"Context-Aware Activity Recognition Systems","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":"Huawei Technologies (Canada); University of Toronto","funders":"","keywords":"Computer science; Software portability; Interface (matter); Embedded system; Software deployment; Mobile device; Participatory sensing; Real-time computing; Operating system","score_opus":0.041802953478724536,"score_gpt":0.2607015934689875,"score_spread":0.218898639990263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230438372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39716205,0.00031645104,0.54345226,0.025881302,0.0030519643,0.0005301383,0.000032509688,0.001468394,0.028104935],"genre_scores_gemma":[0.99501145,0.000003315411,0.0031286802,0.0005476866,0.00016400195,0.000013519204,7.797339e-7,0.000008892004,0.0011216665],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988017,0.0000937562,0.0001898703,0.00037085477,0.0002775387,0.00026627837],"domain_scores_gemma":[0.997498,0.001229768,0.00011414103,0.0009747111,0.0000816027,0.0001018211],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025071987,0.00012568994,0.0001552397,0.00009318157,0.00007943753,0.00015454162,0.001316003,0.000060669397,0.00017950153],"category_scores_gemma":[0.00044210858,0.00008244386,0.000054530836,0.00017409601,0.000037031656,0.0011564129,0.00036809602,0.000058272843,0.0019869895],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014594083,0.000100003395,0.023842996,0.000028194718,0.00008461884,0.000079508274,0.0010173639,0.0000021918522,0.017370116,0.007470644,0.013269414,0.9367204],"study_design_scores_gemma":[0.0063159713,0.00077464187,0.18726844,0.0009554646,0.00008910762,0.00046357754,0.0004894524,0.0023619274,0.12939388,0.051169142,0.61714536,0.0035730402],"about_ca_topic_score_codex":0.000051559055,"about_ca_topic_score_gemma":0.00007310905,"teacher_disagreement_score":0.9331473,"about_ca_system_score_codex":0.00003078027,"about_ca_system_score_gemma":0.00004548726,"threshold_uncertainty_score":0.9987901},"labels":[],"label_agreement":null},{"id":"W4231433643","doi":"10.1007/978-1-4939-7131-2_100204","title":"Context Networks","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Context (archaeology); Computer science; Geography; Archaeology","score_opus":0.035220089049065,"score_gpt":0.23090305165767513,"score_spread":0.19568296260861012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231433643","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.050708e-7,0.00022765866,0.42763728,0.00016119647,0.0011589917,0.00019436779,0.0000035293415,0.00032766562,0.5702884],"genre_scores_gemma":[0.022095736,0.0000321967,0.00093162013,0.0015096536,0.0009816106,0.000013807302,0.000010509195,0.000044333687,0.97438055],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99821675,0.000029476363,0.00039362104,0.00071303616,0.00036073418,0.0002863744],"domain_scores_gemma":[0.99790317,0.00021760979,0.00027796804,0.001126426,0.00032126645,0.00015356662],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00025997235,0.00036419943,0.00047368906,0.00015786546,0.00009360499,0.0002917256,0.0011027727,0.00042620228,0.004082325],"category_scores_gemma":[0.000013813047,0.00034067387,0.00022848557,0.000042671472,0.000098478486,0.00038761582,0.0004914539,0.00033898506,0.007537574],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038882126,0.000011079904,0.0000032886921,0.000011836365,0.00011211509,0.000034162993,0.00012793657,6.461141e-7,0.0000015619141,0.34354535,0.19795944,0.4581887],"study_design_scores_gemma":[0.00018407285,0.000077345074,0.000003941422,0.0001567555,0.000011928029,0.00007534714,0.000005799968,0.0044706943,0.000016233198,0.013836679,0.98067206,0.00048912055],"about_ca_topic_score_codex":0.000028948707,"about_ca_topic_score_gemma":0.00020225841,"teacher_disagreement_score":0.78271264,"about_ca_system_score_codex":0.00008154482,"about_ca_system_score_gemma":0.000096544005,"threshold_uncertainty_score":0.9999045},"labels":[],"label_agreement":null},{"id":"W4231643149","doi":"10.22215/etd/2017-12004","title":"Probability-Based Context-Aware Robotic System","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Computer science; Particle filter; Context (archaeology); Task (project management); Markov decision process; Artificial intelligence; Partially observable Markov decision process; Human–computer interaction; Service (business); Fuzzy logic; Process (computing); Distributed computing; Machine learning; Markov process; Systems engineering; Markov chain; Engineering; Kalman filter; Markov model","score_opus":0.05297754324102316,"score_gpt":0.2776439948813902,"score_spread":0.22466645164036703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231643149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004810644,0.0005116111,0.75672853,0.0008746148,0.014782306,0.0049832277,0.000056583434,0.004035209,0.21321726],"genre_scores_gemma":[0.9652007,0.0000013169024,0.0012907046,0.00012890228,0.00019171493,0.00043454586,0.00033088966,0.000059582864,0.03236163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611336,0.00033170704,0.000796042,0.0013815996,0.0008447771,0.00053249905],"domain_scores_gemma":[0.99463844,0.000358144,0.0010476278,0.002824897,0.0008867031,0.00024417575],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007063405,0.00063265854,0.0010526354,0.0003388989,0.0005371604,0.0012280062,0.0027259565,0.000603669,0.00010083917],"category_scores_gemma":[0.00017979494,0.00059647067,0.0004407206,0.00021600486,0.000053950524,0.00090217596,0.00009141969,0.0005251692,0.0010823047],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021700466,0.0009366165,0.0013770901,0.0152236335,0.00077147334,0.00042038073,0.0027869975,0.00022772257,0.00046918704,0.069430076,0.016219143,0.8919207],"study_design_scores_gemma":[0.018510751,0.00252015,0.0259687,0.04703934,0.0015851525,0.0008615871,0.015361583,0.6474873,0.05315277,0.00926029,0.15508786,0.023164518],"about_ca_topic_score_codex":0.00068548974,"about_ca_topic_score_gemma":0.0035797423,"teacher_disagreement_score":0.9603901,"about_ca_system_score_codex":0.0004365435,"about_ca_system_score_gemma":0.0010927633,"threshold_uncertainty_score":0.9998088},"labels":[],"label_agreement":null},{"id":"W4232025056","doi":"10.4018/978-1-60566-960-1.ch022","title":"An Adaptable Context Management Framework for Pervasive Computing","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition 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":"Queen's University","funders":"","keywords":"Ubiquitous computing; Context management; Computer science; Context-aware pervasive systems; Context awareness; Context (archaeology); Autonomic computing; End-user computing; World Wide Web; Human–computer interaction; Knowledge management; Data science; Cloud computing; Utility computing; Cloud computing security","score_opus":0.03498698967729343,"score_gpt":0.28082012392002237,"score_spread":0.24583313424272893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232025056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000030039566,0.00012225677,0.5521402,0.000075971795,0.0015617625,0.0010882475,0.00011040345,0.000348561,0.44452256],"genre_scores_gemma":[0.7485633,0.0000035086143,0.19950284,0.0025630365,0.0014608105,0.0001654185,0.000021174277,0.00014099827,0.047578912],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99687314,0.000047375805,0.00060960447,0.0012575586,0.00059147755,0.000620854],"domain_scores_gemma":[0.9968055,0.00028829888,0.00057229,0.0015883554,0.00043228,0.00031329787],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037446112,0.00063437066,0.0007740133,0.00012807579,0.00035992888,0.0006581411,0.0018001631,0.0007562464,0.00002101403],"category_scores_gemma":[0.00002887753,0.00069406076,0.00039909352,0.00003749463,0.00010318535,0.0002798127,0.0005613367,0.00065183674,0.00017275792],"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":[0.000015065989,0.000018608862,0.0000042875035,0.00006850903,0.00014473821,0.000039457234,0.00017369258,0.0000015044099,0.000013937612,0.79333943,0.0003969697,0.20578383],"study_design_scores_gemma":[0.0006885377,0.0002548902,0.000022331054,0.00084820023,0.000095490934,0.00010358298,0.000106249645,0.0018178548,0.00012974713,0.8158501,0.17904478,0.001038239],"about_ca_topic_score_codex":0.0000894614,"about_ca_topic_score_gemma":0.00024112924,"teacher_disagreement_score":0.74853325,"about_ca_system_score_codex":0.00027550926,"about_ca_system_score_gemma":0.00018355425,"threshold_uncertainty_score":0.99955106},"labels":[],"label_agreement":null},{"id":"W4232413020","doi":"10.1145/1461893","title":"Proceedings of the 1st ACM workshop on Vision networks for behavior analysis","year":2008,"lang":"en","type":"paratext","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Scope (computer science); Field (mathematics); Focus (optics); Pleasure; Event (particle physics); Virtual reality; Multimedia; Data science; Human–computer interaction","score_opus":0.04652173906841744,"score_gpt":0.30929009577940075,"score_spread":0.2627683567109833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232413020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012426088,0.000699616,0.8529015,0.0020718528,0.008539847,0.006691703,0.00018976773,0.0003408498,0.116138764],"genre_scores_gemma":[0.6877029,0.0003192811,0.004896038,0.0006521481,0.0009297995,0.0011363229,0.00009933792,0.00008486436,0.3041793],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997339,0.000046585286,0.0006588723,0.00091526925,0.00065699185,0.00038327402],"domain_scores_gemma":[0.99646395,0.0006669434,0.0008382694,0.0012068799,0.00072462694,0.00009930835],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036799975,0.00040584957,0.00088904274,0.0004994457,0.00024512174,0.0002780371,0.0030384867,0.000506393,0.0002067688],"category_scores_gemma":[0.0001252034,0.00027905343,0.00097570417,0.0022976785,0.00008299337,0.0003745141,0.000812784,0.00045166072,0.0002024089],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037811184,0.00038154057,0.00044172985,0.000080418446,0.0006540076,0.0000011402449,0.00029110533,0.00057072064,0.000091020185,0.00025318406,0.8990069,0.09819042],"study_design_scores_gemma":[0.0038777094,0.001425945,0.020352978,0.0037465594,0.0046666637,0.00013398724,0.00053081865,0.21323086,0.0062443246,0.0001973418,0.74045646,0.0051363516],"about_ca_topic_score_codex":0.000056507768,"about_ca_topic_score_gemma":0.00006803825,"teacher_disagreement_score":0.8480055,"about_ca_system_score_codex":0.000118034164,"about_ca_system_score_gemma":0.00010940767,"threshold_uncertainty_score":0.99996614},"labels":[],"label_agreement":null},{"id":"W4232560372","doi":"10.1002/9781119970422.ch1","title":"Introduction","year":2011,"lang":"en","type":"other","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Ubiquitous computing; Computer science; Context-aware pervasive systems; Mobile computing; Mobile device; Wireless; End-user computing; The Internet; Multimedia; Wireless sensor network; Wireless network; Ubiquitous robot; Human–computer interaction; Utility computing; Telecommunications; World Wide Web; Computer network; Cloud computing; Operating system; Artificial intelligence","score_opus":0.024698622355460935,"score_gpt":0.22441367636549528,"score_spread":0.19971505401003434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232560372","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.1723456e-8,0.00006648066,0.36552176,0.0002890337,0.0016192197,0.00009725922,0.0000011719017,0.0007529914,0.631652],"genre_scores_gemma":[0.00018091951,0.0000119159795,0.0065405807,0.00010257917,0.0018692855,0.000017382667,0.000003851802,0.00010046065,0.991173],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992039,0.000045442608,0.00010480509,0.00038892703,0.00013640462,0.00012047726],"domain_scores_gemma":[0.9991252,0.000011251656,0.00011659799,0.00067456585,0.000028704326,0.000043687658],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000090287125,0.00013040038,0.00016657158,0.00022406415,0.000014744924,0.000060678405,0.00046339852,0.00014101059,0.016185204],"category_scores_gemma":[0.000011373463,0.000117511765,0.00005133152,0.00014541401,0.000015006989,0.00014480577,0.00011940425,0.00009435211,0.011529213],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.0470151e-7,0.000012929127,0.000002649197,0.000008052099,0.0000160953,0.0000014112824,0.00002321095,3.1137326e-9,0.0000070023966,0.0071311384,0.92194647,0.07085083],"study_design_scores_gemma":[0.000052316078,0.000011718402,0.000008085574,0.00001700361,0.0000026872574,0.000021505406,0.0000026278333,0.000024327415,0.00011078449,0.00021210505,0.9993897,0.0001471243],"about_ca_topic_score_codex":0.00040154924,"about_ca_topic_score_gemma":0.00019861165,"teacher_disagreement_score":0.35952103,"about_ca_system_score_codex":0.000019297673,"about_ca_system_score_gemma":0.000029519606,"threshold_uncertainty_score":0.9892404},"labels":[],"label_agreement":null},{"id":"W4232564330","doi":"10.22215/etd/2005-07514","title":"Dynamap, a mobile and context-aware pedestrian navigation application","year":2005,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Computer science; Humanities; Cartography; Geography; Art","score_opus":0.009923632234520268,"score_gpt":0.26881384146626514,"score_spread":0.2588902092317449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232564330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25902793,0.0033779875,0.68474287,0.00081160024,0.0025726291,0.0071828933,0.000118622025,0.0024143152,0.039751124],"genre_scores_gemma":[0.98897934,0.00007942713,0.0006617747,0.00012498636,0.0002300033,0.0016185693,0.0012602239,0.000037812824,0.0070078694],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99771297,0.000110748755,0.00054280064,0.00091407774,0.00044309063,0.0002763246],"domain_scores_gemma":[0.998115,0.00017332411,0.00048229579,0.0006791637,0.00038888238,0.00016136022],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028923218,0.00037832497,0.00044115967,0.00024302454,0.0001975981,0.00043204756,0.0005579922,0.0004197856,0.00006442103],"category_scores_gemma":[0.000025436018,0.0003959981,0.00010107719,0.00040577498,0.000027090005,0.0009415371,0.00007227008,0.00033492869,0.00028381924],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001394775,0.00006312876,0.00015288086,0.00015995742,0.000034758858,0.0000031149905,0.0010540154,0.000002436952,0.00031573523,0.0019026607,0.00058225275,0.9957151],"study_design_scores_gemma":[0.009964286,0.0016048398,0.033239316,0.0039252723,0.000556976,0.0009443119,0.031161971,0.2986115,0.026268946,0.008267405,0.57518137,0.010273831],"about_ca_topic_score_codex":0.00051085796,"about_ca_topic_score_gemma":0.0036250104,"teacher_disagreement_score":0.98544127,"about_ca_system_score_codex":0.000153916,"about_ca_system_score_gemma":0.00021300842,"threshold_uncertainty_score":0.9998492},"labels":[],"label_agreement":null},{"id":"W4232620566","doi":"10.4108/eai.6-7-2017.152902","title":"Context-based Project Management","year":2017,"lang":"en","type":"article","venue":"EAI Endorsed Transactions on Context-aware Systems and Applications","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Context (archaeology); Software; Constant (computer programming); Computer science; Context management; Software engineering; Engineering management; Ubiquitous computing; Engineering; Human–computer interaction; Operating system; Programming language; Geography","score_opus":0.03184412504371953,"score_gpt":0.27823464978201784,"score_spread":0.2463905247382983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232620566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036786153,0.0003060247,0.9863973,0.0014763381,0.00070463953,0.003495669,0.00019579467,0.0005957947,0.0064605717],"genre_scores_gemma":[0.98925143,0.000047142927,0.0007184771,0.00033820645,0.00014280478,0.005433189,0.000016263448,0.00004424091,0.0040082135],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970043,0.00019476784,0.00068093766,0.0011150764,0.00050408504,0.00050088344],"domain_scores_gemma":[0.996271,0.00031575884,0.0005302863,0.002367415,0.00026826968,0.00024725904],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00045033044,0.00046077042,0.00057731365,0.00041799463,0.002380008,0.0013661768,0.0013387948,0.00018232927,0.000026129874],"category_scores_gemma":[0.0000120852455,0.0004525201,0.00024238275,0.0003084264,0.00021917014,0.00089373527,0.00004372252,0.0003683121,0.00023122375],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004876634,0.00065703824,0.00020410858,0.00036973334,0.00037331137,0.000034031247,0.00039208328,0.00019313167,0.00012529339,0.06478456,0.0014073276,0.9314106],"study_design_scores_gemma":[0.0076985387,0.00041845455,0.0029242334,0.0009846237,0.00022626376,0.00021632243,0.0030595711,0.048752833,0.002033244,0.00061739195,0.93104774,0.0020208019],"about_ca_topic_score_codex":0.00091787725,"about_ca_topic_score_gemma":0.00048251048,"teacher_disagreement_score":0.9888836,"about_ca_system_score_codex":0.00013254827,"about_ca_system_score_gemma":0.00014654761,"threshold_uncertainty_score":0.99979264},"labels":[],"label_agreement":null},{"id":"W4233286306","doi":"10.2196/mhealth.9177","title":"Addendum of: Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications","year":2017,"lang":"en","type":"erratum","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"mHealth; Robustness (evolution); Addendum; Computer science; Mobile phone; Artificial intelligence; Simulation; Human–computer interaction; Computer vision; Machine learning; Physical medicine and rehabilitation; Medicine; Telecommunications; Psychological intervention; Nursing","score_opus":0.06071108658791191,"score_gpt":0.36983319147328286,"score_spread":0.30912210488537095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233286306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05717178,0.0037966622,0.91860723,0.00046926114,0.007060523,0.010349826,0.00051454245,0.00041517158,0.0016149915],"genre_scores_gemma":[0.9720433,0.002321323,0.009215865,0.000115002644,0.0014896697,0.0057368297,0.00049487693,0.00011357562,0.008469567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99678475,0.00029742744,0.00090100407,0.0009453334,0.00043848972,0.00063298875],"domain_scores_gemma":[0.99625677,0.0004143546,0.0019828626,0.0006800308,0.0003734996,0.0002925054],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010062886,0.0004057519,0.00091566314,0.0009436021,0.0008803126,0.00023140707,0.0005897942,0.00034288852,0.0000043455275],"category_scores_gemma":[0.00004927682,0.00039231055,0.000096965785,0.0007333446,0.00012147908,0.0006361358,0.00013717152,0.0009767248,0.0000026366304],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005578649,0.0019864049,0.020346072,0.026560785,0.00029161078,0.000014971746,0.008611731,0.0038843476,0.00007686704,0.00053547235,0.012176572,0.9249573],"study_design_scores_gemma":[0.008115737,0.0074111307,0.034816395,0.008492781,0.00054638594,0.00019315853,0.0018345579,0.48297024,0.00018685685,0.0010104424,0.45042473,0.0039975788],"about_ca_topic_score_codex":0.0041653705,"about_ca_topic_score_gemma":0.028804112,"teacher_disagreement_score":0.9209597,"about_ca_system_score_codex":0.0002891793,"about_ca_system_score_gemma":0.0015545307,"threshold_uncertainty_score":0.9998529},"labels":[],"label_agreement":null},{"id":"W4233328228","doi":"10.4018/978-1-4666-4422-9.ch085","title":"State-of-the-Art Assistive Technology for People with Dementia","year":2013,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Mental health; Dementia; Scope (computer science); Work (physics); Independent living; Telecare; Health care; Home automation; Plan (archaeology); Computer science; Applied psychology; Psychology; Human–computer interaction; Knowledge management; Engineering; Telemedicine; Gerontology; Medicine; Psychiatry; Telecommunications","score_opus":0.014799682297571759,"score_gpt":0.22722888449315456,"score_spread":0.2124292021955828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233328228","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024263294,0.0001412391,0.2903877,0.00031420222,0.0006673773,0.00174588,0.000227854,0.0002317947,0.70604134],"genre_scores_gemma":[0.8423672,0.0000013388578,0.012538185,0.00029380756,0.00009860517,0.0005037502,0.00000436944,0.000069399495,0.14412339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99824893,0.000023543154,0.0003993754,0.00060487736,0.00039615645,0.00032712036],"domain_scores_gemma":[0.9977088,0.00010896477,0.0006132432,0.0009405969,0.0005504855,0.00007792215],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011280643,0.00035998496,0.0005739777,0.000115589035,0.00011548846,0.000106562424,0.0011344904,0.0002555989,0.000016774797],"category_scores_gemma":[0.000023307457,0.00027689387,0.00020391944,0.00006754067,0.00014672964,0.00012396806,0.00047559396,0.0002114627,0.00018464909],"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":[0.000026537567,0.000022243134,0.00044862015,0.000094835006,0.0005536896,0.000006367915,0.0000618098,0.0000014125972,0.000046326724,0.8765277,0.007517075,0.114693366],"study_design_scores_gemma":[0.0020262108,0.00096405187,0.0014297714,0.0014036348,0.00038797344,0.00031848726,0.000023821958,0.00051940675,0.001952286,0.72574186,0.2636245,0.0016080289],"about_ca_topic_score_codex":0.000061247076,"about_ca_topic_score_gemma":0.00079815683,"teacher_disagreement_score":0.8421245,"about_ca_system_score_codex":0.00015541934,"about_ca_system_score_gemma":0.00035898527,"threshold_uncertainty_score":0.99996835},"labels":[],"label_agreement":null},{"id":"W4233470818","doi":"10.1109/twc.2011.09.4416-tc1310-filler","title":"Call for papers","year":2013,"lang":"en","type":"paratext","venue":"IEEE Transactions on Communications","topic":"Context-Aware Activity Recognition Systems","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 Manitoba","funders":"","keywords":"Computer science; Telecommunications; Engineering","score_opus":0.06486297646993994,"score_gpt":0.30716628557203274,"score_spread":0.2423033091020928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233470818","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000028483187,0.00048006477,0.87417716,0.0042117466,0.00332349,0.0016103857,0.0006261708,0.00027609817,0.11529202],"genre_scores_gemma":[0.23770005,0.003589803,0.047818087,0.0019491656,0.00030665213,0.012187852,0.00041271973,0.00020883793,0.6958268],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99757475,0.00036040795,0.00063360773,0.00067301723,0.00033243472,0.0004257922],"domain_scores_gemma":[0.9916624,0.0018188981,0.0003418573,0.0054994915,0.00048006506,0.00019728212],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025103524,0.00044479358,0.00054539397,0.00045262082,0.00093106186,0.00041973506,0.0044512837,0.00045330345,0.00076605164],"category_scores_gemma":[0.000013337652,0.00047800987,0.00050181564,0.0005182379,0.00016592075,0.00053686864,0.000026036332,0.0009411347,0.013729056],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000135021455,0.0009052516,1.5744358e-7,0.00014579622,0.0004905642,5.2768286e-7,0.0007582464,0.0006763049,0.00075705873,0.0010886551,0.64680874,0.34835517],"study_design_scores_gemma":[0.00049579435,0.00012575352,0.0000024150636,0.00020111764,0.00006481124,0.000017817347,0.000041230964,0.011447618,0.0011084451,0.0001422332,0.9857586,0.0005941479],"about_ca_topic_score_codex":0.0003091454,"about_ca_topic_score_gemma":0.00044732928,"teacher_disagreement_score":0.8263591,"about_ca_system_score_codex":0.00026758405,"about_ca_system_score_gemma":0.00037840582,"threshold_uncertainty_score":0.9997672},"labels":[],"label_agreement":null},{"id":"W4234126694","doi":"10.1145/2980024.2872398","title":"Sidewinder","year":2016,"lang":"en","type":"article","venue":"ACM SIGARCH Computer Architecture News","topic":"Context-Aware Activity Recognition Systems","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":"Huawei Technologies (Canada); University of Toronto","funders":"","keywords":"Computer science; Software portability; Interface (matter); Embedded system; Software deployment; Participatory sensing; Mobile device; Real-time computing; Operating system","score_opus":0.023212185531594685,"score_gpt":0.24907305364666749,"score_spread":0.2258608681150728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234126694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015015007,0.00006165864,0.9613737,0.019740567,0.0011758978,0.00042287644,0.000007430117,0.00073308754,0.0014697904],"genre_scores_gemma":[0.86812675,0.000008308013,0.12637189,0.0036371343,0.0011586853,0.000063181746,0.0000024976866,0.00004111722,0.0005904318],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99663717,0.00041777288,0.00046268746,0.001045349,0.00069386,0.00074316707],"domain_scores_gemma":[0.9948875,0.0019386311,0.00016128388,0.0024979112,0.00017848345,0.00033619738],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039875053,0.00038610704,0.00042174524,0.0003601682,0.00017485849,0.00027735872,0.0032267077,0.00013482601,0.00010733119],"category_scores_gemma":[0.00024195062,0.0002543035,0.00024160984,0.0005079759,0.00011417667,0.00064418424,0.0020566697,0.00032381178,0.0007594751],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010864577,0.000052434218,0.0012604027,0.000011384169,0.000051971656,0.000045526984,0.00069888134,0.000019285138,0.004289401,0.0009840119,0.007289208,0.98528665],"study_design_scores_gemma":[0.0060334764,0.0011802211,0.0260859,0.00068492565,0.00003586067,0.0018546917,0.000028047903,0.004308548,0.02066024,0.14399576,0.79219663,0.0029357101],"about_ca_topic_score_codex":0.00006543551,"about_ca_topic_score_gemma":0.0001180815,"teacher_disagreement_score":0.98235095,"about_ca_system_score_codex":0.000094847914,"about_ca_system_score_gemma":0.00015022143,"threshold_uncertainty_score":0.99999094},"labels":[],"label_agreement":null},{"id":"W4234396608","doi":"10.1145/2544174.2500615","title":"Experience report","year":2013,"lang":"en","type":"article","venue":"ACM SIGPLAN Notices","topic":"Context-Aware Activity Recognition Systems","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":"Modular design; Computer science; Embedded system; Open source; Instrumentation (computer programming); Software engineering; Code (set theory); Source code; Gambit; Operating system; Programming language; Software; Simulation; Set (abstract data type)","score_opus":0.03905577636607352,"score_gpt":0.2778498679371863,"score_spread":0.23879409157111278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234396608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91971046,0.000109711335,0.057044383,0.0029926621,0.0014453593,0.0004575281,0.0000023485188,0.00062849507,0.017609049],"genre_scores_gemma":[0.9900568,0.0000016415268,0.008201923,0.0004535319,0.00012348409,0.00010049929,0.0000034264683,0.000008024711,0.0010506521],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985481,0.000067681874,0.00030208396,0.00046011107,0.0003426354,0.00027940274],"domain_scores_gemma":[0.99767447,0.00046027577,0.00019913686,0.0013871161,0.00014956511,0.0001294401],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020459927,0.00013760851,0.00017532504,0.00008238167,0.00011373862,0.00044324278,0.0015067551,0.000057917056,0.00027052438],"category_scores_gemma":[0.0005490345,0.00012278094,0.00005144368,0.00025413322,0.00004676359,0.0019673735,0.00043826803,0.00010262172,0.0019823087],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011820537,0.00041700326,0.14310332,0.00012019462,0.0001482516,0.0016047781,0.022718683,0.000030008754,0.024690725,0.004990499,0.05154301,0.75062174],"study_design_scores_gemma":[0.0024133336,0.00062854675,0.45679,0.00046536664,0.000055650584,0.00553944,0.0054756384,0.033700906,0.070376545,0.018772254,0.40137324,0.0044090836],"about_ca_topic_score_codex":0.0006891986,"about_ca_topic_score_gemma":0.000046042383,"teacher_disagreement_score":0.7462126,"about_ca_system_score_codex":0.000023656681,"about_ca_system_score_gemma":0.00004225382,"threshold_uncertainty_score":0.99879473},"labels":[],"label_agreement":null},{"id":"W4234852123","doi":"10.1007/978-0-387-39940-9_2783","title":"Human Centered 38 H Human-Computer Interaction Computing","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.04211490184657262,"score_gpt":0.28809598165869055,"score_spread":0.24598107981211792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234852123","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007297079,0.00083669135,0.16526742,0.00007907422,0.008548496,0.0020441215,0.00063238834,0.00082523783,0.8210369],"genre_scores_gemma":[0.45676327,0.00036323676,0.008978298,0.0003129671,0.013337996,0.000093910356,0.004194912,0.0005016236,0.5154538],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99435306,0.00027859345,0.0021066356,0.0015283108,0.0011418712,0.00059155544],"domain_scores_gemma":[0.9942281,0.00027070104,0.002328885,0.0023901665,0.00049885525,0.00028329418],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009243413,0.0009068161,0.0015449089,0.000858077,0.00032167733,0.0003504873,0.0018655757,0.0004534413,0.00012082892],"category_scores_gemma":[0.000027351289,0.00097510003,0.00046385362,0.0001596064,0.00009767076,0.0014233895,0.0009397401,0.0009525402,0.00041524516],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006307397,0.0012587261,0.00035703802,0.005245041,0.0020110148,0.0012929316,0.004562378,0.0002875841,0.0033604584,0.31027374,0.2281064,0.44318163],"study_design_scores_gemma":[0.0017642775,0.0006205681,0.00023803969,0.009453559,0.00015093702,0.0005240495,0.00010224126,0.006568226,0.00012980704,0.0007201576,0.9773593,0.002368856],"about_ca_topic_score_codex":0.0004063039,"about_ca_topic_score_gemma":0.0000863649,"teacher_disagreement_score":0.74925286,"about_ca_system_score_codex":0.00030358337,"about_ca_system_score_gemma":0.00014496101,"threshold_uncertainty_score":0.99926996},"labels":[],"label_agreement":null},{"id":"W4234922622","doi":"10.22215/etd/2011-07212","title":"Architecture for adaptive remote patient monitoring","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Usability; Computer science; Scalability; Population; Graphical user interface; Medicine; Database; Human–computer interaction","score_opus":0.045979990201133594,"score_gpt":0.2732552388579621,"score_spread":0.22727524865682852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234922622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007855264,0.00022237301,0.9486724,0.000034864104,0.0050911987,0.001290682,0.000019210078,0.000388895,0.036425084],"genre_scores_gemma":[0.77722955,0.000018648027,0.19069041,0.00010505263,0.000815015,0.000360235,0.00017351743,0.00011135905,0.030496232],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983938,0.00006142012,0.00033124984,0.00062872766,0.0002992637,0.00028558445],"domain_scores_gemma":[0.9984601,0.0001953078,0.0003246208,0.00054082973,0.0003810398,0.00009808456],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011728054,0.00029753003,0.0003380574,0.00022074665,0.00011968003,0.00012323362,0.0006216949,0.00024905172,0.000017738144],"category_scores_gemma":[0.000051137453,0.0002745549,0.00021636029,0.00017010928,0.00000798745,0.00025243277,0.00006512766,0.00026438377,0.0000679849],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052507836,0.000026012976,0.000006662175,0.000064087355,0.000061145496,0.0000036482393,0.0037376585,0.0000019649851,0.00024687365,0.00062131876,0.00034846365,0.99482965],"study_design_scores_gemma":[0.005570574,0.0064591626,0.006330976,0.0086459685,0.00050127273,0.00026208773,0.015859952,0.027889483,0.57368064,0.13209438,0.2116868,0.011018717],"about_ca_topic_score_codex":0.00035426024,"about_ca_topic_score_gemma":0.00038657864,"teacher_disagreement_score":0.98381096,"about_ca_system_score_codex":0.000063744614,"about_ca_system_score_gemma":0.00012598286,"threshold_uncertainty_score":0.9999707},"labels":[],"label_agreement":null},{"id":"W4235392299","doi":"10.15353/joci.v2i2.2086","title":"Learning in Communities: A Distributed Intelligence Perspective","year":2006,"lang":"en","type":"article","venue":"The Journal of Community Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Computer science; Distributed learning; Human intelligence; Human–computer interaction; Data science; Knowledge management; Cognitive science; Artificial intelligence; Psychology","score_opus":0.03878726270883993,"score_gpt":0.2844453997411701,"score_spread":0.2456581370323302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235392299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45889446,0.000059744914,0.5371899,0.0006166444,0.00008775288,0.00008943288,0.0000029687185,0.00003330556,0.0030257772],"genre_scores_gemma":[0.9991178,0.000040105508,0.00063392165,0.00015658842,0.00002497553,0.0000014045004,0.0000031103534,0.000004511089,0.0000175848],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99681234,0.0017180962,0.0009040287,0.000008549768,0.0003625843,0.00019438134],"domain_scores_gemma":[0.9953942,0.0024812175,0.0008687189,0.0005602725,0.0006548969,0.000040736955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003942772,0.00012527457,0.00027345674,0.00024812814,0.00043847488,0.0001863621,0.0022120713,0.000050882783,0.0000067910787],"category_scores_gemma":[0.00025317608,0.00009240774,0.00008163302,0.0006059414,0.00013676897,0.0014103296,0.00047193174,0.0019842389,0.000017725819],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005762317,0.00042221116,0.0022657441,0.00006376808,0.0000703342,0.0000030433737,0.9263988,0.03682543,0.00002834325,0.009178183,0.0007419772,0.023944527],"study_design_scores_gemma":[0.00045998377,0.00039327514,0.0043408833,0.00025737332,0.000016268697,0.00064709625,0.9396641,0.03584116,0.0002872938,0.016131295,0.0017644813,0.0001967542],"about_ca_topic_score_codex":0.0026658322,"about_ca_topic_score_gemma":0.00089291635,"teacher_disagreement_score":0.54022336,"about_ca_system_score_codex":0.00025406136,"about_ca_system_score_gemma":0.00013202983,"threshold_uncertainty_score":0.86206406},"labels":[],"label_agreement":null},{"id":"W4235563903","doi":"10.22215/etd/2010-09042","title":"Development of models for predicting severity of childhood falls","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University; Canadian Heritage; Public Health Agency of Canada; Library and Archives Canada","funders":"","keywords":"Humanities; Psychology; Computer science; Art","score_opus":0.026980219146876788,"score_gpt":0.2671605467218891,"score_spread":0.24018032757501231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235563903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.396514,0.000032234544,0.5929655,0.000010158218,0.0009756248,0.0009560376,0.00004064748,0.000109835186,0.008395993],"genre_scores_gemma":[0.85115033,0.000001720618,0.1475902,0.000009948406,0.000038426504,0.00010808918,0.00014052182,0.000016733757,0.0009440018],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982363,0.00002712231,0.0007523768,0.0004018597,0.00040318898,0.00017916092],"domain_scores_gemma":[0.9979698,0.00018563074,0.00075938785,0.00041335565,0.00061519234,0.000056628192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046494696,0.0002031116,0.00048381923,0.0001842333,0.0000713256,0.000035605728,0.0006859247,0.00029333835,0.000012254467],"category_scores_gemma":[0.0000754353,0.00020095216,0.00015270634,0.00016208742,0.000010949758,0.0004416273,0.00008144267,0.00020249291,0.0000028826455],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044558965,0.00062396034,0.0003522793,0.0016906188,0.00038573777,5.321232e-7,0.0488336,0.000026306283,0.01422582,0.004023943,0.00016528391,0.92962736],"study_design_scores_gemma":[0.003097428,0.00028662,0.032403212,0.0027360094,0.00015090224,0.000024030061,0.0063335453,0.1194241,0.8201777,0.010040877,0.0031372034,0.0021884295],"about_ca_topic_score_codex":0.000062136955,"about_ca_topic_score_gemma":0.0012649092,"teacher_disagreement_score":0.9274389,"about_ca_system_score_codex":0.000027991624,"about_ca_system_score_gemma":0.00061956776,"threshold_uncertainty_score":0.81945914},"labels":[],"label_agreement":null},{"id":"W4235664356","doi":"10.1007/s11036-014-0502-2","title":"Context-Aware Systems and Applications","year":2014,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Context (archaeology); Human–computer interaction; Geology","score_opus":0.010073271213082009,"score_gpt":0.2289391790902038,"score_spread":0.2188659078771218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235664356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064976723,0.0019437668,0.9936091,0.00017195026,0.0000857638,0.0016601678,0.000011840956,0.0002654666,0.0016022172],"genre_scores_gemma":[0.99003726,0.00017980294,0.00016456954,0.00024095413,0.0005154245,0.008533512,0.000019338106,0.000015556509,0.00029356673],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986849,0.00008608569,0.00029398096,0.0005570408,0.00013374118,0.0002442772],"domain_scores_gemma":[0.99850833,0.00036785795,0.00014716589,0.0006590523,0.00012772607,0.00018985779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030219872,0.00017160652,0.00024694626,0.00006997423,0.00042290884,0.00037005986,0.00034466814,0.00010765613,0.0000043271107],"category_scores_gemma":[0.0000044702406,0.00016837525,0.000038995862,0.0003425762,0.000102128804,0.00026755797,0.00018768298,0.00015118031,0.000038338483],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015183504,0.00008126788,0.0012935085,0.000048642327,0.000026160013,2.7321408e-7,0.000053871616,0.0022326582,0.00003277813,0.15011702,0.0012886723,0.84482366],"study_design_scores_gemma":[0.00030136152,0.00004461544,0.00086688204,0.000032131786,0.000015962987,0.000051414394,0.00014810268,0.25285286,0.0000115445455,0.0009942459,0.74440825,0.00027265438],"about_ca_topic_score_codex":0.00006685048,"about_ca_topic_score_gemma":0.000030884054,"teacher_disagreement_score":0.9934445,"about_ca_system_score_codex":0.000021153066,"about_ca_system_score_gemma":0.00001891467,"threshold_uncertainty_score":0.68661433},"labels":[],"label_agreement":null},{"id":"W4236853429","doi":"10.1109/ipsn.2016.7460664","title":"DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":402,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Inference; Mobile device; Bottleneck; Machine learning; Mobile processor; Overhead (engineering); Computer architecture; Embedded system; Distributed computing; Mobile technology; Mobile Web; Operating system","score_opus":0.0249704824860318,"score_gpt":0.2809048529370151,"score_spread":0.25593437045098333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236853429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11495366,0.00003861771,0.88238376,0.00020971912,0.0003496406,0.0005141117,0.000004593337,0.0005667521,0.0009791304],"genre_scores_gemma":[0.98984855,0.0000040752584,0.007902534,0.0005174434,0.00007134538,0.00040231404,0.0000014163679,0.00001867213,0.0012336374],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99836874,0.00009910387,0.00027998516,0.0005838835,0.00028633495,0.00038193658],"domain_scores_gemma":[0.997178,0.0017396741,0.00015610206,0.0004700146,0.00031590846,0.00014026629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029281338,0.00020626088,0.00024504677,0.0001328606,0.00019167853,0.00021405988,0.0007149395,0.00009889991,0.00028967307],"category_scores_gemma":[0.00042144733,0.00013959069,0.00011153033,0.00022681232,0.000031648473,0.0011377825,0.0002046176,0.00011011035,0.0007515271],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030720625,0.00014670599,0.014428133,0.000047828595,0.000045533434,0.000005861448,0.0007108479,0.000023842074,0.0026608368,0.0027893712,0.00047002756,0.9786403],"study_design_scores_gemma":[0.015687156,0.009556332,0.05750624,0.0036251121,0.00008580361,0.00015967748,0.0017249263,0.033602096,0.20248368,0.010082231,0.65771085,0.0077758958],"about_ca_topic_score_codex":0.000014010829,"about_ca_topic_score_gemma":0.00010874831,"teacher_disagreement_score":0.9708644,"about_ca_system_score_codex":0.00007926639,"about_ca_system_score_gemma":0.00008477091,"threshold_uncertainty_score":0.96596104},"labels":[],"label_agreement":null},{"id":"W4236876997","doi":"10.22215/etd/2006-07967","title":"Autonomous patient monitoring with a pressure sensor array","year":2006,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; Pressure sensor; Metric (unit); Artificial intelligence; Noise (video); Pattern recognition (psychology); Real-time computing; Speech recognition; Engineering","score_opus":0.012881534683795394,"score_gpt":0.2346951628207574,"score_spread":0.221813628136962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236876997","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32863662,0.0014715869,0.29341722,0.00027633074,0.010718723,0.0037032038,0.00006835567,0.0035466996,0.35816127],"genre_scores_gemma":[0.9332333,0.0000026574826,0.01045835,0.000023149794,0.00028986434,0.00019628575,0.00009829963,0.0000485064,0.05564959],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979332,0.00008240278,0.00036730716,0.0007246985,0.00057236943,0.00031998625],"domain_scores_gemma":[0.9984038,0.00009668888,0.00037815422,0.00067880185,0.0003470014,0.0000955417],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000084796,0.00036619182,0.00039795475,0.00020116258,0.00012920669,0.0003944309,0.00050005474,0.00023011814,0.000026364061],"category_scores_gemma":[0.000012197738,0.00030771838,0.00010636861,0.00027184788,0.000010951087,0.0004895082,0.00003272789,0.00030489068,0.0001100613],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024585557,0.0011180908,0.007649534,0.0015644354,0.0013823282,0.00039517155,0.018769154,0.0007158735,0.020255424,0.0010973088,0.009249368,0.93755746],"study_design_scores_gemma":[0.003349061,0.0019498781,0.04432245,0.0043552453,0.00059233425,0.00051199866,0.00675431,0.004667503,0.611753,0.0004408593,0.31400537,0.0072979815],"about_ca_topic_score_codex":0.0007247254,"about_ca_topic_score_gemma":0.00040713404,"teacher_disagreement_score":0.93025947,"about_ca_system_score_codex":0.00007546887,"about_ca_system_score_gemma":0.00021311115,"threshold_uncertainty_score":0.9999375},"labels":[],"label_agreement":null},{"id":"W4237118080","doi":"10.4018/978-1-5225-5484-4.ch027","title":"Enhancing Self-Reflection With Wearable Sensors Workshop","year":2018,"lang":"en","type":"book-chapter","venue":"Wearable Technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reflection (computer programming); Wearable computer; Computer science; Data science; Wearable technology; Human–computer interaction; Engineering; Engineering ethics","score_opus":0.026743906442693227,"score_gpt":0.24280384166099225,"score_spread":0.21605993521829903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237118080","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028825812,0.007609109,0.19828242,0.0025416673,0.002029464,0.0022971854,0.000016038839,0.03807813,0.7462634],"genre_scores_gemma":[0.13325945,0.002813933,0.050482456,0.00014194421,0.00038874737,0.00025744815,0.000005870702,0.00026335212,0.8123868],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967015,0.000044181957,0.0005306386,0.0013387102,0.0006841064,0.0007008822],"domain_scores_gemma":[0.9966999,0.0002750877,0.0005931429,0.0019195616,0.0004408418,0.000071432216],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00048040162,0.00069263286,0.00079280214,0.00082818663,0.0003966786,0.0005080644,0.001490299,0.0011475374,0.0001594223],"category_scores_gemma":[0.00011395649,0.00061931816,0.00016818977,0.0004428508,0.00029297065,0.000904928,0.0006878451,0.0011154308,0.002103812],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002734986,0.0004766747,0.00014951109,0.001443313,0.0033595092,0.0010038248,0.0044955793,0.00015653178,0.009769412,0.063768804,0.037243392,0.87785995],"study_design_scores_gemma":[0.0015793332,0.00173353,0.000016842563,0.012234167,0.00028594054,0.0015829608,0.0020462868,0.0026795038,0.1215218,0.09840653,0.7530959,0.0048172],"about_ca_topic_score_codex":0.000040078212,"about_ca_topic_score_gemma":0.00030462653,"teacher_disagreement_score":0.87304276,"about_ca_system_score_codex":0.0003941024,"about_ca_system_score_gemma":0.000246815,"threshold_uncertainty_score":0.9996258},"labels":[],"label_agreement":null},{"id":"W4237646711","doi":"10.22215/etd/2012-09526","title":"Context-aware algorithms for sleep apnea monitoring and sensor acceptance using unobtrusive pressure sensors arrays","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sleep apnea; Polysomnography; Context (archaeology); Computer science; Obstructive sleep apnea; Medicine; Machine learning; Apnea; Algorithm; Artificial intelligence","score_opus":0.04828034905830567,"score_gpt":0.3092399717575321,"score_spread":0.2609596226992264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237646711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12902853,0.0038036625,0.8492697,0.00013403961,0.011341873,0.0036727684,0.00027030378,0.0008195309,0.0016596033],"genre_scores_gemma":[0.9692393,0.000029526176,0.02174172,0.00004333523,0.0014011987,0.00026522094,0.00011877601,0.000119088305,0.0070418348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965261,0.00017973324,0.0007006256,0.001229486,0.0005817036,0.00078235613],"domain_scores_gemma":[0.99657243,0.0006404725,0.0007761837,0.00079005456,0.00091778976,0.00030305484],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039933444,0.00073316874,0.0009423021,0.000330744,0.00047055556,0.00056318345,0.0007186682,0.0006404564,0.000040879728],"category_scores_gemma":[0.000121441975,0.0007504208,0.00027476717,0.00034457675,0.000049454135,0.0016552655,0.00014242917,0.0004768462,0.000028441029],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019405034,0.000267306,0.0042496277,0.0025262577,0.00160484,0.000041936866,0.018352618,0.00023868652,0.018466532,0.0008427774,0.00054617715,0.9526692],"study_design_scores_gemma":[0.0076521253,0.00052955287,0.018473001,0.0035347377,0.0020744025,0.0007824158,0.056037813,0.6325971,0.2249262,0.0008575503,0.0425926,0.009942497],"about_ca_topic_score_codex":0.00027396122,"about_ca_topic_score_gemma":0.00009309983,"teacher_disagreement_score":0.9427267,"about_ca_system_score_codex":0.00011622289,"about_ca_system_score_gemma":0.00012917066,"threshold_uncertainty_score":0.9994947},"labels":[],"label_agreement":null},{"id":"W4237896258","doi":"10.1007/978-1-4614-6170-8_100809","title":"Context Adaptable Application","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.02674356811670635,"score_gpt":0.21938456434165415,"score_spread":0.1926409962249478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237896258","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.9255293e-7,0.00011138911,0.50882787,0.00026171363,0.00022827947,0.00027052048,0.0000038072876,0.0002742919,0.49002182],"genre_scores_gemma":[0.07796088,0.0000179321,0.0017977066,0.00095857255,0.00028431692,0.00006792705,0.000020867537,0.000039033574,0.91885275],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998295,0.000026594304,0.00037852413,0.0006991138,0.0003885463,0.00021222337],"domain_scores_gemma":[0.9979008,0.00019536896,0.0003245973,0.001207798,0.0002502004,0.000121240715],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002918967,0.00030253892,0.00043743433,0.00016024156,0.0000847508,0.00019075329,0.0009107183,0.00029807934,0.00041253908],"category_scores_gemma":[0.000013257945,0.00029746498,0.00016167825,0.000039742044,0.000038520888,0.00026773592,0.00026773254,0.0002489078,0.0074416786],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.957489e-7,0.000003940451,0.0000011594674,0.000011659501,0.000019952788,0.0000011523081,0.000020341851,3.1231025e-7,0.000019722947,0.5672404,0.011473181,0.42120716],"study_design_scores_gemma":[0.00016253522,0.000033484786,0.0000034066047,0.000062266954,0.000009728968,0.000028386346,0.0000031598024,0.004046131,0.000105466206,0.018231886,0.97695285,0.00036069626],"about_ca_topic_score_codex":0.00012261006,"about_ca_topic_score_gemma":0.00022570562,"teacher_disagreement_score":0.9654797,"about_ca_system_score_codex":0.00009347404,"about_ca_system_score_gemma":0.000093149276,"threshold_uncertainty_score":0.9999477},"labels":[],"label_agreement":null},{"id":"W4238123579","doi":"10.2196/preprints.15923","title":"Overview of Policies, Guidelines, and Standards for Active Assisted Living Data Exchange: Thematic Analysis (Preprint)","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Research Institute for Aging; Public Health Ontario; University of Toronto; University Health Network; Health Canada; Canadian Standards Association; University of Waterloo","funders":"","keywords":"Thematic analysis; Preprint; Transparency (behavior); Population ageing; Population; Business; Computer science; Internet privacy; Public relations; Political science; Medicine; Qualitative research; World Wide Web; Sociology; Computer security; Environmental health","score_opus":0.29906894477404883,"score_gpt":0.42892584125366157,"score_spread":0.12985689647961274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238123579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034992904,0.0027210223,0.98635817,0.0006995248,0.00038660114,0.0019014949,0.0026185883,0.00016399584,0.0016512909],"genre_scores_gemma":[0.92833567,0.0012072789,0.06816007,0.0003215796,0.00017803101,0.0002860804,0.00031058,0.000051980907,0.0011487517],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995632,0.0003499987,0.0011373806,0.0014573147,0.0011041913,0.00031915077],"domain_scores_gemma":[0.9895618,0.0018300164,0.0012599643,0.0046058176,0.0026258463,0.000116536976],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.004844868,0.00041700123,0.0015037466,0.0006410786,0.00007712864,0.00038734073,0.0027441834,0.00026864957,0.00012920168],"category_scores_gemma":[0.0025711313,0.00036856264,0.00036038214,0.00074700924,0.000057009063,0.0006366587,0.010445864,0.00027396312,0.0000051816523],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050602917,0.0004443195,0.002628571,0.013045084,0.010515419,0.0000032919304,0.0057092695,0.00040623764,0.00026818458,0.0026441156,0.022168288,0.9421166],"study_design_scores_gemma":[0.0011653537,0.00016801375,0.023599759,0.007301299,0.0029223133,0.000042867192,0.0012599325,0.9400995,0.00070562493,0.002961637,0.01793318,0.0018405166],"about_ca_topic_score_codex":0.0024110184,"about_ca_topic_score_gemma":0.0016600156,"teacher_disagreement_score":0.9402761,"about_ca_system_score_codex":0.0002807243,"about_ca_system_score_gemma":0.0009804474,"threshold_uncertainty_score":0.9998766},"labels":[],"label_agreement":null},{"id":"W4239558676","doi":"10.1145/1125451.1125801","title":"RoutePlanner","year":2006,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Computer science; Focus (optics); Human–computer interaction; Iterative design; Software engineering; Engineering","score_opus":0.008631187702047396,"score_gpt":0.1997029028361183,"score_spread":0.1910717151340709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239558676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007748366,0.000020089203,0.7898802,0.00084384286,0.00026676044,0.00005850059,7.049567e-7,0.00036520968,0.20081629],"genre_scores_gemma":[0.9839631,1.687985e-7,0.0053513767,0.0002845182,0.0000982825,0.000007561825,0.0000013258095,0.0000030124572,0.010290686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99944025,0.000025481446,0.00010458081,0.00017526465,0.00013111251,0.00012330215],"domain_scores_gemma":[0.999583,0.000052216736,0.000027544043,0.00026867454,0.000041631185,0.000026933423],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000095890224,0.000055078315,0.00006852246,0.00004836469,0.000038190847,0.00012221193,0.00028032588,0.000024523739,0.00007192733],"category_scores_gemma":[0.000005167215,0.000046718156,0.00003320451,0.00015227952,0.000007890845,0.00042455032,0.000070609254,0.000035549274,0.0009469373],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037966868,0.0002328117,0.014182262,0.000011915712,0.000022565377,0.000063735715,0.0001911029,0.000032661348,0.0040700794,0.45683914,0.260584,0.26376593],"study_design_scores_gemma":[0.0013541469,0.00010531148,0.061718363,0.000032841403,0.000006946307,0.0003536786,0.000055522793,0.041879132,0.024757251,0.03375426,0.8349868,0.0009957264],"about_ca_topic_score_codex":0.00038808936,"about_ca_topic_score_gemma":0.00011774766,"teacher_disagreement_score":0.9762147,"about_ca_system_score_codex":0.000015377682,"about_ca_system_score_gemma":0.000015494303,"threshold_uncertainty_score":0.99983096},"labels":[],"label_agreement":null},{"id":"W4240343763","doi":"10.2196/preprints.28701","title":"Smart Assistive Technology for Cooking for People With Cognitive Impairments Following a Traumatic Brain Injury: User Experience Study (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Institut Universitaire de Gériatrie de Montréal; Université de Sherbrooke; Health and Social Services Centre University Institute of Geriatrics of Sherbrooke; Centre Hospitalier Universitaire de Sherbrooke; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Usability; Context (archaeology); Session (web analytics); Cognition; Applied psychology; Web usability; Computer science; Psychology; Human–computer interaction; World Wide Web","score_opus":0.03999972994919622,"score_gpt":0.3322922046382993,"score_spread":0.2922924746891031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240343763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47273272,0.000018065062,0.51887214,0.0002829272,0.0005094623,0.00708417,0.000036267003,0.00037653153,0.00008773526],"genre_scores_gemma":[0.95169806,7.6318867e-7,0.026426287,0.00026267173,0.00004812158,0.02111258,0.00004350369,0.0000670812,0.0003409423],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99507743,0.00031244065,0.00087299757,0.0023572773,0.0006361729,0.0007436937],"domain_scores_gemma":[0.99487156,0.0019529136,0.00069364783,0.0013132181,0.0010093597,0.00015932969],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011729951,0.0006981452,0.001409427,0.00058466085,0.00042296917,0.0009418786,0.0014709692,0.0003802981,0.00002239979],"category_scores_gemma":[0.0009565403,0.00066137075,0.00046625626,0.00083785044,0.00007539638,0.0008013977,0.0021982368,0.00054445234,0.0000082369215],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012286846,0.008888022,0.5009319,0.0021836308,0.008553284,0.0003446776,0.21168041,0.0001043215,0.0014002855,0.0006795036,0.0008254589,0.2631798],"study_design_scores_gemma":[0.034503974,0.015531821,0.15001908,0.016356645,0.0017758094,0.0005147538,0.666651,0.07771723,0.022471612,0.0028937745,0.0013362649,0.010228025],"about_ca_topic_score_codex":0.00038057423,"about_ca_topic_score_gemma":0.0016308457,"teacher_disagreement_score":0.49244583,"about_ca_system_score_codex":0.00035159418,"about_ca_system_score_gemma":0.0006836879,"threshold_uncertainty_score":0.9995838},"labels":[],"label_agreement":null},{"id":"W4240622938","doi":"10.1007/978-3-319-05939-6","title":"Context-Aware Systems and Applications","year":2014,"lang":"en","type":"book","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Context (archaeology); Computer science; Data science; History; Archaeology","score_opus":0.022858751958062366,"score_gpt":0.23803557377431347,"score_spread":0.2151768218162511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240622938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003381479,0.00084907067,0.9955532,0.0005205999,0.0005467943,0.0010580868,0.000076375676,0.00010654859,0.0012555168],"genre_scores_gemma":[0.11475176,0.00018545777,0.88223296,0.0006087584,0.0008529971,0.00058289355,0.00012543831,0.000051461804,0.0006082654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985881,0.00003514194,0.00066379405,0.00021758287,0.00026572827,0.00022968558],"domain_scores_gemma":[0.99758023,0.0007248548,0.00061561784,0.00075103104,0.00026090973,0.000067340174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052360893,0.00028341362,0.00048209715,0.00022369916,0.00095210125,0.00047544463,0.0019371648,0.00023502133,2.165846e-7],"category_scores_gemma":[0.0000435886,0.00023724925,0.00014398413,0.00031933218,0.0004145742,0.0004686184,0.000907021,0.000367811,0.0000011543207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026532282,0.00005787277,0.000025814368,0.0023859604,0.0003503564,1.4817483e-7,0.004230601,0.48733956,0.000012533074,0.18479945,0.0019990904,0.31879595],"study_design_scores_gemma":[0.00022003517,0.00004578626,0.000024997127,0.00032743523,0.000038861337,0.000033792912,0.000009903795,0.75014347,0.000016615904,0.000558721,0.24825306,0.00032733774],"about_ca_topic_score_codex":0.000048352496,"about_ca_topic_score_gemma":0.00008711351,"teacher_disagreement_score":0.31846863,"about_ca_system_score_codex":0.000087928434,"about_ca_system_score_gemma":0.00027740232,"threshold_uncertainty_score":0.96747434},"labels":[],"label_agreement":null},{"id":"W4241380135","doi":"10.2196/preprints.21016","title":"Enabling Remote Patient Monitoring Through the Use of Smart Thermostat Data in Canada: Exploratory Study (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thermostat; Preprint; Computer science; Data collection; Agency (philosophy); Data science; Engineering; Statistics; World Wide Web; Mathematics","score_opus":0.26544251140080116,"score_gpt":0.3079054254389559,"score_spread":0.04246291403815472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241380135","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78709424,0.00028290728,0.2038061,0.001399341,0.0034012548,0.0034899705,0.0001044779,0.0001921569,0.00022954372],"genre_scores_gemma":[0.99566853,0.00004732373,0.0038802922,0.00020388623,0.00008748306,0.000059817503,0.0000107957385,0.000028974453,0.000012910702],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995128,0.00093410624,0.0010612478,0.0014872187,0.001039092,0.0003503401],"domain_scores_gemma":[0.99299455,0.001010137,0.00064949284,0.0049779713,0.00027008448,0.00009775156],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.00072513625,0.00038402714,0.00066523155,0.00009225209,0.00009344929,0.0003287996,0.0038579735,0.00009343764,0.000010974359],"category_scores_gemma":[0.00045015087,0.00030446422,0.00007449949,0.00047250683,0.00003246451,0.0011567858,0.014230731,0.00094465364,0.00000989526],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111602814,0.0006970088,0.087363064,0.00044234915,0.0008204797,0.0005399427,0.11457241,0.009128034,0.0004343787,0.00009584571,0.0023849178,0.78340995],"study_design_scores_gemma":[0.0048183743,0.0010420999,0.08912717,0.0067282035,0.00031000248,0.00007734229,0.23174718,0.57691264,0.023909526,0.003410741,0.055343468,0.006573247],"about_ca_topic_score_codex":0.919972,"about_ca_topic_score_gemma":0.86526954,"teacher_disagreement_score":0.7768367,"about_ca_system_score_codex":0.000665758,"about_ca_system_score_gemma":0.00276914,"threshold_uncertainty_score":0.99994075},"labels":[],"label_agreement":null},{"id":"W4243476142","doi":"10.4018/978-1-4666-3998-0.ch002","title":"A Task Context Aware Physical Distribution Knowledge Service System","year":2013,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Task (project management); Context (archaeology); Service (business); Computer science; Context awareness; Resource (disambiguation); Knowledge management; Process management; Human–computer interaction; Systems engineering; Engineering; Business; Computer network","score_opus":0.023898490307902916,"score_gpt":0.24610649032675108,"score_spread":0.22220800001884816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243476142","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027120384,0.00030903786,0.08652701,0.00016667928,0.0018251614,0.0013298402,0.001207345,0.0012191902,0.90714455],"genre_scores_gemma":[0.96439105,4.916786e-7,0.00004622359,0.00026191276,0.0008076555,0.00015097973,0.00005961538,0.000052168096,0.034229912],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99665415,0.00013278745,0.0006642457,0.0012479719,0.0006962887,0.0006045701],"domain_scores_gemma":[0.9962726,0.00017371794,0.00059214915,0.0014581381,0.0011040313,0.00039936026],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001880638,0.0008228702,0.0010870831,0.00007691056,0.0002355895,0.00056854455,0.0015465985,0.00059085025,0.000018295194],"category_scores_gemma":[0.000016785561,0.0008212551,0.0004713636,0.0000940475,0.00008573294,0.00039411298,0.00084302336,0.0005199872,0.010249034],"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":[0.000009396704,0.0000360924,0.0000025697689,0.0003083696,0.00014671314,0.000046578592,0.0002538791,3.43585e-7,0.000023988638,0.9283489,0.009318332,0.061504826],"study_design_scores_gemma":[0.00355378,0.0005860989,0.00014373637,0.008122966,0.0005148023,0.0017571375,0.0004277708,0.034311254,0.00035099965,0.12477097,0.8199642,0.005496306],"about_ca_topic_score_codex":0.00050368765,"about_ca_topic_score_gemma":0.00034938316,"teacher_disagreement_score":0.96411985,"about_ca_system_score_codex":0.0013602386,"about_ca_system_score_gemma":0.00050623016,"threshold_uncertainty_score":0.9994238},"labels":[],"label_agreement":null},{"id":"W4243767044","doi":"10.4018/978-1-4666-2026-1.ch007","title":"Micro Context-Awareness for Autonomic Pervasive Computing","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Systems, Applications & Products in Data Processing (Canada); Université de Sherbrooke","funders":"","keywords":"Ubiquitous computing; Computer science; Context-aware pervasive systems; Context awareness; Autonomic computing; Context (archaeology); Macro; End-user computing; Software; Human–computer interaction; Context management; Data science; Software engineering; Cloud computing; Utility computing; Cloud computing security; Operating system","score_opus":0.04603135225199966,"score_gpt":0.2741844709511436,"score_spread":0.22815311869914395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243767044","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005471523,0.0015888846,0.37371543,0.00019488444,0.004083896,0.002063891,0.0005949765,0.00081745797,0.61639345],"genre_scores_gemma":[0.95824534,0.0000025000263,0.0041887215,0.0012219656,0.0013230465,0.00008854999,0.000023545095,0.00011044964,0.034795914],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.996722,0.000066211236,0.00083268166,0.001162013,0.00040084505,0.00081629027],"domain_scores_gemma":[0.99674493,0.00043927686,0.0008354162,0.0011321986,0.00051008083,0.00033811986],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004237234,0.00079613394,0.001107668,0.0001486058,0.00034395757,0.00043581967,0.0015857288,0.00063970464,0.00002230125],"category_scores_gemma":[0.000035985737,0.0008785373,0.0006629785,0.000030620442,0.0001347436,0.00031993576,0.00074506155,0.00037142413,0.0005968947],"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":[0.000021361706,0.000019552439,0.00007442152,0.00009489192,0.00023477744,0.000018016004,0.00034314455,0.0000018911564,0.00010990281,0.78711486,0.0022504013,0.20971677],"study_design_scores_gemma":[0.004421949,0.0004375702,0.0003871249,0.002375644,0.00043768823,0.001213538,0.00014523645,0.003910659,0.002060355,0.2437919,0.735842,0.0049763247],"about_ca_topic_score_codex":0.00019004954,"about_ca_topic_score_gemma":0.00017258068,"teacher_disagreement_score":0.95769817,"about_ca_system_score_codex":0.00081771106,"about_ca_system_score_gemma":0.0007773297,"threshold_uncertainty_score":0.9993665},"labels":[],"label_agreement":null},{"id":"W4244627266","doi":"10.22215/etd/2010-09724","title":"Context-aware smart home monitoring through the analysis of pressure sequences","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Context (archaeology); Computer science; Population; Sagittal plane; Health care; Nursing homes; Artificial intelligence; Medicine; Real-time computing; Simulation; Nursing; Geography","score_opus":0.03232269833721411,"score_gpt":0.2995974817152688,"score_spread":0.26727478337805466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244627266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7146666,0.008784961,0.17841467,0.0019466279,0.029727483,0.004114705,0.0005057312,0.0017851733,0.060054038],"genre_scores_gemma":[0.98813546,0.000049809707,0.0004793754,0.00004539595,0.00016643699,0.00010916771,0.000121602774,0.000021267626,0.010871464],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99718815,0.00022543836,0.0006963247,0.0007317613,0.00085057545,0.00030772254],"domain_scores_gemma":[0.9963249,0.000681555,0.000847579,0.0013208056,0.0007570191,0.00006812678],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044764022,0.00038245053,0.00091623707,0.00038716683,0.00023165507,0.0003421749,0.0020514966,0.00045863507,0.0001520783],"category_scores_gemma":[0.0000781264,0.0002692549,0.0005563843,0.0016849721,0.00007872284,0.0009171364,0.0001313385,0.00066659146,0.000039346698],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001924267,0.0007937022,0.06475402,0.0019429502,0.043827325,0.00007344331,0.110723555,0.0004758015,0.015844595,0.024176048,0.004235405,0.73296076],"study_design_scores_gemma":[0.0027850536,0.00060291943,0.49118802,0.003039358,0.023496978,0.00008974464,0.07741309,0.043634526,0.20493947,0.008842946,0.13604271,0.0079251705],"about_ca_topic_score_codex":0.004276413,"about_ca_topic_score_gemma":0.008313537,"teacher_disagreement_score":0.72503555,"about_ca_system_score_codex":0.00002855099,"about_ca_system_score_gemma":0.00022656527,"threshold_uncertainty_score":0.999976},"labels":[],"label_agreement":null},{"id":"W4246183297","doi":"10.1007/978-1-4939-7131-2_100203","title":"Context Adaptable Application","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Context (archaeology); Geography; Archaeology","score_opus":0.03476551822638728,"score_gpt":0.23361752796561125,"score_spread":0.19885200973922396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246183297","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011191078,0.00013081533,0.4617282,0.0001901942,0.00031090307,0.0003179247,0.0000072230278,0.0002987989,0.53701484],"genre_scores_gemma":[0.027317464,0.000020328624,0.002447002,0.00073114684,0.0004527192,0.000063907704,0.000020688485,0.00003969063,0.96890706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99827296,0.000019783687,0.00036615744,0.00073090626,0.00039058272,0.00021961823],"domain_scores_gemma":[0.9978633,0.00012741909,0.00030678618,0.0012102912,0.00037478656,0.000117404226],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00024666847,0.00029868828,0.0003802609,0.00016334285,0.00009665554,0.00020375475,0.00094338245,0.00030392493,0.0018086017],"category_scores_gemma":[0.000011456415,0.00029268485,0.00014816798,0.000049225448,0.00006776032,0.0004743315,0.0003243482,0.00020633322,0.015213677],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030862484,0.000013512585,0.0000019634288,0.000018060255,0.000053637814,0.0000037642724,0.00012152713,9.351748e-8,0.000048762293,0.5087887,0.06675522,0.42419165],"study_design_scores_gemma":[0.00014780552,0.000049865666,0.0000028321674,0.000066362954,0.000009871543,0.000033276214,0.000008667165,0.0017040385,0.00020841323,0.03000228,0.96741015,0.0003564236],"about_ca_topic_score_codex":0.00010204785,"about_ca_topic_score_gemma":0.0002977659,"teacher_disagreement_score":0.900655,"about_ca_system_score_codex":0.0001090538,"about_ca_system_score_gemma":0.00012877448,"threshold_uncertainty_score":0.99995255},"labels":[],"label_agreement":null},{"id":"W4246203854","doi":"10.1109/mic.2014.58","title":"Table of contents","year":2014,"lang":"en","type":"article","venue":"IEEE Internet Computing","topic":"Context-Aware Activity Recognition Systems","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":"Regional Municipality of Niagara","funders":"","keywords":"Computer science; Table (database); World Wide Web; Database","score_opus":0.03178129913916287,"score_gpt":0.256141861385095,"score_spread":0.22436056224593212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246203854","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23802137,0.000019277082,0.7547154,0.00006970825,0.001310404,0.00008722988,8.7640717e-7,0.00014163149,0.0056341146],"genre_scores_gemma":[0.9954133,3.073164e-7,0.0037356934,0.00020845524,0.00014278268,0.0000021812787,7.5335765e-7,0.000009656954,0.00048687728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863946,0.00014417918,0.00039766103,0.00032840803,0.00024616308,0.000244127],"domain_scores_gemma":[0.9986727,0.00036506308,0.00027789842,0.00040618388,0.00020879455,0.000069311776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048750805,0.00012671734,0.00028115098,0.00010691063,0.000032982723,0.000106831365,0.00083198166,0.000049486276,0.0000139007825],"category_scores_gemma":[0.00010528462,0.00012712124,0.000079977544,0.00021958222,0.000034821405,0.00029290788,0.00023827153,0.00012285833,0.00013752548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026233845,0.00036626263,0.028519355,0.00020498005,0.00022312716,0.000020657886,0.004578264,0.00066273,0.03743061,0.01890164,0.015462378,0.89360374],"study_design_scores_gemma":[0.0008156722,0.00017500676,0.0025046987,0.0004050367,0.000006931031,0.00007475849,0.000041087347,0.89920807,0.08153196,0.0005853491,0.014325942,0.00032550734],"about_ca_topic_score_codex":0.00032554092,"about_ca_topic_score_gemma":0.000022938097,"teacher_disagreement_score":0.8985453,"about_ca_system_score_codex":0.000024062621,"about_ca_system_score_gemma":0.000017836655,"threshold_uncertainty_score":0.51838535},"labels":[],"label_agreement":null},{"id":"W4250515167","doi":"10.4018/978-1-60960-499-8.ch010","title":"How It Started","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Thriving; The Internet; Quarter (Canadian coin); Mobile device; Internet access; Internet privacy; Telecommunications; Business; Computer science; Geography; World Wide Web; Psychology","score_opus":0.053426885526875426,"score_gpt":0.24208584324193072,"score_spread":0.1886589577150553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250515167","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008650588,0.00015058648,0.079742685,0.00044265267,0.0013276623,0.00044621323,0.000114917864,0.0005032492,0.9172634],"genre_scores_gemma":[0.38123652,0.000002835649,0.0012431592,0.0013334749,0.0004972696,0.000039806553,0.0000054139236,0.000060097238,0.6155814],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976381,0.000047743175,0.00037163886,0.000891533,0.00061423477,0.0004367152],"domain_scores_gemma":[0.99760705,0.00006600683,0.000408168,0.001338002,0.00032060227,0.00026015638],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001652965,0.0005396228,0.0006066228,0.000118080156,0.00010795287,0.0005528696,0.0013867364,0.0004893685,0.000064997774],"category_scores_gemma":[0.000024310202,0.00055449153,0.00032624946,0.000031603766,0.000092873524,0.00032189637,0.0005338056,0.00035981365,0.0012145402],"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":[0.0000073654955,0.000008793613,0.0000042079673,0.000021698237,0.00011020887,0.000113452195,0.000117820535,2.0487805e-8,0.000018747129,0.90728503,0.01220954,0.080103084],"study_design_scores_gemma":[0.00037156502,0.00012275405,0.000018803927,0.00026777753,0.000041702133,0.0001950631,0.0000089790165,0.00004230341,0.00010025776,0.41842493,0.579609,0.00079686166],"about_ca_topic_score_codex":0.00012486831,"about_ca_topic_score_gemma":0.00027370526,"teacher_disagreement_score":0.56739944,"about_ca_system_score_codex":0.0002772259,"about_ca_system_score_gemma":0.00031093534,"threshold_uncertainty_score":0.99969065},"labels":[],"label_agreement":null},{"id":"W4251412798","doi":"10.22215/etd/2013-06717","title":"Domain independent context awareness framework","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition 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":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Domain (mathematical analysis); Computer science; Context (archaeology); Mathematics; Geography","score_opus":0.023471256352151887,"score_gpt":0.284714942226416,"score_spread":0.26124368587426416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251412798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09041278,0.00055147125,0.8478807,0.00036735658,0.008457605,0.0013158196,0.000015002057,0.0008604188,0.050138835],"genre_scores_gemma":[0.94330466,0.000023518784,0.008930219,0.0006485145,0.00035902305,0.000527845,0.00022862654,0.00006715385,0.045910437],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99672914,0.00022475456,0.0006453782,0.0010291584,0.0009004093,0.00047115106],"domain_scores_gemma":[0.9969902,0.00046573475,0.00049621594,0.0012818382,0.0005422728,0.0002237391],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003620195,0.0005084363,0.00068881415,0.00034770247,0.00020377252,0.00087474834,0.0017940021,0.0008559396,0.0011065525],"category_scores_gemma":[0.000076114375,0.00047841988,0.00026103746,0.000442266,0.000023235134,0.00093980925,0.00018104272,0.00076363643,0.0038556666],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003682819,0.00033785577,0.0009205425,0.000394548,0.0003762091,0.00008198683,0.009334551,0.0000016727132,0.0007757222,0.2510774,0.021825656,0.714837],"study_design_scores_gemma":[0.00496308,0.00077519857,0.045778427,0.007640388,0.00022524067,0.00043485887,0.03994021,0.0033868065,0.03774505,0.34941673,0.4976343,0.012059725],"about_ca_topic_score_codex":0.0011997905,"about_ca_topic_score_gemma":0.0018149229,"teacher_disagreement_score":0.85289186,"about_ca_system_score_codex":0.00015985248,"about_ca_system_score_gemma":0.0004528289,"threshold_uncertainty_score":0.9998066},"labels":[],"label_agreement":null},{"id":"W4252735791","doi":"10.18573/book3","title":"Human Computer Interaction and Emerging Technologies: Adjunct Proceedings from the INTERACT 2019 Workshops","year":2020,"lang":"en","type":"paratext","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Programa Operacional Regional do Centro; Natural Sciences and Engineering Research Council of Canada; African Academy of Sciences; Vlaamse regering; The Wellcome Trust DBT India Alliance; Ministero dell’Istruzione, dell’Università e della Ricerca; Aalto-Yliopisto","keywords":"Adjunct; Computer science; Human–computer interaction","score_opus":0.03142507560797749,"score_gpt":0.28186160958317574,"score_spread":0.25043653397519827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252735791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006797135,0.0031820033,0.85390687,0.03942758,0.01850597,0.0031603402,0.0001920504,0.0032122668,0.07161576],"genre_scores_gemma":[0.94944185,0.00048193836,0.0075930245,0.0036978142,0.003254086,0.00029741784,0.00035162564,0.0001530414,0.03472923],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971235,0.000089514906,0.00065134495,0.0013210424,0.0004262917,0.0003883271],"domain_scores_gemma":[0.9978468,0.0005324002,0.00063965406,0.0006393568,0.00025237902,0.00008943786],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024913912,0.0005844811,0.0007112208,0.0002559627,0.00032498004,0.0017358286,0.0020068737,0.00042944375,0.0004352681],"category_scores_gemma":[0.00006502331,0.00042206052,0.00018992646,0.00058898324,0.00010662771,0.0014933769,0.0021726862,0.0014970633,0.0031240848],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068980908,0.0000273041,0.000056383924,0.000039889776,0.00015427744,0.0000049170085,0.0012074056,0.0000022750123,0.00044653364,0.0001791741,0.7998257,0.19804923],"study_design_scores_gemma":[0.0005707263,0.00020680393,0.00029881674,0.0017036187,0.000055925586,0.00009092396,0.0026769578,0.018943481,0.0009705226,0.0003633263,0.97298115,0.0011377635],"about_ca_topic_score_codex":0.0005277112,"about_ca_topic_score_gemma":0.00008348698,"teacher_disagreement_score":0.9426447,"about_ca_system_score_codex":0.00014141247,"about_ca_system_score_gemma":0.000062420244,"threshold_uncertainty_score":0.9998231},"labels":[],"label_agreement":null},{"id":"W4253012898","doi":"10.21203/rs.3.rs-109382/v1","title":"“Chatty Devices” and Edge-based Activity Classification","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Factory (object-oriented programming); Enhanced Data Rates for GSM Evolution; Product (mathematics); Automation; Process (computing); Robot; Human–computer interaction; Set (abstract data type); Artificial intelligence; Manufacturing engineering; Engineering","score_opus":0.18737766351294954,"score_gpt":0.4126699600092796,"score_spread":0.22529229649633004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253012898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097953215,0.00097019895,0.8563077,0.03469084,0.0010352934,0.003503341,0.00015641976,0.00093394646,0.0044490877],"genre_scores_gemma":[0.9971771,0.000039354447,0.0016985361,0.00016025464,0.0003289909,0.00040338997,0.000042770767,0.00003183318,0.00011775055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942779,0.0017267335,0.00031675308,0.0014653704,0.0016122847,0.0006009437],"domain_scores_gemma":[0.99560696,0.0013279639,0.00022874822,0.0013880797,0.0010114743,0.00043674608],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0021773633,0.00032624288,0.0004884867,0.00053270214,0.00034882073,0.0012340824,0.0013808135,0.0004154938,0.000033752764],"category_scores_gemma":[0.00066359306,0.00033761436,0.00015589608,0.0008420717,0.00016927804,0.0006018488,0.003082537,0.0020424838,0.00026648818],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015368088,0.00061501283,0.013849521,0.006464767,0.00018635982,0.0001478916,0.0021438445,0.000081807004,0.0040761107,0.0032139341,0.008381149,0.9606859],"study_design_scores_gemma":[0.0023867178,0.00089663983,0.26262197,0.00418316,0.00004214732,0.00003871448,0.0004038677,0.63673717,0.015880961,0.013639579,0.060872816,0.0022962731],"about_ca_topic_score_codex":0.0005035278,"about_ca_topic_score_gemma":0.0002841351,"teacher_disagreement_score":0.95838964,"about_ca_system_score_codex":0.00040377304,"about_ca_system_score_gemma":0.0013657622,"threshold_uncertainty_score":0.9999076},"labels":[],"label_agreement":null},{"id":"W4253295989","doi":"10.12688/f1000research.4790.1","title":"Combining low sampling frequency smartphone sensors and video for a Wearable Mobility Monitoring System","year":2014,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Ottawa Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stairs; Accelerometer; False positive paradox; Wearable computer; Sitting; Computer science; Context (archaeology); Gait; Real-time computing; Activity recognition; Simulation; Physical medicine and rehabilitation; Artificial intelligence; Embedded system; Medicine; Engineering; Geography","score_opus":0.09617345855688418,"score_gpt":0.3461766951226823,"score_spread":0.2500032365657981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253295989","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50798994,0.0009063268,0.48450163,0.0002786655,0.0022499652,0.002606255,0.00006992381,0.0007005749,0.000696721],"genre_scores_gemma":[0.9750547,0.000048765738,0.022668857,0.000011796259,0.000628847,0.0012349684,0.0000142427,0.00007174747,0.00026606867],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99440295,0.0008608894,0.0008259704,0.0017553353,0.0011296957,0.0010251468],"domain_scores_gemma":[0.9932405,0.0030616203,0.00035324172,0.0019114732,0.0010117239,0.0004214861],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0055919876,0.00048015476,0.00095343665,0.0005090672,0.00056660495,0.0014101979,0.0016888273,0.00047291047,0.0000075739076],"category_scores_gemma":[0.0009024011,0.0005149865,0.00024100672,0.00040495183,0.00015120547,0.0004945483,0.0028645878,0.001322836,0.00008419163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007361417,0.0019268645,0.14637038,0.089571476,0.0024904038,0.00033511943,0.022097122,0.008994014,0.07580704,0.014291636,0.0025341404,0.6348457],"study_design_scores_gemma":[0.005238326,0.00083353213,0.031854227,0.022745384,0.0001167534,0.0003424659,0.0025032088,0.8810854,0.03038889,0.019044315,0.0020627168,0.0037847965],"about_ca_topic_score_codex":0.0012646199,"about_ca_topic_score_gemma":0.000027227625,"teacher_disagreement_score":0.87209135,"about_ca_system_score_codex":0.00059061183,"about_ca_system_score_gemma":0.000487254,"threshold_uncertainty_score":0.99973017},"labels":[],"label_agreement":null},{"id":"W4254924541","doi":"10.4018/978-1-4666-4422-9.ch031","title":"DeepKøver","year":2013,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","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":"Université de Moncton; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Realisation; Modular design; Activities of daily living; Key (lock); Disabled people; Computer science; Order (exchange); Psychology; Human–computer interaction; Business; Applied psychology; Computer security; Operating system","score_opus":0.028298015682724876,"score_gpt":0.23972020728923985,"score_spread":0.21142219160651496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254924541","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020847177,0.00020490291,0.04680509,0.00012460869,0.0013366728,0.00049814896,0.000044705426,0.0005099001,0.9504551],"genre_scores_gemma":[0.23654997,0.000002456371,0.0030343167,0.001822297,0.000716117,0.0000798974,0.000004022323,0.00007231705,0.7577186],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99770766,0.000035309145,0.00043651368,0.0008142803,0.0006024046,0.00040382243],"domain_scores_gemma":[0.9978578,0.000076394135,0.000323664,0.0012226233,0.00027100445,0.00024851065],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001278825,0.00048871624,0.0005435658,0.00009470067,0.00009980942,0.00042667316,0.0012954619,0.0004702343,0.00024078878],"category_scores_gemma":[0.000015681428,0.0004923186,0.00030827013,0.000025370466,0.00007439851,0.000282838,0.0005950049,0.00032745037,0.011757557],"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":[0.000001849939,0.000005168048,0.00000434461,0.000015414089,0.00006680851,0.000049040995,0.00003427451,1.2449831e-7,0.00001014137,0.8815811,0.01583737,0.10239433],"study_design_scores_gemma":[0.00033517665,0.00006942608,0.00003913576,0.00024092982,0.000031049727,0.00025857493,0.0000031765915,0.00019662768,0.000053624462,0.5350728,0.46287283,0.0008266198],"about_ca_topic_score_codex":0.00014915869,"about_ca_topic_score_gemma":0.00007520948,"teacher_disagreement_score":0.44703546,"about_ca_system_score_codex":0.0003028925,"about_ca_system_score_gemma":0.00023794331,"threshold_uncertainty_score":0.9997528},"labels":[],"label_agreement":null},{"id":"W4280619244","doi":"10.1108/sr-05-2021-0157","title":"Activity recognition on smartphones using an AKNN based support vectors","year":2022,"lang":"en","type":"article","venue":"Sensor Review","topic":"Context-Aware Activity Recognition Systems","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":"Institut du Savoir Montfort; Université du Québec à Montréal","funders":"Academy of Finland","keywords":"Activity recognition; Support vector machine; Computer science; Machine learning; Artificial intelligence; Metric (unit); Inertial measurement unit; Set (abstract data type); Word error rate; Data mining; Engineering","score_opus":0.13594054054772986,"score_gpt":0.32261914413512266,"score_spread":0.1866786035873928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280619244","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90526384,0.004662996,0.065562956,0.007378513,0.0057241097,0.0050790478,0.00037694196,0.0017882922,0.004163333],"genre_scores_gemma":[0.9926749,0.00030378735,0.0019643684,0.004545973,0.00013585172,0.00016130188,0.00006155378,0.000038155915,0.000114103575],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968155,0.0012925824,0.00034472154,0.0006474835,0.0005928672,0.00030683746],"domain_scores_gemma":[0.9983586,0.0002270662,0.00031622002,0.00082641304,0.00012790399,0.00014379542],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011374259,0.00023054436,0.0004583535,0.00017023478,0.0003718967,0.00009624597,0.0004733576,0.000036607813,0.0010202602],"category_scores_gemma":[0.00011516918,0.00023718858,0.00020177227,0.00075136503,0.000024085753,0.00062825397,0.00015679837,0.00030107825,0.00029886587],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028867264,0.00064997416,0.00019336109,0.0009221084,0.000035044315,0.00010904178,0.00014180111,0.000053125314,0.004288452,0.00003302594,0.00195379,0.9915914],"study_design_scores_gemma":[0.004045485,0.0045930543,0.006051748,0.008685654,0.00051646563,0.0024528487,0.00026803667,0.10824963,0.04188819,0.00068778463,0.81720316,0.0053579593],"about_ca_topic_score_codex":0.000116627234,"about_ca_topic_score_gemma":0.000018702915,"teacher_disagreement_score":0.9862335,"about_ca_system_score_codex":0.00023405567,"about_ca_system_score_gemma":0.00018247234,"threshold_uncertainty_score":0.99989295},"labels":[],"label_agreement":null},{"id":"W4280630662","doi":"10.21203/rs.3.rs-1614908/v2","title":"Attention Vision Transformers for Human Fall Detection","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Transformer; Inertial measurement unit; Computer science; Artificial intelligence; Accelerometer; Machine translation; Activity recognition; Sentence; Computer vision; Pattern recognition (psychology); Speech recognition; Simulation; Engineering; Voltage","score_opus":0.12092626645475077,"score_gpt":0.4400089321862193,"score_spread":0.3190826657314685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280630662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10051296,0.0001961406,0.88922423,0.0010732465,0.0014034734,0.0045603244,0.00014893658,0.00046009946,0.002420563],"genre_scores_gemma":[0.9952047,0.000037614667,0.00040129764,0.000018055634,0.00025567715,0.0026675465,0.00021043868,0.00004359636,0.0011610911],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99471575,0.0011083466,0.00047640063,0.0012072274,0.0017984947,0.00069380424],"domain_scores_gemma":[0.99744606,0.0005307151,0.00017459794,0.00080912095,0.00084929005,0.00019020335],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0042322986,0.00026765052,0.0003677262,0.0010354322,0.0011628858,0.00073027064,0.0012113512,0.00031269377,0.00009794167],"category_scores_gemma":[0.00015194897,0.00029472116,0.0004623365,0.0006982728,0.00007301965,0.00057797064,0.0014076851,0.0016696062,0.00006515543],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011854294,0.00050656794,0.0007298673,0.0029261848,0.0001569586,0.000024651417,0.0015599008,0.00008824861,0.023216104,0.0016466342,0.0019341059,0.9670922],"study_design_scores_gemma":[0.011327751,0.014119301,0.07801366,0.0060529257,0.00016423431,0.00016517367,0.007256687,0.25574082,0.02362518,0.10528638,0.49182436,0.0064235195],"about_ca_topic_score_codex":0.0009928532,"about_ca_topic_score_gemma":0.0011795631,"teacher_disagreement_score":0.96066874,"about_ca_system_score_codex":0.00092150114,"about_ca_system_score_gemma":0.00028750647,"threshold_uncertainty_score":0.99995047},"labels":[],"label_agreement":null},{"id":"W4281675702","doi":"10.3390/ijerph19127046","title":"Validation of a Speech Database for Assessing College Students’ Physical Competence under the Concept of Physical Literacy","year":2022,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Competence (human resources); Support vector machine; Artificial intelligence; Speech recognition; Natural language processing; Psychology","score_opus":0.1151668899670334,"score_gpt":0.44378573824262957,"score_spread":0.3286188482755962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281675702","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96019197,0.00012673906,0.032717526,0.0060927276,0.00017115299,0.00028832266,0.0003815008,0.0000025738993,0.000027494727],"genre_scores_gemma":[0.99900854,0.000023995754,0.00059489807,0.000132754,0.00017500091,0.000015178921,0.000020615384,0.000004967386,0.000024068004],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9964235,0.00070411427,0.00039500932,0.00016148441,0.0021161477,0.00019978387],"domain_scores_gemma":[0.99739486,0.0015803059,0.00047884803,0.00016216429,0.0002467921,0.00013701389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002776182,0.000067507586,0.00020131031,0.00021398028,0.00020628294,0.00015622361,0.0010567417,0.000010159039,0.00002604378],"category_scores_gemma":[0.00012659689,0.0000531114,0.00007849799,0.00019085685,0.0002033086,0.0010148359,0.0006735708,0.00032204424,6.5799486e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005460582,0.016662737,0.01743681,0.00016452328,0.0009362193,0.00007697511,0.02748452,0.00039446657,0.06405576,0.04481189,0.0029255177,0.8245045],"study_design_scores_gemma":[0.036294963,0.03408131,0.28063363,0.0019547378,0.00008217647,0.004603218,0.14509355,0.21896198,0.09551174,0.051061276,0.12972794,0.0019934785],"about_ca_topic_score_codex":0.000042506774,"about_ca_topic_score_gemma":0.0000012903482,"teacher_disagreement_score":0.822511,"about_ca_system_score_codex":0.00030734116,"about_ca_system_score_gemma":0.0004037709,"threshold_uncertainty_score":0.216582},"labels":[],"label_agreement":null},{"id":"W4282941274","doi":"10.2196/23887","title":"Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study","year":2022,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Agency for Healthcare Research and Quality","keywords":"Rehabilitation; Medicine; Skilled Nursing Facility; Cohort; Longitudinal study; Population; Physical therapy; Physical medicine and rehabilitation; Wearable computer; Nursing; Medical emergency; Computer science","score_opus":0.06537319548174073,"score_gpt":0.3770077193247729,"score_spread":0.31163452384303214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282941274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957771,0.000030263378,0.000790793,0.00023535007,0.0004045636,0.00243146,0.0001427171,0.000096319796,0.000091446025],"genre_scores_gemma":[0.9991086,0.0000036004515,0.00004237056,0.00005614597,0.000037713708,0.00066440884,0.000016865075,0.000009346302,0.000060938684],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99684966,0.00071258855,0.0003716475,0.0007465228,0.00084773486,0.00047184012],"domain_scores_gemma":[0.99836737,0.00018081554,0.00041612415,0.0005683101,0.00012742211,0.00033996435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077001686,0.00020179423,0.00056960137,0.00016651717,0.00076192315,0.00003551002,0.0003313389,0.00003620583,0.000024439376],"category_scores_gemma":[0.00002269393,0.0002067047,0.00008196507,0.00045850192,0.00007211818,0.0003125981,0.00041926184,0.00035085753,0.000008104134],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013740035,0.0058766133,0.85224843,0.000112718626,0.0000140890725,0.0000054750662,0.005009223,9.4613335e-7,0.000013451169,0.000037135,0.0001505344,0.13639396],"study_design_scores_gemma":[0.0012978676,0.001331581,0.99597204,0.000011855935,0.000038851183,0.000014054081,0.00032835253,0.0006833071,0.00002201761,0.000029597823,0.00010809379,0.00016240659],"about_ca_topic_score_codex":0.00044847175,"about_ca_topic_score_gemma":0.00007867771,"teacher_disagreement_score":0.14372356,"about_ca_system_score_codex":0.0006472721,"about_ca_system_score_gemma":0.00030394702,"threshold_uncertainty_score":0.8429173},"labels":[],"label_agreement":null},{"id":"W4284696135","doi":"10.2196/38512","title":"Monitoring Physical Behavior in Rehabilitation Using a Machine Learning–Based Algorithm for Thigh-Mounted Accelerometers: Development and Validation Study","year":2022,"lang":"en","type":"article","venue":"JMIR Bioinformatics and Biotechnology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Accelerometer; Rehabilitation; Algorithm; Computer science; Physical medicine and rehabilitation; Artificial intelligence; Machine learning; Simulation; Physical therapy; Medicine","score_opus":0.03723977398451626,"score_gpt":0.30929987370941153,"score_spread":0.27206009972489525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284696135","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.859475,0.000016295886,0.13905674,0.0001491928,0.00008102812,0.0011067824,0.000006806723,0.000107313346,7.911864e-7],"genre_scores_gemma":[0.86920005,0.0000011718437,0.13019453,0.000007955848,0.000008026939,0.00057128293,0.000009143912,0.0000058216,0.000002002089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902713,0.0000854135,0.00032355645,0.00021667194,0.00017858668,0.00016864766],"domain_scores_gemma":[0.9994006,0.00019028355,0.00018255116,0.00015097455,0.000047643884,0.000027939075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005129702,0.00012220048,0.00019862303,0.0005000303,0.00031099835,0.00010134802,0.0001784446,0.000077578385,5.616758e-7],"category_scores_gemma":[0.000040060313,0.00012173829,0.000022669094,0.00044360137,0.000036174482,0.0003386525,0.0003163739,0.0002791311,5.002422e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014527006,0.00050193473,0.014516341,0.000058693033,0.00001695877,0.0000027602064,0.0070682787,0.000037905207,0.002194154,0.000035394813,8.403781e-7,0.9755522],"study_design_scores_gemma":[0.0012637957,0.0011099355,0.0068330755,0.000022207088,0.000009262494,0.000018212988,0.0045986054,0.978462,0.007169171,0.000035876586,0.00028762268,0.00019026312],"about_ca_topic_score_codex":0.000037790156,"about_ca_topic_score_gemma":0.00000904072,"teacher_disagreement_score":0.9784241,"about_ca_system_score_codex":0.00012728114,"about_ca_system_score_gemma":0.000042949257,"threshold_uncertainty_score":0.49643433},"labels":[],"label_agreement":null},{"id":"W4285134718","doi":"10.1109/tai.2022.3178065","title":"Toward Personalization of User Preferences in Partially Observable Smart Home Environments","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Personalization; Reinforcement learning; Computer science; Preference; Home automation; Baseline (sea); Human–computer interaction; Machine learning; Artificial intelligence; World Wide Web; Telecommunications","score_opus":0.11718370842104574,"score_gpt":0.2788703902975181,"score_spread":0.16168668187647234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285134718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10345544,0.00002125663,0.8948918,0.00022324765,0.0008714435,0.0003186178,0.000031234973,0.000057584813,0.00012940203],"genre_scores_gemma":[0.9984913,0.000022880091,0.0008905429,0.00010179789,0.0000148584995,0.00020793545,0.0000030871986,0.000011814808,0.00025581644],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977864,0.00031685855,0.0005654765,0.00047352305,0.0005936154,0.0002640984],"domain_scores_gemma":[0.99916786,0.0001730675,0.00016924688,0.00037656145,0.000043107804,0.00007013744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046023092,0.00016266604,0.00023753694,0.00028212054,0.00021472675,0.00006937226,0.00064915756,0.0000535031,0.0005086699],"category_scores_gemma":[0.000009074099,0.00018657355,0.000106858104,0.0008051732,0.00007066906,0.0005621335,0.000014189536,0.00027713776,0.000116760064],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041332774,0.004373134,0.0018407733,0.000120303615,0.00014341855,0.00004739455,0.019797573,0.29756254,0.040412415,0.011869553,0.00007848128,0.6233411],"study_design_scores_gemma":[0.0002973934,0.0012684512,0.0026586326,0.00014520789,0.000041785355,0.000041426163,0.0043253447,0.34096935,0.6337355,0.0120290825,0.003417769,0.0010700732],"about_ca_topic_score_codex":0.0004138657,"about_ca_topic_score_gemma":0.00074720755,"teacher_disagreement_score":0.8950358,"about_ca_system_score_codex":0.0001667908,"about_ca_system_score_gemma":0.00010677597,"threshold_uncertainty_score":0.76082486},"labels":[],"label_agreement":null},{"id":"W4285327108","doi":"10.1109/hpcc-dss-smartcity-dependsys53884.2021.00221","title":"Assessing Stroke Patients Movements Using Inertial Measurements Through the Advances of Ensemble Learning Technology","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Inertial frame of reference; Stroke (engine); Artificial intelligence; Ensemble learning; Inertial measurement unit; Aerospace engineering; Engineering; Physics","score_opus":0.08285284647735294,"score_gpt":0.3234160364244281,"score_spread":0.24056318994707518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285327108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4957255,0.00020522936,0.49862155,0.00014408995,0.00045926266,0.00013794158,9.629897e-7,0.00009431937,0.004611124],"genre_scores_gemma":[0.9847674,0.000006431889,0.014856279,0.00012329638,0.000027074946,0.000008911473,0.0000019576307,0.000009065627,0.00019960423],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981822,0.00022983587,0.0003803799,0.00036237825,0.0005864924,0.0002587633],"domain_scores_gemma":[0.99853975,0.00011017152,0.00031349596,0.0004010031,0.0006094782,0.000026107069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026831136,0.00013654667,0.00023164148,0.00009130336,0.00024080329,0.00019072267,0.0004699562,0.00006968288,0.000021173917],"category_scores_gemma":[0.0002489508,0.000107487394,0.00006788443,0.0006395529,0.00005416085,0.0018087527,0.00044874463,0.00018446498,0.000008289621],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008434612,0.0004304147,0.21778098,0.00005785559,0.00017654388,0.000008956779,0.0011133557,0.0009614057,0.27086365,0.0008181977,0.00003417078,0.50774604],"study_design_scores_gemma":[0.0021942738,0.00017841619,0.006769432,0.0003307665,0.00003625002,0.000028524692,0.0037269741,0.018057827,0.95952487,0.0016132665,0.006993093,0.0005462971],"about_ca_topic_score_codex":0.00008117666,"about_ca_topic_score_gemma":0.000048585684,"teacher_disagreement_score":0.6886612,"about_ca_system_score_codex":0.00007463973,"about_ca_system_score_gemma":0.0001279143,"threshold_uncertainty_score":0.43832088},"labels":[],"label_agreement":null},{"id":"W4286111648","doi":"10.3390/bios12070549","title":"Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition","year":2022,"lang":"en","type":"article","venue":"Biosensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Classifier (UML); Orientation (vector space); Accelerometer; Deep learning; Heuristic; Invariant (physics); Computer vision; Precision and recall; Mathematics","score_opus":0.09954009723169419,"score_gpt":0.25772993344165773,"score_spread":0.15818983620996355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286111648","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98593634,0.000015744134,0.012088476,0.00025034565,0.00043945166,0.0005161354,0.000037657195,0.00012732673,0.00058851764],"genre_scores_gemma":[0.99842167,0.000006099414,0.00114538,0.00004658342,0.00005937418,0.000206205,0.000021557,0.000020167006,0.000072972354],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9975913,0.0006078899,0.0003484587,0.0005788636,0.0005808004,0.00029268998],"domain_scores_gemma":[0.99870425,0.00027441687,0.0003569176,0.00046508928,0.00012255549,0.00007673635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006797577,0.00020236896,0.0003214202,0.00046217314,0.00026769727,0.000072706796,0.00035201674,0.000044104407,0.000043081964],"category_scores_gemma":[0.000096788644,0.00020542531,0.00006591215,0.0010072999,0.000049448914,0.0009864244,0.0002211012,0.00033761983,0.00001167392],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001773611,0.00506146,0.019512266,0.0006606554,0.00058017526,0.0023093338,0.047040433,0.0025871107,0.4354637,0.014384121,0.00066929206,0.46995786],"study_design_scores_gemma":[0.009998316,0.0036789298,0.6816257,0.0007529461,0.00014239791,0.0014932818,0.007833918,0.008660937,0.27895465,0.0023635465,0.001723288,0.0027720765],"about_ca_topic_score_codex":0.0011151453,"about_ca_topic_score_gemma":0.00036078252,"teacher_disagreement_score":0.6621134,"about_ca_system_score_codex":0.00022473425,"about_ca_system_score_gemma":0.00010201866,"threshold_uncertainty_score":0.8377001},"labels":[],"label_agreement":null},{"id":"W4286209045","doi":"10.1002/sim.9529","title":"Modeling and forecasting of at home activity in older adults using passive sensor technology","year":2022,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Categorical variable; Quarter (Canadian coin); Life expectancy; Computer science; Gerontology; Medicine; Machine learning; Geography; Environmental health","score_opus":0.04229577504528906,"score_gpt":0.2954046475400237,"score_spread":0.2531088724947347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286209045","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5992325,0.000072420895,0.40011808,0.00019254013,0.0001468086,0.00016707466,0.000032946085,0.000014757201,0.00002286604],"genre_scores_gemma":[0.9787287,0.000008640454,0.021164764,0.000029361965,0.000019698497,0.000024803978,0.0000036991169,0.000008624847,0.000011706509],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861276,0.00015392952,0.00037805512,0.00032950888,0.00031461637,0.00021114122],"domain_scores_gemma":[0.9989327,0.0005161031,0.0001948673,0.00021903998,0.000101067795,0.00003621927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005343977,0.000107933396,0.0003627437,0.0005460492,0.00008859437,0.0000056229537,0.0001947697,0.00004325063,0.000017299835],"category_scores_gemma":[0.00043848233,0.00011165926,0.000009600963,0.00066280493,0.00009440531,0.000101852645,0.00047958863,0.00029148968,2.8694046e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032399734,0.0004044898,0.07868604,0.0008298113,0.000062347484,0.0010570863,0.02264273,0.027708827,0.016766954,0.007909752,0.00009887606,0.8435091],"study_design_scores_gemma":[0.0014259183,0.00010939672,0.0019643656,0.00025922502,0.0000042975994,0.00011275433,0.001357477,0.9922757,0.00015994914,0.00221283,0.000005742375,0.00011231716],"about_ca_topic_score_codex":0.0009002698,"about_ca_topic_score_gemma":0.0006851398,"teacher_disagreement_score":0.9645669,"about_ca_system_score_codex":0.00021864212,"about_ca_system_score_gemma":0.000050840215,"threshold_uncertainty_score":0.45533326},"labels":[],"label_agreement":null},{"id":"W4287178897","doi":"10.1016/j.irbm.2023.100770","title":"Detection of Social Isolation Based on Meal-Taking Activity and Mobility of Elderly People Living Alone","year":2023,"lang":"en","type":"article","venue":"IRBM","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Social isolation; Activities of daily living; Gerontology; Isolation (microbiology); Meal; Elderly people; Pandemic; Coronavirus disease 2019 (COVID-19); Meal preparation; Psychology; Medicine; Physical therapy; Disease; Food science; Psychiatry","score_opus":0.0314083618403794,"score_gpt":0.27416053100066173,"score_spread":0.24275216916028233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287178897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7867031,0.0000026891973,0.21261567,0.00009841967,0.00015279431,0.00014520226,0.000004379718,0.0000980702,0.00017966965],"genre_scores_gemma":[0.9997626,7.254497e-7,0.00015820561,0.000009051649,0.000033421533,0.000015402633,7.7693994e-7,0.0000058645537,0.0000139489275],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989715,0.00020413441,0.00018806178,0.0002510625,0.0002614546,0.0001237416],"domain_scores_gemma":[0.99873817,0.00060758035,0.0003037576,0.00020966154,0.00011404275,0.000026789568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006886913,0.00008125284,0.00020117973,0.00017237016,0.000102518614,0.0000321375,0.00011621241,0.00006646577,0.0000074401655],"category_scores_gemma":[0.00023575574,0.000089428744,0.000060499908,0.00056241616,0.0000259386,0.00035790988,0.000068682515,0.00008662926,0.0000057872317],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006320753,0.0002406531,0.01279288,0.00024356529,0.000023517026,0.0000011634901,0.0048151463,0.00013670561,0.3803955,0.00008365037,0.000019680205,0.6011843],"study_design_scores_gemma":[0.00027562454,0.00019417341,0.7143263,0.000071330534,0.000006742616,0.000001748301,0.00009588909,0.24390543,0.04088732,0.000116089104,0.000015634767,0.000103723265],"about_ca_topic_score_codex":0.00047047492,"about_ca_topic_score_gemma":0.0004920631,"teacher_disagreement_score":0.70153344,"about_ca_system_score_codex":0.00004314616,"about_ca_system_score_gemma":0.000039546085,"threshold_uncertainty_score":0.36467984},"labels":[],"label_agreement":null},{"id":"W4288471702","doi":"10.4017/gt.2022.21.1.590.07","title":"An overview of Markov Chains for monitoring indoor movements and ambient motion detection","year":2022,"lang":"en","type":"article","venue":"Gerontechnology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain; Hidden Markov model; Computer science; Motion (physics); Motion sensors; Artificial intelligence; Machine learning","score_opus":0.0632937323954968,"score_gpt":0.3088752353800755,"score_spread":0.2455815029845787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288471702","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5849379,0.00032058283,0.41351658,0.00014287992,0.0005319941,0.0003750017,0.000011377139,0.00015336678,0.000010371338],"genre_scores_gemma":[0.997719,0.00002506367,0.0017589484,0.00003230223,0.000026146883,0.00039314388,0.0000024840072,0.000008291943,0.00003464773],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990028,0.00008977719,0.00022409817,0.0003400158,0.00016052798,0.00018273783],"domain_scores_gemma":[0.9993046,0.00004250917,0.00018286954,0.00037294015,0.00006666872,0.000030437443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036842,0.000092102164,0.00018050484,0.00025316235,0.00017552763,0.000021466287,0.0003524525,0.000068925685,0.0000048151132],"category_scores_gemma":[0.00002603095,0.000108369735,0.000039695795,0.00025836803,0.000032204818,0.00027265467,0.00031530994,0.00012744215,8.7913764e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018351186,0.000116503536,0.0036402429,0.000043508124,0.000022920603,0.0000014774265,0.00029201226,0.00000812607,0.07926634,0.001303811,0.0000069532202,0.91527975],"study_design_scores_gemma":[0.00491289,0.0050477996,0.053165946,0.00012084335,0.000041929783,0.00029304982,0.003842026,0.11529441,0.7945716,0.0085565,0.013204899,0.0009481262],"about_ca_topic_score_codex":0.00008921004,"about_ca_topic_score_gemma":0.000046009718,"teacher_disagreement_score":0.9143316,"about_ca_system_score_codex":0.000121159064,"about_ca_system_score_gemma":0.000015654541,"threshold_uncertainty_score":0.44191897},"labels":[],"label_agreement":null},{"id":"W4288766932","doi":"10.2196/38211","title":"An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study","year":2022,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Dementia; Outlier; Computer science; Univariate; Generalizability theory; Multivariate statistics; Artificial intelligence; Data mining; Statistics; Medicine; Machine learning; Mathematics","score_opus":0.03617638418028334,"score_gpt":0.3049046729364046,"score_spread":0.26872828875612126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288766932","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5438245,0.0000113158,0.45330074,0.0000680978,0.00010073668,0.002406425,0.00004731791,0.00017026644,0.00007058074],"genre_scores_gemma":[0.99319977,4.6662765e-7,0.0037065623,0.00012971646,0.00004157034,0.002727571,0.00015634918,0.000020122727,0.000017901855],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99767005,0.0005290402,0.00031385268,0.00079055724,0.00037670849,0.0003197926],"domain_scores_gemma":[0.9985892,0.00015298302,0.00018906135,0.0009114486,0.00006513644,0.00009213486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013051946,0.00014680198,0.00027161432,0.0003119601,0.00068642624,0.00013088243,0.0008145954,0.000017831035,0.000029659366],"category_scores_gemma":[0.00001340416,0.0001621352,0.00003341428,0.00077613426,0.000013295144,0.0014413531,0.0004777525,0.0002060577,0.0000019783008],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001265734,0.0013075408,0.97680473,0.00006401668,0.000106436055,0.0000060441753,0.008793917,0.0042882147,0.00012791598,0.000021736845,0.000073789495,0.00839302],"study_design_scores_gemma":[0.0009027417,0.00043878835,0.57289845,0.00002157817,0.000017793462,0.000033160188,0.00857808,0.41678995,0.0000043145283,0.0000043021532,0.000115518655,0.00019535187],"about_ca_topic_score_codex":0.0011692366,"about_ca_topic_score_gemma":0.0041210046,"teacher_disagreement_score":0.4495942,"about_ca_system_score_codex":0.00026298832,"about_ca_system_score_gemma":0.00019062158,"threshold_uncertainty_score":0.66116816},"labels":[],"label_agreement":null},{"id":"W4289201574","doi":"10.1017/s0714980822000198","title":"Passive Remote Monitoring and Aging in Place: A Scoping Review","year":2022,"lang":"en","type":"review","venue":"Canadian Journal on Aging / La Revue canadienne du vieillissement","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Western University; University of New Brunswick","funders":"Canadian Institutes of Health Research; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"CINAHL; Scopus; Inclusion (mineral); Intervention (counseling); MEDLINE; Medicine; Gerontology; Psychology; Psychological intervention; Nursing; Political science; Social psychology","score_opus":0.04238187823757147,"score_gpt":0.29044632505732687,"score_spread":0.2480644468197554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289201574","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007313041,0.9925028,0.00040262085,0.0024457532,0.0025112326,0.0015981325,0.00003349088,0.000043414042,0.00038944202],"genre_scores_gemma":[0.0003187543,0.9979139,0.00031885374,0.0004697091,0.00051143346,0.00016114513,0.000016480186,0.00009422828,0.00019549904],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9943738,0.0013407101,0.0015300036,0.0010977085,0.00030282242,0.0013549761],"domain_scores_gemma":[0.9948129,0.0009491458,0.0011804295,0.000875096,0.00014427255,0.0020381748],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030809431,0.0007615336,0.002180605,0.0018683994,0.0007944872,0.0008480163,0.0014896807,0.00017246963,0.00014440423],"category_scores_gemma":[0.0007243065,0.00081197394,0.0004167335,0.001368411,0.00005721631,0.00053588056,0.00023411818,0.0019855911,0.0000176826],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[4.357745e-7,0.000007194581,0.000023835579,0.021272546,0.00007900237,0.0044938284,0.0010115165,0.000010855792,6.724743e-8,0.00012057073,0.00038299314,0.9725972],"study_design_scores_gemma":[0.00016232996,0.000040105442,0.000010209169,0.37695232,0.0000838893,0.0038141431,0.00008555401,0.00006451234,1.6321442e-7,0.000018644287,0.61824614,0.00052199303],"about_ca_topic_score_codex":0.016238367,"about_ca_topic_score_gemma":0.14892061,"teacher_disagreement_score":0.97207516,"about_ca_system_score_codex":0.0055093644,"about_ca_system_score_gemma":0.0041703195,"threshold_uncertainty_score":0.9994331},"labels":[],"label_agreement":null},{"id":"W4292230836","doi":"10.1109/icc45855.2022.9839267","title":"Improving Human Activity Recognition using ML and Wearable Sensors","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Accelerometer; Linear discriminant analysis; Decision tree; Context (archaeology); Wearable computer; Artificial intelligence; Activity recognition; Machine learning; Wearable technology; Random forest; Gyroscope; Field (mathematics); Identification (biology); Discriminant; Ranging; Statistical classification; Pattern recognition (psychology); Embedded system; Engineering; Mathematics","score_opus":0.23472260268493308,"score_gpt":0.36877941439237905,"score_spread":0.13405681170744596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292230836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.883521,0.00010949369,0.079111814,0.007901383,0.0017969792,0.00079555274,0.00033636641,0.00045147614,0.025975905],"genre_scores_gemma":[0.9958779,0.000057297806,0.0030957349,0.000224325,0.000058053774,0.0001998541,0.000045132816,0.000017552908,0.00042412413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977687,0.0006299247,0.00032657027,0.00048631013,0.00056811405,0.00022035686],"domain_scores_gemma":[0.99762726,0.0003465168,0.00036646903,0.0012601665,0.00030789245,0.00009170866],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00066617696,0.00018300205,0.00019760073,0.00035895393,0.0014434042,0.0004106379,0.0019439093,0.00005077848,0.00038839635],"category_scores_gemma":[0.00007543426,0.00022810741,0.00007918765,0.00035255053,0.00011106233,0.00089180254,0.0013680041,0.00066531927,0.000046446236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009116971,0.0015643373,0.0011812041,0.000039351315,0.00032470672,0.000027741382,0.0034692693,0.00048306235,0.38000387,0.04909225,0.00090667955,0.5628163],"study_design_scores_gemma":[0.0009981084,0.00024651416,0.0013460618,0.00012413313,0.000028997303,0.00022703178,0.0012230497,0.9776104,0.004223984,0.008908624,0.004355376,0.00070771045],"about_ca_topic_score_codex":0.0011937792,"about_ca_topic_score_gemma":0.00026883252,"teacher_disagreement_score":0.9771274,"about_ca_system_score_codex":0.00036067236,"about_ca_system_score_gemma":0.00015691662,"threshold_uncertainty_score":0.9998566},"labels":[],"label_agreement":null},{"id":"W4292847794","doi":"10.1145/3524458.3547226","title":"Web-based human activity recognition using images and descriptive web pages","year":2022,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Autonomy; Generalization; Activity recognition; Web application; Machine learning; Artificial intelligence; Data collection; Order (exchange); Process (computing); World Wide Web","score_opus":0.0871399878770566,"score_gpt":0.279007848161102,"score_spread":0.19186786028404543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292847794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85542005,0.0000509464,0.13929227,0.0005901817,0.00033900116,0.00037619457,0.00005600288,0.00043029664,0.003445057],"genre_scores_gemma":[0.9946605,0.0000027852711,0.004728102,0.0002620475,0.000045278135,0.00008314794,0.000007260127,0.000015159475,0.0001957341],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807113,0.0005436109,0.00019347771,0.00055516796,0.0003707789,0.00026585942],"domain_scores_gemma":[0.9990586,0.00021743153,0.00016460374,0.00034147396,0.00012195234,0.00009592675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060858397,0.00018032754,0.00022667914,0.00026224693,0.000730499,0.00029421152,0.00032050448,0.000040507104,0.00023618084],"category_scores_gemma":[0.000036867958,0.00019445739,0.00007528865,0.00041903643,0.00006598007,0.0010520085,0.00044042047,0.0002499883,0.000019193834],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004109514,0.00043805497,0.0033768169,0.000054013526,0.0000671801,0.000069441754,0.00067335746,0.00003632533,0.6078122,0.00015180526,0.002962808,0.3843169],"study_design_scores_gemma":[0.008453284,0.0014515562,0.023803731,0.00020443041,0.00013181947,0.0006672494,0.002923886,0.6817184,0.26127282,0.004403856,0.011991254,0.0029776972],"about_ca_topic_score_codex":0.00031478028,"about_ca_topic_score_gemma":0.0001621022,"teacher_disagreement_score":0.68168205,"about_ca_system_score_codex":0.00020150535,"about_ca_system_score_gemma":0.00016440812,"threshold_uncertainty_score":0.7929742},"labels":[],"label_agreement":null},{"id":"W4293493554","doi":"10.36227/techrxiv.19172369.v3","title":"Deep Reinforcement Learning in Human Activity Recognition: A Survey","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition 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":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Reinforcement learning; Field (mathematics); Activity recognition; Computer science; Artificial intelligence; Deep learning; Key (lock); Human–computer interaction; Data science; Machine learning; Computer security","score_opus":0.10120361642330274,"score_gpt":0.31386440901213597,"score_spread":0.21266079258883325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293493554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11900732,0.000052935855,0.84162974,0.00033022766,0.0014732188,0.0012765389,0.000009073317,0.00080086285,0.03542009],"genre_scores_gemma":[0.9953119,0.000017080212,0.0007022873,0.00014708663,0.00008417187,0.00059411867,0.00027043084,0.000029886189,0.0028430165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947617,0.002009772,0.00066780794,0.0012754461,0.00079868274,0.00048658508],"domain_scores_gemma":[0.9973551,0.0006064774,0.00056802336,0.0011146298,0.00022121632,0.00013452981],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031209225,0.00042013742,0.0006559402,0.0006037878,0.00032166636,0.0005038887,0.0014347986,0.00025640527,0.0019085038],"category_scores_gemma":[0.00025141975,0.00049530755,0.00021017763,0.000674003,0.000034420507,0.00060516834,0.0049688444,0.0021447516,0.00014570911],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013870875,0.0011517638,0.057033647,0.00067667867,0.000434997,0.00033183565,0.0060671857,0.08271603,0.0006757019,0.000764913,0.0017827959,0.8482257],"study_design_scores_gemma":[0.0034861001,0.00078529987,0.224779,0.0007155003,0.000042925116,0.00009757444,0.0005712255,0.7488993,0.0020253705,0.0059467847,0.008108188,0.004542783],"about_ca_topic_score_codex":0.011000042,"about_ca_topic_score_gemma":0.008545068,"teacher_disagreement_score":0.8763046,"about_ca_system_score_codex":0.00071939226,"about_ca_system_score_gemma":0.00032070826,"threshold_uncertainty_score":0.99974984},"labels":[],"label_agreement":null},{"id":"W4294012891","doi":"10.1016/j.compbiomed.2022.106060","title":"Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects","year":2022,"lang":"en","type":"review","venue":"Computers in Biology and Medicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":211,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Activity recognition; Convolutional neural network; Artificial intelligence; Deep learning; Data science; Machine learning; Human–computer interaction","score_opus":0.2487808128299344,"score_gpt":0.39351245984377003,"score_spread":0.14473164701383565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294012891","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010535536,0.99364454,0.0033335115,0.0005201739,0.0012022342,0.0010310994,0.000105971936,0.000016246382,0.000040843785],"genre_scores_gemma":[0.00063485914,0.99835455,0.0003505221,0.00015038493,0.00023778039,0.00003022166,0.00023078431,0.000008543534,0.0000023725167],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972322,0.0011630406,0.0006332175,0.00062797085,0.00015432264,0.00018926646],"domain_scores_gemma":[0.9975934,0.00066749094,0.0008939583,0.00073470024,0.000060038805,0.000050437808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014405916,0.0002584526,0.0014896212,0.00015366725,0.00009666585,0.000006854855,0.0007742675,0.00013733188,0.000008242393],"category_scores_gemma":[0.00009014696,0.00017532066,0.00007219842,0.00033398138,0.0003790237,0.00021367618,0.00126798,0.00052791275,1.4836127e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032388793,0.000030532578,0.00001428694,0.0059976312,0.00006149312,0.000003769595,0.000064336826,3.6280986e-7,0.0000011413479,0.00012626855,0.00024955787,0.99344736],"study_design_scores_gemma":[0.001471887,0.00065816456,0.0016683802,0.07140477,0.00050664577,0.0012524812,0.00003186533,0.01554051,4.949097e-7,0.0014979484,0.9052893,0.0006775467],"about_ca_topic_score_codex":0.000019822279,"about_ca_topic_score_gemma":0.000032284886,"teacher_disagreement_score":0.99276984,"about_ca_system_score_codex":0.00005286938,"about_ca_system_score_gemma":0.000103201666,"threshold_uncertainty_score":0.7149369},"labels":[],"label_agreement":null},{"id":"W4294018485","doi":"10.2196/39005","title":"In-Home Monitoring Technology for Aging in Place: Scoping Review","year":2022,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Scopus; Wearable computer; Wearable technology; Grey literature; Computer science; Data science; Gerontology; MEDLINE; Medicine","score_opus":0.12707648830263274,"score_gpt":0.5075468344650397,"score_spread":0.38047034616240694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294018485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45074025,0.08535432,0.11638315,0.33407965,0.0083441315,0.004288153,0.000007774601,0.00007969036,0.00072286656],"genre_scores_gemma":[0.9961282,0.0024922967,0.0006112899,0.00024433472,0.00023318204,0.0002353508,1.6559554e-7,0.00001080713,0.00004437351],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9948669,0.0011516188,0.0008013802,0.00026104346,0.0024796803,0.00043937564],"domain_scores_gemma":[0.99325097,0.005452751,0.00030211668,0.00022755242,0.0006055619,0.00016106092],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01597798,0.00008908144,0.00044758932,0.0018828436,0.000118037846,0.0000663773,0.0018017428,0.00006732179,0.00017398072],"category_scores_gemma":[0.008324876,0.000083905754,0.000091587965,0.0017824377,0.00006748059,0.00082095625,0.001099974,0.002758985,0.000007827031],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033366005,0.000859062,0.008081244,0.0029622135,0.00009569742,0.0038128777,0.0032950114,0.000047325633,0.0016946314,0.0007986577,0.004681245,0.97333837],"study_design_scores_gemma":[0.017768014,0.0059562097,0.0034859243,0.79219896,0.000026073269,0.013058179,0.019118484,0.027489727,0.015477503,0.019604927,0.0842786,0.0015374029],"about_ca_topic_score_codex":0.00005083305,"about_ca_topic_score_gemma":0.000040131807,"teacher_disagreement_score":0.971801,"about_ca_system_score_codex":0.0008742538,"about_ca_system_score_gemma":0.0011735449,"threshold_uncertainty_score":0.9995417},"labels":[],"label_agreement":null},{"id":"W4294666599","doi":"10.1115/1.4055504","title":"Gait Phase Detection in Walking and Stairs Using Machine Learning","year":2022,"lang":"en","type":"article","venue":"Journal of Biomechanical Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stairs; Artificial intelligence; Computer science; Swing; Gait; Binary classification; Gait analysis; Machine learning; Inertial measurement unit; Simulation; Physical medicine and rehabilitation; Support vector machine; Engineering; Medicine","score_opus":0.01897912884810324,"score_gpt":0.2491277554973927,"score_spread":0.23014862664928945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294666599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44427267,0.0001852164,0.5551214,0.00004123809,0.00031575945,0.00003741092,7.002276e-7,0.00002379503,0.0000017604539],"genre_scores_gemma":[0.99550074,0.000007869328,0.0044043204,0.000013796829,0.000057372443,0.0000023832786,2.0328085e-7,0.000010375116,0.0000029202743],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989177,0.00010238266,0.00038225696,0.00013929346,0.00029017043,0.00016819706],"domain_scores_gemma":[0.9994494,0.00013594661,0.00021022458,0.00008014756,0.00004129912,0.000082982435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010367645,0.00009153242,0.00021042959,0.00053857896,0.00009162281,0.000071546616,0.00020237229,0.000030056262,0.000010122655],"category_scores_gemma":[0.00011982941,0.00009763235,0.000060439274,0.0005681472,0.000004081581,0.00044892158,0.00022819571,0.00052440923,3.646444e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026499863,0.000075682765,0.000043652974,0.000022682267,0.000023818122,0.00014154956,0.00031181774,0.011212063,0.8224244,0.000089160094,4.5425494e-7,0.16562821],"study_design_scores_gemma":[0.00095947186,0.00031124084,0.000025210225,0.00004515411,0.000005528651,0.0011467913,0.00009369344,0.97862923,0.01786822,0.00006319607,0.00073967234,0.00011256623],"about_ca_topic_score_codex":0.000030890664,"about_ca_topic_score_gemma":0.000004705971,"teacher_disagreement_score":0.9674172,"about_ca_system_score_codex":0.00022115215,"about_ca_system_score_gemma":0.000028235218,"threshold_uncertainty_score":0.39813316},"labels":[],"label_agreement":null},{"id":"W4294789909","doi":"10.48550/arxiv.2209.00990","title":"Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transfer of learning; Artificial intelligence; Accelerometer; Machine learning; Activity recognition; Supervised learning; Representation (politics); Field (mathematics); Class (philosophy); Domain (mathematical analysis); Task (project management); Labeled data; Pattern recognition (psychology); Artificial neural network; Mathematics","score_opus":0.08157651385813679,"score_gpt":0.21371971371619994,"score_spread":0.13214319985806317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294789909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5114511,0.000015431406,0.47870436,0.000065106244,0.00028916917,0.001036574,0.00003928506,0.001030683,0.0073682447],"genre_scores_gemma":[0.9962626,0.000029261486,0.002079883,0.00004140908,0.000091742746,0.000027862177,0.00020159216,0.000046358502,0.0012192677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953122,0.0016480162,0.0003236475,0.001921752,0.0003293137,0.0004650468],"domain_scores_gemma":[0.996933,0.0004559235,0.00078585226,0.0011094881,0.0005007074,0.00021505274],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005966805,0.00050942955,0.00068075355,0.000494238,0.00069433666,0.00029967923,0.0012355576,0.00029408696,0.00046563486],"category_scores_gemma":[0.00008374595,0.00061579904,0.00027802945,0.0011269057,0.00011389753,0.0013709887,0.001395002,0.0014565721,0.00020593125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0063195815,0.012781363,0.22712173,0.004496231,0.015773704,0.017847976,0.03680495,0.32088396,0.032146603,0.04071417,0.0022537196,0.28285602],"study_design_scores_gemma":[0.0060760565,0.0009483753,0.0042532114,0.00043278362,0.0005313437,0.000084450294,0.00093979004,0.9656891,0.0016881494,0.016168935,0.00073446747,0.0024533393],"about_ca_topic_score_codex":0.0011062511,"about_ca_topic_score_gemma":0.00014024499,"teacher_disagreement_score":0.64480513,"about_ca_system_score_codex":0.0008538732,"about_ca_system_score_gemma":0.00038283927,"threshold_uncertainty_score":0.9996293},"labels":[],"label_agreement":null},{"id":"W4295420234","doi":"10.1109/embc48229.2022.9871042","title":"The Use of Conductive Lycra Fabric in the Prototype Design of a Wearable Device to Monitor Physiological Signals","year":2022,"lang":"en","type":"article","venue":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Queen's University","keywords":"Wearable computer; Computer science; Wearable technology; Maintainability; Human–computer interaction; Embedded system","score_opus":0.17675456666490127,"score_gpt":0.3346476881299275,"score_spread":0.15789312146502624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295420234","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8061147,0.00026191957,0.18377785,0.005846554,0.0017755745,0.002009762,0.000114742135,0.000036615747,0.00006230626],"genre_scores_gemma":[0.99680555,0.00004652323,0.0022782572,0.0002943227,0.00006197761,0.0003693275,0.000005815322,0.000006800868,0.00013145516],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978357,0.0006527627,0.0005512157,0.0002968678,0.00043215495,0.0002313035],"domain_scores_gemma":[0.99701744,0.0017980572,0.0003215912,0.0003974308,0.0004323097,0.000033183725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017602285,0.00016328537,0.0003679149,0.00013589032,0.00009018468,0.000016566992,0.001862983,0.000072812414,0.000047562768],"category_scores_gemma":[0.000829906,0.00009617524,0.00011749339,0.0009270203,0.00023760507,0.00016851122,0.00037584844,0.00051615393,0.0000020679454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005253471,0.00072430895,0.0055373167,0.00014778928,0.0006858134,0.0000045864,0.041748356,0.26791704,0.6478146,0.0075668134,0.02229669,0.0050313734],"study_design_scores_gemma":[0.0063509,0.006532276,0.06129642,0.0019225049,0.00012115006,0.00017194891,0.025028367,0.81872284,0.01967495,0.006310046,0.052122876,0.0017457335],"about_ca_topic_score_codex":0.00055099657,"about_ca_topic_score_gemma":0.00003594665,"teacher_disagreement_score":0.6281396,"about_ca_system_score_codex":0.000091204274,"about_ca_system_score_gemma":0.00011775364,"threshold_uncertainty_score":0.39219123},"labels":[],"label_agreement":null},{"id":"W4297060521","doi":"","title":"Context Modeling for the design of a context gap calculator","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition Systems","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":"Université TÉLUQ; Université du Québec à Montréal","funders":"","keywords":"Calculator; Context (archaeology); Computer science; Context model; Artificial intelligence; Geology; Operating system","score_opus":0.06628787031541072,"score_gpt":0.2658899838343771,"score_spread":0.1996021135189664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297060521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044793193,0.0021024763,0.9828502,0.006077394,0.0006548592,0.001925689,0.000083937644,0.00024378116,0.0015823762],"genre_scores_gemma":[0.93257844,0.00011652861,0.065280974,0.0002087182,0.000055859422,0.0004354941,0.000046398658,0.000046657464,0.0012309017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99193275,0.0050852583,0.0009237881,0.0010305946,0.0005931399,0.00043449338],"domain_scores_gemma":[0.982076,0.007144943,0.0009964821,0.0036012346,0.0060148216,0.0001665156],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.010056633,0.00041867164,0.00066084124,0.00020792852,0.0004772861,0.0006148569,0.003580833,0.0003338681,0.000039378247],"category_scores_gemma":[0.0025736687,0.00036696522,0.00043751398,0.0003717283,0.00031075734,0.00031334657,0.002059402,0.00049386546,0.00003941022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011499136,0.0011179909,0.00038446998,0.0007467422,0.0009259436,0.00000455962,0.045178413,0.0043325624,0.0031520822,0.17903733,0.0069640204,0.7580409],"study_design_scores_gemma":[0.0006726708,0.000001336572,0.000042467804,0.0015316425,0.000056315843,0.000012001886,0.00024999195,0.97353697,0.013280547,0.005754098,0.0044658664,0.00039610488],"about_ca_topic_score_codex":0.0017537304,"about_ca_topic_score_gemma":0.000923862,"teacher_disagreement_score":0.96920437,"about_ca_system_score_codex":0.00014104192,"about_ca_system_score_gemma":0.0006787979,"threshold_uncertainty_score":0.9998782},"labels":[],"label_agreement":null},{"id":"W4297904035","doi":"","title":"Human Activity Recognition from Multimodal Data","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Activity recognition; Artificial intelligence","score_opus":0.06311940751932868,"score_gpt":0.2805015619193504,"score_spread":0.2173821544000217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297904035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19776434,0.00038170803,0.7637663,0.007249251,0.0011598957,0.0010507432,0.0020841856,0.001119696,0.025423905],"genre_scores_gemma":[0.9349219,0.000080162325,0.05637738,0.0001373383,0.00009035566,0.00024614262,0.005549285,0.00006774949,0.0025296374],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9830703,0.012338578,0.000717211,0.0023850116,0.0010016036,0.0004872909],"domain_scores_gemma":[0.98558074,0.0024633943,0.0011414891,0.008971464,0.0015737973,0.000269128],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.007466353,0.0005324777,0.0006525406,0.0003386401,0.0008912844,0.0013482333,0.007472245,0.00037401603,0.00084069005],"category_scores_gemma":[0.0013808283,0.0006583451,0.00026514824,0.0005714205,0.00016834833,0.0011733399,0.016792132,0.0016151734,0.0001872599],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017897226,0.002076565,0.0017997453,0.0001733195,0.00035786477,0.000037822094,0.009202065,0.000027028756,0.008853743,0.003987958,0.0050276374,0.9684383],"study_design_scores_gemma":[0.0056745857,0.0000051423463,0.061764088,0.006167816,0.0004223823,0.00009284762,0.0006661331,0.6529995,0.092245154,0.05819195,0.11525321,0.006517142],"about_ca_topic_score_codex":0.020918421,"about_ca_topic_score_gemma":0.0072154715,"teacher_disagreement_score":0.9619212,"about_ca_system_score_codex":0.00033926067,"about_ca_system_score_gemma":0.0005064926,"threshold_uncertainty_score":0.99968845},"labels":[],"label_agreement":null},{"id":"W4297999823","doi":"10.1145/3563950","title":"Synthetic Behavior Sequence Generation Using Generative Adversarial Networks","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Computing for Healthcare","topic":"Context-Aware Activity Recognition Systems","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":"McMaster University","funders":"","keywords":"Computer science; Similarity (geometry); Generative grammar; Sequence (biology); Machine learning; Adversarial system; Reinforcement learning; Artificial intelligence; Variety (cybernetics); Function (biology); Data mining; Population","score_opus":0.15914771381212353,"score_gpt":0.352951869221925,"score_spread":0.19380415540980148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297999823","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06869923,0.000082607425,0.92450887,0.0020069317,0.0031406446,0.0011826395,0.00008751793,0.00028631478,0.000005271396],"genre_scores_gemma":[0.9450678,0.0000032892963,0.053429443,0.00069985166,0.00037086004,0.00032801076,0.000037449816,0.000031191936,0.000032067914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727404,0.0005635826,0.00048155192,0.0007798035,0.00042910036,0.00047193893],"domain_scores_gemma":[0.99803287,0.00046216734,0.00025092883,0.0008485858,0.00025950244,0.00014595532],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006770915,0.00023744116,0.00029143036,0.00023306884,0.002593175,0.00015708884,0.0008031205,0.000092949005,0.000020967313],"category_scores_gemma":[0.000037057198,0.00028710393,0.00019771492,0.00059333374,0.000036687266,0.00033875424,0.00006699504,0.0005084002,0.0000041859325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004871791,0.00027960475,0.000142067,0.00004316789,0.000056617617,0.00001773275,0.0018776637,0.47014394,0.0020448605,0.0011217532,0.00009342388,0.52413046],"study_design_scores_gemma":[0.0005697405,0.00038411815,0.000047059402,0.00003295251,0.000030631123,0.00017279614,0.00020201219,0.9964812,0.00090330845,0.00020042901,0.0006664565,0.0003093323],"about_ca_topic_score_codex":0.00047787707,"about_ca_topic_score_gemma":0.00015109622,"teacher_disagreement_score":0.8763686,"about_ca_system_score_codex":0.00072663167,"about_ca_system_score_gemma":0.0003349759,"threshold_uncertainty_score":0.9999581},"labels":[],"label_agreement":null},{"id":"W4300531409","doi":"10.1145/2534903.2534914","title":"An empirical study on the user's context in mobile videoconferencing devices","year":2013,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Polytechnique Montréal; Bombardier (Canada); Université de Montréal","funders":"","keywords":"Videoconferencing; Context (archaeology); Computer science; Mobile device; Empirical research; Multimedia; Human–computer interaction; World Wide Web; Geography; Mathematics; Statistics","score_opus":0.06673121384149944,"score_gpt":0.32779202554083375,"score_spread":0.2610608116993343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300531409","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.976872,0.000017234204,0.016100597,0.0012704842,0.00015298279,0.0015524863,5.4422566e-7,0.00017538098,0.0038582683],"genre_scores_gemma":[0.99700975,3.4996413e-7,0.00011309593,0.0020504675,0.000039958042,0.00062519475,3.9656868e-7,0.000008119652,0.00015267346],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980792,0.00057153084,0.0003122669,0.00046245006,0.00032047048,0.00025410464],"domain_scores_gemma":[0.9980368,0.0008562248,0.000083419676,0.00079190166,0.00013686136,0.00009479497],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006457355,0.00015844066,0.00022355725,0.00012720721,0.000106894295,0.0005996935,0.0009719396,0.000049546128,0.00033550544],"category_scores_gemma":[0.000051101302,0.00009867562,0.000043239288,0.0003501451,0.00002435336,0.0012226785,0.0001645684,0.00020145522,0.0010006045],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000076865535,0.0017427012,0.62901264,0.000007072335,0.000045471268,0.000018543375,0.018277355,0.000021664817,0.00064929464,0.0022976736,0.002665635,0.34525424],"study_design_scores_gemma":[0.0012664057,0.0016530079,0.89546263,0.00008241425,0.0000064731585,0.000017322694,0.049351133,0.043474685,0.0015581847,0.0008779796,0.0056402525,0.0006094857],"about_ca_topic_score_codex":0.0021246152,"about_ca_topic_score_gemma":0.00462593,"teacher_disagreement_score":0.34464476,"about_ca_system_score_codex":0.000057021807,"about_ca_system_score_gemma":0.000067833826,"threshold_uncertainty_score":0.99977726},"labels":[],"label_agreement":null},{"id":"W4301440293","doi":"","title":"A pervasive framework for multi-agent personalization in smart homes","year":2008,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke","funders":"","keywords":"Personalization; Computer science; Ubiquitous computing; Internet privacy; World Wide Web; Human–computer interaction","score_opus":0.048083274287843515,"score_gpt":0.2744151782588207,"score_spread":0.22633190397097722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301440293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017044669,0.0010331952,0.9722143,0.005614013,0.000599323,0.0013565831,0.00007857115,0.00033499827,0.0017243873],"genre_scores_gemma":[0.59411114,0.0008344693,0.4012027,0.00022500804,0.00004379488,0.00068916474,0.00033651505,0.000057728637,0.0024994332],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929246,0.0040670973,0.0007125498,0.00127828,0.00054746744,0.00047005175],"domain_scores_gemma":[0.99152994,0.0027787697,0.00071460323,0.0022175817,0.0025681306,0.00019098858],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0036006768,0.0004296193,0.0005838602,0.00044008213,0.0003482675,0.0006107233,0.0020814405,0.00047743472,0.000031221352],"category_scores_gemma":[0.0030162847,0.00049681513,0.00036359107,0.00063811004,0.00014642462,0.00040595417,0.0015688472,0.00070880126,0.000049783524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065790344,0.004993141,0.03249891,0.0021339678,0.0005113104,0.00007331912,0.22155242,0.0003879019,0.0013207758,0.3366549,0.0039998796,0.39580768],"study_design_scores_gemma":[0.0038029533,0.0000034533593,0.031914737,0.0135833165,0.00007319376,0.0001225402,0.0010975153,0.8689063,0.018841097,0.026987296,0.031908914,0.0027586543],"about_ca_topic_score_codex":0.0011072692,"about_ca_topic_score_gemma":0.0023305463,"teacher_disagreement_score":0.8685184,"about_ca_system_score_codex":0.0003512123,"about_ca_system_score_gemma":0.00056802644,"threshold_uncertainty_score":0.99974835},"labels":[],"label_agreement":null},{"id":"W4301656030","doi":"10.5753/wscad.2005.18992","title":"Um Suporte à Computação Pervasiva para o Holoparadigma","year":2005,"lang":"pt","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Hewlett-Packard (Canada)","funders":"","keywords":"Humanities; Computer science; Physics; Political science; Philosophy","score_opus":0.08243717095532832,"score_gpt":0.3040132763362741,"score_spread":0.2215761053809458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301656030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10168838,0.0016511766,0.81887966,0.028245766,0.0036976568,0.0014372022,0.000059074777,0.0014369364,0.04290415],"genre_scores_gemma":[0.9555534,0.000047615238,0.0046019005,0.0029197794,0.0010039922,0.000035570843,0.000012903499,0.000054283882,0.035770565],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99475104,0.00044444564,0.0011279256,0.0015101366,0.0009983378,0.0011680968],"domain_scores_gemma":[0.9963834,0.00048478885,0.0004591669,0.0016708861,0.00037724097,0.00062448485],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008960355,0.00068016077,0.0009028912,0.0003552287,0.00039818083,0.0009965588,0.0017772656,0.0003292309,0.0019883618],"category_scores_gemma":[0.00006490498,0.00069515407,0.00045587606,0.0009371825,0.00016514299,0.0021490566,0.00076109474,0.00058390014,0.012673797],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048615475,0.0013378054,0.009553677,0.00013437489,0.0003786909,0.00035790616,0.0066320375,0.00035603577,0.00044701982,0.005448661,0.122345954,0.8529592],"study_design_scores_gemma":[0.0023310035,0.00045089202,0.016206954,0.0002711014,0.000085242384,0.0016209864,0.0005135029,0.48360142,0.00242009,0.00022660635,0.49035135,0.0019208447],"about_ca_topic_score_codex":0.0002608286,"about_ca_topic_score_gemma":0.0003558305,"teacher_disagreement_score":0.853865,"about_ca_system_score_codex":0.00026278835,"about_ca_system_score_gemma":0.00040234922,"threshold_uncertainty_score":0.99955},"labels":[],"label_agreement":null},{"id":"W4302575380","doi":"10.1109/icc45855.2022.9838761","title":"A Real-Time IoT System and ML algorithms: A Comparative Study","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Context (archaeology); Accelerometer; Wearable computer; Machine learning; Inertial measurement unit; Gyroscope; Wearable technology; Decision tree; Real-time computing; Artificial intelligence; Decision tree learning; Algorithm; Data mining; Embedded system; Engineering","score_opus":0.17542129595854342,"score_gpt":0.37906044137060213,"score_spread":0.20363914541205871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302575380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55796933,0.00031120802,0.05251789,0.024018,0.0054203793,0.006400453,0.0016485014,0.002543036,0.3491712],"genre_scores_gemma":[0.99514306,0.0000335072,0.0022001658,0.00011018135,0.000051619314,0.0011204431,0.000054883953,0.000014685985,0.0012714656],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966511,0.0011917085,0.00051566976,0.00057917234,0.00084868993,0.00021364524],"domain_scores_gemma":[0.99648553,0.0007233534,0.00036317942,0.0018742952,0.00043740295,0.000116218944],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008282174,0.0002288305,0.0003480414,0.00038163437,0.0009084718,0.0004041559,0.0037688555,0.000036403253,0.00029030454],"category_scores_gemma":[0.000031984266,0.00025663807,0.00008015408,0.0004893844,0.00011748758,0.00036162988,0.0019666282,0.00053826335,0.00021600576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031184917,0.010296693,0.004114306,0.00005999091,0.0022971458,0.00017456384,0.07806661,0.000605302,0.020238373,0.78872555,0.016097996,0.079011634],"study_design_scores_gemma":[0.0020919172,0.0010476405,0.003854392,0.00013489828,0.00004674249,0.0002843134,0.029374579,0.9483799,0.00016410976,0.0015759411,0.012235941,0.0008096692],"about_ca_topic_score_codex":0.00061527814,"about_ca_topic_score_gemma":0.00020159729,"teacher_disagreement_score":0.9477745,"about_ca_system_score_codex":0.00044873694,"about_ca_system_score_gemma":0.00021088538,"threshold_uncertainty_score":0.99998856},"labels":[],"label_agreement":null},{"id":"W4306404439","doi":"10.1186/s12984-022-01085-5","title":"Criterion validity of ActiGraph monitoring devices for step counting and distance measurement in adults and older adults: a systematic review","year":2022,"lang":"en","type":"review","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centres Intégré Universitaires de Santé et de Services Sociaux; Université du Québec à Chicoutimi; Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Centre for Interdisciplinary Research in Rehabilitation","keywords":"Criterion validity; CINAHL; Data extraction; Medicine; Physical therapy; MEDLINE; Computer science; Physical medicine and rehabilitation; Statistics; Mathematics; Construct validity; Psychometrics; Nursing","score_opus":0.04153115725450298,"score_gpt":0.2932806424114943,"score_spread":0.25174948515699136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306404439","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002677305,0.978203,0.01718195,0.000051131778,0.0003905219,0.0014759689,0.000006824377,0.000012704338,5.8052655e-7],"genre_scores_gemma":[0.03849444,0.9590512,0.0022325094,0.0000046938103,0.0000393903,0.00015971887,7.071069e-7,0.000016764669,5.6254277e-7],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9975149,0.00038797077,0.001269787,0.00027149753,0.00043103477,0.00012481808],"domain_scores_gemma":[0.996388,0.0018156058,0.0012709333,0.00017564437,0.00029094785,0.00005886491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002090869,0.00021754595,0.0013898963,0.00029437235,0.000052688694,0.00007231983,0.00016708484,0.000045641937,1.9654051e-7],"category_scores_gemma":[0.0015341407,0.00018006297,0.00018075797,0.00029778207,0.000018867737,0.00050470774,0.00005728094,0.00024302534,2.3034309e-8],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074320687,0.000025148689,0.000085689346,0.82523304,0.000030108542,0.0000027485064,0.0005129937,0.0000020499845,0.00000213685,0.000004262598,0.0000028054023,0.17409156],"study_design_scores_gemma":[0.0004382002,0.00043793846,0.0010599124,0.99228567,0.00029558444,0.00020437602,0.00024067544,0.0014793685,5.087087e-7,0.0000047045187,0.0033571206,0.00019591594],"about_ca_topic_score_codex":0.000006586207,"about_ca_topic_score_gemma":0.0000040427194,"teacher_disagreement_score":0.17389564,"about_ca_system_score_codex":0.00012008583,"about_ca_system_score_gemma":0.00004905861,"threshold_uncertainty_score":0.73427546},"labels":[],"label_agreement":null},{"id":"W4308250795","doi":"10.4017/gt.2022.21.s.566.opp1","title":"Building personalized virtual networks of care with the ADel electronic home assistant for older adults","year":2022,"lang":"en","type":"article","venue":"Gerontechnology","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Université de Montréal; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Ministry of Education, Recreation and Sports","funders":"","keywords":"Gerontology; Medicine; Computer science; Psychology","score_opus":0.007346951178316255,"score_gpt":0.22420803315024235,"score_spread":0.2168610819719261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308250795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19852516,0.0019090015,0.7968498,0.0015371355,0.00020637651,0.000692145,0.000020456368,0.0002312383,0.00002870514],"genre_scores_gemma":[0.9972943,0.000006411084,0.0016542353,0.0001474132,0.000030985615,0.00075065665,0.0000070837973,0.000018532417,0.000090390444],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99844885,0.00012202527,0.00022279496,0.00046494816,0.00026222668,0.00047916855],"domain_scores_gemma":[0.99875593,0.00023986788,0.0002490094,0.0005677077,0.00016013414,0.00002734678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003076115,0.00015999716,0.00031510217,0.00015483072,0.00032383585,0.000029657085,0.0011273267,0.00009844635,0.000016308562],"category_scores_gemma":[0.000015837093,0.00012443044,0.000112040856,0.0004835726,0.00015093261,0.00012879827,0.00042323844,0.0004202635,0.0000010742589],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011823361,0.000250302,0.0005561536,0.00012433721,0.00042209262,0.00002657102,0.010122352,0.0010673386,0.0021446773,0.26704156,0.0016012939,0.71546096],"study_design_scores_gemma":[0.03803384,0.02474875,0.0037416315,0.000865763,0.00042736018,0.0030034946,0.13696562,0.34451303,0.01288416,0.0055435062,0.42494056,0.0043323147],"about_ca_topic_score_codex":0.000041884232,"about_ca_topic_score_gemma":0.00021800256,"teacher_disagreement_score":0.7987691,"about_ca_system_score_codex":0.00023855107,"about_ca_system_score_gemma":0.00016443116,"threshold_uncertainty_score":0.5074126},"labels":[],"label_agreement":null},{"id":"W4308877343","doi":"10.3389/fpsyt.2022.1038008","title":"Real-time location systems technology in the care of older adults with cognitive impairment living in residential care: A scoping review","year":2022,"lang":"en","type":"review","venue":"Frontiers in Psychiatry","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Toronto; Toronto Rehabilitation Institute; University Health Network","funders":"Toronto Rehabilitation Institute; AGE-WELL","keywords":"Cognitive impairment; Gerontology; Residential care; Cognition; Medicine; Dementia; Independent living; Psychology; Psychiatry; Disease","score_opus":0.011440005041730799,"score_gpt":0.2860737202884138,"score_spread":0.274633715246683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308877343","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007071899,0.98489475,0.003035008,0.00009037026,0.003404218,0.008201914,0.000025464884,0.00006988261,0.00020765972],"genre_scores_gemma":[0.0025308281,0.9924026,0.0011922577,0.000034010485,0.00008319865,0.0036348647,0.00006671927,0.00004643507,0.000009068193],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9944607,0.0018730541,0.001535253,0.0010202867,0.00066619687,0.0004445207],"domain_scores_gemma":[0.9971482,0.00034840475,0.0012643838,0.0010058833,0.00019237692,0.00004073874],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012566795,0.0004805317,0.0020960746,0.001654698,0.00009011593,0.00008050206,0.0015130258,0.00030569074,0.000009499897],"category_scores_gemma":[0.00011983559,0.000396143,0.0002070131,0.0036037052,0.000084051615,0.0003226534,0.0003389053,0.00084985513,0.0000064385968],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026208956,0.00025022807,0.0032267317,0.3376203,0.00010485281,0.00005633537,0.011846688,0.000007544056,1.1365825e-8,0.00004580493,0.0016792535,0.64513606],"study_design_scores_gemma":[0.000734257,0.00020988104,0.00022143174,0.97732955,0.00017120686,0.00012575668,0.018815082,0.000064905325,6.949441e-8,0.000011012594,0.0018298358,0.00048701355],"about_ca_topic_score_codex":0.0007197744,"about_ca_topic_score_gemma":0.0012369553,"teacher_disagreement_score":0.644649,"about_ca_system_score_codex":0.00081740046,"about_ca_system_score_gemma":0.0019365448,"threshold_uncertainty_score":0.999849},"labels":[],"label_agreement":null},{"id":"W4309158109","doi":"10.1109/wimob55322.2022.9941359","title":"A Security-Centric Deep Learning Enabled Camera Solution for Real-Time Human Fall Detection","year":2022,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 British Columbia","funders":"","keywords":"Computer science; Task (project management); Real-time computing; Deep learning; Artificial intelligence; Inference; Garbage collection; Embedded system; Object detection; Garbage; Pattern recognition (psychology); Engineering","score_opus":0.016039711049821437,"score_gpt":0.24265266699331828,"score_spread":0.22661295594349684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309158109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15508576,0.00003582686,0.8381298,0.00024856857,0.0004425793,0.0008163885,0.000003616659,0.0008515281,0.0043859067],"genre_scores_gemma":[0.99595785,0.0000033547667,0.0008973095,0.00008235558,0.00010423128,0.00039612112,0.000018681363,0.000017724416,0.0025223563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980354,0.00041104163,0.00029741914,0.0005130776,0.00036194996,0.0003811391],"domain_scores_gemma":[0.9990263,0.00023172067,0.0002059672,0.00030506356,0.0001525499,0.000078390134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008182844,0.00014893878,0.00021429843,0.0002883973,0.0011922523,0.00018814595,0.00041891081,0.00005156425,0.00015716227],"category_scores_gemma":[0.00006415042,0.00017107489,0.00013436178,0.00062144845,0.000015740894,0.0005702242,0.00036295605,0.00026011397,0.00008440253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012054373,0.0007166825,0.0010948514,0.00013947973,0.00019419292,0.00003143781,0.00792421,0.0009330264,0.34125867,0.00502034,0.0027631323,0.6398034],"study_design_scores_gemma":[0.0019652308,0.0008573163,0.0005509452,0.000012981382,0.00002854818,0.0001547452,0.00044565042,0.9446048,0.0069616246,0.00233858,0.041465644,0.0006139154],"about_ca_topic_score_codex":0.0013793258,"about_ca_topic_score_gemma":0.00053045544,"teacher_disagreement_score":0.94367176,"about_ca_system_score_codex":0.0003846312,"about_ca_system_score_gemma":0.00004888298,"threshold_uncertainty_score":0.9169961},"labels":[],"label_agreement":null},{"id":"W4309344223","doi":"10.1109/smc53654.2022.9945513","title":"Multimodal Human Activity Recognition for Smart Healthcare Applications","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Activity recognition; Modalities; Convolutional neural network; Computer science; Wearable computer; Robustness (evolution); Sensor fusion; Artificial intelligence; Deep learning; Assisted living; Machine learning; Human–computer interaction; Embedded system","score_opus":0.1138413629557532,"score_gpt":0.33483149302410964,"score_spread":0.22099013006835644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309344223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34173834,0.00044809867,0.59155226,0.011275962,0.011173021,0.009350258,0.0044829994,0.0010711772,0.02890785],"genre_scores_gemma":[0.99224424,0.00003679784,0.00032907707,0.00036729313,0.0003411339,0.0033693719,0.00022854285,0.000028980501,0.0030545376],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970731,0.00035718456,0.00052634644,0.0008655372,0.0008410558,0.0003367845],"domain_scores_gemma":[0.9979822,0.00029145586,0.0004625095,0.0005131478,0.0005840583,0.0001666271],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007150472,0.00028334063,0.00035728977,0.0003242246,0.00072018936,0.00047605386,0.0009172515,0.00010018312,0.00012319311],"category_scores_gemma":[0.00002727936,0.00032954145,0.00012310213,0.00023242949,0.00006362078,0.00036291016,0.00028305448,0.00040367647,0.0000632302],"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":[0.00043375298,0.002053792,0.0039846892,0.0007271081,0.0006793553,0.000044253964,0.003740655,0.00031281204,0.020481495,0.5808215,0.01124778,0.37547278],"study_design_scores_gemma":[0.008999851,0.0033580794,0.009818907,0.0006506027,0.00015378802,0.0007567825,0.0064052637,0.49684694,0.004511528,0.04537359,0.41916108,0.0039635585],"about_ca_topic_score_codex":0.001171084,"about_ca_topic_score_gemma":0.0004402879,"teacher_disagreement_score":0.6505059,"about_ca_system_score_codex":0.00029046327,"about_ca_system_score_gemma":0.00015953196,"threshold_uncertainty_score":0.99991566},"labels":[],"label_agreement":null},{"id":"W4309553930","doi":"10.1007/978-3-031-21333-5_13","title":"NEARS-Hub, a Lightweight Edge Computing for Real-Time Monitoring in Smart Environments","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université du Québec à Chicoutimi; Institut Universitaire de Gériatrie de Montréal; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université de Sherbrooke","funders":"","keywords":"Interoperability; Flexibility (engineering); Scalability; Computer science; Enhanced Data Rates for GSM Evolution; Provisioning; Edge computing; Operationalization; Node (physics); Edge device; Distributed computing; Computer network; Engineering; Telecommunications; World Wide Web; Database; Operating system; Cloud computing","score_opus":0.022439283859302533,"score_gpt":0.23555968419731513,"score_spread":0.2131204003380126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309553930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002733177,0.014460656,0.94977325,0.00023037818,0.0085080415,0.0044632703,0.000052436786,0.0002844269,0.019494334],"genre_scores_gemma":[0.98867637,0.0006734698,0.001075265,0.00007239209,0.002283514,0.0002542145,0.0000696396,0.00014772253,0.0067474064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99676806,0.0002109975,0.000904949,0.0010876482,0.00043430386,0.00059404114],"domain_scores_gemma":[0.99709654,0.0015705957,0.00052245305,0.0006567825,0.000032451586,0.0001211899],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010182384,0.0005569048,0.0010601751,0.0003686502,0.00023153589,0.0003483662,0.0006318195,0.0005619846,0.00001813543],"category_scores_gemma":[0.000034825964,0.00057104876,0.00017092569,0.00016225451,0.000046125366,0.00023937333,0.0005092897,0.0008505811,0.000011055256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029802055,0.00037581794,0.0336786,0.002044079,0.0009375179,0.0010138345,0.008397957,0.23900226,0.0007870208,0.016743649,0.0013849689,0.6953363],"study_design_scores_gemma":[0.0017900902,0.0003066189,0.00095664477,0.003478097,0.00004762591,0.00019967428,0.000015375972,0.8485932,0.00003133974,0.0018890097,0.14099307,0.0016992784],"about_ca_topic_score_codex":0.0002997867,"about_ca_topic_score_gemma":0.00007773686,"teacher_disagreement_score":0.9859432,"about_ca_system_score_codex":0.00041857435,"about_ca_system_score_gemma":0.00005541204,"threshold_uncertainty_score":0.9996741},"labels":[],"label_agreement":null},{"id":"W4309657827","doi":"10.1177/20556683221140345","title":"Human-like tele-health robotics for older adults – A preliminary feasibility trial and vision","year":2022,"lang":"en","type":"article","venue":"Journal of Rehabilitation and Assistive Technologies Engineering","topic":"Context-Aware Activity Recognition Systems","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":"Robotics; Artificial intelligence; Computer science; Computer vision; Physical medicine and rehabilitation; Medicine; Psychology; Robot","score_opus":0.018693380532868776,"score_gpt":0.29935689242091446,"score_spread":0.28066351188804567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309657827","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.553968,0.0010138944,0.43880633,0.0043722913,0.0005070202,0.0010834641,0.000010275599,0.00023528069,0.0000034345742],"genre_scores_gemma":[0.9409794,0.00001068456,0.05888474,0.000016784888,0.000020214045,0.00007208653,9.464763e-7,0.0000073578976,0.000007765154],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99882424,0.00008450179,0.00046655256,0.00024638776,0.00023050897,0.00014781041],"domain_scores_gemma":[0.99830365,0.0008792084,0.00039588776,0.00018401945,0.00019132688,0.00004590346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011124518,0.00011756322,0.00029889648,0.00033049658,0.0002230322,0.00006199119,0.00022430417,0.00005414595,8.6380004e-7],"category_scores_gemma":[0.0008157284,0.00011043358,0.00008378313,0.00024412487,0.00005252866,0.00038110695,0.00023600181,0.0002785725,7.368779e-8],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00388408,0.0010026706,0.0051157423,0.0011973459,0.00013220368,0.000013358215,0.0037286007,0.0043887557,0.001186931,0.005188536,0.002255328,0.9719064],"study_design_scores_gemma":[0.09929465,0.08145486,0.52749723,0.0022221864,0.00010270914,0.000832167,0.03662814,0.23713821,0.00041790705,0.0049773864,0.0077486136,0.0016859605],"about_ca_topic_score_codex":0.0000027692085,"about_ca_topic_score_gemma":0.0000013062164,"teacher_disagreement_score":0.9702205,"about_ca_system_score_codex":0.00020486224,"about_ca_system_score_gemma":0.000055598866,"threshold_uncertainty_score":0.4503351},"labels":[],"label_agreement":null},{"id":"W4309740763","doi":"10.3390/electronics11223816","title":"A Generic Preprocessing Architecture for Multi-Modal IoT Sensor Data in Artificial General Intelligence","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Preprocessor; Artificial intelligence; Unsupervised learning; Cluster analysis; Modal; Modular design; Machine learning; Data pre-processing; Pattern recognition (psychology)","score_opus":0.10645568083857497,"score_gpt":0.33195911472930434,"score_spread":0.22550343389072935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309740763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09776513,0.0014868877,0.8990406,0.0006061071,0.0003378253,0.0005484388,0.000056128752,0.00013497756,0.000023928187],"genre_scores_gemma":[0.89881283,0.000014563365,0.099781424,0.00043114068,0.00026466497,0.00032217862,0.000084464446,0.000035496927,0.00025322373],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975532,0.00022244306,0.0003737209,0.0009055932,0.00034657592,0.0005984345],"domain_scores_gemma":[0.9985262,0.00015941201,0.00016286258,0.0010232753,0.000064657834,0.0000635955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088602514,0.0001818176,0.00023733875,0.00019788975,0.00030984438,0.00017058858,0.0017036662,0.00005121904,0.0000146711445],"category_scores_gemma":[0.00013745858,0.00020349865,0.000062880965,0.00073805073,0.00002671171,0.0002491207,0.000981301,0.00048294218,0.000007821014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007167879,0.00031987642,0.0002027723,0.000044073015,0.00003179531,0.000019407727,0.0017381143,0.0139724715,0.016377602,0.001474314,0.00018880607,0.96555907],"study_design_scores_gemma":[0.0002657022,0.00016793376,0.00006742514,0.000009847303,0.0000068974787,0.00013112143,0.000098364006,0.9539346,0.0069023552,0.0024380207,0.03565399,0.00032372176],"about_ca_topic_score_codex":0.000043307482,"about_ca_topic_score_gemma":0.0008602082,"teacher_disagreement_score":0.96523535,"about_ca_system_score_codex":0.00029300884,"about_ca_system_score_gemma":0.0005506501,"threshold_uncertainty_score":0.8298434},"labels":[],"label_agreement":null},{"id":"W4310897187","doi":"","title":"Validation of SmartStep: a data-driven inertial-signal step detection method on elderly people","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; SIGNAL (programming language); Inertial frame of reference; Artificial intelligence","score_opus":0.035716579600235346,"score_gpt":0.2745667771509974,"score_spread":0.2388501975507621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310897187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052516986,0.00014909686,0.9370173,0.0023558666,0.0007020898,0.00086794695,0.0002279823,0.00037727342,0.0057854573],"genre_scores_gemma":[0.9039859,0.00005907508,0.09363407,0.000057972888,0.00004423132,0.00021318444,0.00097331573,0.00004829814,0.000983909],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9820879,0.013920137,0.0009571558,0.0015638863,0.0011121216,0.00035876452],"domain_scores_gemma":[0.9876652,0.0033375162,0.0014624002,0.005389966,0.0019757315,0.00016922657],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009451205,0.0004184144,0.00066602335,0.0005571603,0.00042328404,0.00058931473,0.0042581228,0.00027709303,0.0002445552],"category_scores_gemma":[0.0011832485,0.00048367694,0.00026033772,0.0009426184,0.0000793823,0.00064873445,0.0057493323,0.0009680341,0.000038099053],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004732243,0.0012114409,0.00088402396,0.0003615914,0.0002683204,0.0000071739387,0.010392215,0.0018962969,0.015144634,0.009455514,0.0007854934,0.95954597],"study_design_scores_gemma":[0.0013635891,0.000011105311,0.0028808347,0.0015676565,0.00014366135,0.000051790623,0.0003206385,0.74600583,0.22703451,0.0019453977,0.017513413,0.0011615788],"about_ca_topic_score_codex":0.0044659656,"about_ca_topic_score_gemma":0.003627252,"teacher_disagreement_score":0.9583844,"about_ca_system_score_codex":0.00026527827,"about_ca_system_score_gemma":0.0004953822,"threshold_uncertainty_score":0.99976146},"labels":[],"label_agreement":null},{"id":"W4311680957","doi":"10.22215/etd/2022-15280","title":"Methods for Gait Analysis in a Supportive Smart Home","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"","keywords":"Gait; Asynchronous communication; Gait analysis; Computer science; Ceiling (cloud); Interpolation (computer graphics); Work (physics); Protocol (science); Physical medicine and rehabilitation; Artificial intelligence; Simulation; Motion (physics); Real-time computing; Engineering; Telecommunications; Medicine","score_opus":0.03698704187874821,"score_gpt":0.3884000219613902,"score_spread":0.351412980082642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311680957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004305534,0.00016118499,0.9809176,0.00013677296,0.0012823512,0.0009820721,0.000049791717,0.00017627371,0.011988455],"genre_scores_gemma":[0.4868737,0.000033223936,0.34204453,0.0006531369,0.00015573666,0.0067237373,0.005891283,0.000120097204,0.15750459],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736476,0.00052374596,0.00061406364,0.0008631258,0.00031829884,0.00031600674],"domain_scores_gemma":[0.99758774,0.0010552976,0.000422987,0.00062098354,0.00023314057,0.00007984861],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016286841,0.00026569303,0.0008135346,0.0016091395,0.00009922718,0.00018199241,0.0008685954,0.0001689866,0.00090155075],"category_scores_gemma":[0.00016202778,0.00028005172,0.0005648739,0.0026244102,0.000007941616,0.00039884206,0.000119731085,0.00028380723,0.000026271347],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012395554,0.00035613836,0.0027003423,0.00028065976,0.0024126507,0.000030615465,0.010345075,0.000034885303,0.00047319106,0.005907843,0.00232908,0.97500557],"study_design_scores_gemma":[0.0068592634,0.0016698951,0.19033448,0.00033802955,0.004822026,0.000063035695,0.032568365,0.40040955,0.013701824,0.039125204,0.30133677,0.008771537],"about_ca_topic_score_codex":0.0008463559,"about_ca_topic_score_gemma":0.006753218,"teacher_disagreement_score":0.966234,"about_ca_system_score_codex":0.0002158319,"about_ca_system_score_gemma":0.00028770993,"threshold_uncertainty_score":0.9999652},"labels":[],"label_agreement":null},{"id":"W4311681012","doi":"10.22215/etd/2022-15324","title":"The Supportive Smart Home System: Implications and Solutions for Service Providers","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Health Canada","keywords":"Packet loss; Computer network; Computer science; Network traffic control; Jitter; Cloud computing; Network packet; Robustness (evolution); Telecommunications","score_opus":0.03476749408330306,"score_gpt":0.28378310902505066,"score_spread":0.2490156149417476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311681012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019729195,0.0044820816,0.7450673,0.062268488,0.017403439,0.029937545,0.0021453665,0.0046140784,0.11435251],"genre_scores_gemma":[0.952081,0.000055914952,0.0012886589,0.00058714615,0.00016287273,0.014047662,0.0015767546,0.000079434845,0.030120505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99810976,0.00014531727,0.00044767448,0.00062131375,0.0002713506,0.0004046076],"domain_scores_gemma":[0.9972038,0.00094993145,0.0004155574,0.00072852866,0.0006011025,0.00010105607],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006638052,0.00023280858,0.0002761306,0.00014649297,0.0019487555,0.00057852897,0.00084409636,0.00011772144,0.000013171143],"category_scores_gemma":[0.00006710106,0.00019260202,0.00012643095,0.0004723686,0.000023211518,0.00051384827,0.00021387756,0.0002153836,0.000030878356],"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":[0.000107769556,0.00022071743,0.00026197886,0.0026414464,0.0008202767,0.0000043541477,0.01349733,0.000010052344,0.0010523672,0.73595643,0.05097227,0.19445498],"study_design_scores_gemma":[0.0038809963,0.00092177524,0.054246202,0.00082237786,0.0009437443,0.0006793997,0.19192211,0.045390986,0.00059482205,0.03100196,0.6648646,0.004731026],"about_ca_topic_score_codex":0.00069553853,"about_ca_topic_score_gemma":0.007718029,"teacher_disagreement_score":0.9323518,"about_ca_system_score_codex":0.00020633423,"about_ca_system_score_gemma":0.00053474476,"threshold_uncertainty_score":0.99935055},"labels":[],"label_agreement":null},{"id":"W4311973436","doi":"10.1002/alz.067374","title":"Smart Home Tech: an interface to provide feedback to caregivers of persons living with cognitive impairment","year":2022,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Carleton University; Bruyère; University of Ottawa","funders":"","keywords":"Activities of daily living; Cognition; Human–computer interaction; Interface (matter); Focus (optics); Focus group; Computer science; Cognitive impairment; Tracking (education); Motion sensors; Applied psychology; Psychology; Multimedia; Medicine; Artificial intelligence; Physical therapy","score_opus":0.026702677159893825,"score_gpt":0.2607924088487746,"score_spread":0.23408973168888075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311973436","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79155594,0.0020080537,0.19879048,0.0017074976,0.0008742908,0.003217052,0.00015488849,0.00039990415,0.0012919212],"genre_scores_gemma":[0.99453765,5.403842e-7,0.0045566526,0.00037877136,0.000025986665,0.0004244583,0.0000055887685,0.000028472776,0.000041883377],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9975848,0.000282362,0.00030981403,0.00074098347,0.000648782,0.0004332198],"domain_scores_gemma":[0.9985748,0.00015867568,0.00018917854,0.00056518294,0.00023048639,0.00028167406],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048438556,0.0002490508,0.00030623126,0.0003256127,0.00028830118,0.00014241562,0.0009138048,0.000028953558,0.00018560389],"category_scores_gemma":[0.000020216654,0.00025932532,0.00008382406,0.000817148,0.000043381067,0.00066169904,0.0011811769,0.00018061064,0.00009422298],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070894044,0.0031049037,0.049547046,0.00007995089,0.0254442,0.00015680495,0.24537762,0.0011300964,0.016151477,0.0004299022,0.016562458,0.6413066],"study_design_scores_gemma":[0.011995902,0.055861834,0.20062704,0.0028608472,0.022943301,0.0012251812,0.3365041,0.010368651,0.23026669,0.00018034397,0.1165608,0.010605315],"about_ca_topic_score_codex":0.00083009806,"about_ca_topic_score_gemma":0.00049915246,"teacher_disagreement_score":0.6307013,"about_ca_system_score_codex":0.000055041997,"about_ca_system_score_gemma":0.00018327404,"threshold_uncertainty_score":0.9999859},"labels":[],"label_agreement":null},{"id":"W4312065865","doi":"10.1007/s12652-022-04486-5","title":"Interdisciplinary perspectives on privacy awareness in lifelogging technology development","year":2022,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Agencia Estatal de Investigación; RWTH Aachen University; Forskningsrådet om Hälsa, Arbetsliv och Välfärd; Canadian Institutes of Health Research; Ministero dell’Istruzione, dell’Università e della Ricerca; Vetenskapsrådet; Bundesministerium für Bildung und Forschung; Joint Programming Initiative More Years, Better Lives","keywords":"Lifelog; Variety (cybernetics); Internet privacy; Population; Computer science; Work (physics); Context (archaeology); Human–computer interaction; Medicine; Engineering; Artificial intelligence","score_opus":0.04703151256812755,"score_gpt":0.3163174717931268,"score_spread":0.26928595922499926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312065865","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7344504,0.00039585488,0.26383248,0.0005877848,0.0004715917,0.00012775521,2.735326e-7,0.000043339016,0.00009049483],"genre_scores_gemma":[0.9935997,0.00001623585,0.0061876336,0.00008766099,0.00007398775,0.0000065711665,3.307457e-7,0.000009587618,0.000018258279],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9979744,0.00017276415,0.0007860854,0.00036082734,0.00042951852,0.00027641386],"domain_scores_gemma":[0.99865144,0.00025803794,0.00059505063,0.00022623624,0.00020375673,0.000065499815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012842116,0.00016756674,0.00037231535,0.0012074554,0.00060164195,0.00014429307,0.0009682337,0.000038154925,0.000018113245],"category_scores_gemma":[0.000076801836,0.00016449828,0.00007475493,0.0006611221,0.000055902212,0.00033157854,0.0019918887,0.00065762404,0.0000049874216],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017817195,0.001191574,0.012844152,0.00006601591,0.00014540751,0.00057078374,0.19802971,0.0056699254,0.0015130684,0.020149231,0.00009573997,0.7595462],"study_design_scores_gemma":[0.0059088725,0.009533567,0.03428712,0.005487363,0.00007348379,0.011083324,0.5547058,0.25048614,0.050966457,0.056683414,0.016860595,0.003923836],"about_ca_topic_score_codex":0.0000052241244,"about_ca_topic_score_gemma":0.0000053095127,"teacher_disagreement_score":0.7556224,"about_ca_system_score_codex":0.00038135296,"about_ca_system_score_gemma":0.0001920757,"threshold_uncertainty_score":0.67080456},"labels":[],"label_agreement":null},{"id":"W4312087026","doi":"10.1002/alz.067365","title":"Smart Home Tech: an interface to provide feedback to caregivers of persons living with cognitive impairment","year":2022,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition 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":true,"ca_institutions":"Carleton University; Bruyère; University of Ottawa","funders":"","keywords":"Interface (matter); Activities of daily living; Cognition; Focus (optics); Focus group; Human–computer interaction; Computer science; Cognitive impairment; Motion sensors; Tracking (education); Sleep (system call); Applied psychology; Psychology; Multimedia; Artificial intelligence; Psychiatry","score_opus":0.026702677159893825,"score_gpt":0.2607924088487746,"score_spread":0.23408973168888075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312087026","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79155594,0.0020080537,0.19879048,0.0017074976,0.0008742908,0.003217052,0.00015488849,0.00039990415,0.0012919212],"genre_scores_gemma":[0.99453765,5.403842e-7,0.0045566526,0.00037877136,0.000025986665,0.0004244583,0.0000055887685,0.000028472776,0.000041883377],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9975848,0.000282362,0.00030981403,0.00074098347,0.000648782,0.0004332198],"domain_scores_gemma":[0.9985748,0.00015867568,0.00018917854,0.00056518294,0.00023048639,0.00028167406],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048438556,0.0002490508,0.00030623126,0.0003256127,0.00028830118,0.00014241562,0.0009138048,0.000028953558,0.00018560389],"category_scores_gemma":[0.000020216654,0.00025932532,0.00008382406,0.000817148,0.000043381067,0.00066169904,0.0011811769,0.00018061064,0.00009422298],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070894044,0.0031049037,0.049547046,0.00007995089,0.0254442,0.00015680495,0.24537762,0.0011300964,0.016151477,0.0004299022,0.016562458,0.6413066],"study_design_scores_gemma":[0.011995902,0.055861834,0.20062704,0.0028608472,0.022943301,0.0012251812,0.3365041,0.010368651,0.23026669,0.00018034397,0.1165608,0.010605315],"about_ca_topic_score_codex":0.00083009806,"about_ca_topic_score_gemma":0.00049915246,"teacher_disagreement_score":0.6307013,"about_ca_system_score_codex":0.000055041997,"about_ca_system_score_gemma":0.00018327404,"threshold_uncertainty_score":0.9999859},"labels":[],"label_agreement":null},{"id":"W4312557520","doi":"10.1109/access.2022.3218785","title":"Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Missing data; Imputation (statistics); Computer science; Bayesian probability; Time series; Data mining; Gaussian process; Gaussian; Artificial intelligence; Machine learning","score_opus":0.061025797928644494,"score_gpt":0.3102451591558624,"score_spread":0.24921936122721794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312557520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016847622,0.00017600747,0.97155577,0.0027413566,0.0012659369,0.0005778399,0.000118773336,0.0006063261,0.0061103413],"genre_scores_gemma":[0.9902672,0.0000017006228,0.007802155,0.0003770706,0.0002970259,0.00007365663,0.00011560189,0.00003330663,0.0010322711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971083,0.00048001384,0.00039354997,0.00094413594,0.0006751045,0.00039893403],"domain_scores_gemma":[0.9976903,0.00016818313,0.00027417508,0.0016207141,0.00008870286,0.00015791309],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007808047,0.00023120294,0.00030888722,0.00023634444,0.00071997347,0.0012898577,0.0035824461,0.000054311007,0.00019193382],"category_scores_gemma":[0.000047118025,0.00025105325,0.00006798887,0.0010192923,0.00005759662,0.0031624592,0.0018669458,0.00031695995,0.000078011246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021256508,0.0014117339,0.010462981,0.00068745704,0.00064871006,0.0014601585,0.012527095,0.015331941,0.004392274,0.0015832287,0.075980574,0.8753013],"study_design_scores_gemma":[0.0015928502,0.00019903056,0.0049427478,0.00011640472,0.00007319273,0.0033427589,0.0007479444,0.94903654,0.005886183,0.0023388078,0.029863328,0.0018601993],"about_ca_topic_score_codex":0.0001518327,"about_ca_topic_score_gemma":0.00002156386,"teacher_disagreement_score":0.9734196,"about_ca_system_score_codex":0.00012271841,"about_ca_system_score_gemma":0.00021483602,"threshold_uncertainty_score":0.99999416},"labels":[],"label_agreement":null},{"id":"W4312601839","doi":"10.1145/3544794.3558465","title":"Improving Noise Robustness of Single Sensor Data in Human Activity Recognition With UMAP and Additional Data","year":2022,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Concordia University","funders":"","keywords":"Robustness (evolution); Computer science; Macro; Pattern recognition (psychology); Artificial intelligence; Noise (video); Data mining","score_opus":0.110347802307636,"score_gpt":0.27667884808740506,"score_spread":0.16633104577976907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312601839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6193234,0.000018295561,0.37079793,0.00039060102,0.00018126392,0.00054423965,0.0078003886,0.00014538423,0.00079850724],"genre_scores_gemma":[0.9868198,5.7236537e-7,0.010699985,0.000040252045,0.000050120696,0.000043047654,0.0022502013,0.000011494289,0.00008451061],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804103,0.0002702508,0.00024919925,0.00080905785,0.00044194792,0.00018852258],"domain_scores_gemma":[0.99764717,0.00039392363,0.00024846056,0.0015595116,0.00009388994,0.000057019235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007462873,0.00013221567,0.00022658946,0.00020164298,0.00020329386,0.00011108213,0.0012195194,0.00003127506,0.0005963712],"category_scores_gemma":[0.000099129415,0.00013335215,0.000013054394,0.00042256262,0.00006249233,0.0025302288,0.0031858943,0.00020422322,0.000002551495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020870054,0.0023459052,0.0025206055,0.00028925232,0.00011456489,0.00009283234,0.0005043881,0.0003077081,0.074149944,0.00014313225,0.0077440613,0.9115789],"study_design_scores_gemma":[0.0046122232,0.000995799,0.017034708,0.0003073968,0.000059784925,0.0009368258,0.0014975768,0.9606322,0.007801481,0.00026430047,0.004438705,0.0014189676],"about_ca_topic_score_codex":0.0008830354,"about_ca_topic_score_gemma":0.001681645,"teacher_disagreement_score":0.9603245,"about_ca_system_score_codex":0.000072037044,"about_ca_system_score_gemma":0.00012318786,"threshold_uncertainty_score":0.6529846},"labels":[],"label_agreement":null},{"id":"W4312759888","doi":"10.1007/978-3-031-18872-5_17","title":"Low-Code Internet of Things Application Development for Edge Analytics","year":2022,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Context-Aware Activity Recognition Systems","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":"Artificial Intelligence in Medicine (Canada)","funders":"Science Foundation Ireland; European Commission","keywords":"Analytics; Computer science; Python (programming language); Software analytics; Software deployment; Data science; Software engineering; Internet of Things; World Wide Web; Software; Software development; Software development process; Operating system","score_opus":0.016712287021489722,"score_gpt":0.2606784322276566,"score_spread":0.24396614520616686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312759888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000060294085,0.003514628,0.91572505,0.0008624821,0.00014103451,0.0010895876,0.000018344535,0.0002561321,0.07833245],"genre_scores_gemma":[0.74036866,0.027105786,0.2118058,0.0014549615,0.000029189072,0.0037528006,0.0016220844,0.00008109846,0.013779636],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985394,0.000022999753,0.0009234886,0.000198884,0.000191801,0.0001234212],"domain_scores_gemma":[0.9974924,0.00020226107,0.0011061623,0.0009005486,0.00027487194,0.000023795328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045111115,0.00017795611,0.0003286412,0.00094067695,0.00010296513,0.000044247914,0.0012665117,0.00024259581,0.0000160871],"category_scores_gemma":[0.00006303105,0.00020549765,0.000044693636,0.00020666167,0.0001335826,0.0020467178,0.00073904486,0.00036292645,0.000014319834],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058472247,0.000011435646,0.000017885748,0.00008729289,0.000009695661,3.6193732e-8,0.0007944415,0.0000074558116,0.000002927447,0.43731278,0.000023034534,0.56172717],"study_design_scores_gemma":[0.00034908566,0.000046725087,0.000012441075,0.00015211393,0.0000056473446,0.000009620609,0.00021518729,0.0071340217,0.000526998,0.04940733,0.94192666,0.00021419027],"about_ca_topic_score_codex":0.0000064166484,"about_ca_topic_score_gemma":0.000059470087,"teacher_disagreement_score":0.9419036,"about_ca_system_score_codex":0.00018621022,"about_ca_system_score_gemma":0.0001011869,"threshold_uncertainty_score":0.8379951},"labels":[],"label_agreement":null},{"id":"W4312817501","doi":"10.1109/icjece.2022.3199227","title":"Deep Incremental Learning for Personalized Human Activity Recognition on Edge Devices","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Deep learning; Activity recognition; Machine learning; Artificial intelligence; Software deployment; Wearable computer; Enhanced Data Rates for GSM Evolution; Edge computing; Wearable technology; Human–computer interaction; Embedded system; Operating system","score_opus":0.023509322594011776,"score_gpt":0.2198667576536786,"score_spread":0.19635743505966682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312817501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34613696,0.00020008467,0.65299624,0.0001741332,0.0003204428,0.00011708,0.0000026114476,0.00002412118,0.000028349756],"genre_scores_gemma":[0.9978355,0.0000015413774,0.0017317439,0.00015107305,0.00023378947,0.000015495882,0.0000032141943,0.000010281441,0.000017373322],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991059,0.00008858412,0.00019529327,0.00017351098,0.00017321827,0.00026354697],"domain_scores_gemma":[0.9991829,0.0002515768,0.00012956052,0.00006250551,0.00008690022,0.0002865818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034003344,0.00011491325,0.00020230327,0.00038973574,0.00037733692,0.00015341463,0.00025611464,0.000028948878,0.00001592191],"category_scores_gemma":[0.00003697407,0.00012401596,0.000094440715,0.00026761685,0.000010102575,0.00026583645,0.000039473765,0.00039591274,9.3766636e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006277983,0.0001074384,0.002042715,0.00006972254,0.0002242002,0.00017967234,0.0013297014,0.010490171,0.0033478232,0.0029842749,0.00069713866,0.97846437],"study_design_scores_gemma":[0.001539317,0.0022528637,0.0075919847,0.00007440589,0.00002589707,0.0009026634,0.000035889858,0.961743,0.0006938936,0.0002452257,0.024461035,0.00043379777],"about_ca_topic_score_codex":0.00023828204,"about_ca_topic_score_gemma":0.00021796564,"teacher_disagreement_score":0.97803056,"about_ca_system_score_codex":0.0002555636,"about_ca_system_score_gemma":0.0001387958,"threshold_uncertainty_score":0.50572246},"labels":[],"label_agreement":null},{"id":"W4313216271","doi":"10.1016/j.arr.2022.101830","title":"Is research on ‘smart living environments’ based on unobtrusive technologies for older adults going in circles? Evidence from an umbrella review","year":2022,"lang":"en","type":"review","venue":"Ageing Research Reviews","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre for Interdisciplinary Research in Rehabilitation; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; University of Toronto; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université de Sherbrooke; Institut Universitaire de Gériatrie de Montréal","funders":"Fonds de Recherche du Québec - Santé; Fonds de recherche du Québec; Agence Nationale de la Recherche","keywords":"Human–computer interaction; Psychology; Cognitive science; Computer science","score_opus":0.46542235421744244,"score_gpt":0.5015080203940748,"score_spread":0.03608566617663239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313216271","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011955771,0.9809993,0.0019303327,0.0010011945,0.0002806672,0.0151886,0.00007367629,0.00019527212,0.00031902234],"genre_scores_gemma":[0.0001762032,0.98306745,0.0010329224,0.00043912223,0.00014590472,0.01463108,0.00006388032,0.00014030263,0.00030314992],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9698459,0.018241512,0.0023467236,0.0038409869,0.0038309414,0.0018939229],"domain_scores_gemma":[0.95141697,0.041458316,0.0010474713,0.005522395,0.0002650809,0.00028979318],"candidate_categories":["metaresearch","metaepi_narrow","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.0426856,0.0009446421,0.0037986815,0.0026828244,0.0010068414,0.000696115,0.0069609433,0.00052926334,0.0003682138],"category_scores_gemma":[0.025194583,0.00083621807,0.0010649862,0.0052045304,0.00029360966,0.0010351747,0.0029432727,0.0052838973,0.0014689395],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011005172,0.00024882893,0.00001049857,0.048081353,0.000030138926,0.00015046832,0.00027911545,7.125429e-7,0.0000015488007,0.000020032632,0.0045213983,0.9466449],"study_design_scores_gemma":[0.00013285312,0.00038923437,0.000024014453,0.41117728,0.00003042268,0.0000060546267,0.000061113606,0.00056400726,0.0000038747066,0.000047166697,0.58712244,0.00044154443],"about_ca_topic_score_codex":0.00058126065,"about_ca_topic_score_gemma":0.00014090311,"teacher_disagreement_score":0.94620335,"about_ca_system_score_codex":0.0025071318,"about_ca_system_score_gemma":0.0009779558,"threshold_uncertainty_score":0.99940884},"labels":[],"label_agreement":null},{"id":"W4313220191","doi":"10.1016/j.inffus.2022.12.023","title":"Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation","year":2022,"lang":"en","type":"article","venue":"Information Fusion","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Centers for Mechanical Engineering Research and Education, Massachusetts Institute of Technology; Basic and Applied Basic Research Foundation of Guangdong Province; China Postdoctoral Science Foundation; Science, Technology and Innovation Commission of Shenzhen Municipality; National Natural Science Foundation of China; Massachusetts Institute of Technology","keywords":"Computer science; Artificial intelligence; Classifier (UML); Machine learning; Pattern recognition (psychology); Benchmark (surveying); Domain adaptation","score_opus":0.05018607380328232,"score_gpt":0.27665558171694415,"score_spread":0.22646950791366183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313220191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.337133,0.00004305979,0.65823907,0.00014874374,0.00063475955,0.000770013,0.000062328756,0.00020511614,0.0027639142],"genre_scores_gemma":[0.9976469,0.0000060057105,0.0017380447,0.00011820734,0.0000373309,0.00018533065,0.00010141948,0.0000050981093,0.00016165903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999023,0.0000918249,0.00031704112,0.00016007063,0.0002658793,0.0001421866],"domain_scores_gemma":[0.9990065,0.00029340334,0.00020377064,0.00019906745,0.0002447885,0.00005244488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050160644,0.00010420262,0.00011414412,0.00022527932,0.00071019365,0.00030943693,0.00020366174,0.000037667258,0.000044021584],"category_scores_gemma":[0.00013312578,0.000109789326,0.000050881386,0.0003014768,0.00001982345,0.0030165135,0.0002953172,0.00007286678,0.000054439664],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009195037,0.00005368675,0.0014614735,0.00008375994,0.000011825613,4.3352242e-7,0.01669834,0.0013051525,0.00093930337,0.003869307,0.0010259708,0.9744588],"study_design_scores_gemma":[0.001974858,0.00027761207,0.020269979,0.000023795781,0.000009905776,0.000036061036,0.002396029,0.82412684,0.0009125088,0.0014066432,0.14822753,0.0003382599],"about_ca_topic_score_codex":0.000060046823,"about_ca_topic_score_gemma":0.000025412373,"teacher_disagreement_score":0.97412056,"about_ca_system_score_codex":0.00011015325,"about_ca_system_score_gemma":0.00008288539,"threshold_uncertainty_score":0.5462307},"labels":[],"label_agreement":null},{"id":"W4313436112","doi":"10.3390/s23010006","title":"Federated Learning via Augmented Knowledge Distillation for Heterogenous Deep Human Activity Recognition Systems","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thunder Bay Regional Research Institute; Lakehead University","funders":"","keywords":"Computer science; Distillation; Activity recognition; Deep learning; Artificial intelligence; Human–computer interaction; Machine learning; Embedded system; Chemistry; Chromatography","score_opus":0.04525981913289185,"score_gpt":0.2785962715836858,"score_spread":0.23333645245079393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313436112","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65403855,0.000060594197,0.34290177,0.00005614643,0.0009037247,0.0009151975,0.000026258736,0.00047810003,0.00061964424],"genre_scores_gemma":[0.9984893,0.0000011700104,0.0001544968,0.00001659823,0.00014162231,0.00039008842,0.0001395582,0.000034957095,0.0006321766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973926,0.00092337985,0.00035533388,0.0006122322,0.0003344541,0.00038202756],"domain_scores_gemma":[0.9986646,0.00033337463,0.000354146,0.0002511596,0.00029190272,0.000104801904],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00068390043,0.00022536489,0.00031464506,0.00023214327,0.0017015152,0.00031366458,0.0002494733,0.000073080104,0.000029333043],"category_scores_gemma":[0.00007491236,0.00026517792,0.00012101563,0.00049196667,0.000024921921,0.00036995782,0.0002031301,0.00029967667,0.00007556283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002195051,0.00094405253,0.0011312074,0.00033829064,0.00036856075,0.000043102133,0.0054219943,0.014053098,0.11431751,0.00019977258,0.00040957073,0.86255336],"study_design_scores_gemma":[0.001194028,0.00062748196,0.00086475065,0.000039317074,0.000029200943,0.00018368944,0.00050874805,0.9834252,0.0046887626,0.00015823079,0.0077360533,0.000544525],"about_ca_topic_score_codex":0.00026560354,"about_ca_topic_score_gemma":0.00016453689,"teacher_disagreement_score":0.9693721,"about_ca_system_score_codex":0.00045139017,"about_ca_system_score_gemma":0.000048966085,"threshold_uncertainty_score":0.99998003},"labels":[],"label_agreement":null},{"id":"W4317522925","doi":"10.2196/43726","title":"An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study","year":2023,"lang":"en","type":"article","venue":"JMIR Biomedical Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Verily Life Sciences","keywords":"Generalizability theory; Biometrics; Wearable computer; Computer science; Ground truth; Artificial intelligence; Wearable technology; Machine learning; Set (abstract data type); Algorithm; Data set; Data mining; Statistics; Mathematics","score_opus":0.05985076518709303,"score_gpt":0.3398835450617325,"score_spread":0.2800327798746395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317522925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6123968,0.000012301602,0.38594872,0.00009235254,0.00041563547,0.00047115298,0.00008705143,0.0005722207,0.0000037971997],"genre_scores_gemma":[0.95686734,0.000007910436,0.042672854,0.000044094442,0.00021406455,0.000057062047,0.00010054137,0.00002682401,0.000009328022],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975583,0.00013949187,0.0003564419,0.0008332189,0.00064832764,0.00046422606],"domain_scores_gemma":[0.9981059,0.00033169796,0.00007007766,0.0008223689,0.00007239419,0.000597561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007900223,0.00021322753,0.00028997054,0.0007013641,0.00013264392,0.00028597328,0.00068477134,0.0001032245,0.000009265204],"category_scores_gemma":[0.0001009542,0.00022242765,0.000031987533,0.0020370693,0.0000369137,0.001094018,0.00080855854,0.00024890344,0.000036816695],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005353423,0.0012960343,0.037019435,0.00017099084,0.00047818874,0.0005693783,0.0061086523,0.00359823,0.074906684,0.00005988227,0.0006777811,0.8750612],"study_design_scores_gemma":[0.0005827874,0.000101002144,0.12687181,0.00009073276,0.000015815258,0.000003739517,0.00026269315,0.8703565,0.00006973033,0.0000082609895,0.001377701,0.00025923594],"about_ca_topic_score_codex":0.0020149192,"about_ca_topic_score_gemma":0.00014338137,"teacher_disagreement_score":0.874802,"about_ca_system_score_codex":0.00009556888,"about_ca_system_score_gemma":0.00008455864,"threshold_uncertainty_score":0.9070337},"labels":[],"label_agreement":null},{"id":"W4317624908","doi":"10.1016/j.inffus.2023.01.015","title":"Multi-level feature fusion for multimodal human activity recognition in Internet of Healthcare Things","year":2023,"lang":"en","type":"article","venue":"Information Fusion","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":181,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; King Saud University","keywords":"Computer science; Convolutional neural network; Activity recognition; Wearable computer; Feature (linguistics); Artificial intelligence; Sensor fusion; Reliability (semiconductor); Deep learning; Modality (human–computer interaction); Pattern recognition (psychology); Machine learning; Embedded system","score_opus":0.08742623048540878,"score_gpt":0.3193409502391366,"score_spread":0.23191471975372785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317624908","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6616655,0.000006637701,0.33412978,0.0017676766,0.0006093521,0.0012296378,0.00009698126,0.0003514614,0.00014298399],"genre_scores_gemma":[0.98953426,0.000008668449,0.009517011,0.00024677342,0.000031010386,0.00012974771,0.00038214016,0.000009186867,0.00014122414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845946,0.0001215905,0.00052696065,0.00024266932,0.00038048546,0.00026883528],"domain_scores_gemma":[0.9984712,0.00020148758,0.00049678533,0.0003061803,0.00044945604,0.00007491559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009007069,0.00017246579,0.0002650732,0.0007254211,0.00014007319,0.00011176257,0.00037507495,0.00021866449,0.00001128045],"category_scores_gemma":[0.00020739571,0.0001735379,0.00010799575,0.0007784369,0.000022389608,0.00426058,0.00027991933,0.0002373798,0.00013536817],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008320367,0.00010173336,0.0007940463,0.0003707006,0.000008959583,0.000001414881,0.011950818,0.000019708497,0.017564805,0.0001651507,0.0013653259,0.9675741],"study_design_scores_gemma":[0.0049957046,0.0004106383,0.15799455,0.0009890515,0.000008194256,0.000021258084,0.00095208676,0.7956548,0.032312974,0.00074671576,0.0052941814,0.0006198894],"about_ca_topic_score_codex":0.0013136793,"about_ca_topic_score_gemma":0.00049696764,"teacher_disagreement_score":0.96695423,"about_ca_system_score_codex":0.00015263927,"about_ca_system_score_gemma":0.000072432944,"threshold_uncertainty_score":0.70766705},"labels":[],"label_agreement":null},{"id":"W4317655921","doi":"10.1186/s12938-023-01065-3","title":"Privacy-protecting behaviours of risk detection in people with dementia using videos","year":2023,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Toronto Rehabilitation Institute; AGE-WELL; Alzheimer's Association","keywords":"Dementia; Computer science; Segmentation; Anomaly detection; Artificial intelligence; Noise (video); Computer security; Internet privacy; Medicine; Image (mathematics); Disease","score_opus":0.019532313167406248,"score_gpt":0.25107310431653296,"score_spread":0.2315407911491267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317655921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56055796,0.000016714595,0.43889186,0.000043441025,0.00017763513,0.00010862457,0.00001146271,0.00019182287,4.8597764e-7],"genre_scores_gemma":[0.983978,0.0000039346887,0.01589309,0.0000038302687,0.00007678761,0.000014022434,0.000011824142,0.000016819433,0.00000170678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986941,0.000055564626,0.00033272052,0.0002792076,0.00037268398,0.0002656802],"domain_scores_gemma":[0.99922574,0.00021470577,0.00013427383,0.0002743613,0.00005635893,0.0000945777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056714675,0.00012721554,0.00022332721,0.00053399644,0.0000457661,0.000027086282,0.00030765412,0.00008247923,0.0000038919125],"category_scores_gemma":[0.0004307449,0.00012065904,0.000043640597,0.0019676692,0.000021997736,0.00021830056,0.00018961584,0.00027223746,0.0000041646267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057621837,0.0008832717,0.053415764,0.00052513386,0.00028227086,0.000180656,0.0042099943,0.029096615,0.2394823,0.00004612584,0.00002165618,0.6717986],"study_design_scores_gemma":[0.00058595976,0.00009198627,0.09167068,0.00025278996,0.000018226732,0.00003605481,0.00006346989,0.90188473,0.0050968,0.000009604639,0.00011379496,0.00017589606],"about_ca_topic_score_codex":0.0008437844,"about_ca_topic_score_gemma":0.000317719,"teacher_disagreement_score":0.87278813,"about_ca_system_score_codex":0.00007370953,"about_ca_system_score_gemma":0.000058773963,"threshold_uncertainty_score":0.49203327},"labels":[],"label_agreement":null},{"id":"W4318200332","doi":"10.3390/s23031390","title":"Human Activity Recognition with an HMM-Based Generative Model","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hidden Markov model; Generative grammar; Computer science; Pattern recognition (psychology); Artificial intelligence; Speech recognition","score_opus":0.09214994951530925,"score_gpt":0.300073390858856,"score_spread":0.20792344134354673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318200332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8403123,0.0000012287985,0.15684614,0.00046536303,0.00009985263,0.00026891258,0.000026019114,0.00081300794,0.001167161],"genre_scores_gemma":[0.9936537,5.632892e-7,0.005386235,0.00019902737,0.00008389927,0.000056907957,0.000033832424,0.000024342899,0.0005614761],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827635,0.00027733968,0.00015857012,0.00058774283,0.00037879785,0.00032118903],"domain_scores_gemma":[0.9988681,0.00009358467,0.00012773919,0.000558938,0.00020965148,0.00014200182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003422308,0.0001961267,0.00021484341,0.00025644945,0.00027976016,0.00018890487,0.00030349495,0.000083151645,0.000013511904],"category_scores_gemma":[0.000024451772,0.00018095123,0.000062074636,0.00071580004,0.000055545977,0.00076129753,0.000060447557,0.00017432476,0.00031747404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029139713,0.0012776502,0.0013672228,0.00016421257,0.0002925581,0.0004840974,0.009651586,0.1689567,0.1637378,0.0018979432,0.003801659,0.6480772],"study_design_scores_gemma":[0.0005743077,0.00024708253,0.0013656578,0.00003418893,0.000008502008,0.000012566472,0.00008798758,0.96132904,0.0347406,0.0011952971,0.0000762718,0.00032850806],"about_ca_topic_score_codex":0.00013605651,"about_ca_topic_score_gemma":0.0004565362,"teacher_disagreement_score":0.79237235,"about_ca_system_score_codex":0.00007363956,"about_ca_system_score_gemma":0.000109826105,"threshold_uncertainty_score":0.7378977},"labels":[],"label_agreement":null},{"id":"W4318256937","doi":"10.1145/3582524.3582533","title":"Report on the 1st Early Career Researchers' Roundtable for Information Access Research (ECRs4IR 2022) at CHIIR 2022","year":2022,"lang":"en","type":"article","venue":"ACM SIGIR Forum","topic":"Context-Aware Activity Recognition Systems","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 Regina","funders":"","keywords":"Implementation; Event (particle physics); Computer science; Career development; Conjunction (astronomy); Medical education; Public relations; Political science; Medicine","score_opus":0.22925515299234653,"score_gpt":0.39115706814448803,"score_spread":0.1619019151521415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318256937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7843607,0.0004233696,0.04220005,0.12474354,0.004441903,0.0092888195,0.00053923175,0.00078014605,0.033222236],"genre_scores_gemma":[0.9728454,0.0000095372425,0.00016642454,0.0011519789,0.00014048477,0.0029499023,0.00011506281,0.000029576928,0.022591673],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9943505,0.0007854323,0.00057957385,0.0006411118,0.0025569925,0.0010863688],"domain_scores_gemma":[0.9930922,0.00297554,0.00030620824,0.0026443873,0.0008018113,0.00017987247],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007950233,0.00021299704,0.00026145036,0.00053603965,0.0027349414,0.0018986737,0.0047333157,0.00009361364,0.00026082748],"category_scores_gemma":[0.0030000275,0.0001807165,0.0001807074,0.0018723775,0.00008824861,0.005139846,0.007106356,0.00097453163,0.00023185206],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005198291,0.00030343584,0.02830566,0.00009704678,0.00020736974,0.00012517301,0.004696428,0.00014236214,0.0009084081,0.025135983,0.8434211,0.096137226],"study_design_scores_gemma":[0.00087613997,0.00078211114,0.0061418363,0.000030815158,0.0000068819163,0.0001801161,0.0017942025,0.0030143643,0.0028164664,0.00903655,0.9749179,0.00040261392],"about_ca_topic_score_codex":0.0012728706,"about_ca_topic_score_gemma":0.00029616416,"teacher_disagreement_score":0.18848467,"about_ca_system_score_codex":0.0013088414,"about_ca_system_score_gemma":0.00045286474,"threshold_uncertainty_score":0.99913746},"labels":[],"label_agreement":null},{"id":"W4318262271","doi":"10.1016/b978-0-32-399163-6.00014-7","title":"Role of smart technologies in detecting cognitive impairment and enhancing assisted living","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Dementia; Economic shortage; Cognition; Assisted living; Cognitive impairment; Health care; Gerontology; Population; Medicine; Population ageing; Class (philosophy); Psychology; Disease; Psychiatry; Computer science; Environmental health; Political science; Pathology; Artificial intelligence","score_opus":0.021568876094122988,"score_gpt":0.24297815793659788,"score_spread":0.22140928184247488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318262271","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015348009,0.0021832623,0.0017568296,0.00007266739,0.0005935256,0.0022336065,0.000038958693,0.00161334,0.9761598],"genre_scores_gemma":[0.8516499,0.00004314503,0.0005015026,0.000019894951,0.000038311282,0.00013243008,0.0000015594213,0.00006379822,0.14754951],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9978179,0.00008180387,0.00068984996,0.00071132055,0.00036190345,0.00033723182],"domain_scores_gemma":[0.997552,0.001207618,0.00056291133,0.00044927394,0.00017610558,0.000052100455],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086585776,0.00036935366,0.0007142539,0.0007134085,0.00009504657,0.00010771015,0.0004367518,0.0003528443,0.0000051928705],"category_scores_gemma":[0.00028255666,0.00039117693,0.0001396235,0.000090292175,0.000117677366,0.00017061828,0.0010066981,0.00055782415,0.000036493984],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044807707,0.000009740346,0.00015677963,0.00013011185,0.00006677116,0.000024468953,0.0012120352,6.226056e-8,0.0014197758,0.00025934394,5.865236e-7,0.99671584],"study_design_scores_gemma":[0.010682758,0.007025879,0.053730946,0.33937228,0.0015385034,0.0029925106,0.043240085,0.010798944,0.19606127,0.19781485,0.116064474,0.020677501],"about_ca_topic_score_codex":0.000008204078,"about_ca_topic_score_gemma":0.00085991836,"teacher_disagreement_score":0.97603834,"about_ca_system_score_codex":0.00012966644,"about_ca_system_score_gemma":0.00012526242,"threshold_uncertainty_score":0.999854},"labels":[],"label_agreement":null},{"id":"W4318812094","doi":"10.1007/978-3-031-24801-6_32","title":"Indoor Positioning and Navigation Using Bluetooth Low Energy and Cloud Service in Healthcare Perspective","year":2022,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Bluetooth; Computer science; Global Positioning System; Beacon; Mobile device; Cloud computing; Navigation system; Bluetooth Low Energy; Wearable computer; Hybrid positioning system; Wireless; Telecommunications; Real-time computing; Embedded system; Positioning system; World Wide Web; Engineering","score_opus":0.04263587172443938,"score_gpt":0.30120706063259695,"score_spread":0.25857118890815756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318812094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035034116,0.008459011,0.8335343,0.034853566,0.002733982,0.0035390658,0.00023765235,0.0005648322,0.08104345],"genre_scores_gemma":[0.9687095,0.0016489557,0.026434375,0.0028941988,0.00006532558,0.00006265659,0.00008679326,0.000015705242,0.00008248622],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982676,0.00012417865,0.0006013399,0.00039141733,0.0004063417,0.00020913841],"domain_scores_gemma":[0.9977711,0.0002782287,0.00040881487,0.0009056787,0.0005310669,0.00010509687],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010036172,0.00020995467,0.00027948382,0.0010938231,0.00073270773,0.00070087606,0.0010676626,0.00010917392,0.000003379425],"category_scores_gemma":[0.00002420568,0.00024839424,0.000022112088,0.0007459304,0.00035644782,0.00783126,0.0024469292,0.000442631,0.0000019240063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059832755,0.000021715095,0.0003340732,0.00009047393,0.000007767498,0.0000016308812,0.013304893,0.00019485425,0.000013053625,0.86121774,0.000012805166,0.12479502],"study_design_scores_gemma":[0.00085071806,0.00010809663,0.003090532,0.0010892722,0.0000084556705,0.00036924964,0.0007331469,0.95833695,0.000015151903,0.017612185,0.01711491,0.0006713375],"about_ca_topic_score_codex":0.0013390837,"about_ca_topic_score_gemma":0.00036865557,"teacher_disagreement_score":0.9581421,"about_ca_system_score_codex":0.00052894786,"about_ca_system_score_gemma":0.00037752127,"threshold_uncertainty_score":0.99999684},"labels":[],"label_agreement":null},{"id":"W4319430866","doi":"10.1109/smartblock4health56071.2022.10034526","title":"Managing the Fall Risk in Smart Homes Using Monocular Cameras, Ambient Sensors and an Interactive Social Robot","year":2022,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université de Montréal; Université du Québec à Chicoutimi","funders":"","keywords":"Falling (accident); Humanoid robot; Independence (probability theory); Computer science; Monocular; Neglect; Robot; Artificial intelligence; Population; Social robot; Computer security; Computer vision; Physical medicine and rehabilitation; Human–computer interaction; Psychology; Mobile robot; Robot control; Medicine; Environmental health; Psychiatry","score_opus":0.029852763493401244,"score_gpt":0.28034922925851674,"score_spread":0.2504964657651155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319430866","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89819396,0.00006747848,0.09968912,0.0010494477,0.0003000374,0.00028676464,0.0000058214764,0.00008317881,0.0003241996],"genre_scores_gemma":[0.99826866,0.000007862576,0.0012766159,0.00027275432,0.000046065335,0.000033982436,0.000001683833,0.000010287263,0.00008210806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980089,0.0008337805,0.0002121557,0.0004235979,0.00028561987,0.0002359333],"domain_scores_gemma":[0.9993107,0.00018241258,0.00014542154,0.00027081938,0.00004287113,0.000047780988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068841176,0.00013328309,0.00018989302,0.00017870207,0.00057742157,0.00026465193,0.00038946644,0.00002169702,0.000021677331],"category_scores_gemma":[0.000018841354,0.000115433206,0.000056182682,0.00037981925,0.00004306316,0.00071413,0.0006711159,0.00033374966,0.00000419862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010394716,0.0005608621,0.09421472,0.000034330573,0.0002963357,0.00023704184,0.086760834,0.01145571,0.0039980123,0.0021682624,0.00044350474,0.7997264],"study_design_scores_gemma":[0.0006781157,0.0000936628,0.03936862,0.000015992844,0.00001855988,0.00011691974,0.016192012,0.9396386,0.0005150137,0.0013458641,0.0016310483,0.00038556335],"about_ca_topic_score_codex":0.008692727,"about_ca_topic_score_gemma":0.0014108731,"teacher_disagreement_score":0.9281829,"about_ca_system_score_codex":0.000198649,"about_ca_system_score_gemma":0.000037957387,"threshold_uncertainty_score":0.9979085},"labels":[],"label_agreement":null},{"id":"W4321165801","doi":"10.2196/43092","title":"Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Bern","keywords":"Computer science; Assisted living; Microservices; Software; Component (thermodynamics); Component-based software engineering; Service (business); End user; Software development; Embedded system; World Wide Web; Cloud computing; Operating system","score_opus":0.3353488634281944,"score_gpt":0.47197734812041364,"score_spread":0.13662848469221922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321165801","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74756813,0.000031624888,0.24483018,0.0012075752,0.00042122582,0.0040542516,0.000038222217,0.00077172014,0.0010770849],"genre_scores_gemma":[0.9425406,0.0000029751873,0.055424917,0.0000056041113,0.00011244739,0.0013675382,0.00004846874,0.000034043613,0.00046342323],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99405205,0.0012158672,0.0010112941,0.00079027994,0.0019550961,0.0009754365],"domain_scores_gemma":[0.99321324,0.0035169793,0.00028175756,0.00079408323,0.0018038573,0.00039005748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023416094,0.00018180435,0.00049221603,0.001365782,0.001260861,0.00059693545,0.0016362373,0.00019790912,0.0000062196673],"category_scores_gemma":[0.0018060196,0.00015709075,0.000054838376,0.002920923,0.000385684,0.0018792206,0.003359324,0.000747875,0.000075006035],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024358687,0.0006931513,0.0004054069,0.0032785195,0.0002023679,0.00043750988,0.056445323,0.0000015284517,0.072599225,0.004069473,0.015300681,0.84632325],"study_design_scores_gemma":[0.008176605,0.005595576,0.005698604,0.009219312,0.000019633871,0.0066169915,0.14757377,0.06259486,0.25763786,0.0035177781,0.4907115,0.00263751],"about_ca_topic_score_codex":0.00006639601,"about_ca_topic_score_gemma":0.000029735284,"teacher_disagreement_score":0.84368575,"about_ca_system_score_codex":0.00042149262,"about_ca_system_score_gemma":0.00075239915,"threshold_uncertainty_score":0.9697651},"labels":[],"label_agreement":null},{"id":"W4321193179","doi":"10.1109/icce56470.2023.10043463","title":"Comparison of Wearable Sensor-based Fall Event Detection by 1-D and 2-D Convolutional Neural Networks","year":2023,"lang":"en","type":"article","venue":"2023 IEEE International Conference on Consumer Electronics (ICCE)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Accelerometer; Gyroscope; Event (particle physics); Computer science; Wearable computer; Convolutional neural network; Artificial intelligence; Deep learning; Spectrogram; Noise (video); Computer vision; Real-time computing; Pattern recognition (psychology); Embedded system; Engineering","score_opus":0.06347175874893854,"score_gpt":0.33152462338488126,"score_spread":0.2680528646359427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321193179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39821625,0.0008318425,0.592645,0.0032337394,0.002601983,0.0006192885,0.00011989596,0.0004634987,0.0012685198],"genre_scores_gemma":[0.99856645,0.00012874544,0.00010408473,0.00018041708,0.00006434084,0.00006251107,0.00006358167,0.000018504608,0.0008113531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974402,0.00020702746,0.0005653368,0.00060789316,0.0007168849,0.0004626257],"domain_scores_gemma":[0.9981484,0.0004931778,0.00036079308,0.0003271959,0.0005460576,0.00012439273],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045687796,0.00026286405,0.00036683847,0.00033487612,0.00015123354,0.00020050164,0.0006124824,0.00014778892,0.00007490053],"category_scores_gemma":[0.00007004722,0.00028757108,0.00012099694,0.0004940146,0.00011777704,0.00033789693,0.00010171795,0.00046595497,0.00011678537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000991679,0.0017017061,0.04994755,0.00018612367,0.0017146457,0.000050953982,0.00078675983,0.04584759,0.15485644,0.04424368,0.031052519,0.66862035],"study_design_scores_gemma":[0.0007688559,0.00023570945,0.0012238492,0.00006144657,0.000014562725,0.000014191762,0.00004738078,0.9851532,0.00855368,0.00046136137,0.0031886343,0.00027713075],"about_ca_topic_score_codex":0.00024632135,"about_ca_topic_score_gemma":0.00031649673,"teacher_disagreement_score":0.9393056,"about_ca_system_score_codex":0.00020858725,"about_ca_system_score_gemma":0.00021766928,"threshold_uncertainty_score":0.9999576},"labels":[],"label_agreement":null},{"id":"W4321448639","doi":"10.2196/37347","title":"Health Monitoring Using Smart Home Technologies: Scoping Review","year":2023,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Institute of Aging; University of Toronto; University of Waterloo; Research Institute for Aging; University Health Network","funders":"Mitacs","keywords":"CINAHL; Interoperability; Health care; mHealth; Telemedicine; Home automation; Scopus; Internet privacy; Computer science; Medicine; Data science; MEDLINE; World Wide Web; Nursing; Telecommunications; Political science; Psychological intervention","score_opus":0.18413321170606756,"score_gpt":0.43914948786294516,"score_spread":0.2550162761568776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321448639","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39974514,0.4061399,0.07262029,0.08955024,0.0078776665,0.010743991,0.00004857628,0.012870368,0.00040381443],"genre_scores_gemma":[0.8930748,0.099780716,0.004044191,0.0024548832,0.00027682722,0.0002744505,0.000007900368,0.00003982048,0.000046378173],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99676096,0.00029969806,0.0007697705,0.0006228548,0.000420784,0.0011259074],"domain_scores_gemma":[0.9979607,0.00034010483,0.0004749593,0.00066949567,0.00008697497,0.00046775406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022181976,0.00024135312,0.00071346486,0.00042457678,0.0006207681,0.000108701024,0.00044941707,0.0001101635,0.0000015798586],"category_scores_gemma":[0.00015598233,0.00023831619,0.000064981214,0.001800877,0.00005013995,0.0007190145,0.00035657134,0.00036468322,0.00007981941],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008455223,0.000086614426,0.031787876,0.074024245,0.000016688968,0.000015678906,0.0014989409,0.0000023665148,0.000021185278,0.0006924731,0.0032291745,0.8886163],"study_design_scores_gemma":[0.0057033673,0.002644925,0.22604603,0.70830303,0.00011974832,0.0020013778,0.0058001573,0.021717628,0.00047910307,0.003385323,0.018605333,0.005193972],"about_ca_topic_score_codex":0.00028852027,"about_ca_topic_score_gemma":0.000025851108,"teacher_disagreement_score":0.8834223,"about_ca_system_score_codex":0.00030024204,"about_ca_system_score_gemma":0.0010591852,"threshold_uncertainty_score":0.9718252},"labels":[],"label_agreement":null},{"id":"W4321851180","doi":"","title":"Detection of social isolation based on meal-taking activity and mobility of elderly people living alone","year":2021,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"","keywords":"Social isolation; Meal; Isolation (microbiology); Computer science; Elderly people; Gerontology; Psychology; Medicine; Food science; Chemistry; Biology; Bioinformatics; Psychotherapist","score_opus":0.022770086870178084,"score_gpt":0.2420392482137527,"score_spread":0.2192691613435746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321851180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48578495,0.00033840284,0.5069123,0.0035093578,0.00026697837,0.00021706305,0.000027421147,0.000052098945,0.002891435],"genre_scores_gemma":[0.992956,0.00005528157,0.0058433395,0.000028909633,0.00002470901,0.000020929063,0.000013580517,0.000020246593,0.0010370063],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9888332,0.009050345,0.0005815829,0.000693726,0.0005494406,0.00029171427],"domain_scores_gemma":[0.9898495,0.0045274245,0.0011344601,0.0010544673,0.003320596,0.0001135561],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005683025,0.00023574803,0.0004881072,0.00019048194,0.00040164756,0.00024153084,0.00038937564,0.00022687584,0.00011324528],"category_scores_gemma":[0.0034094676,0.0003038452,0.00020523761,0.0010557598,0.00024083967,0.0006966637,0.00035913815,0.00033564612,0.000006309678],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043282584,0.0021952954,0.0067060683,0.0006791542,0.000071725146,0.0000028307134,0.022984903,0.000053244516,0.2203867,0.0073491046,0.00002257191,0.7395051],"study_design_scores_gemma":[0.0007707536,0.000006504298,0.20379525,0.0021928013,0.000056391185,0.000022161996,0.00033932072,0.3164095,0.4751828,0.00036048505,0.0005319041,0.00033210826],"about_ca_topic_score_codex":0.0035216126,"about_ca_topic_score_gemma":0.010219284,"teacher_disagreement_score":0.739173,"about_ca_system_score_codex":0.0001517356,"about_ca_system_score_gemma":0.000384902,"threshold_uncertainty_score":0.99994135},"labels":[],"label_agreement":null},{"id":"W4321996042","doi":"10.5194/egusphere-egu23-10594","title":"Automatic labeling of the Training Dataset for Individual and Group Activities Detection","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Computer science; Artificial intelligence; Convolutional neural network; Object detection; Transfer of learning; Context (archaeology); Training set; Activity recognition; Deep learning; Object (grammar); Handshaking; Pattern recognition (psychology); Machine learning; Computer vision","score_opus":0.15061363640441705,"score_gpt":0.30815546116308423,"score_spread":0.15754182475866718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321996042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1914192,0.000054704855,0.8013043,0.0004437878,0.0021360114,0.0013241736,0.0028836108,0.00039353708,0.000040702136],"genre_scores_gemma":[0.9894396,0.0000043490263,0.009924796,0.00007102376,0.00007963422,0.00023850681,0.0001772719,0.000017546357,0.000047270623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855363,0.00015552637,0.00034626352,0.00045318963,0.00030771195,0.00018369008],"domain_scores_gemma":[0.99793214,0.0010032265,0.00036944402,0.0006135183,0.00004594277,0.000035732082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010063549,0.00017759627,0.00032419612,0.00016376676,0.00015091037,0.00028582633,0.0007543341,0.00014796705,0.0000035755068],"category_scores_gemma":[0.00018070011,0.00014076232,0.00009433454,0.00017075882,0.000056014505,0.00035241444,0.0017142714,0.00026176454,0.0000020051514],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047421286,0.00003785858,0.00015295458,0.0009868306,0.0002555582,9.179951e-7,0.006484079,0.000047009376,0.0023584191,0.0005002539,0.0009363552,0.988235],"study_design_scores_gemma":[0.0022746332,0.00035120393,0.012828301,0.0030891774,0.0003898195,0.00014178027,0.007988539,0.90411973,0.027873402,0.034877773,0.004259153,0.0018065153],"about_ca_topic_score_codex":0.00024689507,"about_ca_topic_score_gemma":0.00082064373,"teacher_disagreement_score":0.9864285,"about_ca_system_score_codex":0.000032473217,"about_ca_system_score_gemma":0.000095999516,"threshold_uncertainty_score":0.57401204},"labels":[],"label_agreement":null},{"id":"W4322743804","doi":"10.3390/s23052673","title":"A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Western University","funders":"","keywords":"Assisted living; Scalability; Proof of concept; Computer science; Assisted Living Facility; Interface (matter); Independent living; Augmented reality; Human–computer interaction; Mode (computer interface); Embedded system; Operating system; Gerontology; Medicine","score_opus":0.027266247957225234,"score_gpt":0.2766716667810606,"score_spread":0.24940541882383535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322743804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96380687,0.000029743302,0.034084372,0.000042295596,0.00082673214,0.00076159457,0.000013961509,0.00038510273,0.00004932819],"genre_scores_gemma":[0.9957673,0.0000018279045,0.003874135,0.000008093845,0.000080574966,0.00013283941,0.0000019247154,0.000029479012,0.00010381075],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99784374,0.00014067213,0.00051299843,0.00057642726,0.0005207877,0.00040537698],"domain_scores_gemma":[0.9969395,0.0016947931,0.00026598724,0.0005898575,0.00040038634,0.00010949378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006064372,0.00020670067,0.00042740683,0.000471072,0.00009827332,0.000096405565,0.00046437467,0.00009490616,0.0000019225754],"category_scores_gemma":[0.00017450665,0.00018551272,0.00010584308,0.0014741531,0.00002272397,0.00021026267,0.00017478915,0.00012056047,0.000046853478],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008813338,0.0010725049,0.28132176,0.007017957,0.0005765353,0.0007762217,0.0691635,0.010476196,0.007768209,0.0004811975,0.0006048735,0.6198597],"study_design_scores_gemma":[0.0031088535,0.0003305517,0.65817726,0.019844145,0.000028622866,0.00016082913,0.020077651,0.28953737,0.0077699553,0.000016702215,0.00011214144,0.00083593035],"about_ca_topic_score_codex":0.00016494993,"about_ca_topic_score_gemma":0.000188122,"teacher_disagreement_score":0.6190238,"about_ca_system_score_codex":0.00016206398,"about_ca_system_score_gemma":0.00008268199,"threshold_uncertainty_score":0.7564989},"labels":[],"label_agreement":null},{"id":"W4322746387","doi":"10.1007/978-3-031-28073-3_36","title":"An Analysis of Current Fall Detection Systems and the Role of Smart Devices and Machine Learning in Future Systems","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sheridan College","funders":"","keywords":"Computer science; Support vector machine; Machine learning; Artificial intelligence; Current (fluid); Globe; Real-time computing; Engineering; Electrical engineering; Psychology","score_opus":0.018657962808843267,"score_gpt":0.23715635346695388,"score_spread":0.2184983906581106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322746387","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025799261,0.488367,0.4755931,0.00007277038,0.0052970937,0.0036819777,0.000107893124,0.00024748297,0.00083343795],"genre_scores_gemma":[0.9968291,0.0024815965,0.000005884987,0.000005711422,0.00040417138,0.00007639553,0.000029481691,0.000039646813,0.00012803574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99645203,0.00080883666,0.0011829359,0.00078931515,0.00046269892,0.0003041503],"domain_scores_gemma":[0.99653536,0.0014704202,0.0011419991,0.0005611342,0.00019268852,0.000098385295],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021586197,0.0004730314,0.0018042725,0.00096862856,0.000121870835,0.00038180096,0.0003766225,0.0005575029,5.1195843e-7],"category_scores_gemma":[0.000049900664,0.00034578907,0.00014667386,0.0006507449,0.00014141934,0.00023146348,0.00019101561,0.00087928836,3.8830797e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027744315,0.00006514162,0.1117211,0.0037336457,0.0019718963,0.000028176295,0.0055182376,0.5494918,0.00006423556,0.022113698,0.0000034608863,0.30501115],"study_design_scores_gemma":[0.00057495653,0.00011683873,0.0016210927,0.0017962295,0.00027174785,0.000047380796,0.00022207378,0.99305844,0.0000020014736,0.00018058564,0.001776377,0.00033227025],"about_ca_topic_score_codex":0.006079475,"about_ca_topic_score_gemma":0.018700669,"teacher_disagreement_score":0.9710298,"about_ca_system_score_codex":0.0000678425,"about_ca_system_score_gemma":0.000035023535,"threshold_uncertainty_score":0.9998994},"labels":[],"label_agreement":null},{"id":"W4322762250","doi":"10.1016/j.engappai.2023.106063","title":"B-SMART: A reference architecture for artificially intelligent autonomic smart buildings","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Building automation; Architecture; Smart environment; Smart system; Leverage (statistics); Home automation; Artificial intelligence; Smart material; Computer security; Internet of Things; Telecommunications","score_opus":0.04802000322411873,"score_gpt":0.28792644034041914,"score_spread":0.23990643711630041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322762250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00874489,0.000054755892,0.9877617,0.00095119246,0.00034911794,0.001117775,0.00004560649,0.0008362937,0.00013868905],"genre_scores_gemma":[0.92094254,0.00002014638,0.077277616,0.000038252427,0.0001465546,0.0013805088,0.000032971515,0.000038652077,0.00012272752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774146,0.000029521598,0.00082364003,0.0006716384,0.00027731212,0.00045641913],"domain_scores_gemma":[0.9974649,0.00077814545,0.00023696366,0.0010664477,0.00031168552,0.00014187116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007187727,0.00025975064,0.0003410723,0.00060149236,0.00016270681,0.0001586544,0.001538397,0.0001268729,0.000014870583],"category_scores_gemma":[0.0003459363,0.00029374837,0.00016837068,0.0016106392,0.00007369676,0.00024977233,0.00033480802,0.00023647402,0.0004159688],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016212558,0.00011343967,0.000022801647,0.00011392424,0.000053337997,0.0000012229754,0.0007603066,0.03670649,0.045569703,0.20721908,0.00033755085,0.70908594],"study_design_scores_gemma":[0.000044519133,0.00013673418,0.00014023973,0.00012477154,0.000022881937,0.000014205678,0.00019009906,0.6023788,0.2768257,0.04252558,0.076960534,0.00063592783],"about_ca_topic_score_codex":0.00008287887,"about_ca_topic_score_gemma":0.00005665591,"teacher_disagreement_score":0.9121977,"about_ca_system_score_codex":0.00008833698,"about_ca_system_score_gemma":0.00013476372,"threshold_uncertainty_score":0.9999515},"labels":[],"label_agreement":null},{"id":"W4323287855","doi":"10.2196/40606","title":"Exploring the Role of Active Assisted Living in the Continuum of Care for Older Adults: Thematic Analysis","year":2023,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Research Institute for Aging; Public Health Ontario; University of Waterloo; Canadian Standards Association; University of Toronto; University Health Network; Communitech","funders":"Mitacs; AGE-WELL","keywords":"Thematic analysis; Assisted living; Nursing; Health care; Population; Exploratory research; Work (physics); Psychology; Qualitative research; Medicine; Gerontology; Engineering; Sociology; Political science; Environmental health","score_opus":0.04814913838374688,"score_gpt":0.2913292942782486,"score_spread":0.24318015589450173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323287855","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9834606,0.000093407114,0.014793963,0.0001824609,0.00008740985,0.00084480905,0.000008799874,0.000064769985,0.00046375714],"genre_scores_gemma":[0.99909747,0.000002637288,0.00014347496,0.000018095594,0.000026662037,0.0006842163,0.0000026922135,0.0000073437054,0.000017406399],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998788,0.00021505177,0.00031666682,0.00022497536,0.00026936026,0.00018592874],"domain_scores_gemma":[0.9968045,0.0022644314,0.00026080848,0.00047757666,0.00017716228,0.000015527818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006795514,0.00009739937,0.00030259963,0.00033257526,0.00008950187,0.000060371523,0.0006439455,0.000020528709,0.0000028241782],"category_scores_gemma":[0.00015431132,0.00006299046,0.00020416534,0.0020038106,0.000025153411,0.00042901628,0.00015172792,0.00009520287,0.0000024685155],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012561277,0.00008803874,0.014315029,0.0003888729,0.0003093043,0.000002358836,0.4203059,0.00006244072,0.0010473449,0.00024178068,0.000029299692,0.5631971],"study_design_scores_gemma":[0.0004878585,0.000043695123,0.64941466,0.0018335681,0.00012813274,0.000003885079,0.29350722,0.045634232,0.008402342,0.0002268451,0.00010897655,0.0002085964],"about_ca_topic_score_codex":0.00015839032,"about_ca_topic_score_gemma":0.0005717816,"teacher_disagreement_score":0.6350996,"about_ca_system_score_codex":0.000032043412,"about_ca_system_score_gemma":0.000025755287,"threshold_uncertainty_score":0.25686765},"labels":[],"label_agreement":null},{"id":"W4324148957","doi":"10.3390/s23063081","title":"Domain Adaptation Methods for Lab-to-Field Human Context Recognition","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","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 Victoria","funders":"Defense Advanced Research Projects Agency; Worcester Polytechnic Institute","keywords":"Discriminative model; Computer science; Artificial intelligence; Machine learning; Context (archaeology); Domain adaptation; Field (mathematics); Fidelity; Feature learning; Pattern recognition (psychology); Support vector machine; Deep learning; Classifier (UML)","score_opus":0.11872115586070085,"score_gpt":0.38401585735488863,"score_spread":0.26529470149418777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324148957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17203996,0.000010021494,0.8224918,0.0022074482,0.000750481,0.00073635834,0.000017279172,0.0005870044,0.0011596419],"genre_scores_gemma":[0.7870339,0.0000027174178,0.2086571,0.0016852554,0.00025437764,0.0003312315,0.000051500643,0.00003513335,0.0019487537],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983266,0.00038968454,0.00031117213,0.00046694223,0.00018650622,0.00031912356],"domain_scores_gemma":[0.99779,0.0013503056,0.00012404677,0.0003788743,0.00025264543,0.00010415895],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013275758,0.00014932996,0.0002287433,0.00033790647,0.00027372965,0.00020577792,0.000316464,0.000098962206,0.000019239611],"category_scores_gemma":[0.00037883827,0.0001612625,0.00011908993,0.0007809325,0.000019055393,0.00030301686,0.00010028334,0.000103381615,0.00078788976],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000222213,0.000025621728,0.000031188312,0.000028725051,0.00003252741,0.000005731595,0.0051218397,0.000040950545,0.01547961,0.0035282278,0.003063727,0.97261965],"study_design_scores_gemma":[0.0038293912,0.0015871677,0.0029322992,0.00041322186,0.000057671896,0.00008224353,0.010425463,0.10763194,0.14560835,0.11677708,0.6087508,0.0019043803],"about_ca_topic_score_codex":0.00014683079,"about_ca_topic_score_gemma":0.00021896517,"teacher_disagreement_score":0.9707152,"about_ca_system_score_codex":0.000055211232,"about_ca_system_score_gemma":0.000042196443,"threshold_uncertainty_score":0.9999901},"labels":[],"label_agreement":null},{"id":"W4327519458","doi":"10.1007/978-3-031-28813-5_6","title":"Experimentation of a Nighttime Wandering Assistance System Based on AAL Solutions to Foster Aging at Home","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Assisted living; Set (abstract data type); Activities of daily living; Assisted Living Facility; Event (particle physics); Independent living; Elderly people; Psychology; Aging in place; Work (physics); Computer science; Human–computer interaction; Gerontology; Medicine; Engineering; Psychiatry","score_opus":0.04711947206151549,"score_gpt":0.2578109936963651,"score_spread":0.21069152163484964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327519458","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040069022,0.00006748522,0.99592704,0.0008317988,0.0007347587,0.00063625135,0.00007919024,0.00012805824,0.0011947198],"genre_scores_gemma":[0.3668639,0.000008059044,0.63255143,0.00020576839,0.000107305816,0.0000922222,0.000032710595,0.000028329181,0.000110260626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852043,0.000020565947,0.00065584277,0.0001986739,0.00037458955,0.00022991534],"domain_scores_gemma":[0.9980989,0.00050989614,0.0005086579,0.0006318694,0.00019557294,0.000055064913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045698785,0.00024575493,0.00039370966,0.00038658726,0.0007993001,0.00016936335,0.0012886208,0.00013322303,5.44376e-7],"category_scores_gemma":[0.000035741803,0.00021712945,0.0001948138,0.00033779937,0.00015476895,0.00033203216,0.00080070266,0.0002048989,0.0000024809576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051611605,0.000019318628,0.000006016115,0.00052853225,0.00008615547,2.0565452e-7,0.0038065717,0.95543176,0.00016786752,0.028516188,0.00010482824,0.011327419],"study_design_scores_gemma":[0.0002776413,0.000084895735,0.00006216718,0.0010405742,0.000025734764,0.0000067490632,0.000017336355,0.9949677,0.0006808644,0.00010996251,0.0024305214,0.00029587303],"about_ca_topic_score_codex":0.00001861553,"about_ca_topic_score_gemma":0.0000697063,"teacher_disagreement_score":0.3664632,"about_ca_system_score_codex":0.00023320543,"about_ca_system_score_gemma":0.0001343998,"threshold_uncertainty_score":0.8854282},"labels":[],"label_agreement":null},{"id":"W4361855940","doi":"10.2196/41942","title":"Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; University of Toronto; University of Waterloo; York University","funders":"Mitacs","keywords":"CINAHL; Scopus; Inclusion (mineral); Physical activity; Independent living; Gerontology; Naive Bayes classifier; MEDLINE; World Wide Web; Medicine; Computer science; Psychology; Psychological intervention; Artificial intelligence; Nursing; Political science; Physical therapy","score_opus":0.28967001902633016,"score_gpt":0.5015805018526005,"score_spread":0.21191048282627034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361855940","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9234272,0.001800512,0.0454299,0.028421365,0.000336274,0.0004791417,5.2289613e-7,0.00007807985,0.000026989792],"genre_scores_gemma":[0.9979386,0.000788003,0.00078397774,0.00010466097,0.00027177742,0.000020818943,1.793346e-7,0.000008748994,0.00008324075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99615985,0.00068561285,0.0003477251,0.00022205303,0.0021786443,0.0004061109],"domain_scores_gemma":[0.9982333,0.0006911133,0.00017343179,0.00030578143,0.0003740972,0.00022224798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008820309,0.00010165478,0.00033949027,0.00043277236,0.00013828403,0.0003054521,0.0012283442,0.00008047147,0.000007977443],"category_scores_gemma":[0.0038295276,0.00006486803,0.000091035676,0.0012849437,0.00019868964,0.0007625063,0.0014812177,0.0010745098,0.000023187833],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011899876,0.00013922615,0.0062362114,0.001091262,0.00007360298,0.0002183765,0.0014438297,0.00000791745,0.00114403,0.00082280376,0.0056448863,0.983166],"study_design_scores_gemma":[0.0013787297,0.0010268791,0.052904993,0.2677783,0.00006423485,0.0031392905,0.001067309,0.6523186,0.009727289,0.0062874,0.0034002697,0.0009067172],"about_ca_topic_score_codex":0.00016824833,"about_ca_topic_score_gemma":0.000048081314,"teacher_disagreement_score":0.9822592,"about_ca_system_score_codex":0.000112806054,"about_ca_system_score_gemma":0.00022151941,"threshold_uncertainty_score":0.466827},"labels":[],"label_agreement":null},{"id":"W4364321831","doi":"10.1109/ihsh57076.2022.10092042","title":"Call for Papers","year":2022,"lang":"en","type":"paratext","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.036133912549607766,"score_gpt":0.2805714663307518,"score_spread":0.24443755378114404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364321831","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000028420056,0.0002523798,0.32959205,0.00078085915,0.006561098,0.0007035851,0.00015109716,0.00015452462,0.6618016],"genre_scores_gemma":[0.002949259,0.000034327702,0.003185955,0.0015601497,0.00040756314,0.0009229623,0.00019407537,0.00003561216,0.9907101],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981792,0.000113189984,0.00030083448,0.0007158563,0.00036480563,0.00032614515],"domain_scores_gemma":[0.9983571,0.0004885195,0.00019240571,0.00075359276,0.00010946506,0.00009887733],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00029033885,0.00025282262,0.00040597806,0.00017224866,0.0001901063,0.00023924373,0.0013708819,0.00016184447,0.014216405],"category_scores_gemma":[0.000034360583,0.00024700165,0.00027117663,0.00023992652,0.000017828115,0.00021663695,0.0005214676,0.00027776725,0.0073963604],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029936705,0.00002445054,5.625427e-7,0.00005318197,0.00004496966,0.0000023651264,0.000082335115,0.000006417579,0.000048572765,0.0007671226,0.93062264,0.068344414],"study_design_scores_gemma":[0.00024476502,0.00008089277,0.0000024183057,0.000016909346,0.000006811935,0.000020241387,0.000021703245,0.000831845,0.00009841517,0.000047666046,0.998297,0.00033136804],"about_ca_topic_score_codex":0.00014332234,"about_ca_topic_score_gemma":0.000056490677,"teacher_disagreement_score":0.32890853,"about_ca_system_score_codex":0.0001702181,"about_ca_system_score_gemma":0.00030476437,"threshold_uncertainty_score":0.9999982},"labels":[],"label_agreement":null},{"id":"W4366775507","doi":"10.3390/s23084152","title":"Correlates of Person-Specific Rates of Change in Sensor-Derived Physical Activity Metrics of Daily Living in the Rush Memory and Aging Project","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition 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":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"National Institute on Aging; National Institutes of Health","keywords":"Covariate; Demography; Cognitive decline; Demographics; Gerontology; Variance (accounting); Explained variation; Metric (unit); Multivariate statistics; Statistics; Medicine; Mathematics; Operations management","score_opus":0.14326909000501956,"score_gpt":0.3199054811918311,"score_spread":0.17663639118681154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366775507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9989044,0.00011998868,0.0001336291,0.00015521234,0.00010460815,0.00042727304,0.000009119466,0.00003460086,0.00011114899],"genre_scores_gemma":[0.99972755,0.00006803646,0.00012119123,0.00000694845,0.00003118231,0.000016941261,7.4184413e-7,0.000009628982,0.000017793593],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9983977,0.0005276743,0.00022899067,0.00030105983,0.0003318582,0.00021271256],"domain_scores_gemma":[0.9968057,0.002511211,0.0002605763,0.00031136966,0.000090229405,0.000020876138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009472455,0.00013484067,0.00040877343,0.0006773048,0.000031634692,0.000028045628,0.00028204187,0.000054863733,0.000001186527],"category_scores_gemma":[0.00024352659,0.00011215754,0.000074082076,0.002343068,0.000110560024,0.00030429824,0.00014028659,0.00019480298,0.0000028478196],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060698105,0.00072039024,0.24961114,0.0006695787,0.00008424657,0.00008161087,0.4246355,0.00023053554,0.11548794,0.00021732571,0.00006772458,0.2081333],"study_design_scores_gemma":[0.00051563024,0.00011487507,0.8232844,0.00047099206,0.000009159937,0.0000178073,0.023545383,0.1129511,0.03883522,0.000026937294,0.000009708088,0.00021880046],"about_ca_topic_score_codex":0.0009817355,"about_ca_topic_score_gemma":0.0001285804,"teacher_disagreement_score":0.57367325,"about_ca_system_score_codex":0.000028111659,"about_ca_system_score_gemma":0.000032219734,"threshold_uncertainty_score":0.45736519},"labels":[],"label_agreement":null},{"id":"W4366967224","doi":"10.1145/3544793.3560377","title":"10th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)","year":2022,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Activity recognition; Computer science; Context (archaeology); Wearable computer; Scale (ratio); Human–computer interaction; Data science; Ubiquitous computing; Artificial intelligence; Embedded system","score_opus":0.057603768220053815,"score_gpt":0.31002787903906603,"score_spread":0.2524241108190122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366967224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06914993,0.00001463813,0.89680046,0.0024795379,0.00045062657,0.00037375197,0.000011302317,0.00032308797,0.03039665],"genre_scores_gemma":[0.99641365,0.000001537305,0.0009597,0.00043431384,0.00009991208,0.00006453305,0.0000055424885,0.000007435295,0.002013358],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988921,0.00011098418,0.00012518874,0.0003951039,0.00034331254,0.00013329416],"domain_scores_gemma":[0.99918246,0.00023700559,0.00009693515,0.0003633755,0.00005733625,0.00006285942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026054715,0.000096035925,0.000110304885,0.00015114706,0.00047814802,0.0002020347,0.00037365535,0.00002288378,0.00009559691],"category_scores_gemma":[0.000014512524,0.0001047077,0.000040393115,0.00025610963,0.000028036875,0.00030631886,0.0005433632,0.00020127413,0.000030413448],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000078008115,0.00014373813,0.00039088924,0.0000032702878,0.000030735435,0.000007149587,0.00026283276,0.000048704936,0.0049040336,0.018899176,0.0010956236,0.97420603],"study_design_scores_gemma":[0.0031900746,0.0004122817,0.025727117,0.00008128353,0.000033079767,0.0010559497,0.0013519189,0.20524348,0.011015965,0.01470457,0.73532087,0.0018633889],"about_ca_topic_score_codex":0.000086755055,"about_ca_topic_score_gemma":0.000069969115,"teacher_disagreement_score":0.97234267,"about_ca_system_score_codex":0.00012341792,"about_ca_system_score_gemma":0.000028888791,"threshold_uncertainty_score":0.42698562},"labels":[],"label_agreement":null},{"id":"W4366967648","doi":"10.1145/3544793.3560392","title":"Hierarchical Feature Recovery for Robust Human Activity Recognition in Body Sensor Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"Horizon 2020 Framework Programme","keywords":"Computer science; Wireless sensor network; Missing data; Artificial intelligence; Feature (linguistics); Activity recognition; Pattern recognition (psychology); Soft sensor; Data mining; Complement (music); Computer vision; Machine learning; Process (computing)","score_opus":0.044862136710800034,"score_gpt":0.2667229204005041,"score_spread":0.22186078368970408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366967648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117761716,0.000027964275,0.8727631,0.0024908942,0.0010087009,0.001170576,0.00005887926,0.00035802083,0.0043601263],"genre_scores_gemma":[0.98402077,0.0000033499518,0.01173385,0.0006405182,0.00024044185,0.0006366739,0.00007552596,0.00002543823,0.0026234223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977143,0.00051814097,0.00026504986,0.0007138473,0.0003454826,0.00044319974],"domain_scores_gemma":[0.99859387,0.0005844675,0.0001546639,0.00047068048,0.00009190806,0.000104396255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009954454,0.0002024509,0.000318116,0.00028800455,0.00046555678,0.00019650455,0.00053037645,0.00012294976,0.00020194503],"category_scores_gemma":[0.00007400847,0.00022101143,0.00017611218,0.0006549164,0.000026496957,0.00073680474,0.00045250237,0.00069600117,0.000019240557],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037779784,0.0010749123,0.00075076404,0.00006449376,0.00009352658,0.000084872154,0.0005279948,0.00408942,0.0047061234,0.0007448021,0.028174935,0.95931035],"study_design_scores_gemma":[0.0054021263,0.0017985172,0.01968998,0.00011467996,0.00003524477,0.0004740766,0.0004333837,0.9283642,0.0023731021,0.007549143,0.0318433,0.001922257],"about_ca_topic_score_codex":0.00011762767,"about_ca_topic_score_gemma":0.0003639027,"teacher_disagreement_score":0.9573881,"about_ca_system_score_codex":0.00028852792,"about_ca_system_score_gemma":0.00007560104,"threshold_uncertainty_score":0.9012584},"labels":[],"label_agreement":null},{"id":"W4367046374","doi":"10.1145/3576842.3582390","title":"VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Western University; McMaster University","funders":"","keywords":"Generalizability theory; Artificial intelligence; Computer science; Machine learning; Noise (video); Inertial measurement unit; Feature learning; Pattern recognition (psychology); Mathematics","score_opus":0.0804460112181329,"score_gpt":0.3002067372492848,"score_spread":0.21976072603115193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367046374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4746736,0.000008779469,0.50151694,0.015962254,0.00053843623,0.0017499754,0.000018305154,0.001062905,0.0044687935],"genre_scores_gemma":[0.99640876,0.0000014500254,0.00140368,0.00073655834,0.0001404552,0.0002652134,0.00004323432,0.00001681282,0.0009838224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803406,0.000579583,0.00019701573,0.0004998729,0.00032206066,0.00036739782],"domain_scores_gemma":[0.9980215,0.0012327532,0.00013964753,0.0004331275,0.00011987519,0.00005314097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016504823,0.00017872463,0.00022486821,0.0002742789,0.00034231765,0.0003581826,0.0005255128,0.00013079516,0.000025018124],"category_scores_gemma":[0.00025574327,0.00014100387,0.00013054968,0.0010538533,0.000028546161,0.00061018293,0.00009315021,0.00037136086,0.00017527946],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019519884,0.0007474688,0.0077455468,0.00038410953,0.00013029046,0.00021542059,0.0065135253,0.0009917445,0.06910818,0.0033357355,0.038644146,0.87198865],"study_design_scores_gemma":[0.009986168,0.0015522526,0.12388771,0.000755977,0.000075146236,0.00019893738,0.0039572497,0.72694945,0.03228119,0.009803698,0.087800466,0.0027517744],"about_ca_topic_score_codex":0.00023052859,"about_ca_topic_score_gemma":0.0003459037,"teacher_disagreement_score":0.8692369,"about_ca_system_score_codex":0.000064195934,"about_ca_system_score_gemma":0.00007270646,"threshold_uncertainty_score":0.57499707},"labels":[],"label_agreement":null},{"id":"W4367841545","doi":"10.32920/22734269.v1","title":"Human Action Recognition Using Convolutional Neural Network and Depth Sensor Data","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Convolutional neural network; Action recognition; Pooling; Data set; Artificial intelligence; Computer science; Pattern recognition (psychology); Set (abstract data type); Action (physics); Training set; Computer vision","score_opus":0.444825302238214,"score_gpt":0.38390195133286686,"score_spread":0.06092335090534712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367841545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22252214,0.00013767078,0.7674183,0.0008296617,0.005636035,0.0009873122,0.00028026415,0.0014837277,0.000704861],"genre_scores_gemma":[0.96440166,0.000029132514,0.031553958,0.00024305492,0.0018285548,0.000043932534,0.0012942503,0.000051028994,0.00055441086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969313,0.0003725414,0.0005010307,0.0013703139,0.00044259924,0.00038220617],"domain_scores_gemma":[0.9975857,0.00025948006,0.00040927387,0.0013678317,0.00024233,0.00013536235],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008574129,0.0003239734,0.0003975582,0.00021055681,0.00037009903,0.00068187015,0.0009402245,0.00031227403,0.000035198078],"category_scores_gemma":[0.00007731373,0.00035482328,0.00008127554,0.0002850996,0.00007196348,0.0011163376,0.0044373786,0.00062298396,0.00011131923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083288374,0.00044390143,0.02205356,0.0016044178,0.0014511197,0.0002761024,0.0008453243,0.012069747,0.0043076007,0.0018887245,0.06631696,0.88865924],"study_design_scores_gemma":[0.0003980363,0.00003089415,0.016538661,0.00033083584,0.00006517252,0.00024278795,0.00007314817,0.9724874,0.000056705518,0.0084075155,0.0006900067,0.00067885505],"about_ca_topic_score_codex":0.0012432332,"about_ca_topic_score_gemma":0.0013818085,"teacher_disagreement_score":0.9604176,"about_ca_system_score_codex":0.000136895,"about_ca_system_score_gemma":0.0001561548,"threshold_uncertainty_score":0.9998904},"labels":[],"label_agreement":null},{"id":"W4377293169","doi":"10.3390/s23104924","title":"Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Transmission (telecommunications); Computer science; Tree (set theory); Data transmission; Wearable computer; Real-time computing; Process (computing); Interval (graph theory); Sequence (biology); Data aggregator; Wireless sensor network; Data mining; Scheme (mathematics); Missing data; Artificial intelligence; Machine learning; Computer network; Telecommunications; Embedded system; Mathematics","score_opus":0.3780418867892188,"score_gpt":0.3937205384757478,"score_spread":0.015678651686528988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377293169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18800156,0.00007252597,0.8088181,0.0010694701,0.00045919465,0.0004375265,0.000016468384,0.0008070864,0.0003180687],"genre_scores_gemma":[0.94784415,0.00003062308,0.05037726,0.00004537206,0.00016332824,0.000014012229,0.00008765513,0.000041360076,0.0013962594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997714,0.00026655724,0.0003776235,0.0008178186,0.00038359087,0.00044045268],"domain_scores_gemma":[0.9981006,0.00054775557,0.00021975925,0.0008547357,0.00015345745,0.00012372405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012046144,0.00018001595,0.00023896544,0.00028323082,0.0005797363,0.00044966122,0.000782296,0.00010469411,0.0000069312055],"category_scores_gemma":[0.00031277296,0.00019198658,0.00008327355,0.0009145516,0.000038900453,0.001232452,0.00018668846,0.00020692991,0.00011936889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015573793,0.000023336192,0.00027900562,0.00015466126,0.000031331612,0.000025356456,0.0015651187,0.003123643,0.3888153,0.00011290869,0.00026563145,0.60558814],"study_design_scores_gemma":[0.00030096667,0.000018988227,0.00048552977,0.0002666782,0.000016185748,0.000052132404,0.00069243216,0.9826189,0.0065456918,0.00026465507,0.0084961755,0.00024163788],"about_ca_topic_score_codex":0.00009037041,"about_ca_topic_score_gemma":0.000010903231,"teacher_disagreement_score":0.9794953,"about_ca_system_score_codex":0.00009892317,"about_ca_system_score_gemma":0.00011870771,"threshold_uncertainty_score":0.78289855},"labels":[],"label_agreement":null},{"id":"W4378531216","doi":"10.1007/978-3-031-33377-4_26","title":"A Dynamic and Task-Independent Reward Shaping Approach for Discrete Partially Observable Markov Decision Processes","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Partially observable Markov decision process; Computer science; Markov decision process; Bellman equation; Graph; Dimension (graph theory); Task (project management); State space; Reinforcement learning; Observable; Function (biology); Action (physics); Markov chain; Markov process; Artificial intelligence; Domain (mathematical analysis); Markov model; Machine learning; Mathematical optimization; Theoretical computer science; Mathematics","score_opus":0.045878390466429775,"score_gpt":0.27651459018373165,"score_spread":0.23063619971730187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378531216","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016966637,0.00049963564,0.9954383,0.00042697217,0.0012428572,0.0014233581,0.000034390043,0.00034684016,0.00041797833],"genre_scores_gemma":[0.2879017,0.00014461261,0.7097691,0.00064002525,0.00030414792,0.00023104995,0.00003514782,0.00010818669,0.00086601987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946843,0.000050271745,0.00072705856,0.002456914,0.0012577167,0.00082378805],"domain_scores_gemma":[0.9957797,0.0018468575,0.0004471236,0.0011546913,0.00053551036,0.00023612462],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0018073756,0.00062986667,0.00079139206,0.00077793153,0.00044403266,0.0014038045,0.0025652123,0.00038015228,0.0000032613536],"category_scores_gemma":[0.0005102796,0.00057840033,0.00015230253,0.000864159,0.00036277215,0.0012137941,0.0019174297,0.0005814076,0.000017206847],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041755284,0.000038860962,0.000121802295,0.00065135345,0.000039337447,0.00005134739,0.0011160399,0.008901028,0.00016417146,0.00088057976,0.000039682658,0.987954],"study_design_scores_gemma":[0.0005508385,0.00021558227,0.00020570464,0.0012988982,0.000019803141,0.00013131041,0.0000014587981,0.93756676,0.00017355735,0.058140036,0.0008043036,0.00089175225],"about_ca_topic_score_codex":0.00003198356,"about_ca_topic_score_gemma":0.00053197116,"teacher_disagreement_score":0.9870623,"about_ca_system_score_codex":0.00030092726,"about_ca_system_score_gemma":0.00086279283,"threshold_uncertainty_score":0.99966675},"labels":[],"label_agreement":null},{"id":"W4379376248","doi":"10.2196/42750","title":"Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review","year":2023,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Context (archaeology); Scopus; Sleep (system call); Computer science; Wearable computer; Tracking (education); Data science; mHealth; BitTorrent tracker; Activity tracker; Wearable technology; Health informatics; World Wide Web; Psychology; Eye tracking; MEDLINE; Health care; Artificial intelligence","score_opus":0.12644628943480785,"score_gpt":0.4133676152558068,"score_spread":0.28692132582099894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379376248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8392803,0.13203716,0.005748107,0.016432066,0.0012262729,0.003493437,0.000024301617,0.0006169366,0.0011413958],"genre_scores_gemma":[0.9766527,0.021098165,0.00009838199,0.0017024203,0.00014861235,0.00015523918,0.0000069310545,0.000014068752,0.00012350516],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978722,0.00029459267,0.00048776838,0.0005717211,0.00021606477,0.00055765884],"domain_scores_gemma":[0.998837,0.0003761795,0.00016996088,0.00023896713,0.000053306772,0.00032461737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013756318,0.0001729651,0.00046071934,0.0002663192,0.00021278301,0.00015404446,0.00019403466,0.00007278688,0.0000039587962],"category_scores_gemma":[0.000075559525,0.00016564556,0.000039299593,0.0008703736,0.000046452446,0.0008188514,0.00017444338,0.00026279662,0.00004301264],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023313021,0.00014055156,0.010894233,0.052822478,0.000008246854,0.000045646033,0.007579736,4.2886649e-7,0.000019105812,0.001133183,0.0022837368,0.92504936],"study_design_scores_gemma":[0.006610323,0.0015502024,0.7456258,0.18423639,0.00007631275,0.0010211793,0.0018311803,0.043753188,0.000060649876,0.0018518408,0.010890627,0.0024922832],"about_ca_topic_score_codex":0.00012228236,"about_ca_topic_score_gemma":0.0005557741,"teacher_disagreement_score":0.92255706,"about_ca_system_score_codex":0.00009288974,"about_ca_system_score_gemma":0.00050093414,"threshold_uncertainty_score":0.675483},"labels":[],"label_agreement":null},{"id":"W4379522294","doi":"10.21428/594757db.c7d66349","title":"An Empirical Analysis on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Wearable computer; Computer science; Data mining; Embedded system","score_opus":0.11969436729010122,"score_gpt":0.3600730080887702,"score_spread":0.24037864079866897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379522294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28277254,0.0000022545103,0.7159521,0.0003882919,0.00021377263,0.00016664324,0.00010311881,0.00020264898,0.00019866083],"genre_scores_gemma":[0.98387945,0.0000024047813,0.015453541,0.00009827898,0.000058132566,0.000018393714,0.00008397059,0.0000082568,0.0003975545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985429,0.00013430268,0.0002835812,0.00059189415,0.00025075357,0.0001965688],"domain_scores_gemma":[0.997994,0.00033904766,0.00013110318,0.0013232216,0.00013252204,0.00008012071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069889816,0.000103121565,0.0002794388,0.00043291863,0.000075124575,0.00010845235,0.00073560746,0.00006200489,0.00006377933],"category_scores_gemma":[0.00006209311,0.00009504982,0.00010231844,0.0012458701,0.000024708728,0.000841344,0.00017334035,0.000069413,0.0000877819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004103799,0.00023290634,0.021525964,0.000048766684,0.00058108196,0.000011273697,0.0005833671,0.005072832,0.0018574799,0.00006956511,0.0049991305,0.9649766],"study_design_scores_gemma":[0.00025518503,0.00015638521,0.007817151,0.000013818608,0.00005073657,0.00001018861,0.00021266699,0.9893674,0.0013973045,0.000031034797,0.0005560067,0.00013210313],"about_ca_topic_score_codex":0.00014425498,"about_ca_topic_score_gemma":0.00013866303,"teacher_disagreement_score":0.9842946,"about_ca_system_score_codex":0.000027586671,"about_ca_system_score_gemma":0.000046185556,"threshold_uncertainty_score":0.3876019},"labels":[],"label_agreement":null},{"id":"W4379616855","doi":"10.1109/iconstem56934.2023.10142584","title":"LARVIS - Linux AIRelied Virtual Intelligent System","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Horizon College and Seminary","funders":"","keywords":"Computer science; Operating system; File system; Virtual file system; Volume (thermodynamics); The Internet; Linux Unified Key Setup; World Wide Web; Linux kernel; Self-certifying File System; SSH File Transfer Protocol","score_opus":0.03432860904842891,"score_gpt":0.25444022466833155,"score_spread":0.22011161561990264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379616855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03351758,0.00003901278,0.92901814,0.0012769833,0.002600227,0.00042111624,0.000007184505,0.0047629555,0.028356802],"genre_scores_gemma":[0.9931182,0.000007929326,0.00060238363,0.00023200353,0.0001429372,0.000053715918,0.000005653822,0.000014673594,0.005822482],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833095,0.00012450623,0.00033655055,0.00046265364,0.00041434995,0.00033101253],"domain_scores_gemma":[0.9987047,0.00028705248,0.00008364178,0.00065546343,0.0001272485,0.0001418928],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005700582,0.00015530111,0.00022847825,0.0001970449,0.00012114811,0.00024879957,0.0007757308,0.0000807203,0.00005404226],"category_scores_gemma":[0.00005148533,0.00013968443,0.00011123704,0.0010001592,0.000021648151,0.00044225517,0.00045223458,0.000121561694,0.013069179],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012835827,0.00012373108,0.0006367167,0.00012258654,0.00013342488,0.00020893972,0.0026195066,0.00015256132,0.0018381756,0.19250114,0.052185696,0.7494647],"study_design_scores_gemma":[0.0014539094,0.0005880945,0.003632098,0.00052572566,0.00003017735,0.00038423724,0.0067010396,0.71909803,0.038144168,0.00092072546,0.22674234,0.0017794411],"about_ca_topic_score_codex":0.00011845619,"about_ca_topic_score_gemma":0.00005367302,"teacher_disagreement_score":0.9596006,"about_ca_system_score_codex":0.000105099134,"about_ca_system_score_gemma":0.00007261892,"threshold_uncertainty_score":0.9876993},"labels":[],"label_agreement":null},{"id":"W4379742234","doi":"10.1109/jsen.2023.3282171","title":"Statistical Database of Human Motion Recognition Using Wearable IoT—A Review","year":2023,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Context-Aware Activity Recognition 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":"Rogers Communications (Canada); Telus (Canada); British Columbia Institute of Technology","funders":"","keywords":"Wearable computer; Computer science; Motion (physics); Human–computer interaction; Artificial intelligence; Wearable technology; Motion analysis; Gesture recognition; Activity recognition; Computer vision; Gesture; Embedded system","score_opus":0.13866301755384663,"score_gpt":0.35467626203883584,"score_spread":0.2160132444849892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379742234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4643681,0.00074941834,0.53173536,0.00045289117,0.0015823107,0.00036778534,0.00010787041,0.00019255548,0.00044369942],"genre_scores_gemma":[0.97613096,0.0020940907,0.02058793,0.0002836768,0.0006066225,0.000007609931,0.000037481837,0.000039027702,0.00021257567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977458,0.00051574723,0.0006203219,0.0002716392,0.0005508453,0.00029565408],"domain_scores_gemma":[0.9984604,0.00022979963,0.0004241752,0.00033133646,0.00039468426,0.00015959732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016261109,0.00013848061,0.00033044585,0.00029907242,0.0002392311,0.000117806325,0.00030088553,0.00005384002,0.0001599588],"category_scores_gemma":[0.00025519423,0.00013688233,0.00010425892,0.0007082747,0.0000460625,0.0006075094,0.00006212334,0.00032816388,0.00030464487],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004112031,0.00048999855,0.0010735713,0.003243544,0.00027788576,0.0012336372,0.00096223236,0.0006701231,0.25688806,0.00034273605,0.0557334,0.67904365],"study_design_scores_gemma":[0.009579426,0.0019383823,0.01544482,0.08987577,0.0010423413,0.031836454,0.0010948529,0.63600004,0.1707208,0.027478388,0.009925936,0.0050628223],"about_ca_topic_score_codex":0.000053286312,"about_ca_topic_score_gemma":0.0000069307443,"teacher_disagreement_score":0.67398083,"about_ca_system_score_codex":0.00008109625,"about_ca_system_score_gemma":0.0000850547,"threshold_uncertainty_score":0.5581899},"labels":[],"label_agreement":null},{"id":"W4379932127","doi":"10.1145/3587819.3592553","title":"A Dataset of Food Intake Activities Using Sensors with Heterogeneous Privacy Sensitivity Levels","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Activity recognition; Computer science; Sensitivity (control systems); Artificial intelligence; RGB color model; Machine learning; Engineering","score_opus":0.10010141417869822,"score_gpt":0.29275783849820775,"score_spread":0.19265642431950952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379932127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78537893,0.0000040875643,0.2129796,0.00008554986,0.000115837094,0.00023203195,0.0008771166,0.0002510027,0.0000758326],"genre_scores_gemma":[0.9942509,0.0000010471706,0.0054904977,0.00007574591,0.000036242705,0.000008185818,0.00006355883,0.000018449926,0.000055384127],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99813896,0.00027660673,0.00030375374,0.00049361575,0.00044205628,0.00034501942],"domain_scores_gemma":[0.99823487,0.0004981093,0.0002183018,0.00084502605,0.0001076533,0.00009602188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005128686,0.00020989348,0.0003642336,0.00027430733,0.00010912973,0.00013004798,0.00033445386,0.00006304395,0.000016765734],"category_scores_gemma":[0.000055971264,0.00018119295,0.00006842646,0.00078240945,0.000087526605,0.00094394415,0.0004937635,0.00011711172,0.000043661257],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043351596,0.0015019835,0.0150631275,0.0013323415,0.0030092658,0.003085366,0.021888101,0.017216176,0.6282374,0.0035920686,0.0080425255,0.29659808],"study_design_scores_gemma":[0.0016551234,0.00083984376,0.004478916,0.00030641133,0.000058885013,0.0029373744,0.0010481896,0.2945,0.6891508,0.0004052013,0.003332027,0.0012872287],"about_ca_topic_score_codex":0.00023450905,"about_ca_topic_score_gemma":0.00034082666,"teacher_disagreement_score":0.29531085,"about_ca_system_score_codex":0.00005428138,"about_ca_system_score_gemma":0.00012752168,"threshold_uncertainty_score":0.73888344},"labels":[],"label_agreement":null},{"id":"W4380576197","doi":"10.2196/45231","title":"Implementing Active Assisted Living Technology in the Long-term Care of People Living With Dementia to Address Loneliness: European Survey","year":2023,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; Marie Curie","keywords":"Loneliness; Dementia; Context (archaeology); Assisted living; Long-term care; Gerontology; Psychology; Medicine; Psychiatry","score_opus":0.03715094466791418,"score_gpt":0.31131742517843014,"score_spread":0.27416648051051595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380576197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9621312,0.000050944444,0.03607896,0.0002706104,0.000114784096,0.0006596631,0.000013387243,0.0002907427,0.00038972744],"genre_scores_gemma":[0.9994599,0.0000022087506,0.00025035825,0.000051847674,0.000040381048,0.00013735669,0.0000126397035,0.000026989253,0.00001830491],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973648,0.0007929265,0.0004012438,0.0005126926,0.00037624972,0.0005520731],"domain_scores_gemma":[0.9975666,0.001200908,0.0002735829,0.000619,0.00029440082,0.00004552353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021375725,0.00018915345,0.0002821549,0.00069416594,0.00022488862,0.00021203003,0.0011127911,0.000039220915,0.000010899057],"category_scores_gemma":[0.0003057005,0.0001582742,0.000049451606,0.0031807625,0.000026856958,0.0004479299,0.0012140019,0.00022775409,0.00003081109],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032648436,0.000048217542,0.8863299,0.000116096235,0.00004767123,0.000038265214,0.02303123,0.000027404274,0.0010483467,0.000015469448,0.000040858617,0.08925326],"study_design_scores_gemma":[0.00017704743,0.000044023254,0.9903116,0.0011276715,0.000009304257,0.000024561406,0.0073434506,0.00036628495,0.000379299,0.0000017833403,0.000022637743,0.00019228888],"about_ca_topic_score_codex":0.00044432035,"about_ca_topic_score_gemma":0.009593721,"teacher_disagreement_score":0.10398173,"about_ca_system_score_codex":0.00006505321,"about_ca_system_score_gemma":0.00006155643,"threshold_uncertainty_score":0.6454234},"labels":[],"label_agreement":null},{"id":"W4381386312","doi":"10.1017/s1049023x23001620","title":"CHRONOS Project: The Transformation of Time-dependent Clinical Trajectories into Intelligent Ones Using an Innovative Technological Solution","year":2023,"lang":"en","type":"article","venue":"Prehospital and Disaster Medicine","topic":"Context-Aware Activity Recognition 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":true,"ca_institutions":"Santé Montérégie","funders":"","keywords":"Real-time locating system; Geolocation; Cloud computing; Overcrowding; Computer science; Context (archaeology); Software deployment; Workflow; Process management; Systems engineering; Real-time computing; Engineering; Database; Software engineering","score_opus":0.09030280477924,"score_gpt":0.35038384936145883,"score_spread":0.2600810445822188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381386312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7591151,0.00011490896,0.23820074,0.0014333542,0.00029472736,0.00051252166,0.000003669506,0.00015796731,0.00016702313],"genre_scores_gemma":[0.99946034,0.000038706432,0.00025548865,0.00005160114,0.000112540016,0.00003563852,0.0000116172605,0.000005578477,0.000028475566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849033,0.00018749472,0.0005585361,0.00028847548,0.0003122406,0.00016289805],"domain_scores_gemma":[0.99912584,0.00023412476,0.00017602941,0.0002474415,0.00017132056,0.000045226538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011346684,0.00013537348,0.0002782439,0.00015422326,0.000116582836,0.000036507667,0.00029335354,0.000087806395,0.000008787312],"category_scores_gemma":[0.00017253053,0.00007996209,0.000035888745,0.0007642592,0.00048441414,0.00068531896,0.00015558822,0.00016368213,0.000012073103],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089614274,0.00039357113,0.0071526673,0.00019892889,0.000100564255,0.000006696515,0.12636839,0.00001539259,0.0070618084,0.0026338396,0.00012579828,0.8558527],"study_design_scores_gemma":[0.009396647,0.019220226,0.14783679,0.0032845945,0.00031953445,0.00038686424,0.1515903,0.59748226,0.03408103,0.02973396,0.0041205855,0.0025471898],"about_ca_topic_score_codex":0.0001347448,"about_ca_topic_score_gemma":0.000028540737,"teacher_disagreement_score":0.8533055,"about_ca_system_score_codex":0.000029868557,"about_ca_system_score_gemma":0.00004947711,"threshold_uncertainty_score":0.32607594},"labels":[],"label_agreement":null},{"id":"W4381746829","doi":"10.1109/percomworkshops56833.2023.10150397","title":"Learning Using Privileged Information for Wearable-based Human Activity Recognition","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Concordia University","funders":"","keywords":"Activity recognition; Wearable computer; Computer science; Set (abstract data type); Human–computer interaction; Machine learning; Artificial intelligence; Wearable technology; Embedded system","score_opus":0.09675949243133791,"score_gpt":0.30955301767501675,"score_spread":0.21279352524367884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381746829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31977177,9.757132e-7,0.67729926,0.0002131951,0.00023451506,0.00047956448,0.0000056999397,0.0008993026,0.0010957372],"genre_scores_gemma":[0.993492,6.949715e-7,0.005926194,0.00012228166,0.00006552968,0.00011816242,0.000048725913,0.000011228203,0.00021517454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872446,0.00015548815,0.00026704077,0.00025719678,0.0002857804,0.00031001156],"domain_scores_gemma":[0.9988105,0.0003616751,0.00022226502,0.00025627858,0.00027745852,0.00007178512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091495924,0.00013928887,0.00017918435,0.00040759542,0.00046892083,0.00038771337,0.00025512694,0.00009123763,0.000027636252],"category_scores_gemma":[0.00019908827,0.00014839102,0.00010414148,0.00077676325,0.000018179357,0.0031158074,0.00009091483,0.0001567192,0.00034935327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003879161,0.00007399103,0.0008642792,0.00019078124,0.000037148748,0.0000021513774,0.0007999382,0.0018609595,0.040333975,0.0003392622,0.00069171074,0.954767],"study_design_scores_gemma":[0.001256399,0.00017550719,0.0021807214,0.0001206399,0.000010197543,0.0000068079858,0.00013094266,0.9581602,0.032839637,0.0008010119,0.0039733886,0.0003445064],"about_ca_topic_score_codex":0.00014082767,"about_ca_topic_score_gemma":0.00004213759,"teacher_disagreement_score":0.9562993,"about_ca_system_score_codex":0.00011096893,"about_ca_system_score_gemma":0.00010598771,"threshold_uncertainty_score":0.605121},"labels":[],"label_agreement":null},{"id":"W4381786635","doi":"10.1109/wf-iot54382.2022.10152039","title":"TinyHAR: Benchmarking Human Activity Recognition Systems in Resource Constrained Devices","year":2022,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Edge device; Benchmarking; Convolutional neural network; Inference; Artificial intelligence; Activity recognition; Deep learning; Benchmark (surveying); Machine learning; Quantization (signal processing); Context (archaeology); Computer vision","score_opus":0.04938194676671396,"score_gpt":0.26728901507025177,"score_spread":0.21790706830353782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381786635","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87311226,0.00012760136,0.059911817,0.00090550736,0.0010815442,0.001213812,0.0000472913,0.00080055115,0.06279961],"genre_scores_gemma":[0.99852806,6.6205746e-7,0.00041242858,0.0002785295,0.00010591968,0.00025757682,0.000026397498,0.0000139270505,0.00037651786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99714017,0.0008779142,0.00041471617,0.0006413819,0.00055272423,0.00037307155],"domain_scores_gemma":[0.9986744,0.00041239717,0.00028102583,0.00046545832,0.00007436,0.00009237422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015439433,0.00019132924,0.0003131215,0.00040380968,0.00048031937,0.00030745313,0.0006783472,0.000058748177,0.00031161844],"category_scores_gemma":[0.000035655703,0.0002153425,0.00008377465,0.0008235213,0.000038285267,0.0007852306,0.00054338126,0.0003936839,0.000053443546],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063810425,0.0009420852,0.012893197,0.00023811279,0.00011822631,0.00027620644,0.0033036429,0.00052909076,0.022826098,0.005847384,0.0056055216,0.94735664],"study_design_scores_gemma":[0.011803291,0.0024034139,0.08439821,0.0011942848,0.00008972704,0.0029326815,0.016567323,0.49027938,0.009165115,0.0037993682,0.37103027,0.0063369223],"about_ca_topic_score_codex":0.0014388883,"about_ca_topic_score_gemma":0.0005749163,"teacher_disagreement_score":0.9410197,"about_ca_system_score_codex":0.00029537876,"about_ca_system_score_gemma":0.00009199849,"threshold_uncertainty_score":0.8781412},"labels":[],"label_agreement":null},{"id":"W4381827052","doi":"10.48550/arxiv.2306.12660","title":"Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Computer science; Ubiquitous computing; Digital health; Data science; Home automation; Risk analysis (engineering); Health care; Business; Telecommunications; Human–computer interaction","score_opus":0.44213160277089025,"score_gpt":0.25729582543058627,"score_spread":0.18483577734030399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381827052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95956874,0.00010640641,0.03648045,0.00019973326,0.0027013172,0.00054174475,0.0000488785,0.00026349223,0.00008924207],"genre_scores_gemma":[0.99907345,0.0004951751,0.000062262516,0.000023456185,0.000101290854,0.000007785905,0.000013034445,0.000026052241,0.00019749122],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970372,0.0009311226,0.0004251231,0.0009805927,0.00019296074,0.00043300196],"domain_scores_gemma":[0.99634796,0.001248714,0.0005348212,0.0013951169,0.00033721197,0.00013615127],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009734637,0.00030538448,0.00066009886,0.0005590365,0.00013210054,0.00010031802,0.0019716346,0.00012440157,0.0000027109475],"category_scores_gemma":[0.00007071754,0.00030715394,0.00019256971,0.0015316525,0.00008038629,0.00047393722,0.0024115003,0.0007114526,0.00006548692],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020418095,0.00049077545,0.8281976,0.001242223,0.00060668436,0.00078434113,0.011307285,0.1337018,0.00008945595,0.005543129,0.00076694286,0.017065551],"study_design_scores_gemma":[0.0008328237,0.00014690956,0.9541086,0.0008409969,0.000017556507,0.000009323809,0.0015134347,0.039064933,0.00013814608,0.0025529335,0.00016645319,0.00060789374],"about_ca_topic_score_codex":0.0059405747,"about_ca_topic_score_gemma":0.0015536434,"teacher_disagreement_score":0.12591097,"about_ca_system_score_codex":0.0002552901,"about_ca_system_score_gemma":0.00039257476,"threshold_uncertainty_score":0.9999381},"labels":[],"label_agreement":null},{"id":"W4381894494","doi":"10.1109/ismvl57333.2023.00047","title":"Discovering Emerging Applications of Multi-Valued Logic: Protocols for Human-Autonomy Teaming","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Encoding (memory); Autonomy; Situation awareness; Emergency management; Knowledge management; Computer security; Distributed computing; Artificial intelligence; Engineering","score_opus":0.11802905325855598,"score_gpt":0.3971104708223383,"score_spread":0.27908141756378235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381894494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018857211,0.000003337962,0.9816708,0.0001235206,0.00004711158,0.014377707,0.000008432233,0.0004914885,0.0013918874],"genre_scores_gemma":[0.84191996,3.8837527e-7,0.07929944,0.000054924196,0.00008149665,0.07636599,0.000012487781,0.00002009774,0.0022451822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988098,0.000036922753,0.00036991143,0.0003752975,0.00016642128,0.00024161364],"domain_scores_gemma":[0.9990038,0.00014549619,0.00018688943,0.00048182512,0.00013352546,0.000048443584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004602082,0.00011639255,0.00021261426,0.00019919904,0.00020153704,0.00011129953,0.0005665063,0.00004067331,0.000011003298],"category_scores_gemma":[0.00004230401,0.00011033591,0.00010653171,0.0005853951,0.00002608927,0.0005123464,0.00027334382,0.000059561437,0.000046999263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000114870845,0.00055814587,0.0046484596,0.0009854166,0.0001652456,0.0000036303495,0.005683916,0.00094678253,0.120498896,0.35030636,0.0007413067,0.51545036],"study_design_scores_gemma":[0.0038249083,0.00027996715,0.0057198713,0.00049724313,0.000025611236,0.000014939544,0.0022174895,0.7962549,0.10928914,0.013941954,0.066686384,0.0012476397],"about_ca_topic_score_codex":0.000084825326,"about_ca_topic_score_gemma":0.000046604415,"teacher_disagreement_score":0.90237135,"about_ca_system_score_codex":0.00005494433,"about_ca_system_score_gemma":0.00007474259,"threshold_uncertainty_score":0.44993678},"labels":[],"label_agreement":null},{"id":"W4382118567","doi":"10.1007/978-981-19-8669-7_31","title":"Eliminating Environmental Context for Fall Detection Based on Movement Traces","year":2023,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; False positive paradox; Artificial intelligence; Convolutional neural network; Context (archaeology); TRACE (psycholinguistics); RGB color model; Computer vision; Real-time computing; Geography","score_opus":0.03837603247797977,"score_gpt":0.22995779901615518,"score_spread":0.1915817665381754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382118567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076302695,0.0015363205,0.9375995,0.0039393655,0.006455056,0.008523681,0.00074764376,0.009143546,0.02442466],"genre_scores_gemma":[0.97527057,0.000045725512,0.0004033352,0.00012406296,0.0000938163,0.0007236536,0.00007173258,0.000058585996,0.02320854],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977773,0.000023119734,0.0007660907,0.00074946537,0.00042180374,0.00026221454],"domain_scores_gemma":[0.99801916,0.000405911,0.0007474504,0.00059971947,0.00020858711,0.000019186838],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006960457,0.00039862204,0.00047838886,0.0010600209,0.00032033052,0.0002926162,0.00041245538,0.00051823107,0.0000018440054],"category_scores_gemma":[0.0001497114,0.00038666802,0.00008474751,0.0002547363,0.00011290158,0.00028369046,0.00017857869,0.0002981329,0.00003518509],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014737738,0.000034258584,0.00016276255,0.00033770275,0.00010887398,0.0000060484494,0.000106197156,0.000017833556,0.00091870857,0.3155976,0.0007470969,0.6819482],"study_design_scores_gemma":[0.0056721917,0.004775923,0.0016459881,0.007005076,0.00018593458,0.00010497064,0.009891613,0.1489042,0.025109671,0.14280567,0.64831567,0.0055830977],"about_ca_topic_score_codex":0.000052539,"about_ca_topic_score_gemma":0.00009764855,"teacher_disagreement_score":0.9676403,"about_ca_system_score_codex":0.00020002318,"about_ca_system_score_gemma":0.000042600786,"threshold_uncertainty_score":0.9998585},"labels":[],"label_agreement":null},{"id":"W4382776470","doi":"10.2196/47325","title":"Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Korea Evaluation Institute of Industrial Technology; Ministry of Trade, Industry and Energy; Iran Telecommunication Research Center; Korea Institute of Industrial Technology; Ministry of Science and ICT, South Korea","keywords":"Loneliness; Usability; Computer science; Wearable computer; Human–computer interaction; Computer security; Internet privacy; Embedded system; Medicine","score_opus":0.14697083128936667,"score_gpt":0.4308176885032529,"score_spread":0.28384685721388625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382776470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9424655,0.000033017444,0.04835049,0.0011440251,0.00058838254,0.006244608,0.00004144529,0.0006583982,0.0004741431],"genre_scores_gemma":[0.99768484,6.256879e-7,0.0012021767,0.000017235008,0.000060779257,0.00086821103,0.000005360767,0.000016636017,0.0001441546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99599445,0.001062518,0.00047333917,0.00057365966,0.001187348,0.00070868415],"domain_scores_gemma":[0.9972349,0.0012722915,0.00013024149,0.00057589606,0.00055164594,0.00023504095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065597924,0.00018430766,0.00035824534,0.0005395434,0.00088819646,0.00039190453,0.00042309414,0.0000614197,0.0000017526763],"category_scores_gemma":[0.00016268266,0.00016299242,0.000050088798,0.0010849808,0.000057155205,0.00056979875,0.00042275235,0.00033071716,0.00014527456],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033622226,0.0013495231,0.010953026,0.0055895527,0.00022333153,0.000034484434,0.13182956,0.00014444535,0.00022289171,0.000305623,0.0050746854,0.8439366],"study_design_scores_gemma":[0.0033206122,0.0026572088,0.31999514,0.0015162457,0.0000055102046,0.00001955634,0.028055213,0.6270244,0.0035075787,0.000081770915,0.013232675,0.00058405974],"about_ca_topic_score_codex":0.000080730184,"about_ca_topic_score_gemma":0.000028863946,"teacher_disagreement_score":0.8433526,"about_ca_system_score_codex":0.00043006457,"about_ca_system_score_gemma":0.00046479137,"threshold_uncertainty_score":0.6831379},"labels":[],"label_agreement":null},{"id":"W4383108306","doi":"10.1109/icra48891.2023.10161404","title":"A Deep Learning Human Activity Recognition Framework for Socially Assistive Robots to Support Reablement of Older Adults","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Activities of daily living; Computer science; Robot; Artificial intelligence; Leverage (statistics); Architecture; Deep learning; Human–computer interaction; Embedding; Machine learning; Psychology","score_opus":0.060552961902604924,"score_gpt":0.3321643746703076,"score_spread":0.27161141276770273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383108306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08801954,0.000001952275,0.90796775,0.0007480786,0.00032513915,0.0010909464,0.000015469857,0.00045318753,0.0013779185],"genre_scores_gemma":[0.96363086,0.0000020735326,0.034820024,0.000160641,0.000117498974,0.00044963043,0.000042045227,0.000022899869,0.0007543616],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979508,0.00018710912,0.00038710565,0.000619635,0.0004354501,0.00041991932],"domain_scores_gemma":[0.9979507,0.00075753225,0.0003067909,0.00032889898,0.0005159849,0.0001401288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009386912,0.00019081688,0.00036047434,0.0002838373,0.00028389477,0.00012192163,0.0003890963,0.00014403925,0.00013209954],"category_scores_gemma":[0.00043128958,0.00020290348,0.00016993025,0.0008255198,0.000023693603,0.00050999597,0.0002760854,0.0002008446,0.00020981547],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007556584,0.00022009602,0.0011941388,0.00013133934,0.0000987693,0.000006527663,0.004266235,0.000083741324,0.0019071941,0.001990464,0.0018814127,0.9881445],"study_design_scores_gemma":[0.0124224005,0.010676622,0.61604846,0.005140712,0.00029955464,0.000052470365,0.011317152,0.11603249,0.13974948,0.076624155,0.0060525327,0.005583956],"about_ca_topic_score_codex":0.00021108767,"about_ca_topic_score_gemma":0.0005457053,"teacher_disagreement_score":0.9825606,"about_ca_system_score_codex":0.00012541223,"about_ca_system_score_gemma":0.00011582926,"threshold_uncertainty_score":0.82741636},"labels":[],"label_agreement":null},{"id":"W4383619720","doi":"10.1007/978-3-031-32828-2_3","title":"Keystone for Smart Communities—Smart Households","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in big data","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Architectural engineering; Home automation; Computer science; Engineering; Computer security; Internet privacy; Human–computer interaction; Telecommunications","score_opus":0.5631526713336328,"score_gpt":0.38286390218499394,"score_spread":0.18028876914863884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383619720","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019590887,0.02484967,0.1674351,0.0126780495,0.092682034,0.011977749,0.04001181,0.0060882135,0.6440815],"genre_scores_gemma":[0.0512851,0.01590069,0.00656375,0.0028115618,0.004107077,0.0014028039,0.0069020498,0.00067201175,0.910355],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99728495,0.00010331343,0.00072479696,0.0009089685,0.00050680665,0.00047118647],"domain_scores_gemma":[0.99275,0.0017705398,0.00036472527,0.0047532027,0.00028912997,0.00007242872],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0014809766,0.0005059054,0.0010298415,0.000381223,0.00031310343,0.00015277255,0.0044059455,0.00027102343,0.000007262957],"category_scores_gemma":[0.00021601334,0.00052133977,0.00013464331,0.00017692326,0.0003181401,0.0005341768,0.009173711,0.00055122783,0.00032425174],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036528112,0.00008683515,0.0001598512,0.0009734888,0.0014486852,0.00014862814,0.005254028,0.0000015657444,0.00000281431,0.034002546,0.75466025,0.20322475],"study_design_scores_gemma":[0.00076320797,0.00011037689,0.00005319051,0.0013258,0.00006118021,0.00003301154,0.0011056969,0.00034881404,0.0000042910196,0.014663684,0.9807485,0.00078229525],"about_ca_topic_score_codex":0.00046463244,"about_ca_topic_score_gemma":0.011727694,"teacher_disagreement_score":0.26627347,"about_ca_system_score_codex":0.00019524507,"about_ca_system_score_gemma":0.00014962944,"threshold_uncertainty_score":0.9997238},"labels":[],"label_agreement":null},{"id":"W4384559810","doi":"10.1007/s11357-023-00868-5","title":"Correction to: Pre-frail older adults show improved cognition with StayFitLonger computerized home–based training: a randomized controlled trial","year":2023,"lang":"en","type":"erratum","venue":"GeroScience","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université du Québec à Trois-Rivières; Université de Montréal; Montreal Heart Institute; Institut Universitaire de Gériatrie de Montréal; Bell (Canada)","funders":"","keywords":"Randomized controlled trial; Cognition; Cognitive training; Physical medicine and rehabilitation; Psychology; Gerontology; Physical therapy; Medicine; Psychiatry; Internal medicine","score_opus":0.022215997857067786,"score_gpt":0.25786424477982567,"score_spread":0.23564824692275788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384559810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036923755,0.00007092991,0.81589895,0.00081322226,0.15469398,0.021003483,0.00009951528,0.0018493899,0.0018781855],"genre_scores_gemma":[0.6810877,0.000052237905,0.01205935,0.0036029106,0.006367587,0.032744158,0.00072829373,0.00050117174,0.2628566],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9902289,0.0016418629,0.0017421219,0.0028137015,0.002258139,0.0013152551],"domain_scores_gemma":[0.9908288,0.003972964,0.0019112481,0.0014941117,0.0012000883,0.0005927587],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00448916,0.0011468638,0.004213273,0.0013054593,0.0005840419,0.0018931695,0.0022169366,0.00059952075,0.00005966373],"category_scores_gemma":[0.002641987,0.000910661,0.0009866352,0.002760075,0.0004814656,0.0011617658,0.0004372062,0.0012741768,0.00016866378],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.8081685,0.0004929474,0.0000011381762,0.00016708116,0.00027274137,0.000056674315,0.0042474065,0.000053234344,0.00047598043,0.000012339467,0.14810888,0.037943088],"study_design_scores_gemma":[0.7199657,0.0012295953,0.000055560246,0.0020035626,0.00016719059,0.000027429916,0.0001133646,0.2731504,0.00007204874,0.000029343842,0.0020751536,0.0011106598],"about_ca_topic_score_codex":0.0002145369,"about_ca_topic_score_gemma":0.00051001547,"teacher_disagreement_score":0.80383956,"about_ca_system_score_codex":0.00030286508,"about_ca_system_score_gemma":0.002535149,"threshold_uncertainty_score":0.9993344},"labels":[],"label_agreement":null},{"id":"W4384821450","doi":"10.3390/ijerph20146399","title":"Context-Aware Medical Systems within Healthcare Environments: A Systematic Scoping Review to Identify Subdomains and Significant Medical Contexts","year":2023,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; AGE-WELL; McMaster University","keywords":"Context (archaeology); Health care; Data extraction; Systematic review; Computer science; Phone; Data science; MEDLINE; Geography; Political science","score_opus":0.12903011635514608,"score_gpt":0.4467769204209538,"score_spread":0.3177468040658077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384821450","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28017277,0.13616143,0.099657625,0.46920407,0.0048118248,0.00941474,0.00029835675,0.0001791072,0.00010009388],"genre_scores_gemma":[0.9636977,0.03200826,0.000046451085,0.0038000455,0.00020831497,0.00011532647,0.000014421681,0.000019910487,0.00008962355],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.98838323,0.0023636948,0.0018072367,0.0005189372,0.006215399,0.0007114921],"domain_scores_gemma":[0.9944273,0.0018342254,0.0006851595,0.00033708548,0.00018460077,0.0025316216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01842849,0.00022010018,0.000821148,0.00078501145,0.0002777611,0.00048341925,0.0014590876,0.00014005626,0.000096541145],"category_scores_gemma":[0.003062718,0.00018376907,0.00009037707,0.0004208998,0.00028971667,0.0008863185,0.00093289575,0.0007999127,0.00013315334],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042081403,0.004799634,0.037487287,0.18087207,0.0030725193,0.01160703,0.02075216,0.000012604297,0.0010049611,0.026062524,0.026789606,0.68711877],"study_design_scores_gemma":[0.008203702,0.00555035,0.029390933,0.8996852,0.000034332,0.016248725,0.01867212,0.009346731,0.000075629236,0.0011945539,0.010008949,0.001588788],"about_ca_topic_score_codex":0.0002535671,"about_ca_topic_score_gemma":0.00017701337,"teacher_disagreement_score":0.7188131,"about_ca_system_score_codex":0.0007103822,"about_ca_system_score_gemma":0.0014627099,"threshold_uncertainty_score":0.7493885},"labels":[],"label_agreement":null},{"id":"W4385073800","doi":"10.1145/3610225","title":"Beyond Smart Homes: An In-Depth Analysis of Smart Aging Care System Security","year":2023,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Queen's University","funders":"","keywords":"Internet privacy; Context (archaeology); Computer security; Variety (cybernetics); Computer science; Population ageing; Aging in place; Health care; Home automation; Population; Business; Telecommunications; Medicine; Gerontology; Environmental health","score_opus":0.08269756635745078,"score_gpt":0.35038350132178886,"score_spread":0.26768593496433807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385073800","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002254164,0.9494847,0.041291315,0.000014149115,0.0028471714,0.0016059016,0.00029372566,0.0014871961,0.000721646],"genre_scores_gemma":[0.6298251,0.3646326,0.0026315816,0.000035182413,0.000564592,0.0001531151,0.0017121761,0.00035940585,0.000086240616],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9858943,0.00827168,0.0022575946,0.0018086843,0.00095606357,0.0008116556],"domain_scores_gemma":[0.9890923,0.004699258,0.001786561,0.00359008,0.00059418357,0.00023762161],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009844742,0.0008327677,0.004997985,0.0031943715,0.00021411733,0.00042099005,0.004227444,0.000464924,0.000004070454],"category_scores_gemma":[0.00060903426,0.00084017,0.0012937363,0.009845553,0.00007458838,0.0005066616,0.0023920597,0.00083319267,0.000098739096],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.9431536e-7,0.00006471267,0.007291077,0.02430454,0.0015503169,0.00009843715,0.002830023,0.000058870697,1.3695792e-7,0.00012741477,0.000033469132,0.96364033],"study_design_scores_gemma":[0.0051717446,0.001474546,0.10807373,0.38582772,0.026391113,0.00084628887,0.01676502,0.27735358,0.00002844883,0.0007350084,0.15639837,0.020934414],"about_ca_topic_score_codex":0.003492809,"about_ca_topic_score_gemma":0.0077053783,"teacher_disagreement_score":0.94270587,"about_ca_system_score_codex":0.0006551099,"about_ca_system_score_gemma":0.0005826934,"threshold_uncertainty_score":0.9994049},"labels":[],"label_agreement":null},{"id":"W4385076727","doi":"10.1016/j.buildenv.2023.110651","title":"Unsupervised domain adaptation with and without access to source data for estimating occupancy and recognizing activities in smart buildings","year":2023,"lang":"en","type":"article","venue":"Building and Environment","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Occupancy; Domain adaptation; Adaptation (eye); Computer science; Domain (mathematical analysis); Real-time computing; Environmental science; Data mining; Artificial intelligence; Engineering; Architectural engineering; Mathematics; Psychology","score_opus":0.08416189069732156,"score_gpt":0.2963959288487572,"score_spread":0.21223403815143566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385076727","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5327908,0.00004339571,0.46634707,0.00042541767,0.00003106282,0.00029433952,0.000008927508,0.000052691674,0.00000633104],"genre_scores_gemma":[0.78853714,0.000038017188,0.21115986,0.00008447774,0.000029169969,0.000104604194,0.000009194298,0.000015444417,0.000022114456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866563,0.000056363617,0.00018866757,0.0006622992,0.00017094264,0.00025609654],"domain_scores_gemma":[0.99919087,0.00029767596,0.00008263614,0.00032208752,0.0000065288355,0.0001002321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007170101,0.00015935021,0.00021564457,0.00018668477,0.00020031526,0.00038593763,0.00027099618,0.00004063774,7.5201376e-7],"category_scores_gemma":[0.00005679431,0.00015217873,0.000009551523,0.00017923527,0.000042262876,0.001057428,0.0007226015,0.000077287106,0.0000012384598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007160289,0.00003219232,0.116502464,0.00017377915,0.000036183803,0.0000043619957,0.0055004084,0.0014199854,0.0039681243,0.0000715124,0.00007380242,0.8721456],"study_design_scores_gemma":[0.0025894134,0.00029766472,0.09992868,0.000998219,0.000035282992,0.00007891692,0.002785907,0.8843691,0.000897599,0.0010167091,0.006206432,0.00079609995],"about_ca_topic_score_codex":0.00017772768,"about_ca_topic_score_gemma":0.00007193271,"teacher_disagreement_score":0.8829491,"about_ca_system_score_codex":0.000038664257,"about_ca_system_score_gemma":0.000014502784,"threshold_uncertainty_score":0.6205669},"labels":[],"label_agreement":null},{"id":"W4385187201","doi":"10.1109/iwcmc58020.2023.10182728","title":"Efficient Fall Detection using Bidirectional Long Short-Term Memory","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Context (archaeology); Term (time); Machine learning; Long short term memory; Artificial intelligence; Accelerometer; Data mining; Artificial neural network","score_opus":0.060605728737509655,"score_gpt":0.28900585626565645,"score_spread":0.22840012752814678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385187201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57525516,0.0000098505725,0.4208836,0.00005280086,0.0012441217,0.000097283904,0.0000011176612,0.0007454993,0.0017105489],"genre_scores_gemma":[0.9978325,0.0000011854896,0.00068063015,0.000049194154,0.00017376403,0.0000221436,0.0000020604461,0.0000123258105,0.0012262246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985322,0.00009849304,0.00023382825,0.00044193148,0.0004132467,0.00028034946],"domain_scores_gemma":[0.9992366,0.00015243702,0.00005282709,0.0003363537,0.00012910369,0.00009272026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045372124,0.00013234121,0.00014500316,0.0003690091,0.0002239525,0.00018545054,0.0002865811,0.00006829774,0.00003380266],"category_scores_gemma":[0.00003342663,0.00013148318,0.00010058046,0.0010415816,0.000025049727,0.00025673618,0.00021873869,0.0001141928,0.00052676274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015156146,0.0001909485,0.013633978,0.000058109432,0.00010369356,0.00012255309,0.0009463728,0.0120038595,0.10834901,0.00023717135,0.00035115972,0.863988],"study_design_scores_gemma":[0.00019006821,0.00003207226,0.040972248,0.0000343336,0.0000073797487,0.00021779274,0.00007626685,0.9352158,0.022675093,0.000040330327,0.00025682888,0.0002818037],"about_ca_topic_score_codex":0.00020657468,"about_ca_topic_score_gemma":0.00034945665,"teacher_disagreement_score":0.92321193,"about_ca_system_score_codex":0.00014792565,"about_ca_system_score_gemma":0.000060931,"threshold_uncertainty_score":0.6770645},"labels":[],"label_agreement":null},{"id":"W4385196892","doi":"10.32920/23737437","title":"CageView: A Smart Food Control and Monitoring System for Phenotypical Research In Vivo","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Manitoba; Toronto Metropolitan University","funders":"","keywords":"Schedule; Convolutional neural network; Computer science; Real-time computing; Set (abstract data type); Control (management); Artificial intelligence; Embedded system; Human–computer interaction; Operating system","score_opus":0.2186615725810479,"score_gpt":0.37775383863254747,"score_spread":0.15909226605149956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385196892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04419436,0.0021331708,0.9345514,0.0028984274,0.005939991,0.007111896,0.00015145268,0.0012533742,0.0017659296],"genre_scores_gemma":[0.99380195,0.00004297893,0.0029064945,0.000016661215,0.00044320506,0.0023138556,0.000002059646,0.00003724504,0.0004355542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99609196,0.00074225967,0.00065315276,0.001213298,0.0006677945,0.0006315554],"domain_scores_gemma":[0.99557126,0.002665384,0.00015299954,0.00096755667,0.00045992556,0.00018287697],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004187538,0.00028824646,0.0007726712,0.000618146,0.00015865076,0.0006562983,0.0010519176,0.0003466916,0.0000019029352],"category_scores_gemma":[0.0002544397,0.00027793265,0.00014979136,0.0004635443,0.000054336288,0.00025225698,0.0017242444,0.0008300589,0.000052091786],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000935287,0.0011491843,0.079889104,0.04886544,0.0023704222,0.0005888291,0.011986115,0.00035366497,0.004851527,0.09438767,0.014033275,0.7405895],"study_design_scores_gemma":[0.029290529,0.0038148433,0.04416708,0.051759135,0.0002839107,0.00048404076,0.0077657984,0.73052233,0.013113482,0.05359027,0.057059076,0.008149475],"about_ca_topic_score_codex":0.0009961884,"about_ca_topic_score_gemma":0.00073159626,"teacher_disagreement_score":0.9496076,"about_ca_system_score_codex":0.00038060345,"about_ca_system_score_gemma":0.00026832474,"threshold_uncertainty_score":0.9999673},"labels":[],"label_agreement":null},{"id":"W4385215039","doi":"10.32920/23737437.v1","title":"CageView: A Smart Food Control and Monitoring System for Phenotypical Research In Vivo","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Manitoba; Toronto Metropolitan University","funders":"","keywords":"Schedule; Computer science; Convolutional neural network; Real-time computing; Set (abstract data type); Control (management); Histogram; Artificial intelligence; Embedded system; Operating system","score_opus":0.2186615725810479,"score_gpt":0.37775383863254747,"score_spread":0.15909226605149956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385215039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04419436,0.0021331708,0.9345514,0.0028984274,0.005939991,0.007111896,0.00015145268,0.0012533742,0.0017659296],"genre_scores_gemma":[0.99380195,0.00004297893,0.0029064945,0.000016661215,0.00044320506,0.0023138556,0.000002059646,0.00003724504,0.0004355542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99609196,0.00074225967,0.00065315276,0.001213298,0.0006677945,0.0006315554],"domain_scores_gemma":[0.99557126,0.002665384,0.00015299954,0.00096755667,0.00045992556,0.00018287697],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004187538,0.00028824646,0.0007726712,0.000618146,0.00015865076,0.0006562983,0.0010519176,0.0003466916,0.0000019029352],"category_scores_gemma":[0.0002544397,0.00027793265,0.00014979136,0.0004635443,0.000054336288,0.00025225698,0.0017242444,0.0008300589,0.000052091786],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000935287,0.0011491843,0.079889104,0.04886544,0.0023704222,0.0005888291,0.011986115,0.00035366497,0.004851527,0.09438767,0.014033275,0.7405895],"study_design_scores_gemma":[0.029290529,0.0038148433,0.04416708,0.051759135,0.0002839107,0.00048404076,0.0077657984,0.73052233,0.013113482,0.05359027,0.057059076,0.008149475],"about_ca_topic_score_codex":0.0009961884,"about_ca_topic_score_gemma":0.00073159626,"teacher_disagreement_score":0.9496076,"about_ca_system_score_codex":0.00038060345,"about_ca_system_score_gemma":0.00026832474,"threshold_uncertainty_score":0.9999673},"labels":[],"label_agreement":null},{"id":"W4385337237","doi":"10.1016/j.inffus.2023.101953","title":"A multi-resolution fusion approach for human activity recognition from video data in tiny edge devices","year":2023,"lang":"en","type":"article","venue":"Information Fusion","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; King Saud University","keywords":"Computer science; Edge device; Activity recognition; Artificial intelligence; Convolutional neural network; Deep learning; Inference; Edge computing; Wearable computer; Data stream mining; Real-time computing; Machine learning; Enhanced Data Rates for GSM Evolution; Embedded system; Cloud computing","score_opus":0.14334526936406355,"score_gpt":0.3218688183173329,"score_spread":0.17852354895326933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385337237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26356116,0.000012522563,0.73321795,0.00029290287,0.00042298101,0.0011119229,0.0003338851,0.0005177507,0.0005289125],"genre_scores_gemma":[0.9692921,0.000015104133,0.023823868,0.0001738332,0.00012510736,0.00024346789,0.0062747635,0.000012129537,0.000039635936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791086,0.00018649934,0.00062090467,0.0004816949,0.00046594677,0.0003340776],"domain_scores_gemma":[0.9979863,0.0003110489,0.00046397923,0.00090822205,0.00024370561,0.00008674388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014553093,0.00020938298,0.0002573273,0.00064727705,0.00036626877,0.00037016688,0.0009017782,0.00019920654,0.000019625215],"category_scores_gemma":[0.00030471515,0.00021600453,0.00006890045,0.001009491,0.000024516905,0.008471211,0.00088159,0.00019885144,0.0004780184],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099807294,0.00027457761,0.0013868868,0.00021894765,0.000022018681,0.0000016576772,0.0041453526,0.0002645914,0.014557972,0.00005781054,0.004463702,0.9745067],"study_design_scores_gemma":[0.0017861838,0.00005486155,0.041036066,0.00014655052,0.000009034607,0.0000040639443,0.00034480775,0.9461359,0.001161024,0.00024242037,0.008762379,0.00031675692],"about_ca_topic_score_codex":0.0015102455,"about_ca_topic_score_gemma":0.00084440387,"teacher_disagreement_score":0.97418994,"about_ca_system_score_codex":0.000147976,"about_ca_system_score_gemma":0.00007540908,"threshold_uncertainty_score":0.8808409},"labels":[],"label_agreement":null},{"id":"W4385386598","doi":"10.18280/ria.370313","title":"Qualitative Analysis of Techniques for Device-Free Human Activity Recognition","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Qualitative analysis; Computer science; Psychology; Qualitative research; Sociology","score_opus":0.2077165034172157,"score_gpt":0.4161659031757109,"score_spread":0.2084493997584952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385386598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13673759,0.000019884385,0.86012816,0.00055085745,0.00014557892,0.0006104019,0.00012120313,0.00045150178,0.001234807],"genre_scores_gemma":[0.9934166,0.000014292953,0.0055413726,0.0000396023,0.000048411173,0.00028239048,0.000057525733,0.000015645952,0.0005841633],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979315,0.0002852127,0.0005996426,0.0005919187,0.00026708416,0.00032465332],"domain_scores_gemma":[0.99645674,0.0015081548,0.00041186652,0.0009956995,0.0005497917,0.00007775627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018830579,0.00018171551,0.00049982977,0.0009724639,0.00020218795,0.000089823676,0.0008968665,0.00010969652,0.000051659565],"category_scores_gemma":[0.00055823545,0.000196549,0.00037859747,0.004038305,0.00009059236,0.00055888004,0.00024940557,0.00012513732,0.0001389172],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044111788,0.0004145419,0.00019804116,0.00031664738,0.00082598685,0.0000070182627,0.028260784,0.000667558,0.09827898,0.008613001,0.0017748288,0.8605985],"study_design_scores_gemma":[0.00007097865,0.00027090238,0.00027642166,0.00014693924,0.00019921389,0.0000031479449,0.003887425,0.31154945,0.6590436,0.022671312,0.0014755742,0.00040504336],"about_ca_topic_score_codex":0.00020197645,"about_ca_topic_score_gemma":0.00027979174,"teacher_disagreement_score":0.86019343,"about_ca_system_score_codex":0.00006972104,"about_ca_system_score_gemma":0.00004272869,"threshold_uncertainty_score":0.8015035},"labels":[],"label_agreement":null},{"id":"W4385730409","doi":"10.1109/jcsse58229.2023.10202147","title":"Recognizing Fall Risk Factors with Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Ontario Tech University","funders":"King Mongkut's University of Technology Thonburi","keywords":"Falling (accident); Phone; Medical emergency; Activities of daily living; Sitting; Risk management; Computer science; Computer security; Human factors and ergonomics; Work (physics); Convolutional neural network; Elderly people; Medicine; Poison control; Risk analysis (engineering); Artificial intelligence; Physical therapy; Business; Gerontology; Environmental health; Engineering","score_opus":0.04446680844241125,"score_gpt":0.24244215745897912,"score_spread":0.19797534901656788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385730409","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6818461,0.000029187066,0.30981362,0.000562721,0.0010673809,0.00028062676,0.000013441325,0.0016628067,0.0047241272],"genre_scores_gemma":[0.9956411,0.000006120931,0.0026504414,0.00015465745,0.00018638377,0.000022256227,0.000015726766,0.000014024279,0.001309274],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983718,0.00016081527,0.00021689008,0.0004497177,0.00036788522,0.00043289582],"domain_scores_gemma":[0.99865454,0.0005978859,0.00013103889,0.0003538778,0.00013399335,0.0001286484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003918863,0.00016726047,0.0001953381,0.0001209008,0.00026005448,0.00020126654,0.00044731458,0.00005796264,0.00004897418],"category_scores_gemma":[0.00005872093,0.00012995918,0.000075104166,0.0010512935,0.000041230716,0.0007144085,0.00021208811,0.0001935754,0.00062339107],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003179268,0.0000753077,0.84037024,0.000022017117,0.00023267156,0.00007296674,0.0012860305,0.006733829,0.00009080816,0.005393419,0.039153226,0.10653767],"study_design_scores_gemma":[0.0011557133,0.00028176064,0.45952183,0.000083824634,0.000026563954,0.00010329413,0.00045726684,0.51692206,0.00022378722,0.0027144572,0.0175789,0.0009305276],"about_ca_topic_score_codex":0.00055014365,"about_ca_topic_score_gemma":0.00053408655,"teacher_disagreement_score":0.5101882,"about_ca_system_score_codex":0.00004385036,"about_ca_system_score_gemma":0.00006923645,"threshold_uncertainty_score":0.8012639},"labels":[],"label_agreement":null},{"id":"W4385784798","doi":"10.1007/978-3-319-08234-9_481-1","title":"Fall Risk Detection in Computer Vision","year":2023,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Computer Graphics and Games","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Optometry; Medicine","score_opus":0.012899904365591009,"score_gpt":0.2266245315552392,"score_spread":0.2137246271896482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385784798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006934387,0.0009127998,0.95706385,0.0002403153,0.004677044,0.0009464339,0.00007370474,0.0005161467,0.028635293],"genre_scores_gemma":[0.7080842,0.10310284,0.06538935,0.0016352807,0.008434294,0.0002602302,0.00023747192,0.00093872927,0.1119176],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99726427,0.00012578585,0.00082024693,0.0009350507,0.00054119114,0.0003134481],"domain_scores_gemma":[0.9976243,0.00067368883,0.0006426746,0.00070830644,0.00021242906,0.0001386478],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006285952,0.0004872095,0.00080700807,0.001181975,0.00009248932,0.0001680604,0.00062724657,0.0004573756,0.0000040235964],"category_scores_gemma":[0.0000140692,0.0004922386,0.00027284407,0.0002866662,0.00014061968,0.00041935686,0.0007027551,0.00072079897,0.00004188174],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013066446,0.000046002493,0.00087103253,0.00014160146,0.000101699094,0.000061532635,0.00057220977,0.000032925174,0.0000015906268,0.028461741,0.00095489685,0.9687417],"study_design_scores_gemma":[0.0028651382,0.002354364,0.058608484,0.0033045623,0.0001564129,0.00020500722,0.00001314574,0.3380993,0.00003591061,0.22036803,0.37085792,0.0031317146],"about_ca_topic_score_codex":0.00024982684,"about_ca_topic_score_gemma":0.001140223,"teacher_disagreement_score":0.96560997,"about_ca_system_score_codex":0.000031004154,"about_ca_system_score_gemma":0.00006210132,"threshold_uncertainty_score":0.99975294},"labels":[],"label_agreement":null},{"id":"W4385847493","doi":"10.1145/3582515.3609551","title":"Leveraging Self-Supervised Learning for Human Activity Recognition with Ambient Sensors","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Université de Montréal; Université de Sherbrooke","funders":"AGE-WELL","keywords":"Activity recognition; Computer science; Transfer of learning; Artificial intelligence; Deep learning; Machine learning; Convolutional neural network; Encoder; Data modeling; Supervised learning; Feature extraction; Pattern recognition (psychology); Artificial neural network; Database","score_opus":0.06589076207836665,"score_gpt":0.2764675738616291,"score_spread":0.21057681178326249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385847493","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6638907,0.0000015161557,0.33192918,0.00044487958,0.00014759178,0.0004944708,0.000001924988,0.001791488,0.0012982194],"genre_scores_gemma":[0.9923547,0.0000024527517,0.0059773233,0.00009246648,0.000089166366,0.00018287014,0.000021544707,0.000027645763,0.0012518317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820983,0.00019299272,0.0002039918,0.0006168001,0.00035002708,0.0004263332],"domain_scores_gemma":[0.9987879,0.00038612765,0.00012816349,0.00033951172,0.00024627594,0.00011206281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066284323,0.00019915045,0.00024486508,0.00028173943,0.0004888359,0.00030042976,0.00029122503,0.000068439105,0.000029802593],"category_scores_gemma":[0.00005186718,0.00018319333,0.00010090957,0.00076738786,0.000019576217,0.00086655724,0.00014098804,0.00019606312,0.00028769317],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093298084,0.000398937,0.0063587762,0.00031073103,0.0003122729,0.00007083215,0.009089975,0.000720808,0.06589945,0.00049695664,0.0018580371,0.9143899],"study_design_scores_gemma":[0.0047328863,0.0010858425,0.018578349,0.0003016887,0.000064446554,0.00014261965,0.0018460557,0.8729244,0.08702145,0.0017341666,0.009928266,0.0016398472],"about_ca_topic_score_codex":0.00017750238,"about_ca_topic_score_gemma":0.000075139345,"teacher_disagreement_score":0.91275007,"about_ca_system_score_codex":0.00010150317,"about_ca_system_score_gemma":0.000056976027,"threshold_uncertainty_score":0.74704075},"labels":[],"label_agreement":null},{"id":"W4386074478","doi":"10.11159/icbes23.149","title":"A Prediction Algorithm For Fall Risk Assessment Among Community-Dwelling Elderly People","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Regione Marche; Ministero dello Sviluppo Economico","keywords":"Computer science; Algorithm","score_opus":0.015025604412051273,"score_gpt":0.23121257960407085,"score_spread":0.21618697519201957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386074478","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6396708,0.00009863605,0.35660955,0.00016960852,0.002189593,0.00077322224,0.0000121496205,0.00038136667,0.00009507849],"genre_scores_gemma":[0.9963346,0.000023384875,0.0032352887,0.000014555771,0.00012456384,0.00010049894,4.4503471e-7,0.000010657244,0.0001559758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984078,0.00003067397,0.00032106874,0.00040660604,0.0004626125,0.00037127777],"domain_scores_gemma":[0.99860257,0.00051257946,0.00022330225,0.00019748237,0.00033144726,0.00013262288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017326941,0.00017843494,0.00030229037,0.00046405615,0.00064969464,0.00067543454,0.0008095843,0.000045825353,6.109548e-8],"category_scores_gemma":[0.0000725411,0.00013874451,0.00006241349,0.0019209863,0.000097665514,0.00059333816,0.00032947524,0.00038353784,4.3589267e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002203529,0.00029694595,0.06257261,0.0010352512,0.00021793642,0.0000023027997,0.0027048232,0.013159161,0.0035149304,0.05280227,0.0023253679,0.86134636],"study_design_scores_gemma":[0.0002726318,0.0002474521,0.025021091,0.00028074186,0.000009985795,0.000016427965,0.00003264675,0.97312427,0.00030441664,0.00010290765,0.00044197546,0.00014546982],"about_ca_topic_score_codex":0.00014614247,"about_ca_topic_score_gemma":0.000010271678,"teacher_disagreement_score":0.9599651,"about_ca_system_score_codex":0.000058969235,"about_ca_system_score_gemma":0.000041083626,"threshold_uncertainty_score":0.6513231},"labels":[],"label_agreement":null},{"id":"W4386127600","doi":"10.11159/icbb23.111","title":"Fall Detection Algorithm Using a Smart Wearable System for Remote Health Monitoring","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Information Technology Industry Development Agency; Egypt-Japan University of Science and Technology","keywords":"Accelerometer; Wearable computer; Gyroscope; Computer science; Sitting; Artificial intelligence; Stair climbing; Support vector machine; Statistical classification; Real-time computing; Machine learning; Physical medicine and rehabilitation; Simulation; Computer vision; Algorithm; Computer security; Medicine; Embedded system; Engineering","score_opus":0.0518012971219606,"score_gpt":0.29518971900006896,"score_spread":0.24338842187810836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386127600","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63366455,0.0024848671,0.26537335,0.025439367,0.02774694,0.009211831,0.00004913899,0.034294896,0.0017350521],"genre_scores_gemma":[0.9737655,0.000037689493,0.0245497,0.000019447198,0.00011417452,0.000065856395,1.6085693e-7,0.000028480436,0.0014190173],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982078,0.000014285259,0.00040996046,0.00045371885,0.0003974752,0.0005167249],"domain_scores_gemma":[0.99855834,0.00018860806,0.00061151356,0.00036728807,0.00021828557,0.000055961664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072666,0.0002201475,0.00038853651,0.000693878,0.00037422715,0.00021527034,0.0013388526,0.000112254405,8.262383e-8],"category_scores_gemma":[0.00028786433,0.0001812373,0.00014727689,0.0021336793,0.00006938448,0.0006301084,0.0005973144,0.00026276227,0.00000802234],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001947763,0.00002109176,0.0016756454,0.00035750415,0.000056628156,5.210963e-7,0.0001671894,0.000017123928,0.009472082,0.0010422302,0.0011726149,0.9859979],"study_design_scores_gemma":[0.0008467562,0.0002086094,0.0004865899,0.004419222,0.000022361684,0.000057311034,0.0031836943,0.10867505,0.8711972,0.0032066165,0.0072346516,0.00046192575],"about_ca_topic_score_codex":0.0003194344,"about_ca_topic_score_gemma":0.000041752053,"teacher_disagreement_score":0.985536,"about_ca_system_score_codex":0.0003435135,"about_ca_system_score_gemma":0.00007986583,"threshold_uncertainty_score":0.7390643},"labels":[],"label_agreement":null},{"id":"W4386158794","doi":"10.1109/csci58124.2022.00029","title":"Comparison of Machine Learning Methods for Human Activity Recognition Using Pseudo Free-Living Data","year":2022,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Personalization; Task (project management); Activity recognition; Data modeling; Deep learning; Focus (optics); Engineering","score_opus":0.3306650003447379,"score_gpt":0.46566922105904296,"score_spread":0.13500422071430507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386158794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.090166375,0.0000627917,0.9080761,0.00009778813,0.0003719313,0.00043292882,0.00010151735,0.00020426798,0.00048630495],"genre_scores_gemma":[0.72315216,5.538825e-7,0.2765805,0.000026231086,0.00004280582,0.000040129547,0.000056778605,0.000016423148,0.00008440135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968097,0.0014300987,0.00046275876,0.00067078025,0.0003508681,0.00027579855],"domain_scores_gemma":[0.9961393,0.0018446468,0.0005652917,0.00122792,0.00015664345,0.00006618384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036127528,0.00017138568,0.00046733255,0.00024529794,0.0007870724,0.00012364137,0.0017368107,0.000047810783,0.00021111502],"category_scores_gemma":[0.00064873166,0.00019374046,0.00010437628,0.00049394387,0.000033095563,0.0011462552,0.0033031704,0.00038147345,0.0000024345247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020550415,0.00041522982,0.0051438035,0.00008846583,0.00008544724,8.419209e-7,0.0010454349,0.0004508878,0.10835858,0.00023415564,0.000272677,0.88388395],"study_design_scores_gemma":[0.0003941921,0.00020243361,0.00039885793,0.0000371786,0.000031159543,0.000026847569,0.00032218584,0.98378116,0.0119246105,0.0008564122,0.0017683557,0.0002566221],"about_ca_topic_score_codex":0.0014553508,"about_ca_topic_score_gemma":0.00023866979,"teacher_disagreement_score":0.98333025,"about_ca_system_score_codex":0.0001276514,"about_ca_system_score_gemma":0.00007949175,"threshold_uncertainty_score":0.7900507},"labels":[],"label_agreement":null},{"id":"W4386321924","doi":"10.1109/thms.2023.3303438","title":"Self-Supervised Human Activity Recognition With Localized Time-Frequency Contrastive Representation Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transfer of learning; Artificial intelligence; Accelerometer; Activity recognition; Machine learning; Representation (politics); Supervised learning; Labeled data; Class (philosophy); Domain (mathematical analysis); Field (mathematics); Pattern recognition (psychology); Task (project management); Artificial neural network; Mathematics","score_opus":0.052082508431410726,"score_gpt":0.3014689507831605,"score_spread":0.24938644235174978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386321924","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17203312,0.000025878393,0.8192534,0.00013905822,0.00083777827,0.001741546,0.000093265415,0.003345414,0.0025305387],"genre_scores_gemma":[0.99689496,0.00001033179,0.0006034924,0.000029254692,0.00016175245,0.0007140708,0.00009965796,0.000085023166,0.0014014503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99503785,0.0014474135,0.00073117117,0.0011947982,0.00095893117,0.0006298544],"domain_scores_gemma":[0.9972067,0.000740724,0.0004815499,0.00086049794,0.00046102735,0.00024951616],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009928431,0.00054494984,0.00076125475,0.00087150105,0.0016792716,0.00070494093,0.00059051334,0.00022908625,0.000105162006],"category_scores_gemma":[0.000025490397,0.0005246442,0.0002574966,0.0017813738,0.00011604173,0.001654295,0.000010256158,0.0008388807,0.0011758746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013158324,0.006779058,0.0030340578,0.0026072522,0.0057573966,0.0011393611,0.030306226,0.08491761,0.42462605,0.0016750493,0.0015661082,0.436276],"study_design_scores_gemma":[0.011136518,0.0031837793,0.0014449254,0.0013492078,0.00038953417,0.00043788276,0.0012350719,0.9428209,0.033688672,0.00058886927,0.000980383,0.002744233],"about_ca_topic_score_codex":0.0025774874,"about_ca_topic_score_gemma":0.0005373878,"teacher_disagreement_score":0.8579033,"about_ca_system_score_codex":0.00035570524,"about_ca_system_score_gemma":0.00013267263,"threshold_uncertainty_score":0.9997205},"labels":[],"label_agreement":null},{"id":"W4386803085","doi":"10.23977/acss.2023.070708","title":"Design and Implementation of Child Missing Registration and Mutual Aid Mini Program Based on Java","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Java; Computer science; Missing data; The Internet; World Wide Web; Computer security; Data science; Operating system; Machine learning","score_opus":0.03931851859032668,"score_gpt":0.3276543171487532,"score_spread":0.2883357985584265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386803085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04168893,0.0011016005,0.95537746,0.00037938906,0.00032894788,0.0009430505,0.000004450452,0.00011264826,0.00006350248],"genre_scores_gemma":[0.9856914,0.000109948254,0.013958687,0.000069981514,0.000070995826,0.00007940332,0.000006544226,0.000007769479,0.0000052488313],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843067,0.00029291166,0.0004296157,0.00042121758,0.0002435508,0.00018203688],"domain_scores_gemma":[0.99889624,0.00054396567,0.0002474909,0.0001864179,0.0000629006,0.00006300462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008480916,0.0001457731,0.00027461504,0.00022096121,0.00009807672,0.0002531429,0.00012443913,0.000048070862,6.9876364e-7],"category_scores_gemma":[0.000010146342,0.00013683268,0.000020073358,0.0003265681,0.00004895583,0.00068284787,0.00006106014,0.00006770174,0.0000011496628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001856226,0.000044964283,0.0028269596,0.0002701324,0.000011195303,0.0000127951425,0.00090900966,0.0048558037,0.00023582694,0.00033039818,0.00010701325,0.99037737],"study_design_scores_gemma":[0.0008991355,0.0006405192,0.003159969,0.0005644366,0.000004263458,0.000038700135,0.00017342575,0.99205124,0.00061530643,0.00020778537,0.0014654789,0.00017975125],"about_ca_topic_score_codex":0.000046891008,"about_ca_topic_score_gemma":0.00002087498,"teacher_disagreement_score":0.9901976,"about_ca_system_score_codex":0.000017020391,"about_ca_system_score_gemma":0.000030590887,"threshold_uncertainty_score":0.55798745},"labels":[],"label_agreement":null},{"id":"W4386919582","doi":"10.1109/sas58821.2023.10254188","title":"Deep Learning Ensemble for Recognising Lower Limb Activity","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"Science and Engineering Research Board","keywords":"Computer science; Ensemble learning; Artificial intelligence; Deep learning","score_opus":0.053657126222721716,"score_gpt":0.2911658254955492,"score_spread":0.23750869927282747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386919582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088189244,0.000013031553,0.90019035,0.0007345887,0.000969245,0.00034000954,9.241553e-7,0.0012991269,0.008263497],"genre_scores_gemma":[0.9866254,0.0000060315674,0.0063884645,0.00014582665,0.00014171438,0.00008481796,0.00000356328,0.000021326223,0.0065828804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849194,0.0001249225,0.00018873301,0.00051887,0.00024498964,0.00043053864],"domain_scores_gemma":[0.99818134,0.0010725107,0.000108839566,0.0003648182,0.0001687389,0.0001037383],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007420793,0.00015255992,0.0002220908,0.00021075677,0.00029655718,0.00031804803,0.00039024124,0.00008819697,0.00003920261],"category_scores_gemma":[0.00033352582,0.0001525966,0.00015465375,0.0006943184,0.000017723129,0.00092889596,0.0002397731,0.00017069091,0.00094638555],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028897919,0.00005164417,0.00030928274,0.000028141218,0.000029693934,0.000011758289,0.00034020405,0.000097351665,0.010530676,0.0005036624,0.0016535664,0.98641515],"study_design_scores_gemma":[0.0011728611,0.00069284876,0.003752013,0.000088405235,0.000017106897,0.00005986069,0.00030512104,0.8909038,0.032867096,0.0029844954,0.06629628,0.00086011004],"about_ca_topic_score_codex":0.000044418855,"about_ca_topic_score_gemma":0.00010001511,"teacher_disagreement_score":0.985555,"about_ca_system_score_codex":0.00005712546,"about_ca_system_score_gemma":0.000049181584,"threshold_uncertainty_score":0.9998315},"labels":[],"label_agreement":null},{"id":"W4386937846","doi":"10.1007/978-3-031-43950-6_5","title":"Adaptive Dashboard for IoT Environments: Application for Senior Residences","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","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":"Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"","keywords":"Computer science; Dashboard; Dash; Health professionals; Data science; Population; Visualization; Internet of Things; Health care; Knowledge management; World Wide Web; Medicine; Data mining","score_opus":0.04014470158052275,"score_gpt":0.2736347966459989,"score_spread":0.23349009506547613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386937846","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016300306,0.000104885505,0.99324065,0.0014868755,0.0017058409,0.0027443948,0.000072218405,0.0002564059,0.0003724399],"genre_scores_gemma":[0.13478328,0.000048119786,0.8544043,0.0021235882,0.0020871912,0.0017752837,0.000059230726,0.00016883214,0.0045501976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957325,0.000041252464,0.00062461954,0.0020571442,0.00089287985,0.00065159803],"domain_scores_gemma":[0.99532807,0.002505647,0.0005286989,0.0012509247,0.00023513053,0.00015153972],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013479072,0.00051192223,0.00061333255,0.000702481,0.0003709378,0.00039535816,0.0026331332,0.0003519771,0.000003820229],"category_scores_gemma":[0.00019808993,0.0005023815,0.00023680541,0.0004532313,0.0004465192,0.00057290605,0.00075888215,0.00035988947,0.00008562108],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026254573,0.000026649188,0.000013534456,0.00006731132,0.000030428659,0.000006317832,0.0005416606,0.004114879,0.0005214164,0.01577184,0.00012323757,0.9787565],"study_design_scores_gemma":[0.000633058,0.0004797037,0.000084272884,0.00046583527,0.0000240397,0.000026354732,0.0000016166008,0.6209603,0.003134745,0.35988778,0.013332433,0.00096987624],"about_ca_topic_score_codex":0.000035349043,"about_ca_topic_score_gemma":0.00034778262,"teacher_disagreement_score":0.9777866,"about_ca_system_score_codex":0.00039977476,"about_ca_system_score_gemma":0.0005122289,"threshold_uncertainty_score":0.9997428},"labels":[],"label_agreement":null},{"id":"W4386991040","doi":"10.1007/978-3-031-37337-4","title":"Supportive Smart Homes","year":2023,"lang":"en","type":"book","venue":"Synthesis lectures on technology and health","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Ottawa; Bruyère","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Mitacs; Simon Fraser University; AGE-WELL; University of Ottawa","keywords":"Home automation; Architectural engineering; Psychology; Computer science; Internet privacy; Business; Engineering; Telecommunications","score_opus":0.03463547134098395,"score_gpt":0.2914116859000235,"score_spread":0.25677621455903954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386991040","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008482573,0.023731811,0.09666155,0.43295556,0.007628097,0.0076702116,0.00074199395,0.024575258,0.40518725],"genre_scores_gemma":[0.23705818,0.004913985,0.0021039566,0.010037857,0.00065645925,0.0011305234,0.00008089534,0.00033788994,0.74368024],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99748677,0.00014080975,0.00047007907,0.0010112892,0.00031146963,0.00057957525],"domain_scores_gemma":[0.9974537,0.00089116726,0.00048964005,0.000937151,0.0000852873,0.00014308399],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007086249,0.00042889517,0.00095012604,0.001971898,0.00036504207,0.00008564757,0.00085663603,0.0009874202,0.000038515802],"category_scores_gemma":[0.000261265,0.0003928018,0.00012833448,0.0005143234,0.00023300432,0.00010165329,0.00031889064,0.0010042647,0.00050295045],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010851385,0.00004157112,0.00011112448,0.00028437647,0.00016226094,0.000058605434,0.00019612559,2.1394823e-7,0.000004762568,0.07236721,0.122824125,0.80393875],"study_design_scores_gemma":[0.00047145993,0.0013319185,0.002804692,0.002558071,0.00005801489,0.00033839373,0.00010776388,0.00009835429,0.0021825887,0.44838932,0.54011106,0.0015483287],"about_ca_topic_score_codex":0.000036717134,"about_ca_topic_score_gemma":0.00037488586,"teacher_disagreement_score":0.80239046,"about_ca_system_score_codex":0.00031026936,"about_ca_system_score_gemma":0.0010789396,"threshold_uncertainty_score":0.99985236},"labels":[],"label_agreement":null},{"id":"W4387140183","doi":"10.3390/s23198127","title":"Real-Time Sensor-Embedded Neural Network for Human Activity Recognition","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","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":"École de Technologie Supérieure","funders":"","keywords":"Microcontroller; Activity recognition; Inertial measurement unit; Computer science; Convolutional neural network; Artificial intelligence; Artificial neural network; Inference; Real-time computing; Embedded system","score_opus":0.05936429814553438,"score_gpt":0.2999093647586779,"score_spread":0.24054506661314354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387140183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9881688,0.0000029854962,0.0048914216,0.00067525747,0.0008871966,0.00082962785,0.000058927224,0.0018194414,0.0026663505],"genre_scores_gemma":[0.9914551,0.000006136682,0.0032167395,0.00012357306,0.0007887627,0.00012222171,0.00009345169,0.00005028257,0.0041437717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761087,0.00035140003,0.00032362173,0.0007014492,0.0003381619,0.0006745113],"domain_scores_gemma":[0.9979624,0.0008074569,0.00022701616,0.00064262364,0.00022620424,0.00013426197],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007382214,0.00025917863,0.00037745497,0.00020900034,0.0005184731,0.00029610604,0.00039598878,0.00014919482,0.00003680533],"category_scores_gemma":[0.00013169939,0.00027966787,0.00022683325,0.0009444221,0.00006134006,0.0004995844,0.00015908982,0.00017739322,0.0013763475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025256793,0.00032619818,0.001000726,0.000256365,0.0003573835,0.0002348487,0.003589779,0.004966236,0.25973302,0.0010361572,0.07363702,0.6546097],"study_design_scores_gemma":[0.0031724286,0.0006875952,0.019537572,0.00023962783,0.00008698228,0.00020335321,0.00029542542,0.9200063,0.02752035,0.01258159,0.013543471,0.0021253044],"about_ca_topic_score_codex":0.00020059396,"about_ca_topic_score_gemma":0.00007797044,"teacher_disagreement_score":0.9150401,"about_ca_system_score_codex":0.000078733,"about_ca_system_score_gemma":0.000049522005,"threshold_uncertainty_score":0.99996555},"labels":[],"label_agreement":null},{"id":"W4387357825","doi":"10.1111/dom.15301","title":"Breaking a feedback loop: A reassessment of an investigator initiated <scp>OS‐AID</scp> study","year":2023,"lang":"en","type":"letter","venue":"Diabetes Obesity and Metabolism","topic":"Context-Aware Activity Recognition Systems","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":"York University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Medical Research Council","keywords":"Gateway (web page); Computer science; Loop (graph theory); World Wide Web; Combinatorics; Mathematics","score_opus":0.04574036661001977,"score_gpt":0.2687548718851961,"score_spread":0.22301450527517633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387357825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98768026,0.000549339,0.00048221942,0.0071809404,0.0016221491,0.0014018731,0.0002287726,0.0006129986,0.00024143531],"genre_scores_gemma":[0.88881016,0.00007627962,0.0015759723,0.10515637,0.002476001,0.00047749636,0.00035078984,0.00018810501,0.00088879874],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99431604,0.0014891946,0.0008746397,0.0013797206,0.0010803534,0.0008600713],"domain_scores_gemma":[0.99571013,0.0011996983,0.000852729,0.0013773979,0.00046422312,0.00039583555],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015673089,0.00069589214,0.0016088601,0.00076767226,0.0002504507,0.00061033026,0.0014182297,0.0006597188,0.000006917807],"category_scores_gemma":[0.0006885521,0.00070059614,0.00023818518,0.0012110205,0.00019136186,0.0013248706,0.0009595478,0.0013489965,0.00007166794],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012684499,0.0053437855,0.17258947,0.004673959,0.0059368764,0.002073662,0.026109714,0.000008906747,0.0033174595,0.0008848354,0.59302783,0.1860208],"study_design_scores_gemma":[0.0044118655,0.00066146255,0.6484157,0.0013137426,0.0011396792,0.000047144913,0.00089961843,0.0021921678,0.003511563,0.003346097,0.33265698,0.0014039597],"about_ca_topic_score_codex":0.00055945345,"about_ca_topic_score_gemma":0.000081870596,"teacher_disagreement_score":0.47582623,"about_ca_system_score_codex":0.000055140583,"about_ca_system_score_gemma":0.0003909772,"threshold_uncertainty_score":0.9995445},"labels":[],"label_agreement":null},{"id":"W4387421333","doi":"10.1145/3594739.3605106","title":"11th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Activity recognition; Session (web analytics); Computer science; Context (archaeology); Wearable computer; Scale (ratio); Human–computer interaction; Ubiquitous computing; Data science; Artificial intelligence; World Wide Web; Embedded system","score_opus":0.07986625048859589,"score_gpt":0.33434396060911814,"score_spread":0.2544777101205222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387421333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10412935,0.00000657282,0.86484843,0.002891851,0.0004085956,0.0003406717,0.0000059400927,0.00090311194,0.02646547],"genre_scores_gemma":[0.99632734,0.0000067736682,0.0006831347,0.00018547504,0.00014976748,0.000026844511,0.0000063590865,0.000008366532,0.00260592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990096,0.000055439028,0.00012557373,0.00039035841,0.0002581168,0.000160879],"domain_scores_gemma":[0.9990672,0.0003437458,0.00007407821,0.0003596129,0.00008004284,0.00007530711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024392939,0.000102620856,0.0001169298,0.00020942857,0.00019995117,0.00027017054,0.00028434888,0.000048509177,0.000015642378],"category_scores_gemma":[0.000029040386,0.00010261286,0.000039824572,0.0004469728,0.000033638975,0.0003847145,0.00025502054,0.00012234348,0.00029099258],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030538945,0.000044493445,0.0004355653,0.0000053980116,0.000026115275,0.0000071346863,0.00016021334,0.0000117405825,0.0055216635,0.011619482,0.001414703,0.98075044],"study_design_scores_gemma":[0.0036253987,0.0002519788,0.15427428,0.00045582993,0.000041043793,0.00046459402,0.0008909436,0.4564886,0.03201562,0.02724049,0.32175282,0.0024983843],"about_ca_topic_score_codex":0.00006325577,"about_ca_topic_score_gemma":0.000113168404,"teacher_disagreement_score":0.97825205,"about_ca_system_score_codex":0.00005225215,"about_ca_system_score_gemma":0.0000212235,"threshold_uncertainty_score":0.41844308},"labels":[],"label_agreement":null},{"id":"W4387421368","doi":"10.1145/3594739.3610742","title":"Investigating the Effect of Orientation Variability in Deep Learning-based Human Activity Recognition","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Computer science; Orientation (vector space); Artificial intelligence; Deep learning; Pattern recognition (psychology); Activity recognition; Machine learning; Mathematics","score_opus":0.03353732966876286,"score_gpt":0.29418891701109656,"score_spread":0.2606515873423337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387421368","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83063745,9.1028727e-7,0.1676231,0.00022014984,0.00012873935,0.0004165116,0.0000010931427,0.00025822772,0.0007137946],"genre_scores_gemma":[0.9995155,2.1050214e-7,0.0002706259,0.00002986643,0.00002209513,0.00011372233,0.000013996152,0.000007097087,0.000026891114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99654615,0.0023190186,0.00028430857,0.00035423113,0.00030677806,0.00018949024],"domain_scores_gemma":[0.9958691,0.0034318336,0.00022029033,0.00033861608,0.000098449615,0.000041730862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048948405,0.000118237585,0.00020424245,0.00018521505,0.00016251873,0.00007503035,0.00028982383,0.00006570106,0.000023056162],"category_scores_gemma":[0.0016372816,0.000090347574,0.000069287344,0.001416064,0.000069207235,0.0004943901,0.000106188265,0.00024565746,0.000061140716],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018117309,0.0000738346,0.2350937,0.00020119276,0.00001977834,0.0000030650199,0.0017263009,0.0017107144,0.026261522,0.00026270992,0.000035737055,0.73459333],"study_design_scores_gemma":[0.0012062584,0.0004447783,0.268406,0.00011340771,0.000011308525,0.0000027476087,0.00011088958,0.57196724,0.15455832,0.0029097102,0.000030045738,0.00023930887],"about_ca_topic_score_codex":0.0005910601,"about_ca_topic_score_gemma":0.0004187456,"teacher_disagreement_score":0.734354,"about_ca_system_score_codex":0.000077785626,"about_ca_system_score_gemma":0.00003869538,"threshold_uncertainty_score":0.3684267},"labels":[],"label_agreement":null},{"id":"W4387461057","doi":"10.3390/jcp3040033","title":"A Framework for Synthetic Agetech Attack Data Generation","year":2023,"lang":"en","type":"article","venue":"Journal of Cybersecurity and Privacy","topic":"Context-Aware Activity Recognition Systems","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":"Government of Canada; University of Victoria","funders":"","keywords":"Computer science; Data mining; Intrusion detection system; Focus (optics); Intrusion; Internet of Things; Attack patterns; Machine learning; Computer security","score_opus":0.22017809981988412,"score_gpt":0.3769362495991743,"score_spread":0.1567581497792902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387461057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17599228,0.00040469418,0.8175177,0.004801162,0.0009046003,0.00021724992,0.000028441722,0.00006306751,0.00007078316],"genre_scores_gemma":[0.96500367,0.00022751593,0.034004416,0.00018414472,0.00052546576,0.000004935627,0.000008185095,0.000008184332,0.00003347663],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99882376,0.00011448067,0.00036044474,0.00023655056,0.000270618,0.0001941618],"domain_scores_gemma":[0.9980824,0.00068135466,0.00032921604,0.0006070228,0.00018190552,0.00011811794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013394168,0.00009572187,0.00022505816,0.0001683045,0.00013826418,0.00026495746,0.00091744866,0.000091181115,0.000008422397],"category_scores_gemma":[0.0011327298,0.00008667183,0.00007410584,0.00030428305,0.000027355562,0.0012631662,0.0004055635,0.00022177276,0.000019341354],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010073981,0.00028314564,0.0007268964,0.00029336,0.00034814866,0.00016046602,0.015287065,0.00005074081,0.0038786149,0.040693697,0.07437469,0.86380243],"study_design_scores_gemma":[0.00244787,0.0007714426,0.0027438663,0.00081999187,0.00014730869,0.0013024138,0.0005691581,0.27742815,0.0041347304,0.14497289,0.56382406,0.00083811395],"about_ca_topic_score_codex":0.0000070190154,"about_ca_topic_score_gemma":0.000008273007,"teacher_disagreement_score":0.86296433,"about_ca_system_score_codex":0.000023748069,"about_ca_system_score_gemma":0.00009707476,"threshold_uncertainty_score":0.35343748},"labels":[],"label_agreement":null},{"id":"W4387951158","doi":"10.1109/ccece58730.2023.10288941","title":"A Novel Approach for Fall Detection Using Thermal Imaging and a Stacking Ensemble of Autoencoder and 3D-CNN Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Lakehead University","funders":"","keywords":"Autoencoder; Computer science; Artificial intelligence; Convolutional neural network; Benchmark (surveying); Deep learning; Pattern recognition (psychology); Machine learning; Population","score_opus":0.08663603541839497,"score_gpt":0.27687066937670424,"score_spread":0.19023463395830925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22500682,0.000033187556,0.7740531,0.000029099388,0.000056785444,0.00026143494,0.0000031099228,0.00013453013,0.0004219195],"genre_scores_gemma":[0.8833387,0.0000017549636,0.11652567,0.000031959993,0.000023056435,0.000024670311,8.956091e-7,0.000010453573,0.00004287026],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990921,0.00003183353,0.00019031866,0.0003399544,0.00014516548,0.00020063086],"domain_scores_gemma":[0.99942994,0.00015144245,0.00009515562,0.00016861792,0.00011020068,0.000044618377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004249318,0.00010452582,0.000170724,0.00017482361,0.000118708034,0.0001444396,0.00011418019,0.000036039768,4.3305621e-7],"category_scores_gemma":[0.000019635316,0.00009908232,0.000034091812,0.0002652965,0.000029400422,0.00092841085,0.00018244985,0.000053382355,4.2565256e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024176392,0.00007042164,0.00091104757,0.00024536374,0.000057107416,0.0000017757161,0.004893815,0.012535718,0.5286043,0.0010808016,0.000009944176,0.4515655],"study_design_scores_gemma":[0.00043306855,0.000018254874,0.00040245787,0.000024723658,0.0000077716295,0.000046091704,0.0003247504,0.99393255,0.0042363224,0.00044687052,0.000010717304,0.00011641331],"about_ca_topic_score_codex":0.00041385606,"about_ca_topic_score_gemma":0.00003821324,"teacher_disagreement_score":0.98139685,"about_ca_system_score_codex":0.000023093635,"about_ca_system_score_gemma":0.000032598764,"threshold_uncertainty_score":0.40404597},"labels":[],"label_agreement":null},{"id":"W4387961611","doi":"10.1145/3607828.3617791","title":"Memory-Efficient High-Accuracy Food Intake Activity Recognition with 3D mmWave Radars","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Computer science; Activity recognition; Point cloud; Reduction (mathematics); Artificial intelligence; Radar; Cloud computing; Pattern recognition (psychology); Telecommunications","score_opus":0.042783974904672964,"score_gpt":0.24490023866625044,"score_spread":0.20211626376157749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387961611","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57884735,0.000007299701,0.41286337,0.001171091,0.00073346106,0.0006321588,0.000030981344,0.0013980864,0.0043161777],"genre_scores_gemma":[0.99373,0.0000054762877,0.005141667,0.00023878596,0.00012072473,0.00012355285,0.000024793386,0.000029786548,0.0005852243],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99731785,0.00025827266,0.00031617924,0.00084978115,0.00071858277,0.0005393404],"domain_scores_gemma":[0.99772316,0.00071629294,0.00024745363,0.0008275837,0.00028792577,0.00019756265],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000680163,0.00030167328,0.00035954034,0.00039409526,0.0002571879,0.0003554412,0.0005943692,0.00011176621,0.00014089269],"category_scores_gemma":[0.00017249575,0.00025760435,0.00010041503,0.0017442835,0.000070546754,0.0010348958,0.0003330633,0.00027753023,0.0016345353],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054155335,0.0002086659,0.000052169988,0.000036785787,0.000092071445,0.000053353928,0.0008388748,0.00014720454,0.0017771103,0.00026276702,0.0014535224,0.9950233],"study_design_scores_gemma":[0.014656807,0.005943683,0.057759494,0.0011137781,0.00022039567,0.0014427222,0.003916546,0.32901722,0.5504856,0.0058191787,0.022774765,0.006849775],"about_ca_topic_score_codex":0.00030743523,"about_ca_topic_score_gemma":0.00038009672,"teacher_disagreement_score":0.98817354,"about_ca_system_score_codex":0.00015166884,"about_ca_system_score_gemma":0.00017944902,"threshold_uncertainty_score":0.9999876},"labels":[],"label_agreement":null},{"id":"W4388071876","doi":"10.1109/pimrc56721.2023.10293922","title":"Generating Event Sensor Readings Using Spatial Correlations and a Graph Sensor Adversarial Model for Energy Saving in IoT: GSAVES","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Adversarial system; Wireless sensor network; Computer science; Internet of Things; Event (particle physics); Energy (signal processing); Graph; Real-time computing; Artificial intelligence; Theoretical computer science; Computer network; Computer security; Mathematics; Statistics","score_opus":0.052123429788542223,"score_gpt":0.2824512341282418,"score_spread":0.23032780433969957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388071876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24151246,0.000008854971,0.7574508,0.00020981782,0.00034471098,0.00022872585,0.000012670976,0.00017219242,0.000059774728],"genre_scores_gemma":[0.93464607,0.00000331186,0.06447686,0.000116102085,0.00017008293,0.00004222651,0.000008590416,0.00001771078,0.00051901536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846584,0.000097640856,0.00039607956,0.0004946603,0.00021370311,0.0003320795],"domain_scores_gemma":[0.99893826,0.00050571415,0.00014245704,0.00021350196,0.00011357629,0.000086483495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043416437,0.00016624857,0.00024195405,0.00041264063,0.00026543083,0.00018778302,0.00015378496,0.00009629374,0.000003285596],"category_scores_gemma":[0.00016149241,0.00017532855,0.00008646979,0.0005250252,0.000027524382,0.0004183724,0.00017884227,0.00009100061,0.00000388073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006793978,0.00012859912,0.0058071227,0.000093262446,0.000115143856,0.00004331747,0.012010939,0.702252,0.13630699,0.015565928,0.00051171397,0.12709707],"study_design_scores_gemma":[0.0006387323,0.00002235712,0.00026666594,0.000057978315,0.000008809971,0.000026648853,0.00025273606,0.9969226,0.0006882257,0.000846169,0.000055051678,0.00021401547],"about_ca_topic_score_codex":0.0018661626,"about_ca_topic_score_gemma":0.0020598974,"teacher_disagreement_score":0.69313365,"about_ca_system_score_codex":0.000083483224,"about_ca_system_score_gemma":0.00009887461,"threshold_uncertainty_score":0.71496904},"labels":[],"label_agreement":null},{"id":"W4388538244","doi":"10.18280/jesa.560508","title":"Multimodal System of Ambient Assistance Services for Human Activity Monitoring","year":2023,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Human services; Computer science; Business; Political science","score_opus":0.040133889903829996,"score_gpt":0.29794603462457064,"score_spread":0.25781214472074065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388538244","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8059764,0.00013872822,0.19050708,0.000082444865,0.0016277928,0.00046563186,0.00003085628,0.00083359814,0.00033745065],"genre_scores_gemma":[0.99290466,0.000012207546,0.0062909117,0.000006705054,0.00035064926,0.000066754845,0.0000019256238,0.00004304457,0.0003231509],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9969484,0.00042930845,0.0008374045,0.0004513583,0.0007861108,0.0005473895],"domain_scores_gemma":[0.99704444,0.0004347675,0.0011079402,0.00058235857,0.0006147759,0.00021568658],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014790922,0.00029021382,0.0006252706,0.00045461528,0.00066158536,0.0005727279,0.0011247491,0.00010208148,0.0000037572784],"category_scores_gemma":[0.000078464516,0.00027192978,0.00030856061,0.0008841067,0.00006136511,0.0013772425,0.0002855337,0.00024881304,0.0000667429],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006181085,0.00028779972,0.02357483,0.0058901,0.000493191,0.0002482456,0.004532076,0.0006531093,0.17892618,0.0027884985,0.0004790286,0.78206515],"study_design_scores_gemma":[0.0012986835,0.00029726684,0.80471957,0.0029947464,0.00004764387,0.0005848415,0.00076948194,0.16564387,0.02197007,0.00076000177,0.0004330619,0.00048077345],"about_ca_topic_score_codex":0.00014794644,"about_ca_topic_score_gemma":0.00003634259,"teacher_disagreement_score":0.7815844,"about_ca_system_score_codex":0.00040382793,"about_ca_system_score_gemma":0.00010450694,"threshold_uncertainty_score":0.9999733},"labels":[],"label_agreement":null},{"id":"W4388724848","doi":"10.1007/s40012-023-00389-8","title":"SleepLess: personalized sleep monitoring using smartphones and semi-supervised learning","year":2023,"lang":"en","type":"article","venue":"CSI Transactions on ICT","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Personalization; Sleep (system call); Ground truth; Wearable computer; Machine learning; Modalities; Pipeline (software); Artificial intelligence; Set (abstract data type); Deep learning; Supervised learning; Human–computer interaction; Artificial neural network; World Wide Web; Embedded system","score_opus":0.054549814688982796,"score_gpt":0.2779659126803017,"score_spread":0.2234160979913189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388724848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4057342,0.000094529576,0.5921728,0.00022831898,0.0008040269,0.00015653494,0.0000052155706,0.0006605611,0.00014383343],"genre_scores_gemma":[0.99726045,0.00007075665,0.001929625,0.00003306842,0.00010326221,0.0000515083,0.0000023823493,0.000028769971,0.0005202041],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983691,0.00020119954,0.00024749633,0.00050064473,0.00033516507,0.00034641018],"domain_scores_gemma":[0.9989934,0.00037852162,0.00007210001,0.00032062863,0.00009551706,0.00013984683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035010418,0.0002093318,0.00026070987,0.0002877829,0.0006311932,0.000284828,0.00027218048,0.00009641806,0.000039674313],"category_scores_gemma":[0.000025181684,0.00022580102,0.00012723115,0.00096822134,0.000056333312,0.0007286831,0.00001979358,0.00032964148,0.00013681018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006705791,0.0001826206,0.007780185,0.00017275168,0.00029952696,0.000083530525,0.014966284,0.01492957,0.0420284,0.00012616091,0.000042647367,0.91932124],"study_design_scores_gemma":[0.0027236782,0.000217708,0.009292906,0.0005127981,0.00009748083,0.00022975358,0.004634039,0.951515,0.022937039,0.00015030924,0.006630504,0.0010587403],"about_ca_topic_score_codex":0.000113263384,"about_ca_topic_score_gemma":0.000011429965,"teacher_disagreement_score":0.9365855,"about_ca_system_score_codex":0.00008814527,"about_ca_system_score_gemma":0.000036715828,"threshold_uncertainty_score":0.92078984},"labels":[],"label_agreement":null},{"id":"W4388757723","doi":"10.1109/uemcon59035.2023.10316083","title":"Multi-Microcontroller Sensor Data Acquisition to Enhance UAV Attitude Control, Predictive Maintenance, and Healthcare Solutions","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Lakehead University","funders":"","keywords":"Computer science; Microcontroller; Model predictive control; Perceptron; Data acquisition; Classifier (UML); Machine learning; STM32; Decision tree; Artificial neural network; Robustness (evolution); Artificial intelligence; Boosting (machine learning); Predictive maintenance; Real-time computing; Embedded system; Engineering; Reliability engineering; Control (management)","score_opus":0.05792430480902485,"score_gpt":0.3296438411172836,"score_spread":0.2717195363082588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388757723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006840838,0.0001391908,0.9714295,0.018127043,0.00048586875,0.0013306299,0.0005475481,0.000830248,0.00026910845],"genre_scores_gemma":[0.9832907,0.000044266613,0.011472813,0.0028575913,0.00014168108,0.0002095463,0.00006164336,0.000018349203,0.0019034395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99734473,0.0002487194,0.00039444395,0.0010598387,0.00032516304,0.00062708685],"domain_scores_gemma":[0.99754244,0.0004117537,0.0001672823,0.0012136459,0.00037596902,0.00028893253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088651665,0.00022424181,0.00037925766,0.0002619074,0.00036027955,0.00024221082,0.0008499874,0.00009981584,0.000019375902],"category_scores_gemma":[0.00021030706,0.00020882643,0.000054583514,0.00073602307,0.0000610574,0.0012044762,0.00096201693,0.00017312802,0.00069633935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010421523,0.0013218,0.017808506,0.0005923624,0.0015795307,0.00041070886,0.012293937,0.0009889652,0.16340026,0.010447742,0.35053262,0.4395814],"study_design_scores_gemma":[0.005821372,0.000503624,0.10996003,0.00043384772,0.000054035634,0.00017224978,0.00088556536,0.8528815,0.0010360859,0.0006114748,0.026489511,0.001150738],"about_ca_topic_score_codex":0.00038798907,"about_ca_topic_score_gemma":0.0009143653,"teacher_disagreement_score":0.97644985,"about_ca_system_score_codex":0.000114226976,"about_ca_system_score_gemma":0.00012750475,"threshold_uncertainty_score":0.89502656},"labels":[],"label_agreement":null},{"id":"W4389220681","doi":"10.1016/j.enbuild.2023.113808","title":"Unsupervised domain adaptation without source data for estimating occupancy and recognizing activities in smart buildings","year":2023,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transfer of learning; Classifier (UML); Artificial intelligence; Domain (mathematical analysis); Machine learning; Domain adaptation; Data mining","score_opus":0.06053509487585384,"score_gpt":0.28340531018311793,"score_spread":0.22287021530726409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389220681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4309488,0.00010293054,0.5680361,0.0003547117,0.00017801652,0.0001242474,0.000013488611,0.00019070572,0.0000510506],"genre_scores_gemma":[0.85186523,0.000037848644,0.14752333,0.00016099961,0.00010971799,0.000073378425,0.000046085865,0.000021804173,0.00016163559],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983759,0.000092216316,0.00029600493,0.00070985546,0.00018431466,0.00034173127],"domain_scores_gemma":[0.9986069,0.0007319573,0.00014961002,0.00038123675,0.000046544556,0.00008374427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010180295,0.00019344743,0.0002884857,0.00031854684,0.00027337644,0.00033721526,0.0004208926,0.00009341165,0.0000013093958],"category_scores_gemma":[0.00023636816,0.00020221071,0.000029092513,0.00051465374,0.00005508943,0.0017125562,0.00052899285,0.00010171235,0.0000012492982],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058033565,0.000035172845,0.011705672,0.00015436491,0.0000451398,0.00000907879,0.0049748756,0.00015737899,0.008015673,0.004358579,0.00041732442,0.9700687],"study_design_scores_gemma":[0.0017441447,0.00010617732,0.002244566,0.00052557606,0.000018128043,0.0000665451,0.0018574514,0.95647144,0.0020894345,0.006861249,0.027412536,0.0006027432],"about_ca_topic_score_codex":0.00059584767,"about_ca_topic_score_gemma":0.00032256302,"teacher_disagreement_score":0.969466,"about_ca_system_score_codex":0.000029058037,"about_ca_system_score_gemma":0.00004750301,"threshold_uncertainty_score":0.82459134},"labels":[],"label_agreement":null},{"id":"W4389538428","doi":"10.1109/embc40787.2023.10340481","title":"Cloud Native Remote Monitoring Data Ecosystem for Aging Population based on Commercial AAL Sensors","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Mitacs; University of Waterloo","funders":"","keywords":"Cloud computing; Computer science; Data collection; Population; Assisted living; Health care; Confidentiality; Data science; Computer security; Medicine","score_opus":0.13599611431736647,"score_gpt":0.35067260449084037,"score_spread":0.2146764901734739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389538428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28226587,0.0000037006998,0.70795846,0.0021481365,0.004473394,0.0009220007,0.00016217314,0.001202106,0.0008641419],"genre_scores_gemma":[0.99346113,6.799402e-7,0.0052712546,0.0000905723,0.00078247633,0.000015158967,0.000110436165,0.00002056856,0.0002477275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998092,0.00022785412,0.00032676294,0.00062811887,0.0004167272,0.00030853457],"domain_scores_gemma":[0.99742854,0.0012024164,0.00016220027,0.000980019,0.00014562685,0.00008122092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011346958,0.00016702396,0.00023970378,0.00027885693,0.00027097113,0.00023860448,0.0008454286,0.00007027444,0.000004769516],"category_scores_gemma":[0.00025540628,0.00016703134,0.00007090158,0.0005997337,0.000006015187,0.00074642245,0.00032110128,0.0001225821,0.00012517515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015291617,0.00014751402,0.020818658,0.0003694692,0.00014879637,0.000052292686,0.002417648,0.0066251867,0.0006665167,0.0028432524,0.011110579,0.9546472],"study_design_scores_gemma":[0.0005622571,0.00006102299,0.008263665,0.00017720863,0.0000065995487,0.0000041045328,0.00018641702,0.9866096,0.0012819708,0.0001415493,0.0024780836,0.00022753126],"about_ca_topic_score_codex":0.0004007063,"about_ca_topic_score_gemma":0.0003028106,"teacher_disagreement_score":0.9799844,"about_ca_system_score_codex":0.00015385167,"about_ca_system_score_gemma":0.000051597144,"threshold_uncertainty_score":0.681134},"labels":[],"label_agreement":null},{"id":"W4389576359","doi":"10.1109/acsos-c58168.2023.00051","title":"Variability-Aware Architecture for Human-Chatbot Interactions: Taming Levels of Automation","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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 Waterloo","funders":"","keywords":"Chatbot; Computer science; Human–computer interaction; Context (archaeology); Automation; Architecture; Focus (optics); Service (business); Software engineering; World Wide Web; Engineering","score_opus":0.07437091549513411,"score_gpt":0.33734533688808743,"score_spread":0.26297442139295335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389576359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025930218,0.000002368243,0.97021055,0.0008644268,0.0005068539,0.00053759135,0.00003577266,0.0007137691,0.0011984427],"genre_scores_gemma":[0.98788613,2.726564e-7,0.010729393,0.000071326795,0.000078308774,0.00014343995,0.000014948168,0.000011019453,0.0010651627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986672,0.00014142368,0.00040043134,0.0003653015,0.00022428822,0.00020138815],"domain_scores_gemma":[0.9977951,0.0012288671,0.00018985076,0.0004798534,0.0002581774,0.000048122634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008424622,0.00012252362,0.00022060268,0.00031466907,0.00015080415,0.00009546173,0.00040007022,0.000057639976,0.00007841089],"category_scores_gemma":[0.0002671723,0.00011670589,0.00012318394,0.000613525,0.000025897223,0.00058656815,0.00016842115,0.00010246522,0.000057782192],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014305735,0.00023426344,0.0010827578,0.0006504149,0.00016019134,0.000004225373,0.0062260265,0.00065178616,0.075262256,0.051684182,0.0035185097,0.86051106],"study_design_scores_gemma":[0.0024869977,0.00047841962,0.04148735,0.00073632447,0.000054420747,0.000111109446,0.0014881219,0.69333684,0.07707132,0.15697315,0.02447813,0.0012978016],"about_ca_topic_score_codex":0.00006637752,"about_ca_topic_score_gemma":0.0001271052,"teacher_disagreement_score":0.9619559,"about_ca_system_score_codex":0.000058562262,"about_ca_system_score_gemma":0.00005476298,"threshold_uncertainty_score":0.4759128},"labels":[],"label_agreement":null},{"id":"W4389776095","doi":"10.2196/48526","title":"User Perception of Smart Home Surveillance Among Adults Aged 50 Years and Older: Scoping Review","year":2023,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto; Toronto Rehabilitation Institute; University of Victoria; University Health Network","funders":"","keywords":"Data collection; Internet privacy; mHealth; Aging in place; MEDLINE; Medicine; Computer science; Psychology; Gerontology; Psychological intervention; Nursing","score_opus":0.047487983465959165,"score_gpt":0.3507069908034369,"score_spread":0.30321900733747775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389776095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9901921,0.0058099935,0.0010244113,0.0011882422,0.00026564687,0.0012754794,0.000014614958,0.0001929301,0.00003656376],"genre_scores_gemma":[0.97269034,0.026215905,0.00021556747,0.00065393304,0.000056896122,0.00008524211,0.000011217257,0.000012522495,0.00005838398],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99802065,0.0003459854,0.0005062178,0.0004664554,0.0002798581,0.00038084885],"domain_scores_gemma":[0.99874526,0.00022658339,0.00031226978,0.00035057808,0.00008129182,0.00028399867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013992917,0.00013868397,0.00046343482,0.0001733851,0.00014401322,0.00004744941,0.00017539304,0.000073120704,0.000009528766],"category_scores_gemma":[0.00004923532,0.00013964652,0.00004167199,0.00056438666,0.00006220406,0.00037935382,0.00012265076,0.00016776857,0.000029684456],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005685171,0.000089712594,0.34116423,0.06651835,0.000016086029,0.000014221133,0.00604499,6.12806e-7,0.00005129156,0.00010609699,0.004935134,0.5810024],"study_design_scores_gemma":[0.0005078182,0.0001275319,0.98661005,0.012006656,0.000003997201,0.00001590729,0.00010305496,0.00030560797,0.0000012745286,0.00002009681,0.00016286237,0.00013515391],"about_ca_topic_score_codex":0.0005080208,"about_ca_topic_score_gemma":0.000680418,"teacher_disagreement_score":0.6454458,"about_ca_system_score_codex":0.000042372765,"about_ca_system_score_gemma":0.00018985296,"threshold_uncertainty_score":0.569462},"labels":[],"label_agreement":null},{"id":"W4390046254","doi":"10.1371/journal.pone.0296159","title":"Monitoring mobility in older adults using a Global Positioning System (GPS) smartwatch and accelerometer: A validation study","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"McMaster Institute for Research on Aging, McMaster University; Government of Ontario; AGE-WELL; McMaster University","keywords":"Global Positioning System; Accelerometer; Smartwatch; Assisted GPS; Computer science; Physical medicine and rehabilitation; Wearable computer; Medicine; Telecommunications; Embedded system","score_opus":0.09511716835779778,"score_gpt":0.2892092939263026,"score_spread":0.19409212556850486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390046254","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99539816,0.000036363315,0.002774353,0.000047476235,0.00017860539,0.0010930395,0.000008214048,0.0004286172,0.0000351386],"genre_scores_gemma":[0.9984328,0.0000028861755,0.0012980322,0.000005001681,0.00009287028,0.00014884234,0.000002667599,0.000009773473,0.0000071010336],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99797255,0.00030496359,0.0003710797,0.00056012644,0.00051577773,0.00027547593],"domain_scores_gemma":[0.99911517,0.00014260833,0.00012834888,0.0003845665,0.000148518,0.0000807701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076735206,0.00014946629,0.00031399887,0.00018983625,0.00014892175,0.00032477663,0.00025625993,0.000060136783,0.0000013667717],"category_scores_gemma":[0.00006786608,0.00016857585,0.00003142102,0.0011118219,0.000013134443,0.0011844411,0.00031486232,0.00011544781,0.000027937644],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002881635,0.0023082925,0.97939414,0.0004177365,0.00012092745,0.000054151522,0.0039594043,0.000027104365,0.003934554,0.000027160024,0.0000016084782,0.009726111],"study_design_scores_gemma":[0.0017594922,0.00019171342,0.8690842,0.0027203087,0.00004886904,0.00002116856,0.0026526412,0.11603374,0.0071080206,0.00006232615,1.6808488e-7,0.00031734916],"about_ca_topic_score_codex":0.00088644,"about_ca_topic_score_gemma":0.000039333863,"teacher_disagreement_score":0.116006635,"about_ca_system_score_codex":0.00035402933,"about_ca_system_score_gemma":0.000035322966,"threshold_uncertainty_score":0.68743235},"labels":[],"label_agreement":null},{"id":"W4390109863","doi":"10.1007/978-3-031-49425-3","title":"Computer-Human Interaction Research and Applications","year":2023,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Escuela Politécnica Nacional; Universität Rostock; Universidade Federal Fluminense; Tokyo University of Agriculture; University of Aizu; Universidade de Lisboa; Foundation for Research and Technology-Hellas; Tokyo University of Agriculture and Technology; Daegu University; Universiteit van die Vrystaat; Università degli Studi di Genova; Tokyo City University; Sapienza Università di Roma; Kennesaw State University; LOEWE Zentrum AdRIA; Centre National de la Recherche Scientifique; Korea University; Technische Universiteit Delft; National Chiao Tung University; Universidad del Cauca; University of Wollongong; Shanghai Educational Development Foundation; University of Glasgow; Univerza v Mariboru; Université Bretagne Sud; Indian Institute of Technology Guwahati; Università degli Studi di Milano; Ulsan National Institute of Science and Technology; Utah State University; Northwestern University; Alpen-Adria-Universität Klagenfurt; University of Worcester; Politecnico di Torino; McMaster University; Universitat Politècnica de València; Universität Ulm; Clemson University; North Carolina State University; Aix-Marseille Université","keywords":"Computer science; Information retrieval; Thesaurus; Natural language processing","score_opus":0.17747789766168157,"score_gpt":0.41478472821318446,"score_spread":0.2373068305515029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390109863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013789814,0.00023765485,0.8831315,0.00141328,0.0006214749,0.0016126243,0.000027781518,0.00046415735,0.11235361],"genre_scores_gemma":[0.496445,0.017365174,0.37574214,0.005700249,0.003522593,0.0069426927,0.0024136992,0.00029621416,0.09157227],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99724966,0.00018593666,0.00084781746,0.00054711086,0.00078840833,0.0003810358],"domain_scores_gemma":[0.9946501,0.0011422288,0.00036756607,0.0025575873,0.0011081707,0.00017432847],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0033231827,0.00023899706,0.000325135,0.0028448675,0.0013611661,0.0019953183,0.0035836475,0.00017731245,0.000002816727],"category_scores_gemma":[0.000045307625,0.00026262458,0.00003915993,0.0020990276,0.0014193304,0.009457861,0.005721722,0.0009766286,0.00026937196],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014571424,0.000054221397,0.00007368551,0.00010443794,0.000010711892,6.481057e-7,0.0023541886,0.000034047764,0.0000074525005,0.2869629,0.004585995,0.70581025],"study_design_scores_gemma":[0.00053258345,0.00020505472,0.0029370107,0.0007066601,0.0000059314193,0.00011340193,0.0001426341,0.3554605,0.000011393103,0.037586197,0.6016635,0.00063515024],"about_ca_topic_score_codex":0.000047277692,"about_ca_topic_score_gemma":0.00006585988,"teacher_disagreement_score":0.7051751,"about_ca_system_score_codex":0.0004384447,"about_ca_system_score_gemma":0.00053178566,"threshold_uncertainty_score":0.9999826},"labels":[],"label_agreement":null},{"id":"W4390187312","doi":"10.1109/jsac.2023.3345381","title":"HeadTrack: Real-Time Human–Computer Interaction via Wireless Earphones","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Key Research and Development Program of China; Shenzhen Science and Technology Innovation Program; Key Research and Development Program of Hunan Province of China; Basic and Applied Basic Research Foundation of Guangdong Province; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Computer science; Wireless; Telecommunications","score_opus":0.054790490615987175,"score_gpt":0.3309793096014351,"score_spread":0.27618881898544795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390187312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85411465,0.00003991659,0.12733804,0.009612384,0.002168454,0.00064355886,0.0000138402465,0.0014599062,0.0046092332],"genre_scores_gemma":[0.9960207,0.00026169137,0.0027823881,0.00022154782,0.00028755653,0.00005685615,0.000028603761,0.000032204272,0.00030849155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969387,0.0010412771,0.00077818724,0.00035587317,0.0004598256,0.0004261544],"domain_scores_gemma":[0.9957106,0.0012137747,0.0004509507,0.0016742629,0.0007785999,0.00017178216],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008352048,0.00023978826,0.0003604528,0.0010210968,0.0006997284,0.00047481453,0.0022885564,0.00013480488,0.00003528198],"category_scores_gemma":[0.00006389219,0.00024720636,0.00013074257,0.00272741,0.00007645516,0.0011224108,0.00030911624,0.0011176103,0.0011351339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013122035,0.00347793,0.008608965,0.00006218465,0.0005672436,0.0002973552,0.008780148,0.0020740954,0.25377518,0.006429315,0.053338237,0.6624581],"study_design_scores_gemma":[0.0029438608,0.000832834,0.094886675,0.001537864,0.00004472279,0.002788841,0.0002631676,0.87093884,0.009057189,0.0064938036,0.008599962,0.0016122492],"about_ca_topic_score_codex":0.00020366078,"about_ca_topic_score_gemma":0.00042212725,"teacher_disagreement_score":0.8688647,"about_ca_system_score_codex":0.00036504833,"about_ca_system_score_gemma":0.00014943324,"threshold_uncertainty_score":0.99999803},"labels":[],"label_agreement":null},{"id":"W4390193161","doi":"10.1002/alz.077576","title":"A feasibility study to explore the use of real‐time caregiver reports for people living with dementia at home to label multi‐modal sensor data","year":2023,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","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":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Set (abstract data type); Computer science; Smartwatch; Dementia; Accelerometer; Human–computer interaction; Modal; Wearable computer; Embedded system; Medicine; Operating system","score_opus":0.22926680611874833,"score_gpt":0.3404463807101055,"score_spread":0.11117957459135719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390193161","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9593478,0.0002704719,0.034285683,0.0005589323,0.00045516927,0.004608908,0.0001438457,0.00031917766,0.0000100450325],"genre_scores_gemma":[0.98470813,0.0000029505543,0.014498751,0.00012953144,0.00005170119,0.00043571932,0.000059382128,0.000044588207,0.00006925549],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962842,0.00035254305,0.0006786846,0.0013941629,0.00072980113,0.0005606254],"domain_scores_gemma":[0.99474233,0.00077806046,0.00036872888,0.0034338266,0.00045889095,0.00021815143],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015729562,0.0003159052,0.00049353554,0.00020166588,0.00036699275,0.0002724741,0.0013142074,0.00004658557,0.000033752134],"category_scores_gemma":[0.00022770453,0.00025006002,0.00008871049,0.0010983245,0.00004183071,0.0010707239,0.0032957988,0.00009225274,0.00013334134],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003334293,0.0031022374,0.7842568,0.00007952313,0.026084006,0.00019893388,0.09334955,0.0004917656,0.007606904,0.000018563285,0.05000902,0.034469247],"study_design_scores_gemma":[0.0030751154,0.0017227818,0.9375075,0.0002783876,0.010494355,0.00012060475,0.0072759707,0.028316759,0.0024898192,0.000021054435,0.007021678,0.001675977],"about_ca_topic_score_codex":0.001747334,"about_ca_topic_score_gemma":0.0055994033,"teacher_disagreement_score":0.15325068,"about_ca_system_score_codex":0.000029158138,"about_ca_system_score_gemma":0.000117434814,"threshold_uncertainty_score":0.9999952},"labels":[],"label_agreement":null},{"id":"W4390238639","doi":"10.1016/j.comnet.2023.110156","title":"Leveraging pervasive computing for ambient intelligence: A survey on recent advancements, applications and open challenges","year":2023,"lang":"en","type":"article","venue":"Computer Networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Ambient intelligence; Leverage (statistics); Data science; Ubiquitous computing; Open research; Field (mathematics); Analytics; Context (archaeology); Context-aware pervasive systems; Human–computer interaction; Artificial intelligence; World Wide Web","score_opus":0.1642765570846529,"score_gpt":0.34758982131269406,"score_spread":0.18331326422804115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390238639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013906474,0.00071302423,0.9936614,0.0011083974,0.00093259476,0.001759289,0.000009545002,0.0002902466,0.0001348579],"genre_scores_gemma":[0.96734595,0.002947712,0.026710585,0.0012764832,0.0007397743,0.0006692138,0.00015320236,0.00004753142,0.000109539906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978664,0.00023480195,0.00037254472,0.0008763788,0.0002084081,0.0004414586],"domain_scores_gemma":[0.9973828,0.0014124305,0.00019407392,0.00059582444,0.00027873312,0.00013610937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012832723,0.00022538498,0.00033884216,0.00015118651,0.00038421256,0.00054720265,0.0011989907,0.00007259589,0.0000027580008],"category_scores_gemma":[0.00002789295,0.00023477986,0.00005331096,0.00062459323,0.00003650164,0.00034780096,0.0015808886,0.00018058854,0.00003942186],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011797779,0.000061732935,0.00043186705,0.000024090687,0.00004021794,0.000002562973,0.0007491155,0.008873796,0.0000013642664,0.0018366477,0.002064776,0.985902],"study_design_scores_gemma":[0.00041071858,0.00017787033,0.017233472,0.00019464437,0.0000052363075,0.000009340812,0.00009714421,0.94187117,0.0000264394,0.0012541864,0.038383067,0.00033671746],"about_ca_topic_score_codex":0.000032520744,"about_ca_topic_score_gemma":0.000046137353,"teacher_disagreement_score":0.9855653,"about_ca_system_score_codex":0.000080316466,"about_ca_system_score_gemma":0.000043247644,"threshold_uncertainty_score":0.9574045},"labels":[],"label_agreement":null},{"id":"W4390483246","doi":"10.23977/acss.2023.071101","title":"Research and Innovation in Predictive Remote Control Technology for Mobile Service Robots","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Department of Henan Province","keywords":"Model predictive control; Mobile robot; Service (business); Robot; Remote control; Control (management); Computer science; Mobile service; Business; Artificial intelligence; Marketing","score_opus":0.06132147735330285,"score_gpt":0.36176691561511237,"score_spread":0.3004454382618095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390483246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12897739,0.0027762277,0.8651248,0.00059714395,0.00058212865,0.0017022697,0.0000140605725,0.00016666536,0.00005928817],"genre_scores_gemma":[0.9970148,0.00019032195,0.0020695997,0.00007848822,0.0001228012,0.0004753108,0.0000050923245,0.000011380985,0.000032168595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979012,0.00024975388,0.00056879304,0.00062025595,0.0002654542,0.00039454835],"domain_scores_gemma":[0.99766135,0.0012877804,0.00015475828,0.0002931848,0.0005616888,0.00004124621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023568475,0.0001453899,0.00039003225,0.0017270969,0.00011212432,0.00018545223,0.00035998155,0.00013677913,2.9829778e-7],"category_scores_gemma":[0.00005091205,0.00014187278,0.00001394413,0.0044992585,0.00006769147,0.0008826971,0.0002476911,0.00021940055,0.0000062743625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012535191,0.00013714371,0.016127173,0.0010356512,0.000052962827,0.00006839561,0.0029831014,0.05613381,0.0020260236,0.015636161,0.00027640135,0.90539783],"study_design_scores_gemma":[0.0013917559,0.0004630826,0.0011877113,0.0005405428,0.0000011979984,0.000037233713,0.00039196617,0.98190594,0.00009757409,0.010779183,0.0030346087,0.00016921498],"about_ca_topic_score_codex":0.00009022162,"about_ca_topic_score_gemma":0.00013762411,"teacher_disagreement_score":0.92577213,"about_ca_system_score_codex":0.000058091886,"about_ca_system_score_gemma":0.00004571151,"threshold_uncertainty_score":0.5785404},"labels":[],"label_agreement":null},{"id":"W4390563120","doi":"10.20944/preprints202401.0108.v1","title":"Skeleton-based Privacy-Preserving Smart Activity Sensor for Senior Care and Patient Monitoring","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Context-Aware Activity Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Computer science; Activity recognition; Service (business); Amazon rainforest; Assisted living; Computer security; Artificial intelligence; Data science; Human–computer interaction; Internet privacy; Business; Medicine; Gerontology; Marketing","score_opus":0.10954351641011935,"score_gpt":0.34400491670412603,"score_spread":0.2344614002940067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390563120","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96451473,0.00052389916,0.025596958,0.0011204375,0.003790628,0.002545109,0.00012411774,0.0009077192,0.00087640085],"genre_scores_gemma":[0.9920313,0.000020272106,0.0060894513,0.000054458775,0.00036695396,0.0011698155,0.000014366256,0.00008822791,0.00016518908],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9952615,0.00035527907,0.00064111355,0.002413658,0.0006807662,0.00064767536],"domain_scores_gemma":[0.9949725,0.0007788058,0.00048191554,0.0028798706,0.00058502035,0.000301872],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0006323151,0.0006740297,0.0007792006,0.00037950222,0.00026564221,0.00042603252,0.0016192214,0.00047577455,0.000024910467],"category_scores_gemma":[0.0006217774,0.0007344151,0.0004477643,0.0002701182,0.000068255256,0.000356997,0.011531072,0.0012288103,0.00021142075],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002167891,0.00044876017,0.48008156,0.011705316,0.0008150656,0.00014531544,0.024171287,0.0010610388,0.05542631,0.00011343148,0.00023259455,0.42558256],"study_design_scores_gemma":[0.0020234361,0.00027395322,0.15005207,0.0060068434,0.00031026415,0.0000661713,0.0013927673,0.06709033,0.7436373,0.0035607568,0.022113854,0.0034722043],"about_ca_topic_score_codex":0.0006140049,"about_ca_topic_score_gemma":0.00003578061,"teacher_disagreement_score":0.688211,"about_ca_system_score_codex":0.00052017183,"about_ca_system_score_gemma":0.0005075172,"threshold_uncertainty_score":0.9995107},"labels":[],"label_agreement":null},{"id":"W4390705680","doi":"10.1007/978-3-031-23161-2_481","title":"Fall Risk Detection in Computer Vision","year":2024,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Computer Graphics and Games","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Optometry; Medicine","score_opus":0.0091598616357416,"score_gpt":0.22287703034798123,"score_spread":0.21371716871223964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390705680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061787823,0.0051333704,0.9050299,0.00032371195,0.0058586365,0.0009666604,0.00008225477,0.00042802762,0.07599865],"genre_scores_gemma":[0.7993584,0.06351169,0.04299053,0.0010833512,0.0064203367,0.0001431724,0.00011062754,0.00052966893,0.08585218],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973567,0.00008229204,0.00079589535,0.000990539,0.0004925048,0.00028205273],"domain_scores_gemma":[0.99814326,0.0004315502,0.0004581424,0.0006569901,0.00017697734,0.00013309247],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005065295,0.00050772825,0.00076619047,0.0011471392,0.00006980392,0.00024635304,0.0005641566,0.00044613724,0.0000062061326],"category_scores_gemma":[0.0000066970388,0.0004910541,0.00029819354,0.00023632219,0.0001411977,0.0004163754,0.00069480506,0.0008524384,0.00004163037],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001194768,0.000048163707,0.00036612517,0.0002602281,0.00013209434,0.0000834435,0.000678648,0.000021291455,0.0000019066912,0.071802326,0.0009256705,0.9256682],"study_design_scores_gemma":[0.0010117497,0.0012061473,0.0064722523,0.0025517305,0.00012689122,0.00019972742,0.000005819956,0.21162358,0.000022491759,0.22253887,0.5526625,0.0015782176],"about_ca_topic_score_codex":0.00018990204,"about_ca_topic_score_gemma":0.0007365478,"teacher_disagreement_score":0.9240899,"about_ca_system_score_codex":0.000037998707,"about_ca_system_score_gemma":0.000060431692,"threshold_uncertainty_score":0.99975413},"labels":[],"label_agreement":null},{"id":"W4390946740","doi":"10.1145/3639467","title":"Tensor-Based Viterbi Algorithms for Collaborative Cloud-Edge Cyber-Physical-Social Activity Prediction","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Context-Aware Activity Recognition Systems","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":"St. Francis Xavier University","funders":"Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Viterbi algorithm; Cloud computing; Hidden Markov model; Enhanced Data Rates for GSM Evolution; Tensor (intrinsic definition); Tensor decomposition; Key (lock); Cyber-physical system; Precision and recall; Edge computing; Big data; Algorithm; Data mining; Artificial intelligence; Machine learning; Mathematics","score_opus":0.04019961912686197,"score_gpt":0.2915400377951918,"score_spread":0.2513404186683298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390946740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013782057,0.00007113023,0.976251,0.0027624615,0.0038036513,0.001244208,0.0005329725,0.0012966876,0.00025584502],"genre_scores_gemma":[0.98931235,0.000013999859,0.007482476,0.0002766752,0.0016253114,0.00063347735,0.000032835098,0.000069845446,0.00055305223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971531,0.00034971265,0.00037563266,0.0010648577,0.00047754587,0.00057913805],"domain_scores_gemma":[0.9964896,0.0019488428,0.0001365304,0.00082879903,0.00041783045,0.00017842671],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003673451,0.00043560698,0.00050080416,0.00032118696,0.0007102341,0.00060934015,0.0006142463,0.00030525445,0.00002199877],"category_scores_gemma":[0.00004021269,0.00043673653,0.00046011672,0.0016730945,0.00011684576,0.0009523224,0.000019257404,0.00067497994,0.00008572294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034586294,0.0008345981,0.000014282374,0.00011071183,0.0004903802,0.00003685436,0.0019102562,0.024800006,0.0017384237,0.00042707153,0.0038821183,0.96540946],"study_design_scores_gemma":[0.0014654459,0.0005587816,0.00028962188,0.00016484241,0.00014571082,0.00002766035,0.00017422755,0.9697443,0.0048212595,0.00048571354,0.021536943,0.0005854394],"about_ca_topic_score_codex":0.000033253335,"about_ca_topic_score_gemma":0.000029912884,"teacher_disagreement_score":0.97553027,"about_ca_system_score_codex":0.0003055359,"about_ca_system_score_gemma":0.00018612535,"threshold_uncertainty_score":0.99980843},"labels":[],"label_agreement":null},{"id":"W4390993477","doi":"10.1109/biocas58349.2023.10388962","title":"AīCare: An Affordable, Reliable, and Intelligent Senior Care Ecosystem","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Ecosystem; Business; Computer science","score_opus":0.027086440704715893,"score_gpt":0.26475882009043805,"score_spread":0.23767237938572217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390993477","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61601186,0.0007127743,0.33707768,0.002117028,0.0041400413,0.001993554,0.00007736598,0.0062108673,0.03165881],"genre_scores_gemma":[0.9952058,0.000040909974,0.002344482,0.00016038361,0.000102689635,0.000066336885,0.000017965014,0.000020130172,0.0020412991],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982684,0.00009123742,0.00030855482,0.00062437984,0.0003338634,0.0003735346],"domain_scores_gemma":[0.99859554,0.00012706344,0.00008279377,0.00074095937,0.00022746596,0.00022619865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000404207,0.0001917839,0.00026013303,0.00025299753,0.0001808211,0.00040625353,0.0005459102,0.00009218969,0.000039732626],"category_scores_gemma":[0.000026556525,0.00017191787,0.000060959606,0.000833625,0.000017899603,0.00097244105,0.00040147707,0.000117958036,0.0007177164],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025876852,0.00013971853,0.016347542,0.0007817342,0.00013454787,0.00026158988,0.01906935,0.00022440976,0.0014499597,0.018402588,0.025205696,0.917957],"study_design_scores_gemma":[0.002428103,0.0013416806,0.011351193,0.00071469916,0.000063016894,0.00087158347,0.042329583,0.32510477,0.03801647,0.0030139454,0.57156587,0.0031990935],"about_ca_topic_score_codex":0.0002827002,"about_ca_topic_score_gemma":0.0012357656,"teacher_disagreement_score":0.9147579,"about_ca_system_score_codex":0.00008370152,"about_ca_system_score_gemma":0.00007746362,"threshold_uncertainty_score":0.9225032},"labels":[],"label_agreement":null},{"id":"W4391345087","doi":"10.3390/s24030884","title":"Enhancing Human Activity Recognition in Smart Homes with Self-Supervised Learning and Self-Attention","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Computer science; Activity recognition; Artificial intelligence; Generalization; Machine learning; Transfer of learning; Deep learning; Focus (optics); Encoder; Pattern recognition (psychology)","score_opus":0.015560111803390543,"score_gpt":0.2478244522383195,"score_spread":0.23226434043492897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391345087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98849326,0.0000797267,0.008796832,0.00021989516,0.0001987404,0.00028339887,0.0000015793676,0.0009486181,0.0009779617],"genre_scores_gemma":[0.9971614,0.000028961977,0.002464873,0.000016903154,0.00006433126,0.00002730544,0.000005558797,0.000021499458,0.00020920241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983774,0.00032724245,0.00020441624,0.0005721658,0.00025071882,0.00026806438],"domain_scores_gemma":[0.9993805,0.00023139975,0.00006834721,0.00017080645,0.00007398076,0.00007493684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005563497,0.00018193836,0.00022072642,0.00035272914,0.00016381902,0.0004106387,0.000108723856,0.000085739,0.000011174594],"category_scores_gemma":[0.000025068777,0.00017430508,0.00004866028,0.0005714763,0.000021018412,0.00092513184,0.00007911321,0.00033692826,0.00008116512],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055945577,0.0006354022,0.049537346,0.0014248677,0.0003806493,0.00056707644,0.021323884,0.000040193125,0.13938765,0.00029120725,0.00006527689,0.78629047],"study_design_scores_gemma":[0.0058146734,0.0020444624,0.2713388,0.0057831863,0.00026501945,0.0016535359,0.005136521,0.60501266,0.08955879,0.00185288,0.007690096,0.0038493997],"about_ca_topic_score_codex":0.00022102987,"about_ca_topic_score_gemma":0.0005421485,"teacher_disagreement_score":0.7824411,"about_ca_system_score_codex":0.00012409766,"about_ca_system_score_gemma":0.00006067143,"threshold_uncertainty_score":0.7107955},"labels":[],"label_agreement":null},{"id":"W4391463936","doi":"10.54337/aau645740910","title":"Evaluation of Various Algorithms' Performance in Supervised Binary Classification for Occupant Detection Using a Dataset from a Residential Building","year":2023,"lang":"en","type":"report","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Carleton University","funders":"Aalborg Universitet","keywords":"Binary classification; Binary number; Computer science; Process (computing); Machine learning; Artificial intelligence; Pattern recognition (psychology); Support vector machine; Mathematics; Arithmetic","score_opus":0.38998512017179054,"score_gpt":0.42627358668326354,"score_spread":0.036288466511472994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391463936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27307224,0.00019880611,0.7187964,0.000035526995,0.0032320567,0.0024173367,0.001950571,0.00019615059,0.00010096888],"genre_scores_gemma":[0.98453057,0.00008314324,0.011819712,0.000008389794,0.00038723278,0.00045100698,0.0026361824,0.000047759935,0.000036007823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945694,0.0005334125,0.0011562059,0.0010207525,0.0024029333,0.00031726013],"domain_scores_gemma":[0.9955893,0.00051322096,0.000930412,0.00097501226,0.0019314298,0.000060619495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0077830185,0.00030609313,0.00060357974,0.0010730566,0.00014009832,0.00018830936,0.0006616229,0.00037947617,0.000017746235],"category_scores_gemma":[0.0008349318,0.00033233012,0.00014403231,0.0009667198,0.000030728726,0.00116226,0.000299994,0.00028107144,0.000012882425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010227431,0.00025296715,0.0011234179,0.00068878935,0.0003437041,0.000013335269,0.00067475316,0.001461007,0.04766197,0.0000091049415,0.0013523867,0.9463163],"study_design_scores_gemma":[0.0009359705,0.000093486924,0.015806511,0.00064591796,0.00023219975,0.000030058265,0.0000860535,0.97737956,0.0036236977,0.00015064323,0.0006731304,0.00034274577],"about_ca_topic_score_codex":0.013896769,"about_ca_topic_score_gemma":0.003297843,"teacher_disagreement_score":0.9759186,"about_ca_system_score_codex":0.0013929555,"about_ca_system_score_gemma":0.0026620321,"threshold_uncertainty_score":0.99991286},"labels":[],"label_agreement":null},{"id":"W4391723092","doi":"10.1016/j.jobe.2024.108741","title":"Unsupervised clustering-based domain adaptation for estimating occupancy and recognizing activities in smart buildings","year":2024,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Unavailability; Discriminative model; Artificial intelligence; HVAC; Machine learning; Occupancy; Adaptation (eye); Exploit; Domain (mathematical analysis); Building automation; Data mining; Air conditioning; Engineering","score_opus":0.025581872161651795,"score_gpt":0.2613019435104735,"score_spread":0.23572007134882167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391723092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24301395,0.00046989816,0.75508386,0.00020724456,0.0009864435,0.00013409372,0.0000024047083,0.000096337615,0.0000057610573],"genre_scores_gemma":[0.59256303,0.0000035877579,0.40725595,0.000015897882,0.00013091962,0.000011081073,2.5623712e-7,0.000017302189,0.000001990181],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874187,0.000036196398,0.00051468995,0.00023490087,0.0002224382,0.0002498799],"domain_scores_gemma":[0.9986035,0.00096000015,0.00016267585,0.00011296838,0.00008261519,0.00007820636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011903497,0.00017060508,0.00030701957,0.00075953413,0.00005690001,0.00044198273,0.00022608756,0.00006628891,0.0000010278796],"category_scores_gemma":[0.000266907,0.00017610566,0.00010660424,0.00039087434,0.000010150644,0.001391942,0.000059240647,0.0002680582,3.0347397e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007658554,0.00005856798,0.0011573541,0.002296271,0.00015873897,0.00017915355,0.0055635646,0.27927408,0.111085325,0.0012569893,0.00006245904,0.59883094],"study_design_scores_gemma":[0.0005439589,0.00008766743,0.0003252296,0.002350713,0.000011358014,0.00017286232,0.00011610206,0.9932041,0.0022274756,0.00026911809,0.0005020703,0.00018935412],"about_ca_topic_score_codex":0.000013386251,"about_ca_topic_score_gemma":0.000014775534,"teacher_disagreement_score":0.71393,"about_ca_system_score_codex":0.00017375808,"about_ca_system_score_gemma":0.00009985747,"threshold_uncertainty_score":0.71813804},"labels":[],"label_agreement":null},{"id":"W4391822097","doi":"10.1186/s12877-024-04702-1","title":"Benefits and barriers associated with the use of smart home health technologies in the care of older persons: a systematic review","year":2024,"lang":"en","type":"review","venue":"BMC Geriatrics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Universität Basel","keywords":"Medicine; Usability; Context (archaeology); Data extraction; Independent living; Health care; Empirical research; Gerontology; Applied psychology; MEDLINE; Medical education; Psychology; Computer science","score_opus":0.08717349471158811,"score_gpt":0.30010590586569796,"score_spread":0.21293241115410985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391822097","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002281331,0.99412096,0.0006180451,0.0002716211,0.00012229581,0.004575302,0.00016660504,0.00009875839,0.0000035866988],"genre_scores_gemma":[0.0012095127,0.99796,0.00014037236,0.00006180618,0.0000065730505,0.00057289394,0.0000101966025,0.000024256944,0.000014446316],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9962205,0.0014588817,0.0010484272,0.00045821138,0.00055946776,0.00025449923],"domain_scores_gemma":[0.9949059,0.0021586595,0.001583231,0.0011042312,0.00021502392,0.000032948097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00199101,0.00036635387,0.0025697905,0.0003835914,0.00008026678,0.00017213242,0.0011775307,0.0002034992,4.857575e-7],"category_scores_gemma":[0.001263047,0.00017281694,0.00031537964,0.0028646614,0.00009141865,0.00018417534,0.00031059317,0.00041563474,0.0000028090944],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.369888e-7,0.000013615394,0.000023400471,0.9222389,0.000116655334,0.0000029523758,0.0020052018,4.3613937e-7,2.6667077e-9,0.00016036771,0.00043468273,0.07500328],"study_design_scores_gemma":[0.00015632261,0.00015674558,0.000019272562,0.97741055,0.0017805687,0.000126737,0.0024692402,0.00003410733,6.214276e-8,0.000008957693,0.017478067,0.00035937873],"about_ca_topic_score_codex":0.000070154405,"about_ca_topic_score_gemma":0.0003107135,"teacher_disagreement_score":0.0746439,"about_ca_system_score_codex":0.00016691569,"about_ca_system_score_gemma":0.0008858596,"threshold_uncertainty_score":0.70472705},"labels":[],"label_agreement":null},{"id":"W4391886774","doi":"10.1002/ctm2.1593","title":"Wireless wearable devices for continuous monitoring of body sounds and motions","year":2024,"lang":"en","type":"article","venue":"Clinical and Translational Medicine","topic":"Context-Aware Activity Recognition Systems","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":"McGill University Health Centre","funders":"","keywords":"Wearable computer; Wireless; Computer science; Wearable technology; Telecommunications; Embedded system","score_opus":0.08537797304715085,"score_gpt":0.37618129847397674,"score_spread":0.2908033254268259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391886774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43832624,0.007619522,0.5410493,0.010658797,0.0013752647,0.0003299278,0.000023636283,0.0000992252,0.0005180854],"genre_scores_gemma":[0.9975759,0.00015558224,0.0016646228,0.000041723,0.00040872276,0.0000169836,0.0000033214762,0.000004523896,0.00012864082],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990858,0.000042449075,0.00039397675,0.00024470498,0.0001477991,0.00008529888],"domain_scores_gemma":[0.9978822,0.0018124352,0.000046389396,0.000081661514,0.00010081785,0.000076486554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061101036,0.0000709533,0.00024237923,0.00006300528,0.00006215249,0.000034704968,0.00008314,0.00005472947,0.0000062254226],"category_scores_gemma":[0.000071783244,0.000054975593,0.000046579284,0.00012433271,0.00017536289,0.00023549689,0.000014527873,0.00009437777,0.0000014719623],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002738328,0.00010113613,0.18688649,0.0005137924,0.00017301844,0.000006038673,0.0009053626,0.000002141961,0.00076635444,0.035205994,0.00016353764,0.77524877],"study_design_scores_gemma":[0.0040838066,0.0010307397,0.83616495,0.0033564237,0.00020757075,0.00010157537,0.00039644784,0.07851468,0.00023216486,0.05748936,0.018050445,0.0003718241],"about_ca_topic_score_codex":0.000016245407,"about_ca_topic_score_gemma":0.000006564935,"teacher_disagreement_score":0.77487695,"about_ca_system_score_codex":0.0000016548887,"about_ca_system_score_gemma":0.00003772572,"threshold_uncertainty_score":0.22418396},"labels":[],"label_agreement":null},{"id":"W4391899109","doi":"10.1186/s12938-024-01213-3","title":"The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer","year":2024,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Saskatchewan Health; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Accelerometer; Orientation (vector space); Heuristic; Computer science; Artificial intelligence; Computer vision; Activity recognition; Position (finance); Sitting; Pattern recognition (psychology); Mathematics; Medicine","score_opus":0.03281467155514043,"score_gpt":0.2869684464719514,"score_spread":0.25415377491681096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391899109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8275265,0.0001572614,0.17159842,0.000053241936,0.00027709478,0.00030078535,0.000020909034,0.00006344217,0.0000023064542],"genre_scores_gemma":[0.9984319,0.000008759003,0.0014259325,0.0000035750272,0.00005001369,0.000033835833,0.000031469714,0.000011686981,0.0000028209174],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988116,0.00017579035,0.0002712969,0.00030086536,0.00024447613,0.00019598869],"domain_scores_gemma":[0.99867296,0.0010437162,0.0000487826,0.00014163446,0.000037318838,0.000055610395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009495433,0.00012575295,0.000202064,0.00033207666,0.000034027293,0.00009594117,0.00011803469,0.00007432303,0.0000018203136],"category_scores_gemma":[0.00022795075,0.00010271619,0.000030771113,0.000754042,0.000045670804,0.00023792591,0.00009447578,0.0001958065,7.355745e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005899295,0.000596566,0.0007270719,0.0015828492,0.000058161913,0.00004553977,0.0005859277,0.0015716691,0.83761334,0.000046472003,0.000009943392,0.15710348],"study_design_scores_gemma":[0.0025739998,0.0006508341,0.17994994,0.00406212,0.000029092444,0.0001236598,0.0001041969,0.78079885,0.030108657,0.00054800534,0.00046758787,0.00058307475],"about_ca_topic_score_codex":0.00023761108,"about_ca_topic_score_gemma":0.00010695152,"teacher_disagreement_score":0.80750465,"about_ca_system_score_codex":0.000169197,"about_ca_system_score_gemma":0.000037209844,"threshold_uncertainty_score":0.4188645},"labels":[],"label_agreement":null},{"id":"W4391935120","doi":"10.1109/tnnls.2024.3360990","title":"Deep Reinforcement Learning in Human Activity Recognition: A Survey and Outlook","year":2024,"lang":"en","type":"review","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Reinforcement learning; Field (mathematics); Computer science; Activity recognition; Data science; Artificial intelligence; Deep learning; Domain (mathematical analysis); Human–computer interaction","score_opus":0.07401804098730283,"score_gpt":0.3080288190832962,"score_spread":0.23401077809599338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391935120","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016916392,0.5152361,0.4814861,0.00001608334,0.0015967333,0.0010436295,0.0000044880762,0.00036036762,0.00008735382],"genre_scores_gemma":[0.49011308,0.50789505,0.000009845999,0.000012918699,0.00018531701,0.00035831466,0.000025967405,0.00007277577,0.0013267463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99353844,0.0032745868,0.0009736686,0.0012433926,0.00039397553,0.000575948],"domain_scores_gemma":[0.9974026,0.0013496985,0.00052718533,0.00040175012,0.000096004595,0.00022277374],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0018829572,0.00074522675,0.0017554612,0.00071470306,0.00066946825,0.0011447521,0.00034313128,0.00052237575,0.000010067573],"category_scores_gemma":[0.000025683014,0.0006720528,0.00031401133,0.0009772715,0.000078940524,0.0005723403,0.000025394527,0.0036907543,0.000035690828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009419228,0.0000355179,0.0000114334,0.003005075,0.00014106862,0.000046071476,0.00020590283,0.09512311,3.8565236e-7,0.0000042047895,0.0000196141,0.9013982],"study_design_scores_gemma":[0.00043029818,0.00045201182,0.000024757986,0.012491598,0.0002557444,0.00048524063,0.00006627341,0.939072,3.495061e-7,0.0000024673345,0.045750026,0.0009691902],"about_ca_topic_score_codex":0.0010913189,"about_ca_topic_score_gemma":0.00054555736,"teacher_disagreement_score":0.900429,"about_ca_system_score_codex":0.00020036657,"about_ca_system_score_gemma":0.00007168321,"threshold_uncertainty_score":0.9998922},"labels":[],"label_agreement":null},{"id":"W4392077390","doi":"","title":"Home Human behaviour monitoring based on recorded data from existing smart thermostat in North American houses","year":2023,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Université de Technologie de Compiègne","keywords":"Thermostat; Computer science; Telecommunications; Engineering; Electrical engineering","score_opus":0.10537347646778697,"score_gpt":0.3009324167550645,"score_spread":0.19555894028727752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392077390","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94973445,0.0003169068,0.034318093,0.0087248795,0.001175746,0.0005257862,0.00052974216,0.00069155346,0.003982821],"genre_scores_gemma":[0.98582566,0.0001488073,0.008884834,0.00010271402,0.00009436247,0.000068403686,0.0009174914,0.00007973519,0.003877985],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9878172,0.008277715,0.00083162915,0.0015330404,0.00077450863,0.0007658586],"domain_scores_gemma":[0.98770034,0.005781128,0.000730911,0.0044448334,0.0010551491,0.00028761305],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005734525,0.00043808424,0.00056862284,0.00051548507,0.00062622334,0.0010545471,0.0032568327,0.00012744108,0.00007942606],"category_scores_gemma":[0.0017909586,0.00053729187,0.00013557338,0.0031635894,0.00035488806,0.0009653483,0.0016278323,0.00069279835,0.00038630486],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016916525,0.0010150592,0.6410532,0.00005252108,0.000042899483,0.00008140268,0.007967511,0.00007831716,0.0009012584,0.00066725444,0.00070195104,0.3474217],"study_design_scores_gemma":[0.0010444135,0.0000044693593,0.8445372,0.0026849525,0.000035448425,0.0000063220064,0.00060545176,0.14060031,0.0042652986,0.000182786,0.0052727074,0.0007606756],"about_ca_topic_score_codex":0.04407449,"about_ca_topic_score_gemma":0.08223378,"teacher_disagreement_score":0.34666103,"about_ca_system_score_codex":0.00028585218,"about_ca_system_score_gemma":0.00031749866,"threshold_uncertainty_score":0.9999825},"labels":[],"label_agreement":null},{"id":"W4392293088","doi":"10.1049/htl2.12082","title":"Papers from the 17th Joint Workshop on Augmented Environments for Computer Assisted Interventions at MICCAI 2023: Guest Editors’ Foreword","year":2024,"lang":"en","type":"editorial","venue":"Healthcare Technology Letters","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"University of British Columbia; Concordia University; École de Technologie Supérieure; Western University","funders":"","keywords":"Computer science; Joint (building); Engineering; Architectural engineering","score_opus":0.042865493675369265,"score_gpt":0.3024317961012447,"score_spread":0.2595663024258754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392293088","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004144027,0.0008863389,0.034212142,0.16295512,0.7970393,0.0020481437,0.0014094742,0.0010124304,0.000022702645],"genre_scores_gemma":[0.006593997,0.0002328235,0.0038116795,0.0070984997,0.9745541,0.003105505,0.0030948257,0.00024307704,0.0012654662],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9940633,0.0004391293,0.0012695284,0.0021180788,0.0010985498,0.0010113743],"domain_scores_gemma":[0.99376094,0.002726991,0.0008601876,0.002327074,0.0001320392,0.00019276624],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0007180534,0.0008588166,0.0010916931,0.00075781904,0.0006341028,0.00033334855,0.0024958183,0.0020491164,0.000031716856],"category_scores_gemma":[0.00038121888,0.00072754594,0.0008644575,0.00075591647,0.00038085444,0.00018109103,0.0016399645,0.0028540848,0.0005892545],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037166355,0.00010328151,0.000032303542,0.00024208735,0.00043517767,0.00005433729,0.00014597405,0.0000030230037,0.0002348435,0.00005599101,0.9672204,0.031435393],"study_design_scores_gemma":[0.00085621845,0.00028427123,0.00012441241,0.0034868675,0.00012332958,0.0000151275335,0.00007709855,0.00037136814,0.00009652167,0.00019959034,0.9937336,0.0006316231],"about_ca_topic_score_codex":0.00036286763,"about_ca_topic_score_gemma":0.0014248024,"teacher_disagreement_score":0.17751488,"about_ca_system_score_codex":0.0020064455,"about_ca_system_score_gemma":0.000204448,"threshold_uncertainty_score":0.99951756},"labels":[],"label_agreement":null},{"id":"W4392349984","doi":"10.18280/ts.410106","title":"Fusion of Dual Sensor Features for Fall Risk Assessment with Improved Attention Mechanism","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dual (grammatical number); Mechanism (biology); Sensor fusion; Computer science; Fusion; Artificial intelligence; Risk analysis (engineering); Medicine; Linguistics; Philosophy; Epistemology","score_opus":0.015582849912265159,"score_gpt":0.25872304626770287,"score_spread":0.2431401963554377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392349984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16297117,0.000054813045,0.83479655,0.00047708998,0.00037768224,0.00089307263,0.000086033884,0.00018923586,0.00015433221],"genre_scores_gemma":[0.97690505,0.000007567024,0.02242552,0.000051414707,0.00014770264,0.00016951753,0.000025568776,0.00001807198,0.0002495995],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838126,0.00012246998,0.00033230285,0.00048340825,0.00044168567,0.00023885936],"domain_scores_gemma":[0.99907947,0.00023162356,0.00018005415,0.00023160691,0.00020547639,0.000071794595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066342513,0.00019059928,0.00023457587,0.00016914545,0.00011445925,0.00024379315,0.00022008758,0.000066342654,0.000033368407],"category_scores_gemma":[0.000008590669,0.00015056704,0.00014816676,0.00021696668,0.00002399351,0.0004988099,0.000081488084,0.0001429271,0.000007898667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021353544,0.00058496435,0.0006935697,0.0006458152,0.000669978,0.00004423149,0.0015386709,0.0000808632,0.6177243,0.03740589,0.0015732881,0.33882493],"study_design_scores_gemma":[0.008650137,0.0072752675,0.033074517,0.0014476993,0.0005873531,0.00030703412,0.0011545521,0.8078311,0.11945761,0.010300692,0.008189013,0.0017249978],"about_ca_topic_score_codex":0.000103552695,"about_ca_topic_score_gemma":0.00009505804,"teacher_disagreement_score":0.81393385,"about_ca_system_score_codex":0.000083787105,"about_ca_system_score_gemma":0.000109175315,"threshold_uncertainty_score":0.6139946},"labels":[],"label_agreement":null},{"id":"W4392499491","doi":"10.1145/3643500","title":"exHAR","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Context-Aware Activity Recognition Systems","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":"Debugging; Computer science; Set (abstract data type); Process (computing); Human–computer interaction; Task (project management); Mental model; Data science; Cognitive science; Psychology; Programming language; Engineering","score_opus":0.014322179406096754,"score_gpt":0.25914607134038087,"score_spread":0.2448238919342841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392499491","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9794191,0.0027799436,0.0004431129,0.0058583696,0.0009240124,0.0007661057,0.00001001473,0.002210088,0.0075892163],"genre_scores_gemma":[0.9981517,0.00025184348,0.0006909348,0.000059347814,0.000033164288,0.00026481884,1.02266625e-7,0.000015015798,0.00053306087],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871224,0.000009761596,0.0002458574,0.00052470545,0.00025859664,0.00024884075],"domain_scores_gemma":[0.9986584,0.0003693189,0.00014978756,0.0005975508,0.00020147701,0.000023428958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024823003,0.0002007115,0.00025989104,0.0002901717,0.00013343546,0.00036829966,0.002430769,0.00012738843,0.000006580602],"category_scores_gemma":[0.00077775284,0.00013436847,0.00011380749,0.00062297744,0.00018003523,0.001052158,0.0023092083,0.0004497147,0.000038597074],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005790776,0.00017822442,0.0009332874,0.00044036173,0.00020407542,0.000008000375,0.0020592527,0.0000030759782,0.18257397,0.026098287,0.012232687,0.77521086],"study_design_scores_gemma":[0.00014580971,0.00053416274,0.00034294502,0.0014801347,0.000018944746,0.00012482045,0.003397351,0.00093955797,0.91116667,0.061371833,0.020209394,0.0002683579],"about_ca_topic_score_codex":0.000023201592,"about_ca_topic_score_gemma":0.0000017817513,"teacher_disagreement_score":0.7749425,"about_ca_system_score_codex":0.00007347313,"about_ca_system_score_gemma":0.000030064051,"threshold_uncertainty_score":0.54793876},"labels":[],"label_agreement":null},{"id":"W4392732923","doi":"10.1186/s44247-024-00062-3","title":"NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour","year":2024,"lang":"en","type":"article","venue":"BMC Digital Health","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; Ontario Brain Institute","keywords":"Pipeline (software); Modular design; Wearable computer; Analytics; Open source; Computer science; Human–computer interaction; Data science; Embedded system; Software; Operating system","score_opus":0.13670328095631745,"score_gpt":0.3817874416076558,"score_spread":0.24508416065133837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392732923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09713157,0.0013154286,0.8969249,0.00071831985,0.00025806006,0.0028924972,0.00047038234,0.0001827659,0.00010608246],"genre_scores_gemma":[0.96127987,0.000021765247,0.037027024,0.00024896112,0.00007706779,0.00015567469,0.00006605592,0.000042289827,0.0010812735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741006,0.00008426887,0.00090423337,0.00067583134,0.00036340105,0.00056217756],"domain_scores_gemma":[0.9972535,0.0013627261,0.00038522936,0.00044903558,0.00023700294,0.00031254906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023758828,0.00021921356,0.00061330944,0.0001979435,0.00027001623,0.00094091316,0.0004669886,0.00006809954,0.0000011610865],"category_scores_gemma":[0.00047123194,0.00021329221,0.00014847159,0.0003901848,0.000043398166,0.0015206061,0.00031899576,0.00011737725,0.000006034473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018927465,0.00045875198,0.005113066,0.00445804,0.00006934347,0.0000017954842,0.0037174853,0.0005656257,0.000045769597,0.002862852,0.0027599798,0.979758],"study_design_scores_gemma":[0.0019110143,0.0013069799,0.00040320778,0.00095780403,0.000009333764,0.000048658312,0.0009389648,0.9860204,0.0000669974,0.001733722,0.006342312,0.000260639],"about_ca_topic_score_codex":0.00042932667,"about_ca_topic_score_gemma":0.0003013142,"teacher_disagreement_score":0.98545474,"about_ca_system_score_codex":0.00023038776,"about_ca_system_score_gemma":0.0014380867,"threshold_uncertainty_score":0.90732473},"labels":[],"label_agreement":null},{"id":"W4392791921","doi":"10.2196/46282","title":"Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective","year":2024,"lang":"en","type":"review","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hong Kong Polytechnic University","keywords":"Perspective (graphical); Activities of daily living; Tracking (education); Motion (physics); Health care; mHealth; Match moving; Systematic review; Computer science; Physical medicine and rehabilitation; Psychology; Medicine; Human–computer interaction; Gerontology; MEDLINE; Physical therapy; Nursing; Psychological intervention; Artificial intelligence; Political science","score_opus":0.09798157268221475,"score_gpt":0.411173529180519,"score_spread":0.3131919564983043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392791921","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008369119,0.9926647,0.0019424029,0.0004624488,0.00027811294,0.0043060402,0.000094182535,0.00011863164,0.00004978493],"genre_scores_gemma":[0.026138596,0.97260976,0.00020621352,0.0002692463,0.00015490733,0.0005408926,0.000016872322,0.00004799885,0.000015531541],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.993599,0.0025012947,0.0016932182,0.0011374407,0.000494042,0.00057502073],"domain_scores_gemma":[0.99557525,0.0016726387,0.0016150884,0.0006389273,0.00012373937,0.00037436624],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018153739,0.0005806193,0.0046033533,0.0005628946,0.00016296652,0.00017513854,0.00040493955,0.00020137304,0.0000014335674],"category_scores_gemma":[0.00020653673,0.00047549728,0.00028080316,0.0008717928,0.00007889444,0.00056563964,0.000226094,0.0008480847,0.00000842678],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014634837,0.000051674222,0.0000017563393,0.7176122,0.00003668387,0.000004814975,0.011964747,1.7081794e-8,3.1627163e-8,0.00045007237,0.000049131635,0.2698274],"study_design_scores_gemma":[0.00015562047,0.00030152174,0.000045068682,0.99381536,0.00065437827,0.00010654241,0.002850561,0.00018347973,1.6785637e-7,0.00019045631,0.0012645466,0.00043226767],"about_ca_topic_score_codex":0.0017898665,"about_ca_topic_score_gemma":0.0003254529,"teacher_disagreement_score":0.27620316,"about_ca_system_score_codex":0.0012380994,"about_ca_system_score_gemma":0.0019321688,"threshold_uncertainty_score":0.9997697},"labels":[],"label_agreement":null},{"id":"W4392905165","doi":"10.1109/ccnc51664.2024.10454743","title":"Robust Interactive HMI for Occupancy Estimation in Smart Buildings (WIP)","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Occupancy; Computer science; Real-time computing; Automotive engineering; Engineering; Architectural engineering","score_opus":0.04673034442004016,"score_gpt":0.3019735688240287,"score_spread":0.25524322440398856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392905165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012741422,0.000072594325,0.98043185,0.001420195,0.0010898357,0.0004772103,0.000006422684,0.0003976253,0.0033628694],"genre_scores_gemma":[0.9424686,0.0000017985748,0.056223378,0.00018299103,0.00005052854,0.00016800461,0.0000058045566,0.000011428631,0.0008874608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988584,0.000046643625,0.00026883252,0.0004595142,0.0001563272,0.00021028185],"domain_scores_gemma":[0.9989048,0.0006593657,0.000049843813,0.00023920949,0.0000970791,0.00004968824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038875677,0.0001283816,0.0001678867,0.00031303513,0.000044729426,0.00045999177,0.0003159527,0.000057819667,0.000038020185],"category_scores_gemma":[0.00013955068,0.00012024802,0.00008216424,0.0005010503,0.000013817476,0.001914478,0.00011550296,0.000132488,0.00019087743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028133945,0.00011611397,0.0008718278,0.00018252287,0.00005763492,0.000026891214,0.0028886104,0.00030283525,0.0010900167,0.03337838,0.009303877,0.95175314],"study_design_scores_gemma":[0.00032914296,0.000079479454,0.0009866107,0.00031798126,0.0000061044234,0.000043370605,0.00010934879,0.9761183,0.0031477355,0.004508242,0.014114931,0.00023874293],"about_ca_topic_score_codex":0.0001932989,"about_ca_topic_score_gemma":0.00019343798,"teacher_disagreement_score":0.9758155,"about_ca_system_score_codex":0.00015661944,"about_ca_system_score_gemma":0.00008387825,"threshold_uncertainty_score":0.4903572},"labels":[],"label_agreement":null},{"id":"W4392907792","doi":"10.1016/j.compbiomed.2024.108340","title":"Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices – A scoping review","year":2024,"lang":"en","type":"review","venue":"Computers in Biology and Medicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Population; SAFER; Scopus; Data science; CINAHL; Health care; MEDLINE; Medicine; Computer security","score_opus":0.11288553327231267,"score_gpt":0.4509123026396107,"score_spread":0.33802676936729803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392907792","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000435153,0.7153459,0.28197962,0.00027147864,0.00030316037,0.0019985307,0.0000036697293,0.000029548728,0.000024528577],"genre_scores_gemma":[0.016864374,0.9762878,0.005926134,0.00016389329,0.00018630676,0.00045771856,0.00009599398,0.000016488364,0.0000012850454],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9975224,0.00048183138,0.0011002199,0.00059943,0.00008669231,0.00020943007],"domain_scores_gemma":[0.99680734,0.0022494365,0.00062665006,0.00022606197,0.00005656264,0.000033963377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016502261,0.00024831892,0.0015527307,0.000907517,0.00003640855,0.000014649471,0.00031180828,0.00014289632,6.0584046e-7],"category_scores_gemma":[0.00015453229,0.00019081273,0.00005791741,0.0012684773,0.00009176676,0.0001441368,0.0001690028,0.0003393077,5.807724e-7],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004114365,0.000020268119,0.00077522337,0.03635091,0.00001708847,0.0000031517593,0.00031092454,0.0000011207427,5.412175e-7,0.0010025012,0.000003101595,0.9615111],"study_design_scores_gemma":[0.00012443833,0.00012620325,0.0023231648,0.986712,0.00012381091,0.000034928504,0.000046532652,0.008393833,6.593833e-7,0.00059677096,0.0012387293,0.00027897826],"about_ca_topic_score_codex":0.0006556155,"about_ca_topic_score_gemma":0.009023272,"teacher_disagreement_score":0.96123207,"about_ca_system_score_codex":0.00015986543,"about_ca_system_score_gemma":0.00012509919,"threshold_uncertainty_score":0.7781117},"labels":[],"label_agreement":null},{"id":"W4392981331","doi":"10.1109/acdsa59508.2024.10467538","title":"Adaptable Dashboards to Assist Semi Autonomous Persons in Smart Homes","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.02602141513916997,"score_gpt":0.2690973149892775,"score_spread":0.24307589985010755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392981331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04750459,0.0005634645,0.8165091,0.010676041,0.002441045,0.00055524445,0.000023842027,0.0016838439,0.12004277],"genre_scores_gemma":[0.97245294,0.0000019681481,0.0042186216,0.00051890756,0.00007820717,0.00008156024,0.0000020559667,0.0000146853345,0.022631027],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984572,0.000081431754,0.00024165741,0.0005895367,0.0002783642,0.00035180562],"domain_scores_gemma":[0.9990893,0.00021714649,0.000021830643,0.00046997104,0.00004578993,0.00015598207],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046221277,0.00015936504,0.00023057932,0.0003792833,0.00006447978,0.00070208294,0.00054292526,0.000066986504,0.00020064825],"category_scores_gemma":[0.000042519023,0.00014880953,0.000089747205,0.0009784002,0.000017128878,0.00083964283,0.00028264383,0.00017293748,0.0012524376],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011327572,0.00026416418,0.009807895,0.00017403034,0.00012553955,0.00046854105,0.008296948,0.00013086923,0.0029514704,0.031055594,0.13590758,0.81080604],"study_design_scores_gemma":[0.00047314435,0.00019609035,0.01676288,0.00043739978,0.000013504763,0.00022830705,0.0010120082,0.19851479,0.0030902294,0.00077355,0.7775357,0.0009624367],"about_ca_topic_score_codex":0.0011753472,"about_ca_topic_score_gemma":0.001915745,"teacher_disagreement_score":0.9249484,"about_ca_system_score_codex":0.00023556594,"about_ca_system_score_gemma":0.0002687203,"threshold_uncertainty_score":0.9995252},"labels":[],"label_agreement":null},{"id":"W4393058933","doi":"10.1109/gcwkshps58843.2023.10465052","title":"Context-Aware Hard and Slow Fall Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 New Brunswick; Université de Moncton","funders":"","keywords":"Computer science; Context (archaeology); Geology","score_opus":0.03665111715569397,"score_gpt":0.2442724886021739,"score_spread":0.20762137144647994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393058933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27303594,0.000047572008,0.7187367,0.0018467266,0.00094530144,0.00031531887,0.0000051808097,0.0018233358,0.0032439253],"genre_scores_gemma":[0.99484336,0.000011706041,0.00027031003,0.00038063523,0.00006312454,0.000037920105,0.000001997983,0.000009280125,0.004381671],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890816,0.00007900924,0.00017286002,0.00040369775,0.00020962545,0.00022662805],"domain_scores_gemma":[0.9992185,0.00019691302,0.00005463779,0.00033025577,0.000097460514,0.00010221523],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030813046,0.00011806882,0.00015681384,0.0001817679,0.00013748172,0.00023717478,0.0002571518,0.00006847785,0.000017887336],"category_scores_gemma":[0.00005062259,0.00011096971,0.000049956358,0.0005160149,0.000028496137,0.00068384246,0.00021114049,0.00009945312,0.00096920243],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043820187,0.00001510866,0.0021172264,0.000019163,0.00002286176,0.000018179742,0.00048844045,0.0000014753875,0.0031836568,0.0010212929,0.0040882113,0.98902],"study_design_scores_gemma":[0.004157384,0.00066417956,0.21626,0.0002272094,0.000036662674,0.0007066014,0.0028433767,0.40340677,0.062268782,0.007663785,0.29958183,0.0021834108],"about_ca_topic_score_codex":0.00035921473,"about_ca_topic_score_gemma":0.0009106546,"teacher_disagreement_score":0.9868366,"about_ca_system_score_codex":0.000030297651,"about_ca_system_score_gemma":0.000025250145,"threshold_uncertainty_score":0.99980867},"labels":[],"label_agreement":null},{"id":"W4393160844","doi":"10.1609/aaai.v38i20.30208","title":"Grey-Box Bayesian Optimization for Sensor Placement in Assisted Living Environments","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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 Alberta","funders":"","keywords":"Bayesian probability; Bayesian optimization; Computer science; Artificial intelligence; Environmental science","score_opus":0.08024687492617955,"score_gpt":0.29948301705286773,"score_spread":0.21923614212668818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393160844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0187156,0.000035027846,0.97393143,0.0022646294,0.00079414557,0.0011028141,0.000010811602,0.00012423994,0.003021325],"genre_scores_gemma":[0.99120307,0.000019412906,0.007983294,0.00008628483,0.000060352973,0.0001405132,9.992203e-7,0.00001752551,0.0004885196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802613,0.000033304084,0.00059639954,0.00060232275,0.00042863307,0.00031321897],"domain_scores_gemma":[0.9989495,0.0003471746,0.0002338071,0.00022516982,0.00017764838,0.00006667646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076006056,0.00021456783,0.00024818673,0.0002526406,0.000117645606,0.0004978217,0.00090403896,0.00009536063,0.00008092836],"category_scores_gemma":[0.0004095527,0.00018105064,0.000120407996,0.00060503196,0.00008949646,0.00069556106,0.00024524328,0.00020778901,0.00004794717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014968512,0.0008929172,0.0008244887,0.00057317666,0.00011353061,0.000004224208,0.008391024,0.006004089,0.09278688,0.29433888,0.00041713112,0.595504],"study_design_scores_gemma":[0.00003522576,0.00015388103,0.0001874951,0.0011891051,0.000013301335,0.000007952671,0.00067426555,0.87803674,0.11330435,0.005884953,0.0002576094,0.00025512042],"about_ca_topic_score_codex":0.000024420198,"about_ca_topic_score_gemma":0.00002070111,"teacher_disagreement_score":0.9724875,"about_ca_system_score_codex":0.00015906343,"about_ca_system_score_gemma":0.00008667395,"threshold_uncertainty_score":0.73830307},"labels":[],"label_agreement":null},{"id":"W4393171067","doi":"10.1063/5.0186004","title":"Human activity recognition using ensemble machine learning classifiers","year":2024,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Context-Aware Activity Recognition Systems","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":"St. Stephen's University","funders":"","keywords":"Computer science; Artificial intelligence; Ensemble learning; Machine learning; Activity recognition; Pattern recognition (psychology)","score_opus":0.09909203934251234,"score_gpt":0.3021080234581986,"score_spread":0.20301598411568625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393171067","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49562466,0.00011259732,0.4913786,0.00068988925,0.0005523053,0.00032209832,0.0000062403924,0.0012665053,0.010047126],"genre_scores_gemma":[0.99694294,0.000014210734,0.0020538822,0.00008422459,0.0001659572,0.00004468491,0.0000058705514,0.000031586558,0.0006566704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979015,0.000057478042,0.00030282157,0.0008444474,0.00043081734,0.0004629399],"domain_scores_gemma":[0.99897665,0.0000991861,0.00016868649,0.00016990637,0.00042015951,0.00016542275],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006440089,0.00029379647,0.00030849638,0.00034760617,0.00040200882,0.0016217572,0.000516922,0.00016575935,0.000091227324],"category_scores_gemma":[0.00011351842,0.00030417906,0.00012456141,0.0007477131,0.0000748095,0.0028794259,0.00026608177,0.0006938595,0.00024807372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010861141,0.000076640885,0.0033402238,0.00028270215,0.000082622064,0.000030572854,0.0032980286,0.0000011302485,0.46827608,0.0036481533,0.00040188077,0.5205511],"study_design_scores_gemma":[0.00039627714,0.00025345158,0.0012224527,0.00087565614,0.00005153769,0.00029081423,0.00042150723,0.96120304,0.019704388,0.008549384,0.0061873673,0.0008441103],"about_ca_topic_score_codex":0.00033853643,"about_ca_topic_score_gemma":0.000037669368,"teacher_disagreement_score":0.9612019,"about_ca_system_score_codex":0.00021746982,"about_ca_system_score_gemma":0.00019189806,"threshold_uncertainty_score":0.99994105},"labels":[],"label_agreement":null},{"id":"W4393186053","doi":"10.1109/icpads60453.2023.00330","title":"PORTEND: A Joint Performance Model for Partitioned Early-Exiting DNNs","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"York University; University of Toronto","funders":"","keywords":"Joint (building); Computer science; Engineering","score_opus":0.13348432591784773,"score_gpt":0.27935319687076277,"score_spread":0.14586887095291504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393186053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13242261,0.0000046778,0.8616995,0.0009903203,0.00022622794,0.00035773535,0.000009733252,0.0009312832,0.0033578796],"genre_scores_gemma":[0.9855198,0.000003873904,0.009373662,0.00028872074,0.000084156665,0.00027919607,0.000010318494,0.000014103311,0.0044261683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986485,0.000028185766,0.00031033336,0.00038191606,0.00025151114,0.00037954937],"domain_scores_gemma":[0.99915004,0.000121154146,0.00010856421,0.00035707097,0.00016623305,0.00009693917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005826464,0.00012405608,0.00018493667,0.00016773788,0.00022421585,0.0001733058,0.0003086838,0.00004986728,0.000018428436],"category_scores_gemma":[0.000056402867,0.00011919854,0.00010751954,0.00043592037,0.000017946819,0.00092992204,0.00016693884,0.00007879241,0.00049494323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007553101,0.00047983718,0.010552425,0.00068264396,0.00030304462,0.000057903406,0.015863197,0.019044854,0.013941946,0.19751708,0.05336626,0.6881153],"study_design_scores_gemma":[0.00031842958,0.000048648697,0.0025940505,0.000033378426,0.000003495209,0.000011274363,0.000060248913,0.9915231,0.002794553,0.0019872452,0.00045053192,0.00017505074],"about_ca_topic_score_codex":0.000028254111,"about_ca_topic_score_gemma":0.0000268247,"teacher_disagreement_score":0.9724782,"about_ca_system_score_codex":0.00003727278,"about_ca_system_score_gemma":0.00007605632,"threshold_uncertainty_score":0.63616586},"labels":[],"label_agreement":null},{"id":"W4393190295","doi":"10.3390/s24072119","title":"Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Wearable computer; Wavelet packet decomposition; Activity recognition; Support vector machine; Pattern recognition (psychology); Computer science; Artificial intelligence; Discrete wavelet transform; Wavelet transform; Selection (genetic algorithm); Entropy (arrow of time); Machine learning; Data mining","score_opus":0.0634569301542508,"score_gpt":0.31802343856195875,"score_spread":0.25456650840770795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393190295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57431865,0.00002477667,0.42415196,0.00015221744,0.00028129976,0.00033980404,0.00005958254,0.0002618132,0.00040986453],"genre_scores_gemma":[0.9952727,0.0000021879007,0.003757601,0.00004676173,0.00006459842,0.00006277439,0.00003541924,0.000025109199,0.0007328492],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981827,0.00031197068,0.00032772246,0.00060825446,0.00031370638,0.0002556254],"domain_scores_gemma":[0.99834204,0.00070809433,0.00020387842,0.0002905843,0.00040318698,0.00005221383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066743715,0.00019678271,0.00039509128,0.0010126731,0.00019973413,0.00022534178,0.00013856737,0.00012777474,0.00007794156],"category_scores_gemma":[0.00009662845,0.00020093744,0.00037292234,0.0022946598,0.00005515652,0.0005459926,0.000021207848,0.00015755006,0.00006534386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008429932,0.0009116106,0.0014572471,0.0010077946,0.007280809,0.000013402556,0.0036235098,0.020593723,0.48844108,0.0056296713,0.0016952365,0.46850294],"study_design_scores_gemma":[0.00032568566,0.00044347535,0.0023075265,0.00010738482,0.00048123283,0.0000035198307,0.00009564881,0.9474045,0.046526868,0.00078552874,0.0012592068,0.00025942046],"about_ca_topic_score_codex":0.00050390244,"about_ca_topic_score_gemma":0.0005471879,"teacher_disagreement_score":0.9268108,"about_ca_system_score_codex":0.00013821412,"about_ca_system_score_gemma":0.000092077054,"threshold_uncertainty_score":0.8193991},"labels":[],"label_agreement":null},{"id":"W4393227484","doi":"10.1007/s00521-024-09645-7","title":"Few-shot transfer learning for wearable IMU-based human activity recognition","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Inertial measurement unit; Computational Science and Engineering; Transfer of learning; Computer science; Wearable computer; Activity recognition; Artificial intelligence; Shot (pellet); Pattern recognition (psychology); Human–computer interaction; Computer vision; Machine learning; Materials science","score_opus":0.07061333100416113,"score_gpt":0.32270334209553825,"score_spread":0.25209001109137713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393227484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20241195,0.00011903009,0.79494697,0.0007518605,0.00010717477,0.00053952244,0.000008999832,0.0006193667,0.0004951224],"genre_scores_gemma":[0.99806476,0.0000030075023,0.0012254495,0.000105483814,0.00020715923,0.00022616025,0.000021205231,0.000017025219,0.00012975078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988345,0.00009289593,0.00018671209,0.0005289615,0.00012853718,0.00022838096],"domain_scores_gemma":[0.9990161,0.00057654467,0.0000364998,0.00019927968,0.00009285496,0.00007873315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031487836,0.0001425696,0.00015891944,0.000118351454,0.0006512502,0.00048527436,0.00019270653,0.00006330162,0.000005481001],"category_scores_gemma":[0.000013598226,0.00014802748,0.000095081414,0.0003604632,0.00003602723,0.00029510813,0.0000424215,0.00026020515,0.000026765094],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004642217,0.000057808826,0.000105303385,0.00015701656,0.000016312391,0.000001104268,0.00017854357,0.0005534382,0.02094574,0.0013055254,0.00010507682,0.9765695],"study_design_scores_gemma":[0.00042373504,0.00014162646,0.0008475625,0.00017064615,0.000026694379,0.000029640176,0.000030821106,0.97074896,0.009523584,0.0016559516,0.01607237,0.0003284192],"about_ca_topic_score_codex":0.00004282587,"about_ca_topic_score_gemma":0.000012905601,"teacher_disagreement_score":0.97624105,"about_ca_system_score_codex":0.000031708907,"about_ca_system_score_gemma":0.00003834299,"threshold_uncertainty_score":0.6036385},"labels":[],"label_agreement":null},{"id":"W4393261213","doi":"10.18280/mmep.110306","title":"Artificial Butterfly Optimizer Based Two-Layer Convolutional Neural Network with Polarized Attention Mechanism for Human Activity Recognition","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Butterfly; Mechanism (biology); Convolutional neural network; Computer science; Artificial intelligence; Layer (electronics); Pattern recognition (psychology); Artificial neural network; Biology; Physics; Materials science; Nanotechnology","score_opus":0.06797132604329087,"score_gpt":0.251230553298145,"score_spread":0.1832592272548541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393261213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05644557,0.000033192788,0.941792,0.00039269432,0.00023264068,0.0005440459,0.000012014849,0.00052115845,0.000026699985],"genre_scores_gemma":[0.8542794,7.722537e-7,0.14522491,0.000026352662,0.00016254839,0.00022671885,0.000015979445,0.000031763837,0.00003154578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985834,0.00004095698,0.00030693892,0.00046655122,0.0002449782,0.00035717798],"domain_scores_gemma":[0.99918664,0.00034952344,0.000058169306,0.00018602281,0.00011353626,0.000106126725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000556508,0.00023140319,0.00028285678,0.00012061336,0.0001772271,0.00046335507,0.00012773991,0.00009499054,0.0000073936676],"category_scores_gemma":[0.00001534279,0.00020443978,0.00010019527,0.00019468841,0.000023177336,0.0004564182,0.00003922508,0.00022660126,0.000013657244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004210437,0.00012993508,0.0000033325282,0.0011549644,0.0001194748,0.0000073868036,0.00020135347,0.85492694,0.028510446,0.10869261,0.000014696688,0.006196767],"study_design_scores_gemma":[0.00033567465,0.00009860476,0.0000023447546,0.00056658225,0.000039410592,0.00003373515,0.000002260029,0.91519904,0.0007729933,0.0826829,0.000011285754,0.0002551491],"about_ca_topic_score_codex":0.00001751714,"about_ca_topic_score_gemma":0.00000244621,"teacher_disagreement_score":0.79783386,"about_ca_system_score_codex":0.000047978225,"about_ca_system_score_gemma":0.00002729771,"threshold_uncertainty_score":0.8336812},"labels":[],"label_agreement":null},{"id":"W4393462071","doi":"10.5281/zenodo.7843703","title":"Wi-Fi (CSI and RSSI) data of six basic knife activities for cooking (chopping, cubing, French cutting, julienning, mincing, and slicing)","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Slicing; Computer science; Business; Computer graphics (images)","score_opus":0.08702555841105854,"score_gpt":0.29068325241893006,"score_spread":0.20365769400787154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393462071","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002395651,0.0006350025,0.029836554,0.0004293838,0.00079167355,0.0019402611,0.96063894,0.0013379266,0.0019946136],"genre_scores_gemma":[0.012242304,0.0006548584,0.0012046029,0.00024987466,0.0006808376,0.0000013809653,0.9815997,0.0017629354,0.0016034949],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99586356,0.00052403676,0.00071098976,0.0015085797,0.0007357741,0.0006570491],"domain_scores_gemma":[0.99582905,0.00045869226,0.00087657134,0.0019540887,0.00060997676,0.00027164657],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0022050054,0.00044981443,0.0006635522,0.0008076676,0.0019186093,0.0020997077,0.0034451396,0.00024803935,0.00030446582],"category_scores_gemma":[0.0023272312,0.00050858315,0.0000816746,0.00074833934,0.00032993156,0.0012666247,0.008184565,0.00060339435,0.00059791503],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014399363,0.00006757491,0.000006579476,0.0007650651,0.00012322885,0.000013131967,0.00078181183,0.0000028005688,0.00026310165,0.000088391644,0.9473383,0.050535653],"study_design_scores_gemma":[0.0007046133,0.0002915809,0.00011003766,0.0006656649,0.00006855838,0.00019151527,0.00026849032,0.0029433053,0.000115171606,0.00004445192,0.99412024,0.0004763751],"about_ca_topic_score_codex":0.0006304077,"about_ca_topic_score_gemma":0.00003819938,"teacher_disagreement_score":0.050059278,"about_ca_system_score_codex":0.00013855235,"about_ca_system_score_gemma":0.000035646273,"threshold_uncertainty_score":0.99983704},"labels":[],"label_agreement":null},{"id":"W4393508083","doi":"10.5281/zenodo.20341016","title":"Dataset: A multi-scale probabilistic atlas of the human connectome","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke","funders":"","keywords":"Atlas (anatomy); Connectome; Scale (ratio); Probabilistic logic; Computer science; Cartography; Functional connectivity; Geography; Artificial intelligence; Geology; Neuroscience; Biology; Paleontology","score_opus":0.06739125044373977,"score_gpt":0.2820783351505674,"score_spread":0.21468708470682762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393508083","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016724059,0.00006406176,0.004472032,0.00027795965,0.0003031103,0.00080084306,0.99300575,0.00022453169,0.00068444444],"genre_scores_gemma":[0.0036624873,0.000020294063,0.00018033243,0.000117017174,0.00011653025,2.260934e-7,0.9951959,0.00046161152,0.00024557844],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963593,0.0011874472,0.0005237728,0.0008290655,0.0007283868,0.00037205996],"domain_scores_gemma":[0.9958952,0.0000993041,0.00047107175,0.0024863342,0.0008926361,0.0001554676],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000869036,0.00026677555,0.0003929208,0.00024128257,0.0015934581,0.0011433845,0.0049911244,0.00015906911,0.0038211679],"category_scores_gemma":[0.0011004064,0.00023798487,0.00013178626,0.0009933348,0.00027349198,0.00035729734,0.006424488,0.00058662903,0.0034250258],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003853154,0.00025931268,5.0434926e-7,0.0002564263,0.000053441356,0.000014801604,0.00015790063,0.0000049039363,0.00087104994,0.00009352619,0.9940744,0.004209866],"study_design_scores_gemma":[0.00043879374,0.000082837265,0.0000899278,0.00018659346,0.000029006835,0.00017981487,0.000050444814,0.00022232805,0.00023339893,0.00003247833,0.9982023,0.00025203795],"about_ca_topic_score_codex":0.00015794978,"about_ca_topic_score_gemma":0.000023866474,"teacher_disagreement_score":0.0042916997,"about_ca_system_score_codex":0.00017870043,"about_ca_system_score_gemma":0.000025054876,"threshold_uncertainty_score":0.99989355},"labels":[],"label_agreement":null},{"id":"W4393508748","doi":"10.5281/zenodo.5176211","title":"Supplement to Creating Mobile Self-Triage Applications: Requirements and Usability Perspectives","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Usability; Triage; Computer science; Human–computer interaction; Medical emergency; Medicine","score_opus":0.04209412765451447,"score_gpt":0.29714856187978816,"score_spread":0.2550544342252737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393508748","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027639227,0.00040805803,0.06384062,0.0008338472,0.00019178772,0.0039829165,0.9263818,0.0011663805,0.0029181782],"genre_scores_gemma":[0.008159854,0.00042910883,0.004529488,0.00021789096,0.0004529879,0.000008719418,0.9852536,0.0007187776,0.00022962502],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99649704,0.0007719609,0.00046495162,0.0012119815,0.00065246044,0.00040160373],"domain_scores_gemma":[0.99684495,0.00011952644,0.00024381408,0.0014493109,0.0010567032,0.0002856924],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013176408,0.00026719613,0.00033252503,0.00033073328,0.0019021834,0.0022250358,0.0018794558,0.00011038684,0.0037240249],"category_scores_gemma":[0.00065708114,0.00030645807,0.000072162635,0.0008769684,0.00008627685,0.0005161759,0.004616203,0.0003476158,0.00261137],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008330024,0.00031509614,0.0000014071383,0.00018922349,0.00007799006,0.000010791943,0.0014274914,0.0000024029418,0.00011133856,0.0002539551,0.9573323,0.04026966],"study_design_scores_gemma":[0.00032187757,0.00019704277,0.000041405376,0.00007415815,0.000021578004,0.00008817939,0.00097014103,0.00006439281,0.00006477235,0.000047253518,0.99781996,0.00028921137],"about_ca_topic_score_codex":0.000078545796,"about_ca_topic_score_gemma":0.000003763931,"teacher_disagreement_score":0.059311133,"about_ca_system_score_codex":0.00051274395,"about_ca_system_score_gemma":0.000019761315,"threshold_uncertainty_score":0.9999387},"labels":[],"label_agreement":null},{"id":"W4393641564","doi":"10.5281/zenodo.5176210","title":"Supplement to Creating Mobile Self-Triage Applications: Requirements and Usability Perspectives","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Usability; Triage; Computer science; World Wide Web; Human–computer interaction; Medical emergency; Medicine","score_opus":0.04209412765451447,"score_gpt":0.29714856187978816,"score_spread":0.2550544342252737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393641564","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027639227,0.00040805803,0.06384062,0.0008338472,0.00019178772,0.0039829165,0.9263818,0.0011663805,0.0029181782],"genre_scores_gemma":[0.008159854,0.00042910883,0.004529488,0.00021789096,0.0004529879,0.000008719418,0.9852536,0.0007187776,0.00022962502],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99649704,0.0007719609,0.00046495162,0.0012119815,0.00065246044,0.00040160373],"domain_scores_gemma":[0.99684495,0.00011952644,0.00024381408,0.0014493109,0.0010567032,0.0002856924],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013176408,0.00026719613,0.00033252503,0.00033073328,0.0019021834,0.0022250358,0.0018794558,0.00011038684,0.0037240249],"category_scores_gemma":[0.00065708114,0.00030645807,0.000072162635,0.0008769684,0.00008627685,0.0005161759,0.004616203,0.0003476158,0.00261137],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008330024,0.00031509614,0.0000014071383,0.00018922349,0.00007799006,0.000010791943,0.0014274914,0.0000024029418,0.00011133856,0.0002539551,0.9573323,0.04026966],"study_design_scores_gemma":[0.00032187757,0.00019704277,0.000041405376,0.00007415815,0.000021578004,0.00008817939,0.00097014103,0.00006439281,0.00006477235,0.000047253518,0.99781996,0.00028921137],"about_ca_topic_score_codex":0.000078545796,"about_ca_topic_score_gemma":0.000003763931,"teacher_disagreement_score":0.059311133,"about_ca_system_score_codex":0.00051274395,"about_ca_system_score_gemma":0.000019761315,"threshold_uncertainty_score":0.9999387},"labels":[],"label_agreement":null},{"id":"W4393673900","doi":"10.5281/zenodo.7843704","title":"Wi-Fi (CSI and RSSI) data of six basic knife activities for cooking (chopping, cubing, French cutting, julienning, mincing, and slicing)","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Slicing; Computer science; Computer graphics (images)","score_opus":0.08702555841105854,"score_gpt":0.29068325241893006,"score_spread":0.20365769400787154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393673900","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002395651,0.0006350025,0.029836554,0.0004293838,0.00079167355,0.0019402611,0.96063894,0.0013379266,0.0019946136],"genre_scores_gemma":[0.012242304,0.0006548584,0.0012046029,0.00024987466,0.0006808376,0.0000013809653,0.9815997,0.0017629354,0.0016034949],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99586356,0.00052403676,0.00071098976,0.0015085797,0.0007357741,0.0006570491],"domain_scores_gemma":[0.99582905,0.00045869226,0.00087657134,0.0019540887,0.00060997676,0.00027164657],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0022050054,0.00044981443,0.0006635522,0.0008076676,0.0019186093,0.0020997077,0.0034451396,0.00024803935,0.00030446582],"category_scores_gemma":[0.0023272312,0.00050858315,0.0000816746,0.00074833934,0.00032993156,0.0012666247,0.008184565,0.00060339435,0.00059791503],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014399363,0.00006757491,0.000006579476,0.0007650651,0.00012322885,0.000013131967,0.00078181183,0.0000028005688,0.00026310165,0.000088391644,0.9473383,0.050535653],"study_design_scores_gemma":[0.0007046133,0.0002915809,0.00011003766,0.0006656649,0.00006855838,0.00019151527,0.00026849032,0.0029433053,0.000115171606,0.00004445192,0.99412024,0.0004763751],"about_ca_topic_score_codex":0.0006304077,"about_ca_topic_score_gemma":0.00003819938,"teacher_disagreement_score":0.050059278,"about_ca_system_score_codex":0.00013855235,"about_ca_system_score_gemma":0.000035646273,"threshold_uncertainty_score":0.99983704},"labels":[],"label_agreement":null},{"id":"W4394564303","doi":"10.1109/access.2024.3386351","title":"Adaptive Hierarchical Classification for Human Activity Recognition Using Inertial Measurement Unit (IMU) Time-Series Data","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Bhabha Atomic Research Centre; Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Interpretability; Computer science; Inertial measurement unit; Artificial intelligence; Activity recognition; AdaBoost; Decision tree; Random forest; Machine learning; Data mining; Classifier (UML); Boosting (machine learning); Units of measurement; Pattern recognition (psychology)","score_opus":0.5144295705122292,"score_gpt":0.41298966955378447,"score_spread":0.10143990095844468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394564303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08352672,0.000053731095,0.9127093,0.000678989,0.0012311495,0.0007873811,0.00019726061,0.00042105047,0.00039441537],"genre_scores_gemma":[0.9958816,0.0000034949785,0.0031146347,0.00005982111,0.00062201725,0.0001300749,0.00009521353,0.000028502855,0.000064643136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757445,0.00029126857,0.000340327,0.0008921068,0.0006011097,0.0003007329],"domain_scores_gemma":[0.99812853,0.0002647711,0.00015548238,0.0009114537,0.00043655428,0.00010323077],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011666731,0.0002192785,0.0002559091,0.0002612572,0.00032068085,0.001217153,0.0014844692,0.00012063886,0.000025235071],"category_scores_gemma":[0.00013248624,0.00022012717,0.00008190833,0.00056007475,0.00007697919,0.0058257016,0.00042365785,0.00025174377,0.0000814185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015582953,0.00024684286,0.00008185273,0.00019876643,0.00020912678,0.000014920316,0.00038381235,0.000030032177,0.22357252,0.000662023,0.0026439894,0.7718003],"study_design_scores_gemma":[0.00079757976,0.00026792818,0.0019153956,0.00061612175,0.00012691152,0.00006204265,0.000043464723,0.9137997,0.06575246,0.008549292,0.0072514787,0.0008176141],"about_ca_topic_score_codex":0.00025692378,"about_ca_topic_score_gemma":0.00031514527,"teacher_disagreement_score":0.91376966,"about_ca_system_score_codex":0.00022334697,"about_ca_system_score_gemma":0.0003076481,"threshold_uncertainty_score":0.9998197},"labels":[],"label_agreement":null},{"id":"W4394567017","doi":"10.2196/45978","title":"Factors That Influence Successful Adoption of Real-Time Location Systems for Use in a Dementia Care Setting: Mixed Methods Study","year":2024,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Brock University; Public Health Ontario; University Health Network; University of Toronto; Toronto Rehabilitation Institute","funders":"","keywords":"Task (project management); Dementia; Real-time locating system; Health care; Perception; Psychology; Descriptive statistics; Applied psychology; Nursing; Medicine; Computer science; Disease; Engineering","score_opus":0.053614718064578334,"score_gpt":0.3612666766106673,"score_spread":0.30765195854608895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394567017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7148213,0.0002951362,0.28288814,0.00001648105,0.00041163113,0.0013485901,0.000008118842,0.0001922821,0.000018345232],"genre_scores_gemma":[0.9953033,0.0000031318616,0.00416052,0.000004641855,0.00002338449,0.00044300457,0.000017085717,0.000018995777,0.000025984891],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979106,0.0005713228,0.00045590336,0.0005265771,0.0003020661,0.00023348565],"domain_scores_gemma":[0.9980432,0.00097366434,0.0002154157,0.00041825065,0.0003008435,0.000048674166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011332051,0.00018052674,0.00029521334,0.0004354069,0.000073817464,0.00053192355,0.0003744097,0.00006387553,0.0000014705404],"category_scores_gemma":[0.00008811593,0.00017243782,0.00007347567,0.0007420592,0.000017050696,0.0018414655,0.00015338136,0.00011139376,0.0000057889583],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017124561,0.00024214723,0.8051903,0.0027737047,0.0002401378,0.000015945994,0.05573348,0.0021941373,0.011775361,0.0002545314,0.0001427649,0.12142036],"study_design_scores_gemma":[0.00085034856,0.00023414913,0.65491223,0.0024309978,0.000095969524,0.000009653535,0.026668381,0.307381,0.0061712684,0.000052012398,0.00057961856,0.0006143556],"about_ca_topic_score_codex":0.001588257,"about_ca_topic_score_gemma":0.00020244562,"teacher_disagreement_score":0.30518687,"about_ca_system_score_codex":0.00017578319,"about_ca_system_score_gemma":0.00010047022,"threshold_uncertainty_score":0.703181},"labels":[],"label_agreement":null},{"id":"W4394714448","doi":"10.5220/0009118700002513","title":"A Mobile Application for Physical Activity Recognition using Acceleration Data from Wearable Sensors for Cardiac Rehabilitation","year":2020,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Wearable computer; Acceleration; Rehabilitation; Computer science; Wearable technology; Accelerometer; Human–computer interaction; Embedded system; Medicine; Physical therapy; Physics","score_opus":0.16329225164076822,"score_gpt":0.3488036203501069,"score_spread":0.1855113687093387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394714448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30753493,0.000010949263,0.6884976,0.00057405315,0.00020071496,0.0024520175,0.0004913213,0.00019649626,0.00004192256],"genre_scores_gemma":[0.9094354,0.0000023710968,0.08827673,0.00011460578,0.0005494045,0.0010676191,0.00051390403,0.000020068492,0.000019921923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998298,0.00015504265,0.00022252252,0.00090240483,0.00022350196,0.00019855575],"domain_scores_gemma":[0.99740416,0.0012670305,0.00020384659,0.0006916902,0.0003359805,0.000097298805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037394182,0.0001598344,0.00029557745,0.000050685896,0.00018985175,0.00022239043,0.0004366226,0.00007370365,0.000005245646],"category_scores_gemma":[0.00034418062,0.00017213618,0.00012650424,0.00029153228,0.000021603219,0.0025153118,0.00016370606,0.00008116385,0.000049849536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026249734,0.00035061638,0.00012611347,0.000152632,0.0000905692,1.2462421e-7,0.0023595192,0.0005168888,0.23435032,0.00013470603,0.0030395668,0.75861645],"study_design_scores_gemma":[0.0005382567,0.00030583318,0.0001366892,0.000019535275,0.00003709902,3.685873e-7,0.00019212546,0.96732366,0.025033027,0.0013050094,0.0048881564,0.0002202601],"about_ca_topic_score_codex":0.00021898122,"about_ca_topic_score_gemma":0.000022216194,"teacher_disagreement_score":0.96680677,"about_ca_system_score_codex":0.00009213313,"about_ca_system_score_gemma":0.00009712654,"threshold_uncertainty_score":0.70195097},"labels":[],"label_agreement":null},{"id":"W4394714556","doi":"10.2139/ssrn.4791534","title":"Bridging Bim with Ai: A Gpt-Powered Assistant for Real-Time Modeling Actions","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Bridging (networking); Computer science; Systems engineering; Engineering; Computer security","score_opus":0.02870359815014962,"score_gpt":0.2874620964908778,"score_spread":0.25875849834072817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394714556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015964873,0.0014953096,0.97547823,0.003621954,0.001242255,0.0007652001,0.00004273944,0.0004996551,0.00088977563],"genre_scores_gemma":[0.991069,0.0009540551,0.0047572902,0.00008191646,0.0009814674,0.00019572007,0.00002150204,0.00011255886,0.001826466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99471027,0.00020083395,0.00074374914,0.0010486325,0.0006960084,0.0026005032],"domain_scores_gemma":[0.9976774,0.0001589308,0.0005224653,0.0008212194,0.00061248563,0.0002074934],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0026205226,0.0005338441,0.0007141812,0.0006484884,0.00048438855,0.0015362496,0.0012641927,0.00028958736,0.0000104059145],"category_scores_gemma":[0.00006588509,0.00047611457,0.00051243266,0.00045337935,0.00003516816,0.0005486835,0.0007317261,0.005502231,0.000082469836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007978581,0.000772598,0.00010345244,0.0011078104,0.00959571,0.00024804645,0.003465139,0.04172828,0.006948209,0.30993202,0.0022299567,0.62307096],"study_design_scores_gemma":[0.0006537331,0.0003015289,0.0000045337397,0.00093147677,0.00019367626,0.0024035368,0.00024936505,0.72438157,0.00008566464,0.26943022,0.0006828105,0.0006818889],"about_ca_topic_score_codex":0.00033394937,"about_ca_topic_score_gemma":0.0010758806,"teacher_disagreement_score":0.97510415,"about_ca_system_score_codex":0.00314589,"about_ca_system_score_gemma":0.011554388,"threshold_uncertainty_score":0.99976903},"labels":[],"label_agreement":null},{"id":"W4394951289","doi":"10.1109/jsen.2024.3388893","title":"Centaur: Robust Multimodal Fusion for Human Activity Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fusion; Sensor fusion; Activity recognition; Computer science; Centaur; Artificial intelligence; Computer vision; Pattern recognition (psychology); Physics","score_opus":0.07571047175349337,"score_gpt":0.3072349701069163,"score_spread":0.23152449835342293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394951289","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5188796,0.000063768486,0.47511885,0.00072916143,0.004092486,0.0003197406,0.000031709274,0.00027982157,0.00048489386],"genre_scores_gemma":[0.9919311,0.000023506229,0.0058233105,0.00008344934,0.0014860701,0.000021378059,0.000005992586,0.000033175573,0.0005920214],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979658,0.00025858334,0.00038968978,0.0004986378,0.00045118565,0.00043611077],"domain_scores_gemma":[0.9986197,0.00037393224,0.0001889203,0.00028544207,0.00030713028,0.00022491589],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008973723,0.00023490166,0.0002699662,0.0003762934,0.00055212964,0.0010576725,0.0004026626,0.0001419402,0.00008654275],"category_scores_gemma":[0.00006713879,0.00022062595,0.00032091234,0.0003332281,0.00003843599,0.0015403617,0.000060434086,0.000553632,0.00022314642],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005885836,0.00022474353,0.00008073439,0.000121851124,0.00014031479,0.00023198797,0.0011384664,0.000352121,0.12143881,0.00011544469,0.011678944,0.86441773],"study_design_scores_gemma":[0.004854528,0.0012050105,0.0033373646,0.0020160645,0.0001903176,0.010735896,0.00043373165,0.7196569,0.19142886,0.013231895,0.05059898,0.002310452],"about_ca_topic_score_codex":0.000058086916,"about_ca_topic_score_gemma":0.00005528707,"teacher_disagreement_score":0.8621073,"about_ca_system_score_codex":0.0002426075,"about_ca_system_score_gemma":0.00011805328,"threshold_uncertainty_score":0.9999793},"labels":[],"label_agreement":null},{"id":"W4395079775","doi":"10.18280/ria.380230","title":"GeriatricCare 4.0: A Novel 3D Context-Based CareVision Framework for Fall Detection, Fall Classification and Fall Alerts for Elderlies","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Fall of man; Computer science; Geography; Political science","score_opus":0.07240764313328675,"score_gpt":0.3072889139774623,"score_spread":0.23488127084417554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395079775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008518968,0.002107302,0.9837275,0.0018066324,0.0014666088,0.0017798175,0.00008561045,0.00043229578,0.00007523084],"genre_scores_gemma":[0.96166784,0.000078244295,0.036604002,0.00024114942,0.00029863947,0.00074066187,0.000026910784,0.00004545003,0.0002971115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974299,0.00007591763,0.0007103551,0.0010946194,0.00024463242,0.0004445457],"domain_scores_gemma":[0.9959323,0.0024263293,0.00021190931,0.00070267153,0.00057674566,0.00015005367],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00072245725,0.0003223052,0.00039322348,0.00032225501,0.00037134677,0.0007877548,0.0005377592,0.0002623667,0.000007910378],"category_scores_gemma":[0.00061782345,0.0003245945,0.0002546949,0.00066836074,0.0000843571,0.0006099233,0.00010300461,0.00023916102,0.000069847076],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053558757,0.00012264779,0.0001801516,0.0008970844,0.000051355688,0.000003110224,0.0026034557,0.0005462339,0.0050818278,0.027441213,0.00043928108,0.9625801],"study_design_scores_gemma":[0.00013938239,0.00034064197,0.00008232645,0.00070644566,0.00004424032,0.000031653133,0.0012128544,0.9080189,0.017156465,0.0056939367,0.066102624,0.0004705704],"about_ca_topic_score_codex":0.00014447194,"about_ca_topic_score_gemma":0.00046249255,"teacher_disagreement_score":0.9621095,"about_ca_system_score_codex":0.00014804954,"about_ca_system_score_gemma":0.00013981614,"threshold_uncertainty_score":0.9999206},"labels":[],"label_agreement":null},{"id":"W4395464764","doi":"10.2196/51874","title":"Objective Assessment of Physical Activity at Home Using a Novel Floor-Vibration Monitoring System: Validation and Comparison With Wearable Activity Trackers and Indirect Calorimetry Measurements","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Wearable computer; Calorimetry; Physical activity; Vibration; Computer science; Activity tracker; Environmental science; Simulation; Acoustics; Physical medicine and rehabilitation; Embedded system; Medicine; Physics","score_opus":0.18109164844896017,"score_gpt":0.4326959481665427,"score_spread":0.2516042997175826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395464764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8375106,0.00007231637,0.16107246,0.000028549222,0.00014262482,0.0007896965,0.000017300688,0.000086842745,0.00027962058],"genre_scores_gemma":[0.99857825,0.000004250047,0.0011975112,4.506647e-7,0.000062656036,0.00012777226,0.0000016884335,0.000016427108,0.000010965654],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99674827,0.0007297845,0.00022033222,0.000518165,0.0014179071,0.0003655557],"domain_scores_gemma":[0.9983633,0.00060865976,0.00018349872,0.00026028638,0.0004550196,0.0001292221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018057647,0.00020893797,0.0004401361,0.0005502359,0.00041603745,0.00049555994,0.00018386844,0.000087351924,0.0000010777794],"category_scores_gemma":[0.00003021576,0.00017842469,0.000050555078,0.0011395433,0.00015189033,0.0035669135,0.00032351224,0.0005256501,0.000004252024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037983843,0.0013146248,0.044633824,0.0037141219,0.0007387439,0.00000844568,0.038676366,0.00036525403,0.77400637,0.00034753294,0.000014406659,0.13580047],"study_design_scores_gemma":[0.0007503823,0.0005603644,0.1825482,0.0014502053,0.000023372237,0.00003627788,0.0014680506,0.49786425,0.31503823,0.00001980361,0.0000040830305,0.00023677119],"about_ca_topic_score_codex":0.00031703684,"about_ca_topic_score_gemma":0.000019474748,"teacher_disagreement_score":0.497499,"about_ca_system_score_codex":0.0011968127,"about_ca_system_score_gemma":0.0002762108,"threshold_uncertainty_score":0.7275948},"labels":[],"label_agreement":null},{"id":"W4396530985","doi":"10.2196/58394","title":"Correction: Detecting Anomalies in Daily Activity Routines of Older Persons in Single Resident Smart Homes: Proof-of-Concept Study","year":2024,"lang":"en","type":"erratum","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Proof of concept; Computer science; Psychology; Gerontology; Data science; Medicine; Operating system","score_opus":0.03189219778018192,"score_gpt":0.2923250926890648,"score_spread":0.2604328949088829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396530985","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8780782,0.0039219866,0.020384017,0.0006048011,0.07656916,0.0063015176,0.000045475826,0.000683887,0.013410904],"genre_scores_gemma":[0.9823052,0.0000021222486,0.00009183834,0.000010653342,0.00038738002,0.00030289183,0.0000055096853,0.000049492734,0.016844856],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99630684,0.000582358,0.0008919737,0.0010750839,0.00068747497,0.0004562958],"domain_scores_gemma":[0.99768,0.00056732364,0.0007329738,0.00077184325,0.00018694853,0.000060900475],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009988908,0.00045660362,0.0010593266,0.0013920612,0.00008932249,0.00027682408,0.000762141,0.0002741407,0.000016715894],"category_scores_gemma":[0.00022956014,0.00048219413,0.00021135925,0.0018609643,0.00008060882,0.0008673559,0.0006592426,0.0011881333,0.0000038595476],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086273714,0.0034317204,0.11601984,0.0039632632,0.00043081873,0.0006554232,0.29189694,0.00020106288,0.0011641764,0.00000820498,0.101190664,0.4809516],"study_design_scores_gemma":[0.008437196,0.0043356284,0.53395295,0.08344162,0.00040857415,0.00073915505,0.17712194,0.10432575,0.05852774,0.00031322168,0.020785535,0.0076106605],"about_ca_topic_score_codex":0.0026190465,"about_ca_topic_score_gemma":0.011638438,"teacher_disagreement_score":0.47334096,"about_ca_system_score_codex":0.0004683192,"about_ca_system_score_gemma":0.00044397975,"threshold_uncertainty_score":0.99976295},"labels":[],"label_agreement":null},{"id":"W4396549678","doi":"10.3390/electronics13091739","title":"Enhancing Human Activity Recognition with Siamese Networks: A Comparative Study of Contrastive and Triplet Learning Approaches","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"Ministry of Education - Singapore","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Deep learning; Discriminative model; Machine learning; ENCODE; Classifier (UML); Salient; Adaptability; Robustness (evolution); Pattern recognition (psychology)","score_opus":0.060406746899962865,"score_gpt":0.27962315683331473,"score_spread":0.21921640993335187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396549678","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6657789,0.0012151973,0.33199355,0.000028807151,0.00004435654,0.00051696453,0.0000013507698,0.00014602569,0.0002748626],"genre_scores_gemma":[0.9995058,0.000022608583,0.00023459233,0.0000061045057,0.000054879056,0.000090113586,0.00000510285,0.0000147102155,0.00006606238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982417,0.0004461319,0.00023476142,0.0005231642,0.00024255613,0.0003116547],"domain_scores_gemma":[0.9989886,0.00049825956,0.00016723984,0.0001806822,0.00010580789,0.00005935237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059638615,0.00019762266,0.00039355067,0.00015203095,0.000178698,0.00022433813,0.00016484558,0.00005858446,0.0000033491465],"category_scores_gemma":[0.00002396472,0.00017546923,0.0000397203,0.00055489124,0.000054540014,0.0006819141,0.00008166036,0.00054206833,0.000003486166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057628134,0.0024089494,0.0032471952,0.00036861005,0.0021027513,0.000095097166,0.08954276,0.00216883,0.020623581,0.0023400616,0.00010839516,0.8764175],"study_design_scores_gemma":[0.0062853033,0.018424463,0.008585455,0.0012464765,0.00037714854,0.00032221904,0.018750748,0.89876765,0.042485185,0.0015980575,0.0013709103,0.0017863994],"about_ca_topic_score_codex":0.00004915397,"about_ca_topic_score_gemma":0.0011506221,"teacher_disagreement_score":0.8965988,"about_ca_system_score_codex":0.000114316266,"about_ca_system_score_gemma":0.00014291937,"threshold_uncertainty_score":0.71554273},"labels":[],"label_agreement":null},{"id":"W4396656241","doi":"10.24908/agt.v2i1.17677","title":"Trouble Sleeping?","year":2024,"lang":"en","type":"article","venue":"Aging and (Geron) Technology","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Sleep (system call); Sleep quality; Health care; Computer science; Internet privacy; Psychology; Political science; Psychiatry; Insomnia","score_opus":0.01600506704213003,"score_gpt":0.25479048634029505,"score_spread":0.23878541929816502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396656241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17860132,0.009108122,0.77207583,0.027523207,0.0018404031,0.00024349135,0.0000032380372,0.005624621,0.004979784],"genre_scores_gemma":[0.9967292,0.00004726835,0.002331158,0.0001880679,0.0000618145,0.000029617371,6.4739015e-7,0.000011052336,0.00060116645],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989772,0.000027381142,0.00015700232,0.0004642539,0.000102664875,0.00027152264],"domain_scores_gemma":[0.9993956,0.00010648371,0.000031910477,0.0003899078,0.0000328423,0.00004325255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021820182,0.00011903101,0.00017007331,0.0004139475,0.000108883694,0.00020443996,0.00037251718,0.00012942875,0.000009788647],"category_scores_gemma":[0.000028244216,0.000114998955,0.000037395457,0.00060808525,0.00008133578,0.00038897985,0.00027109368,0.0002532173,0.00018920613],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.473958e-7,0.00001754324,0.001324858,0.00007269041,0.00004368746,0.00008401551,0.0003780146,0.0000010873911,0.0020276434,0.050495822,0.0011506055,0.9444031],"study_design_scores_gemma":[0.0013487324,0.00049180555,0.0016465558,0.0018708846,0.00006803356,0.0032777218,0.0007215135,0.098073505,0.03688256,0.087976635,0.7660482,0.0015938525],"about_ca_topic_score_codex":0.000048382357,"about_ca_topic_score_gemma":0.000023496576,"teacher_disagreement_score":0.9428092,"about_ca_system_score_codex":0.00003540512,"about_ca_system_score_gemma":0.00003535518,"threshold_uncertainty_score":0.46895212},"labels":[],"label_agreement":null},{"id":"W4396879438","doi":"10.2196/46012","title":"Human Digital Twins for Pervasive Healthcare: A Scoping Review (Preprint)","year":2023,"lang":"en","type":"review","venue":"Interactive Journal of Medical Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Computer science; Digital health; Health care; Internet privacy; World Wide Web; Political science","score_opus":0.44402120966877184,"score_gpt":0.5937978658881012,"score_spread":0.1497766562193294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396879438","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000013253186,0.96242154,0.026183106,0.006420656,0.0010696047,0.0036003303,0.00003935061,0.000050190858,0.00021392667],"genre_scores_gemma":[0.000062113904,0.99764484,0.00014957922,0.00023430103,0.0011233846,0.0004808447,0.000010437223,0.000066894136,0.00022758024],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.98715574,0.002771452,0.0028137227,0.0007927959,0.005627694,0.00083857053],"domain_scores_gemma":[0.9703035,0.020931771,0.0022184635,0.000940903,0.0044776984,0.0011276948],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.019091398,0.00041900584,0.003275602,0.0013330185,0.00026554673,0.00076230295,0.004731714,0.00044579827,0.0001563661],"category_scores_gemma":[0.037848122,0.00031631734,0.0016217843,0.001491742,0.00020751171,0.0013730015,0.0019020376,0.004634343,0.0004419071],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007340939,0.00011334454,4.981498e-7,0.10432383,0.00031437926,0.00037344653,0.0001847613,1.6192516e-8,1.625135e-7,0.00020843733,0.0076691564,0.88680464],"study_design_scores_gemma":[0.00023438751,0.00040979363,4.851976e-7,0.60664237,0.000039800594,0.0009723345,0.00006347569,0.000017323195,0.000001012253,0.00037396184,0.3910719,0.00017317364],"about_ca_topic_score_codex":0.000060660524,"about_ca_topic_score_gemma":0.00006480916,"teacher_disagreement_score":0.8866314,"about_ca_system_score_codex":0.0010754296,"about_ca_system_score_gemma":0.007850694,"threshold_uncertainty_score":0.9999289},"labels":[],"label_agreement":null},{"id":"W4399286476","doi":"10.1007/978-3-031-59717-6_22","title":"Personalized Sleep Monitoring Using Smartphones and Semi-supervised Learning","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sleep (system call); Computer science; Human–computer interaction; Operating system","score_opus":0.03936125343915034,"score_gpt":0.2578709781664984,"score_spread":0.21850972472734806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399286476","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002267323,0.0016742671,0.9932348,0.0004173067,0.001008196,0.0004079007,0.00002212766,0.00012861213,0.0008394729],"genre_scores_gemma":[0.13203663,0.00018955351,0.8671684,0.000055397344,0.00030296083,0.00002352444,0.000011876763,0.000030616673,0.00018101459],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987963,0.00001574287,0.00050778873,0.0002051318,0.0002635206,0.00021151101],"domain_scores_gemma":[0.99871993,0.00039091156,0.0003138653,0.0003675109,0.00015463098,0.00005313748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003980472,0.00028283402,0.00039150164,0.00025790106,0.0009040221,0.00051518035,0.0010591378,0.000185749,6.4826554e-7],"category_scores_gemma":[0.000045879406,0.00024141025,0.00017641766,0.00023471276,0.00031213663,0.00054719753,0.0009944733,0.00050051406,7.667337e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003945599,0.000019093834,0.000076452634,0.0013535188,0.00041901943,7.736435e-7,0.015099391,0.8174582,0.00028050886,0.050334264,0.000018151055,0.11493668],"study_design_scores_gemma":[0.00017078898,0.000033404405,0.000021871887,0.00060357375,0.00005642784,0.000040327166,0.00001870827,0.9865829,0.00011275842,0.0007115156,0.011349072,0.00029867163],"about_ca_topic_score_codex":0.00004500768,"about_ca_topic_score_gemma":0.000017277978,"teacher_disagreement_score":0.16912468,"about_ca_system_score_codex":0.00007841137,"about_ca_system_score_gemma":0.00011073999,"threshold_uncertainty_score":0.9844424},"labels":[],"label_agreement":null},{"id":"W4399566859","doi":"10.1145/3660515.3661324","title":"AI-Driven Personalization to Support Human-AI Collaboration","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 British Columbia","funders":"","keywords":"Personalization; Computer science; World Wide Web; Human–computer interaction; Knowledge management","score_opus":0.03141110065362577,"score_gpt":0.33685249643336523,"score_spread":0.30544139577973944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399566859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042813104,0.000021439419,0.9716561,0.013195679,0.0008558918,0.00035124779,0.000009401098,0.0007117269,0.008917231],"genre_scores_gemma":[0.98694885,9.191113e-7,0.001393975,0.003819949,0.00016249587,0.000054395317,0.000024168025,0.000011632056,0.0075836],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99883497,0.00006645376,0.0002050402,0.000410477,0.00032307985,0.00015997824],"domain_scores_gemma":[0.9992785,0.00004828204,0.000026017175,0.0002673582,0.00028301164,0.0000968551],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021297409,0.00010769477,0.00011721893,0.00022537186,0.000108983426,0.0009728691,0.00028074704,0.000054235305,0.00040602215],"category_scores_gemma":[0.000021464362,0.000104859755,0.00004609432,0.0009916305,0.000010531924,0.0014484513,0.00010320914,0.000077307945,0.0015610062],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064074625,0.000115898816,0.0014258149,0.00016897886,0.00008464689,0.000081424114,0.012280505,0.000116445706,0.034306508,0.2798148,0.56933683,0.10226173],"study_design_scores_gemma":[0.00048822942,0.0006424791,0.0014670728,0.00022800425,0.000026394615,0.00011064343,0.0006171851,0.22651605,0.014307673,0.003381992,0.7513396,0.00087464193],"about_ca_topic_score_codex":0.000053291864,"about_ca_topic_score_gemma":0.00036293673,"teacher_disagreement_score":0.98266757,"about_ca_system_score_codex":0.00011866122,"about_ca_system_score_gemma":0.00019086571,"threshold_uncertainty_score":0.9992164},"labels":[],"label_agreement":null},{"id":"W4399620754","doi":"10.1109/iraset60544.2024.10548344","title":"Context-Aware Home System Prototype with Sensors Management","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Sheridan College","funders":"","keywords":"Computer science; Context (archaeology); Context management; Human–computer interaction; Ubiquitous computing","score_opus":0.014064048957676668,"score_gpt":0.2242451267285543,"score_spread":0.21018107777087763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399620754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012840316,0.00024026846,0.9176009,0.0012186612,0.0011790955,0.0025951052,0.000009681524,0.0038595498,0.060456406],"genre_scores_gemma":[0.990859,0.0000029794712,0.0019436987,0.00012214058,0.000077010154,0.00040262594,0.0000024633853,0.000025735775,0.006564396],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99815583,0.00010385276,0.00026785288,0.00068281696,0.00046725583,0.00032236573],"domain_scores_gemma":[0.99896216,0.000104725535,0.000049705715,0.00064226956,0.00011828558,0.00012282262],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031180299,0.00022749975,0.0002512524,0.0002486182,0.00009952725,0.00067547493,0.00052806607,0.00005948154,0.000053413045],"category_scores_gemma":[0.000002087022,0.00016611899,0.00008094963,0.00068825885,0.00003423564,0.00075534283,0.00019126631,0.00014614245,0.001634793],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008801861,0.00014030917,0.00075146597,0.0036899368,0.0008514179,0.003171997,0.0018693667,0.000040812596,0.000105359286,0.4623657,0.0087058805,0.51821977],"study_design_scores_gemma":[0.0044054035,0.002235027,0.0039686495,0.010096235,0.0002613656,0.005617447,0.012908611,0.41406173,0.0056472016,0.0012331699,0.5352496,0.0043155765],"about_ca_topic_score_codex":0.000070948634,"about_ca_topic_score_gemma":0.00004815615,"teacher_disagreement_score":0.97801864,"about_ca_system_score_codex":0.00015900779,"about_ca_system_score_gemma":0.00006335743,"threshold_uncertainty_score":0.9991425},"labels":[],"label_agreement":null},{"id":"W4399914382","doi":"10.1016/j.patrec.2024.06.017","title":"HARWE: A multi-modal large-scale dataset for context-aware human activity recognition in smart working environments","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Toronto","funders":"Huawei Technologies","keywords":"Computer science; Activity recognition; Task (project management); Context (archaeology); Artificial intelligence; Process (computing); Scale (ratio); Modal; Artificial neural network; Modalities; SIGNAL (programming language); Machine learning; Pattern recognition (psychology); Deep neural networks; Speech recognition","score_opus":0.07766638033158467,"score_gpt":0.2979543367091869,"score_spread":0.22028795637760223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399914382","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25911802,0.000053667845,0.7309487,0.002373669,0.0011866202,0.0013638411,0.0046334495,0.00029096327,0.000031068586],"genre_scores_gemma":[0.9870202,0.000013610227,0.0015569333,0.004027102,0.00031232846,0.0011081818,0.0058371313,0.000076397235,0.000048106802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960999,0.00044243553,0.0006850095,0.001456689,0.0004884148,0.000827532],"domain_scores_gemma":[0.9983744,0.0005170087,0.00025253787,0.00062010874,0.000043603995,0.00019234122],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010398088,0.0004919777,0.00049713283,0.00063172734,0.00030906926,0.00069172715,0.0006024436,0.00021855815,0.00018158548],"category_scores_gemma":[0.000045312816,0.0005645275,0.0002579644,0.00046575576,0.000076736775,0.0017806254,0.000268748,0.0005801598,0.0009813275],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044504915,0.00054466364,0.0052115135,0.00026192033,0.00012832633,0.00013635187,0.0012834091,0.0000037091356,0.020238258,0.0000019911406,0.0059785526,0.9661668],"study_design_scores_gemma":[0.034924977,0.0012294183,0.08756731,0.013368667,0.00067278254,0.0008918363,0.0023562196,0.44479847,0.09709744,0.0021331073,0.30351377,0.01144599],"about_ca_topic_score_codex":0.00032885894,"about_ca_topic_score_gemma":0.001820463,"teacher_disagreement_score":0.9547208,"about_ca_system_score_codex":0.00038381072,"about_ca_system_score_gemma":0.000043893226,"threshold_uncertainty_score":0.9997965},"labels":[],"label_agreement":null},{"id":"W4400004661","doi":"10.1007/978-3-031-62871-9_20","title":"Human Activity Recognition Using Convolutional Neural Networks","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.05287082130197344,"score_gpt":0.2673948816811631,"score_spread":0.21452406037918967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400004661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012026257,0.01524692,0.96715415,0.00013522766,0.005734652,0.0010585157,0.000043938362,0.0003280419,0.009095943],"genre_scores_gemma":[0.9942944,0.000094919465,0.00014065996,0.00012233823,0.002794154,0.000046686884,0.00006950105,0.00009300454,0.0023443343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99680084,0.00020069677,0.00074403343,0.0012123653,0.00046879632,0.0005732453],"domain_scores_gemma":[0.9979761,0.0005902469,0.00048798666,0.0005898064,0.00018625976,0.00016960513],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007026763,0.00069792074,0.000982204,0.0004076983,0.0002734265,0.0008539334,0.0004302081,0.0011398568,0.000025315792],"category_scores_gemma":[0.000024019379,0.00068083854,0.00024551715,0.0002262714,0.00013317408,0.00043564558,0.00035177646,0.0016600596,0.000016114212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067873254,0.000092320224,0.00041756575,0.001011505,0.0006791972,0.0006467755,0.00046579848,0.5668922,0.000121107005,0.026186924,0.0006872341,0.4027315],"study_design_scores_gemma":[0.00028987546,0.000073893876,0.00005289986,0.0017238699,0.000060585084,0.000467501,0.0000019777729,0.98652685,0.0000016561007,0.007871355,0.0021954859,0.0007340325],"about_ca_topic_score_codex":0.00042101415,"about_ca_topic_score_gemma":0.0005643283,"teacher_disagreement_score":0.99309176,"about_ca_system_score_codex":0.0003350113,"about_ca_system_score_gemma":0.00006625265,"threshold_uncertainty_score":0.9995643},"labels":[],"label_agreement":null},{"id":"W4400036895","doi":"10.1145/3652037.3663933","title":"Leveraging Pervasive Technologies for monitoring patients during hospitalization: a pilot study presenting the perspectives of the treating clinicians","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"McGill University","funders":"Universitas Brawijaya","keywords":"Computer science; Medicine","score_opus":0.04353007630860846,"score_gpt":0.29883059804208534,"score_spread":0.2553005217334769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400036895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95535314,0.00024870437,0.04078167,0.000661036,0.00071210065,0.0013888223,0.000002713373,0.00051627547,0.00033553646],"genre_scores_gemma":[0.9988954,0.0000055740798,0.0005542083,0.000003540032,0.00009401641,0.00021204853,2.0377709e-7,0.000014873539,0.00022015208],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9985861,0.000118197415,0.00034664804,0.00042902704,0.00031357026,0.00020642592],"domain_scores_gemma":[0.9983031,0.00072801526,0.00016628309,0.0004970507,0.00029351222,0.000012043846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036415036,0.00014146214,0.00017109033,0.00010683436,0.00048269596,0.00035918324,0.0007956965,0.000024796735,0.00000135278],"category_scores_gemma":[0.0005230134,0.00008406612,0.00010866614,0.0006154175,0.000056885838,0.0005941169,0.0005494543,0.00014208774,0.0000016969807],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009901151,0.0005530736,0.8433441,0.00016913612,0.00026598485,0.0000029995692,0.118615836,0.00010924142,0.000638088,0.0009275203,0.00003178843,0.035332367],"study_design_scores_gemma":[0.00129015,0.0010615708,0.42249158,0.0009383156,0.00009704238,0.000011533117,0.52670777,0.02727293,0.018404234,0.001172031,0.000062509636,0.0004903163],"about_ca_topic_score_codex":0.00009127309,"about_ca_topic_score_gemma":0.000023299306,"teacher_disagreement_score":0.42085248,"about_ca_system_score_codex":0.00008653845,"about_ca_system_score_gemma":0.00004606122,"threshold_uncertainty_score":0.37125558},"labels":[],"label_agreement":null},{"id":"W4400036965","doi":"10.1145/3652037.3652041","title":"Fine-Grained Human Activity Recognition in Smart Homes Through Photoplethysmography-Based Hand Gesture Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Photoplethysmogram; Computer science; Gesture; Gesture recognition; Artificial intelligence; Computer vision; Activity recognition; Speech recognition; Pattern recognition (psychology)","score_opus":0.03423877870552629,"score_gpt":0.2754903110479702,"score_spread":0.24125153234244395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400036965","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5943338,0.00012525995,0.4008983,0.00088550954,0.0007758953,0.00051643024,0.000013158116,0.00078377785,0.0016678341],"genre_scores_gemma":[0.99822974,0.000004074351,0.0010186811,0.0001810892,0.000108520944,0.00020242998,0.000020946274,0.000023620278,0.00021088647],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99787337,0.00028226557,0.00031738132,0.0008028807,0.0003673404,0.0003567515],"domain_scores_gemma":[0.9988909,0.0003892038,0.000088951565,0.00043757397,0.000119615936,0.00007376239],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005099428,0.0002811988,0.00030903923,0.0005713296,0.00022024308,0.00075307704,0.0003270216,0.00019582621,0.000090975955],"category_scores_gemma":[0.000053186675,0.0002661326,0.0002187393,0.0016774656,0.000075955446,0.001609885,0.00008423844,0.0004172253,0.00012144071],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053830077,0.00041531754,0.002028908,0.00032546496,0.000095532894,0.0001294179,0.001563441,0.000011613277,0.2485543,0.0004095711,0.00083276763,0.74557984],"study_design_scores_gemma":[0.0027205662,0.0006528777,0.046044566,0.0008837539,0.000050482304,0.0001133896,0.00013779708,0.03689463,0.88121414,0.017533083,0.012235526,0.0015192035],"about_ca_topic_score_codex":0.0018561101,"about_ca_topic_score_gemma":0.013316045,"teacher_disagreement_score":0.74406064,"about_ca_system_score_codex":0.00013076587,"about_ca_system_score_gemma":0.00009850282,"threshold_uncertainty_score":0.9999791},"labels":[],"label_agreement":null},{"id":"W4400037582","doi":"10.1145/3652037.3663930","title":"Perception of the usefulness of socially assistive robots for adherence to home-based rehabilitation exercises for persons with chronic neurological conditions","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke; Université de Montréal","funders":"","keywords":"Perception; Rehabilitation; Physical medicine and rehabilitation; Robot; Psychology; Physical therapy; Human–computer interaction; Computer science; Applied psychology; Medicine; Artificial intelligence","score_opus":0.04847103789541294,"score_gpt":0.29923200703826003,"score_spread":0.25076096914284707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400037582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2856821,0.000009207136,0.7107579,0.0015562676,0.00020247187,0.0015524452,0.0001329829,0.00007291736,0.000033670975],"genre_scores_gemma":[0.98308957,2.4253362e-7,0.015095572,0.00007720598,0.000035661862,0.0015773285,0.000008602191,0.000008805417,0.00010699289],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99892664,0.00011617629,0.00023639397,0.00035897468,0.00021660453,0.00014520915],"domain_scores_gemma":[0.9973005,0.0018927755,0.00010614913,0.00024636532,0.00041528404,0.00003893067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028705286,0.00010879335,0.00021606349,0.00010433928,0.00012844382,0.000066948,0.000313692,0.000059970676,0.000026884129],"category_scores_gemma":[0.00015970589,0.00007260509,0.00017879611,0.00041881073,0.00013116335,0.00028133596,0.000044051805,0.00006192933,0.000003423892],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018112079,0.0023919763,0.02645137,0.006490881,0.00050605513,0.0000047480803,0.02754536,0.024819514,0.11685536,0.09778095,0.016357677,0.6789849],"study_design_scores_gemma":[0.004547389,0.030130558,0.5709177,0.002094293,0.00040265438,0.000023339746,0.0056966883,0.36142328,0.015161721,0.006939721,0.0014296693,0.0012329187],"about_ca_topic_score_codex":0.000043334992,"about_ca_topic_score_gemma":0.00030037662,"teacher_disagreement_score":0.6974075,"about_ca_system_score_codex":0.000085951935,"about_ca_system_score_gemma":0.0005092418,"threshold_uncertainty_score":0.296075},"labels":[],"label_agreement":null},{"id":"W4400114404","doi":"10.1109/i2mtc60896.2024.10560997","title":"Hierarchical Classifier for Improved Human Activity Recognition using Wearable Sensors","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Activity recognition; Computer science; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Wearable technology; Embedded system","score_opus":0.10384482698742321,"score_gpt":0.32910635871773597,"score_spread":0.22526153173031277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400114404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21836177,0.000032734093,0.7755724,0.0010213987,0.00086490216,0.0005666538,0.000023450673,0.00068047567,0.0028762072],"genre_scores_gemma":[0.9807638,0.0000027873969,0.016728273,0.00013778097,0.00029800468,0.000077688885,0.000006796014,0.000027323737,0.0019575462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983348,0.00014969023,0.0002574615,0.00068753114,0.00018827808,0.000382234],"domain_scores_gemma":[0.9988279,0.00046187587,0.00006231409,0.00037794982,0.00014188046,0.00012812762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056422525,0.00019231277,0.00024390439,0.00023391408,0.00026337785,0.0006891563,0.00028232372,0.00014303239,0.000080174854],"category_scores_gemma":[0.000062071136,0.0001800563,0.00019374977,0.00040693025,0.000046936384,0.0013093622,0.00013040166,0.0002677859,0.00012249254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023128709,0.00011013798,0.000015303649,0.00013354154,0.000084400825,0.000010936214,0.00027573225,0.0000050881977,0.27045795,0.0013524366,0.0011595205,0.7263718],"study_design_scores_gemma":[0.00049949647,0.00015919961,0.00013093291,0.00019227793,0.000026788859,0.000100978905,0.000045291476,0.9371058,0.042448647,0.009502103,0.009331473,0.00045701643],"about_ca_topic_score_codex":0.0001834812,"about_ca_topic_score_gemma":0.00007676181,"teacher_disagreement_score":0.9371007,"about_ca_system_score_codex":0.0001486468,"about_ca_system_score_gemma":0.0001371907,"threshold_uncertainty_score":0.7342483},"labels":[],"label_agreement":null},{"id":"W4400114909","doi":"10.1109/i2mtc60896.2024.10560619","title":"The Effect of Sensor Placement in a Cooking Activity Recognition System","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Lakehead University; University of Ottawa","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence","score_opus":0.018758535969509774,"score_gpt":0.26036319761185533,"score_spread":0.24160466164234556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400114909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78611624,0.00018357341,0.20060082,0.0003471937,0.0012153588,0.0008446787,0.000006495912,0.00044026418,0.010245377],"genre_scores_gemma":[0.9994972,0.0000040943482,0.00010986438,0.0000073240017,0.000039652998,0.000096004274,7.386352e-7,0.00000729691,0.00023787886],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99846756,0.00050618686,0.0002507107,0.0003024505,0.00028237014,0.00019070129],"domain_scores_gemma":[0.9975517,0.0019926552,0.0000739381,0.00030275967,0.000045189063,0.000033779754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015876455,0.00011867421,0.00019967525,0.00014414042,0.00007287581,0.00023373758,0.000244644,0.000048823316,0.0000073587503],"category_scores_gemma":[0.000050156374,0.000077158504,0.0000821658,0.00046439175,0.000023207094,0.00041231804,0.00011125776,0.0001418878,0.00011625191],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008232071,0.000029368572,0.0006636582,0.0007558616,0.00008044848,0.000055851888,0.0005024731,0.000023034127,0.009938983,0.0010409128,0.00040355005,0.98642355],"study_design_scores_gemma":[0.0019343196,0.0010891093,0.0020245435,0.003770856,0.000048681904,0.0003556446,0.000952814,0.4448834,0.53912747,0.00014081897,0.0050065136,0.0006657919],"about_ca_topic_score_codex":0.0001778384,"about_ca_topic_score_gemma":0.00013606639,"teacher_disagreement_score":0.98575777,"about_ca_system_score_codex":0.00018459375,"about_ca_system_score_gemma":0.000042706866,"threshold_uncertainty_score":0.31464323},"labels":[],"label_agreement":null},{"id":"W4400142702","doi":"10.1145/3643834.3660731","title":"Fidgets: Building Blocks for a Predictive UI Toolkit","year":2024,"lang":"en","type":"article","venue":"Designing Interactive Systems Conference","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"","keywords":"Computer science; Programming language","score_opus":0.04795092881921082,"score_gpt":0.30371360157260846,"score_spread":0.25576267275339765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400142702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002853222,0.00066635077,0.98280174,0.000292078,0.006226643,0.0016270861,0.00009244174,0.0010145132,0.0044259005],"genre_scores_gemma":[0.98791,0.000006376152,0.008340137,0.00006820856,0.0004493127,0.0012606187,0.00001101159,0.00004628394,0.0019080729],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966072,0.00040840643,0.0006750151,0.0012380212,0.00047230202,0.00059904746],"domain_scores_gemma":[0.99489,0.0031268036,0.00028666377,0.00061987736,0.0008904059,0.00018629056],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010689474,0.0004375727,0.0005833848,0.0004848325,0.00025197098,0.0023476274,0.0010497589,0.00017663681,0.00002592167],"category_scores_gemma":[0.0005238869,0.00042042814,0.00024406204,0.00053988403,0.00006946546,0.0025705565,0.00026672607,0.0004722602,0.00013560551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007119948,0.0006556024,0.0008228055,0.0036748948,0.004572969,0.0008388491,0.06116193,0.0025456373,0.23151585,0.27649775,0.058949858,0.35805184],"study_design_scores_gemma":[0.00056280225,0.00053554313,0.00006695078,0.0043063955,0.00006336916,0.00037498283,0.0014477073,0.93327534,0.01656785,0.0019320253,0.040063735,0.0008033185],"about_ca_topic_score_codex":0.00017732878,"about_ca_topic_score_gemma":0.000010886407,"teacher_disagreement_score":0.98505676,"about_ca_system_score_codex":0.00040618196,"about_ca_system_score_gemma":0.00043144866,"threshold_uncertainty_score":0.99982476},"labels":[],"label_agreement":null},{"id":"W4400171867","doi":"10.2196/57097","title":"Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Activities of daily living; Wearable computer; Physical medicine and rehabilitation; Autonomy; Rehabilitation; Affect (linguistics); Objectivity (philosophy); Quality of life (healthcare); Psychology; Wearable technology; Computer science; Applied psychology; Medicine; Physical therapy; Communication","score_opus":0.025916637896105366,"score_gpt":0.3266614693896167,"score_spread":0.30074483149351133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400171867","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7781597,0.00008765289,0.21842979,0.00010396279,0.00026826785,0.00058720197,0.0000020062857,0.000256405,0.002105057],"genre_scores_gemma":[0.983741,0.000026654647,0.015878437,0.00006079846,0.00007019033,0.00014195254,0.000004621871,0.000012851357,0.00006352949],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973792,0.00045783445,0.00078024896,0.0001922403,0.00091292855,0.00027757848],"domain_scores_gemma":[0.9983819,0.0009050703,0.00019724992,0.00026169835,0.00011168922,0.00014238834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017435781,0.00017889519,0.00037912393,0.00040827194,0.000053225576,0.00017992845,0.00037641948,0.00014116072,0.00007053658],"category_scores_gemma":[0.00027164628,0.0001414559,0.00005712394,0.00088638236,0.00008451318,0.0017986752,0.00019583678,0.0006822477,0.000055572265],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056808127,0.0002265487,0.0006480463,0.0005718955,0.00005774205,0.00004570504,0.07980023,0.000039788796,0.00003084755,0.000028617713,0.00004614342,0.9184476],"study_design_scores_gemma":[0.0026900203,0.001583389,0.001576603,0.0056740423,0.000027837023,0.00024915094,0.07483527,0.90943354,0.0015922552,0.00016877914,0.0016152688,0.0005538325],"about_ca_topic_score_codex":0.00009718926,"about_ca_topic_score_gemma":0.00012721028,"teacher_disagreement_score":0.91789377,"about_ca_system_score_codex":0.00007091034,"about_ca_system_score_gemma":0.0002024553,"threshold_uncertainty_score":0.5768404},"labels":[],"label_agreement":null},{"id":"W4400524082","doi":"10.2196/57601","title":"Feasibility of Measuring Smartphone Accelerometry Data During a Weekly Instrumented Timed Up-and-Go Test After Emergency Department Discharge: Prospective Observational Cohort Study","year":2024,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Observational study; Medicine; Test (biology); Emergency department; Accelerometer; Cohort; Cohort study; Medical emergency; Physical therapy; Computer science; Internal medicine","score_opus":0.09525374536956469,"score_gpt":0.33050922331728466,"score_spread":0.23525547794771998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400524082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99146676,0.00024214546,0.0046689454,0.00021910653,0.0006612422,0.0022404818,0.00011128672,0.00027398093,0.00011606609],"genre_scores_gemma":[0.9984392,0.0000069316357,0.0004174427,0.000009282656,0.00009156281,0.00066514115,0.0000351729,0.000022994274,0.0003122983],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970035,0.00014558992,0.00063635275,0.00115169,0.00074783165,0.00031502687],"domain_scores_gemma":[0.9982729,0.0001750288,0.0001658269,0.0010834971,0.00018818733,0.00011457512],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009818012,0.00025705277,0.00037367688,0.00026277392,0.00015077417,0.00024298423,0.00070045,0.00004480161,0.00009270352],"category_scores_gemma":[0.00015395942,0.00024703203,0.000070808,0.0009927771,0.00003771871,0.0020927684,0.001386659,0.00024571418,0.00003205187],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002013976,0.00040416213,0.99333894,0.0001996299,0.00017883701,0.000015893856,0.0013097575,6.17351e-7,0.0017828752,0.000010313966,0.00007175505,0.002667099],"study_design_scores_gemma":[0.00057528284,0.000056132638,0.99335974,0.00020859066,0.00003498252,0.00002100105,0.00017863479,0.004209878,0.0010120247,0.00006993703,0.00003132259,0.00024245125],"about_ca_topic_score_codex":0.00007487619,"about_ca_topic_score_gemma":0.000109342654,"teacher_disagreement_score":0.006972429,"about_ca_system_score_codex":0.00024270528,"about_ca_system_score_gemma":0.000111674555,"threshold_uncertainty_score":0.9999982},"labels":[],"label_agreement":null},{"id":"W4400597386","doi":"10.21203/rs.3.rs-4611884/v1","title":"Comparative Analysis of Two Methods in Fine-Grained Activity Recognition for Ambient Assisted Living","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Assisted living; Computer science; Chemistry; Environmental science; Medicine; Gerontology","score_opus":0.32035503674556914,"score_gpt":0.5368386729752379,"score_spread":0.21648363622966876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400597386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29818696,0.0004139146,0.69465905,0.0006107476,0.0005846231,0.0032473062,0.00068689906,0.00021350407,0.0013969649],"genre_scores_gemma":[0.9549103,0.00001532647,0.042956907,0.000007721876,0.00009290582,0.0016415942,0.00014245635,0.000024140649,0.00020864802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99004686,0.005555415,0.0008586108,0.0015684551,0.001264091,0.00070655363],"domain_scores_gemma":[0.9848257,0.011167358,0.0004479565,0.0012926784,0.0020812133,0.000185126],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.011715185,0.00038704518,0.0015692599,0.004882897,0.0001468796,0.00053867104,0.0011705956,0.00033128148,0.000056688565],"category_scores_gemma":[0.0019831718,0.00039335014,0.000856536,0.0061862925,0.00014345156,0.00031299356,0.003286602,0.0018001023,0.000030094781],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000276977,0.0023321973,0.003986091,0.006566821,0.0060897823,0.000063437794,0.017576514,0.0019009383,0.035086352,0.0014388517,0.0010500579,0.92363197],"study_design_scores_gemma":[0.00077456946,0.00038555247,0.07567049,0.0049193655,0.00047685305,0.0000055783144,0.0006781327,0.8768936,0.023083886,0.016000684,0.00028570593,0.00082562],"about_ca_topic_score_codex":0.0022095696,"about_ca_topic_score_gemma":0.0060094316,"teacher_disagreement_score":0.9228064,"about_ca_system_score_codex":0.00066691486,"about_ca_system_score_gemma":0.0007769187,"threshold_uncertainty_score":0.9998518},"labels":[],"label_agreement":null},{"id":"W4400680341","doi":"10.1109/ispass61541.2024.00017","title":"Workload Characterization of Commercial Mobile Benchmark Suites","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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 Toronto","funders":"","keywords":"Workload; Benchmark (surveying); Computer science; Characterization (materials science); Operating system","score_opus":0.013599195058031492,"score_gpt":0.25289633278470486,"score_spread":0.23929713772667335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400680341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39788067,0.00022366917,0.590027,0.0004859968,0.0015194607,0.0003636628,0.000017817934,0.0004729985,0.009008732],"genre_scores_gemma":[0.99810725,0.000016716765,0.00062443473,0.00010111774,0.00010708484,0.000040669027,0.000012453158,0.0000063169787,0.0009839813],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991461,0.000062201,0.00023232293,0.00023955887,0.00019361383,0.00012620627],"domain_scores_gemma":[0.99930197,0.00025634564,0.000050193084,0.00026603308,0.000083377476,0.000042059502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021659801,0.00008699367,0.00014688325,0.00013828542,0.000034960103,0.00019280563,0.0003105737,0.000047948273,0.00033888302],"category_scores_gemma":[0.000019237703,0.00007865895,0.000068284615,0.0005066421,0.000023193272,0.00075379485,0.00012289548,0.00007239765,0.00017758242],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025400866,0.000042738266,0.0016086166,0.000058916725,0.00002451591,0.0000053510225,0.00065006013,6.5847445e-7,0.049543675,0.003921038,0.0007847197,0.94335717],"study_design_scores_gemma":[0.0011923686,0.0008399232,0.23326667,0.0022855469,0.000071384056,0.00018816191,0.00030897174,0.09493282,0.3500519,0.0036458755,0.31125873,0.0019576238],"about_ca_topic_score_codex":0.00005309606,"about_ca_topic_score_gemma":0.000020280542,"teacher_disagreement_score":0.9413996,"about_ca_system_score_codex":0.000023285424,"about_ca_system_score_gemma":0.00005462895,"threshold_uncertainty_score":0.37105313},"labels":[],"label_agreement":null},{"id":"W4400727897","doi":"10.1109/iwcmc61514.2024.10592536","title":"Context-Aware Hard and Slow Fall Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Context (archaeology); Geology","score_opus":0.02446297175342113,"score_gpt":0.23984369341917355,"score_spread":0.21538072166575242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400727897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044663012,0.0005561971,0.94698304,0.0014007562,0.00125105,0.00020144186,0.0000039757924,0.0010339236,0.0039065834],"genre_scores_gemma":[0.9950713,0.000014032576,0.0005346934,0.00034989955,0.00009997786,0.000029048762,8.9716696e-7,0.000010242156,0.0038899446],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99894965,0.00006546135,0.00016780356,0.0004587784,0.00018293607,0.00017537606],"domain_scores_gemma":[0.99934906,0.00019274103,0.000025003643,0.00027099357,0.00006993487,0.000092290284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023964414,0.00012458791,0.0001397107,0.00014603276,0.00008725251,0.00063919695,0.00020911152,0.00006873678,0.00003214742],"category_scores_gemma":[0.00002373599,0.00010974271,0.000059043785,0.00027919904,0.000028487157,0.0009841606,0.00013657416,0.0001357138,0.0004582434],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021720466,0.000010593144,0.00021606573,0.000039871607,0.000027101965,0.000023320406,0.00038582267,3.0438608e-7,0.002070426,0.0031307566,0.0018521128,0.99224144],"study_design_scores_gemma":[0.0010458686,0.00039603937,0.009929843,0.00043976356,0.00003976452,0.0013078926,0.00074938335,0.38478798,0.038641397,0.0059633753,0.5554752,0.0012235112],"about_ca_topic_score_codex":0.00032936808,"about_ca_topic_score_gemma":0.0007059984,"teacher_disagreement_score":0.99101794,"about_ca_system_score_codex":0.00004568287,"about_ca_system_score_gemma":0.000040215415,"threshold_uncertainty_score":0.6163791},"labels":[],"label_agreement":null},{"id":"W4400728075","doi":"10.1109/iwcmc61514.2024.10592505","title":"Ontology Alignment: First Step towards Voice Control for Smart-Home Assistive Scenarios","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Sherbrooke","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Ontology; Home automation; Assistive technology; Human–computer interaction; Assisted living; Control (management); Artificial intelligence; Telecommunications; Medicine","score_opus":0.026566890189501667,"score_gpt":0.2697845974174337,"score_spread":0.24321770722793204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400728075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009122056,0.00038474333,0.9764868,0.009304531,0.0025622759,0.0009748144,0.00008135783,0.00072276173,0.008570524],"genre_scores_gemma":[0.9886428,0.0000055328146,0.0057988167,0.0011126224,0.00024808315,0.00045256657,0.000008475324,0.000022467408,0.00370861],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978913,0.00012959859,0.00039509262,0.0007866165,0.0003239353,0.00047344228],"domain_scores_gemma":[0.9978372,0.0012062868,0.00008634518,0.00050243846,0.00021182116,0.00015589477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005721703,0.00025101748,0.0004120913,0.00019386294,0.00017972663,0.00049942534,0.00066208025,0.00014219749,0.00008907658],"category_scores_gemma":[0.00010244333,0.00021865091,0.00022400015,0.00032202993,0.000056851914,0.0007390944,0.00016160439,0.00015205654,0.00071465847],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017667832,0.0004732019,0.0021322807,0.00064081384,0.0012805589,0.00022567183,0.0026932235,0.00003606667,0.0008275799,0.1011844,0.087847166,0.80248237],"study_design_scores_gemma":[0.0027163615,0.00066011027,0.0038543388,0.00022873688,0.00008793172,0.00019753733,0.00016491671,0.31568715,0.00071330986,0.0023523008,0.67256224,0.0007750617],"about_ca_topic_score_codex":0.00048757586,"about_ca_topic_score_gemma":0.0017127811,"teacher_disagreement_score":0.9877306,"about_ca_system_score_codex":0.00022757024,"about_ca_system_score_gemma":0.00021949677,"threshold_uncertainty_score":0.91857266},"labels":[],"label_agreement":null},{"id":"W4400760443","doi":"10.2196/64367","title":"Factors Affecting Clinician Readiness to Adopt Smart Home Technology for Remote Health Monitoring: Systematic Review","year":2024,"lang":"en","type":"review","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Assistive technology; Preprint; Health care; Gerontology; Long-term care; Older people; Aged care; Medicine; Population ageing; Population; Nursing; Psychology; Environmental health; Economic growth; Computer science","score_opus":0.16238404104381698,"score_gpt":0.4453643720428316,"score_spread":0.2829803309990146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400760443","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000035557323,0.9363643,0.045393962,0.00074176525,0.0038759373,0.012311091,0.00002828733,0.0012444117,0.00003672023],"genre_scores_gemma":[0.00015996861,0.9946256,0.0020908993,0.00021028743,0.00053951744,0.0017354792,0.000023582528,0.0001651099,0.0004495077],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9941029,0.00066413416,0.002336749,0.001545368,0.00048135925,0.00086951366],"domain_scores_gemma":[0.9944532,0.0014818502,0.0016361736,0.0018863484,0.00025025266,0.00029221017],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030791524,0.00089307246,0.0058208574,0.0014346642,0.00027671675,0.00046652814,0.0019858168,0.00037527434,0.0000012076175],"category_scores_gemma":[0.00084112794,0.00069667975,0.0010793244,0.0031349617,0.000024124003,0.00034799307,0.0007788612,0.00090019184,0.00028353083],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.380481e-7,0.000009417568,0.000003174591,0.6000177,0.00010073911,0.0000074777113,0.00015650144,2.7198583e-8,4.649286e-8,0.00002699665,0.00032964867,0.39934808],"study_design_scores_gemma":[0.00005213572,0.00008150677,9.929848e-7,0.8234689,0.00048659282,0.000118677526,0.000094476985,0.00003776686,9.058695e-7,0.000038604547,0.175046,0.0005734331],"about_ca_topic_score_codex":0.000033127533,"about_ca_topic_score_gemma":0.000012037175,"teacher_disagreement_score":0.39877465,"about_ca_system_score_codex":0.0008482508,"about_ca_system_score_gemma":0.00055543735,"threshold_uncertainty_score":0.99954844},"labels":[],"label_agreement":null},{"id":"W4400813120","doi":"10.1145/3670854","title":"Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Computing for Healthcare","topic":"Context-Aware Activity Recognition Systems","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":"Queen's University","funders":"","keywords":"Computer science; Home automation; Ubiquitous computing; Digital health; Data science; Risk analysis (engineering); Health care; Telecommunications; Business; Human–computer interaction","score_opus":0.24203669176793555,"score_gpt":0.3652743073151061,"score_spread":0.12323761554717053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400813120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41406718,0.0012890508,0.5704852,0.008308296,0.00443531,0.0009503004,0.00009302317,0.00036520936,0.000006431492],"genre_scores_gemma":[0.99618244,0.00013704906,0.0031879484,0.00017175195,0.00014986501,0.00012001172,0.0000074355726,0.000030106252,0.000013411668],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99740136,0.0005198933,0.0006644679,0.00057995215,0.00034542824,0.00048890355],"domain_scores_gemma":[0.9945814,0.004014852,0.00014821821,0.00084109604,0.0002873001,0.0001271465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011904078,0.00021862154,0.00043368156,0.0004283667,0.0003811733,0.00014812722,0.0008497313,0.00006161611,0.0000013296076],"category_scores_gemma":[0.000055792403,0.00018968829,0.00017286114,0.0012684754,0.00003634233,0.00037940586,0.000039206818,0.00049583963,0.000010064882],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004635111,0.00011408894,0.0041229064,0.0010857533,0.00006937161,0.000007013133,0.008256878,0.0022015495,0.000033877528,0.0003479628,0.00006636999,0.9836479],"study_design_scores_gemma":[0.0038764316,0.005004463,0.7186505,0.011556843,0.000046253394,0.00036917388,0.009625474,0.23303154,0.004157366,0.0038647663,0.007743069,0.0020740826],"about_ca_topic_score_codex":0.0025277424,"about_ca_topic_score_gemma":0.0010182483,"teacher_disagreement_score":0.9815738,"about_ca_system_score_codex":0.00020940145,"about_ca_system_score_gemma":0.00038829815,"threshold_uncertainty_score":0.77352643},"labels":[],"label_agreement":null},{"id":"W4400876177","doi":"10.3390/s24144706","title":"Automated Detection of In-Home Activities with Ultra-Wideband Sensors","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","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":"Glenrose Rehabilitation Hospital; University of Alberta","funders":"","keywords":"Computer science; Wideband; Embedded system; Real-time computing; Telecommunications; Engineering; Electrical engineering","score_opus":0.013041109121908851,"score_gpt":0.23766839263428075,"score_spread":0.2246272835123719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400876177","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9862064,0.000088258384,0.010680471,0.00014018659,0.00042788888,0.00021524719,0.000008131963,0.0010698752,0.0011635147],"genre_scores_gemma":[0.99935085,0.0000073862,0.00020765059,0.00001599793,0.00003893255,0.000013794165,0.0000011592205,0.00001942021,0.00034481622],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986306,0.00015980666,0.00026820996,0.00039641856,0.00030322166,0.0002417516],"domain_scores_gemma":[0.9990525,0.00042585976,0.00008241031,0.00031886168,0.00006470294,0.000055659682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024616992,0.00016821153,0.00026667744,0.00045094793,0.000042623833,0.00014083095,0.00018281047,0.000082563914,0.000008300051],"category_scores_gemma":[0.00003171504,0.00014444877,0.00006702863,0.0009311015,0.00006938416,0.0005804545,0.000027284827,0.00017891178,0.000045750698],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028828037,0.0003901947,0.009200753,0.0013761392,0.00057795644,0.0011372126,0.027424715,0.010153162,0.71402365,0.0017887672,0.0004651529,0.233174],"study_design_scores_gemma":[0.0010045682,0.00045376748,0.012845669,0.000969874,0.000031257227,0.0008047043,0.0016292403,0.30075303,0.67788357,0.0003659155,0.0024577405,0.0008006656],"about_ca_topic_score_codex":0.0002591488,"about_ca_topic_score_gemma":0.00034558648,"teacher_disagreement_score":0.29059985,"about_ca_system_score_codex":0.00009068172,"about_ca_system_score_gemma":0.00007253306,"threshold_uncertainty_score":0.589045},"labels":[],"label_agreement":null},{"id":"W4401110396","doi":"10.1109/cai59869.2024.00028","title":"Adaptive Home Energy Management: Human-Centric RL Approach for Diverse Situations","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Alberta","funders":"","keywords":"Computer science; Energy management; Energy (signal processing)","score_opus":0.053903252000320694,"score_gpt":0.26954432285864754,"score_spread":0.21564107085832684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401110396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010490655,0.00015814169,0.945455,0.00019082663,0.0004216608,0.00037905065,0.000016302583,0.00054000184,0.052734077],"genre_scores_gemma":[0.95226556,0.000013358183,0.03084709,0.00015121422,0.00012812803,0.0002986447,0.00003006186,0.000014063938,0.016251907],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987171,0.00005315549,0.00019281177,0.00053750764,0.00024898874,0.00025041946],"domain_scores_gemma":[0.9992856,0.00014911962,0.000040663665,0.00035661235,0.000092022376,0.00007598435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019511371,0.00014108037,0.00014659252,0.00035684934,0.00020674297,0.00038272818,0.0004908279,0.000047906866,0.000053714604],"category_scores_gemma":[0.000004743096,0.0001320158,0.00013294768,0.00070455106,0.000023456718,0.00079410523,0.00028315696,0.000059807597,0.00012360177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031846075,0.0001229659,0.000011638579,0.00006636309,0.00016245096,0.000016622753,0.0004313586,0.0000502133,0.00004691003,0.7434004,0.013456061,0.24223182],"study_design_scores_gemma":[0.0012136057,0.00018323094,0.00035309402,0.00008742166,0.0001088506,0.000039292947,0.0010819355,0.8718067,0.00039998474,0.028527275,0.09541464,0.00078399567],"about_ca_topic_score_codex":0.0000719276,"about_ca_topic_score_gemma":0.000027969723,"teacher_disagreement_score":0.9521606,"about_ca_system_score_codex":0.000096835094,"about_ca_system_score_gemma":0.000028651486,"threshold_uncertainty_score":0.5383448},"labels":[],"label_agreement":null},{"id":"W4401361894","doi":"10.1016/j.eswa.2024.124966","title":"Extraction of sequential patterns from the web for human activity recognition","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; USable; Exploit; Autonomy; Field (mathematics); Activity recognition; Activities of daily living; Assisted living; Artificial intelligence; Machine learning; Data science; World Wide Web; Computer security","score_opus":0.059416170887688455,"score_gpt":0.3251723815078517,"score_spread":0.26575621062016325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401361894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03809101,0.00045088358,0.9571161,0.00071545376,0.0005362466,0.0020380507,0.00038441032,0.0003414963,0.00032631567],"genre_scores_gemma":[0.9918858,0.000015383614,0.00109957,0.000036378122,0.000737638,0.005961416,0.00010405293,0.000024600853,0.00013519812],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99853355,0.00014876312,0.00032864354,0.0005175296,0.00029383358,0.00017770182],"domain_scores_gemma":[0.99811053,0.00070411037,0.00021495984,0.0006905769,0.0002203676,0.000059480943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033126562,0.00016546172,0.00021583724,0.000095467956,0.00027432528,0.00034485658,0.00044001738,0.00008775853,0.000016328278],"category_scores_gemma":[0.000010767222,0.000120737415,0.000106616346,0.00030426527,0.000044058444,0.00065136724,0.000052358268,0.00013688882,0.00005449545],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007367056,0.00062785763,0.0008149474,0.0006012032,0.00074124034,0.0000075972803,0.0058053816,0.00004372204,0.5187512,0.022750665,0.011179678,0.43860283],"study_design_scores_gemma":[0.0034192228,0.00077223097,0.00611495,0.0036917648,0.00031771112,0.000496971,0.0035003605,0.17458901,0.12484908,0.0074827075,0.67235625,0.0024097639],"about_ca_topic_score_codex":0.002367905,"about_ca_topic_score_gemma":0.00035536298,"teacher_disagreement_score":0.95601654,"about_ca_system_score_codex":0.00010217963,"about_ca_system_score_gemma":0.000118555006,"threshold_uncertainty_score":0.4923529},"labels":[],"label_agreement":null},{"id":"W4401381426","doi":"10.1145/3677525.3678671","title":"Utilizing Self-Supervised Learning for Recognizing Human Activity in Older Adults through Labeling Applications in Real-World Smart Homes","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Institut Universitaire de Gériatrie de Montréal; Université du Québec à Chicoutimi; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Computer science; Activity recognition; Human–computer interaction; Artificial intelligence; Machine learning","score_opus":0.05244833726543793,"score_gpt":0.32473765508808455,"score_spread":0.27228931782264665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401381426","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5774275,0.00031619222,0.40543407,0.0007010985,0.00038189377,0.0024282369,0.000005530793,0.0018246389,0.011480816],"genre_scores_gemma":[0.9824583,0.000042671265,0.015988646,0.000060640086,0.00009109663,0.0008496664,0.000015814683,0.00003866657,0.0004544921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975283,0.00021513413,0.00051693193,0.0009803621,0.00025176178,0.0005074837],"domain_scores_gemma":[0.99809104,0.0011972412,0.00010070731,0.00042324982,0.000116541785,0.00007123397],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000956954,0.0002649409,0.0003966984,0.0005977418,0.0002549988,0.00046851116,0.00047511832,0.000113412134,0.000027473592],"category_scores_gemma":[0.000059231854,0.00027482116,0.00013120002,0.0018213651,0.000020942454,0.0017855791,0.00022480256,0.00048322894,0.000037508315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036388734,0.0008219282,0.08487928,0.0017949501,0.00013060211,0.000034731533,0.029912328,0.00010918249,0.0089543,0.016250098,0.0002646967,0.8568115],"study_design_scores_gemma":[0.004584462,0.00019588084,0.055425875,0.0047691874,0.000052780124,0.0000242277,0.007495954,0.8894144,0.016074874,0.0051929345,0.014630579,0.002138815],"about_ca_topic_score_codex":0.0031222817,"about_ca_topic_score_gemma":0.012827987,"teacher_disagreement_score":0.88930523,"about_ca_system_score_codex":0.0002952461,"about_ca_system_score_gemma":0.00012136109,"threshold_uncertainty_score":0.9999704},"labels":[],"label_agreement":null},{"id":"W4401456030","doi":"10.1145/3643794.3648275","title":"Evaluation of APIs for Data Exchange with Building Management Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Computer science; Data exchange; Data management; Database","score_opus":0.2008829338680323,"score_gpt":0.36579887899510277,"score_spread":0.16491594512707047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401456030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013633278,0.0010810925,0.9902416,0.00021388567,0.00068257394,0.0011899873,0.000023585082,0.00017947484,0.0050245007],"genre_scores_gemma":[0.9798829,0.000008434059,0.019100487,0.00001601342,0.00007604977,0.00026432532,0.000016392298,0.000009977135,0.0006254318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839956,0.00010729703,0.00019391235,0.00044117917,0.00073076,0.0001272919],"domain_scores_gemma":[0.9986252,0.00016361375,0.00006187952,0.0008234813,0.0002960075,0.00002981287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032079294,0.00008456858,0.00013011832,0.00016855869,0.00003515017,0.00025773465,0.0006509342,0.000023389777,0.000016699374],"category_scores_gemma":[0.000022273602,0.00006574687,0.00002375403,0.000332794,0.0000107619,0.00090414996,0.00030023616,0.000029289939,0.000016695161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004720093,0.00003591265,0.00002283893,0.0010778143,0.00027331716,0.0000040537266,0.00029209236,0.00010247641,0.00029691224,0.03429251,0.0063477485,0.9572496],"study_design_scores_gemma":[0.00032565626,0.00005364668,0.000028068309,0.00038017367,0.000103869046,0.000017069095,0.000130814,0.980898,0.00043385624,0.00040109686,0.017121943,0.0001058135],"about_ca_topic_score_codex":0.00010845848,"about_ca_topic_score_gemma":0.000045805627,"teacher_disagreement_score":0.9807955,"about_ca_system_score_codex":0.00007724786,"about_ca_system_score_gemma":0.00006684028,"threshold_uncertainty_score":0.26810798},"labels":[],"label_agreement":null},{"id":"W4401545172","doi":"10.3390/buildings14082499","title":"A GPT-Powered Assistant for Real-Time Interaction with Building Information Models","year":2024,"lang":"en","type":"article","venue":"Buildings","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Computer science; Systems engineering; Architectural engineering; Human–computer interaction; Engineering; Simulation; Aerospace engineering","score_opus":0.020176557801783214,"score_gpt":0.2636840366544493,"score_spread":0.24350747885266608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401545172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03440148,0.000040486273,0.9597211,0.0007152669,0.0005572218,0.00054064504,0.000019852769,0.00082340644,0.003180534],"genre_scores_gemma":[0.94099283,0.000010622884,0.0582959,0.00011469035,0.00010012824,0.00018069036,0.000014868763,0.00002060194,0.00026963992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866104,0.000034443772,0.00034787718,0.00036455176,0.00031595485,0.00027613473],"domain_scores_gemma":[0.99892074,0.00029536613,0.0001532806,0.00031128095,0.00023434847,0.000085003034],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00047341918,0.00018387752,0.00020859373,0.0003473767,0.00013138939,0.0011628377,0.0003514164,0.000076382166,0.000013666491],"category_scores_gemma":[0.000046287183,0.00016112371,0.00010110612,0.00049150264,0.00001958315,0.0066823056,0.00010151209,0.00013037225,0.000090763446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027545288,0.00010218123,0.000031608597,0.0006384858,0.00030955882,0.000027319309,0.008437853,0.0010255114,0.06194855,0.17497866,0.015526727,0.7366981],"study_design_scores_gemma":[0.00053873926,0.000217996,0.000034172434,0.0007140425,0.0000289961,0.00018139214,0.00013014246,0.88355994,0.0057930863,0.0052572223,0.10313404,0.0004102492],"about_ca_topic_score_codex":0.00012781944,"about_ca_topic_score_gemma":0.0000073776614,"teacher_disagreement_score":0.90659136,"about_ca_system_score_codex":0.00022998301,"about_ca_system_score_gemma":0.00012144816,"threshold_uncertainty_score":0.99987406},"labels":[],"label_agreement":null},{"id":"W4401597494","doi":"10.1109/icasspw62465.2024.10627018","title":"Towards Collaborative Multimodal Federated Learning for Human Activity Recognition in Smart Workplace Environments","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Huawei Technologies (Canada); University of Toronto","funders":"","keywords":"Computer science; Activity recognition; Human–computer interaction; Knowledge management; Collaborative learning; Artificial intelligence","score_opus":0.03536492444722671,"score_gpt":0.2945081338223574,"score_spread":0.2591432093751307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401597494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2540917,0.0000520249,0.73893416,0.0006549937,0.00046792772,0.000953977,0.00001521975,0.00038665676,0.004443354],"genre_scores_gemma":[0.9934699,0.000006560497,0.0031215395,0.00006079857,0.000065726425,0.0003469219,0.000034404726,0.000022187094,0.0028719425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981067,0.0003313961,0.00027720872,0.00068984117,0.00026568526,0.00032917628],"domain_scores_gemma":[0.9991716,0.0004160162,0.000082807666,0.00017278662,0.00007206071,0.00008473579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006632322,0.00021654053,0.000262635,0.0002525349,0.00024235417,0.00066208374,0.00021674074,0.00013470769,0.000074949465],"category_scores_gemma":[0.000105602354,0.00022011356,0.00008018469,0.00071079517,0.000032734453,0.0013386945,0.00012944885,0.000315945,0.00019147695],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007081447,0.0002619151,0.0009875002,0.00008230793,0.000093668394,0.000035324298,0.001990068,0.000100432386,0.03369666,0.00035710656,0.00060402765,0.96172017],"study_design_scores_gemma":[0.0044600638,0.0009831141,0.018531304,0.00086906826,0.000040458723,0.000047221383,0.0012885944,0.7834766,0.14004542,0.0038858552,0.04456581,0.0018065063],"about_ca_topic_score_codex":0.00031695975,"about_ca_topic_score_gemma":0.0007820876,"teacher_disagreement_score":0.9599137,"about_ca_system_score_codex":0.00031437684,"about_ca_system_score_gemma":0.00013248893,"threshold_uncertainty_score":0.897597},"labels":[],"label_agreement":null},{"id":"W4401597552","doi":"10.1109/icasspw62465.2024.10626042","title":"Persota FL: A Robust-to-Noise Personalized Over the Air Federated Learning for Human Activity Recognition","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Huawei Technologies (Canada); University of Toronto","funders":"","keywords":"Computer science; Noise (video); Artificial intelligence; Activity recognition; Machine learning; Speech recognition","score_opus":0.06673971972122557,"score_gpt":0.3019668943446437,"score_spread":0.23522717462341813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401597552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23286818,0.00006207309,0.75789744,0.0037290906,0.0005704575,0.0010395267,0.000012710015,0.0010138779,0.0028066668],"genre_scores_gemma":[0.99005663,0.0000026386958,0.0014384015,0.0007352999,0.00024882672,0.0003953992,0.000016894392,0.00003579556,0.0070701065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978484,0.00035984296,0.00024193287,0.00076570536,0.00038396,0.00040016585],"domain_scores_gemma":[0.9984039,0.0008213572,0.000079951256,0.00031170883,0.0002473764,0.00013568476],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009726468,0.00025402755,0.00026557333,0.0002089958,0.00079061405,0.001173476,0.0004285709,0.00010777907,0.00024171996],"category_scores_gemma":[0.00019209478,0.00019240876,0.00025164915,0.0006946054,0.00004614176,0.0010102285,0.00018589513,0.00036005187,0.00034884133],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016400342,0.00030039187,0.00026363618,0.0003319262,0.00046809576,0.00004350684,0.0109385885,0.00033748467,0.064209364,0.0039697187,0.026727645,0.89224565],"study_design_scores_gemma":[0.0023206826,0.0007419538,0.0047681867,0.00061906886,0.000115073584,0.00018429484,0.0015779904,0.8505337,0.0121768145,0.0021449227,0.1233288,0.0014885132],"about_ca_topic_score_codex":0.00041981004,"about_ca_topic_score_gemma":0.00038322379,"teacher_disagreement_score":0.89075714,"about_ca_system_score_codex":0.00017274272,"about_ca_system_score_gemma":0.00013133533,"threshold_uncertainty_score":0.9998634},"labels":[],"label_agreement":null},{"id":"W4401672165","doi":"10.2196/57320","title":"In-Home Positioning for Remote Home Health Monitoring in Older Adults: Systematic Review","year":2024,"lang":"en","type":"review","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta; Glenrose Rehabilitation Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; AGE-WELL","keywords":"CINAHL; Medicine; Dementia; MEDLINE; Systematic review; Gerontology; Cognition; Quality of life (healthcare); Physical medicine and rehabilitation; Telemedicine; Cognitive impairment; Physical therapy; Health care; Disease; Pathology; Nursing; Psychological intervention; Psychiatry","score_opus":0.046067551902374,"score_gpt":0.3810003054810483,"score_spread":0.3349327535786743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401672165","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000016714763,0.97596526,0.011973324,0.0004621673,0.0016456385,0.009647443,0.000018165823,0.00024963138,0.000036704274],"genre_scores_gemma":[0.00015906617,0.99490255,0.0012859895,0.00025032362,0.00031529064,0.0027977566,0.000022703487,0.00008880371,0.00017750134],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9947716,0.00074553845,0.0022311828,0.0011250054,0.00045374257,0.00067291554],"domain_scores_gemma":[0.9971154,0.00068637397,0.0009850281,0.0009655067,0.000108813,0.00013891663],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024431814,0.00059822085,0.0038564475,0.0011037416,0.00010213875,0.00043915596,0.0010458255,0.00018153999,0.0000029764462],"category_scores_gemma":[0.00010398038,0.000531152,0.00062260934,0.0019200203,0.000012684816,0.00085249654,0.00034370177,0.00075249036,0.00015250518],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.2289103e-7,0.000016877557,0.0000010093255,0.74315864,0.000029781668,0.000039036473,0.0004904067,1.1497014e-7,1.7728567e-8,0.000029897763,0.00015044416,0.25608346],"study_design_scores_gemma":[0.00021490364,0.000023352586,0.000003424699,0.98590213,0.00013020684,0.00025054955,0.000048993774,0.0005056241,1.0082128e-7,0.00007242621,0.0123522645,0.0004960092],"about_ca_topic_score_codex":0.000108018954,"about_ca_topic_score_gemma":0.00002142709,"teacher_disagreement_score":0.25558746,"about_ca_system_score_codex":0.0013052833,"about_ca_system_score_gemma":0.00042097538,"threshold_uncertainty_score":0.999714},"labels":[],"label_agreement":null},{"id":"W4401691890","doi":"10.1109/mcas.2024.3358277","title":"AiCare: An Affordable, Reliable, and Intelligent Senior Care Ecosystem [Innovations Corner]","year":2024,"lang":"en","type":"article","venue":"IEEE Circuits and Systems Magazine","topic":"Context-Aware Activity Recognition Systems","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 Alberta","funders":"","keywords":"Ecosystem; Computer science; Environmental resource management; Engineering; Systems engineering; Environmental science; Ecology; Biology","score_opus":0.03121719801690478,"score_gpt":0.26235996978037823,"score_spread":0.23114277176347345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401691890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36827815,0.028173938,0.56676,0.00084728445,0.016523378,0.0033434492,0.0005030125,0.0024531141,0.013117679],"genre_scores_gemma":[0.99719447,0.000120942306,0.00011240826,0.000079473786,0.00048448588,0.00015334252,0.000030766012,0.000044012595,0.0017801119],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99725133,0.00015404352,0.00072542153,0.0010031244,0.00042969198,0.00043635984],"domain_scores_gemma":[0.99812394,0.00018543562,0.0001585678,0.00074141216,0.00050167827,0.00028894865],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007089947,0.00035643534,0.0005333591,0.00043122887,0.00027138274,0.0017484281,0.0004041872,0.00017456974,0.000009603679],"category_scores_gemma":[0.000027500468,0.00031864177,0.00007013896,0.0009423796,0.000051330324,0.0012945365,0.00011917047,0.00026800146,0.00017092536],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001964183,0.00031559615,0.007510943,0.012098232,0.00066693884,0.0009423254,0.02316326,0.0006254396,0.02265313,0.09531375,0.0331692,0.8035215],"study_design_scores_gemma":[0.0015536759,0.0009693515,0.003692429,0.00452971,0.00017740797,0.0053398362,0.0055119866,0.34438223,0.0016970544,0.0006906981,0.62888944,0.002566166],"about_ca_topic_score_codex":0.00016621896,"about_ca_topic_score_gemma":0.00028123788,"teacher_disagreement_score":0.80095536,"about_ca_system_score_codex":0.00014428709,"about_ca_system_score_gemma":0.0001636934,"threshold_uncertainty_score":0.99992657},"labels":[],"label_agreement":null},{"id":"W4401808941","doi":"10.1109/sas60918.2024.10636508","title":"The Effect of Activity Granularity on A Kitchen Activity Recognition System","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":"Lakehead University; University of Ottawa","funders":"","keywords":"Granularity; Computer science; Activity recognition; Artificial intelligence; Pattern recognition (psychology); Operating system","score_opus":0.0193453142259886,"score_gpt":0.25971125888026597,"score_spread":0.24036594465427738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401808941","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7942756,0.00009725592,0.18920344,0.0007320307,0.0018231042,0.0009341515,0.000025269543,0.001021596,0.011887573],"genre_scores_gemma":[0.99938536,0.00000524248,0.000089771405,0.000014965746,0.00010327855,0.00013503637,0.000001595596,0.000016047703,0.0002486971],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99724275,0.001029111,0.00025325108,0.0005959522,0.00056995236,0.00030899144],"domain_scores_gemma":[0.9954162,0.0034452404,0.0001372947,0.000800884,0.00010564334,0.00009472248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021505791,0.00025026075,0.0003716321,0.00015867139,0.00025173317,0.00045552084,0.00059390854,0.00013069602,0.000009042838],"category_scores_gemma":[0.00016027027,0.00015890966,0.00027337667,0.0006369653,0.00007085964,0.0008840673,0.00019106909,0.00037904357,0.00034418795],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012233548,0.00005083036,0.0001775366,0.00043633682,0.00010635143,0.000020118061,0.00012511402,0.0000012846034,0.008496816,0.001372852,0.00041187057,0.9886786],"study_design_scores_gemma":[0.0011073869,0.001801653,0.006649414,0.0012970772,0.00009905169,0.00019355716,0.000057885896,0.08481232,0.8990472,0.00087526644,0.0033617252,0.00069743674],"about_ca_topic_score_codex":0.00042502448,"about_ca_topic_score_gemma":0.00010899629,"teacher_disagreement_score":0.98798114,"about_ca_system_score_codex":0.00021498518,"about_ca_system_score_gemma":0.000082012986,"threshold_uncertainty_score":0.6480148},"labels":[],"label_agreement":null},{"id":"W4401943523","doi":"10.1109/icdh62654.2024.00033","title":"Triage-Bot: An Assistive Triage Framework","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Ontario Tech University; Queen's University","funders":"","keywords":"Triage; Computer science; Human–computer interaction; Medical emergency; Medicine","score_opus":0.04543338792794627,"score_gpt":0.32469505310133767,"score_spread":0.2792616651733914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401943523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004954748,0.00026190342,0.96588427,0.0017970015,0.0024177718,0.00030353313,0.000009803293,0.0013908867,0.022980094],"genre_scores_gemma":[0.976671,0.000006165098,0.017928867,0.0005110695,0.00051089254,0.000059305687,0.000004172396,0.00001864123,0.0042899125],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803996,0.0002830954,0.00031377526,0.0006994863,0.00037465463,0.0002890467],"domain_scores_gemma":[0.9977728,0.0011413116,0.000054630356,0.00076079625,0.000092448005,0.00017802019],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00078908907,0.00018666091,0.0002671335,0.00021576836,0.00010990148,0.0011141616,0.000728085,0.00014498556,0.00047780605],"category_scores_gemma":[0.00022879192,0.00015771748,0.00014756534,0.00082269765,0.000033035754,0.0016139598,0.00018398392,0.0003392057,0.0015910838],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020514452,0.00016088186,0.000208968,0.000041607054,0.000110303736,0.00021936258,0.0016385082,0.0000028626505,0.0004494535,0.1573994,0.011192786,0.82855535],"study_design_scores_gemma":[0.0029915487,0.0014958675,0.015208452,0.0014180847,0.00012957542,0.0005645403,0.001249574,0.27533376,0.012881901,0.11030422,0.5753008,0.0031216182],"about_ca_topic_score_codex":0.00007554014,"about_ca_topic_score_gemma":0.000037637205,"teacher_disagreement_score":0.9717162,"about_ca_system_score_codex":0.000084878775,"about_ca_system_score_gemma":0.00014545796,"threshold_uncertainty_score":0.99992275},"labels":[],"label_agreement":null},{"id":"W4401993448","doi":"10.1038/s41390-024-03446-3","title":"Correction: A recommendation for the use of electrical biosensing technology in neonatology","year":2024,"lang":"en","type":"erratum","venue":"Pediatric Research","topic":"Context-Aware Activity Recognition Systems","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 Calgary","funders":"","keywords":"Neonatology; Medicine; Medical physics; Intensive care medicine; Computer science; Computational biology; Biology; Genetics; Pregnancy","score_opus":0.1943786518931204,"score_gpt":0.39824116944238774,"score_spread":0.20386251754926735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401993448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012212347,0.025410388,0.6006652,0.1153428,0.24094386,0.009975732,0.00015644648,0.0010995951,0.005184695],"genre_scores_gemma":[0.46347848,0.043212775,0.021117534,0.0007604521,0.03366908,0.006840171,0.00081556983,0.0007067852,0.42939913],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966442,0.0006674892,0.00064636534,0.0008019856,0.00059973286,0.0006402254],"domain_scores_gemma":[0.98911315,0.008930997,0.00022872046,0.00069200556,0.000981848,0.000053252148],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0024902408,0.00020458573,0.00047850443,0.004126843,0.00018491628,0.00027781786,0.0009854853,0.0007733125,0.000012750472],"category_scores_gemma":[0.004189054,0.0001669582,0.00014435464,0.007168512,0.00012573761,0.00028543477,0.00064880686,0.0029478897,0.000067628236],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001447281,0.000033447795,0.00022989354,0.000100777535,0.000021552929,0.000011335956,0.00007883537,0.0000016828643,0.0000052657447,0.00048458038,0.7387087,0.26030946],"study_design_scores_gemma":[0.00023222646,0.0002355201,0.00018440776,0.0000781151,0.000027794407,0.00014480628,0.000042047508,0.17418131,0.00005460628,0.0028331408,0.8217816,0.000204461],"about_ca_topic_score_codex":0.00043138166,"about_ca_topic_score_gemma":0.00034143054,"teacher_disagreement_score":0.5795477,"about_ca_system_score_codex":0.00035939255,"about_ca_system_score_gemma":0.001274983,"threshold_uncertainty_score":0.99935234},"labels":[],"label_agreement":null},{"id":"W4402038426","doi":"10.2196/56972","title":"Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis","year":2024,"lang":"en","type":"review","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wearable computer; Activity tracker; Meta-analysis; Computer science; mHealth; Wearable technology; Medicine; Psychological intervention; Embedded system","score_opus":0.24232487143451586,"score_gpt":0.487136580505991,"score_spread":0.24481170907147515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402038426","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000033575352,0.98646826,0.0040194695,0.0014270479,0.00014240116,0.0073570036,0.00036062667,0.00014382279,0.000078012934],"genre_scores_gemma":[0.00084993395,0.9940301,0.00042305424,0.00048001585,0.000055695033,0.003818932,0.000031221312,0.000041081425,0.00026996352],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.99331325,0.0017550959,0.0024229167,0.0011731542,0.0007317523,0.0006038517],"domain_scores_gemma":[0.9903757,0.0049496796,0.0026905092,0.0010245136,0.00023352816,0.0007260895],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0059381374,0.0006201098,0.010119981,0.0010199547,0.00030975285,0.00018418228,0.000627285,0.0003109655,0.00003787125],"category_scores_gemma":[0.0009502303,0.00043389617,0.0017926035,0.0024987564,0.000095847805,0.00047644015,0.00021304269,0.0007616394,0.0000150678925],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016668632,0.000042589447,2.6357958e-7,0.93708104,0.012523472,0.000003749298,0.00008552762,2.1472928e-8,9.887488e-9,0.00036961603,0.00030971502,0.049582325],"study_design_scores_gemma":[0.00022739264,0.00017182768,0.00000424055,0.14658657,0.8335685,0.00020384435,0.000035867615,0.0007543881,2.5564242e-7,0.00024263268,0.017576711,0.00062772917],"about_ca_topic_score_codex":0.0005311883,"about_ca_topic_score_gemma":0.00072714005,"teacher_disagreement_score":0.82104504,"about_ca_system_score_codex":0.00021814232,"about_ca_system_score_gemma":0.0018677807,"threshold_uncertainty_score":0.9998113},"labels":[],"label_agreement":null},{"id":"W4402169966","doi":"10.2196/52153","title":"Sensing In Exergames for Efficacy and Motion Quality: Scoping Review of Recent Publications","year":2024,"lang":"en","type":"review","venue":"JMIR Serious Games","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Quality (philosophy); Computer science; World Wide Web; Physics","score_opus":0.16851536299649666,"score_gpt":0.4544942876539035,"score_spread":0.2859789246574068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402169966","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000056121785,0.9824762,0.010189631,0.0008109503,0.00056297856,0.005592803,0.00003113254,0.0001866218,0.00014411566],"genre_scores_gemma":[0.00001977446,0.9974003,0.0015130561,0.000077829856,0.000080408485,0.0006840729,0.00005355382,0.000037577174,0.00013342987],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99682355,0.000467923,0.0013862398,0.00077901164,0.0002707485,0.00027251206],"domain_scores_gemma":[0.99727166,0.00079129613,0.00080951833,0.0007772802,0.00026768396,0.000082555634],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013085186,0.00035278877,0.0019298972,0.0005439363,0.000050354185,0.0002597228,0.0004149521,0.00018375569,0.0000069692715],"category_scores_gemma":[0.00057927275,0.0003130187,0.00029409776,0.0012420481,0.000047608755,0.0004013063,0.00028515764,0.00024967728,0.000016536991],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.613899e-7,0.000024990286,4.788504e-7,0.23532736,0.000029873388,9.156406e-7,0.00010836229,2.3867841e-8,5.25493e-7,0.00016273983,0.0003370488,0.76400715],"study_design_scores_gemma":[0.0001198011,0.000024827774,0.000010078806,0.49786267,0.0001040625,0.000052271134,0.000009193324,0.000043347634,0.0000016600287,0.00007330431,0.5014524,0.00024642618],"about_ca_topic_score_codex":0.000021690537,"about_ca_topic_score_gemma":0.00011930113,"teacher_disagreement_score":0.7637607,"about_ca_system_score_codex":0.00015006996,"about_ca_system_score_gemma":0.0004878654,"threshold_uncertainty_score":0.99993217},"labels":[],"label_agreement":null},{"id":"W4402178711","doi":"10.1123/jmpb.2023-0058","title":"Shaking Up Activity Counts: Assessing the Comparability of Accelerometers and Activity Count Computation","year":2024,"lang":"en","type":"article","venue":"Journal for the Measurement of Physical Behaviour","topic":"Context-Aware Activity Recognition 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":"Ontario Tech University; University of Guelph","funders":"","keywords":"Comparability; Accelerometer; Computation; Physical activity; Computer science; Medicine; Statistics; Physical medicine and rehabilitation; Mathematics; Algorithm","score_opus":0.1546257581221968,"score_gpt":0.3809355701627028,"score_spread":0.226309812040506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402178711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6067324,0.00009778551,0.39186013,0.0002770287,0.00072432973,0.00024123189,0.000007483141,0.000018869336,0.00004071148],"genre_scores_gemma":[0.99963105,0.0000027478773,0.00024593837,0.000008036598,0.000086834276,0.000012111042,2.1624643e-7,0.0000073428973,0.0000057430466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984951,0.0001829416,0.00021803829,0.00017971151,0.00079182175,0.00013239551],"domain_scores_gemma":[0.9982764,0.00085135276,0.0002673144,0.00019111573,0.00037453277,0.00003926762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018274639,0.00011377519,0.00023898025,0.000054529923,0.00020488839,0.00032671867,0.00031844393,0.000026565307,0.0000024145213],"category_scores_gemma":[0.00007118269,0.00006721964,0.00018683921,0.00018924163,0.0000918868,0.00064385607,0.000073263276,0.0002587533,0.0000014182136],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011017188,0.00092510314,0.007309624,0.0005419956,0.0004656479,0.0000023095336,0.0025702526,0.0006065839,0.19269645,0.0012114124,0.00085668627,0.79270375],"study_design_scores_gemma":[0.000930577,0.0004190833,0.24288714,0.000835442,0.00041961385,0.000061995626,0.00045155763,0.70197093,0.047208503,0.003853778,0.00060352765,0.0003578464],"about_ca_topic_score_codex":0.000030748568,"about_ca_topic_score_gemma":0.000007440654,"teacher_disagreement_score":0.79234594,"about_ca_system_score_codex":0.00015323539,"about_ca_system_score_gemma":0.00012444961,"threshold_uncertainty_score":0.31505555},"labels":[],"label_agreement":null},{"id":"W4402187468","doi":"10.1109/abc61795.2024.10651644","title":"Daily Routine Recognition from Longitudinal, Real-Life Wearable Sensor Data for the Elderly","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Wearable computer; Computer science; Wearable technology; Embedded system","score_opus":0.14514506580409275,"score_gpt":0.31963197625041717,"score_spread":0.17448691044632442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402187468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014706092,0.0010988856,0.9683501,0.00895585,0.0018005581,0.0007206738,0.000592226,0.0008067958,0.0029687819],"genre_scores_gemma":[0.9824931,0.00023445411,0.012306182,0.00043491716,0.00083213556,0.0001371855,0.0002766165,0.000032936354,0.0032524567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978714,0.0001245609,0.0003768304,0.00096088916,0.00034171142,0.00032459802],"domain_scores_gemma":[0.995374,0.0024701655,0.00008764895,0.0017708951,0.00018254766,0.00011472994],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009701818,0.00019984433,0.00024017987,0.00009418127,0.00024984693,0.0012477192,0.0014092263,0.00008844885,0.0002456694],"category_scores_gemma":[0.00028047047,0.00014122699,0.00010291452,0.0004410183,0.000039858416,0.0018318733,0.00049226446,0.00018845685,0.0010176036],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044405126,0.000099529185,0.00031412466,0.00009176295,0.00041557354,0.000031365882,0.00048236758,0.00000888726,0.0009016587,0.0015439434,0.1296878,0.86637855],"study_design_scores_gemma":[0.0011761304,0.00035019798,0.0031859993,0.0005163081,0.0002057983,0.00009316099,0.0007547891,0.84115624,0.0015100065,0.005301916,0.14492138,0.0008280682],"about_ca_topic_score_codex":0.0031718402,"about_ca_topic_score_gemma":0.00080552005,"teacher_disagreement_score":0.967787,"about_ca_system_score_codex":0.000046917092,"about_ca_system_score_gemma":0.00018425446,"threshold_uncertainty_score":0.99978906},"labels":[],"label_agreement":null},{"id":"W4402216304","doi":"10.1109/jsen.2024.3451291","title":"Deep Transfer Learning for Detection of Upper and Lower Body Movements: Transformer With Convolutional Neural Network","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Vector Institute; Western University; Canada Research Chairs","keywords":"Transfer of learning; Convolutional neural network; Computer science; Transformer; Deep learning; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Machine learning; Engineering; Electrical engineering; Voltage","score_opus":0.013665135718189158,"score_gpt":0.23542549150275766,"score_spread":0.22176035578456849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402216304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35962546,0.00027408334,0.63873833,0.000170286,0.0009205801,0.00015732083,0.0000020091395,0.00004117376,0.0000707944],"genre_scores_gemma":[0.99847144,0.000034759665,0.0008059497,0.00006206411,0.00040238924,0.000011321696,7.7867037e-7,0.000017896451,0.00019340373],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874836,0.000108624554,0.00031300442,0.00024508275,0.0003068157,0.0002781073],"domain_scores_gemma":[0.9993586,0.00021338629,0.000053675674,0.00007919856,0.00019358077,0.0001015936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041065057,0.0001465146,0.00019935126,0.0001415185,0.000215895,0.00022137874,0.00011760327,0.000063963744,0.000022537393],"category_scores_gemma":[0.000009611021,0.000118774304,0.00013018177,0.00022446696,0.000055876968,0.00076582335,0.00000522227,0.00034671774,0.0000038075095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012630016,0.00026984542,0.004272583,0.00047566922,0.0015494741,0.00019908982,0.0064032865,0.12396316,0.25336292,0.0011396323,0.0004903143,0.606611],"study_design_scores_gemma":[0.0021939636,0.0014438586,0.0035306597,0.00042839802,0.00009359924,0.0033085775,0.00024436656,0.9604016,0.019370114,0.0010527433,0.007459527,0.00047260974],"about_ca_topic_score_codex":0.000007862743,"about_ca_topic_score_gemma":0.000017297038,"teacher_disagreement_score":0.8364384,"about_ca_system_score_codex":0.000047886533,"about_ca_system_score_gemma":0.00005608632,"threshold_uncertainty_score":0.48434755},"labels":[],"label_agreement":null},{"id":"W4402401573","doi":"10.1109/jiot.2024.3457372","title":"FEDge-HAR: An Optimized Private Mobile Edge-Enabled IoT Paradigm for Privacy of Human Activity Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Ningbo Municipal Bureau of Science and Technology","keywords":"Computer science; Internet of Things; Enhanced Data Rates for GSM Evolution; Edge computing; Computer security; Information privacy; Internet privacy; Activity recognition; Mobile telephony; Computer network; Human–computer interaction; Telecommunications; Artificial intelligence; Mobile radio","score_opus":0.05172679627628167,"score_gpt":0.31810056154792776,"score_spread":0.2663737652716461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402401573","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5318777,0.00011018577,0.46543813,0.0001653728,0.0016397107,0.00045059834,0.000013163127,0.00012349128,0.00018164479],"genre_scores_gemma":[0.98098856,0.00001737551,0.018129554,0.000056777903,0.00034426508,0.0000754869,0.0000057566535,0.000038972612,0.00034327814],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973974,0.0003067824,0.00090602064,0.0005180053,0.000493976,0.00037781076],"domain_scores_gemma":[0.99760395,0.00045074557,0.0008466203,0.00047787794,0.0004199328,0.0002008527],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018282509,0.00028301586,0.00063757965,0.00048645094,0.00012461642,0.0006607703,0.0013000143,0.00015920379,0.0000832746],"category_scores_gemma":[0.00013110234,0.00026290314,0.00044146177,0.00030347193,0.00008918486,0.0027666003,0.00017547316,0.0005744,0.000023634022],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007189736,0.0013165104,0.00013559447,0.0011859159,0.0010395405,0.00010399537,0.018081063,0.00019657069,0.4313235,0.0012548008,0.006042552,0.538601],"study_design_scores_gemma":[0.0034602645,0.0029015178,0.00018232937,0.0030087538,0.00016255377,0.0011998513,0.00013273527,0.18225005,0.7742078,0.025603326,0.006065237,0.0008256007],"about_ca_topic_score_codex":0.0001186332,"about_ca_topic_score_gemma":0.0000053372073,"teacher_disagreement_score":0.5377754,"about_ca_system_score_codex":0.00018170766,"about_ca_system_score_gemma":0.00016826516,"threshold_uncertainty_score":0.9999823},"labels":[],"label_agreement":null},{"id":"W4402494392","doi":"10.1007/s11633-024-1526-0","title":"Correction to: Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications","year":2024,"lang":"en","type":"article","venue":"Machine Intelligence Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Computer graphics (images); Information retrieval","score_opus":0.1218150086592629,"score_gpt":0.41155579611317383,"score_spread":0.28974078745391096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402494392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.111580186,0.000098745615,0.87820834,0.0033235664,0.0011115568,0.0014677284,0.000031567903,0.00036930531,0.003808986],"genre_scores_gemma":[0.99676263,0.00006384414,0.00080629723,0.00016877646,0.00019173032,0.0004390294,0.000016769674,0.000023239956,0.001527709],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964999,0.0005568102,0.00047946852,0.00083787844,0.0012184872,0.00040747123],"domain_scores_gemma":[0.9968297,0.0015168649,0.00006904045,0.00087377627,0.00042668649,0.0002839634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021541798,0.00019619802,0.00023661328,0.0012049788,0.00028226172,0.00048106018,0.0008866643,0.00006042357,0.000102982725],"category_scores_gemma":[0.00013693428,0.0001764829,0.00009203207,0.0022601038,0.00009720674,0.0005185191,0.00034955028,0.00070807035,0.00046513122],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027326107,0.00016347063,0.002121372,0.00014731113,0.000031236446,0.0000056631234,0.0074373567,0.0002606898,0.02091819,0.01117873,0.0019262977,0.95578235],"study_design_scores_gemma":[0.00006334136,0.0008711076,0.005545205,0.0007872312,0.000008533461,0.000019042218,0.00045411105,0.51778126,0.46499074,0.0053517893,0.0037687696,0.0003588932],"about_ca_topic_score_codex":0.0037281793,"about_ca_topic_score_gemma":0.0014192306,"teacher_disagreement_score":0.9554235,"about_ca_system_score_codex":0.00046177715,"about_ca_system_score_gemma":0.00018258381,"threshold_uncertainty_score":0.71967643},"labels":[],"label_agreement":null},{"id":"W4402721799","doi":"10.1145/3675094.3677561","title":"12th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Computer science; Natural language processing","score_opus":0.05921415699872257,"score_gpt":0.33107395449887533,"score_spread":0.27185979750015277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402721799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023721233,0.00005294272,0.94070214,0.0023203727,0.0005441243,0.00023132721,0.0000048167126,0.0005508322,0.031872198],"genre_scores_gemma":[0.996044,0.000006886896,0.0010872745,0.00018896694,0.00022373763,0.000021508924,0.000003207111,0.000009044971,0.0024154088],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990396,0.00003933317,0.00012249721,0.00043984756,0.000230765,0.00012799268],"domain_scores_gemma":[0.999199,0.0003306968,0.000037192713,0.00030434257,0.00006034056,0.000068466135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019508453,0.00010792513,0.00010463591,0.00017210231,0.00013479668,0.0006652728,0.0002422795,0.000049078593,0.000044254884],"category_scores_gemma":[0.000015174935,0.00010190429,0.00004564941,0.00025670335,0.000034462926,0.0005357444,0.00017699176,0.00016171047,0.00015446285],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018637132,0.00003487977,0.00007377794,0.000010540819,0.00003218345,0.000009442322,0.0001259729,0.000003119984,0.0048816702,0.02436949,0.0009623196,0.96949476],"study_design_scores_gemma":[0.0010559253,0.00016051235,0.00936829,0.000772876,0.000042597505,0.0005364888,0.00030405138,0.3693473,0.030860275,0.022310797,0.5637668,0.001474087],"about_ca_topic_score_codex":0.000058373356,"about_ca_topic_score_gemma":0.00010885297,"teacher_disagreement_score":0.9723227,"about_ca_system_score_codex":0.000072789786,"about_ca_system_score_gemma":0.000031716067,"threshold_uncertainty_score":0.64152414},"labels":[],"label_agreement":null},{"id":"W4402830256","doi":"10.1109/jsen.2024.3464416","title":"CDFL: Efficient Federated Human Activity Recognition Using Contrastive Learning and Deep Clustering","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"","keywords":"Computer science; Cluster analysis; Artificial intelligence; Deep learning; Pattern recognition (psychology)","score_opus":0.04631548503441584,"score_gpt":0.3028261332030844,"score_spread":0.25651064816866853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402830256","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55401134,0.00013214564,0.44452935,0.00007024445,0.00080072024,0.00010464134,0.0000017548772,0.00015335795,0.00019642062],"genre_scores_gemma":[0.9980416,0.000019295576,0.0014022868,0.000026002546,0.00040864554,0.0000028997385,7.264596e-7,0.000026153899,0.000072344985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980764,0.0004023401,0.0003381725,0.000441438,0.000361704,0.00037997199],"domain_scores_gemma":[0.9989454,0.0003291455,0.0001989999,0.00007904758,0.0002484578,0.00019892679],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008343453,0.00022422982,0.00028011116,0.00033887086,0.0008702797,0.0019152623,0.00013370444,0.00010356629,0.000028356],"category_scores_gemma":[0.00008667852,0.00022007896,0.000106572916,0.00035657515,0.000057373625,0.0007946471,0.00007705492,0.0008341108,0.000040330237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058643618,0.00010879048,0.00027785683,0.000114482486,0.00026494224,0.00092315854,0.003881984,0.011524425,0.4177601,0.000013079554,0.000061717445,0.56501085],"study_design_scores_gemma":[0.00046475572,0.0001117662,0.00058448274,0.00048606438,0.00003007586,0.004743994,0.00026340026,0.9828111,0.009906533,0.00008792624,0.00020350161,0.00030639308],"about_ca_topic_score_codex":0.00005500844,"about_ca_topic_score_gemma":0.000037641286,"teacher_disagreement_score":0.97128665,"about_ca_system_score_codex":0.00023616255,"about_ca_system_score_gemma":0.00008261134,"threshold_uncertainty_score":0.99912083},"labels":[],"label_agreement":null},{"id":"W4402830452","doi":"10.1109/jiot.2024.3462954","title":"Semantic-DARTS: Elevating Semantic Learning for Mobile Differentiable Architecture Search","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Context-Aware Activity Recognition 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 Waterloo","funders":"National Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization; National Key Research and Development Program of China; Key Research and Development Program of Heilongjiang","keywords":"Computer science; Architecture; Artificial intelligence; Differentiable function; Mobile computing; Computer network","score_opus":0.02340716417334995,"score_gpt":0.2841549632815281,"score_spread":0.2607477991081782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402830452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29499033,0.0003639859,0.7023397,0.0002991612,0.0013985487,0.00023387025,0.0000014200136,0.00014656356,0.00022643068],"genre_scores_gemma":[0.9888143,0.000014095361,0.007874601,0.00007920067,0.0003949488,0.000025922327,0.0000016434641,0.000036045858,0.002759235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976671,0.0002129013,0.00064062566,0.00042124622,0.0005920177,0.0004661111],"domain_scores_gemma":[0.99821883,0.00080309145,0.0002707005,0.00024450838,0.00032443248,0.00013844222],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015098212,0.00022598486,0.00039488095,0.00045518464,0.00016318203,0.001230597,0.00094599335,0.00009893676,0.00006954744],"category_scores_gemma":[0.00016358167,0.00019417942,0.0003324802,0.00029936866,0.000051336094,0.0010949878,0.0002024093,0.0010880477,0.0000427186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010494803,0.00028519332,0.002109813,0.0025999283,0.0011271837,0.0002729178,0.052478537,0.004660682,0.16766147,0.0014051506,0.009131862,0.7581623],"study_design_scores_gemma":[0.000691192,0.0008157902,0.00010421193,0.004140411,0.000065237844,0.0028343203,0.0006348439,0.8830255,0.09075636,0.004068469,0.012364428,0.0004991872],"about_ca_topic_score_codex":0.00008589257,"about_ca_topic_score_gemma":0.0000061664127,"teacher_disagreement_score":0.87836486,"about_ca_system_score_codex":0.00009720853,"about_ca_system_score_gemma":0.00012841796,"threshold_uncertainty_score":0.9998062},"labels":[],"label_agreement":null},{"id":"W4402980467","doi":"10.1109/otcon60325.2024.10687360","title":"Real-time Settings and Their Transformation through Distributed Intelligence Communication Technologies","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Horizon College and Seminary","funders":"","keywords":"Computer science; Transformation (genetics); Distributed computing; Real-time computing","score_opus":0.022604615272232047,"score_gpt":0.26326020508322423,"score_spread":0.2406555898109922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402980467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071312333,0.0005096599,0.97439694,0.010065365,0.000062193954,0.00024239125,0.000020954703,0.0025745938,0.0049966914],"genre_scores_gemma":[0.99227595,0.00054183876,0.006994178,0.0000393991,0.000005689676,0.000034944343,0.000029336547,0.0000056562067,0.000073025825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992181,0.00006885055,0.00023273412,0.00024332045,0.000101315636,0.00013566231],"domain_scores_gemma":[0.9990517,0.00037362854,0.000043492597,0.00044348784,0.00006997595,0.000017727798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032961383,0.00011547269,0.00012806608,0.00007887169,0.00011860388,0.00042571765,0.0004788948,0.000079703976,0.000011845192],"category_scores_gemma":[0.00003589975,0.00008873468,0.000031060434,0.0004843655,0.000078346995,0.0018708439,0.00017358847,0.00013566816,0.00010808196],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023239616,0.000021953048,0.000010631865,0.000085700805,0.000030369814,0.0000015600475,0.008386841,0.0000039668976,0.007343314,0.08435099,0.0013202141,0.89844215],"study_design_scores_gemma":[0.00018142331,0.00016393294,0.00018774236,0.0007544787,0.00001709025,0.0002668775,0.008777898,0.5747792,0.20316555,0.15017943,0.06080094,0.0007254357],"about_ca_topic_score_codex":0.00009564784,"about_ca_topic_score_gemma":0.000014209832,"teacher_disagreement_score":0.9851447,"about_ca_system_score_codex":0.000045310102,"about_ca_system_score_gemma":0.0000260478,"threshold_uncertainty_score":0.4105205},"labels":[],"label_agreement":null},{"id":"W4403318103","doi":"10.1145/3672539.3686706","title":"Dynamic Abstractions: Building the Next Generation of Cognitive Tools and Interfaces","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Montréal; University of Toronto","funders":"","keywords":"Computer science; Human–computer interaction; Software engineering","score_opus":0.12514841635119056,"score_gpt":0.3264860438217349,"score_spread":0.20133762747054434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403318103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4131676,0.00080463354,0.5832089,0.00086367317,0.0004675168,0.00017834918,0.000008459139,0.000107744345,0.0011931265],"genre_scores_gemma":[0.9981216,0.00004479199,0.0014452516,0.00006481363,0.00003695091,0.000023978377,0.0000023191744,0.00000432585,0.00025594843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934393,0.00005830473,0.00017527114,0.00021881431,0.00012355996,0.00008012649],"domain_scores_gemma":[0.99880505,0.0009155951,0.000047406036,0.00012749669,0.00008397787,0.000020488362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026494512,0.000071457485,0.00008523601,0.00008334551,0.000077365505,0.0006674725,0.00014716607,0.000029711202,0.000034703182],"category_scores_gemma":[0.000084348736,0.000049424925,0.000029821846,0.00017120925,0.000043150936,0.0014992184,0.00009090131,0.00009400263,0.00003811957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020422863,0.000020200285,0.000030575386,0.000034117496,0.00006497754,0.0000034085047,0.0015088944,0.0000122820275,0.0977444,0.007953226,0.00025406343,0.89237183],"study_design_scores_gemma":[0.00022985155,0.00012354029,0.002916703,0.00054486614,0.000039325492,0.00023409724,0.002289042,0.9139038,0.075629964,0.0020571197,0.0017492488,0.0002824911],"about_ca_topic_score_codex":0.000054471217,"about_ca_topic_score_gemma":0.00010040646,"teacher_disagreement_score":0.9138915,"about_ca_system_score_codex":0.000023362967,"about_ca_system_score_gemma":0.000037233553,"threshold_uncertainty_score":0.6436452},"labels":[],"label_agreement":null},{"id":"W4403351173","doi":"10.1016/j.ijnss.2024.10.009","title":"Advances in applying somatosensory interaction technology in geriatric care: A bibliometric analysis","year":2024,"lang":"en","type":"review","venue":"International Journal of Nursing Sciences","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Shanghai Municipal Health Commission","keywords":"Geriatric care; Psychology; Somatosensory system; Gerontology; Medicine; Nursing; Neuroscience","score_opus":0.08082475799791837,"score_gpt":0.44693919892134915,"score_spread":0.36611444092343076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403351173","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029154913,0.9825958,0.010430128,0.00032413114,0.005376921,0.00022543287,0.0000049823298,0.000041137202,0.0007099131],"genre_scores_gemma":[0.075609535,0.9223672,0.0017515838,0.000009170362,0.00021205803,0.000028661167,0.000001936578,0.000010887839,0.000008950018],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99612427,0.00026792684,0.0014987789,0.00056589686,0.0012509325,0.00029220557],"domain_scores_gemma":[0.9972052,0.0005626779,0.0015081045,0.00019996599,0.00046454262,0.000059531478],"candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0013521537,0.00028543564,0.0012204164,0.2849899,0.000057692545,0.0008279449,0.0026091146,0.00019439953,0.0000055227197],"category_scores_gemma":[0.00030729148,0.00023006265,0.00056504004,0.18885806,0.0001680282,0.0025189135,0.0001686781,0.00075481116,0.000023908462],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024291794,0.00007772302,0.00019690508,0.00023600958,0.00015940625,0.00017603431,0.0003458386,0.00012273405,0.0000016230151,0.00033017554,0.000015318143,0.9983358],"study_design_scores_gemma":[0.0014348009,0.00072182,0.0006958208,0.15425041,0.0034516542,0.013391046,0.0067875306,0.010928675,0.000044008644,0.01695744,0.78896886,0.0023679356],"about_ca_topic_score_codex":0.000022975002,"about_ca_topic_score_gemma":0.000041574644,"teacher_disagreement_score":0.99596786,"about_ca_system_score_codex":0.0013572206,"about_ca_system_score_gemma":0.000645704,"threshold_uncertainty_score":0.9381683},"labels":[],"label_agreement":null},{"id":"W4403390347","doi":"10.1109/access.2024.3480814","title":"Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition: A Review","year":2024,"lang":"en","type":"review","venue":"IEEE Access","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep de Chicoutimi; Université du Québec à Chicoutimi; Q & T Research; Université de Montréal; Université de Sherbrooke","funders":"AGE-WELL; Natural Sciences and Engineering Research Council of Canada; Réseau québécois de recherche sur le vieillissement; Université de Sherbrooke","keywords":"Computer science; Activity recognition; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Machine learning","score_opus":0.2038097312799016,"score_gpt":0.42246322780866086,"score_spread":0.21865349652875926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403390347","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004611925,0.85345876,0.13768853,0.000215356,0.0016773785,0.0049390383,0.00017093612,0.0012303467,0.0006150173],"genre_scores_gemma":[0.0002342754,0.9931052,0.00097915,0.0005031477,0.00081067596,0.003720554,0.00018536925,0.00014832478,0.00031330585],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945363,0.0012871567,0.001125726,0.0017634856,0.0006102834,0.0006770938],"domain_scores_gemma":[0.9950842,0.0019299651,0.0010441833,0.001021528,0.0006730172,0.000247071],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014080838,0.0009804318,0.0032169241,0.00052393426,0.00040849065,0.0013779713,0.002163385,0.00046789765,0.000065353066],"category_scores_gemma":[0.00028355306,0.000862794,0.0015691381,0.0014874323,0.000057614237,0.0014405559,0.0003251831,0.0012269437,0.00064042315],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032382989,0.00009972571,7.6476863e-7,0.10157519,0.0003478055,0.0000444225,0.00002991585,3.8929824e-7,0.0000020883806,0.000010587398,0.0037784243,0.89410746],"study_design_scores_gemma":[0.00046954816,0.00013766518,6.814735e-7,0.12044341,0.0020527411,0.00010448269,0.000003146199,0.0015150418,0.000027539629,0.00010770779,0.874043,0.0010950705],"about_ca_topic_score_codex":0.00005806214,"about_ca_topic_score_gemma":0.00001911044,"teacher_disagreement_score":0.8930124,"about_ca_system_score_codex":0.0004215694,"about_ca_system_score_gemma":0.0009005511,"threshold_uncertainty_score":0.9996587},"labels":[],"label_agreement":null},{"id":"W4403675482","doi":"10.1109/biorob60516.2024.10719807","title":"Reinforcement Learning for Control of Human Locomotion in Simulation","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"University of Toronto","funders":"","keywords":"Reinforcement learning; Computer science; Reinforcement; Control (management); Artificial intelligence; Human–computer interaction; Engineering","score_opus":0.03932292210613274,"score_gpt":0.3183529776514953,"score_spread":0.27903005554536253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403675482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009962479,0.000024248824,0.9878872,0.00017459357,0.00013582755,0.00040021475,3.033074e-7,0.00011560923,0.0012994999],"genre_scores_gemma":[0.9989567,3.978392e-7,0.0003411341,0.000023826218,0.000028692179,0.00003900731,0.0000035995788,0.000004109109,0.0006025253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993352,0.000047119647,0.00024476656,0.00016022587,0.00012105402,0.000091604554],"domain_scores_gemma":[0.9994448,0.0003102824,0.000051499956,0.00010548166,0.000071379705,0.000016594146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004239025,0.00005304591,0.0001078275,0.00016676067,0.000029942395,0.00007044135,0.000103529186,0.000030005502,0.000023307699],"category_scores_gemma":[0.00003719185,0.00005067818,0.000048590213,0.00017954408,0.000006799703,0.00042948846,0.00002418589,0.000055123815,0.0000136917315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015426993,0.000060073897,0.0016716679,0.00028205244,0.000042284842,0.0000029870819,0.0017281309,0.6473638,0.012728862,0.05862162,0.00011522551,0.2773679],"study_design_scores_gemma":[0.00042527253,0.00011804622,0.0003827407,0.00007879474,0.0000024437488,6.382497e-7,0.00002469804,0.9954831,0.0014324032,0.0007741728,0.0012218705,0.0000557947],"about_ca_topic_score_codex":0.00005286437,"about_ca_topic_score_gemma":0.00003094297,"teacher_disagreement_score":0.98899424,"about_ca_system_score_codex":0.000050872943,"about_ca_system_score_gemma":0.000020630812,"threshold_uncertainty_score":0.20665962},"labels":[],"label_agreement":null},{"id":"W4403794871","doi":"10.1080/20476965.2024.2395574","title":"Next-generation fall detection: harnessing human pose estimation and transformer technology","year":2024,"lang":"en","type":"review","venue":"Health Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Science and Engineering Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Computer science; Wearable technology; Transformer; Computer security; Artificial intelligence; Embedded system; Engineering","score_opus":0.18002517745017702,"score_gpt":0.39327775467361514,"score_spread":0.21325257722343813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403794871","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009994491,0.772183,0.22187403,0.00029185152,0.003141775,0.0017773085,0.000013925348,0.0006199785,0.000088170294],"genre_scores_gemma":[0.01320754,0.98374027,0.00055939297,0.00005882093,0.00094237295,0.0009941636,0.000074299176,0.000081899336,0.00034122192],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99616927,0.00056163216,0.0014479246,0.0009937367,0.0003760861,0.00045133516],"domain_scores_gemma":[0.99821144,0.000090978785,0.00074452005,0.0006385692,0.00013134489,0.00018313497],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011748828,0.00050346885,0.0016563731,0.0011196928,0.0005502942,0.0011952912,0.00040734198,0.00056290446,0.0000015888164],"category_scores_gemma":[0.000024379548,0.00044053257,0.00019282289,0.0011854799,0.000051241208,0.0008939028,0.00007640008,0.00062250637,0.0001811252],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.2752022e-7,0.000013510475,3.3993146e-7,0.030576847,0.000043236152,0.0000072417374,0.0002073692,0.0000011294227,0.000011834521,0.0010502067,0.00026128723,0.9678268],"study_design_scores_gemma":[0.00015569279,0.00016118014,4.652682e-7,0.025090434,0.000142394,0.001809066,0.00006582129,0.028065711,0.000004516383,0.000098830016,0.94388074,0.00052516983],"about_ca_topic_score_codex":0.00044338,"about_ca_topic_score_gemma":0.00033963795,"teacher_disagreement_score":0.9673016,"about_ca_system_score_codex":0.0005793152,"about_ca_system_score_gemma":0.0005857975,"threshold_uncertainty_score":0.9998416},"labels":[],"label_agreement":null},{"id":"W4403815291","doi":"10.1093/eurpub/ckae144.1168","title":"A Comparative Study of Sensor Quantity and Data Granularity in AAL Systems for Activity Recognition","year":2024,"lang":"en","type":"article","venue":"European Journal of Public Health","topic":"Context-Aware Activity Recognition Systems","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":"Research Institute for Aging; University of Waterloo","funders":"","keywords":"Granularity; Computer science; Pattern recognition (psychology); Artificial intelligence; Data mining; Operating system","score_opus":0.4172161409569002,"score_gpt":0.4140018628697525,"score_spread":0.003214278087147693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403815291","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61667216,0.00055926584,0.38051492,0.0009163569,0.0006143661,0.0005509373,0.00008202037,0.000027088623,0.000062894855],"genre_scores_gemma":[0.99857616,0.000047906186,0.0011738626,0.000027375467,0.0001463522,0.0000031810196,0.000006281442,0.000011346681,0.000007501826],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9911393,0.0068573793,0.00091900426,0.00040822552,0.0004090305,0.00026706888],"domain_scores_gemma":[0.9976274,0.00065966236,0.0006579583,0.00044119573,0.0003918502,0.00022197733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021862462,0.00013443513,0.00057384686,0.0004784474,0.000088823006,0.00043174255,0.00065142487,0.000020662575,0.0000014275635],"category_scores_gemma":[0.00034575202,0.00011875416,0.000049882066,0.00053670007,0.000038906506,0.0020637796,0.000254193,0.00036753577,0.0000049140654],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010825506,0.0036714335,0.012370215,0.0018501594,0.00048550693,0.00037202492,0.03645822,0.00002079294,0.00044975016,0.00055753085,0.0029380836,0.94071805],"study_design_scores_gemma":[0.015869325,0.018511869,0.5984256,0.004405026,0.00012270454,0.003457486,0.03011776,0.29711407,0.00011626552,0.00034290995,0.03001642,0.0015005564],"about_ca_topic_score_codex":0.00042873787,"about_ca_topic_score_gemma":0.00040126187,"teacher_disagreement_score":0.93921745,"about_ca_system_score_codex":0.00011083765,"about_ca_system_score_gemma":0.00050565857,"threshold_uncertainty_score":0.7577137},"labels":[],"label_agreement":null},{"id":"W4403816023","doi":"10.1093/eurpub/ckae144.1636","title":"Data ecosystem using smart home technology to support healthy aging","year":2024,"lang":"en","type":"article","venue":"European Journal of Public Health","topic":"Context-Aware Activity Recognition Systems","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":"Ecosystem; Ecology; Biology","score_opus":0.22068182243977671,"score_gpt":0.38213005594094634,"score_spread":0.16144823350116963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403816023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031684197,0.0011709147,0.8988045,0.063134775,0.004128595,0.0002193979,0.000057849673,0.00028219237,0.00051756995],"genre_scores_gemma":[0.9789203,0.000042378884,0.01789815,0.0021287752,0.00090425205,8.982783e-7,0.000005819309,0.00004582384,0.00005359718],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99347925,0.003139337,0.0013862984,0.00052126404,0.0006216787,0.0008521688],"domain_scores_gemma":[0.9968452,0.00017689863,0.00057795347,0.00111507,0.00030766087,0.0009772425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028240422,0.00019384875,0.00048573772,0.0016819653,0.00019869913,0.00080689066,0.0025778904,0.000033341377,0.000018598806],"category_scores_gemma":[0.00031069762,0.00017784131,0.000085472595,0.0018348692,0.000022755337,0.0026635055,0.0010916103,0.0006071158,0.0004023423],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019462268,0.00009365984,0.0011073046,0.00030100497,0.00008723175,0.0006809231,0.0017520599,0.0000066297453,0.00010500946,0.001508043,0.018035656,0.9763205],"study_design_scores_gemma":[0.0003678589,0.0009265461,0.0008516581,0.00069056224,0.000005981684,0.0081251655,0.00048765028,0.0062299673,0.000012114882,0.000032298423,0.9820036,0.00026655162],"about_ca_topic_score_codex":0.000015806234,"about_ca_topic_score_gemma":0.000029608058,"teacher_disagreement_score":0.97605395,"about_ca_system_score_codex":0.00051968556,"about_ca_system_score_gemma":0.0027417804,"threshold_uncertainty_score":0.9787623},"labels":[],"label_agreement":null},{"id":"W4403816026","doi":"10.1093/eurpub/ckae144.1633","title":"Telemonitoring activities of daily living in home healthcare services to support aging in place","year":2024,"lang":"en","type":"article","venue":"European Journal of Public Health","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"HEC Montréal; McGill University Health Centre; Université de Sherbrooke; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal","funders":"","keywords":"Aging in place; Health care; Activities of daily living; Gerontology; Medicine; Physical therapy; Political science","score_opus":0.0652566003913092,"score_gpt":0.3198344052302795,"score_spread":0.2545778048389703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403816026","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9153869,0.0018287875,0.061610345,0.018345194,0.0019883432,0.00021765669,0.0000070048022,0.000085506435,0.0005302414],"genre_scores_gemma":[0.99756414,0.000088513465,0.0015476049,0.00036328865,0.00033484123,0.0000018323094,4.904377e-7,0.000027472272,0.00007182017],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9941822,0.0031620162,0.001221349,0.00031247325,0.00056555896,0.00055639615],"domain_scores_gemma":[0.9981848,0.0004955568,0.00047344482,0.0002811741,0.00015778834,0.00040724932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016471459,0.00016076637,0.00046705492,0.0015734789,0.000058031746,0.00042626998,0.0008597213,0.000023003555,0.000009004993],"category_scores_gemma":[0.00012231768,0.00015868322,0.000080926235,0.0013272063,0.000016379525,0.0021832576,0.0002993218,0.0005774563,0.000019388002],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036074453,0.00009051783,0.036811557,0.0010996058,0.00002086282,0.00046506058,0.057416033,0.000052373285,0.00017233103,0.00018702996,0.00019567228,0.90348536],"study_design_scores_gemma":[0.00199209,0.003850525,0.851743,0.027934913,0.0000075848943,0.0024533703,0.041198216,0.00676311,0.00033890808,0.000104666,0.06237184,0.0012417742],"about_ca_topic_score_codex":0.00040117285,"about_ca_topic_score_gemma":0.00057941984,"teacher_disagreement_score":0.90224355,"about_ca_system_score_codex":0.0004882509,"about_ca_system_score_gemma":0.0012520988,"threshold_uncertainty_score":0.6470914},"labels":[],"label_agreement":null},{"id":"W4403831444","doi":"10.1681/asn.2024rmgbhzsb","title":"Machine Learning-Derived Predictors of Clinical Outcomes in CureGN Participants with FSGS","year":2024,"lang":"en","type":"article","venue":"Journal of the American Society of Nephrology","topic":"Context-Aware Activity Recognition 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":"Toronto General Hospital; University of British Columbia","funders":"","keywords":"Medicine; Intensive care medicine; Internal medicine","score_opus":0.05294102341855199,"score_gpt":0.3425925476566973,"score_spread":0.2896515242381453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403831444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98098886,0.0002456168,0.012015332,0.0062454874,0.00035473556,0.000100620266,0.0000041470803,0.000020438883,0.000024761388],"genre_scores_gemma":[0.9962663,0.00014808871,0.0024533295,0.0009681758,0.00009913441,0.0000024650774,1.5917396e-7,0.000010535761,0.00005178933],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99749076,0.00069232524,0.0009865451,0.00019531345,0.00039733705,0.0002377001],"domain_scores_gemma":[0.9966777,0.0012509,0.0015321268,0.00028631807,0.00017200764,0.00008093804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015027445,0.00013150438,0.0009166818,0.00008352707,0.00003224039,0.000019680094,0.0007547626,0.000062399275,0.000008019605],"category_scores_gemma":[0.00017733849,0.0000784063,0.0005951031,0.0005758886,0.0007140916,0.00023674936,0.00019930536,0.00067424675,0.0000020469224],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017201992,0.00029278608,0.96610147,0.000048733877,0.0011433908,0.00002244829,0.003783376,0.00031260657,0.0056204316,0.000048012225,0.002261704,0.02019304],"study_design_scores_gemma":[0.0011534275,0.0021804015,0.97007114,0.0001864654,0.00010665409,0.00020639428,0.0007360256,0.016387602,0.0003619543,0.00018812498,0.0082774,0.00014439388],"about_ca_topic_score_codex":0.00019871898,"about_ca_topic_score_gemma":0.000057340072,"teacher_disagreement_score":0.020048646,"about_ca_system_score_codex":0.00003694579,"about_ca_system_score_gemma":0.0002932504,"threshold_uncertainty_score":0.31973162},"labels":[],"label_agreement":null},{"id":"W4403899100","doi":"10.54392/irjmt2462","title":"GastroSmart: Precision GI Health Monitoring with Non-Invasive GMR","year":2024,"lang":"en","type":"article","venue":"International Research Journal of Multidisciplinary Technovation","topic":"Context-Aware Activity Recognition Systems","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Medicine; Environmental science","score_opus":0.08046587779363792,"score_gpt":0.418873604022607,"score_spread":0.3384077262289691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403899100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35442492,0.00033753013,0.6338332,0.008748409,0.0016387696,0.00038739882,0.000009579797,0.0001216596,0.00049847073],"genre_scores_gemma":[0.97163975,0.00013532527,0.027269166,0.000009893123,0.0005891768,0.00003221657,0.000005033931,0.000021260343,0.00029816316],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99587286,0.00010495285,0.0007511981,0.0003971606,0.002514086,0.0003597483],"domain_scores_gemma":[0.99548125,0.0009586239,0.0003726065,0.00033378787,0.002702536,0.00015118733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045565534,0.00016373886,0.00023746556,0.0020455187,0.00026294668,0.000546699,0.0012875219,0.00008021658,0.000013838053],"category_scores_gemma":[0.000531639,0.00013010124,0.00010469297,0.0015930581,0.00009619447,0.0025093048,0.0004908913,0.0009683075,0.00005552068],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043018867,0.0006257499,0.01875137,0.0002592416,0.0004024666,0.00096069137,0.004183125,0.0007671986,0.06825558,0.010105595,0.005457097,0.8898017],"study_design_scores_gemma":[0.009709282,0.01763972,0.17439647,0.050741713,0.000050300867,0.013159588,0.009387353,0.31418312,0.28966162,0.09669263,0.022030517,0.002347697],"about_ca_topic_score_codex":0.000031155854,"about_ca_topic_score_gemma":0.000016575616,"teacher_disagreement_score":0.887454,"about_ca_system_score_codex":0.0009461856,"about_ca_system_score_gemma":0.0011013934,"threshold_uncertainty_score":0.5305375},"labels":[],"label_agreement":null},{"id":"W4404183126","doi":"10.1136/jnnp-2024-abn.69","title":"Selecting amantadine as the 3rd experimental arm for MND-SMART using a data-driven framework","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Discovery Centre","funders":"","keywords":"Computer science; Amantadine; Psychology; Neuroscience","score_opus":0.1219876729551249,"score_gpt":0.37735273777960054,"score_spread":0.25536506482447563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404183126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054659348,0.00042813053,0.939125,0.00145969,0.0015810402,0.00060289196,0.000015781941,0.00052571547,0.0016024197],"genre_scores_gemma":[0.9569666,0.0000019104564,0.041392025,0.0006697863,0.00044736464,0.000060377322,0.000009673526,0.000022715858,0.00042956977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984012,0.00008765294,0.0002595427,0.0006584425,0.00027595586,0.0003171614],"domain_scores_gemma":[0.9979159,0.0010315635,0.000063545885,0.00085287873,0.00006511938,0.000070996015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054221414,0.00016897157,0.00018305829,0.000094245086,0.0003084267,0.001001016,0.0011634551,0.00007090084,0.00010134916],"category_scores_gemma":[0.00012111641,0.00012329829,0.00008891451,0.00049868907,0.000035424742,0.0011502054,0.00070037175,0.00022377157,0.00015165903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001331248,0.0012542004,0.001484449,0.00060809834,0.0018402971,0.00027465326,0.03934535,0.00036680573,0.13111857,0.26184216,0.05280354,0.5089288],"study_design_scores_gemma":[0.00018319441,0.00012875238,0.000014632238,0.00024971756,0.000023564848,0.00034132728,0.0007617105,0.9430177,0.020392552,0.002110044,0.032482747,0.00029403763],"about_ca_topic_score_codex":0.00025603126,"about_ca_topic_score_gemma":0.00005312036,"teacher_disagreement_score":0.9426509,"about_ca_system_score_codex":0.00010204458,"about_ca_system_score_gemma":0.00014422338,"threshold_uncertainty_score":0.9652821},"labels":[],"label_agreement":null},{"id":"W4404270432","doi":"10.2196/53447","title":"Using a Device-Free Wi-Fi Sensing System to Assess Daily Activities and Mobility in Low-Income Older Adults: Protocol for a Feasibility Study","year":2024,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging","keywords":"Socioeconomic status; Activities of daily living; Dementia; Cognition; Ethnic group; Gerontology; Computer science; Applied psychology; Medicine; Psychology; Population; Physical therapy; Environmental health","score_opus":0.4137093278713815,"score_gpt":0.5620594048493872,"score_spread":0.14835007697800573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404270432","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112289995,0.0000013376297,0.033429593,0.0001129829,0.000025690722,0.85362345,0.00003825657,0.00039904955,0.00007967805],"genre_scores_gemma":[0.33170563,4.1983776e-9,0.0024450172,0.000009448276,0.00008913486,0.665683,5.2671993e-7,0.000035279365,0.000031978863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99188673,0.0027130437,0.00091709464,0.0019040008,0.0015125832,0.0010665654],"domain_scores_gemma":[0.994623,0.002062015,0.00013963612,0.0019800407,0.0008355429,0.00035976025],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009546069,0.00040559567,0.00069655053,0.00089473603,0.00041254004,0.0024764787,0.001276984,0.00017943517,0.0000061506894],"category_scores_gemma":[0.0007095592,0.000367141,0.00012621252,0.0020836797,0.00014120548,0.002089926,0.0019348793,0.0008121236,0.000014673066],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013822787,0.027350072,0.17672394,0.27216005,0.0005414051,0.0019472458,0.08964106,0.00013307304,0.014915435,0.0021067453,0.0022152285,0.39844295],"study_design_scores_gemma":[0.0420075,0.012606585,0.11119907,0.25108907,0.000018310506,0.00052493083,0.038045734,0.516348,0.01135383,0.0019702592,0.010967958,0.003868728],"about_ca_topic_score_codex":0.00043668598,"about_ca_topic_score_gemma":0.0010854997,"teacher_disagreement_score":0.51621497,"about_ca_system_score_codex":0.0014644621,"about_ca_system_score_gemma":0.0009688778,"threshold_uncertainty_score":0.99987805},"labels":[],"label_agreement":null},{"id":"W4404627482","doi":"10.1109/ojim.2024.3502885","title":"Device-Free Human Activity Recognition: A Systematic Literature Review","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of Instrumentation and Measurement","topic":"Context-Aware Activity Recognition Systems","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 Ottawa","funders":"","keywords":"Systematic review; Computer science; Biology; MEDLINE","score_opus":0.12314152241075971,"score_gpt":0.3405661763293261,"score_spread":0.2174246539185664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404627482","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09897436,0.48085213,0.333451,0.03727297,0.017450886,0.015714465,0.00010559248,0.00044834102,0.01573027],"genre_scores_gemma":[0.991277,0.0053427047,0.0020864194,0.0009116209,0.00015379456,0.00009041032,0.000002290758,0.000014229419,0.00012154752],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99748844,0.00049526605,0.0007292982,0.0002503773,0.0008988635,0.0001377746],"domain_scores_gemma":[0.9981636,0.00006756079,0.00052258885,0.00030847391,0.0007900149,0.00014779142],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003413745,0.00017192953,0.00046147365,0.0002160421,0.00014079252,0.0015865011,0.0007393682,0.00004521465,0.00003260916],"category_scores_gemma":[0.00012774735,0.00013486664,0.00012509119,0.00045821682,0.000018598499,0.0029057043,0.00011721182,0.00024309155,0.000027437238],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006532975,0.00042280767,0.00006530482,0.15411967,0.0011175467,0.00043381145,0.0041516516,0.000001023938,0.034501582,0.0011325353,0.021862797,0.78212595],"study_design_scores_gemma":[0.0041737603,0.0009726995,0.00048564514,0.93769723,0.0006581302,0.0069765286,0.00050579634,0.00049302424,0.037673775,0.0031775665,0.0063011055,0.0008847225],"about_ca_topic_score_codex":0.000010884039,"about_ca_topic_score_gemma":0.00003720733,"teacher_disagreement_score":0.89230263,"about_ca_system_score_codex":0.00022699127,"about_ca_system_score_gemma":0.00019776115,"threshold_uncertainty_score":0.99944997},"labels":[],"label_agreement":null},{"id":"W4404628314","doi":"10.1109/ciot63799.2024.10757166","title":"Demo-ScanIoT, an Application to collect IoT dataset for HomeGuard","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Computer science; Internet of Things; World Wide Web; Data science; Information retrieval","score_opus":0.031542369533719083,"score_gpt":0.3238155762562856,"score_spread":0.2922732067225665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404628314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016863196,0.00003876607,0.9926255,0.0020623736,0.00038365653,0.0009589392,0.00084767013,0.0004169994,0.0009798221],"genre_scores_gemma":[0.95975906,0.000001433676,0.035321362,0.0014626557,0.00029162114,0.00080640696,0.00063215796,0.000023176957,0.0017021155],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987709,0.000046653036,0.00019223525,0.00059265073,0.00018812389,0.0002094733],"domain_scores_gemma":[0.9988367,0.0002451793,0.000026759455,0.00066072994,0.00008136806,0.0001492451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040769557,0.00011522733,0.00014311542,0.00017775194,0.000101323545,0.00051302096,0.00061069464,0.00004711091,0.000028569204],"category_scores_gemma":[0.000031158943,0.000107589185,0.000045569577,0.00066564436,0.000009478533,0.00048371457,0.00013781109,0.000055533397,0.0005419244],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010706106,0.00006117885,0.000029468003,0.00005924298,0.00003114535,0.000003361179,0.00032923382,0.000017558368,0.005470493,0.010047495,0.34784243,0.6360977],"study_design_scores_gemma":[0.0001641826,0.00017453628,0.00014621211,0.00002242166,0.0000078488865,0.00003173929,0.00003819461,0.14245474,0.004192907,0.0011716906,0.85136145,0.00023409014],"about_ca_topic_score_codex":0.00016237346,"about_ca_topic_score_gemma":0.00047996666,"teacher_disagreement_score":0.9580727,"about_ca_system_score_codex":0.00008285532,"about_ca_system_score_gemma":0.000114809685,"threshold_uncertainty_score":0.6965522},"labels":[],"label_agreement":null},{"id":"W4404915309","doi":"10.1109/vis55277.2024.00016","title":"Visualizations on Smart Watches while Running: It Actually Helps!","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Victoria","funders":"","keywords":"Smartwatch; Computer science; Human–computer interaction; Embedded system; Wearable computer","score_opus":0.06152510419174516,"score_gpt":0.30717282084417974,"score_spread":0.24564771665243457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404915309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006199648,0.000089292655,0.8690067,0.009832925,0.0015475007,0.0002831042,0.000007284066,0.0016373163,0.11139625],"genre_scores_gemma":[0.98053086,0.000007170865,0.0013532398,0.0015445418,0.00014197796,0.000045336215,0.000008991728,0.000018527793,0.016349373],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99859715,0.000062256324,0.00024633703,0.00049974525,0.00034988843,0.00024462602],"domain_scores_gemma":[0.9989113,0.0003819549,0.000035374556,0.00046242838,0.0001010603,0.00010784784],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029905734,0.00016093427,0.00016053334,0.00021571804,0.00013546183,0.00080934056,0.0004801877,0.000070654096,0.00033819606],"category_scores_gemma":[0.000050751474,0.000138488,0.000097655684,0.000633605,0.000024204875,0.0008489507,0.00017233701,0.00013598605,0.002981405],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012885513,0.0005068084,0.000986498,0.00019103225,0.0002954376,0.00015015446,0.019125223,0.00010196661,0.004764945,0.4119101,0.29506195,0.26689303],"study_design_scores_gemma":[0.00032881292,0.00028916512,0.0008021726,0.00045596034,0.000020369715,0.00009762491,0.00078046427,0.13020813,0.013804229,0.0023588769,0.85013765,0.00071655813],"about_ca_topic_score_codex":0.0000630177,"about_ca_topic_score_gemma":0.0000965114,"teacher_disagreement_score":0.9743312,"about_ca_system_score_codex":0.00006768714,"about_ca_system_score_gemma":0.0001388813,"threshold_uncertainty_score":0.99779487},"labels":[],"label_agreement":null},{"id":"W4405113355","doi":"10.1016/j.procs.2024.11.119","title":"SereniSens: a Multimodal AI Framework with LLMs for Stress Prediction through Sleep Biometrics","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Biometrics; Stress (linguistics); Sleep (system call); Speech recognition; Artificial intelligence; Operating system","score_opus":0.023259854594836,"score_gpt":0.2860154001024071,"score_spread":0.26275554550757113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007671155,0.00026506907,0.9846774,0.0013329927,0.0038300317,0.00093307806,0.00006595188,0.0011213964,0.00010296541],"genre_scores_gemma":[0.6601111,0.0000070468163,0.33876094,0.0004105444,0.0005473094,0.000120542056,0.0000049664072,0.000019013823,0.000018527682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99625766,0.000039919014,0.00038712364,0.0015103263,0.0011048132,0.00070014276],"domain_scores_gemma":[0.9975334,0.0007076221,0.0001252501,0.0007086483,0.0007044102,0.00022071059],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00075720425,0.00031691542,0.0002968516,0.0008092088,0.00044350163,0.0023192635,0.0017106107,0.00012927463,0.0000038231437],"category_scores_gemma":[0.00016994895,0.00026070463,0.000103869505,0.0066556255,0.00033177398,0.004490327,0.0005120293,0.00033868582,0.00005332284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047329075,0.00040015604,0.003128586,0.0008721657,0.00012269912,0.000084488915,0.011061781,0.0015701512,0.0007957461,0.035366915,0.0020532291,0.94449675],"study_design_scores_gemma":[0.0003583437,0.0004484352,0.0006183698,0.0004754675,0.000016216056,0.0001870496,0.000029740304,0.98409045,0.0046161897,0.004154532,0.0046143522,0.00039083316],"about_ca_topic_score_codex":0.000025068484,"about_ca_topic_score_gemma":0.000008927845,"teacher_disagreement_score":0.98252034,"about_ca_system_score_codex":0.0001856862,"about_ca_system_score_gemma":0.0005985476,"threshold_uncertainty_score":0.9999845},"labels":[],"label_agreement":null},{"id":"W4405180601","doi":"10.23919/ics.2024.3512503","title":"A Multi-Modal System Featuring Wireless Flexible Sensor Patches and a Depth-Sensing Imager for Home-Based Monitoring of Rehabilitation Exercises","year":2024,"lang":"en","type":"article","venue":"Integrated Circuits and Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Modal; Rehabilitation; Wireless; Wireless sensor network; Computer science; Real-time computing; Remote sensing; Telecommunications; Geology; Computer network; Medicine; Materials science; Physical therapy","score_opus":0.038582336069644264,"score_gpt":0.27440490587032285,"score_spread":0.2358225698006786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405180601","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.588763,0.0020670476,0.4065155,0.000045914527,0.001328553,0.0008204971,0.00004291839,0.00038659837,0.000029996092],"genre_scores_gemma":[0.995061,0.000009677373,0.00454107,0.0000028871375,0.0001300201,0.00012213181,0.0000058483106,0.00003617766,0.00009123196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789256,0.00022316849,0.00060695445,0.000680116,0.00028600797,0.0003112191],"domain_scores_gemma":[0.99752057,0.001237441,0.00019264477,0.00032646323,0.0006023359,0.000120552795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008367164,0.00028346284,0.00058769656,0.00040333642,0.00016358463,0.0008623957,0.0001696048,0.00015943675,2.0891389e-7],"category_scores_gemma":[0.00011885272,0.00024227165,0.00010767901,0.00053820666,0.00007788349,0.0006086682,0.000040100662,0.00017176931,0.0000025211602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085375315,0.00014548047,0.027399464,0.018799447,0.00035433733,0.000103393846,0.012945201,0.0002678733,0.44132233,0.0014645476,0.0000926301,0.49701992],"study_design_scores_gemma":[0.0015999095,0.0003137724,0.006974716,0.014364329,0.00009833068,0.00032351728,0.017512606,0.9125888,0.045279056,0.00003258183,0.0002120027,0.0007003898],"about_ca_topic_score_codex":0.00072426273,"about_ca_topic_score_gemma":0.00002213638,"teacher_disagreement_score":0.9123209,"about_ca_system_score_codex":0.00016722112,"about_ca_system_score_gemma":0.00015006364,"threshold_uncertainty_score":0.98795515},"labels":[],"label_agreement":null},{"id":"W4405260208","doi":"10.1561/1100000093","title":"WaterHCI: Water in Human-Computer Interaction","year":2024,"lang":"en","type":"article","venue":"Foundations and Trends® in Human–Computer Interaction","topic":"Context-Aware Activity Recognition Systems","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":"University of New Brunswick","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.05045424932423511,"score_gpt":0.3412390725740723,"score_spread":0.29078482324983723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405260208","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59440124,0.00005959871,0.3934523,0.0015368246,0.0069587296,0.00036533823,0.000007524819,0.00058300793,0.002635444],"genre_scores_gemma":[0.99588406,0.000007656416,0.0018423916,0.0001362202,0.0008174795,0.00012259702,0.00021968989,0.00003403979,0.0009358544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997041,0.00026391528,0.00088471785,0.0010564387,0.00028681883,0.0004671113],"domain_scores_gemma":[0.99899685,0.00019032766,0.0001187147,0.00049229525,0.00010571615,0.00009608637],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00048870733,0.00037470198,0.00038108675,0.002447968,0.00028813168,0.0020580855,0.00039486247,0.00015371514,0.00040999302],"category_scores_gemma":[0.000003713933,0.00033180733,0.00016038884,0.0007122854,0.00005473908,0.0044688634,0.00037720497,0.00064617983,0.00026282776],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022152522,0.00053284457,0.0013189898,0.00013615908,0.00013407739,0.00018863317,0.009452416,0.0010979149,0.0046274457,0.009737281,0.0026172407,0.97013485],"study_design_scores_gemma":[0.0027149292,0.0008128042,0.03824765,0.001971809,0.000060108687,0.0011274309,0.0003078319,0.7939434,0.005986086,0.007941612,0.14498998,0.0018963417],"about_ca_topic_score_codex":0.00086507236,"about_ca_topic_score_gemma":0.0015847125,"teacher_disagreement_score":0.96823853,"about_ca_system_score_codex":0.0004295692,"about_ca_system_score_gemma":0.000019293502,"threshold_uncertainty_score":0.9999134},"labels":[],"label_agreement":null},{"id":"W4405265879","doi":"10.37934/araset.53.2.1836","title":"Classification of Elderly’s Home Activities using Tree-Based Model","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Research in Applied Sciences and Engineering Technology","topic":"Context-Aware Activity Recognition Systems","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":"College of Veterinarians of British Columbia","funders":"Sunway University","keywords":"Decision tree; Random forest; Computer science; Tree (set theory); Set (abstract data type); Machine learning; Decision tree learning; Artificial intelligence; Activities of daily living; Population; Data science; Psychology; Medicine; Environmental health","score_opus":0.09312807801936686,"score_gpt":0.35537050092129874,"score_spread":0.2622424229019319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405265879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47982347,0.00059362163,0.51855946,0.00068065414,0.000115487426,0.00008413293,7.063666e-7,0.000045490084,0.00009697849],"genre_scores_gemma":[0.9254484,0.00005897891,0.07445704,0.0000020033137,0.00001622572,0.000008914688,4.798596e-8,0.000005325564,0.0000030358794],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871445,0.000025393461,0.00032037825,0.00022431444,0.0004434517,0.0002720078],"domain_scores_gemma":[0.9992653,0.00034704048,0.000093368864,0.00015724846,0.00009021399,0.000046821562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017420587,0.00008317858,0.00021801914,0.0023906583,0.000066110915,0.000101845755,0.0005582007,0.00008957637,5.0917123e-7],"category_scores_gemma":[0.000060101283,0.00007128206,0.000031675663,0.0021143917,0.00027870614,0.00046584624,0.00011057247,0.00048251284,3.2856966e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012011638,0.000033031818,0.000079372774,0.00013377953,0.00000915393,0.000014412925,0.0001437905,0.15190125,0.5925436,0.04793981,0.0000045052643,0.20718528],"study_design_scores_gemma":[0.0001896117,0.00017599112,0.000084053536,0.00028395647,0.0000014069766,0.00004269913,0.00032962614,0.97393143,0.0150368195,0.009732255,0.00011528103,0.00007685707],"about_ca_topic_score_codex":0.0000023381385,"about_ca_topic_score_gemma":0.0000049642545,"teacher_disagreement_score":0.8220302,"about_ca_system_score_codex":0.00010795783,"about_ca_system_score_gemma":0.00027342784,"threshold_uncertainty_score":0.2906798},"labels":[],"label_agreement":null},{"id":"W4405491182","doi":"10.1109/embc53108.2024.10782799","title":"Enhanced In-Home Human Activity Recognition Using Multimodal Sensing and Spatiotemporal Machine Learning Architecture","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Computer science; Architecture; Activity recognition; Artificial intelligence; Human–computer interaction; Pattern recognition (psychology); Machine learning; Geography","score_opus":0.037548875161136676,"score_gpt":0.2858311717202682,"score_spread":0.2482822965591315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405491182","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5276643,0.000047847123,0.4712607,0.00013879567,0.00016551628,0.0001435042,0.000002481653,0.00026424613,0.00031263995],"genre_scores_gemma":[0.9879481,0.000003496901,0.011800717,0.00003559881,0.000096217176,0.0000037353286,0.000008639808,0.00002036323,0.00008313888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998357,0.0002928759,0.00024505067,0.00060907163,0.0002240597,0.00027196074],"domain_scores_gemma":[0.99934536,0.0002501496,0.00007855538,0.0001897109,0.00005589767,0.00008030837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004592917,0.00020426603,0.0002517737,0.00041684706,0.00017525864,0.000446905,0.00012166382,0.00010344746,0.000022135102],"category_scores_gemma":[0.00004537101,0.0002003388,0.00006112827,0.00047878653,0.00004055824,0.0008923179,0.00017760793,0.00052452815,0.000020213236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008584984,0.00002324626,0.00043826696,0.000071936054,0.000016149508,0.000041934916,0.0011859206,0.00011403108,0.13620079,0.000025359746,0.0000014150459,0.8618724],"study_design_scores_gemma":[0.00045881496,0.00007656774,0.0029675364,0.00038056378,0.000008109492,0.00016992375,0.000058699177,0.9609321,0.03304931,0.0014059432,0.000096151685,0.00039631463],"about_ca_topic_score_codex":0.0028734559,"about_ca_topic_score_gemma":0.0022581837,"teacher_disagreement_score":0.96081805,"about_ca_system_score_codex":0.00011395996,"about_ca_system_score_gemma":0.00005353555,"threshold_uncertainty_score":0.81695795},"labels":[],"label_agreement":null},{"id":"W4405497411","doi":"10.2196/57622","title":"Evaluating a Smart Textile Loneliness Monitoring System for Older People: Co-Design and Qualitative Focus Group Study","year":2024,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Loneliness; Focus group; Wearable computer; Wearable technology; Perspective (graphical); Intervention (counseling); Qualitative research; Engineering; Computer science; Applied psychology; Internet privacy; Psychology; Business; Embedded system; Medicine; Marketing; Nursing","score_opus":0.12171336931935685,"score_gpt":0.43560836395111274,"score_spread":0.3138949946317559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405497411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2071983,0.00049894874,0.78789747,0.00012733885,0.00095211394,0.0026350555,0.000006736264,0.00061517954,0.0000688563],"genre_scores_gemma":[0.98937595,9.443668e-7,0.008049475,0.0000079597685,0.00032420058,0.0020842794,0.0000022335742,0.000038141774,0.00011680331],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705,0.00079815043,0.00046847493,0.00082695927,0.0004908265,0.00036562103],"domain_scores_gemma":[0.99635565,0.0028125017,0.0001393627,0.00037508344,0.00021819932,0.00009918464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003721895,0.00025216263,0.00038677847,0.00026253756,0.00031452905,0.0009965283,0.00034405253,0.000056043373,0.0000035844032],"category_scores_gemma":[0.00009684037,0.00024120626,0.000083399,0.0005576722,0.000019211628,0.0010225105,0.00018436559,0.00018341666,0.000028746714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041492163,0.0002908152,0.0035239584,0.002131988,0.0003276626,0.00007773731,0.38870612,0.00011526822,0.0040636077,0.00078502984,0.0005371536,0.59939915],"study_design_scores_gemma":[0.003386202,0.00091983686,0.011340467,0.004418553,0.00013584564,0.00022201295,0.15031897,0.8243271,0.0027732647,0.00060182926,0.00029462946,0.0012612904],"about_ca_topic_score_codex":0.00013743337,"about_ca_topic_score_gemma":0.00001893985,"teacher_disagreement_score":0.82421184,"about_ca_system_score_codex":0.00018203231,"about_ca_system_score_gemma":0.00008563542,"threshold_uncertainty_score":0.9836106},"labels":[],"label_agreement":null},{"id":"W4405602138","doi":"10.1109/picom64201.2024.00025","title":"Multimodal Sequential Deep Learning for Agitation Detection in People Living with Dementia","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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; Toronto Metropolitan University; University Health Network","funders":"","keywords":"Dementia; Assisted living; Computer science; Artificial intelligence; Deep learning; Medicine; Gerontology","score_opus":0.017586154570383376,"score_gpt":0.2581169417607062,"score_spread":0.24053078719032284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405602138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14246874,0.000054935223,0.85605747,0.00009603302,0.0003446953,0.00028855292,3.0642232e-7,0.00031450336,0.0003747879],"genre_scores_gemma":[0.99059975,0.000001651353,0.008958887,0.00002995582,0.00006521559,0.00013381937,0.0000025955112,0.000012501075,0.00019561133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990383,0.000090104215,0.00017323683,0.00034886357,0.00016421184,0.00018528986],"domain_scores_gemma":[0.9992834,0.0004140526,0.00004314162,0.000106105705,0.00011974956,0.00003356661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039866645,0.00009611788,0.00010326938,0.00022890218,0.00009389338,0.0004288245,0.00012614469,0.00004325705,0.000022983746],"category_scores_gemma":[0.000092597926,0.000088984176,0.000046807876,0.00042870743,0.0000080818545,0.0011631965,0.000054624772,0.0001199321,0.00003903199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015304919,0.00004966098,0.0060847276,0.0001574209,0.000081047205,0.00000957475,0.0053533027,0.002235155,0.008094047,0.000665102,0.000018437882,0.9772362],"study_design_scores_gemma":[0.000270191,0.00011338501,0.018222637,0.00012962698,0.000012146208,0.000027988208,0.00041529592,0.977797,0.0024149579,0.000103518476,0.00033480785,0.00015847167],"about_ca_topic_score_codex":0.00035672626,"about_ca_topic_score_gemma":0.0076784687,"teacher_disagreement_score":0.9770777,"about_ca_system_score_codex":0.000092160226,"about_ca_system_score_gemma":0.00004641476,"threshold_uncertainty_score":0.42847657},"labels":[],"label_agreement":null},{"id":"W4405618790","doi":"10.1007/978-3-031-77571-0_41","title":"Wireless Alert Technology for Elderly Care at Home","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Wireless; Telecommunications; Computer science; Computer security; Internet privacy","score_opus":0.015753080785496632,"score_gpt":0.23226903129880003,"score_spread":0.21651595051330338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405618790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038133303,0.12369019,0.85279834,0.0006798078,0.0070730024,0.002471908,0.00009045702,0.00059562165,0.012219355],"genre_scores_gemma":[0.9800327,0.00043781707,0.0001792737,0.00009611401,0.0012043975,0.0003365024,0.000061168124,0.00011391839,0.01753814],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99740046,0.000038698985,0.0006277294,0.001154098,0.00027674146,0.0005022798],"domain_scores_gemma":[0.99793535,0.0006817676,0.0002833571,0.000788336,0.00021095382,0.000100220124],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00025847598,0.00057621795,0.001029544,0.0005375633,0.00015444765,0.00039289982,0.00065102783,0.0014661187,0.0000059707963],"category_scores_gemma":[0.000017367753,0.00051552104,0.00020159401,0.00020926981,0.00009951005,0.00012103847,0.00041681345,0.00076828606,0.00002604308],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004076191,0.000014860807,0.00026719316,0.0023701417,0.00034402602,0.0002459312,0.0011952401,0.0035837702,0.00003645779,0.0807161,0.0013949223,0.9097906],"study_design_scores_gemma":[0.0014917827,0.0007983062,0.000012388014,0.009466783,0.0001320292,0.0012180958,0.00007044252,0.30121702,0.000059523885,0.04452479,0.6384346,0.0025742569],"about_ca_topic_score_codex":0.000056467754,"about_ca_topic_score_gemma":0.0010126968,"teacher_disagreement_score":0.97965133,"about_ca_system_score_codex":0.00030458719,"about_ca_system_score_gemma":0.0000682765,"threshold_uncertainty_score":0.9998302},"labels":[],"label_agreement":null},{"id":"W4405733892","doi":"10.18280/i2m.230603","title":"Sleep Quality Index for Sensor Data in Eldercare Monitoring","year":2024,"lang":"fr","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sleep (system call); Sleep quality; Index (typography); Computer science; Medicine; World Wide Web; Psychiatry; Operating system","score_opus":0.24613904963401861,"score_gpt":0.4291964492482069,"score_spread":0.18305739961418827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405733892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34359667,0.007664649,0.59816474,0.024410812,0.022342924,0.0017401283,0.0010595558,0.00058001454,0.00044050574],"genre_scores_gemma":[0.9889077,0.00013223014,0.008666419,0.00022350874,0.00096178683,0.00019236562,0.00019198524,0.000036634916,0.0006873545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99534005,0.0009770045,0.0010092364,0.0013864423,0.00060337805,0.0006838976],"domain_scores_gemma":[0.99622667,0.0018979154,0.00023816687,0.0012494219,0.00025297084,0.0001348367],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003227538,0.00037290357,0.00049951265,0.00037726207,0.00015701381,0.0010562699,0.0012227425,0.00038651028,0.000060843755],"category_scores_gemma":[0.0007183996,0.00041733944,0.00015456074,0.00086684583,0.00012058414,0.0034834032,0.00058056094,0.0005443092,0.00015486135],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071041235,0.00011313025,0.114182375,0.000701568,0.0001586127,0.00008007954,0.0021104938,0.00021175407,0.00078692177,0.0020071075,0.000865676,0.8787112],"study_design_scores_gemma":[0.006212826,0.00076147914,0.34136802,0.002223617,0.00025357908,0.0002900278,0.006908284,0.5221171,0.0065405765,0.013392245,0.097688265,0.0022439733],"about_ca_topic_score_codex":0.001132652,"about_ca_topic_score_gemma":0.0006967877,"teacher_disagreement_score":0.8764673,"about_ca_system_score_codex":0.00050161686,"about_ca_system_score_gemma":0.00037353064,"threshold_uncertainty_score":0.99998075},"labels":[],"label_agreement":null},{"id":"W4405902202","doi":"10.48550/arxiv.2412.19254","title":"Leveraging Self-Training and Variational Autoencoder for Agitation Detection in People with Dementia Using Wearable Sensors","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoencoder; Wearable computer; Training (meteorology); Dementia; Computer science; Artificial intelligence; Physical medicine and rehabilitation; Training set; Psychology; Machine learning; Human–computer interaction; Pattern recognition (psychology); Medicine; Embedded system; Deep learning; Geography; Internal medicine","score_opus":0.08374787501556262,"score_gpt":0.2049069851148038,"score_spread":0.12115911009924117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405902202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39903185,0.000031187807,0.6000831,0.00004270035,0.0002535339,0.00031838025,0.000004642077,0.00013548482,0.00009911744],"genre_scores_gemma":[0.9818596,0.000009271508,0.01795174,0.000024560271,0.000047591846,0.0000057731404,0.000005761361,0.000018591365,0.000077075594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985226,0.000117803655,0.00018264962,0.0008411123,0.00009868,0.00023716304],"domain_scores_gemma":[0.99905163,0.00021391618,0.00019121454,0.00024440725,0.00023769723,0.000061141596],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041673548,0.00020425195,0.00024048792,0.0004731411,0.00015888867,0.00028171937,0.00021142646,0.00013975383,0.000003249809],"category_scores_gemma":[0.000028654176,0.00024652627,0.00006495196,0.00054931716,0.000019397537,0.0005624428,0.0003933589,0.00031065356,0.0000050304834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052545973,0.000090687245,0.0063312193,0.00062083104,0.0005290446,0.0000837839,0.012328953,0.96707606,0.0003671947,0.0065932088,0.00001039034,0.005916064],"study_design_scores_gemma":[0.0004871386,0.00003195834,0.0022309162,0.0002903705,0.00010452722,0.000025767677,0.0005595107,0.98299986,0.00007067551,0.012896514,0.000027817443,0.00027495358],"about_ca_topic_score_codex":0.00044736057,"about_ca_topic_score_gemma":0.0007196006,"teacher_disagreement_score":0.5828278,"about_ca_system_score_codex":0.0003476327,"about_ca_system_score_gemma":0.00031389136,"threshold_uncertainty_score":0.9999987},"labels":[],"label_agreement":null},{"id":"W4405908240","doi":"10.1109/wf-iot62078.2024.10811273","title":"Federated Learning for Hierarchical Fall Detection and Human Activity Recognition","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"Ministry of Education","keywords":"Computer science; Activity recognition; Artificial intelligence; Machine learning","score_opus":0.04583481502952763,"score_gpt":0.2907823820524197,"score_spread":0.24494756702289205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405908240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31305242,0.000020968097,0.684137,0.00033065566,0.00025013374,0.00025181237,0.0000016755305,0.0006238065,0.001331539],"genre_scores_gemma":[0.99799937,0.0000030849726,0.0009203926,0.000047919657,0.00012239047,0.00009840364,0.0000060048715,0.000013185331,0.00078924716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890673,0.00015147746,0.0001472853,0.00046973053,0.00013636373,0.00018841127],"domain_scores_gemma":[0.99929696,0.0004117578,0.000036564114,0.0000861881,0.00009234833,0.00007617884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004128998,0.00012066864,0.00013801637,0.00016680721,0.00036165837,0.00084649806,0.000082315615,0.000090749636,0.000013007877],"category_scores_gemma":[0.00007426321,0.0001157746,0.00006264438,0.00024405186,0.000025399573,0.0008961518,0.000077570265,0.00025370237,0.00004857453],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010117812,0.000022697897,0.00005744185,0.00005618833,0.000023044766,0.0000035494427,0.00012595807,7.9050585e-7,0.04971351,0.00036794232,0.00006221558,0.9495565],"study_design_scores_gemma":[0.0011217139,0.0009849664,0.005623348,0.00025464714,0.000035787598,0.00029293713,0.00010442414,0.86414164,0.09197959,0.012157702,0.022492722,0.00081054674],"about_ca_topic_score_codex":0.00021194047,"about_ca_topic_score_gemma":0.0006126068,"teacher_disagreement_score":0.948746,"about_ca_system_score_codex":0.00005577129,"about_ca_system_score_gemma":0.000031973796,"threshold_uncertainty_score":0.81628},"labels":[],"label_agreement":null},{"id":"W4405930431","doi":"10.1007/978-3-031-74640-6_25","title":"Multimodal Sensor Fusion for Daily Living Activity Recognition in Active Assisted Living for Older Adults","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; University of Toronto; University of Waterloo","funders":"","keywords":"Assisted living; Activity recognition; Sensor fusion; Computer science; Psychology; Physical medicine and rehabilitation; Medicine; Artificial intelligence; Gerontology","score_opus":0.05592006971486409,"score_gpt":0.3058842812513628,"score_spread":0.24996421153649873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405930431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004261021,0.00020559384,0.96649516,0.00092622906,0.0011803922,0.0038680423,0.0002569105,0.00027946668,0.022527162],"genre_scores_gemma":[0.86510813,0.0005098675,0.13131441,0.0004448618,0.00014335207,0.00097208645,0.00015373259,0.00004108185,0.0013124815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976249,0.00007875921,0.0008597011,0.0006400923,0.00042513682,0.00037142533],"domain_scores_gemma":[0.9939681,0.0031259307,0.00055659696,0.0013443372,0.0008935222,0.00011147005],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001991091,0.00034641018,0.00043953853,0.0017591708,0.00051950756,0.0009473883,0.0017078789,0.00026230334,0.0000061959427],"category_scores_gemma":[0.0003681264,0.00037459534,0.00013403693,0.0005907775,0.00030133952,0.009213889,0.0018181809,0.00053996785,0.000038218055],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018898909,0.00007121114,0.000021747492,0.00024133964,0.000011842561,2.9621117e-7,0.007291955,0.000021419137,0.000026168636,0.005479132,0.00006475047,0.98675126],"study_design_scores_gemma":[0.0007838808,0.00012485677,0.00980863,0.005912714,0.000016866792,0.000038098307,0.00025791916,0.97130364,0.00005458155,0.0020639275,0.008960387,0.00067451264],"about_ca_topic_score_codex":0.00006688756,"about_ca_topic_score_gemma":0.00018986956,"teacher_disagreement_score":0.9860767,"about_ca_system_score_codex":0.0004707449,"about_ca_system_score_gemma":0.00033950873,"threshold_uncertainty_score":0.9998706},"labels":[],"label_agreement":null},{"id":"W4405966877","doi":"10.1093/geroni/igae098.3963","title":"EFRAILTY.ORG: ANALYSIS OF INITIAL USERS","year":2024,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.06274540962962889,"score_gpt":0.36078947016155655,"score_spread":0.29804406053192767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405966877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5465177,0.00004027964,0.4496324,0.00079948356,0.00050979684,0.000071481285,0.000004318887,0.00014356566,0.002280896],"genre_scores_gemma":[0.9988938,9.81428e-7,0.00078152196,0.00017583031,0.00004319522,0.000012606822,0.000012214788,0.0000052268197,0.000074638476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883384,0.00005776617,0.00050121016,0.0002527248,0.00023625648,0.000118196185],"domain_scores_gemma":[0.99925196,0.00020023668,0.00011100374,0.00023796519,0.00018651456,0.000012311269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068057247,0.000074014766,0.00017857422,0.0026490053,0.000023661432,0.00012679149,0.00024379701,0.000038728802,0.000034462835],"category_scores_gemma":[0.000090095346,0.00007973083,0.000051539533,0.012686006,0.000020427573,0.0007627751,0.00008220074,0.00012853088,0.000024757459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000830756,0.00011503994,0.17979455,0.00021864967,0.0010950931,0.000100077494,0.014246506,0.0012901465,0.025186658,0.20593917,0.00078118034,0.57122463],"study_design_scores_gemma":[0.00082096056,0.00006222728,0.21769705,0.0008283006,0.00018566033,0.000023390683,0.00086514186,0.7240488,0.03040486,0.0054671266,0.018804308,0.0007921979],"about_ca_topic_score_codex":0.00013138274,"about_ca_topic_score_gemma":0.000058746966,"teacher_disagreement_score":0.72275865,"about_ca_system_score_codex":0.00007016938,"about_ca_system_score_gemma":0.000077561,"threshold_uncertainty_score":0.60952055},"labels":[],"label_agreement":null},{"id":"W4405966962","doi":"10.1093/geroni/igae098.3549","title":"EXPLORING BED SENSOR SLEEP TECH: INSIGHTS FROM INTERDISCIPLINARY TEAM IN A GERIATRIC ASSESSMENT INPATIENT SETTING","year":2024,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Context-Aware Activity Recognition Systems","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 British Columbia Hospital; University of British Columbia; Vancouver General Hospital","funders":"","keywords":"Sleep (system call); Medicine; Psychology; Physical medicine and rehabilitation; Computer science","score_opus":0.06264811210196237,"score_gpt":0.3152784439056877,"score_spread":0.25263033180372535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405966962","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79349095,0.0000761449,0.20169215,0.0011351753,0.0016248657,0.0003390933,0.0000036828624,0.0003358273,0.0013021269],"genre_scores_gemma":[0.9910316,0.0000034256007,0.008319455,0.000119890596,0.0001914828,0.00027372345,0.000023198269,0.000021266651,0.000015995827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975257,0.00017799213,0.0009746096,0.0006576665,0.00039137923,0.00027267143],"domain_scores_gemma":[0.99886,0.0004143559,0.0001977514,0.00036152764,0.00013664125,0.000029697567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007209245,0.000199744,0.0002473189,0.0024474093,0.000094694005,0.0004426565,0.00035094682,0.00006331732,0.000007911937],"category_scores_gemma":[0.000071735805,0.00021159959,0.000039894054,0.005402227,0.000015947975,0.0024035876,0.0006827214,0.00049971015,0.000035746616],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012666308,0.00026050408,0.03669567,0.0002584824,0.00006480711,0.0003418519,0.05932775,0.0008646626,0.03732188,0.009395224,0.00017595447,0.8552805],"study_design_scores_gemma":[0.0018366125,0.00013557849,0.14201568,0.0051340293,0.000013631858,0.00005436785,0.010122868,0.80408055,0.014842133,0.017856741,0.002514623,0.0013931919],"about_ca_topic_score_codex":0.00016456332,"about_ca_topic_score_gemma":0.0000826588,"teacher_disagreement_score":0.8538874,"about_ca_system_score_codex":0.0006643165,"about_ca_system_score_gemma":0.000117999174,"threshold_uncertainty_score":0.8628781},"labels":[],"label_agreement":null},{"id":"W4406264173","doi":"10.1109/icce-asia63397.2024.10774046","title":"MACeIP: A Multimodal Ambient Context-Enriched Intelligence Platform in Smart Cities","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Ambient intelligence; Computer science; Context (archaeology); Context awareness; Smart city; Human–computer interaction; Internet of Things; World Wide Web; Geography","score_opus":0.04095589092801432,"score_gpt":0.279990487589944,"score_spread":0.23903459666192967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406264173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09773286,0.000658222,0.88648534,0.0013006048,0.0017451966,0.0005294931,0.000007220053,0.0008725886,0.010668471],"genre_scores_gemma":[0.9946288,0.000017582262,0.0019420302,0.00044950348,0.000061634426,0.000106872445,0.0000031764268,0.000015842406,0.0027745513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979829,0.00007286013,0.00048523818,0.0006480091,0.0003934105,0.00041758805],"domain_scores_gemma":[0.99872667,0.00054115715,0.000051231294,0.00046583032,0.00009241895,0.0001226756],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005743012,0.00023219561,0.00029111104,0.00044114492,0.00006356018,0.000629979,0.00070220075,0.00010205488,0.00021313601],"category_scores_gemma":[0.00007195038,0.00020770791,0.00012419098,0.00083862414,0.000067464694,0.0013911113,0.0003386998,0.0003037079,0.0010339362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002087493,0.00015160098,0.0024046698,0.0001345422,0.00006253504,0.0002464424,0.009475298,0.000030458703,0.0013534905,0.12104271,0.0017401746,0.8633372],"study_design_scores_gemma":[0.00074585323,0.00027551936,0.006416174,0.00063284434,0.000013320062,0.0003369448,0.0048740013,0.91007787,0.014285932,0.025752006,0.035324648,0.0012648877],"about_ca_topic_score_codex":0.0014372562,"about_ca_topic_score_gemma":0.0022028226,"teacher_disagreement_score":0.9100474,"about_ca_system_score_codex":0.0002118792,"about_ca_system_score_gemma":0.00014749372,"threshold_uncertainty_score":0.9997439},"labels":[],"label_agreement":null},{"id":"W4406330970","doi":"10.3390/s25020430","title":"In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against Variability","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Robustness (evolution); Inertial measurement unit; Artificial intelligence; Computer science; Machine learning; Variance (accounting); Wearable computer; Statistics; Mathematics","score_opus":0.025315440202924127,"score_gpt":0.2764825822121709,"score_spread":0.2511671420092468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406330970","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6629274,0.00004347788,0.3344473,0.00058756873,0.00017174458,0.00015283494,4.2919996e-7,0.000025134399,0.0016440871],"genre_scores_gemma":[0.9982094,0.000008032594,0.0016210427,0.000075396885,0.000014471529,0.000011127278,3.795385e-7,0.000004247186,0.000055884586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982726,0.00073414884,0.00032184375,0.00033780074,0.00015000912,0.00018362208],"domain_scores_gemma":[0.9985387,0.00093875185,0.000108835746,0.00032845745,0.00006205511,0.0000232239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016452362,0.00010199221,0.00023643485,0.00016486406,0.00007242276,0.0001235318,0.00029290837,0.00007350459,0.000001937637],"category_scores_gemma":[0.00029001568,0.000087824475,0.000030606265,0.0007199889,0.000066063105,0.00066317647,0.00018338903,0.000294858,8.743493e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018668763,0.00017220217,0.042325903,0.00021373587,0.000030263516,0.000033164593,0.0074630305,0.83539116,0.00074793265,0.007977364,0.000011084048,0.10561549],"study_design_scores_gemma":[0.00029256815,0.0000045753895,0.034013037,0.000146,0.000002437518,0.0000031463567,0.00033434152,0.9614627,0.00017710729,0.0034187788,0.000055624918,0.00008972762],"about_ca_topic_score_codex":0.0001681012,"about_ca_topic_score_gemma":0.00018739286,"teacher_disagreement_score":0.335282,"about_ca_system_score_codex":0.00006601793,"about_ca_system_score_gemma":0.00007992305,"threshold_uncertainty_score":0.35813782},"labels":[],"label_agreement":null},{"id":"W4406569969","doi":"10.1016/j.gaitpost.2025.01.020","title":"Editorial for the Special Issue: \"Wearable sensors for movement analysis in real-world environments: applications in clinical populations and disease management\"","year":2025,"lang":"en","type":"letter","venue":"Gait & Posture","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"Fonds de Recherche du Québec - Santé","keywords":"Wearable computer; Computer science; Physical medicine and rehabilitation; Movement (music); Human–computer interaction; Medicine; Embedded system; Aesthetics; Art","score_opus":0.02766450066320962,"score_gpt":0.31993917293698604,"score_spread":0.2922746722737764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406569969","genre_codex":"commentary","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017473745,0.00034525833,0.24938317,0.680434,0.047433116,0.017920226,0.0022244372,0.00014051593,0.0019445333],"genre_scores_gemma":[0.0037220868,0.0009516436,0.010207822,0.24909101,0.6370018,0.018371731,0.0049710996,0.00014111766,0.0755417],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997325,0.00019806565,0.00076739915,0.00092384254,0.00042467052,0.00036099151],"domain_scores_gemma":[0.9975545,0.0010848697,0.00030206062,0.00091662083,0.000056956156,0.00008499155],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063579465,0.00029481098,0.00054199446,0.00062492955,0.00021557102,0.0002395547,0.000686198,0.00030830066,0.000018681341],"category_scores_gemma":[0.00008591991,0.00025781183,0.0003442137,0.0009773446,0.00005428812,0.00019310335,0.00027020075,0.000624291,0.000007671162],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003711034,0.000110060915,0.0054237447,0.00009338354,0.000331453,0.0000052062314,0.00008319134,0.00013039079,2.937777e-7,0.00047401598,0.9852595,0.008051693],"study_design_scores_gemma":[0.0007400658,0.000017999084,0.045194797,0.00006026386,0.0005040675,6.8737364e-8,0.000038326332,0.0019763384,2.50364e-7,0.0023169958,0.9489157,0.00023513654],"about_ca_topic_score_codex":0.000252298,"about_ca_topic_score_gemma":0.0020291768,"teacher_disagreement_score":0.5895687,"about_ca_system_score_codex":0.00020218879,"about_ca_system_score_gemma":0.00007338652,"threshold_uncertainty_score":0.9999874},"labels":[],"label_agreement":null},{"id":"W4406600371","doi":"10.58459/icce.2013.1051","title":"Towards a Descriptive View of Context Usage in Context-Aware U-Learning System","year":2013,"lang":"en","type":"article","venue":"International Conference on Computers in Education","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Descriptive research; Descriptive statistics; Computer science; Psychology; Sociology; History; Social science; Statistics; Mathematics","score_opus":0.046141599504487124,"score_gpt":0.2967912587689496,"score_spread":0.2506496592644625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406600371","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5275828,0.00027477375,0.42667785,0.0044311574,0.009489315,0.0019692266,0.000015393201,0.00026977417,0.02928968],"genre_scores_gemma":[0.9968464,0.00002296512,0.0022292938,0.00036502886,0.0000890139,0.00027391297,0.00001941032,0.000013086942,0.00014092821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974952,0.00042323288,0.00074893906,0.00056501996,0.00050903007,0.0002585848],"domain_scores_gemma":[0.99797255,0.0002924082,0.00042889005,0.00034488007,0.00086745794,0.00009383104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004864981,0.00024334925,0.00040777426,0.00075082236,0.000043332006,0.0002832694,0.0011171753,0.000111020505,0.00010406568],"category_scores_gemma":[0.000113219976,0.00025829952,0.00008789182,0.00046636193,0.00006557687,0.0011497969,0.0001943401,0.00037192917,0.00017219145],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019819314,0.00039445836,0.008003168,0.00006618924,0.000033890414,0.0000057533102,0.00601416,0.00010750122,0.00028354203,0.10450431,0.00030391576,0.88026327],"study_design_scores_gemma":[0.003014349,0.0005876119,0.117850915,0.009807245,0.000011085162,0.00012393603,0.040055394,0.8165333,0.001474789,0.0059820283,0.0033378738,0.0012214279],"about_ca_topic_score_codex":0.0033555536,"about_ca_topic_score_gemma":0.00037827413,"teacher_disagreement_score":0.87904185,"about_ca_system_score_codex":0.0007679964,"about_ca_system_score_gemma":0.0006006549,"threshold_uncertainty_score":0.99998695},"labels":[],"label_agreement":null},{"id":"W4406620918","doi":"10.1049/htl2.70000","title":"Guest editorial: Papers from the 18th joint workshop on Augmented Environments for Computer Assisted Interventions (AE‐CAI) at MICCAI 2024: Guest editors’ foreword","year":2025,"lang":"en","type":"editorial","venue":"Healthcare Technology Letters","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Concordia University; École de Technologie Supérieure; Western University","funders":"Strong","keywords":"Computer science; Joint (building); Engineering; Architectural engineering","score_opus":0.03301791664272397,"score_gpt":0.2998289929895134,"score_spread":0.2668110763467894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406620918","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012138129,0.0004154473,0.022177657,0.09771166,0.8742015,0.002661522,0.0016937248,0.0009839426,0.000033192962],"genre_scores_gemma":[0.00075315917,0.00016563277,0.0017589288,0.003485249,0.9881823,0.0021128107,0.002454171,0.00012937025,0.00095837156],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99195313,0.0007539541,0.0017831017,0.002549418,0.001628702,0.0013316761],"domain_scores_gemma":[0.9899032,0.004831882,0.0015026565,0.0031648832,0.00033658068,0.00026078615],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0010786208,0.0011598163,0.0015941686,0.0009919289,0.0010512156,0.0003845769,0.0035672395,0.0033154516,0.000025201887],"category_scores_gemma":[0.0010902255,0.0010411526,0.0010108376,0.0010015905,0.0005011608,0.00028641144,0.0020345636,0.003593934,0.00019578334],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009146511,0.00020354455,0.00008585284,0.0002565266,0.00051384466,0.000028036604,0.00012973913,0.00000396852,0.00023107315,0.00003505315,0.98713785,0.011283019],"study_design_scores_gemma":[0.0017187753,0.0003328697,0.000121475605,0.0036355127,0.00015723224,0.0000067598517,0.00007756799,0.00010125121,0.0001509745,0.00009737572,0.9928039,0.0007963558],"about_ca_topic_score_codex":0.00069925375,"about_ca_topic_score_gemma":0.0016985124,"teacher_disagreement_score":0.11398083,"about_ca_system_score_codex":0.0026555054,"about_ca_system_score_gemma":0.00043751724,"threshold_uncertainty_score":0.99920386},"labels":[],"label_agreement":null},{"id":"W4406775175","doi":"10.3389/fcomp.2025.1514933","title":"WIMUSim: simulating realistic variabilities in wearable IMUs for human activity recognition","year":2025,"lang":"en","type":"article","venue":"Frontiers in Computer Science","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"Horizon 2020 Framework Programme","keywords":"Wearable computer; Computer science; Human–computer interaction; Artificial intelligence; Embedded system","score_opus":0.03139511520530828,"score_gpt":0.29460258160352243,"score_spread":0.26320746639821413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406775175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09592106,0.000045562883,0.89889055,0.00035651104,0.0030061684,0.0007394586,0.0000069493926,0.00014217015,0.0008915374],"genre_scores_gemma":[0.8486209,0.0000025847473,0.15091033,0.0002029517,0.00007421605,0.00010929809,0.0000022395307,0.000007324612,0.000070144226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715644,0.00019350514,0.00050463335,0.0010918947,0.00040277088,0.0006507451],"domain_scores_gemma":[0.99805015,0.00068726705,0.00017812774,0.000720001,0.0002772935,0.000087135835],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027002383,0.00022516988,0.0004292742,0.0011476445,0.00038388532,0.0005513767,0.0013854272,0.0001028506,0.0000019572346],"category_scores_gemma":[0.00037197783,0.0002547342,0.00007809022,0.0026833075,0.00029261227,0.0019787801,0.0005124965,0.00026109358,0.0000032169235],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023610557,0.00019308024,0.009882039,0.00013362973,0.000010439931,0.000008214871,0.0010921056,0.00472119,0.0010203369,0.0015689549,0.0005400331,0.98080635],"study_design_scores_gemma":[0.00096038176,0.000091586044,0.010792083,0.0004480394,0.000004177739,0.0000038072853,0.000057451278,0.916395,0.0015770793,0.069146,0.00020368917,0.0003207251],"about_ca_topic_score_codex":0.00051623373,"about_ca_topic_score_gemma":0.00023619435,"teacher_disagreement_score":0.9804856,"about_ca_system_score_codex":0.0006315967,"about_ca_system_score_gemma":0.00044850042,"threshold_uncertainty_score":0.99999046},"labels":[],"label_agreement":null},{"id":"W4406820161","doi":"10.1016/j.ipm.2025.104074","title":"Exploring long- and short-term knowledge state graph representations with adaptive fusion for knowledge tracing","year":2025,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Context-Aware Activity Recognition Systems","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":"York University","funders":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China","keywords":"Term (time); Tracing; Computer science; Graph; Fusion; Artificial intelligence; Theoretical computer science; Programming language; Linguistics; Physics","score_opus":0.07738686477959825,"score_gpt":0.30535855352309854,"score_spread":0.2279716887435003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406820161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026219057,0.00017311944,0.95737505,0.00014889402,0.00023302826,0.001003557,0.000003979505,0.00025904432,0.014584247],"genre_scores_gemma":[0.98985475,0.000040950246,0.008683961,0.00005866409,0.000016727385,0.0008034965,0.000015313946,0.0000073490737,0.00051878195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882406,0.00003816246,0.00043609302,0.00028807193,0.00018109234,0.00023252894],"domain_scores_gemma":[0.99900365,0.00011219115,0.00016652865,0.00026478528,0.00039429398,0.000058570327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004005688,0.00017385437,0.00017281048,0.0006609165,0.00046757676,0.000725591,0.00026903243,0.000026189771,0.0000010770441],"category_scores_gemma":[0.000017289054,0.00016312872,0.000040067443,0.00082824304,0.00004046384,0.006978504,0.00024631343,0.00008743153,0.000010803607],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000307487,0.0000501058,0.00033115433,0.0007946293,0.00005286181,0.0000012028902,0.010961249,0.00015745212,0.000008187736,0.0024150964,0.00011395203,0.98508334],"study_design_scores_gemma":[0.010828936,0.0007833523,0.20955318,0.013833214,0.0005452423,0.000089958725,0.034211576,0.67363185,0.0077699553,0.012467357,0.033045482,0.003239921],"about_ca_topic_score_codex":0.0000072208427,"about_ca_topic_score_gemma":0.000054016098,"teacher_disagreement_score":0.9818434,"about_ca_system_score_codex":0.00009581587,"about_ca_system_score_gemma":0.00007497545,"threshold_uncertainty_score":0.69968903},"labels":[],"label_agreement":null},{"id":"W4406821256","doi":"10.1016/j.iot.2025.101502","title":"A contactless method for recognition of daily living activities for older adults based on ambient assisted living technology","year":2025,"lang":"en","type":"article","venue":"Internet of Things","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; University of Toronto; University of Waterloo","funders":"","keywords":"Assisted living; Activities of daily living; Independent living; Human–computer interaction; Computer science; Psychology; Gerontology; Medicine","score_opus":0.022075557538408872,"score_gpt":0.29351852052553673,"score_spread":0.27144296298712783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406821256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0982088,0.000030131849,0.8990472,0.00053672335,0.0005853051,0.000921732,0.000028434328,0.00015311988,0.0004885444],"genre_scores_gemma":[0.9239924,7.913429e-7,0.07482707,0.00037654786,0.000017101445,0.0003931956,0.0000052441787,0.000015527474,0.0003721483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833333,0.00015275506,0.0005539239,0.00050248974,0.00021177146,0.00024571765],"domain_scores_gemma":[0.99358594,0.004877775,0.0005880505,0.00041314997,0.00050233223,0.000032737138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010355613,0.00019538522,0.00050326483,0.0007390075,0.000051170682,0.00007822964,0.0006634653,0.00018512228,0.000011663271],"category_scores_gemma":[0.001338359,0.00020126266,0.00023946606,0.00034473813,0.00005092336,0.0004893086,0.00020828661,0.00015533851,0.0000015701615],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036315448,0.00077495974,0.0006937558,0.0015204462,0.00023774637,0.0000014157756,0.0066353404,0.0000074852783,0.021079684,0.0020571651,0.00094884424,0.96568],"study_design_scores_gemma":[0.002478972,0.0012457512,0.0030855478,0.022667062,0.00010297211,0.000013761871,0.002316462,0.48532355,0.47961065,0.0017194947,0.00091381447,0.00052195677],"about_ca_topic_score_codex":0.00029959876,"about_ca_topic_score_gemma":0.00005688774,"teacher_disagreement_score":0.96515805,"about_ca_system_score_codex":0.00011453777,"about_ca_system_score_gemma":0.00010310152,"threshold_uncertainty_score":0.8207254},"labels":[],"label_agreement":null},{"id":"W4407271909","doi":"10.1101/2025.02.07.25321857","title":"Exploring bed sensor sleep technology: insights from an interdisciplinary team in a geriatric assessment inpatient setting","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Hospital and Health Sciences Centre; Vancouver Coastal Health; University of British Columbia","funders":"Vancouver Coastal Health Research Institute","keywords":"Sleep (system call); Medicine; Psychology; Computer science","score_opus":0.051429460537931404,"score_gpt":0.32167974191759185,"score_spread":0.27025028137966045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407271909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92324305,0.00016376543,0.06866338,0.0013341578,0.0035093941,0.0010962061,0.000046379235,0.0008126446,0.0011310163],"genre_scores_gemma":[0.9783712,0.000024569554,0.019995853,0.00008267869,0.00022734351,0.001172186,0.000058390448,0.000030826774,0.00003694803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952409,0.00068099576,0.0011102681,0.0018824425,0.0005818518,0.0005035008],"domain_scores_gemma":[0.99647146,0.0004106696,0.000627303,0.002113529,0.00022639426,0.00015061005],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0006226419,0.0005795653,0.00091234903,0.0022881778,0.00021355305,0.00034552964,0.0021990368,0.00045879892,0.000008195834],"category_scores_gemma":[0.000116301286,0.0006024573,0.00018872955,0.001472817,0.000058924306,0.001045806,0.01003717,0.0017109048,0.00003171532],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007452896,0.0021879426,0.26669794,0.0008511293,0.000595842,0.0007868501,0.04192522,0.0015531027,0.008392394,0.0011826435,0.00013985451,0.67561257],"study_design_scores_gemma":[0.004438377,0.00083668076,0.43824205,0.009228737,0.00022673252,0.00006697436,0.010516502,0.46389887,0.021324573,0.043575767,0.002329317,0.0053154347],"about_ca_topic_score_codex":0.0002707104,"about_ca_topic_score_gemma":0.00041910357,"teacher_disagreement_score":0.67029715,"about_ca_system_score_codex":0.0007177798,"about_ca_system_score_gemma":0.00038055767,"threshold_uncertainty_score":0.99964267},"labels":[],"label_agreement":null},{"id":"W4407900133","doi":"10.1109/tmc.2025.3545437","title":"MoCo: Urban User Mobile Contact Detection Based on Cellular Signaling Trace","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Calgary","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Computer science; TRACE (psycholinguistics); Mobile computing; Cellular network; Computer network; Human–computer interaction","score_opus":0.012879257999351773,"score_gpt":0.24567063704613445,"score_spread":0.23279137904678268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407900133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13750201,0.000064972395,0.85773945,0.00005351001,0.002190414,0.0011735129,0.000010317686,0.00076751295,0.0004983173],"genre_scores_gemma":[0.99691796,0.0000020727389,0.0018426111,0.00035174633,0.000108158616,0.00035051303,0.0000020439174,0.000036243044,0.00038864344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696004,0.00041940002,0.0006374102,0.0009764221,0.0004886168,0.0005180839],"domain_scores_gemma":[0.99720615,0.0013049282,0.00022418116,0.00089613406,0.00022135701,0.00014722106],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066543417,0.00040852517,0.00045226992,0.00068437046,0.0007637498,0.00038108559,0.00066638045,0.00020910377,0.00005112995],"category_scores_gemma":[0.000012864736,0.00045051557,0.00035613714,0.0012158297,0.00004106756,0.00047545493,0.000008528215,0.000702877,0.0001325027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007198815,0.0006990126,0.000049551854,0.00008704285,0.00009134416,0.000021705482,0.0005334295,0.49904338,0.08585823,0.000058440044,0.0000913746,0.41339448],"study_design_scores_gemma":[0.00091038214,0.00043781233,0.000041251184,0.0003135799,0.00003016407,0.000006476394,0.00013273784,0.60524267,0.38977978,0.000019616326,0.0027348432,0.00035068628],"about_ca_topic_score_codex":0.00008774897,"about_ca_topic_score_gemma":0.000028033643,"teacher_disagreement_score":0.85941595,"about_ca_system_score_codex":0.00036279054,"about_ca_system_score_gemma":0.00015845087,"threshold_uncertainty_score":0.99979466},"labels":[],"label_agreement":null},{"id":"W4407948239","doi":"10.1007/978-3-031-83117-1_9","title":"A Do-It-Yourself Approach to Ambient Assisted Living","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Assisted living; Computer science; Gerontology; Medicine","score_opus":0.03220461151841259,"score_gpt":0.2498510641120863,"score_spread":0.2176464525936737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407948239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017806147,0.006233533,0.8627914,0.00027176418,0.002502738,0.0013295808,0.000013398427,0.00018239269,0.12665737],"genre_scores_gemma":[0.97316,0.00015784301,0.0016342448,0.0009280258,0.0010996616,0.00023455897,0.000022968836,0.00007129778,0.02269135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99636954,0.0002550709,0.0008658422,0.0013880233,0.0005422441,0.00057928043],"domain_scores_gemma":[0.99667007,0.0013962922,0.0003934113,0.0011038104,0.00020676247,0.0002296438],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001059137,0.0006684606,0.0011924949,0.000589483,0.00017538361,0.0009827655,0.0008690647,0.00089138787,0.000010019539],"category_scores_gemma":[0.0001511326,0.0006162091,0.00020490224,0.000374419,0.000047772468,0.00017869724,0.00060919597,0.0009591225,0.000015974512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051972173,0.00029124887,0.00080187345,0.0017486102,0.00074816844,0.00018318045,0.0048621823,0.067936644,0.000026369578,0.056439362,0.016610088,0.8503003],"study_design_scores_gemma":[0.0008246106,0.00027186624,0.0005819885,0.019773385,0.00013676847,0.000630117,0.00006643422,0.7902154,0.0000035944695,0.0014991479,0.18322003,0.0027766668],"about_ca_topic_score_codex":0.00020147164,"about_ca_topic_score_gemma":0.00022844075,"teacher_disagreement_score":0.97314227,"about_ca_system_score_codex":0.00025292946,"about_ca_system_score_gemma":0.0001302082,"threshold_uncertainty_score":0.9996289},"labels":[],"label_agreement":null},{"id":"W4408086404","doi":"10.1007/978-3-031-71385-9_12","title":"Recruitment and EDI Challenges in NeuroIS Research: A Case for Mobile Research Infrastructure","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in information systems and organisation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; McMaster University Medical Centre","funders":"","keywords":"Business; Knowledge management; Computer science","score_opus":0.20052928006757834,"score_gpt":0.3672153779015254,"score_spread":0.16668609783394708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408086404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014970364,0.045780417,0.75382215,0.011130013,0.007956693,0.066729374,0.00084926357,0.0008654877,0.097896256],"genre_scores_gemma":[0.99465257,0.00205722,0.0007645032,0.00010961321,0.0002754487,0.0012939502,0.00008394149,0.000029651957,0.00073310686],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972322,0.000347359,0.00083492213,0.00050766626,0.00067097694,0.0004068595],"domain_scores_gemma":[0.99613667,0.0022854756,0.00028191533,0.0004957816,0.00071242143,0.00008772148],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0037691526,0.00028821017,0.000481468,0.0019186797,0.00030190605,0.0006533236,0.00026842783,0.00062117353,0.000007816992],"category_scores_gemma":[0.00056628836,0.00027630012,0.00003770273,0.00030701092,0.0000941143,0.0012236123,0.00035544325,0.00094034785,0.000011054827],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004813539,0.000018291297,0.000113362075,0.0027145087,0.000036109464,0.00005360431,0.03243636,0.00023757204,0.0000378781,0.05987546,0.00063736626,0.90379137],"study_design_scores_gemma":[0.0039096894,0.0013744719,0.00079094776,0.005312257,0.000027955282,0.0027748153,0.0052355803,0.046011284,0.00037769368,0.08663291,0.8460089,0.0015435083],"about_ca_topic_score_codex":0.00037255345,"about_ca_topic_score_gemma":0.0009866161,"teacher_disagreement_score":0.9796822,"about_ca_system_score_codex":0.00048807342,"about_ca_system_score_gemma":0.0002863461,"threshold_uncertainty_score":0.99996895},"labels":[],"label_agreement":null},{"id":"W4408092236","doi":"10.1109/tim.2025.3547487","title":"PALM: Personalized Active Learning for mmWave-Based Activity Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Context-Aware Activity Recognition 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 Ottawa","funders":"National Science and Technology Council","keywords":"Computer science; Activity recognition; Artificial intelligence; Machine learning; Human–computer interaction; Embedded system; Pattern recognition (psychology)","score_opus":0.07266513216966322,"score_gpt":0.2879162874484085,"score_spread":0.2152511552787453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408092236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044980854,0.0000129251275,0.95129716,0.0011952941,0.000807569,0.0009432867,0.000021123506,0.00018250312,0.000559277],"genre_scores_gemma":[0.9959219,0.000016416503,0.0026637064,0.00046119897,0.000016913522,0.0006648569,0.0000069128914,0.000010270308,0.0002377862],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99840957,0.00023350523,0.0002420657,0.0004623272,0.00044177435,0.00021074586],"domain_scores_gemma":[0.9990437,0.00018778689,0.00014044407,0.00017321898,0.00037049263,0.000084346524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005162126,0.0001931885,0.00020559499,0.00033618018,0.0005010921,0.00016851627,0.000120226294,0.00008273171,0.000031121937],"category_scores_gemma":[0.000025380015,0.00020885062,0.00013025661,0.00032927428,0.0000473946,0.0005223985,0.0000018157378,0.00019887243,0.000013306661],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030526865,0.00024714123,0.00002694357,0.0000730987,0.00011081921,3.8307402e-7,0.00041290958,0.0002000286,0.018549193,0.00010639851,0.000046378736,0.97992146],"study_design_scores_gemma":[0.01110207,0.00077548844,0.001990379,0.00055240205,0.0001843527,0.0000082886945,0.001030159,0.06730711,0.9095833,0.00084533164,0.005966072,0.0006551046],"about_ca_topic_score_codex":0.000054236556,"about_ca_topic_score_gemma":0.000100862955,"teacher_disagreement_score":0.97926635,"about_ca_system_score_codex":0.0003907207,"about_ca_system_score_gemma":0.00021364144,"threshold_uncertainty_score":0.85166806},"labels":[],"label_agreement":null},{"id":"W4408100126","doi":"10.1109/jiot.2025.3547405","title":"Continuous Human Activity Recognition in IoT Environments With BAOA and AFSG-TPD GANs","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Context-Aware Activity Recognition 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":"Queen's University","funders":"","keywords":"Computer science; Internet of Things; Activity recognition; Embedded system; Artificial intelligence","score_opus":0.017147969589727332,"score_gpt":0.24975129120107492,"score_spread":0.23260332161134759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408100126","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80291057,0.000041204796,0.19494084,0.00029906153,0.00035496417,0.00013990178,0.0000016971405,0.000021637672,0.0012901041],"genre_scores_gemma":[0.9975891,0.00001084474,0.0014070469,0.000189249,0.000037651997,0.000007678887,5.0475364e-7,0.000008856494,0.00074907043],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856585,0.0001986483,0.00039510333,0.00030988705,0.0003054876,0.00022503526],"domain_scores_gemma":[0.99908596,0.00014494642,0.0004162853,0.00020440693,0.00006750419,0.00008091327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000688876,0.00016895989,0.0003381178,0.00036092708,0.000069638496,0.0002312667,0.0004523967,0.00008823868,0.000022845796],"category_scores_gemma":[0.00004157704,0.00015316713,0.00006547278,0.00017655478,0.0000894046,0.00092902855,0.00011895817,0.0005045714,0.0000090708745],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039439584,0.0010066464,0.06280059,0.00016593425,0.0004634899,0.00033835095,0.01080984,0.00002115426,0.18020815,0.00025342655,0.0015703778,0.7419676],"study_design_scores_gemma":[0.015450314,0.0032749132,0.22676952,0.01058111,0.0002083906,0.004854047,0.0012858489,0.021862574,0.6900488,0.01793401,0.0056020617,0.002128375],"about_ca_topic_score_codex":0.0003712129,"about_ca_topic_score_gemma":0.00012235701,"teacher_disagreement_score":0.73983926,"about_ca_system_score_codex":0.00014936003,"about_ca_system_score_gemma":0.000047271653,"threshold_uncertainty_score":0.62459743},"labels":[],"label_agreement":null},{"id":"W4408338238","doi":"10.1016/j.procs.2025.02.240","title":"Task Generator 2.0: Integrating Interactive Technology with Personalized Task Generation","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition 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":"3v Geomatics (Canada)","funders":"","keywords":"Computer science; Task (project management); Generator (circuit theory); Human–computer interaction; Multimedia; Systems engineering; Power (physics)","score_opus":0.01309708990084671,"score_gpt":0.2565759540533312,"score_spread":0.24347886415248451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408338238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063169114,0.00011757864,0.9319339,0.0018955158,0.0012493584,0.00046290277,0.0000027469202,0.00049392605,0.0006749553],"genre_scores_gemma":[0.8086224,0.0000027331512,0.19001313,0.00092521263,0.00019364952,0.00011568984,0.0000024835788,0.000008786278,0.00011591936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99714774,0.00008280347,0.0003786503,0.0012705437,0.00059536664,0.0005249039],"domain_scores_gemma":[0.99767697,0.0001451391,0.00024187993,0.00068442855,0.0011202509,0.00013131842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069341663,0.00029485152,0.00033224068,0.001045285,0.0005756482,0.0010104291,0.0019113896,0.000094097064,0.000004772223],"category_scores_gemma":[0.00018410204,0.00024472477,0.000057533045,0.004333707,0.0005396782,0.002374115,0.0007911284,0.0003416408,0.00004296675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031404114,0.00025783043,0.0040717493,0.00007369322,0.000097430326,0.00004247493,0.0065071397,0.00013794663,0.15455134,0.11418927,0.0023064858,0.71773326],"study_design_scores_gemma":[0.0011017885,0.00034593674,0.0006456282,0.00032910093,0.000018375507,0.00025091012,0.00025277367,0.9222962,0.06691636,0.002414957,0.0047478057,0.00068011385],"about_ca_topic_score_codex":0.000023279496,"about_ca_topic_score_gemma":0.000048717087,"teacher_disagreement_score":0.9221583,"about_ca_system_score_codex":0.0002854605,"about_ca_system_score_gemma":0.001166398,"threshold_uncertainty_score":0.9979586},"labels":[],"label_agreement":null},{"id":"W4408355865","doi":"10.1109/icassp49660.2025.10888558","title":"OSR: Toward Developing Efficient Federated Learning-based Human Activity Recognition using Optimal Server Representations","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition 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 Toronto","funders":"","keywords":"Computer science; Activity recognition; Federated learning; Server; Artificial intelligence; Human–computer interaction; Operating system","score_opus":0.09204825444403314,"score_gpt":0.34076230180013967,"score_spread":0.24871404735610653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408355865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41671687,0.0000057976827,0.5800892,0.0006940702,0.00020708326,0.00027825852,0.0000017399373,0.00040118356,0.0016057767],"genre_scores_gemma":[0.98223174,5.0019713e-7,0.016883722,0.00030976857,0.00003335026,0.000049639897,0.00001891552,0.000013315686,0.00045908106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977229,0.0004737241,0.00038504848,0.0007113568,0.00035161752,0.00035531772],"domain_scores_gemma":[0.9985894,0.00031579248,0.0002041962,0.00029988203,0.0005094976,0.000081224876],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000570196,0.0002278975,0.0002804531,0.00041478343,0.0008308622,0.0006798826,0.0003514164,0.00012038557,0.00007202426],"category_scores_gemma":[0.00018109025,0.00024584934,0.00012595228,0.0012546898,0.00004489036,0.0006263298,0.0002553529,0.00029546302,0.00007742031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002553531,0.0022205056,0.014796547,0.0008534774,0.00088509213,0.00015603052,0.0037045083,0.275416,0.17265935,0.0053952653,0.0019926059,0.5216653],"study_design_scores_gemma":[0.0010293091,0.000049984406,0.007953837,0.0002486063,0.000025114403,0.000013906724,0.00021487921,0.9103469,0.07915323,0.00015703845,0.00035185582,0.00045530096],"about_ca_topic_score_codex":0.0008451516,"about_ca_topic_score_gemma":0.00018374571,"teacher_disagreement_score":0.63493097,"about_ca_system_score_codex":0.00035858105,"about_ca_system_score_gemma":0.00046534176,"threshold_uncertainty_score":0.9999994},"labels":[],"label_agreement":null},{"id":"W4408374997","doi":"10.1155/ijta/8095704","title":"Overview Study of Partially Observable Hidden Markov Models for Ambient Movement Guidance Support","year":2025,"lang":"en","type":"review","venue":"International Journal of Telemedicine and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Observable; Movement (music); Hidden Markov model; Computer science; Computational biology; Artificial intelligence; Biology; Physics","score_opus":0.09639490683100707,"score_gpt":0.3867527038633815,"score_spread":0.2903577970323744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408374997","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009872422,0.5928791,0.40380013,0.0005336275,0.0004675464,0.0019151634,0.00010342409,0.000014119561,0.00027700583],"genre_scores_gemma":[0.0009391907,0.9913738,0.0052613537,0.00030846885,0.00056890544,0.00096190197,0.000035516805,0.000015033053,0.00053586066],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970474,0.000095417025,0.0016876126,0.00032181357,0.0007025468,0.00014521542],"domain_scores_gemma":[0.9958879,0.0005280812,0.0017652045,0.00039869844,0.0013232804,0.000096859185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089227397,0.00022826312,0.001101709,0.00038307166,0.00005616808,0.00007365517,0.001426928,0.00007172042,0.000015740283],"category_scores_gemma":[0.00007174117,0.00018325716,0.00028920773,0.00030723834,0.000030275389,0.00033056876,0.0003025688,0.0001808337,0.0000017436594],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072022026,0.00047310902,0.000025218686,0.0017138991,0.0005201112,0.0000059198487,0.00010887144,0.000007610263,0.0000017078709,0.0024694768,0.0033529962,0.9913139],"study_design_scores_gemma":[0.001137296,0.00045552538,0.0000307388,0.006589069,0.00047219638,0.000074658514,0.000118726384,0.00058618514,0.0000067826577,0.0023649298,0.98797774,0.00018612991],"about_ca_topic_score_codex":0.00003977402,"about_ca_topic_score_gemma":0.00003070883,"teacher_disagreement_score":0.9911277,"about_ca_system_score_codex":0.00011228179,"about_ca_system_score_gemma":0.00054811634,"threshold_uncertainty_score":0.747301},"labels":[],"label_agreement":null},{"id":"W4408423660","doi":"10.1109/lsens.2025.3551337","title":"Human Activity Recognition Using WiFi Signal Features and Efficient Residual Packet Attention Network","year":2025,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Network packet; Residual; SIGNAL (programming language); Pattern recognition (psychology); Computer network; Speech recognition; Real-time computing; Artificial intelligence; Algorithm","score_opus":0.03098577820918613,"score_gpt":0.27160433309076815,"score_spread":0.24061855488158201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408423660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9188535,0.000032405405,0.076575726,0.0025382342,0.0011656911,0.00035702082,0.0000118263515,0.00017796528,0.00028759104],"genre_scores_gemma":[0.9966212,0.0000016109162,0.0016503861,0.001107722,0.0003631141,0.000012224609,0.0000067844776,0.00001455929,0.00022241446],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99780834,0.00051097025,0.00027129287,0.00065111415,0.00033963108,0.00041866652],"domain_scores_gemma":[0.99903315,0.00021816716,0.00019957875,0.00036854672,0.00009466947,0.00008589553],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005509276,0.0002451734,0.00028349023,0.00033289782,0.00047725491,0.00036054262,0.00023548128,0.00012164816,0.000005337017],"category_scores_gemma":[0.000022977154,0.0002637158,0.0001008328,0.00064935855,0.000108963526,0.00034518083,0.000107549735,0.00030497843,0.000017757953],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006352121,0.00014383497,0.0012621724,0.00010698122,0.00017405755,0.00007623558,0.00047133196,0.006096532,0.9020476,0.00014977384,0.028992038,0.060415927],"study_design_scores_gemma":[0.010606831,0.0006711202,0.39051914,0.0049448,0.0007676624,0.0014594313,0.0006324474,0.2829214,0.293691,0.0036577764,0.003919392,0.0062089665],"about_ca_topic_score_codex":0.00012452086,"about_ca_topic_score_gemma":0.000041144835,"teacher_disagreement_score":0.6083566,"about_ca_system_score_codex":0.00013557756,"about_ca_system_score_gemma":0.000035481404,"threshold_uncertainty_score":0.9999815},"labels":[],"label_agreement":null},{"id":"W4408557973","doi":"10.2196/69052","title":"Detecting Older Adults’ Behavior Changes During Adverse External Events Using Ambient Sensing: Longitudinal Observational Study","year":2025,"lang":"en","type":"article","venue":"JMIR Nursing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Nursing Research","keywords":"Observational study; Preprint; Adverse effect; Medicine; Environmental science; Internal medicine; Computer science","score_opus":0.09365428110714744,"score_gpt":0.35919754933518905,"score_spread":0.26554326822804164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408557973","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93580496,0.000047292247,0.060737085,0.00015639038,0.0017326034,0.0012341453,0.000002370174,0.00023139235,0.00005375491],"genre_scores_gemma":[0.9959762,3.815161e-7,0.003530194,0.000043334898,0.00019858965,0.00006806358,0.0000015873535,0.000020296118,0.00016131457],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99754155,0.00020281275,0.0004309131,0.0007636481,0.000590412,0.00047068464],"domain_scores_gemma":[0.9987371,0.00010880216,0.00027379917,0.00049660954,0.0002807709,0.00010289554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034026208,0.00027319803,0.00030141094,0.00041655396,0.0006046675,0.00018375865,0.0004064027,0.00008433867,0.000009988387],"category_scores_gemma":[0.000045530327,0.00031222086,0.00011252299,0.00073613605,0.000036671616,0.0007834992,0.00023001686,0.00027236805,0.000010741935],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013790467,0.0019941395,0.6460285,0.00014480481,0.00010167842,0.00026106878,0.013031026,0.00010852718,0.03771529,0.000040984807,0.000019930148,0.30041614],"study_design_scores_gemma":[0.0015140868,0.000071636234,0.97580266,0.0026900198,0.000048357888,0.00017265562,0.0021883869,0.012852732,0.004285821,0.000042771117,0.000007649433,0.0003232163],"about_ca_topic_score_codex":0.00015018425,"about_ca_topic_score_gemma":0.00014582684,"teacher_disagreement_score":0.32977417,"about_ca_system_score_codex":0.00059657014,"about_ca_system_score_gemma":0.00010180606,"threshold_uncertainty_score":0.999933},"labels":[],"label_agreement":null},{"id":"W4408608483","doi":"10.2196/preprints.69107","title":"Passive Remote Monitoring Technologies’ Influence on Home Care Clients’ Ability to Stay Home: Multiprovincial Randomized Controlled Trial (Preprint)","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Preprint; Randomized controlled trial; Aged care; Medicine; Gerontology; Computer science; World Wide Web; Surgery","score_opus":0.017841241787756274,"score_gpt":0.28849749431631766,"score_spread":0.2706562525285614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408608483","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86692536,0.0002730098,0.07986946,0.0013154157,0.015074209,0.030626893,0.00007953806,0.0045659794,0.0012701509],"genre_scores_gemma":[0.9875437,0.000039135834,0.0064054783,0.00007776016,0.000623961,0.0049217897,0.000008851187,0.0000667272,0.00031261708],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9896191,0.0016065106,0.002495931,0.003612897,0.0017469928,0.0009185891],"domain_scores_gemma":[0.9889844,0.0041300436,0.0011174896,0.0039846934,0.0014702708,0.00031309167],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0033406378,0.0011920803,0.004556888,0.0011534931,0.00025737207,0.0015354077,0.0037579227,0.001177032,0.000020395146],"category_scores_gemma":[0.0075456696,0.0009530179,0.0017972146,0.0008050875,0.0002518137,0.0003762735,0.010387223,0.0027244082,0.00063530426],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.61999124,0.0003162287,0.0001774265,0.0006812157,0.0007445774,0.00012185525,0.0046031806,0.0017469724,0.00042087934,0.0008217121,0.00007506608,0.37029964],"study_design_scores_gemma":[0.9663542,0.0005305176,0.00026163805,0.0018477408,0.00021028698,0.000008991127,0.0010173757,0.009371527,0.0043292395,0.014086767,0.00016013329,0.0018216202],"about_ca_topic_score_codex":0.0010584454,"about_ca_topic_score_gemma":0.000105289975,"teacher_disagreement_score":0.368478,"about_ca_system_score_codex":0.0017588817,"about_ca_system_score_gemma":0.0010943012,"threshold_uncertainty_score":0.99957633},"labels":[],"label_agreement":null},{"id":"W4408615726","doi":"10.1145/3708359.3712070","title":"A Dynamic Bayesian Network Based Framework for Multimodal Context-Aware Interactions","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Dynamic Bayesian network; Bayesian network; Context (archaeology); Bayesian probability; Artificial intelligence; Machine learning","score_opus":0.01713242366401683,"score_gpt":0.3086441808193927,"score_spread":0.29151175715537586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408615726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003043437,0.00005629624,0.98655057,0.006739789,0.0024137218,0.0009151136,0.000020880618,0.0006214672,0.0023778053],"genre_scores_gemma":[0.8245344,8.660202e-7,0.1698592,0.0032703239,0.000078249075,0.0003828822,0.000013547506,0.0000147953615,0.0018457078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814,0.00014348174,0.0004273039,0.0006481636,0.00018220958,0.0004588761],"domain_scores_gemma":[0.9962604,0.0023918157,0.00014694926,0.0007965187,0.00028583826,0.0001184534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003323832,0.00024534584,0.0003505931,0.00023116125,0.0003495038,0.0003390427,0.00077726576,0.0001512264,0.0001260394],"category_scores_gemma":[0.0002294856,0.00024428585,0.00025925558,0.0007578491,0.000044534347,0.000577494,0.00017558539,0.00029710494,0.00007318464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013395204,0.00047541776,0.0028110377,0.00016350119,0.00032122328,0.000012590891,0.00041771066,0.0014484749,0.00014086324,0.22852924,0.026215307,0.73933065],"study_design_scores_gemma":[0.0007723743,0.00005431724,0.000823604,0.0003357442,0.000018937757,0.0000060815287,0.00014096267,0.9497077,0.00018078877,0.021235941,0.026433336,0.0002902309],"about_ca_topic_score_codex":0.00012233933,"about_ca_topic_score_gemma":0.0012248683,"teacher_disagreement_score":0.94825923,"about_ca_system_score_codex":0.00017750422,"about_ca_system_score_gemma":0.00025236627,"threshold_uncertainty_score":0.9961688},"labels":[],"label_agreement":null},{"id":"W4408734875","doi":"10.1007/978-981-96-2468-3_77","title":"Hybrid Classification Model Development for Enhancement the Data Driven Based Human Falling Detection Performance","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Falling (accident); Computer science; Psychology","score_opus":0.046910096697851594,"score_gpt":0.2540489244006531,"score_spread":0.20713882770280148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408734875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002559855,0.0002063645,0.9974477,0.000164488,0.00023162732,0.0008907651,0.0000073705323,0.00017336597,0.0006223096],"genre_scores_gemma":[0.9642277,0.000020607582,0.034335796,0.0001365175,0.00013570178,0.00026715038,0.0001351926,0.00003993787,0.0007014346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797,0.000015797817,0.00051665027,0.00077492313,0.00035765834,0.00036498744],"domain_scores_gemma":[0.9980857,0.0006417059,0.00018409583,0.0009188453,0.00012333412,0.000046349644],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041663848,0.00035815503,0.00034346586,0.00038076573,0.00019432147,0.0001327689,0.0012414582,0.00021198577,0.0000022953086],"category_scores_gemma":[0.0001780793,0.0003239185,0.00007772418,0.00017671342,0.000012948269,0.00022238579,0.00020689388,0.00069590175,0.000004434353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009775413,0.000022278453,0.0000022865252,0.0001522838,0.000050524446,0.0000011281795,0.00005517289,0.16644143,0.0067387545,0.0011832855,0.000018523266,0.8253246],"study_design_scores_gemma":[0.0002167321,0.00004164979,0.000017663977,0.0002721248,0.000018547144,0.0000028046966,8.285695e-8,0.9693142,0.023652447,0.00049153523,0.0056362497,0.0003359316],"about_ca_topic_score_codex":0.000004033563,"about_ca_topic_score_gemma":0.000046401947,"teacher_disagreement_score":0.9639717,"about_ca_system_score_codex":0.00062234415,"about_ca_system_score_gemma":0.00026915633,"threshold_uncertainty_score":0.99992126},"labels":[],"label_agreement":null},{"id":"W4408793165","doi":"10.1016/j.chaos.2025.116308","title":"Safe control of context-aware systems modeled by Boolean dynamics","year":2025,"lang":"en","type":"article","venue":"Chaos Solitons & Fractals","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Context (archaeology); Dynamics (music); Control (management); Computer science; Mathematics; Theoretical computer science; Artificial intelligence; Physics","score_opus":0.008489646781246366,"score_gpt":0.2430697348956148,"score_spread":0.23458008811436842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408793165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017071158,0.0022022023,0.9671412,0.001798257,0.0016455901,0.001232322,0.00050610595,0.00035366393,0.008049509],"genre_scores_gemma":[0.99633944,0.000024454695,0.00013455313,0.0006124539,0.00008666736,0.00019453895,0.00004800074,0.000029420242,0.0025304626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965944,0.00039366618,0.0010520736,0.000775433,0.0005667253,0.00061764923],"domain_scores_gemma":[0.996337,0.00094760786,0.0005860028,0.0013211438,0.00059825694,0.0002100056],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00072652695,0.00041435534,0.0010515613,0.00036245037,0.00020930773,0.00029239836,0.0014655535,0.00029868426,0.000023836446],"category_scores_gemma":[0.00018158622,0.00042307642,0.00029581538,0.0006358957,0.00015552562,0.00086055376,0.00039398708,0.0003533672,0.000094561976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004560625,0.0030516102,0.015139136,0.0022095584,0.0032519372,0.0001360772,0.003878209,0.0022166553,0.029281158,0.22851175,0.094579674,0.6172882],"study_design_scores_gemma":[0.0029556162,0.00014902416,0.00028744413,0.000690209,0.000087420995,0.000044891804,0.0011349311,0.9713107,0.0046692397,0.0014784005,0.016473921,0.00071824575],"about_ca_topic_score_codex":0.0008083997,"about_ca_topic_score_gemma":0.00010858844,"teacher_disagreement_score":0.9792683,"about_ca_system_score_codex":0.000336098,"about_ca_system_score_gemma":0.0003525444,"threshold_uncertainty_score":0.9998221},"labels":[],"label_agreement":null},{"id":"W4408793665","doi":"10.1109/icctit64404.2024.10928538","title":"AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Western University","funders":"","keywords":"Computer science; Architecture; Computer architecture; Geography","score_opus":0.02557519988338085,"score_gpt":0.28365167806555264,"score_spread":0.2580764781821718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408793665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15237778,0.0013027542,0.84315264,0.0018367231,0.00066521706,0.00016362486,0.000040282586,0.00033162427,0.00012935347],"genre_scores_gemma":[0.9912203,0.000010561369,0.00842335,0.00013194962,0.00017066649,0.0000030522087,0.000011886435,0.000009102103,0.000019114243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986962,0.000118384836,0.00032521997,0.0004338842,0.0002374959,0.00018878281],"domain_scores_gemma":[0.99899405,0.00045981017,0.0000844568,0.00027693313,0.00006243594,0.00012232522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012287754,0.0001376427,0.00027116793,0.00013458544,0.00009471901,0.00025966277,0.00029383064,0.000046744783,0.0000055474643],"category_scores_gemma":[0.000020232825,0.00011871766,0.000053978947,0.00035757307,0.00003647514,0.00032018466,0.00029889846,0.0001989454,0.000014397371],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008925578,0.00008549215,0.011590457,0.00032580853,0.00019807975,0.000051746232,0.010178096,0.00022142641,0.0077288295,0.0076831244,0.0007689941,0.96115905],"study_design_scores_gemma":[0.0015037714,0.0004991879,0.105582766,0.0039394316,0.00003729295,0.000187437,0.00096571137,0.82117224,0.031212498,0.01517246,0.018411756,0.0013154685],"about_ca_topic_score_codex":0.0007266605,"about_ca_topic_score_gemma":0.00007437011,"teacher_disagreement_score":0.9598436,"about_ca_system_score_codex":0.000057448025,"about_ca_system_score_gemma":0.00009972759,"threshold_uncertainty_score":0.48411658},"labels":[],"label_agreement":null},{"id":"W4409603298","doi":"10.1101/2025.04.17.25326032","title":"iPlayer: an open-source tool for time-windowed video annotation of human physical activity and behaviour","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Context-Aware Activity Recognition 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":"Université de Montréal; McMaster University","funders":"","keywords":"Annotation; Open source; Computer science; Artificial intelligence","score_opus":0.044245500161935465,"score_gpt":0.33520599257632117,"score_spread":0.2909604924143857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409603298","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8675788,0.000011699691,0.12984192,0.00014505064,0.00023278127,0.0016577769,0.000119207456,0.00014906116,0.00026370864],"genre_scores_gemma":[0.99691224,0.0000010665689,0.0015648996,0.00004250991,0.00012415422,0.00029081732,0.000048814603,0.000021487109,0.0009940197],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99756044,0.0004091093,0.0003670609,0.0010655753,0.0003456938,0.00025213818],"domain_scores_gemma":[0.99734527,0.0004960896,0.00053556386,0.0011688151,0.00035365406,0.0001006198],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086190924,0.00032810215,0.000757599,0.00021842732,0.000182593,0.00046676263,0.0015964792,0.00022332145,0.0000068480163],"category_scores_gemma":[0.00013463885,0.00035056318,0.00014810865,0.00021117851,0.00006987581,0.00091631577,0.002279359,0.00036599964,0.000006619833],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029365547,0.0037499454,0.056058552,0.0014610422,0.00045988834,0.00001465646,0.00977877,0.00017296508,0.18206416,0.0021480322,0.0009547966,0.7428435],"study_design_scores_gemma":[0.0052640503,0.0019869127,0.61004716,0.0015767402,0.00041424148,0.000028089997,0.00012858355,0.20286508,0.16585292,0.0075569544,0.0017170325,0.002562216],"about_ca_topic_score_codex":0.0003091932,"about_ca_topic_score_gemma":0.00005670339,"teacher_disagreement_score":0.74028134,"about_ca_system_score_codex":0.000068242654,"about_ca_system_score_gemma":0.00020110545,"threshold_uncertainty_score":0.9998946},"labels":[],"label_agreement":null},{"id":"W4409625267","doi":"10.1007/978-3-031-85908-3_4","title":"MobiVitalsConnect: A Comprehensive Mobile Healthcare System for Real-Time Patient Monitoring and Data Visualization","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Sheridan College","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visualization; Computer science; Health care; World Wide Web; Real-time computing; Data mining; Political science","score_opus":0.07314189646189875,"score_gpt":0.35175466855362125,"score_spread":0.2786127720917225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409625267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000985623,0.002883097,0.9511167,0.000948666,0.0017109454,0.0058030263,0.00069389556,0.00064488867,0.035213158],"genre_scores_gemma":[0.7688877,0.010320158,0.21524312,0.00079953606,0.00023796064,0.0012832346,0.0017204129,0.000061917046,0.0014459775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977916,0.000085511594,0.0009026648,0.0005680843,0.00040499732,0.000247147],"domain_scores_gemma":[0.9943485,0.0008654489,0.00057301624,0.0030507562,0.0010421516,0.00012010367],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00089253346,0.00026889952,0.0004285986,0.0009844283,0.0006739395,0.0008801494,0.0027498077,0.00014961997,7.676976e-7],"category_scores_gemma":[0.000054430464,0.00029301873,0.00003713426,0.0004401872,0.00032981506,0.0072087566,0.0047489577,0.00021647013,0.000012415148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007841995,0.000026956484,0.00005891845,0.0006821363,0.000024493045,5.0213714e-7,0.0038248047,0.00007208913,0.00003232932,0.20685709,0.00040613962,0.7880067],"study_design_scores_gemma":[0.00062577816,0.00021511209,0.0002786891,0.0024989136,0.000018976809,0.00005678143,0.00041007542,0.90657556,0.0000628267,0.0009103052,0.087759934,0.0005870386],"about_ca_topic_score_codex":0.00007264644,"about_ca_topic_score_gemma":0.000008988935,"teacher_disagreement_score":0.9065035,"about_ca_system_score_codex":0.00027668473,"about_ca_system_score_gemma":0.00040759187,"threshold_uncertainty_score":0.9999522},"labels":[],"label_agreement":null},{"id":"W4409625268","doi":"10.1007/978-3-031-85908-3_9","title":"Advancing Fall Detection and Prevention: Integrating Wearable Devices and a Camera-Based Analysis","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lions Gate Hospital; Health PEI; Burnaby Hospital; University of British Columbia","funders":"","keywords":"Wearable computer; Computer science; Embedded system","score_opus":0.025289057585671442,"score_gpt":0.29353605347943007,"score_spread":0.2682469958937586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409625268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003826456,0.00084031676,0.97117555,0.0006762585,0.0001189594,0.0003692827,0.000006376009,0.00008439155,0.026346225],"genre_scores_gemma":[0.81160456,0.0014399085,0.1845645,0.0009528835,0.000025059644,0.000077927194,0.000027389538,0.000007693295,0.0013000995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984604,0.0000777874,0.0006260752,0.00035811093,0.00029888802,0.00017874912],"domain_scores_gemma":[0.997529,0.0004788315,0.00042273485,0.0010532662,0.00042808696,0.00008812024],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013191577,0.00020445272,0.00033432955,0.0018544539,0.000622959,0.0010608017,0.00097926,0.00010615025,0.000002430735],"category_scores_gemma":[0.000077378376,0.00021219357,0.00005473665,0.0010288638,0.0003766422,0.0062064887,0.0011957315,0.00034371487,0.0000037368566],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002095511,0.000008973671,0.00052937673,0.00007637032,0.000033994926,1.8842725e-7,0.0009816358,0.00013439641,0.0000062024333,0.015267874,0.000014880519,0.982944],"study_design_scores_gemma":[0.00025887863,0.000053083517,0.0024998372,0.0006059783,0.00004802561,0.000014296954,0.0000808252,0.9733027,0.000017420354,0.0017091688,0.021140419,0.00026933305],"about_ca_topic_score_codex":0.00012613347,"about_ca_topic_score_gemma":0.0012788125,"teacher_disagreement_score":0.98267466,"about_ca_system_score_codex":0.00012887538,"about_ca_system_score_gemma":0.00026617793,"threshold_uncertainty_score":0.9999762},"labels":[],"label_agreement":null},{"id":"W4409761146","doi":"10.1109/vrw66409.2025.00091","title":"StudyXR: A Framework to Streamline User Study in Immersive Environments","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Ontario Tech University","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.0168069241363422,"score_gpt":0.3025180563198357,"score_spread":0.2857111321834935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409761146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33811772,0.000016417971,0.6575328,0.00114012,0.0003264358,0.0010401744,0.000001778518,0.000038097398,0.001786483],"genre_scores_gemma":[0.9923485,0.0000012967167,0.0030774295,0.0011678062,0.000019569663,0.00014091234,5.6865395e-7,0.0000057614857,0.0032381886],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998405,0.0001782597,0.0003061657,0.00056703977,0.00029185115,0.00025172948],"domain_scores_gemma":[0.99881315,0.00033187325,0.000047590445,0.0007004299,0.000025182964,0.00008177605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003297426,0.00015443837,0.00025075194,0.0003233111,0.00005718506,0.00010710578,0.0007030859,0.000057335816,0.00006420246],"category_scores_gemma":[0.000098910954,0.00014723343,0.000043298227,0.0008832844,0.000011513227,0.00032602038,0.0006498854,0.00017362952,0.00036600727],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006994274,0.0060692634,0.78717,0.000017530643,0.0003280752,0.0001480298,0.01660481,0.00021309617,0.0013615914,0.006691013,0.003567106,0.17775957],"study_design_scores_gemma":[0.0034923423,0.00093689613,0.9515753,0.00027415282,0.000031793083,0.0000047618205,0.015991163,0.0021053306,0.0042148638,0.0023626569,0.018164301,0.000846458],"about_ca_topic_score_codex":0.00034222053,"about_ca_topic_score_gemma":0.00058408274,"teacher_disagreement_score":0.65445536,"about_ca_system_score_codex":0.00014403232,"about_ca_system_score_gemma":0.00004422507,"threshold_uncertainty_score":0.60040045},"labels":[],"label_agreement":null},{"id":"W4410019015","doi":"10.1016/j.aei.2025.103413","title":"A Graph-Based approach for individual fall risk assessment through a wearable inertial measurement unit sensor","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Liberty Mutual Insurance","keywords":"Wearable computer; Units of measurement; Inertial measurement unit; Unit (ring theory); Computer science; Inertial frame of reference; Engineering; Artificial intelligence; Mathematics; Embedded system; Physics","score_opus":0.032135961657637165,"score_gpt":0.2646916356414444,"score_spread":0.23255567398380722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410019015","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031807548,0.0000733982,0.99351484,0.00007495686,0.00045406295,0.00090990844,0.000036940586,0.00045630024,0.001298814],"genre_scores_gemma":[0.3552905,0.00000921535,0.64409536,0.00018293486,0.00002212961,0.00034477407,0.000018380606,0.000012683491,0.000024007668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813527,0.000034286906,0.0006330303,0.00021146666,0.0005639841,0.00042196835],"domain_scores_gemma":[0.99855167,0.00021193441,0.00023161204,0.00055604713,0.00037356786,0.000075158096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007134818,0.00027244448,0.00033321235,0.00027679902,0.00015917531,0.00024963496,0.00059245736,0.00009624135,9.628113e-7],"category_scores_gemma":[0.00018130886,0.00027603045,0.0001352836,0.0007098851,0.000019782161,0.0013061877,0.0001356792,0.00028619592,0.0000036041552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018151486,0.00014485352,0.0003584646,0.00068957306,0.00024339197,7.561376e-7,0.001149682,0.95058805,0.00048946164,0.0034492791,0.00023022767,0.042638093],"study_design_scores_gemma":[0.0019006472,0.00009999663,0.00038726573,0.00022061568,0.000045254426,0.0000034733323,0.00020618297,0.97782046,0.0023762826,0.0002174873,0.016365891,0.00035644474],"about_ca_topic_score_codex":0.000020498448,"about_ca_topic_score_gemma":0.00000575213,"teacher_disagreement_score":0.35210976,"about_ca_system_score_codex":0.00017660357,"about_ca_system_score_gemma":0.00022269966,"threshold_uncertainty_score":0.9999692},"labels":[],"label_agreement":null},{"id":"W4410036589","doi":"10.1007/978-3-031-87499-4_22","title":"Enhancing Autonomy of Context-Aware Self-healing in Fog Native Environments","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Autonomy; Context (archaeology); Self-healing; Human–computer interaction","score_opus":0.015175086656802332,"score_gpt":0.24540886835713271,"score_spread":0.23023378170033038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410036589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029782733,0.0004955386,0.99372566,0.0003055534,0.0014182958,0.00080549944,0.00001356053,0.00011741046,0.00282067],"genre_scores_gemma":[0.9222997,0.000029039309,0.076173715,0.00078929117,0.00014979014,0.000036100086,0.0000057479824,0.000028953908,0.000487666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953675,0.00013343606,0.001103109,0.001732891,0.0010019977,0.0006610283],"domain_scores_gemma":[0.99624217,0.0015718166,0.0006859451,0.001176385,0.00018870483,0.00013500973],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013907966,0.00059521967,0.0010133408,0.0016397368,0.00016085814,0.00022355751,0.0026431887,0.00039941963,0.000017910774],"category_scores_gemma":[0.00013170797,0.00062809483,0.0001818906,0.00084054127,0.00039378752,0.0008924245,0.0015682213,0.0009665816,0.00002268525],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009990081,0.000120600576,0.0006074826,0.00021789489,0.00005628619,0.000083483385,0.0060538324,0.0039704624,0.00043341314,0.0040704114,0.000008451731,0.98436767],"study_design_scores_gemma":[0.0023674814,0.00048356657,0.0013059822,0.009041373,0.000043078082,0.00010184582,0.00001293655,0.8887569,0.042487726,0.044663746,0.008030496,0.002704915],"about_ca_topic_score_codex":0.00015420603,"about_ca_topic_score_gemma":0.00055949687,"teacher_disagreement_score":0.98166275,"about_ca_system_score_codex":0.001335417,"about_ca_system_score_gemma":0.0016194102,"threshold_uncertainty_score":0.99961704},"labels":[],"label_agreement":null},{"id":"W4410341907","doi":"10.1109/icnc64010.2025.10993997","title":"On-Body e-Hub: Accessibility Technology for People with Disabilities","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Calgary","funders":"","keywords":"Computer science; Internet privacy; Business","score_opus":0.018402242823394924,"score_gpt":0.2961495541838513,"score_spread":0.2777473113604564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410341907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17888902,0.00001518036,0.80015016,0.0051769265,0.00026848068,0.00067207107,0.0000072062617,0.0005702647,0.0142506985],"genre_scores_gemma":[0.9912718,4.7692475e-7,0.004401303,0.00034439465,0.000015196915,0.0004122957,0.0000017222184,0.000005482758,0.0035473115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987993,0.00004352233,0.00021282435,0.0005568189,0.0001454085,0.00024209805],"domain_scores_gemma":[0.9976747,0.001149206,0.000058001147,0.000877388,0.00020431585,0.000036375153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025351893,0.00014057125,0.00026552138,0.00021557012,0.00014201587,0.00016243722,0.0007731812,0.00008598633,0.000047435093],"category_scores_gemma":[0.00036176006,0.000103595725,0.000059589343,0.0007795255,0.000091326205,0.0004307248,0.0002231739,0.00009985188,0.00003365295],"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":[0.000104425264,0.0005447043,0.09130218,0.00023584142,0.00010399149,0.0000012404394,0.00058342883,0.000008131606,0.00029946383,0.7543496,0.005369395,0.14709759],"study_design_scores_gemma":[0.0069017294,0.0027018618,0.06953061,0.0006398319,0.000075054755,0.000053464417,0.008682416,0.05201437,0.06364101,0.7703383,0.023406357,0.002015001],"about_ca_topic_score_codex":0.00006459333,"about_ca_topic_score_gemma":0.0005786697,"teacher_disagreement_score":0.8123828,"about_ca_system_score_codex":0.000083870575,"about_ca_system_score_gemma":0.00015038156,"threshold_uncertainty_score":0.4224511},"labels":[],"label_agreement":null},{"id":"W4410490166","doi":"10.1177/27723577251337010","title":"Comparative analysis of two methods in fine-grained activity recognition for ambient assisted living","year":2025,"lang":"en","type":"article","venue":"Journal of Smart Cities and Society","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Assisted living; Computer science; Medicine; Gerontology","score_opus":0.07834092687409662,"score_gpt":0.38353642148876205,"score_spread":0.30519549461466544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410490166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4821116,0.00009074871,0.5171036,0.00024259281,0.00017235609,0.000103402876,0.000011890753,0.0000063856514,0.00015738107],"genre_scores_gemma":[0.943112,0.00003359532,0.056625064,0.000097274315,0.00002747445,0.000011201208,0.000001271393,0.0000021718683,0.00008992212],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99865973,0.0003507027,0.0005158174,0.00016485997,0.00017147559,0.00013742858],"domain_scores_gemma":[0.9965951,0.0021731928,0.00056900166,0.0001233028,0.00049594254,0.00004350534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020975836,0.00010825375,0.0006828979,0.00032071778,0.00009838612,0.0000904094,0.00016743396,0.00006039951,0.0000059736844],"category_scores_gemma":[0.00014572746,0.00009818219,0.0005033275,0.00096804183,0.00006598379,0.00046977232,0.000085335014,0.00016761263,7.8305234e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041030234,0.0017810704,0.11339767,0.0009199134,0.0141857015,0.000008427225,0.09000447,0.0005368554,0.060974333,0.002875515,0.0052438187,0.7096619],"study_design_scores_gemma":[0.0028889924,0.00050235924,0.7224396,0.00094190834,0.0010760911,0.000021269825,0.009969642,0.24194863,0.015413195,0.0034732474,0.0009173392,0.00040772828],"about_ca_topic_score_codex":0.00010494109,"about_ca_topic_score_gemma":0.00027656334,"teacher_disagreement_score":0.7092542,"about_ca_system_score_codex":0.00008360896,"about_ca_system_score_gemma":0.00011449327,"threshold_uncertainty_score":0.40037534},"labels":[],"label_agreement":null},{"id":"W4410536486","doi":"10.1109/icst62969.2025.10989022","title":"Addressing Data Leakage in HumanEval Using Combinatorial Test Design","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Ontario Tech University","funders":"","keywords":"Computer science; Leakage (economics); Reliability engineering; Test (biology); Engineering; Geology","score_opus":0.37319231829259186,"score_gpt":0.3999106756467645,"score_spread":0.02671835735417266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410536486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004945908,0.00006191469,0.9879932,0.00023056768,0.0014957758,0.00030124173,0.000004924632,0.0001873505,0.0047791516],"genre_scores_gemma":[0.97058845,9.711965e-7,0.028881026,0.00018080842,0.00008486258,0.000006091248,0.0000050878234,0.000006884796,0.00024582224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983976,0.00024809357,0.00031291536,0.0005459621,0.00024161994,0.00025381893],"domain_scores_gemma":[0.9977325,0.0009774349,0.000080777805,0.0010908053,0.00007568991,0.00004277346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001028604,0.00013003888,0.00022143856,0.0002586372,0.00013804313,0.00043278356,0.0014879612,0.00007686955,0.000024063072],"category_scores_gemma":[0.00040893166,0.00013371056,0.000024979416,0.00067425956,0.00003524356,0.0014008771,0.0010546031,0.00017619,0.000034080967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012822963,0.0032818916,0.047175687,0.000465858,0.00023401938,0.0006621763,0.0025498646,0.000942943,0.10291204,0.14115696,0.034326263,0.66616404],"study_design_scores_gemma":[0.0024854736,0.00006450287,0.0023442206,0.00056222244,0.000017320095,0.00003878127,0.000117500356,0.96889126,0.00879859,0.013400709,0.0027614308,0.00051800173],"about_ca_topic_score_codex":0.00034991038,"about_ca_topic_score_gemma":0.000112036134,"teacher_disagreement_score":0.9679483,"about_ca_system_score_codex":0.00011426432,"about_ca_system_score_gemma":0.00028301182,"threshold_uncertainty_score":0.54525584},"labels":[],"label_agreement":null},{"id":"W4410582155","doi":"10.1109/tai.2025.3569517","title":"Unsupervised Domain Adaptation With Source Data for Estimating Occupancy and Recognizing Activities in Smart Buildings","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Occupancy; Domain adaptation; Adaptation (eye); Computer science; Domain (mathematical analysis); Pattern recognition (psychology); Artificial intelligence; Engineering; Mathematics; Psychology; Architectural engineering","score_opus":0.09931836748195774,"score_gpt":0.31703966070077155,"score_spread":0.2177212932188138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410582155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044736926,0.000028287748,0.9531444,0.00058295013,0.00056403136,0.00065989373,0.000036073405,0.00016656895,0.00008089704],"genre_scores_gemma":[0.81940705,0.000006951186,0.18021557,0.00012582498,0.000025120742,0.00014451717,0.0000043946884,0.000013190375,0.000057375866],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981654,0.00012291947,0.00045930038,0.00074667146,0.00020482829,0.00030082374],"domain_scores_gemma":[0.9979598,0.001134002,0.00011952141,0.0006180999,0.00010673399,0.00006182547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006682747,0.00021263967,0.00026835452,0.00044679013,0.00035140757,0.0003347349,0.00056499953,0.000085870735,0.0000054318853],"category_scores_gemma":[0.00005768885,0.00021868064,0.000042675678,0.00080438517,0.00009516187,0.0012837364,0.00001427659,0.00025048523,0.0000073112],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014656156,0.0001378931,0.000050028728,0.0000674277,0.000032432352,0.0000023819462,0.0020355578,0.0060375626,0.0015828535,0.0008650178,0.000007827994,0.9890345],"study_design_scores_gemma":[0.00017910021,0.00015040278,0.00003773262,0.0005495693,0.00002353596,0.00001270366,0.0027703429,0.9539904,0.03693083,0.004733299,0.00031252755,0.000309546],"about_ca_topic_score_codex":0.00042062497,"about_ca_topic_score_gemma":0.002734429,"teacher_disagreement_score":0.9887249,"about_ca_system_score_codex":0.00008483646,"about_ca_system_score_gemma":0.00015070595,"threshold_uncertainty_score":0.89175373},"labels":[],"label_agreement":null},{"id":"W4410613955","doi":"10.1109/jiot.2025.3572655","title":"Is It Instrumentation? Evaluation of Validity Constraints in Smart Homes","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Université de Sherbrooke","funders":"Canada Excellence Research Chairs, Government of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Instrumentation (computer programming); Computer science; Reliability engineering; Engineering; Operating system","score_opus":0.09847360444196807,"score_gpt":0.3619238770054308,"score_spread":0.26345027256346276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410613955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8321387,0.000040884002,0.16102244,0.0012203817,0.0016016022,0.00017100504,0.000002802372,0.000011229042,0.0037909488],"genre_scores_gemma":[0.99763584,0.0000092642995,0.0017792523,0.00036424908,0.000021294469,0.000005511536,5.8650414e-7,0.00000325153,0.00018075942],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99785846,0.00037637883,0.00073534634,0.00018864192,0.00070597813,0.00013518397],"domain_scores_gemma":[0.9982441,0.0002151196,0.00060549664,0.00019569426,0.00069762615,0.000041943567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030066296,0.00010368728,0.0002789347,0.00047409892,0.000026624233,0.000116541145,0.0005990374,0.00006490329,0.00019550332],"category_scores_gemma":[0.0002474559,0.00010331789,0.000119189004,0.00031311417,0.00008847398,0.001122359,0.000084467094,0.00025129522,0.000010279074],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008864761,0.0005759844,0.15298623,0.00021811164,0.00044042832,0.00001814997,0.032803934,0.00010174005,0.017296111,0.0015095458,0.016269427,0.7776917],"study_design_scores_gemma":[0.010592057,0.0006301975,0.13951734,0.0065659806,0.00023236178,0.00082339614,0.004303734,0.24012637,0.5487734,0.04649743,0.0011468879,0.00079085777],"about_ca_topic_score_codex":0.00020070917,"about_ca_topic_score_gemma":0.000034381934,"teacher_disagreement_score":0.7769008,"about_ca_system_score_codex":0.00019020823,"about_ca_system_score_gemma":0.00034146625,"threshold_uncertainty_score":0.42131814},"labels":[],"label_agreement":null},{"id":"W4410905594","doi":"10.64026/jccn/2025004","title":"Real Time Data and Decisions in Aarhus and Surrey Smart","year":2025,"lang":"en","type":"article","venue":"Journal of Computer and Communication Networks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.030444719342643634,"score_gpt":0.29227131131631046,"score_spread":0.2618265919736668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410905594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09522004,0.0049526524,0.89591134,0.002562819,0.00025209293,0.0001372934,0.000002305149,0.000023631503,0.0009378565],"genre_scores_gemma":[0.97258264,0.0055168727,0.021592803,0.00021592049,0.000048493785,0.0000013782596,0.0000045308966,0.0000034107377,0.00003396004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998814,0.00034945662,0.00044312715,0.00017175885,0.00011605273,0.00010558905],"domain_scores_gemma":[0.9974532,0.0013229407,0.00021662489,0.0007968294,0.0001359363,0.0000744796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011839406,0.000090710564,0.00025795703,0.00019729148,0.000115356444,0.00029285508,0.0009507548,0.00006311553,0.000001129011],"category_scores_gemma":[0.0000329302,0.00008284491,0.000020422645,0.0002624484,0.000050685627,0.0007436365,0.0014735127,0.00028223082,6.5665057e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026247417,0.000076099794,0.014621088,0.0000066004745,0.000049648246,0.0000068003574,0.00039799057,0.000076248245,0.000015426194,0.002347222,0.004150316,0.9782263],"study_design_scores_gemma":[0.0009389618,0.000065009845,0.12327423,0.00046045473,0.000015221591,0.00022773753,0.000031543026,0.8639906,0.000001957101,0.0024874054,0.008387272,0.00011961302],"about_ca_topic_score_codex":0.000037503727,"about_ca_topic_score_gemma":0.00007024448,"teacher_disagreement_score":0.9781067,"about_ca_system_score_codex":0.000019730784,"about_ca_system_score_gemma":0.00005453892,"threshold_uncertainty_score":0.33783177},"labels":[],"label_agreement":null},{"id":"W4410984035","doi":"10.2196/58078","title":"Trade-Offs Between Simplifying Inertial Measurement Unit–Based Movement Recordings and the Attainability of Different Levels of Analyses: Systematic Assessment of Method Variations","year":2025,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Inertial measurement unit; Accelerometer; Movement (music); Unit (ring theory); Computer science; Units of measurement; Physical medicine and rehabilitation; Econometrics; Psychology; Artificial intelligence; Mathematics; Acoustics; Medicine","score_opus":0.21076974009523936,"score_gpt":0.45968122492621094,"score_spread":0.24891148483097159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410984035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1731717,0.00036479603,0.81977123,0.0041381745,0.00011642244,0.002308646,0.00004296384,0.000025476576,0.000060596278],"genre_scores_gemma":[0.9942842,0.000022310818,0.0051967525,0.00027539942,0.000016965276,0.00019303059,0.0000024803876,0.000005802936,0.0000030427443],"study_design_codex":"systematic_review","study_design_gemma":"observational","domain_scores_codex":[0.99481416,0.0022246202,0.0016148353,0.00039583628,0.0006732479,0.00027728642],"domain_scores_gemma":[0.9954179,0.0021790788,0.001268911,0.00059160334,0.00038870444,0.00015378914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065121306,0.00019944113,0.0012013494,0.00031834372,0.00020036384,0.00003670841,0.00032829077,0.000075251155,0.0000020781513],"category_scores_gemma":[0.0002945515,0.00013692705,0.00012054006,0.0006564954,0.00010886652,0.00013642639,0.0001448745,0.00019223084,4.0369166e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042112643,0.002334905,0.25634345,0.45069158,0.0017099832,0.0000013246952,0.015806561,0.00026459506,0.004134466,0.0895595,0.00013723018,0.17859527],"study_design_scores_gemma":[0.0040805223,0.00047688565,0.89502615,0.0049524447,0.0005115396,0.0000013007968,0.0010170562,0.08746849,0.0012987087,0.0049494123,0.000009145977,0.0002083241],"about_ca_topic_score_codex":0.0010135242,"about_ca_topic_score_gemma":0.00009670888,"teacher_disagreement_score":0.8211125,"about_ca_system_score_codex":0.00025691386,"about_ca_system_score_gemma":0.0009196195,"threshold_uncertainty_score":0.5583723},"labels":[],"label_agreement":null},{"id":"W4411087005","doi":"10.1109/rtas65571.2025.00020","title":"ParRP: Enabling Space Isolation in Caches with Shared Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Isolation (microbiology); Space (punctuation); Parallel computing; Computer architecture; Operating system","score_opus":0.06331655768424743,"score_gpt":0.2895781173295196,"score_spread":0.22626155964527217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411087005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011487181,0.00007898625,0.9744661,0.003000704,0.00013535998,0.00022526894,0.0000052952787,0.00017226177,0.010428858],"genre_scores_gemma":[0.9867107,0.0000027446579,0.011599318,0.00021273976,0.000019564275,0.0000142688195,0.000018021967,0.0000037218404,0.0014188859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990121,0.00007060845,0.00016555749,0.00044517376,0.00014899408,0.00015758188],"domain_scores_gemma":[0.9987621,0.00020328612,0.000054933873,0.0008862071,0.00006358562,0.000029890243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003668673,0.000088772846,0.00013456517,0.00019780123,0.000050519116,0.00025251423,0.0007763209,0.000039191236,0.000022191856],"category_scores_gemma":[0.00007689249,0.000075680604,0.000011637981,0.0007203932,0.000012640273,0.0016009466,0.0004261396,0.00009328069,0.000035550296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013508284,0.0005921247,0.30452076,0.0002940955,0.00023550668,0.000105997504,0.0058534923,0.0004497874,0.0098642865,0.037866555,0.019787282,0.62029505],"study_design_scores_gemma":[0.00264286,0.00010536123,0.059199233,0.0007823869,0.000020750733,0.000036548892,0.0013372327,0.8822572,0.0076794014,0.0021027373,0.043032516,0.0008037396],"about_ca_topic_score_codex":0.00073306024,"about_ca_topic_score_gemma":0.0030887742,"teacher_disagreement_score":0.97522354,"about_ca_system_score_codex":0.000048438862,"about_ca_system_score_gemma":0.00013102558,"threshold_uncertainty_score":0.30861655},"labels":[],"label_agreement":null},{"id":"W4411110021","doi":"10.36227/techrxiv.174918043.39615584/v1","title":"Embedded TinyML Approach for Fall Detection in Geriatric Care","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Université Laval","keywords":"Geriatric care; Computer science; Medicine; Psychology; Nursing","score_opus":0.029956822262086322,"score_gpt":0.2783567108249542,"score_spread":0.2483998885628679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411110021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030473042,0.00023792012,0.968788,0.000065403765,0.0015830687,0.002126948,0.00003818044,0.00042467556,0.02368849],"genre_scores_gemma":[0.93872917,0.000009522236,0.056862324,0.00009892116,0.00020404125,0.0015737214,0.00006638842,0.000015320942,0.0024406102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765724,0.00018347467,0.00049375644,0.0010979968,0.00025294276,0.00031456337],"domain_scores_gemma":[0.9981933,0.0002286953,0.0002315325,0.0009410582,0.00034234696,0.00006305201],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041139175,0.0003058323,0.0004938354,0.00067892426,0.00008490673,0.00031871066,0.0010609148,0.00043730045,0.000004442173],"category_scores_gemma":[0.00009232477,0.00031980942,0.000254475,0.0005572889,0.000012473387,0.00023839742,0.0011944835,0.00045096406,0.000012874827],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003940459,0.00020015622,0.00046533882,0.0023407976,0.000099434954,0.000004107618,0.0045766216,0.0014798827,0.0002045996,0.0008598314,0.0007690404,0.9889608],"study_design_scores_gemma":[0.0034326883,0.00020474923,0.0019723712,0.00046839213,0.0000943957,0.000032425138,0.002638758,0.9626102,0.011362816,0.0049411715,0.009897273,0.0023447797],"about_ca_topic_score_codex":0.000964952,"about_ca_topic_score_gemma":0.001236512,"teacher_disagreement_score":0.986616,"about_ca_system_score_codex":0.00034509206,"about_ca_system_score_gemma":0.00037958135,"threshold_uncertainty_score":0.9999254},"labels":[],"label_agreement":null},{"id":"W4411409579","doi":"10.1109/serp4iot66600.2025.00010","title":"A Pattern-Driven Middleware Architecture for IoT Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Middleware (distributed applications); Architecture; Computer architecture; Internet of Things; Distributed computing; Embedded system","score_opus":0.06750720832472365,"score_gpt":0.3115479907858304,"score_spread":0.24404078246110675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411409579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084135117,0.000053777916,0.9872385,0.0063653183,0.0005426909,0.00049979356,0.00010218684,0.00027749682,0.004078886],"genre_scores_gemma":[0.94760376,0.0000014217794,0.044859253,0.0032038768,0.0001238533,0.000107334374,0.00006294851,0.000010499816,0.0040270514],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987756,0.000060464026,0.00019808958,0.00059158646,0.00014929721,0.00022494835],"domain_scores_gemma":[0.99787104,0.00038997424,0.000056520523,0.0015274308,0.000100396275,0.000054612134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000198217,0.00012687358,0.00018494693,0.00013914665,0.00009792297,0.0002002607,0.0019777839,0.000060518956,0.0000285909],"category_scores_gemma":[0.00009492249,0.00011021951,0.0000629064,0.0002841416,0.000019218292,0.00023911192,0.0009771354,0.00010030483,0.000052018026],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053930776,0.00005514592,0.0010791809,0.000083954954,0.00005958542,0.000003861865,0.00028852216,0.000012025461,0.00028375525,0.0019426005,0.033596672,0.9625893],"study_design_scores_gemma":[0.0024134717,0.00014856899,0.003511102,0.00043314617,0.000043827928,0.00005717514,0.0002570029,0.32738772,0.0033890854,0.01231294,0.6492381,0.0008078773],"about_ca_topic_score_codex":0.00010509819,"about_ca_topic_score_gemma":0.0006623999,"teacher_disagreement_score":0.96178144,"about_ca_system_score_codex":0.000027122716,"about_ca_system_score_gemma":0.0001401731,"threshold_uncertainty_score":0.44946212},"labels":[],"label_agreement":null},{"id":"W4411427361","doi":"10.18280/ijsse.150410","title":"Abnormal Human Activity Recognition and Classification Using Knowledge Graphs","year":2025,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.03482425575965295,"score_gpt":0.2863956617430206,"score_spread":0.2515714059833677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411427361","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6437871,0.00037295942,0.35431528,0.00034241486,0.0007811772,0.000057543286,0.0000072210432,0.000027049906,0.00030928067],"genre_scores_gemma":[0.99872124,0.00016984977,0.0009760168,0.000023741228,0.00009379937,0.0000010803143,0.000001990805,0.000003806349,0.000008466868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923474,0.000047731664,0.0003102628,0.00013847699,0.00017596125,0.000092826245],"domain_scores_gemma":[0.9991852,0.00013839216,0.00018283563,0.00007412289,0.00036239842,0.00005706179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004530598,0.00009684975,0.00016069216,0.00039684554,0.00007750686,0.00015094076,0.00022772397,0.000061294435,0.0000029552737],"category_scores_gemma":[0.00006368653,0.00010306361,0.00005633832,0.00016195039,0.000024397146,0.0010466176,0.00011055206,0.00021968542,9.962911e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003019514,0.0005162606,0.0047764834,0.00032440736,0.0009901422,0.0000872626,0.0061395066,0.00063530286,0.14276244,0.083028235,0.000098943645,0.7603391],"study_design_scores_gemma":[0.0037776157,0.00018935963,0.13498665,0.0019305715,0.00009576051,0.0016717976,0.00025444225,0.81647575,0.01073527,0.02161483,0.0075328215,0.0007351502],"about_ca_topic_score_codex":0.000020549294,"about_ca_topic_score_gemma":0.000012817726,"teacher_disagreement_score":0.8158404,"about_ca_system_score_codex":0.000092800714,"about_ca_system_score_gemma":0.00004544693,"threshold_uncertainty_score":0.4202812},"labels":[],"label_agreement":null},{"id":"W4411655648","doi":"10.3389/fphys.2025.1639428","title":"Editorial: Physio-logging in humans: recent advances and limitations in wearable devices for biomedical applications","year":2025,"lang":"en","type":"editorial","venue":"Frontiers in Physiology","topic":"Context-Aware Activity Recognition 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":"Université de Sherbrooke","funders":"","keywords":"Logging; Wearable computer; Computer science; Neuroscience; Data science; Biology; Ecology; Embedded system","score_opus":0.020104352468415645,"score_gpt":0.29269117218756324,"score_spread":0.2725868197191476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411655648","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001852164,0.0064560976,0.07663655,0.00036676592,0.91487944,0.0012199251,0.0000815454,0.00007005569,0.00027107587],"genre_scores_gemma":[0.0012666343,0.014507213,0.014245784,0.00009757205,0.96378165,0.0053317086,0.00049776735,0.000036587666,0.00023508472],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99717396,0.00031146538,0.00066707283,0.001054276,0.00031437163,0.0004788298],"domain_scores_gemma":[0.9961408,0.002823505,0.0003000173,0.00046249555,0.00020925494,0.00006395462],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006096032,0.0002979598,0.0008393226,0.0009871015,0.00010634585,0.00008046703,0.00087285024,0.00070062355,0.000001149095],"category_scores_gemma":[0.0008570398,0.00032557631,0.00007065637,0.00093423267,0.00016807907,0.000558839,0.00026114,0.00072862516,0.0000032029518],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035784014,0.00016458459,0.000081728926,0.00044681053,0.000021581001,7.122676e-7,0.0002539785,0.000033883334,0.00009136725,0.00008194518,0.8763501,0.12243752],"study_design_scores_gemma":[0.0010091211,0.000071926566,0.00021953287,0.00039916259,0.000007683178,1.1417717e-7,0.00023950779,0.0012721972,0.000005201761,0.018685361,0.97781456,0.0002756623],"about_ca_topic_score_codex":0.00010405286,"about_ca_topic_score_gemma":0.0012766565,"teacher_disagreement_score":0.122161865,"about_ca_system_score_codex":0.00032589523,"about_ca_system_score_gemma":0.0005617802,"threshold_uncertainty_score":0.99991965},"labels":[],"label_agreement":null},{"id":"W4412106540","doi":"10.1177/07334648251357031","title":"Anomaly Detection Technologies for Dementia Care: Monitoring Goals, Sensor Applications, and Trade-offs in Home-Based Solutions—A Narrative Review","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Gerontology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"AGE-WELL","keywords":"Psychological intervention; Wearable computer; Computer science; Dementia; Autonomy; Thematic analysis; Psychology; Medicine; Nursing; Qualitative research; Embedded system","score_opus":0.027723631005962056,"score_gpt":0.2933808214670448,"score_spread":0.26565719046108277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412106540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01724782,0.04515241,0.9312345,0.004322936,0.00040509662,0.0013392697,0.000003568797,0.00010248783,0.00019195816],"genre_scores_gemma":[0.98449695,0.00075057475,0.014147575,0.00012381516,0.000040475843,0.0004304703,8.8774294e-7,0.0000054172065,0.00000383449],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986848,0.00007945196,0.0006068354,0.0002774569,0.00012307389,0.00022838615],"domain_scores_gemma":[0.9987499,0.00032285976,0.0004748052,0.00024178327,0.00017891468,0.000031715215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052045746,0.00014446327,0.00046134533,0.0004000364,0.00016585707,0.00004732542,0.00033928765,0.00014369389,6.371512e-7],"category_scores_gemma":[0.00005597038,0.00014064234,0.0000810239,0.00043372656,0.00006553395,0.0002121884,0.00006672172,0.00023541745,8.820776e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011843947,0.00011579945,0.0027052346,0.00091639085,0.0001614374,0.0000056218037,0.0008203394,0.0000342557,0.007554013,0.0023149098,0.00030955396,0.984944],"study_design_scores_gemma":[0.041784573,0.0049704914,0.13749817,0.013995079,0.002509608,0.0017404035,0.08131703,0.012884242,0.3150226,0.060476106,0.32240933,0.0053923866],"about_ca_topic_score_codex":0.0000093752415,"about_ca_topic_score_gemma":0.00009541769,"teacher_disagreement_score":0.9795516,"about_ca_system_score_codex":0.00019826689,"about_ca_system_score_gemma":0.00015716426,"threshold_uncertainty_score":0.5735228},"labels":[],"label_agreement":null},{"id":"W4412146332","doi":"10.1177/20552076251336004","title":"Perspectives of older adults on the ethics of active assisted living technologies: A Scoping review and conceptual framework","year":2025,"lang":"en","type":"review","venue":"Digital Health","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; University of Toronto; University of Waterloo; Conestoga College","funders":"Mitacs","keywords":"Assisted living; Engineering ethics; Conceptual framework; Sociology; Psychology; Gerontology; Medicine; Engineering; Social science","score_opus":0.09617012266948383,"score_gpt":0.40608970652286963,"score_spread":0.3099195838533858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412146332","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000002909765,0.9878665,0.006736856,0.0014565606,0.0001306136,0.0029761782,0.00015045879,0.00013959367,0.0005403153],"genre_scores_gemma":[0.00425798,0.9950292,0.00023005188,0.00024653575,0.000013666107,0.00017516497,0.000005135362,0.000014339959,0.00002788656],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9971037,0.0006686752,0.00091393257,0.00064923376,0.00039677124,0.00026772267],"domain_scores_gemma":[0.97937083,0.017902086,0.0014732883,0.00085097324,0.00034895673,0.00005384787],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008950083,0.0003397766,0.0021496036,0.00023099032,0.00012910625,0.00009228335,0.0008612664,0.00037576837,0.0000036860167],"category_scores_gemma":[0.00887551,0.00023486493,0.0002908813,0.0010701903,0.0003671645,0.00033664444,0.00057344214,0.0012288118,0.00000346685],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001150713,0.000069472866,0.0000011410773,0.1558236,0.000056225195,5.238026e-7,0.0034789883,4.582047e-9,1.6310906e-9,0.004993161,0.000036990128,0.83553874],"study_design_scores_gemma":[0.000048613914,0.00016411676,0.000009751107,0.991277,0.00004290963,0.000014224263,0.0049521085,0.000001986754,5.533897e-7,0.00013387766,0.0031699631,0.00018490099],"about_ca_topic_score_codex":0.00002402355,"about_ca_topic_score_gemma":0.000011291104,"teacher_disagreement_score":0.8354534,"about_ca_system_score_codex":0.00017178636,"about_ca_system_score_gemma":0.0020805735,"threshold_uncertainty_score":0.99947315},"labels":[],"label_agreement":null},{"id":"W4412171536","doi":"10.1109/memea65319.2025.11068011","title":"Comparative Analysis of Automated Rules-Based Scoring Versus Human Scoring of Sleep Apnea","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Ottawa; Bruyère; Royal Ottawa Mental Health Centre; Carleton University","funders":"","keywords":"Sleep apnea; Computer science; Apnea; Artificial intelligence; Data mining; Medicine; Internal medicine","score_opus":0.06373990278501908,"score_gpt":0.3505173365575344,"score_spread":0.28677743377251536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412171536","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5692037,0.000029934288,0.4150881,0.00004088227,0.00029142512,0.00017826329,0.000008926798,0.00039565781,0.014763133],"genre_scores_gemma":[0.9961912,2.0010356e-7,0.0036671765,0.000021341613,0.000007681653,0.000014552754,0.000009411366,0.000003247075,0.000085209685],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841785,0.00015224151,0.0005503595,0.00038001014,0.00030919976,0.00019033648],"domain_scores_gemma":[0.9980886,0.0005738859,0.00031241865,0.0006320001,0.00034356277,0.000049527887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000356407,0.00015261798,0.000688252,0.0011060161,0.00009198366,0.00006261876,0.0006152635,0.000052126034,0.00003662863],"category_scores_gemma":[0.000041522246,0.00015305342,0.00023839662,0.0027914904,0.000068811416,0.00032603982,0.0001892823,0.000086095926,0.000011156298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008696225,0.0034758418,0.18712394,0.0016184826,0.03491573,0.000051952968,0.011492766,0.10087616,0.36104968,0.16161112,0.0012504149,0.13566428],"study_design_scores_gemma":[0.0010210028,0.000083897874,0.024694664,0.000096622854,0.0002681216,1.7667071e-7,0.0001713651,0.86388826,0.109550886,0.00003771811,0.000031529187,0.0001557273],"about_ca_topic_score_codex":0.000600971,"about_ca_topic_score_gemma":0.0003753244,"teacher_disagreement_score":0.7630121,"about_ca_system_score_codex":0.000079622594,"about_ca_system_score_gemma":0.00008522339,"threshold_uncertainty_score":0.6241337},"labels":[],"label_agreement":null},{"id":"W4412535621","doi":"10.1016/j.enbuild.2025.116145","title":"Semi-supervised mixture of probabilistic principal component analyzers for modeling human behavior","year":2025,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principal component analysis; Component (thermodynamics); Probabilistic logic; Biological system; Computer science; Mixture model; Artificial intelligence; Pattern recognition (psychology); Data mining; Chemistry; Biology; Thermodynamics; Physics","score_opus":0.024634061122984177,"score_gpt":0.2719246588827,"score_spread":0.24729059775971585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412535621","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5272434,0.00010627708,0.47203645,0.00013776588,0.00011394043,0.00015009467,0.000004981075,0.000050779,0.0001563029],"genre_scores_gemma":[0.99504036,0.0000041471158,0.0044552754,0.00010062311,0.000029659213,0.00011048877,0.000008303307,0.000006182751,0.00024497416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999004,0.000040336745,0.00028825697,0.0003592139,0.00013351043,0.00017468381],"domain_scores_gemma":[0.9993447,0.00010488815,0.00008767105,0.00025277695,0.00015521828,0.000054732725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022802052,0.00013078895,0.00025331217,0.00015814124,0.00015779823,0.00006931039,0.00031477748,0.00008255621,0.0000037531813],"category_scores_gemma":[0.00003041454,0.00012499803,0.000091535854,0.00022817796,0.000040662555,0.0002025668,0.00015794623,0.00006138787,1.5269234e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011079381,0.00090319914,0.005872517,0.0008395233,0.00034601922,0.000014471617,0.0021034684,0.00818618,0.31520638,0.5064422,0.00087419094,0.1591011],"study_design_scores_gemma":[0.0018727072,0.000189857,0.0007666829,0.00048434257,0.0001413849,0.000018247947,0.00012225978,0.9445147,0.035425648,0.008596564,0.0073374254,0.0005301822],"about_ca_topic_score_codex":0.0001925719,"about_ca_topic_score_gemma":0.00007600385,"teacher_disagreement_score":0.93632853,"about_ca_system_score_codex":0.000035151388,"about_ca_system_score_gemma":0.00004470986,"threshold_uncertainty_score":0.5097272},"labels":[],"label_agreement":null},{"id":"W4412564281","doi":"10.36227/techrxiv.175322671.12208812/v1","title":"GA: A Comprehensive Survey on LLM-based GUI Agent","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition 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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Survey research; Human–computer interaction; Psychology; Applied psychology","score_opus":0.12236438158492702,"score_gpt":0.3280943184286443,"score_spread":0.20572993684371726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412564281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009137994,0.00013664059,0.951445,0.0018832494,0.0057110945,0.0015430489,0.00042697717,0.0010061655,0.028709875],"genre_scores_gemma":[0.9813983,0.000012544281,0.005346634,0.005031658,0.00014240383,0.00023004808,0.00024412115,0.000023495159,0.0075708204],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9958894,0.0010692587,0.0005826449,0.0013936331,0.00067408115,0.00039095394],"domain_scores_gemma":[0.9945523,0.0018634727,0.00034041904,0.0022583914,0.0008198363,0.00016555387],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006694826,0.00050122465,0.0007642517,0.00046061867,0.00011785929,0.00049285096,0.0019748528,0.00033842545,0.00011199806],"category_scores_gemma":[0.00019150286,0.0004841334,0.00033176588,0.00044160464,0.000048287944,0.00011647956,0.0021466287,0.00072115526,0.0006304157],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005586375,0.005036637,0.015641663,0.0024528056,0.0020029636,0.00052047917,0.0018669602,0.013903512,0.00029734382,0.010083099,0.56143755,0.38619834],"study_design_scores_gemma":[0.0060108593,0.0014035972,0.38154322,0.005761501,0.00014119977,0.000029751052,0.00012150951,0.38017944,0.01090257,0.0050398074,0.2025747,0.0062918505],"about_ca_topic_score_codex":0.0040351036,"about_ca_topic_score_gemma":0.0016180497,"teacher_disagreement_score":0.9722603,"about_ca_system_score_codex":0.00022010984,"about_ca_system_score_gemma":0.0008442214,"threshold_uncertainty_score":0.99976104},"labels":[],"label_agreement":null},{"id":"W4412719141","doi":"10.1109/tccn.2025.3592620","title":"A Graph Neural Network-Based Dual Attention Fusion Network for CSI-Based Activity Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Key Research and Development Program of China; Simon Fraser University; National Natural Science Foundation of China","keywords":"Computer science; Dual (grammatical number); Artificial neural network; Graph; Artificial intelligence; Theoretical computer science","score_opus":0.06583630283179329,"score_gpt":0.30090884788943945,"score_spread":0.23507254505764616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412719141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010920705,0.00078386965,0.98227596,0.001573071,0.0015542611,0.00188911,0.00009529638,0.00042001627,0.00048767836],"genre_scores_gemma":[0.98471856,0.00038173705,0.011312517,0.0013350047,0.0002111594,0.0018356813,0.00010580958,0.000033421427,0.00006612963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969465,0.0008862636,0.000542308,0.00075302174,0.0002640498,0.0006078144],"domain_scores_gemma":[0.993752,0.004098012,0.00034506613,0.0010392232,0.0006275801,0.0001381063],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0010169965,0.0003971709,0.0004454395,0.0004112853,0.0023221637,0.00038172345,0.0005839547,0.00023393332,0.000009644674],"category_scores_gemma":[0.000019554602,0.00044817541,0.00034366065,0.0016546346,0.00023381549,0.0005988301,0.000033354776,0.0006222415,0.000009352699],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003127617,0.00045605827,0.0003071408,0.00006226409,0.00014002202,0.0000015750495,0.000045697587,0.004307676,0.00022849797,0.00012724764,0.0002784422,0.99373263],"study_design_scores_gemma":[0.0033746492,0.00044581722,0.0013698553,0.0020441022,0.00029310634,0.000010870734,0.000059634447,0.9849427,0.0008301488,0.0024379832,0.003509416,0.0006817337],"about_ca_topic_score_codex":0.00007072095,"about_ca_topic_score_gemma":0.00076571916,"teacher_disagreement_score":0.9930509,"about_ca_system_score_codex":0.000116106676,"about_ca_system_score_gemma":0.00018002142,"threshold_uncertainty_score":0.999797},"labels":[],"label_agreement":null},{"id":"W4412964775","doi":"10.1109/i2mtc62753.2025.11079034","title":"Cross-Body Transfer Learning for Human Activity Recognition","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"AGE-WELL; Carleton University","keywords":"Computer science; Transfer of learning; Artificial intelligence","score_opus":0.047242675742952246,"score_gpt":0.34257982000578546,"score_spread":0.2953371442628332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412964775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26176295,0.0000075459743,0.7215506,0.0003485223,0.00031669895,0.00039443752,0.0000036004649,0.00038528192,0.0152303865],"genre_scores_gemma":[0.98827785,0.0000013962591,0.0009832873,0.00020624883,0.00006124836,0.00017770831,0.000010500032,0.000009205442,0.01027254],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99873006,0.00012556533,0.00022835028,0.0004893455,0.00015838059,0.00026830594],"domain_scores_gemma":[0.9989699,0.00038241438,0.000040562958,0.00029573916,0.00025603504,0.000055323744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005096301,0.00014793836,0.0002125769,0.00019162807,0.0004136743,0.00042074788,0.0003695494,0.0001054157,0.000112378795],"category_scores_gemma":[0.000089288114,0.00015128429,0.00015413645,0.0003625563,0.00003638887,0.0009828481,0.00007546739,0.00018947429,0.000077349105],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037330326,0.00018779565,0.002499852,0.000107792956,0.000074401,0.0000021886137,0.000254457,0.000016646216,0.058470782,0.008911994,0.00039643288,0.9290403],"study_design_scores_gemma":[0.008146679,0.0009152824,0.07363876,0.00046535477,0.00009229606,0.000038883263,0.00018806508,0.08036114,0.7042105,0.035659347,0.094378404,0.001905267],"about_ca_topic_score_codex":0.00015421175,"about_ca_topic_score_gemma":0.00012755377,"teacher_disagreement_score":0.92713505,"about_ca_system_score_codex":0.00006911305,"about_ca_system_score_gemma":0.00006760076,"threshold_uncertainty_score":0.6169194},"labels":[],"label_agreement":null},{"id":"W4413198398","doi":"10.1007/978-981-95-0568-5_6","title":"Transforming Digital Reminder Systems for Dementia Care into Behavioral Anomaly Detectors: A Proof-of-Concept Using LSTM Autoencoders","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Anomaly detection; Dementia; Anomaly (physics); Detector; Proof of concept; Artificial intelligence; Medicine; Internal medicine; Operating system; Telecommunications; Physics","score_opus":0.057610786490878335,"score_gpt":0.3154515934599823,"score_spread":0.25784080696910394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413198398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003195292,0.0015072026,0.98592216,0.00006282675,0.0005078309,0.0014582077,0.00006746898,0.00007240554,0.010082383],"genre_scores_gemma":[0.90529615,0.00006269654,0.094124265,0.00006705947,0.000030147823,0.00012325298,0.0000962647,0.000012739023,0.00018742082],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977877,0.000032108477,0.0010868333,0.00036988017,0.00045973723,0.0002637452],"domain_scores_gemma":[0.9965651,0.00026941946,0.0005943055,0.0014479975,0.0010347799,0.000088412686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005715599,0.00027982506,0.00042387962,0.0012108507,0.0005114594,0.001010351,0.002457624,0.00017953562,0.0000011950502],"category_scores_gemma":[0.000034003904,0.00030141673,0.00011646483,0.0004945903,0.0005915698,0.00979695,0.001051127,0.00027469336,0.0000012845296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007436594,0.0000441963,0.00014849074,0.00050004607,0.00004404165,4.0582856e-7,0.021427883,0.0012985384,0.000016419299,0.04470452,0.000052664098,0.93175536],"study_design_scores_gemma":[0.0006923757,0.00017403251,0.0000350567,0.0016608634,0.00005073955,0.000031553398,0.00070010213,0.9545847,0.00038905116,0.000812797,0.040272463,0.00059627666],"about_ca_topic_score_codex":0.00009108982,"about_ca_topic_score_gemma":0.00003491122,"teacher_disagreement_score":0.9532862,"about_ca_system_score_codex":0.00033009492,"about_ca_system_score_gemma":0.00085657684,"threshold_uncertainty_score":0.9999438},"labels":[],"label_agreement":null},{"id":"W4413332417","doi":"10.1016/j.procs.2025.07.161","title":"Hybrid CNN-LSTM-GRU with Attention for Human Activity Recognition","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","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":"Acadia University","funders":"","keywords":"Computer science; Artificial intelligence; Speech recognition; Activity recognition; Pattern recognition (psychology)","score_opus":0.027488652559972473,"score_gpt":0.27822942683999285,"score_spread":0.2507407742800204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22200449,0.00001344306,0.7744243,0.0007333204,0.0009117066,0.00081908493,0.000006804303,0.00037102905,0.00071580784],"genre_scores_gemma":[0.94808143,0.0000016662572,0.050844915,0.00042490335,0.00017799971,0.00027605475,0.0000068813515,0.000010710533,0.00017542529],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99719673,0.00006538442,0.00030894656,0.0012423829,0.0006091306,0.00057740725],"domain_scores_gemma":[0.99772966,0.0002300471,0.00024529,0.00066906866,0.00096921966,0.00015672493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011318198,0.0002624787,0.00029923173,0.00057382137,0.0008124641,0.00091507775,0.001476009,0.000052931417,0.0000036230103],"category_scores_gemma":[0.00008144895,0.00024333113,0.00009775444,0.0015108947,0.00029237577,0.0029319671,0.00048194602,0.00018213091,0.000045925433],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003429304,0.00027313078,0.0014887144,0.0001820947,0.00003188287,0.0000066767443,0.0002485553,0.000017201317,0.016524885,0.003767483,0.0009275605,0.97649753],"study_design_scores_gemma":[0.005519223,0.0018426336,0.065645956,0.0014733933,0.000096215794,0.00034748582,0.000045343706,0.67709655,0.19008507,0.050295636,0.0052240924,0.0023284392],"about_ca_topic_score_codex":0.000039741306,"about_ca_topic_score_gemma":0.000032990927,"teacher_disagreement_score":0.9741691,"about_ca_system_score_codex":0.00019746505,"about_ca_system_score_gemma":0.0005373124,"threshold_uncertainty_score":0.99227554},"labels":[],"label_agreement":null},{"id":"W4413332499","doi":"10.1016/j.procs.2025.07.223","title":"GRU-Based Multi-Modal Human Activity Recognition","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition 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":"Acadia University","funders":"","keywords":"Computer science; Modal; Artificial intelligence; Activity recognition; Pattern recognition (psychology); Human–computer interaction","score_opus":0.04397431282124299,"score_gpt":0.3071591680633702,"score_spread":0.2631848552421272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10919713,0.000020216406,0.88604224,0.0009886831,0.0016160386,0.00049845927,0.000004812222,0.0006486262,0.0009838186],"genre_scores_gemma":[0.9178852,9.062187e-7,0.0808092,0.00097462966,0.00012440978,0.00010051247,0.000002814157,0.000009104181,0.0000932432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99679196,0.00012346875,0.00036302416,0.0013300491,0.00074196176,0.00064952223],"domain_scores_gemma":[0.9977017,0.00023685445,0.00021517271,0.0008931102,0.00073344435,0.00021971135],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012661689,0.000294544,0.00031842393,0.00080007187,0.0007931687,0.00096366595,0.002295258,0.00009973493,0.000008191719],"category_scores_gemma":[0.00014786296,0.0003015893,0.00011958183,0.0029128876,0.00040060235,0.0025398934,0.00077180675,0.00030381346,0.0001583894],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012651833,0.00059927657,0.0034307707,0.00012800582,0.000019130488,0.000015216375,0.0004614934,0.0000820128,0.028344078,0.0024991108,0.000555638,0.96385264],"study_design_scores_gemma":[0.0014810533,0.0001736235,0.036405005,0.00026493898,0.000012172249,0.000028351707,0.000008040432,0.8742146,0.08312025,0.0027117825,0.00090529805,0.00067488465],"about_ca_topic_score_codex":0.000088253684,"about_ca_topic_score_gemma":0.00006057159,"teacher_disagreement_score":0.96317774,"about_ca_system_score_codex":0.0002532846,"about_ca_system_score_gemma":0.00095690694,"threshold_uncertainty_score":0.9999436},"labels":[],"label_agreement":null},{"id":"W4413468376","doi":"10.1002/sdtp.18204","title":"38‐1: Advance HMI for AI‐Enabled Hardware and Applications","year":2025,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Transparency (behavior); Power consumption; Visualization; Embedded system; Computer hardware; Computer architecture; Human–computer interaction; Power (physics); Artificial intelligence","score_opus":0.00758418378792098,"score_gpt":0.26379202859542467,"score_spread":0.2562078448075037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413468376","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018664332,0.0012398547,0.29437497,0.027821068,0.0007206189,0.005415741,0.00014276286,0.0014713798,0.6669472],"genre_scores_gemma":[0.99578476,0.00005966337,0.0014426196,0.00057466555,0.000043587806,0.0009078683,0.000007723203,0.000012351687,0.0011667751],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99855244,0.000039832754,0.00039355177,0.0005564653,0.00019340594,0.00026427562],"domain_scores_gemma":[0.99833757,0.00051027857,0.00014386796,0.00071127905,0.00020226848,0.00009476493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023799618,0.00017555943,0.0003429149,0.00013938629,0.00015751204,0.00008260853,0.00068403425,0.0001326257,0.0000042391903],"category_scores_gemma":[0.000109185974,0.00017353342,0.0001297124,0.00048236997,0.00013794811,0.00035300548,0.00027723884,0.00015151776,0.0000063579496],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044162396,0.0002441769,0.0013937904,0.00037387206,0.000062106345,0.000001741214,0.00006644669,0.000042762982,0.8842018,0.09677503,0.0014733572,0.0153207565],"study_design_scores_gemma":[0.002350689,0.00040515335,0.007994628,0.0005479526,0.00008633107,0.000039721333,0.00011509755,0.00026813959,0.09834637,0.006672822,0.8823014,0.0008717068],"about_ca_topic_score_codex":0.00001369905,"about_ca_topic_score_gemma":0.0000357482,"teacher_disagreement_score":0.9939183,"about_ca_system_score_codex":0.00007715996,"about_ca_system_score_gemma":0.000121010096,"threshold_uncertainty_score":0.70764875},"labels":[],"label_agreement":null},{"id":"W4413639947","doi":"10.1109/compsac65507.2025.00183","title":"Addressing Context Awareness Requirements of IoT Systems: A Model-Driven Approach","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Context (archaeology); Internet of Things; Context model; Context awareness; Systems engineering; Computer security; Engineering; Artificial intelligence","score_opus":0.1799943502979542,"score_gpt":0.3512889613040899,"score_spread":0.1712946110061357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413639947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029444909,0.00023461466,0.94052136,0.0001244575,0.0004758424,0.00052444276,0.000010074157,0.00022660194,0.028437724],"genre_scores_gemma":[0.9900501,0.0000017838181,0.007569786,0.00017721114,0.000022847551,0.00010126388,0.0000036807087,0.000009624068,0.0020637014],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979342,0.00020457205,0.00059642096,0.0005495116,0.00042977126,0.00028547746],"domain_scores_gemma":[0.9982973,0.00015005832,0.00026121153,0.0008055213,0.00041109536,0.00007486103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004537587,0.00019737113,0.00047909084,0.0002946237,0.00013432844,0.00020826553,0.0009568463,0.00010896892,0.000005337782],"category_scores_gemma":[0.000052184365,0.00018154741,0.00011060301,0.00060939085,0.00006107118,0.00038045432,0.00041140334,0.00011481058,0.000015553273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001356404,0.002227396,0.019316185,0.0042964825,0.0012668717,0.00002647485,0.0074134325,0.079618424,0.023675423,0.47445464,0.014697119,0.3728719],"study_design_scores_gemma":[0.0006642611,0.00002082111,0.00013505996,0.00048807764,0.000014633104,0.000007854025,0.00045836729,0.9951491,0.0022384615,0.00031034683,0.0003226544,0.00019041653],"about_ca_topic_score_codex":0.00039595354,"about_ca_topic_score_gemma":0.000034291046,"teacher_disagreement_score":0.9606052,"about_ca_system_score_codex":0.00011475031,"about_ca_system_score_gemma":0.00033755272,"threshold_uncertainty_score":0.74032885},"labels":[],"label_agreement":null},{"id":"W4413677485","doi":"10.1109/jiot.2025.3602828","title":"LDA-FedHAR: Federated Human Activity Recognition for Wearable Devices Through Local HAR Data Alignment","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Context-Aware Activity Recognition Systems","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; Carleton University","funders":"Foundation Research Project of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Wearable computer; Activity recognition; Wearable technology; Artificial intelligence; Speech recognition; Embedded system","score_opus":0.09561594755256243,"score_gpt":0.34062931624127624,"score_spread":0.24501336868871382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413677485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097511284,0.00015034793,0.8971646,0.0011731284,0.0019059408,0.0003459881,0.0000279393,0.00009222008,0.0016285902],"genre_scores_gemma":[0.9899281,0.000021315513,0.008173988,0.0007349021,0.00015385359,0.000021666603,0.000014660227,0.00001571944,0.00093578454],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997703,0.00023966502,0.00068230246,0.0005610712,0.00045706655,0.00035689745],"domain_scores_gemma":[0.9977519,0.00034337273,0.00073115865,0.0005592174,0.0005208025,0.00009353543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001445178,0.00023916486,0.00044681918,0.00019763774,0.00030215306,0.0008640923,0.0017924595,0.00013809963,0.000055551474],"category_scores_gemma":[0.00012236407,0.00022879147,0.00016554697,0.00024645004,0.00007971033,0.0042771846,0.00042985863,0.00044308754,0.000032757616],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040752074,0.001134442,0.0008410774,0.00056423893,0.0014257799,0.000057580783,0.0032917417,0.000043636635,0.06455278,0.0006794584,0.11483349,0.81216824],"study_design_scores_gemma":[0.0054708556,0.0010473691,0.00086250546,0.006096121,0.0002642892,0.00079923624,0.0009314959,0.21371223,0.7020549,0.02197573,0.04556597,0.001219267],"about_ca_topic_score_codex":0.0007100828,"about_ca_topic_score_gemma":0.00014246938,"teacher_disagreement_score":0.89241683,"about_ca_system_score_codex":0.00019135799,"about_ca_system_score_gemma":0.00019322944,"threshold_uncertainty_score":0.93298453},"labels":[],"label_agreement":null},{"id":"W4413862749","doi":"10.1007/s44163-025-00503-6","title":"A two-phase hybrid clustering framework exploring transitional activities in HAR","year":2025,"lang":"en","type":"article","venue":"Discover Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Artificial intelligence; Autoencoder; Pattern recognition (psychology); Unsupervised learning; Feature extraction; Convolutional neural network; Leverage (statistics); Hidden Markov model; Machine learning; Data mining; Deep learning","score_opus":0.10326577765752963,"score_gpt":0.3538569049535054,"score_spread":0.25059112729597577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413862749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18977286,0.00007245768,0.80681217,0.00067975465,0.0011951729,0.0002538139,0.000020904305,0.00013233819,0.0010605229],"genre_scores_gemma":[0.9955459,0.000016275711,0.0036781835,0.00034308748,0.0001344763,0.00020064364,0.0000056256054,0.000012516267,0.00006328469],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784744,0.00015575222,0.00057925575,0.0006432425,0.0003473415,0.00042695363],"domain_scores_gemma":[0.9986728,0.0005916149,0.00009297762,0.00050158787,0.00006472074,0.00007633351],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041966146,0.0002396269,0.00031492094,0.00043478954,0.00017698426,0.00051452947,0.0006814782,0.000054610864,0.000064062304],"category_scores_gemma":[0.00013194534,0.0002677164,0.00013886718,0.0008322456,0.00009932074,0.0022883033,0.00023148069,0.00037574067,0.000092331815],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001822677,0.0007309338,0.00035932555,0.0000859518,0.000057927344,0.00013561733,0.005884406,0.0048761563,0.0059973486,0.25249395,0.000045021243,0.72915107],"study_design_scores_gemma":[0.00041445528,0.00014554427,0.00038890287,0.0016447869,0.000023401073,0.000047681227,0.004677169,0.37391075,0.3614602,0.25513077,0.0010534705,0.0011028379],"about_ca_topic_score_codex":0.00026885467,"about_ca_topic_score_gemma":0.0005491464,"teacher_disagreement_score":0.8057731,"about_ca_system_score_codex":0.00016328167,"about_ca_system_score_gemma":0.00018292236,"threshold_uncertainty_score":0.9999775},"labels":[],"label_agreement":null},{"id":"W4413863383","doi":"10.1007/978-3-032-01628-7_41","title":"Method for Adapting the SUS and UTAUT-2 for Individuals with Autism, Traumatic Brain Injury and Intellectual Disability","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; The Quebec Population Health Research Network; Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Traumatic brain injury; Autism; Intellectual disability; Psychology; Clinical psychology; Psychiatry; Neuroscience; Physical medicine and rehabilitation; Medicine","score_opus":0.06057575758566139,"score_gpt":0.3203301332911561,"score_spread":0.25975437570549476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413863383","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023732006,0.00020165017,0.9788671,0.004191663,0.00012245942,0.0032141109,0.00019733785,0.00015291851,0.012815432],"genre_scores_gemma":[0.10363804,0.000043896933,0.6592134,0.0028526068,0.00027552235,0.0017524055,0.00006719531,0.00014777383,0.23200911],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99791163,0.00016651966,0.0005151859,0.00085698755,0.00025912045,0.00029056252],"domain_scores_gemma":[0.98141825,0.01752253,0.00028160773,0.0005674948,0.00012056724,0.000089561494],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023093957,0.00040243406,0.0006786192,0.00012755748,0.00031181183,0.0004316603,0.0005093222,0.00023420763,0.000024966594],"category_scores_gemma":[0.0005304328,0.0002686638,0.00013625307,0.000080173646,0.00023422045,0.0002938072,0.0003969972,0.00027934572,0.0000015403567],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046814534,0.000019900786,0.000031033203,0.0004188266,0.00025720242,4.035066e-7,0.0025868223,3.885329e-7,0.0000050131666,0.1340177,0.003367051,0.8592488],"study_design_scores_gemma":[0.004214317,0.0028677483,0.00050732645,0.0026989656,0.0006942529,0.00029874887,0.002015072,0.10294402,0.00029320974,0.2688488,0.6118634,0.0027541947],"about_ca_topic_score_codex":0.00006579497,"about_ca_topic_score_gemma":0.00038713662,"teacher_disagreement_score":0.85649467,"about_ca_system_score_codex":0.00007033324,"about_ca_system_score_gemma":0.00016474488,"threshold_uncertainty_score":0.9999766},"labels":[],"label_agreement":null},{"id":"W4414199133","doi":"10.1109/cvprw67362.2025.00172","title":"ActNAS: Generating Efficient YOLO Models Using Activation NAS","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Deep River Science Academy","funders":"","keywords":"Activation function; Leverage (statistics); Inference; Artificial neural network; Deep neural networks; Function (biology)","score_opus":0.0612386703587275,"score_gpt":0.2921252175464666,"score_spread":0.2308865471877391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414199133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20213917,0.00001854173,0.7900302,0.00033068762,0.00038141423,0.00019102285,5.745139e-7,0.00023031274,0.006678104],"genre_scores_gemma":[0.96575737,5.8187237e-7,0.032946147,0.00059948105,0.000054130167,0.000016200704,0.0000012950859,0.000005865778,0.0006189514],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987562,0.00010967531,0.00026825612,0.0004011199,0.0002426009,0.00022217959],"domain_scores_gemma":[0.999084,0.00017160876,0.0001015139,0.00041647657,0.0001808799,0.0000455212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003390953,0.00012962014,0.0001614654,0.00021742823,0.00022816566,0.00030917962,0.00037373762,0.0000691285,0.000013438145],"category_scores_gemma":[0.000055403572,0.00012386839,0.00006332087,0.0006066352,0.00001570395,0.00074232864,0.00024604518,0.000107178304,0.00001687621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012960132,0.0002777407,0.00025652553,0.00007360796,0.000113254486,0.000006153951,0.0019693284,0.2861806,0.33106926,0.0904792,0.000998785,0.28856257],"study_design_scores_gemma":[0.00018653754,0.000007944762,0.00003665035,0.0000644982,0.0000034747554,0.0000050040267,0.000085909756,0.9496585,0.048858743,0.00076626264,0.00020146558,0.0001249883],"about_ca_topic_score_codex":0.00018464841,"about_ca_topic_score_gemma":0.000015746497,"teacher_disagreement_score":0.7636182,"about_ca_system_score_codex":0.00016205753,"about_ca_system_score_gemma":0.00015842222,"threshold_uncertainty_score":0.50512064},"labels":[],"label_agreement":null},{"id":"W4414305964","doi":"10.1007/s40860-025-00255-1","title":"A uniform approach to HAR recognition in unobtrusive indoor monitoring systems","year":2025,"lang":"en","type":"article","venue":"Journal of Reliable Intelligent Environments","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Chicoutimi","funders":"Politecnico di Milano","keywords":"Activity recognition; Generalization; Train; Face (sociological concept); Artificial neural network; Reductionism; Detector","score_opus":0.030174554491049062,"score_gpt":0.2581912356326163,"score_spread":0.22801668114156726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414305964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19305924,0.0007349509,0.79814005,0.00021303457,0.0028306085,0.0007373651,0.000004806377,0.000026002966,0.0042539327],"genre_scores_gemma":[0.98905236,0.00037663808,0.009016685,0.000115760915,0.00013510225,0.000054713604,0.0000018642965,0.000015479194,0.001231428],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975587,0.00013747194,0.0010108033,0.00034841377,0.000601743,0.00034286073],"domain_scores_gemma":[0.99870133,0.00014202867,0.0005105016,0.0003842763,0.00009373864,0.00016809918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010146743,0.0002175101,0.0004440153,0.00084470655,0.000072939074,0.0002047502,0.0008180103,0.00012734573,0.000007424232],"category_scores_gemma":[0.00011503118,0.00020789339,0.00012709419,0.000724141,0.000022641738,0.0008897937,0.00025396567,0.0004149924,0.00016453012],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070015166,0.0053945403,0.12858433,0.00080901984,0.001290302,0.0004446463,0.008017234,0.07413329,0.021657579,0.0016641241,0.0064458256,0.75085896],"study_design_scores_gemma":[0.011042905,0.0031379822,0.053846274,0.01980206,0.00039621795,0.0017745419,0.016303072,0.08234568,0.36630335,0.007798513,0.4330329,0.004216478],"about_ca_topic_score_codex":0.00009173013,"about_ca_topic_score_gemma":0.0000014605857,"teacher_disagreement_score":0.7959931,"about_ca_system_score_codex":0.0008911675,"about_ca_system_score_gemma":0.00009791677,"threshold_uncertainty_score":0.8477647},"labels":[],"label_agreement":null},{"id":"W4414445686","doi":"10.1016/j.ijhcs.2025.103624","title":"Defining a recommendation system to configure personalized assistance for individuals with cognitive impairments due to a traumatic brain injury","year":2025,"lang":"en","type":"article","venue":"International Journal of Human-Computer Studies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université de Montréal","funders":"Fonds de Recherche du Québec-Société et Culture; Agence Nationale de la Recherche","keywords":"Multidisciplinary approach; Process (computing); Relevance (law); Set (abstract data type); Cognition; Toolbox; Assistive technology; Multidisciplinary team; Traumatic brain injury","score_opus":0.042751603458784725,"score_gpt":0.37656842694341236,"score_spread":0.33381682348462766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414445686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12092788,0.00016352574,0.86695015,0.008948847,0.0018217996,0.00077669264,0.000079277575,0.00006644193,0.00026537062],"genre_scores_gemma":[0.9662609,0.0000025706884,0.030026644,0.003041078,0.0002695211,0.00019179283,0.00000985591,0.00001503784,0.00018260822],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99761945,0.00022308705,0.0008746734,0.00040266346,0.0006271668,0.00025298004],"domain_scores_gemma":[0.99538076,0.0012057931,0.0007078444,0.00014719668,0.0024477022,0.00011072276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011728711,0.0002626998,0.0006647477,0.0008640958,0.00023673648,0.0004492226,0.0008691963,0.00004812405,0.000005874888],"category_scores_gemma":[0.00024286075,0.0002240482,0.00017749998,0.0003780888,0.00004294932,0.0006625138,0.00028437425,0.00016768381,0.000013589518],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018744828,0.0011225428,0.007112417,0.00089804176,0.018110085,0.0004660092,0.097896695,0.0002057045,0.00055080024,0.04553727,0.099803224,0.7264227],"study_design_scores_gemma":[0.12320359,0.046282914,0.18764582,0.1826791,0.0029219827,0.011631466,0.1823335,0.037774883,0.016577503,0.017168056,0.18064348,0.011137702],"about_ca_topic_score_codex":0.0000092881155,"about_ca_topic_score_gemma":0.000042508203,"teacher_disagreement_score":0.84533304,"about_ca_system_score_codex":0.000479231,"about_ca_system_score_gemma":0.00016191513,"threshold_uncertainty_score":0.91364205},"labels":[],"label_agreement":null},{"id":"W4414705105","doi":"10.3390/s25196035","title":"Fall Detection by Deep Learning-Based Bimodal Movement and Pose Sensing with Late Fusion","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","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":"Université du Québec à Montréal","funders":"","keywords":"Robustness (evolution); RGB color model; False positive paradox; Autoencoder; Deep neural networks; Artificial neural network; Abnormality; Deep learning","score_opus":0.005714525031718935,"score_gpt":0.2069438110926951,"score_spread":0.20122928606097618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414705105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66219467,0.00003663926,0.3365882,0.00038384149,0.00015258844,0.0001522483,7.9788794e-7,0.00015912522,0.0003318708],"genre_scores_gemma":[0.9981458,0.0000038110118,0.00090972945,0.00035031702,0.000018430572,0.0000030113733,0.000002222983,0.000008718058,0.00055794505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989115,0.00016851432,0.00015153864,0.00037442968,0.00020056749,0.00019342205],"domain_scores_gemma":[0.9994027,0.00013357721,0.00008942492,0.00021128258,0.00010261968,0.000060370396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020463316,0.00013678476,0.00014772988,0.00016350311,0.00019931616,0.00015717301,0.00009019836,0.00006063311,0.0000019012696],"category_scores_gemma":[0.00003204801,0.00012482863,0.000030179945,0.00033831692,0.000033626784,0.00015724477,0.00007369011,0.00016102953,0.000012523491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010588828,0.00007538423,0.0048245904,0.000066307926,0.000067670975,0.000040307325,0.0008952552,0.0057608434,0.07261784,0.00004648528,0.00006265112,0.9154368],"study_design_scores_gemma":[0.0010559447,0.00022183255,0.0036903718,0.00011616565,0.000016069387,0.000014584011,0.00017100305,0.9253698,0.06662791,0.00014259615,0.002304898,0.00026880598],"about_ca_topic_score_codex":0.00051996077,"about_ca_topic_score_gemma":0.0007679776,"teacher_disagreement_score":0.91960895,"about_ca_system_score_codex":0.00006863669,"about_ca_system_score_gemma":0.000026512973,"threshold_uncertainty_score":0.5090364},"labels":[],"label_agreement":null},{"id":"W4414768686","doi":"10.2196/75234","title":"Interpretation of Health-Smart Home Data and Implications for Clinical Decision-Making: Inductive Content Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Nursing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Nursing Research","keywords":"Interpretation (philosophy); Representation (politics); Visualization; Data visualization; Content analysis; Health data","score_opus":0.1990490125577237,"score_gpt":0.4871991347013693,"score_spread":0.28815012214364555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414768686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045809194,0.0003575763,0.9481874,0.0043826816,0.00043048116,0.00062103005,0.000056567333,0.000043605032,0.00011143416],"genre_scores_gemma":[0.9778257,0.000011588906,0.021830712,0.00021636696,0.000020326288,0.0000544315,0.000024025143,0.0000040452765,0.000012829796],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99832845,0.00019587661,0.000705946,0.00052392826,0.000106785155,0.00013900135],"domain_scores_gemma":[0.9965643,0.0018423522,0.00040118693,0.0008582204,0.0002874119,0.000046490753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010941442,0.000087841414,0.0004108159,0.00038879484,0.00013448934,0.00009907737,0.00054247386,0.000060587077,8.102416e-7],"category_scores_gemma":[0.00041439428,0.00008803374,0.00010325219,0.000873084,0.00008450473,0.0005530541,0.00018995647,0.000090402165,9.4732775e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002876831,0.00011790525,0.021358214,0.000016688708,0.00015774711,5.2504557e-8,0.0007786903,0.0000021759647,0.000026135222,0.0018648719,0.0007767561,0.974872],"study_design_scores_gemma":[0.000712088,0.00016300005,0.82174313,0.0011176367,0.00017106492,0.000005226754,0.0008254265,0.15390754,0.00001718721,0.019906944,0.0012472988,0.00018346985],"about_ca_topic_score_codex":0.000018806551,"about_ca_topic_score_gemma":0.000038110953,"teacher_disagreement_score":0.97468853,"about_ca_system_score_codex":0.00009005365,"about_ca_system_score_gemma":0.00015193628,"threshold_uncertainty_score":0.35899118},"labels":[],"label_agreement":null},{"id":"W4414918015","doi":"10.3390/s25196203","title":"Sensor Input Type and Location Influence Outdoor Running Terrain Classification via Deep Learning Approaches","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Inertial measurement unit; Deep learning; Convolutional neural network; SIGNAL (programming language); Acceleration; Preprocessor; Terrain; Pattern recognition (psychology)","score_opus":0.03889294272321766,"score_gpt":0.2613999537842792,"score_spread":0.22250701106106155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414918015","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.690908,0.00013530231,0.30530933,0.0010043469,0.00022929309,0.00027262475,3.4056333e-7,0.00027938213,0.0018613548],"genre_scores_gemma":[0.9950738,0.0000076798315,0.003937518,0.00015397699,0.000037363014,0.000016983111,0.0000055754304,0.000009619754,0.0007574751],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986606,0.0002027465,0.0002692625,0.0004752945,0.00018360096,0.0002084735],"domain_scores_gemma":[0.9989065,0.0002016406,0.00015576021,0.00034465772,0.00033088055,0.000060593367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004212167,0.00015009614,0.000177453,0.00023044855,0.00021335293,0.0002107492,0.00023003417,0.00010311038,0.00000216133],"category_scores_gemma":[0.00043663275,0.00015712884,0.000022677546,0.0010730776,0.00006759897,0.0004430908,0.00012219159,0.00022752653,0.00006719252],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003727534,0.00008606515,0.059513554,0.00022327885,0.0000955611,0.000009502871,0.010474875,0.008889504,0.017666055,0.0048254393,0.00007619045,0.8981027],"study_design_scores_gemma":[0.00033614528,0.000051964988,0.12849937,0.00013382666,0.00001620504,0.000036419835,0.0014630904,0.8641648,0.0016774498,0.0003904414,0.0029291187,0.0003011514],"about_ca_topic_score_codex":0.00008288789,"about_ca_topic_score_gemma":0.00003440866,"teacher_disagreement_score":0.8978016,"about_ca_system_score_codex":0.00007553615,"about_ca_system_score_gemma":0.0000670218,"threshold_uncertainty_score":0.64075285},"labels":[],"label_agreement":null},{"id":"W4415188266","doi":"10.23977/jemm.2025.100203","title":"Development of A Sitting Posture Health Detection System Based on the Centernet Model","year":2025,"lang":"en","type":"article","venue":"Journal of Engineering Mechanics and Machinery","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Shanxi University","keywords":"Sitting; Body posture; Anxiety; Lying; Spinal Curvatures; Mental health","score_opus":0.011956024103592568,"score_gpt":0.2166501484251095,"score_spread":0.20469412432151693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415188266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03714964,0.0001238879,0.96179676,0.00037870818,0.00041940404,0.00008316711,0.0000012586331,0.00002231822,0.000024882922],"genre_scores_gemma":[0.9794976,0.0000041435733,0.020331498,0.00013148513,0.000021012373,0.0000023679413,1.7614418e-7,0.000004888822,0.000006811733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992144,0.000038884402,0.00037281224,0.0000905354,0.00018458591,0.00009877356],"domain_scores_gemma":[0.99934864,0.000117426025,0.00027732263,0.000121652294,0.000092002236,0.00004295346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010106981,0.00008798909,0.00019141295,0.00019689166,0.000061709216,0.00004380273,0.0001884313,0.000031679254,1.6907411e-7],"category_scores_gemma":[0.00004150014,0.00006197856,0.000054004126,0.00015461142,0.0000011859247,0.00008762758,0.000054738557,0.00018704757,1.3092234e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012856662,0.00029278424,0.00006527225,0.0023495646,0.0004482098,0.000023030343,0.004511836,0.44105214,0.13855843,0.091013454,0.00024253753,0.3213142],"study_design_scores_gemma":[0.00019051322,0.000048568105,0.000049806502,0.0006247281,0.0000043952055,0.000023518749,0.000069345224,0.9957831,0.0029348133,0.00004633371,0.00017744373,0.00004744013],"about_ca_topic_score_codex":0.000005113749,"about_ca_topic_score_gemma":0.000004295775,"teacher_disagreement_score":0.942348,"about_ca_system_score_codex":0.00009821318,"about_ca_system_score_gemma":0.00013564678,"threshold_uncertainty_score":0.25274122},"labels":[],"label_agreement":null},{"id":"W4415420638","doi":"10.1038/s44385-025-00039-5","title":"Age estimation via electrocardiogram from smartwatches","year":2025,"lang":"en","type":"article","venue":"npj Biomedical Innovations.","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Concordia University","keywords":"Smartwatch; Limiting; Reliability (semiconductor); Estimation; Set (abstract data type); Binary classification; Data set","score_opus":0.016196551239976046,"score_gpt":0.2754603692546089,"score_spread":0.2592638180146329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415420638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011244133,0.000068324596,0.976236,0.008177766,0.00091897766,0.00023670444,0.000010674147,0.00048834016,0.0026191005],"genre_scores_gemma":[0.958502,0.0000036017786,0.03899547,0.0017359098,0.0001287001,0.00010047719,0.0002065591,0.000007328149,0.00031995523],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99824643,0.000100613535,0.00050047814,0.0004449828,0.00044235482,0.00026513622],"domain_scores_gemma":[0.9986624,0.00033800062,0.00012367686,0.00055135455,0.00025019076,0.0000743357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004453089,0.00015628201,0.0002502799,0.0005784404,0.00017241675,0.00024442017,0.0006030636,0.00017192632,0.00004285718],"category_scores_gemma":[0.00028772443,0.000153079,0.000078980454,0.003983453,0.00012746148,0.00050872215,0.00018798368,0.00024791993,0.00019063229],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045462207,0.00016551198,0.0006164401,0.000015969126,0.00011550426,0.000014677945,0.00013555397,0.0000017805089,0.010426876,0.01783614,0.021753617,0.9489134],"study_design_scores_gemma":[0.0027178503,0.00023806428,0.052545253,0.0003879975,0.00008437409,0.00004083497,0.00009534697,0.21603297,0.022576518,0.251116,0.45302033,0.0011444319],"about_ca_topic_score_codex":0.00024299818,"about_ca_topic_score_gemma":0.000022787,"teacher_disagreement_score":0.9477689,"about_ca_system_score_codex":0.00011120874,"about_ca_system_score_gemma":0.00018725355,"threshold_uncertainty_score":0.6242381},"labels":[],"label_agreement":null},{"id":"W4415490130","doi":"10.36227/techrxiv.176127211.13892804/v1","title":"Design and Implementation of a Semi-Autonomous Quadruped Assistive Robot for Remote Elderly Monitoring and COVID-19 Response","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Wycliffe College","funders":"","keywords":"Robot; Focus (optics); Assistive device; Human–robot interaction; Telerobotics; Mobile robot; Robotics","score_opus":0.06687635720553922,"score_gpt":0.37468548650242073,"score_spread":0.30780912929688153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415490130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08394457,0.00061529287,0.9094519,0.0027242727,0.0005179731,0.002575627,0.00003221494,0.00008885473,0.00004930179],"genre_scores_gemma":[0.9408552,0.000068656875,0.05782898,0.0001810608,0.00004537296,0.00010046826,0.0000021714238,0.00001592996,0.000902172],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960545,0.0012809103,0.0009416725,0.001001743,0.00029031117,0.00043088492],"domain_scores_gemma":[0.99153364,0.006701847,0.0005288317,0.00047856406,0.0004732486,0.00028388394],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0034989975,0.0003512624,0.0006202417,0.0005362901,0.00045469965,0.0004631002,0.00031820373,0.00017655692,0.000018522707],"category_scores_gemma":[0.00077490864,0.0003895353,0.00010078316,0.00061453186,0.00015359957,0.00078356965,0.00031728263,0.00015136955,0.0000017009273],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002273317,0.00008631997,0.0030584564,0.00066131394,0.00032777514,0.000010963256,0.007990682,0.00024418728,0.03079411,0.00033466372,0.00031638244,0.9539018],"study_design_scores_gemma":[0.019881982,0.006090638,0.074968696,0.0015096151,0.0006665054,0.00019436555,0.021850772,0.6384705,0.22622982,0.003894944,0.0042198114,0.0020224007],"about_ca_topic_score_codex":0.0017678,"about_ca_topic_score_gemma":0.00014689071,"teacher_disagreement_score":0.95187944,"about_ca_system_score_codex":0.0004048343,"about_ca_system_score_gemma":0.0018314901,"threshold_uncertainty_score":0.99985564},"labels":[],"label_agreement":null},{"id":"W4415704567","doi":"10.1016/j.buildenv.2025.113836","title":"Query-Distill: Cloud–fog collaboration with knowledge distillation and confidence-based querying for smart buildings","year":2025,"lang":"en","type":"article","venue":"Building and Environment","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Robustness (evolution); Occupancy; Software deployment; Efficient energy use; Building automation; Electricity; Distillation; Activity recognition","score_opus":0.012438807785925855,"score_gpt":0.24789609269583684,"score_spread":0.235457284909911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415704567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20135769,0.00046196935,0.7962536,0.0010210234,0.00018270279,0.0004561454,0.000008654502,0.000071764116,0.00018640314],"genre_scores_gemma":[0.9820649,0.00003895007,0.01740566,0.00009884027,0.00004334184,0.00012680709,0.000007659417,0.000009470392,0.00020438501],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890274,0.00006155433,0.00021582244,0.0004957293,0.00013620938,0.00018795214],"domain_scores_gemma":[0.9992401,0.00029977525,0.000116637486,0.00022694096,0.000042519318,0.00007400379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037143755,0.00016569337,0.00020133096,0.0001223286,0.0002899727,0.00026371612,0.00011788019,0.00006604161,0.0000025841016],"category_scores_gemma":[0.00003861755,0.00015521787,0.000026250258,0.0001733662,0.00008326653,0.0002885951,0.00008588298,0.00006514177,0.0000019946733],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036768432,0.0003941113,0.059832506,0.00085179287,0.00024393818,0.000009946441,0.0031997217,0.002908616,0.031380147,0.12972553,0.0028823947,0.7682036],"study_design_scores_gemma":[0.008200783,0.0010831043,0.059136733,0.0020782643,0.00026251975,0.000058803475,0.00095694215,0.45623022,0.03604013,0.011488927,0.42244837,0.0020152146],"about_ca_topic_score_codex":0.00007059347,"about_ca_topic_score_gemma":0.0000448844,"teacher_disagreement_score":0.7807072,"about_ca_system_score_codex":0.00012858959,"about_ca_system_score_gemma":0.00006539756,"threshold_uncertainty_score":0.63296014},"labels":[],"label_agreement":null},{"id":"W4416028763","doi":"10.1016/j.scsadv.2025.100005","title":"Federated optimal transport domain adaptation using Internet of Things sensor data for activity recognition","year":2025,"lang":"en","type":"article","venue":"Sustainable Cities and Society Advances","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adaptation (eye); Federated learning; Domain (mathematical analysis); Activity recognition; Internet of Things; Domain adaptation; Information privacy; Data sharing; Information sharing","score_opus":0.03938885623586352,"score_gpt":0.28756159644612417,"score_spread":0.24817274021026065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416028763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21630801,0.00045985985,0.7821272,0.00021517191,0.00012128654,0.00041680204,0.000048045134,0.00006137134,0.0002422406],"genre_scores_gemma":[0.9150675,0.00015708445,0.083469294,0.00013433391,0.000028968436,0.000038210103,0.00007192828,0.000008445178,0.0010242226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988373,0.00007050502,0.0002525771,0.0004258551,0.00014707145,0.00026671766],"domain_scores_gemma":[0.998806,0.00029334286,0.00019394906,0.00021609839,0.0004569346,0.000033658358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006015645,0.00014347737,0.00027557617,0.00006008135,0.00030957226,0.00015253136,0.00025645908,0.000083828076,0.0000026899613],"category_scores_gemma":[0.000060445527,0.00014871391,0.00008916585,0.00027859447,0.000119607976,0.0035147348,0.00015836873,0.00009854868,1.2556995e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010552638,0.0006550986,0.0019829585,0.014711626,0.0012852087,0.000035184403,0.08686557,0.0015708968,0.007825595,0.027345594,0.0016958506,0.8549712],"study_design_scores_gemma":[0.0017480516,0.00019228463,0.00037564329,0.00040118513,0.00007515008,0.000013932446,0.1802529,0.78015006,0.0050736475,0.017607994,0.013624387,0.00048474353],"about_ca_topic_score_codex":0.00054610404,"about_ca_topic_score_gemma":0.00002238312,"teacher_disagreement_score":0.8544864,"about_ca_system_score_codex":0.000092247785,"about_ca_system_score_gemma":0.00021013891,"threshold_uncertainty_score":0.6064377},"labels":[],"label_agreement":null},{"id":"W4416082172","doi":"10.2196/68669","title":"Designing a Gait Recognition Algorithm for Older Adults Using Mobility Aids: Prospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Accelerometer; Measure (data warehouse); Gait; Wearable computer; Work (physics); Calibration; Step detection","score_opus":0.07455867953251961,"score_gpt":0.40295020204531035,"score_spread":0.32839152251279075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416082172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34716457,0.000028811068,0.6429325,0.000032238135,0.00023211935,0.008654966,0.000034760662,0.00019755785,0.0007225004],"genre_scores_gemma":[0.97452235,0.0000021276958,0.018813366,0.000027983266,0.00006712271,0.0064236205,0.000014568899,0.000016826978,0.00011204914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954935,0.0012627409,0.00059031765,0.00090094993,0.0010173891,0.000735136],"domain_scores_gemma":[0.99461025,0.0015905817,0.00018040687,0.00065590185,0.0028342372,0.00012862391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00555113,0.00025025895,0.00044841453,0.0008340743,0.0008340683,0.0005056046,0.0007412807,0.00012476913,0.000016323931],"category_scores_gemma":[0.00036678088,0.00023990353,0.00014444522,0.0020993797,0.00013658371,0.0023104718,0.0005800582,0.0005881257,0.000068504254],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017542466,0.0027676728,0.034065843,0.00031524728,0.00035061623,0.000009810645,0.033344153,0.0000033831059,0.00024091791,0.00014386555,0.00076097355,0.9278221],"study_design_scores_gemma":[0.010489543,0.0034087852,0.30945677,0.0022664228,0.000058561396,0.000038742135,0.048700705,0.5950202,0.019564172,0.009614406,0.00022047285,0.001161246],"about_ca_topic_score_codex":0.00027640493,"about_ca_topic_score_gemma":0.000048485825,"teacher_disagreement_score":0.92666084,"about_ca_system_score_codex":0.00086274743,"about_ca_system_score_gemma":0.0004080558,"threshold_uncertainty_score":0.9782982},"labels":[],"label_agreement":null},{"id":"W4416244666","doi":"10.26507/paper.4584","title":"Infraestructura local para espacios inteligentes: uso de docker y microservicios en el monitoreo de adultos mayores","year":2025,"lang":"es","type":"article","venue":"Encuentro Internacional de Educación en Ingeniería","topic":"Context-Aware Activity Recognition Systems","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":"Université de Moncton","funders":"","keywords":"Internet of Things; Raspberry pi; Work (physics); Persona","score_opus":0.007487525030759343,"score_gpt":0.31509540925620294,"score_spread":0.3076078842254436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416244666","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76431614,0.0018195038,0.22100045,0.005900725,0.005044739,0.0008666911,0.0001655918,0.00035814111,0.00052803307],"genre_scores_gemma":[0.98525226,0.0003964348,0.006312428,0.003546185,0.0021848849,0.00039300375,0.00004790273,0.00012459845,0.0017422799],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9914203,0.0017561767,0.0017136115,0.001818675,0.0010559775,0.002235249],"domain_scores_gemma":[0.9944785,0.0017443389,0.0007103616,0.0014792952,0.0007207501,0.0008667167],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0015835646,0.001304794,0.0012301209,0.0011585759,0.00050190423,0.001471178,0.0034892648,0.0009191892,0.00040100917],"category_scores_gemma":[0.0009921986,0.0014153405,0.0008767051,0.0010935221,0.00052235124,0.0011635543,0.0016297707,0.0020497683,0.0007074387],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016576184,0.0021463681,0.5962887,0.0019203067,0.004019682,0.0013209447,0.08647783,0.0012301173,0.05391033,0.034283552,0.019596538,0.19714801],"study_design_scores_gemma":[0.007882741,0.0010724812,0.3019995,0.011760107,0.0009465704,0.0061836895,0.023185713,0.075048976,0.22747016,0.018375361,0.32022557,0.005849118],"about_ca_topic_score_codex":0.0021779449,"about_ca_topic_score_gemma":0.00021058467,"teacher_disagreement_score":0.30062902,"about_ca_system_score_codex":0.0037065023,"about_ca_system_score_gemma":0.00284379,"threshold_uncertainty_score":0.9999704},"labels":[],"label_agreement":null},{"id":"W4416267799","doi":"10.1016/j.enbuild.2025.116692","title":"Scalable activity recognition in smart buildings via generalized domain adaptation of IoT sensor data","year":2025,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Activity recognition; Scalability; Software deployment; Robustness (evolution); Software portability; Domain (mathematical analysis); Domain adaptation; Adaptation (eye); Generalization","score_opus":0.037217182770672624,"score_gpt":0.2628638170885767,"score_spread":0.22564663431790408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416267799","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5137322,0.00012351168,0.48497778,0.00046596906,0.00019250714,0.000089220135,0.0000129658465,0.000055417175,0.00035043887],"genre_scores_gemma":[0.9602056,0.000064206884,0.03913659,0.00021814808,0.00003965955,0.000025041012,0.000027511627,0.000009078232,0.000274155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982968,0.00023042387,0.0003579719,0.00064965314,0.00021870753,0.0002464217],"domain_scores_gemma":[0.9987597,0.00027773745,0.00021788846,0.0005607183,0.00012387412,0.000060072834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081246183,0.00017794367,0.00033992413,0.00039107673,0.00011337471,0.00010421468,0.00050725427,0.00013645421,0.0000117483],"category_scores_gemma":[0.000100968056,0.00018778059,0.000045489105,0.0008169457,0.00006479636,0.00092299224,0.00044729974,0.00012051717,0.000002525079],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013031023,0.00022277061,0.002914493,0.00009440911,0.00006955399,0.000011317758,0.00042713224,0.00009717075,0.12531123,0.007514694,0.00049165887,0.86271524],"study_design_scores_gemma":[0.008437081,0.0003439265,0.016750978,0.0016507553,0.000105851774,0.0001507613,0.00046218824,0.4361302,0.35543457,0.055068757,0.12368781,0.0017771411],"about_ca_topic_score_codex":0.0044022514,"about_ca_topic_score_gemma":0.0010278039,"teacher_disagreement_score":0.86093813,"about_ca_system_score_codex":0.000060180497,"about_ca_system_score_gemma":0.000079789934,"threshold_uncertainty_score":0.7657471},"labels":[],"label_agreement":null},{"id":"W4416427683","doi":"10.1016/j.jestch.2025.102230","title":"DC-PFL: A dynamic clustering-based personalized federated learning method for human activity recognition","year":2025,"lang":"en","type":"article","venue":"Engineering Science and Technology an International Journal","topic":"Context-Aware Activity Recognition 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":"Trinity College","funders":"Zhongyuan University of Technology","keywords":"Federated learning; Activity recognition; Adaptability; Robustness (evolution); Cluster analysis; Personalization; Raw data; Distributed learning","score_opus":0.017740468303247117,"score_gpt":0.32529108606734614,"score_spread":0.30755061776409903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416427683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1928757,0.000020197673,0.80404156,0.0019854868,0.0006303868,0.00012369266,0.0000026842567,0.00026740058,0.00005287629],"genre_scores_gemma":[0.95116305,0.0000046876758,0.04856323,0.000101320125,0.00003874953,0.000042629512,0.0000033840236,0.000007227588,0.00007572426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987267,0.000048236874,0.00021236988,0.00040332048,0.0003420577,0.0002672649],"domain_scores_gemma":[0.9985424,0.00014824333,0.00014154763,0.00013318077,0.00096069905,0.00007388235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014218893,0.00013987461,0.00016825914,0.0014647258,0.00064824166,0.00069229974,0.0008299703,0.0001153739,0.0000059830136],"category_scores_gemma":[0.00062204624,0.0001464209,0.00004680844,0.0008458094,0.00014307308,0.0012336841,0.00015035852,0.0004502658,0.000001986013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047878955,0.00012779087,0.0008330281,0.000029145642,0.00011589404,0.000028858265,0.00020620743,0.0020422908,0.2651297,0.0053752274,0.000020051211,0.72604394],"study_design_scores_gemma":[0.0009406964,0.00015213608,0.001572193,0.00014781885,0.000008932334,0.00028787318,0.000075431904,0.98154724,0.011685774,0.0021763232,0.0012126442,0.00019292776],"about_ca_topic_score_codex":0.000013217622,"about_ca_topic_score_gemma":0.00003236608,"teacher_disagreement_score":0.97950494,"about_ca_system_score_codex":0.00026719135,"about_ca_system_score_gemma":0.0002631016,"threshold_uncertainty_score":0.6675862},"labels":[],"label_agreement":null},{"id":"W4416524583","doi":"10.1016/j.eij.2025.100805","title":"Continuous Well-Being assessment and actionable feedback using explainable regression for Edge-Enabled wearable devices","year":2025,"lang":"en","type":"article","venue":"Egyptian Informatics Journal","topic":"Context-Aware Activity Recognition Systems","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":"Université de Moncton","funders":"","keywords":"Interpretability; Wearable computer; Wearable technology; Counterfactual thinking; Regression; Mean squared error; Decision support system; Range (aeronautics)","score_opus":0.01987196475374084,"score_gpt":0.30450137540627636,"score_spread":0.28462941065253555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416524583","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08582938,0.00026551218,0.8868362,0.0006253944,0.0014794363,0.00056910806,0.0000022246172,0.00010183649,0.024290951],"genre_scores_gemma":[0.7900618,0.00012884535,0.20461412,0.0007939533,0.00024727554,0.000050732167,0.0000039544116,0.00002255069,0.004076788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979448,0.00010579045,0.000863037,0.00019114243,0.0003842456,0.00051101076],"domain_scores_gemma":[0.99791634,0.00034919468,0.00068771513,0.00033652352,0.0005297057,0.00018049055],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015448494,0.00023934261,0.0004130069,0.00047848027,0.0012476951,0.0019567078,0.0005115762,0.00012547678,0.000035063233],"category_scores_gemma":[0.0000789932,0.00021223303,0.0001148446,0.00043845875,0.00004083378,0.0040972983,0.00026717782,0.00041546708,0.000016620363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005619427,0.0010173698,0.030540034,0.0067347037,0.0021248783,0.00014774689,0.04096383,0.0092728585,0.006045953,0.03856355,0.084525004,0.77950215],"study_design_scores_gemma":[0.004577363,0.00031409482,0.001757674,0.0037130336,0.00011026117,0.0014203286,0.012085504,0.8304173,0.003371802,0.009162702,0.1322411,0.00082885433],"about_ca_topic_score_codex":0.000020543197,"about_ca_topic_score_gemma":0.000008327924,"teacher_disagreement_score":0.8211444,"about_ca_system_score_codex":0.00035777685,"about_ca_system_score_gemma":0.0005222703,"threshold_uncertainty_score":0.99907935},"labels":[],"label_agreement":null},{"id":"W4416640425","doi":"10.1016/j.procs.2025.10.171","title":"Preface","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","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":"Acadia University","funders":"","keywords":"Publicity; Domain (mathematical analysis); Wish; Track (disk drive); Technical report; Special Interest Group","score_opus":0.010974833357541607,"score_gpt":0.25911692007914333,"score_spread":0.24814208672160173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010152072,0.00009662543,0.97689146,0.0018744344,0.002274567,0.00029489992,7.2979543e-7,0.00054184615,0.007873388],"genre_scores_gemma":[0.9194876,0.0000035651708,0.07852786,0.0013895133,0.000103319064,0.000041931875,2.770739e-7,0.000004207178,0.00044174228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775946,0.000039933977,0.00026561983,0.00089755474,0.0005633813,0.00047406694],"domain_scores_gemma":[0.99833274,0.00016385943,0.00009359004,0.00081985665,0.00044205782,0.00014788553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084989006,0.00016676441,0.00019113887,0.00046163602,0.00035586435,0.0008206052,0.0029016654,0.000044715875,0.0000051260504],"category_scores_gemma":[0.000112964946,0.00015796689,0.00005643541,0.0029936703,0.00025567785,0.002103661,0.0013033221,0.00015241011,0.00018867175],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036101692,0.00012590227,0.0034499124,0.000067318,0.000014851436,0.000009473416,0.0010903487,0.00006568852,0.003212248,0.069868475,0.004624683,0.9174675],"study_design_scores_gemma":[0.0009725121,0.00016799997,0.028831217,0.00031042245,0.000010434676,0.0001337711,0.000028221195,0.8621758,0.042139366,0.018804247,0.0455203,0.0009057082],"about_ca_topic_score_codex":0.0000131186025,"about_ca_topic_score_gemma":0.000005460955,"teacher_disagreement_score":0.9165618,"about_ca_system_score_codex":0.00011495805,"about_ca_system_score_gemma":0.00079475465,"threshold_uncertainty_score":0.7913115},"labels":[],"label_agreement":null},{"id":"W4416640815","doi":"10.1016/j.procs.2025.10.217","title":"A Review on Sensor-based HAR Models Using GNN: AI in Healthcare","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Harrison McCain Foundation","keywords":"Transformative learning; Health care; Workspace; Healthcare system; Health professionals; Deep learning; Healthcare industry","score_opus":0.06821368816659576,"score_gpt":0.3385507640499174,"score_spread":0.2703370758833217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037790532,0.0019655167,0.9744441,0.017462954,0.0010402891,0.00084306573,0.0000025422105,0.00024655138,0.00021595675],"genre_scores_gemma":[0.87603366,0.000106884916,0.065303415,0.058375835,0.00007761584,0.00007313861,0.0000010412897,0.000010430635,0.000018004634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99681467,0.0001673225,0.0005159104,0.0011852504,0.00070095545,0.0006159226],"domain_scores_gemma":[0.9979272,0.00022010338,0.00016971257,0.000913658,0.0005841227,0.00018518731],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014858663,0.0002577562,0.00042904055,0.00078048964,0.0002624315,0.000402082,0.0018441641,0.000065286506,0.0000013275987],"category_scores_gemma":[0.0001164208,0.00024850978,0.00008685162,0.004250447,0.00016406985,0.0016306062,0.00050191476,0.0003350046,0.000028105916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036443886,0.00074178044,0.0049859295,0.0067590983,0.00002355264,0.00011524523,0.0011372846,0.017553264,0.0014617875,0.037389748,0.0049556303,0.9248402],"study_design_scores_gemma":[0.00032636686,0.000067702116,0.0003794748,0.0053291675,0.00000336656,0.000021788406,0.0000031459217,0.9898801,0.0005863059,0.002376847,0.0007586866,0.0002670887],"about_ca_topic_score_codex":0.00015966523,"about_ca_topic_score_gemma":0.000019621399,"teacher_disagreement_score":0.9723268,"about_ca_system_score_codex":0.00037294053,"about_ca_system_score_gemma":0.0017738293,"threshold_uncertainty_score":0.9999967},"labels":[],"label_agreement":null},{"id":"W4416704893","doi":"10.26868/25222708.2025.1234","title":"Explainable domain adaptation without source data for activity recognition","year":2025,"lang":"","type":"article","venue":"Building Simulation Conference proceedings","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Process (computing); Activity recognition; Transfer of learning; Transparency (behavior); Domain (mathematical analysis); Adaptation (eye); Energy (signal processing); Domain adaptation","score_opus":0.1433214221211334,"score_gpt":0.3512055922935967,"score_spread":0.2078841701724633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416704893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18600729,0.00009795941,0.8060455,0.0017453751,0.0012475485,0.003101794,0.00016882163,0.0005027096,0.0010830269],"genre_scores_gemma":[0.9518367,0.000026325324,0.04529415,0.00027008198,0.0004232672,0.0004213663,0.0001487394,0.00006276539,0.0015165916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938902,0.00018361348,0.001259498,0.002718443,0.0009012349,0.0010469797],"domain_scores_gemma":[0.9914087,0.0015235764,0.0014845782,0.0011796678,0.0041397708,0.00026368274],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0032766156,0.0007959126,0.00096584996,0.0009440835,0.0014010218,0.0032027217,0.0022980522,0.00057566527,0.00005483511],"category_scores_gemma":[0.002757851,0.000975031,0.00021310424,0.0018240188,0.00019041041,0.008896215,0.0012849316,0.00059238914,0.000052725438],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072579976,0.00043853224,0.0025188143,0.0014429206,0.00027965958,0.0000012009617,0.0054696817,0.0037502071,0.008997865,0.0082702255,0.00069261296,0.9674125],"study_design_scores_gemma":[0.0024869458,0.00019288884,0.00084910303,0.0018780922,0.00018179018,0.00000843281,0.0016546191,0.9469352,0.0027789918,0.021411894,0.020685378,0.0009367126],"about_ca_topic_score_codex":0.00025002664,"about_ca_topic_score_gemma":0.00005184797,"teacher_disagreement_score":0.9664758,"about_ca_system_score_codex":0.0005326616,"about_ca_system_score_gemma":0.0011112887,"threshold_uncertainty_score":0.999899},"labels":[],"label_agreement":null},{"id":"W4416729968","doi":"10.2196/77322","title":"Using Indoor Movement Complexity in Smart Homes to Detect Frailty in Older Adults: Multiple-Methods Case Series Study","year":2025,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Movement (music); Home automation; Series (stratigraphy); Confounding; Time series; Healthy aging","score_opus":0.0952765574114291,"score_gpt":0.3968227561478014,"score_spread":0.3015461987363723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416729968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73758125,0.000041685027,0.26002613,0.00021723231,0.0002988777,0.0016375316,0.000004440912,0.0001074531,0.00008542539],"genre_scores_gemma":[0.9471536,4.1610906e-7,0.051999792,0.00039763324,0.00002194504,0.00036880252,8.594013e-7,0.000011441582,0.00004546384],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973564,0.00074942963,0.00055679254,0.0006942965,0.00023309239,0.0004099447],"domain_scores_gemma":[0.99865,0.00046018336,0.00011132665,0.00059503433,0.00009939516,0.00008404989],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012099457,0.00023825477,0.0004339194,0.000770788,0.00014935112,0.00024577894,0.0004547566,0.000058316564,0.000010845802],"category_scores_gemma":[0.00017771432,0.0002553942,0.00006130535,0.0015636628,0.000031831532,0.0007409935,0.0007626379,0.00028070915,0.000006347704],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006481865,0.0005208935,0.6159387,0.00015049153,0.000038403574,0.0010344448,0.02243126,0.00013530269,0.0012583442,0.000045245753,0.000033921297,0.35834816],"study_design_scores_gemma":[0.0052638836,0.00016426922,0.89002377,0.0015313572,0.00000965355,0.00023693172,0.017013386,0.07971998,0.0044170893,0.0005529093,0.00029787494,0.00076890964],"about_ca_topic_score_codex":0.0051077525,"about_ca_topic_score_gemma":0.023142692,"teacher_disagreement_score":0.35757923,"about_ca_system_score_codex":0.0003667301,"about_ca_system_score_gemma":0.000093624134,"threshold_uncertainty_score":0.9999898},"labels":[],"label_agreement":null},{"id":"W4416786473","doi":"10.5770/cgj.28.909","title":"Notice of Retraction: Predicting falls among older persons using machine learning [abstract]","year":2025,"lang":"en","type":"article","venue":"Canadian Geriatrics Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Notice; Poison control; Injury prevention; Older people; Human factors and ergonomics; MEDLINE","score_opus":0.02213921780509946,"score_gpt":0.24985758300689834,"score_spread":0.2277183652017989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416786473","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75307703,0.0007854247,0.2327097,0.0010934146,0.0052414173,0.00028968122,0.000028796088,0.0000930663,0.0066814767],"genre_scores_gemma":[0.9957536,0.000021222639,0.003317499,0.00010771374,0.00034405512,0.0000017663816,0.0000018430171,0.000013742492,0.00043858855],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99820656,0.00016657966,0.0005455771,0.00028287183,0.00035459478,0.0004438247],"domain_scores_gemma":[0.9980209,0.00028753505,0.0005416277,0.0002584771,0.0004385587,0.00045292897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011523537,0.00016661399,0.00027412886,0.001048418,0.000641293,0.00047442323,0.0005444451,0.00019212141,0.00007083067],"category_scores_gemma":[0.0005256485,0.00018847303,0.00014908917,0.0011492543,0.000040413648,0.001086847,0.00006737531,0.0010944189,0.000006454639],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001943054,0.00012354979,0.8799551,0.00027590198,0.0004851131,0.0007362441,0.006305492,0.0054929205,0.0031734628,0.0007365821,0.0030037875,0.09969238],"study_design_scores_gemma":[0.0030293392,0.00019309981,0.48521018,0.0014791269,0.00035205059,0.003631421,0.004450086,0.4633707,0.0017482926,0.00034888796,0.034669008,0.0015177854],"about_ca_topic_score_codex":0.027372448,"about_ca_topic_score_gemma":0.024587173,"teacher_disagreement_score":0.45787778,"about_ca_system_score_codex":0.00061274966,"about_ca_system_score_gemma":0.0015677295,"threshold_uncertainty_score":0.99321157},"labels":[{"model":"gemma","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"},{"model":"gpt","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4416874325","doi":"10.1109/icdcsw63273.2025.00130","title":"FedGraph: Probing-Based Personalized Federated Learning for Human Activity Recognition with Multimodal Physiological Signals","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"Ministry of Education; Academy of Medical Sciences","keywords":"Activity recognition; Personalization; Robustness (evolution); Metadata; Graph; Inertial measurement unit; Gaze; Federated learning","score_opus":0.06956129039414691,"score_gpt":0.3104542810994098,"score_spread":0.2408929907052629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416874325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34868252,0.000045523477,0.6451257,0.0007908855,0.00029561284,0.0030081605,0.000026772706,0.00056583586,0.0014590182],"genre_scores_gemma":[0.9894751,0.0000029234657,0.0038466807,0.0006454762,0.00014558401,0.0012412999,0.00012129427,0.000049695773,0.004471947],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934017,0.0017613954,0.00085487356,0.0022141028,0.0006450318,0.0011228637],"domain_scores_gemma":[0.9950058,0.001592443,0.0008032037,0.0005321603,0.0017848787,0.00028153489],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014572084,0.0009094748,0.0013169757,0.00061790994,0.002595688,0.0016775928,0.00069925544,0.0005551987,0.0005838689],"category_scores_gemma":[0.00039442276,0.00078931265,0.00053268933,0.001697776,0.00048231462,0.0012086037,0.00022882482,0.00096651586,0.000096632844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003417487,0.003424918,0.0010354714,0.0011489834,0.001084973,0.000028872904,0.0008904435,0.0014176187,0.50279766,0.00045066193,0.0012009282,0.483102],"study_design_scores_gemma":[0.014644613,0.0048106746,0.0036961867,0.0021976384,0.00028534632,0.000017563103,0.000514364,0.85945654,0.11014923,0.0010513453,0.0012263443,0.0019501669],"about_ca_topic_score_codex":0.00047331912,"about_ca_topic_score_gemma":0.0003482936,"teacher_disagreement_score":0.8580389,"about_ca_system_score_codex":0.00032555073,"about_ca_system_score_gemma":0.0008975734,"threshold_uncertainty_score":0.99945575},"labels":[],"label_agreement":null},{"id":"W4416962810","doi":"10.1109/star66750.2025.11264776","title":"From IMUs to Smartwatches: Measuring Performance in Practical Shooting","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Surgical Specialties (Canada)","funders":"Arm","keywords":"Wearable computer; Inertial measurement unit; Units of measurement; Activity recognition; Key (lock); Smartwatch; Athletes","score_opus":0.055303724257745684,"score_gpt":0.3006079167893671,"score_spread":0.24530419253162142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416962810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42574245,0.00001711235,0.5477231,0.009359873,0.0005711576,0.00024014858,7.219204e-7,0.00021954763,0.016125904],"genre_scores_gemma":[0.9777853,0.0000012064273,0.020170884,0.0014105341,0.00006412123,0.00003767345,5.205029e-7,0.0000046696823,0.000525089],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986623,0.00010165318,0.0002968484,0.00043676014,0.0002368745,0.00026557987],"domain_scores_gemma":[0.9988189,0.00055755494,0.00004441923,0.00041894233,0.00008291434,0.00007731927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058215164,0.000114726536,0.00019143122,0.00021284963,0.00006506001,0.00021074504,0.00041369518,0.00005514171,0.00002654863],"category_scores_gemma":[0.00028592238,0.00011308061,0.000034256438,0.0006766008,0.0000098148685,0.0008824564,0.0003796963,0.00018868597,0.00025464213],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005217995,0.00032461292,0.14249833,0.00008604668,0.000077307275,0.00006707008,0.00662546,0.00020235252,0.0061196103,0.004336816,0.0038068595,0.83580333],"study_design_scores_gemma":[0.0036690796,0.00025215908,0.4114988,0.002581765,0.000033117478,0.00007492079,0.0036726042,0.3692102,0.11350887,0.0037148145,0.089681275,0.002102362],"about_ca_topic_score_codex":0.0007970153,"about_ca_topic_score_gemma":0.000686369,"teacher_disagreement_score":0.833701,"about_ca_system_score_codex":0.0001287983,"about_ca_system_score_gemma":0.00014382608,"threshold_uncertainty_score":0.46112937},"labels":[],"label_agreement":null},{"id":"W4417084524","doi":"10.2139/ssrn.5876551","title":"TCubes: Non-Privacy Invasive Dataset for Activities of Daily Living as Thermal Cubes","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Benchmarking; Suite; Activity recognition; Sample (material); Gesture; Work (physics)","score_opus":0.024000054380845747,"score_gpt":0.2933249094581974,"score_spread":0.26932485507735165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417084524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23263057,0.006969566,0.7438973,0.0014755109,0.005374996,0.0035610264,0.0044839773,0.000103805185,0.0015032028],"genre_scores_gemma":[0.9854588,0.008703717,0.0009985648,0.00026250302,0.0012716308,0.00027372057,0.00031254767,0.00008618025,0.0026323432],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98770547,0.0012631732,0.0024132167,0.0018987895,0.0014122552,0.005307068],"domain_scores_gemma":[0.9872148,0.0055482914,0.0033659956,0.002360894,0.0011566234,0.00035336328],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.007870239,0.0013271904,0.0022155223,0.0012624455,0.0010568316,0.0012497549,0.0062926915,0.0007697593,0.00012811008],"category_scores_gemma":[0.0018537636,0.0013930933,0.0013201849,0.00074595766,0.00035231907,0.0028454568,0.0043458077,0.0074570156,0.000065142114],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016436944,0.0032444298,0.0033071227,0.005020159,0.016655903,0.000072811636,0.03991458,0.0024178384,0.015617891,0.03452237,0.0056621186,0.87192106],"study_design_scores_gemma":[0.021551348,0.018777555,0.0032206266,0.05944893,0.0061691967,0.012876328,0.12895432,0.07927905,0.09027134,0.50791526,0.05492829,0.016607763],"about_ca_topic_score_codex":0.0012391564,"about_ca_topic_score_gemma":0.0018169681,"teacher_disagreement_score":0.8553133,"about_ca_system_score_codex":0.002926105,"about_ca_system_score_gemma":0.042738218,"threshold_uncertainty_score":0.99994797},"labels":[],"label_agreement":null},{"id":"W4417169892","doi":"10.1109/icecs66544.2025.11270821","title":"Fall Detection by Bimodal Sensing with Late Fusion","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Anomaly detection; Deep learning; Fusion; Inertial measurement unit; Sensor fusion; Inertial frame of reference","score_opus":0.010629983968718075,"score_gpt":0.2260966099082018,"score_spread":0.2154666259394837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417169892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24073188,0.00015213485,0.7427956,0.0013542916,0.0015347145,0.00048925134,0.0000049741093,0.0003067807,0.012630361],"genre_scores_gemma":[0.9855441,0.000022496002,0.001505455,0.0007306588,0.00008500789,0.0000040188734,0.000002776472,0.000019784986,0.0120856995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968996,0.0003767496,0.0005518045,0.0010827951,0.0005333903,0.00055563124],"domain_scores_gemma":[0.99805856,0.00028059105,0.00025520448,0.0007761362,0.00046589292,0.00016361977],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005569481,0.00040295738,0.0004423907,0.0004056764,0.0005524763,0.00089246524,0.0004165344,0.0002399788,0.00004599518],"category_scores_gemma":[0.00004606234,0.00036541562,0.00012747402,0.0016451371,0.00010681365,0.0011634617,0.00042868528,0.0004050001,0.00024744848],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000845643,0.00009996936,0.0007012448,0.000066634486,0.0001005886,0.000019639865,0.00042595764,0.000040221654,0.035002746,0.000085686894,0.00068739464,0.96268535],"study_design_scores_gemma":[0.0022630407,0.0005492,0.0017882793,0.00083999446,0.000096857395,0.0001549323,0.00030475276,0.86705095,0.10557481,0.00044507915,0.019949565,0.0009825518],"about_ca_topic_score_codex":0.003341574,"about_ca_topic_score_gemma":0.0039046549,"teacher_disagreement_score":0.9617028,"about_ca_system_score_codex":0.00024864014,"about_ca_system_score_gemma":0.00023958093,"threshold_uncertainty_score":0.9998798},"labels":[],"label_agreement":null},{"id":"W4417229650","doi":"10.1061/9780784486139.101","title":"Feasibility of Applying a Wearable Electrodermal Activity Sensor for Individual Fall Risk Assessment","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Alberta","funders":"","keywords":"Arousal; Wearable computer; Hazard; Wearable technology; Test (biology)","score_opus":0.05868202881838293,"score_gpt":0.34421526875684977,"score_spread":0.28553323993846685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417229650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3378721,0.00015508961,0.65203667,0.0006469901,0.0008114539,0.004047337,0.00022283764,0.00014107671,0.0040664403],"genre_scores_gemma":[0.97564816,0.000046767414,0.021520168,0.00018770543,0.00010744759,0.0006914916,0.000006429269,0.000025637853,0.0017662173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99332494,0.001407552,0.0012694981,0.0018362601,0.0010127368,0.0011490283],"domain_scores_gemma":[0.9932618,0.0025985448,0.0011145751,0.0016592711,0.0011336856,0.00023212735],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0045426004,0.0006253879,0.0013174118,0.00051616144,0.000696886,0.00071656634,0.0013776986,0.00039507536,0.00007493356],"category_scores_gemma":[0.00058958196,0.00066320127,0.00070026406,0.0014436493,0.0002154942,0.0016582788,0.0009774724,0.00086338446,0.00001803986],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047274618,0.0028847344,0.14184505,0.0012538525,0.0012491201,0.000004197679,0.00082158676,0.00013611479,0.015722882,0.0035878147,0.0009374678,0.83108443],"study_design_scores_gemma":[0.010658612,0.0016219876,0.26844934,0.0010138546,0.0009689105,0.00004044948,0.0009369402,0.61139363,0.09275204,0.0064585386,0.0037015278,0.0020041969],"about_ca_topic_score_codex":0.0016214518,"about_ca_topic_score_gemma":0.00086323696,"teacher_disagreement_score":0.8290802,"about_ca_system_score_codex":0.00057129905,"about_ca_system_score_gemma":0.0022194705,"threshold_uncertainty_score":0.99958193},"labels":[],"label_agreement":null},{"id":"W4417398607","doi":"10.2196/80102","title":"Integrating Care Context With Skeleton and Depth Information for Older Adult Activity Recognition in a Care Facility Using Care-Assessment-Aware Spatiotemporal Transformer: Method and Validation Study","year":2025,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Skeleton (computer programming); Data collection; Activity recognition","score_opus":0.02527492462943503,"score_gpt":0.3423990048449916,"score_spread":0.31712408021555655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417398607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5046815,0.00003213295,0.49310264,0.00004911464,0.00006001165,0.0018864777,0.00004149854,0.000060415867,0.00008617497],"genre_scores_gemma":[0.98805743,0.0000020755938,0.011219946,0.000056790235,0.000012518977,0.00051614427,0.00012506072,0.000008199219,0.0000018352309],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99822456,0.00034831237,0.0004165869,0.00050707307,0.00025521367,0.00024823064],"domain_scores_gemma":[0.99845225,0.00024968115,0.00023732308,0.00023094214,0.0007730011,0.000056801717],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039192193,0.0002593062,0.0003657406,0.00040663424,0.00032069616,0.00045286148,0.00013275557,0.00009973955,9.804392e-7],"category_scores_gemma":[0.000041277777,0.00025086655,0.00004964691,0.00045598505,0.000027613376,0.003164787,0.00006210326,0.00025673158,3.2681427e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043140182,0.000036938898,0.09037105,0.0007863442,0.000025207928,0.0000010935665,0.066267714,0.000021706823,0.00019038728,0.000013885431,0.0000011210973,0.8422414],"study_design_scores_gemma":[0.011997773,0.000838878,0.3014212,0.0028335853,0.00016660725,0.00003082749,0.5677849,0.09320523,0.020217417,0.00014300848,0.00012374597,0.0012368652],"about_ca_topic_score_codex":0.0025757037,"about_ca_topic_score_gemma":0.0069745583,"teacher_disagreement_score":0.84100455,"about_ca_system_score_codex":0.00044470947,"about_ca_system_score_gemma":0.000245749,"threshold_uncertainty_score":0.99999434},"labels":[],"label_agreement":null},{"id":"W4417426391","doi":"10.1145/3779303","title":"Health Monitoring with Earables: A Survey","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Internet of Things","topic":"Context-Aware Activity Recognition Systems","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Wearable computer; Health care; Wearable technology; Component (thermodynamics); Everyday life; State of health; State (computer science)","score_opus":0.035097144124223045,"score_gpt":0.2935487346106267,"score_spread":0.25845159048640365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417426391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034543145,0.00006887934,0.96202064,0.0017045381,0.00066808145,0.00020243802,0.0000057689144,0.00017039497,0.00061610964],"genre_scores_gemma":[0.9849526,0.000015280915,0.012765888,0.00031690346,0.000008067513,0.000022039621,0.0000011060259,0.000009413368,0.0019086916],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986221,0.0001928154,0.00033701808,0.0003646698,0.0002695518,0.00021383325],"domain_scores_gemma":[0.9982914,0.0005338868,0.00016292986,0.0007657243,0.00017868305,0.000067380184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005995253,0.00014910038,0.00030186382,0.00031742788,0.000099664656,0.00011285364,0.0010177616,0.00005616768,0.000013805542],"category_scores_gemma":[0.000056130582,0.00013633448,0.00007949067,0.00059977017,0.00004901686,0.0006815913,0.000027076207,0.0002708168,0.00002308671],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032530678,0.0008015256,0.01533162,0.000381848,0.0006503725,0.000009055644,0.012715069,0.0002530283,0.00056887016,0.0009855679,0.001163165,0.9668146],"study_design_scores_gemma":[0.0170193,0.010841511,0.2752059,0.03402201,0.00027565658,0.00050516176,0.0057777097,0.06463667,0.5534336,0.0113346055,0.021897791,0.0050500743],"about_ca_topic_score_codex":0.0065005473,"about_ca_topic_score_gemma":0.00031844256,"teacher_disagreement_score":0.9617645,"about_ca_system_score_codex":0.00012275067,"about_ca_system_score_gemma":0.00016903286,"threshold_uncertainty_score":0.98269266},"labels":[],"label_agreement":null},{"id":"W46334052","doi":"10.1007/978-3-642-28572-1_6","title":"Reducing Uncertainty in Human-Robot Interaction: A Cost Analysis Approach","year":2013,"lang":"en","type":"book-chapter","venue":"Springer tracts in advanced robotics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Task (project management); Robot; Computer science; Human–computer interaction; Interface (matter); Function (biology); Uncertainty reduction theory; Process (computing); Artificial intelligence; Simulation; Engineering; Systems engineering","score_opus":0.05112435886865724,"score_gpt":0.29494381980354,"score_spread":0.24381946093488277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W46334052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006740301,0.0004585198,0.72936386,0.0002797813,0.0017750281,0.0021255454,0.000014608146,0.00038008305,0.26492852],"genre_scores_gemma":[0.8300305,0.00018608572,0.102741666,0.00020199895,0.00036933713,0.00027643767,0.00012752577,0.0001712663,0.06589521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99546295,0.0001239061,0.0014345001,0.0016006635,0.0006779784,0.00069997535],"domain_scores_gemma":[0.99648553,0.00043968798,0.0009609456,0.0016171734,0.00028540805,0.00021125306],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00060875044,0.0007520345,0.001449789,0.0020548534,0.00012954594,0.00036589737,0.0012361467,0.0005484356,0.0001141031],"category_scores_gemma":[0.000105337895,0.00084307883,0.00042311146,0.000847383,0.0001008359,0.0014338692,0.00044991905,0.0019021594,0.00014616155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014494575,0.00021940489,0.00015280671,0.00011956429,0.00030896024,0.00009569251,0.00059149007,0.8957243,0.00018535819,0.018765561,0.00003171519,0.08379063],"study_design_scores_gemma":[0.005920759,0.0004020008,0.008860018,0.0063332804,0.0010463875,0.00027029135,0.00061931857,0.9008165,0.00043650108,0.019168451,0.047943648,0.0081828255],"about_ca_topic_score_codex":0.0003019638,"about_ca_topic_score_gemma":0.00089874497,"teacher_disagreement_score":0.82935643,"about_ca_system_score_codex":0.0012227917,"about_ca_system_score_gemma":0.0001576094,"threshold_uncertainty_score":0.999402},"labels":[],"label_agreement":null},{"id":"W4865795","doi":"10.1007/978-3-642-29154-8_42","title":"Policy-Based Personalized Context Dissemination for Location-Aware Services","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Negotiation; Context (archaeology); Computer science; Architecture; Protocol (science); Context awareness; Tracking (education); Internet privacy; World Wide Web; Knowledge management; Business; Human–computer interaction; Political science; Psychology; Geography; Medicine","score_opus":0.025194102034647916,"score_gpt":0.2668050341849883,"score_spread":0.24161093215034038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4865795","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000057052916,0.0005098387,0.99556166,0.0018701759,0.00046273772,0.00097297976,0.00015405982,0.0000857613,0.00032570635],"genre_scores_gemma":[0.2508593,0.00004306138,0.7477495,0.00048603566,0.0003561907,0.0001668916,0.00018276396,0.000028600003,0.00012768447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986982,0.000015576683,0.00058630266,0.00017333125,0.00028112446,0.00024549925],"domain_scores_gemma":[0.9973692,0.0008194226,0.0006665509,0.0005592981,0.000524422,0.00006107102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045866947,0.00028488063,0.0003848648,0.00030439472,0.0008548023,0.0002934253,0.0017139866,0.00021616429,9.0705345e-7],"category_scores_gemma":[0.0000688071,0.00024482017,0.00022999982,0.00028304025,0.0002789284,0.0006615049,0.00037201864,0.00021762522,9.301426e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011823982,0.00008340594,0.000019927227,0.0029743607,0.00031413228,5.973221e-8,0.011772877,0.29380643,0.00005229769,0.31623644,0.000197811,0.37453043],"study_design_scores_gemma":[0.0003775111,0.000057626727,0.00003450893,0.00064835604,0.000056053846,0.0000074241475,0.000018559256,0.95412886,0.00019319454,0.0010441386,0.043047786,0.0003859649],"about_ca_topic_score_codex":0.000057189405,"about_ca_topic_score_gemma":0.0001390996,"teacher_disagreement_score":0.6603224,"about_ca_system_score_codex":0.000115617266,"about_ca_system_score_gemma":0.0003280303,"threshold_uncertainty_score":0.9983477},"labels":[],"label_agreement":null},{"id":"W53832915","doi":"","title":"Activity recognition through goal-based segmentation","year":2005,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Probabilistic logic; Computer science; Artificial intelligence; Hidden Markov model; Segmentation; Market segmentation; Activity recognition; Motion (physics); SIGNAL (programming language); Pattern recognition (psychology); Process (computing); Statistical model; Machine learning; Computer vision","score_opus":0.04878537638639163,"score_gpt":0.2887566141442606,"score_spread":0.239971237757869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W53832915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075966164,0.000012193538,0.90427846,0.0030194027,0.00027127733,0.00031040792,0.000006012909,0.00051853724,0.0156175345],"genre_scores_gemma":[0.93084264,0.000002823739,0.067037,0.0014253476,0.00014659014,0.0000685798,0.000012128836,0.000009324883,0.0004555344],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986854,0.00015698373,0.00020267021,0.00040796582,0.00032250426,0.00022448192],"domain_scores_gemma":[0.99908626,0.00019660524,0.00013218452,0.00037941174,0.000140241,0.00006527791],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00026746225,0.0001450736,0.00014940613,0.00008849786,0.00014058879,0.00019703411,0.00028413255,0.00007147899,0.00027390805],"category_scores_gemma":[0.0000333135,0.0001431998,0.00008393519,0.00034128383,0.00002289306,0.0026961742,0.000060923754,0.00011084908,0.0014688653],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011369271,0.00017644561,0.00017220766,0.000010611659,0.000013007396,0.0000020284401,0.00025996333,0.000038288294,0.011813111,0.00023974941,0.0014827818,0.9857804],"study_design_scores_gemma":[0.002538464,0.0002240559,0.004042249,0.000076114724,0.00002102644,0.00004808865,0.000118434014,0.14941727,0.81496996,0.002537527,0.025148414,0.0008583842],"about_ca_topic_score_codex":0.00014188168,"about_ca_topic_score_gemma":0.00017875449,"teacher_disagreement_score":0.98492205,"about_ca_system_score_codex":0.00015146474,"about_ca_system_score_gemma":0.00007641289,"threshold_uncertainty_score":0.9993086},"labels":[],"label_agreement":null},{"id":"W56705335","doi":"10.1007/3-540-47922-8_34","title":"Supporting the Needs of Mobile Home Care Workers: A Case Study for Saskatoon District Health System","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Schedule; Presentation (obstetrics); Context (archaeology); Plan (archaeology); Mobile device; Architecture; Health care; Information system; Knowledge management; World Wide Web; Medicine","score_opus":0.03192134223443572,"score_gpt":0.29212924607417323,"score_spread":0.2602079038397375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W56705335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049629104,0.0012929412,0.9874563,0.00019081353,0.0017342514,0.0038581684,0.000051202453,0.00019835371,0.0002550452],"genre_scores_gemma":[0.9902975,0.0000034768327,0.008864214,0.00017006497,0.00030421434,0.00019057286,0.0000046822556,0.00004018955,0.00012510426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99550897,0.00018126311,0.0012717331,0.0011841739,0.0010374853,0.00081637234],"domain_scores_gemma":[0.9943614,0.0016104131,0.0013534481,0.0018761371,0.00060217,0.00019643053],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002117139,0.0005764338,0.0010704678,0.00091589923,0.00059436244,0.0006038858,0.0026902747,0.00020579534,0.000004868182],"category_scores_gemma":[0.00006001623,0.00044464137,0.00028552566,0.001272777,0.0003692774,0.0005440883,0.0010010931,0.0006126992,0.000007553885],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074048867,0.00010245384,0.0008596061,0.0005853486,0.000045070243,0.00036943756,0.040948343,0.002642304,0.0000032232,0.00026943837,0.00005527033,0.9541121],"study_design_scores_gemma":[0.0063962922,0.013205061,0.0005138674,0.010984468,0.00025159528,0.02001426,0.009443072,0.922992,0.0004651295,0.003073788,0.006785357,0.0058751255],"about_ca_topic_score_codex":0.0005474838,"about_ca_topic_score_gemma":0.0007174652,"teacher_disagreement_score":0.9853346,"about_ca_system_score_codex":0.0008030235,"about_ca_system_score_gemma":0.0008020388,"threshold_uncertainty_score":0.99980056},"labels":[],"label_agreement":null},{"id":"W579505347","doi":"10.1007/s12652-015-0281-z","title":"The behavioral profiling based on times series forecasting for smart homes assistance","year":2015,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Profiling (computer programming); Computer science; Software; Computational intelligence; Machine learning; Cognition; Artificial intelligence; Time series; Psychology","score_opus":0.13262184421403345,"score_gpt":0.320474199967393,"score_spread":0.18785235575335957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W579505347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10432269,0.00045037005,0.89262635,0.00067747815,0.0012576756,0.00034770902,0.000001732877,0.000049717855,0.00026630552],"genre_scores_gemma":[0.9626842,0.000010217497,0.03682249,0.00010530718,0.00025544578,0.000005701156,7.5449736e-7,0.000011714954,0.000104172104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816746,0.00015358615,0.000656257,0.00022821984,0.0004780989,0.00031641184],"domain_scores_gemma":[0.9969732,0.0009625531,0.0007336625,0.00021777024,0.00096659176,0.0001462124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002598962,0.00016148294,0.00029593022,0.00015937019,0.0007123692,0.00080335804,0.0005853549,0.00004795543,0.0000014055614],"category_scores_gemma":[0.00043036084,0.00012120073,0.00013325561,0.0001825676,0.00009487616,0.0006061389,0.00011083522,0.00024022227,0.0000020681532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008485309,0.00047047556,0.0074453284,0.00014112625,0.00012988836,0.000095719784,0.009955507,0.003473927,0.00047920845,0.049330227,0.0011661134,0.92646396],"study_design_scores_gemma":[0.0019732832,0.0046808985,0.00053265906,0.0018358915,0.000087235974,0.0005339138,0.008882399,0.9243416,0.025633594,0.016807701,0.013815612,0.00087516574],"about_ca_topic_score_codex":0.000011305327,"about_ca_topic_score_gemma":0.000026152997,"teacher_disagreement_score":0.9255888,"about_ca_system_score_codex":0.00011409369,"about_ca_system_score_gemma":0.00024432308,"threshold_uncertainty_score":0.77467996},"labels":[],"label_agreement":null},{"id":"W652762210","doi":"10.1007/978-3-642-21535-3","title":"Toward useful services for elderly and people with disabilities : 9th International Conference on Smart Homes and Health Telematics, ICOST 2011, Montreal, Canada, June 20-22, 2011: proceedings","year":2011,"lang":"en","type":"book","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Telematics; Health care; Gerontology; Library science; Telecommunications; Engineering; Medicine; Political science; Computer science","score_opus":0.047850228472651093,"score_gpt":0.24832458489232992,"score_spread":0.2004743564196788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W652762210","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034402616,0.0048560724,0.07029838,0.11079527,0.013995665,0.02409821,0.0066224756,0.0020801006,0.7328512],"genre_scores_gemma":[0.12441104,0.0006664254,0.0043499814,0.0013633119,0.00037755162,0.0005583158,0.00031226242,0.000114076574,0.867847],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973637,0.000029012313,0.0005730418,0.00096885406,0.00062726106,0.0004381419],"domain_scores_gemma":[0.99766725,0.00033769308,0.0006036711,0.0003633589,0.00071583025,0.00031221536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003729925,0.0005382802,0.00087968557,0.00020034006,0.00019493498,0.0006090989,0.00076671253,0.00016957993,0.00022166468],"category_scores_gemma":[0.00001537401,0.00043655088,0.00005729273,0.000041877498,0.0001254115,0.0008715608,0.00027411256,0.00025316008,0.000015424483],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020116687,0.00030518838,0.017193416,0.0073652733,0.0007039995,0.000006888246,0.015476618,4.247078e-7,0.000002282517,0.028690007,0.8773691,0.052685626],"study_design_scores_gemma":[0.011009425,0.008934386,0.046938833,0.011423754,0.0003945353,0.0010362355,0.025983458,0.020692589,0.00015710593,0.052990094,0.81248564,0.007953943],"about_ca_topic_score_codex":0.34692237,"about_ca_topic_score_gemma":0.916541,"teacher_disagreement_score":0.5696186,"about_ca_system_score_codex":0.00033669343,"about_ca_system_score_gemma":0.0018273598,"threshold_uncertainty_score":0.9998086},"labels":[],"label_agreement":null},{"id":"W65505258","doi":"","title":"Assessment of Ubiquitous Healthcare Information Systems Benefits","year":2010,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Context-Aware Activity Recognition Systems","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":"Health care; Ubiquitous computing; Context (archaeology); Computer science; Information system; Decision support system; Order (exchange); Healthcare system; Risk analysis (engineering); Knowledge management; Human–computer interaction; Business; Engineering; Data mining","score_opus":0.016874744249430532,"score_gpt":0.27278898856499545,"score_spread":0.2559142443155649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W65505258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18903846,0.00041853116,0.67737526,0.007573467,0.09617913,0.008297647,0.0010230439,0.00035562975,0.019738851],"genre_scores_gemma":[0.9987072,0.000009185843,0.000637954,0.00014185088,0.00031131363,0.00005595099,0.000015375419,0.0000064368173,0.000114776354],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955451,0.00025214851,0.002310935,0.00007798574,0.0015618359,0.00025197642],"domain_scores_gemma":[0.98533607,0.0005368488,0.008643728,0.00045262175,0.004920655,0.000110093286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004896304,0.00016562296,0.0004968837,0.00047139326,0.00024266212,0.00075988134,0.0009953158,0.00024399234,8.8480135e-7],"category_scores_gemma":[0.0008459116,0.00012479437,0.00030603065,0.00056552625,0.000014344299,0.008380486,0.00010895464,0.00042368833,0.000017576493],"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":[0.000093787145,0.0002799578,0.1175413,0.003296,0.0010990041,5.8185066e-7,0.015251417,0.029900227,0.0015606784,0.7449689,0.026260527,0.059747655],"study_design_scores_gemma":[0.0070814863,0.00088614225,0.18670847,0.0015699647,0.0001842834,0.00066871574,0.005724187,0.16990894,0.0016576633,0.00045347863,0.624157,0.0009996485],"about_ca_topic_score_codex":0.00013495477,"about_ca_topic_score_gemma":0.00002047412,"teacher_disagreement_score":0.8096687,"about_ca_system_score_codex":0.00062257855,"about_ca_system_score_gemma":0.0005445302,"threshold_uncertainty_score":0.7327553},"labels":[],"label_agreement":null},{"id":"W6888679269","doi":"10.22034/jipm.2024.715230","title":"Dimensions of the Smart Home Ontology for Elderly People","year":2024,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ontology; Dimension (graph theory); Quarter (Canadian coin); Home automation; Elderly people; Population; Field (mathematics); Maturity (psychological)","score_opus":0.23304417301003097,"score_gpt":0.5178515259059667,"score_spread":0.28480735289593573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888679269","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86249495,0.024660202,0.0965254,0.003387581,0.00801833,0.002016326,0.00015359279,0.00019731118,0.0025462913],"genre_scores_gemma":[0.9978241,0.0006601932,0.0005224862,0.0002001711,0.000110153356,0.00010901126,0.0000026155758,0.00002952002,0.0005417589],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99731696,0.00038368782,0.0008284202,0.00053450564,0.00057552237,0.0003608892],"domain_scores_gemma":[0.9962587,0.0016784592,0.00057032146,0.00087084994,0.00046719445,0.00015452829],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001407045,0.00024612516,0.0007419037,0.0005946919,0.00026133828,0.0012496611,0.004421227,0.00011522009,0.00062229665],"category_scores_gemma":[0.00031819893,0.00016836594,0.00040006763,0.001463746,0.00010762932,0.0026548859,0.0015198309,0.00032979302,0.00002503314],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013686094,0.00063411705,0.18846251,0.00071311736,0.00096423004,0.0000457315,0.0027190964,0.00007683798,0.13256519,0.0059296223,0.15088429,0.5168684],"study_design_scores_gemma":[0.0016954688,0.00014048342,0.6404621,0.0029929245,0.00031035286,0.0004053146,0.0002739232,0.008916068,0.059985556,0.07626408,0.20714176,0.0014119529],"about_ca_topic_score_codex":0.0009558549,"about_ca_topic_score_gemma":0.0008158169,"teacher_disagreement_score":0.51545644,"about_ca_system_score_codex":0.000081871076,"about_ca_system_score_gemma":0.00036635695,"threshold_uncertainty_score":0.99978715},"labels":[],"label_agreement":null},{"id":"W6889464145","doi":"10.25549/wpacards-m84485","title":"WPA block face card for household census of 54th, San Pedro, 55th, Main Streets, in Los Angeles County","year":2012,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Census; Block (permutation group theory); Face (sociological concept); Quarter (Canadian coin)","score_opus":0.018764618539225272,"score_gpt":0.1852722503589804,"score_spread":0.16650763181975514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889464145","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004333677,0.00017160836,0.00075203396,0.000045040106,0.00009447217,0.0005325821,0.9937147,0.00010685632,0.00024901167],"genre_scores_gemma":[0.040375996,0.000023155058,0.00035648028,0.000041152973,0.00011754925,0.0000016447997,0.9588581,0.000046888228,0.00017906979],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99799836,0.00010509315,0.00043934377,0.0005531929,0.0004668358,0.00043720452],"domain_scores_gemma":[0.9979907,0.00033436788,0.00061821786,0.00078070065,0.00006894228,0.00020702847],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015683421,0.00039335524,0.0007429082,0.00040262262,0.00007832043,0.00012928846,0.0015257463,0.00037152623,0.000019561148],"category_scores_gemma":[0.000030612606,0.00043912462,0.00033242733,0.00035624363,0.00020253874,0.00091079634,0.0007757437,0.00029609218,0.00008243051],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010683534,0.00028941815,0.0025145225,0.00048501807,0.00008796938,0.000045405734,0.00006010073,0.000010987067,0.0000064037963,0.000006897355,0.99383855,0.0025479093],"study_design_scores_gemma":[0.00077789254,0.000051878607,0.000056433986,0.00032991506,0.00005229942,0.000018261382,0.0011956664,0.00010414685,0.00005752235,0.000084172716,0.9967998,0.00047200476],"about_ca_topic_score_codex":0.00028252436,"about_ca_topic_score_gemma":0.00019998105,"teacher_disagreement_score":0.036042318,"about_ca_system_score_codex":0.00009114531,"about_ca_system_score_gemma":0.00026970662,"threshold_uncertainty_score":0.99980605},"labels":[],"label_agreement":null},{"id":"W6891460299","doi":"10.3897/phytokeys..23806.figures64-68","title":"Figures 64-68 from: Bahls L, Boynton B, Johnston B (2018) Atlas of diatoms (Bacillariophyta) from diverse habitats in remote regions of western Canada. PhytoKeys 105: 1-186. https://doi.org/10.3897/phytokeys.105.23806","year":2018,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Habitat; Atlas (anatomy); Stizostedion; Site selection","score_opus":0.0387957514760856,"score_gpt":0.24155397003592677,"score_spread":0.20275821855984116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891460299","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03740467,0.0012654077,0.024835523,0.0009978766,0.0020698127,0.0028842972,0.0035954597,0.0017810055,0.92516595],"genre_scores_gemma":[0.6000401,0.00080197566,0.0018618657,0.00030123183,0.0015655515,9.0789723e-7,0.0050415033,0.006522654,0.38386425],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99451166,0.0011174583,0.00096343464,0.0013138762,0.0013816954,0.00071188505],"domain_scores_gemma":[0.9953429,0.00019534792,0.0012128252,0.0019755142,0.0009121899,0.00036119157],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006063037,0.00058889494,0.0009765698,0.00085721596,0.0005066478,0.00039402212,0.0032748722,0.0004006971,0.03222313],"category_scores_gemma":[0.00064411515,0.00065340684,0.00018923357,0.0012289016,0.0004052245,0.00054944534,0.002500425,0.0006606707,0.016914796],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007818194,0.00020242676,0.00024097071,0.00022993759,0.00025621202,0.00006557236,0.0012324508,0.000007622179,0.0020068649,0.00008037772,0.973222,0.022377385],"study_design_scores_gemma":[0.0011369193,0.00021965361,0.0039227963,0.001142254,0.000045145363,0.000007540477,0.0001830436,0.00043384644,0.00140009,0.00008660524,0.9908034,0.0006187046],"about_ca_topic_score_codex":0.17130037,"about_ca_topic_score_gemma":0.011760309,"teacher_disagreement_score":0.56263536,"about_ca_system_score_codex":0.00054343964,"about_ca_system_score_gemma":0.00014158101,"threshold_uncertainty_score":0.9995917},"labels":[],"label_agreement":null},{"id":"W6893781940","doi":"10.5281/zenodo.4150024","title":"REGARDER[[La galerie des coeurs brisés (2020)]EN STREAMING FILM FR COMPLET bih","year":2020,"lang":"fr","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Film festival; Kesterite; TRACE (psycholinguistics)","score_opus":0.08515234816556884,"score_gpt":0.2646509348465597,"score_spread":0.1794985866809909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893781940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026776785,0.0035218229,0.5586208,0.0375757,0.0014262955,0.0021969664,0.002048911,0.004811713,0.363021],"genre_scores_gemma":[0.9860832,0.0004878846,0.0023303877,0.0011390032,0.0009321221,1.8802017e-7,0.00083353324,0.0020319838,0.006161696],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99398625,0.0026043495,0.0006147007,0.0011377602,0.0008207293,0.0008362281],"domain_scores_gemma":[0.99657863,0.00030927957,0.00031522562,0.0008119072,0.0012594084,0.0007255215],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012748422,0.00039155313,0.00045055355,0.00020532278,0.0025616067,0.0038648646,0.0025377576,0.00019554875,0.02227917],"category_scores_gemma":[0.0023926622,0.00048792217,0.00016872912,0.0015922817,0.0006569037,0.0014516645,0.0035060882,0.0007888349,0.023981417],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006145597,0.00022014997,0.000022957895,0.00038301802,0.00013573066,0.00026391904,0.0102335885,0.00012673566,0.0021656943,0.0043738587,0.46152282,0.52049005],"study_design_scores_gemma":[0.0008536765,0.0004536056,0.0011047119,0.00026642822,0.000034560526,0.00083278876,0.0011441855,0.017146887,0.00038120066,0.00020208723,0.9770957,0.00048416547],"about_ca_topic_score_codex":0.00018811478,"about_ca_topic_score_gemma":0.000004132001,"teacher_disagreement_score":0.9593064,"about_ca_system_score_codex":0.00029978476,"about_ca_system_score_gemma":0.000032079835,"threshold_uncertainty_score":0.99975723},"labels":[],"label_agreement":null},{"id":"W6901565798","doi":"10.60692/a1m30-67g61","title":"Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Context-Aware Activity Recognition Systems","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":"Dalhousie University","funders":"","keywords":"Wearable computer; Sports biomechanics; Inertial measurement unit; Wearable technology; Electromyography; Accelerometer","score_opus":0.06368017884529296,"score_gpt":0.23821008397816107,"score_spread":0.1745299051328681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901565798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123016596,0.000065866836,0.8742831,0.0002775705,0.00021857976,0.0010022272,0.00014417846,0.00056284963,0.00042907862],"genre_scores_gemma":[0.99647796,0.0000012375173,0.002555266,0.0006677399,0.00014133897,0.000095957024,0.000021641497,0.000010942513,0.000027895396],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841934,0.00005368165,0.00065441604,0.00030401838,0.000336456,0.00023209871],"domain_scores_gemma":[0.99859524,0.000009379484,0.00041842615,0.00046595625,0.0003021944,0.0002088141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025306738,0.00022734642,0.00032862817,0.00025416326,0.00018124474,0.0008448079,0.00034745006,0.00013841335,0.0000034973807],"category_scores_gemma":[0.000011456398,0.00020557814,0.000083718885,0.0010086909,0.00001642561,0.0025160727,0.00022296852,0.00012891757,0.0002446505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021711734,0.0000382713,0.032077514,0.017849592,0.0007126116,0.00010615028,0.8225871,0.00053910125,0.0013590713,0.04522285,0.000924316,0.07836633],"study_design_scores_gemma":[0.002515102,0.00015317192,0.0012583629,0.0015380316,0.00009230377,0.0013331459,0.009252873,0.95830727,0.003105587,0.0000325856,0.021010552,0.0014010272],"about_ca_topic_score_codex":0.00000612624,"about_ca_topic_score_gemma":1.210956e-7,"teacher_disagreement_score":0.95776814,"about_ca_system_score_codex":0.00007209631,"about_ca_system_score_gemma":0.00005247225,"threshold_uncertainty_score":0.8383233},"labels":[],"label_agreement":null},{"id":"W6902456116","doi":"10.6084/m9.figshare.26992798.v1","title":"Additional file 1 of Benefits and barriers associated with the use of smart home health technologies in the care of older persons: a systematic review","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Context-Aware Activity Recognition Systems","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":"Université Laval","funders":"","keywords":"Home automation; Health care; Telecare; Emerging technologies; MEDLINE; Telehealth; Telemedicine","score_opus":0.06066644045181898,"score_gpt":0.25493951820720334,"score_spread":0.19427307775538435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902456116","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000054675584,0.02889928,0.000004506936,0.0005541155,0.000007550148,0.000907218,0.9695004,0.000055683056,0.00001657378],"genre_scores_gemma":[0.7872175,0.00016617739,0.00014946633,0.0006327722,0.000009203253,0.0054638158,0.20625837,0.000026518905,0.00007620067],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.99892956,0.00022279525,0.00027526828,0.00016805077,0.00029968735,0.000104629304],"domain_scores_gemma":[0.9955943,0.0035681094,0.00030437481,0.0003225194,0.00019593413,0.000014735115],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013952672,0.00009283542,0.00035678459,0.00009251469,0.000041251726,0.00004842354,0.0003769146,0.00004330641,0.030715095],"category_scores_gemma":[0.003140904,0.000050610823,0.00006026242,0.0006783547,0.00002579473,0.00022762631,0.000098719334,0.0001198039,0.000010320974],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.4397796e-7,0.000009414336,0.000004009403,0.05902309,0.000044026485,0.000002729643,0.002127449,9.943373e-7,1.5277757e-7,0.000015839916,0.93634063,0.0024310104],"study_design_scores_gemma":[0.00007766647,0.0001187926,0.00057374575,0.9848601,0.000019775855,0.000032321823,0.002758085,0.00044580136,0.000007344666,0.00000567065,0.010999777,0.00010090541],"about_ca_topic_score_codex":0.000013345096,"about_ca_topic_score_gemma":0.00012877885,"teacher_disagreement_score":0.92583704,"about_ca_system_score_codex":0.0000362356,"about_ca_system_score_gemma":0.00021118611,"threshold_uncertainty_score":0.970171},"labels":[],"label_agreement":null},{"id":"W6924509213","doi":"10.15468/dl.k3884s","title":"Occurrence Download","year":2024,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geodetic datum; Coordinate system; Matching (statistics); Download; Range (aeronautics); Geographic coordinate conversion","score_opus":0.020845050215894737,"score_gpt":0.22993813321040607,"score_spread":0.20909308299451135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6924509213","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003692344,0.00005094517,0.0006861505,0.00043327038,0.0024820978,0.000404393,0.99522537,0.0004464902,0.0002343772],"genre_scores_gemma":[0.000055028475,0.000030527288,0.000006001735,0.0005419124,0.0000034279897,0.000009530779,0.9993533,1.7384446e-8,2.6750627e-7],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977387,0.000119681776,0.00052692427,0.00047137652,0.00078214507,0.00036115033],"domain_scores_gemma":[0.9979817,0.000052363477,0.00032326087,0.0010466769,0.000384113,0.00021187548],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045271122,0.00037468658,0.0003739782,0.00021104596,0.00023683628,0.0010052986,0.0016480616,0.00039428682,0.0005155267],"category_scores_gemma":[0.0001308057,0.00038609735,0.00025183015,0.00077886344,0.00010981932,0.0033659802,0.0012765282,0.00046094446,0.7601716],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074373525,0.0000263721,0.000083049905,0.00025642046,0.00004437915,0.000008627409,0.000074702555,0.0000012241284,3.3754844e-8,5.5922817e-7,0.9883623,0.011134893],"study_design_scores_gemma":[0.00018681996,0.00003142455,0.00001567573,0.000011545541,0.000033628014,0.00003173163,0.00005340312,0.0000033132922,0.0000032392688,0.00000613283,0.9992393,0.00038374282],"about_ca_topic_score_codex":0.00039549315,"about_ca_topic_score_gemma":0.000030265399,"teacher_disagreement_score":0.7596561,"about_ca_system_score_codex":0.0004915055,"about_ca_system_score_gemma":0.00032390314,"threshold_uncertainty_score":0.9998591},"labels":[],"label_agreement":null},{"id":"W6931555795","doi":"10.5281/zenodo.6818936","title":"Tamias ruficaudus","year":2016,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Subspecies; Riparian zone; Nest (protein structural motif); Chromosome; Dorsum","score_opus":0.04928718133489342,"score_gpt":0.2419540854753738,"score_spread":0.19266690414048038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931555795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010536711,0.00005932429,0.8122989,0.005981156,0.000408339,0.0005127783,0.00010933,0.003187587,0.16690591],"genre_scores_gemma":[0.99603206,0.000023422861,0.00060614967,0.00018676934,0.00014404627,5.2300994e-8,0.00004997194,0.0005393617,0.0024181874],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981191,0.00038476122,0.00023035605,0.00049983914,0.0004077188,0.00035819152],"domain_scores_gemma":[0.99833405,0.00007724358,0.000109681685,0.00076335104,0.0005233108,0.00019234398],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006862403,0.0001271013,0.00013666174,0.00022205329,0.0011498291,0.00091229007,0.0018437599,0.00005677958,0.0049781064],"category_scores_gemma":[0.0005943537,0.00010449892,0.00005937485,0.000549111,0.00010129685,0.000856492,0.0015216629,0.00012860907,0.03321632],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000111926765,0.00007578027,0.0000048008183,0.000012200559,0.000022843391,0.000016735876,0.00044560235,7.3941186e-7,0.013131408,0.013366757,0.11161247,0.86129946],"study_design_scores_gemma":[0.00047074317,0.00011290456,0.00051409804,0.000038853173,0.000002890254,0.00020388592,0.000040049294,0.00019093209,0.0018942149,0.0005500536,0.9958069,0.00017449411],"about_ca_topic_score_codex":0.000007788292,"about_ca_topic_score_gemma":2.5209118e-7,"teacher_disagreement_score":0.9854953,"about_ca_system_score_codex":0.00015044198,"about_ca_system_score_gemma":0.000004802445,"threshold_uncertainty_score":0.9959315},"labels":[],"label_agreement":null},{"id":"W6938967979","doi":"10.60692/x10g3-pv785","title":"Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Context-Aware Activity Recognition Systems","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":"Dalhousie University","funders":"","keywords":"Wearable computer; Sports biomechanics; Inertial measurement unit; Wearable technology; Electromyography; Accelerometer","score_opus":0.06368017884529296,"score_gpt":0.23821008397816107,"score_spread":0.1745299051328681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6938967979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123016596,0.000065866836,0.8742831,0.0002775705,0.00021857976,0.0010022272,0.00014417846,0.00056284963,0.00042907862],"genre_scores_gemma":[0.99647796,0.0000012375173,0.002555266,0.0006677399,0.00014133897,0.000095957024,0.000021641497,0.000010942513,0.000027895396],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841934,0.00005368165,0.00065441604,0.00030401838,0.000336456,0.00023209871],"domain_scores_gemma":[0.99859524,0.000009379484,0.00041842615,0.00046595625,0.0003021944,0.0002088141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025306738,0.00022734642,0.00032862817,0.00025416326,0.00018124474,0.0008448079,0.00034745006,0.00013841335,0.0000034973807],"category_scores_gemma":[0.000011456398,0.00020557814,0.000083718885,0.0010086909,0.00001642561,0.0025160727,0.00022296852,0.00012891757,0.0002446505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021711734,0.0000382713,0.032077514,0.017849592,0.0007126116,0.00010615028,0.8225871,0.00053910125,0.0013590713,0.04522285,0.000924316,0.07836633],"study_design_scores_gemma":[0.002515102,0.00015317192,0.0012583629,0.0015380316,0.00009230377,0.0013331459,0.009252873,0.95830727,0.003105587,0.0000325856,0.021010552,0.0014010272],"about_ca_topic_score_codex":0.00000612624,"about_ca_topic_score_gemma":1.210956e-7,"teacher_disagreement_score":0.95776814,"about_ca_system_score_codex":0.00007209631,"about_ca_system_score_gemma":0.00005247225,"threshold_uncertainty_score":0.8383233},"labels":[],"label_agreement":null},{"id":"W6958052491","doi":"10.6068/dp14ba8b84bc839","title":"Trend 2002 - 2009. Statistics Canada. CANSIM: Education, Training and Learning - Fields of Study | Country: Canada | Table: College enrolments, by registration status, program level, Classification of Instructional Programs, Primary Grouping (CIP_PG) and sex | Variable: Mathematics, computer and information sciences, College post-diploma program, Females, Part-time student | Units: #, 2002-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-071.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Statistics education; Census; Economic statistics; Statistical analysis; Publication; Socioeconomic status; Official statistics","score_opus":0.04754096861746865,"score_gpt":0.27399789471592,"score_spread":0.22645692609845136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958052491","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000027429822,0.00080483593,0.0004166341,0.0000066014386,0.00029628593,0.002663605,0.9948501,0.00008897942,0.0008455272],"genre_scores_gemma":[0.00021516166,0.00016047244,0.0082011875,0.000055463464,0.00007399116,0.00012215087,0.9904725,0.00004580827,0.00065326464],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99548304,0.00049849396,0.0012978341,0.0008537682,0.0013893374,0.0004775427],"domain_scores_gemma":[0.99624974,0.0004872425,0.0016874098,0.0009264685,0.00032703922,0.000322104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012400417,0.00047995392,0.00082325237,0.00018697552,0.0002864823,0.00051906396,0.0009262937,0.00024987306,0.00008020899],"category_scores_gemma":[0.00008663534,0.00048957486,3.5878801e-7,0.0005496072,0.00032614704,0.0013718356,0.0004923996,0.00045708127,0.000001345566],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017431477,0.00055915734,0.00013756898,0.0007710863,0.00020247806,0.000008788593,0.00007269988,0.000004542764,0.0000013138545,0.00085515814,0.976402,0.020967769],"study_design_scores_gemma":[0.00088449806,0.00064168515,0.00011850418,0.00013862531,0.000133239,0.00016246302,0.002485347,0.012722015,2.04614e-8,5.432896e-7,0.9822537,0.00045939244],"about_ca_topic_score_codex":0.96049017,"about_ca_topic_score_gemma":0.9039493,"teacher_disagreement_score":0.056540854,"about_ca_system_score_codex":0.00019213298,"about_ca_system_score_gemma":0.011933857,"threshold_uncertainty_score":0.99975556},"labels":[],"label_agreement":null},{"id":"W6962746460","doi":"10.17605/osf.io/uby45","title":"Real time location system techonolgy and older adults with cognitive impairment","year":2021,"lang":"en","type":"other","venue":"Open Science Framework","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dementia; Cognitive impairment; Cognition; Activities of daily living; Staffing; Memory impairment; Cognitive decline; Independent living","score_opus":0.010751460728764282,"score_gpt":0.2722801073529351,"score_spread":0.26152864662417086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6962746460","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022872614,0.00081036997,0.37004566,0.00052496913,0.0014960187,0.0065472913,0.000061365405,0.0008859053,0.61734116],"genre_scores_gemma":[0.6696005,0.00012509474,0.11118418,0.0004565135,0.00067060936,0.001342649,0.000063740925,0.00044007797,0.21611662],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9962434,0.00017826371,0.00031563986,0.0017520864,0.000992845,0.0005177727],"domain_scores_gemma":[0.9970956,0.00026342014,0.0005164027,0.0012984715,0.00053553283,0.0002906108],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009961578,0.0003965069,0.00058758474,0.00041435892,0.0003209303,0.0022468676,0.002851505,0.00032882986,0.00032305566],"category_scores_gemma":[0.00014037896,0.0003375604,0.000038954273,0.002396464,0.00046674896,0.0009048472,0.0018053008,0.00037541476,0.0003163718],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032958112,0.0015026386,0.0026269266,0.0032795535,0.0006778047,0.00084955886,0.020815482,0.0000073199803,0.00054534734,0.023377283,0.07492815,0.8710604],"study_design_scores_gemma":[0.019463968,0.0054641673,0.045301143,0.70439726,0.0007287867,0.006083821,0.039503425,0.047316965,0.0064155175,0.0013953446,0.10552697,0.01840262],"about_ca_topic_score_codex":0.001332079,"about_ca_topic_score_gemma":0.00014466907,"teacher_disagreement_score":0.85265774,"about_ca_system_score_codex":0.0002734586,"about_ca_system_score_gemma":0.0011411078,"threshold_uncertainty_score":0.9999076},"labels":[],"label_agreement":null},{"id":"W6977108981","doi":"10.6084/m9.figshare.26992798","title":"Additional file 1 of Benefits and barriers associated with the use of smart home health technologies in the care of older persons: a systematic review","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Context-Aware Activity Recognition Systems","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":"Université Laval","funders":"","keywords":"Home automation; Health care; Telecare; Emerging technologies; MEDLINE; Telehealth; Telemedicine","score_opus":0.06066644045181898,"score_gpt":0.25493951820720334,"score_spread":0.19427307775538435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977108981","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000054675584,0.02889928,0.000004506936,0.0005541155,0.000007550148,0.000907218,0.9695004,0.000055683056,0.00001657378],"genre_scores_gemma":[0.7872175,0.00016617739,0.00014946633,0.0006327722,0.000009203253,0.0054638158,0.20625837,0.000026518905,0.00007620067],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.99892956,0.00022279525,0.00027526828,0.00016805077,0.00029968735,0.000104629304],"domain_scores_gemma":[0.9955943,0.0035681094,0.00030437481,0.0003225194,0.00019593413,0.000014735115],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013952672,0.00009283542,0.00035678459,0.00009251469,0.000041251726,0.00004842354,0.0003769146,0.00004330641,0.030715095],"category_scores_gemma":[0.003140904,0.000050610823,0.00006026242,0.0006783547,0.00002579473,0.00022762631,0.000098719334,0.0001198039,0.000010320974],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.4397796e-7,0.000009414336,0.000004009403,0.05902309,0.000044026485,0.000002729643,0.002127449,9.943373e-7,1.5277757e-7,0.000015839916,0.93634063,0.0024310104],"study_design_scores_gemma":[0.00007766647,0.0001187926,0.00057374575,0.9848601,0.000019775855,0.000032321823,0.002758085,0.00044580136,0.000007344666,0.00000567065,0.010999777,0.00010090541],"about_ca_topic_score_codex":0.000013345096,"about_ca_topic_score_gemma":0.00012877885,"teacher_disagreement_score":0.92583704,"about_ca_system_score_codex":0.0000362356,"about_ca_system_score_gemma":0.00021118611,"threshold_uncertainty_score":0.970171},"labels":[],"label_agreement":null},{"id":"W6990114535","doi":"","title":"Context-Aware Multisensory Monitoring for Aging-in-Place Applications: An MQTT-Based Indoor Positioning System","year":2025,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"MQTT; Scalability; Smartwatch; Wearable computer; Wearable technology; Beacon; Key (lock); Testbed; Data transmission; Wireless sensor network","score_opus":0.022567781478448512,"score_gpt":0.25066140751912797,"score_spread":0.22809362604067945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990114535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066328696,0.0005344014,0.9114078,0.00020517826,0.0026092578,0.0056028,0.00030353584,0.0014438033,0.071260326],"genre_scores_gemma":[0.79355437,0.000010119496,0.0075402,0.00010537045,0.000312612,0.0003204856,0.00070212927,0.00009426526,0.19736046],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99693674,0.00034537062,0.0005285905,0.0012827698,0.00038482132,0.00052171975],"domain_scores_gemma":[0.9973675,0.00051638647,0.0005543216,0.0007976485,0.00057653827,0.0001876203],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027383465,0.0005434167,0.0006903911,0.00091171276,0.00046018756,0.0004078456,0.0013856612,0.0004788975,0.00022933456],"category_scores_gemma":[0.000020401272,0.000725028,0.00026360768,0.0011394735,0.000027712895,0.00111859,0.00011976699,0.00047868473,0.000041575746],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033695635,0.00026855728,0.0018128473,0.0028727162,0.00019522513,0.00013592289,0.0014114435,0.0006291266,0.000594435,0.0038441413,0.0001419757,0.98775667],"study_design_scores_gemma":[0.02553834,0.0009188557,0.0050599417,0.025325244,0.0010775983,0.0000793238,0.1122254,0.22148138,0.03172449,0.00021022411,0.5685624,0.007796833],"about_ca_topic_score_codex":0.0005085989,"about_ca_topic_score_gemma":0.0022934787,"teacher_disagreement_score":0.97995985,"about_ca_system_score_codex":0.0010955079,"about_ca_system_score_gemma":0.00051472575,"threshold_uncertainty_score":0.99952006},"labels":[],"label_agreement":null},{"id":"W6991710005","doi":"","title":"Indoor human activity monitoring for senior well-being using commodity WiFi devices","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"McGill University","funders":"","keywords":"Activity recognition; Leverage (statistics); Channel state information; Convolutional neural network; Deep learning; Transmitter; Wireless; Key (lock); Feature (linguistics)","score_opus":0.04389448402592717,"score_gpt":0.3008838391156975,"score_spread":0.25698935508977033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991710005","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97454125,0.00008562189,0.000293588,0.000014170342,0.006391679,0.002360277,0.000462612,0.0007177768,0.015132999],"genre_scores_gemma":[0.99290055,0.000011370642,0.0029466373,0.00007295222,0.00029481374,0.0003103369,0.00026281286,0.00024171219,0.0029588407],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99308985,0.00088862673,0.0012042304,0.002245065,0.0013380666,0.0012341817],"domain_scores_gemma":[0.9937106,0.001151329,0.0017951338,0.0018606032,0.0010606454,0.00042172454],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0020621205,0.0012306923,0.0015671712,0.0009917303,0.0027655032,0.000855383,0.002396697,0.0011654914,0.000028043602],"category_scores_gemma":[0.00039213485,0.0014408259,0.0007835982,0.0010645068,0.00005196461,0.004560209,0.00054212956,0.001978169,0.0002385614],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029575123,0.00071720127,0.0018257555,0.0031025351,0.00090558664,0.00007094228,0.00015634907,0.00006908447,0.40829363,0.023698634,0.0000073187734,0.56085724],"study_design_scores_gemma":[0.0045179883,0.00055683294,0.010329972,0.005107664,0.0006869128,0.00015737253,0.0008356137,0.0049213497,0.92015964,0.0124151,0.034319483,0.005992089],"about_ca_topic_score_codex":0.001030059,"about_ca_topic_score_gemma":0.0011819414,"teacher_disagreement_score":0.5548651,"about_ca_system_score_codex":0.0014946221,"about_ca_system_score_gemma":0.00017677707,"threshold_uncertainty_score":0.9988041},"labels":[],"label_agreement":null},{"id":"W6996301382","doi":"","title":"Reconnaissance d'activités et reconnaissance d'objets par affordance pour la reconnaissance de plan d'un robot assistant cognitif","year":2019,"lang":"fr","type":"other","venue":"Archipelago (University of Quebec in Montreal)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Population; Affordance","score_opus":0.04419751366723267,"score_gpt":0.23421864929870348,"score_spread":0.1900211356314708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996301382","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22344218,0.004267859,0.30870336,0.009681274,0.0026262528,0.0036380067,0.0010179215,0.0005354828,0.44608766],"genre_scores_gemma":[0.8175119,0.001067445,0.013317481,0.00036539588,0.00019006993,0.000020504367,0.000060488826,0.00024012737,0.1672266],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98914975,0.0046436368,0.0011201699,0.002587518,0.000803193,0.0016957554],"domain_scores_gemma":[0.9898978,0.0044982065,0.002395797,0.0021732622,0.00043251622,0.00060240255],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0032735239,0.0013389232,0.0024892755,0.0010556759,0.0004147395,0.00027773372,0.0030555942,0.0011081327,0.0013873624],"category_scores_gemma":[0.00060648686,0.0018246276,0.0007487194,0.0011041701,0.001291553,0.0020605274,0.0008577746,0.002090208,0.0011468991],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009817579,0.0011115783,0.009054875,0.001063639,0.0005912583,0.0016178685,0.03280723,0.00033130555,0.0012289191,0.009080772,0.0075974907,0.9345333],"study_design_scores_gemma":[0.015109841,0.00082296366,0.7752992,0.02496734,0.0005225251,0.0014430794,0.015885087,0.030525248,0.0019798637,0.032772746,0.093914516,0.00675759],"about_ca_topic_score_codex":0.18586183,"about_ca_topic_score_gemma":0.84911233,"teacher_disagreement_score":0.92777574,"about_ca_system_score_codex":0.0007275541,"about_ca_system_score_gemma":0.0017886662,"threshold_uncertainty_score":0.99993616},"labels":[],"label_agreement":null},{"id":"W7007815688","doi":"","title":"An abstract spatial device for human behavior models, user performance data collection and game control user interface","year":2011,"lang":"en","type":"article","venue":"NPARC","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"User interface; Interface (matter); Decoupling (probability); Game development tool; Game design; Control (management); Natural user interface; Perception","score_opus":0.13230263484739488,"score_gpt":0.31423197844528267,"score_spread":0.1819293435978878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7007815688","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54387814,0.000008534968,0.45469913,0.000034054112,0.00020212712,0.0005420217,0.000038863967,0.000102105805,0.0004950212],"genre_scores_gemma":[0.9929739,0.0000034179216,0.0064781094,0.000078166115,0.000083084975,0.00016315674,0.000020165737,0.000019192852,0.0001807742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862194,0.00006231375,0.00028487484,0.0005805929,0.00019641628,0.00025384672],"domain_scores_gemma":[0.9985694,0.00007436084,0.00015814143,0.00089788204,0.0001823544,0.00011781543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004288934,0.00016761014,0.00021990109,0.00009661518,0.00019168413,0.00019854502,0.0008517772,0.00009002416,0.000068801826],"category_scores_gemma":[0.000015747844,0.00016894411,0.00003065868,0.00010428287,0.00004321483,0.0028368155,0.00018035996,0.00013740195,0.00001655493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078537664,0.0025407595,0.05801351,0.00036266106,0.00028917723,0.000026225518,0.0122915935,0.00013045786,0.21831024,0.0022732082,0.0049824123,0.6999944],"study_design_scores_gemma":[0.0034966979,0.000910818,0.09810774,0.00010048108,0.00010097458,0.000085571235,0.00012244009,0.8744136,0.01768043,0.00093870773,0.0033214411,0.000721081],"about_ca_topic_score_codex":0.0005150026,"about_ca_topic_score_gemma":0.0007994216,"teacher_disagreement_score":0.87428313,"about_ca_system_score_codex":0.00004356304,"about_ca_system_score_gemma":0.000058715297,"threshold_uncertainty_score":0.6889341},"labels":[],"label_agreement":null},{"id":"W7009059082","doi":"","title":"Development of a Context-aware Telemedicine Framework and it's use in Promoting Safe Aging in Place through Wearable and Smart Home Technology","year":2023,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; AGE-WELL; McMaster University","keywords":"Leverage (statistics); Context (archaeology); Telemedicine; Wearable computer; Wearable technology; Phone; Work (physics); Aging in place","score_opus":0.02483202300878933,"score_gpt":0.2399325909934628,"score_spread":0.21510056798467347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009059082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97761965,0.0005182206,0.013337313,0.0007083239,0.00046253554,0.0011587025,0.000010367088,0.00023347416,0.005951405],"genre_scores_gemma":[0.9366969,0.00013023896,0.012174393,0.00006598266,0.000022879161,0.000014214392,0.000029538243,0.00004920625,0.05081666],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99798924,0.00012348237,0.0004909713,0.00075241725,0.00026267226,0.00038119627],"domain_scores_gemma":[0.9987907,0.00037329318,0.00036283245,0.00028564676,0.000119185876,0.00006834272],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031246926,0.00032041362,0.0006571387,0.00089663145,0.00010718527,0.00009011834,0.0004603985,0.00042933974,0.00023558647],"category_scores_gemma":[0.00009525774,0.00037629734,0.000034866163,0.0018437633,0.000075770775,0.00081553333,0.00039513432,0.0006276584,0.000008469432],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100602025,0.00008913894,0.06701123,0.00086107076,0.00009911638,0.00037891892,0.039225016,0.0000062425866,0.00026686475,0.0005733441,0.00003640554,0.89135206],"study_design_scores_gemma":[0.018150803,0.00094910606,0.17976676,0.08384002,0.00024525408,0.00024281876,0.48652798,0.012160935,0.006192633,0.002866467,0.20341484,0.005642381],"about_ca_topic_score_codex":0.0006889849,"about_ca_topic_score_gemma":0.033772517,"teacher_disagreement_score":0.88570964,"about_ca_system_score_codex":0.00020330794,"about_ca_system_score_gemma":0.00024196348,"threshold_uncertainty_score":0.99986887},"labels":[],"label_agreement":null},{"id":"W7015353066","doi":"","title":"Smart home technologies support independent living","year":2011,"lang":"en","type":"article","venue":"NPARC","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Home automation; Mode (computer interface); Key (lock); Information technology","score_opus":0.04376150765650552,"score_gpt":0.23222967010131332,"score_spread":0.1884681624448078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7015353066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14957505,0.000057639456,0.5663023,0.0010411866,0.0018897464,0.00051867065,0.0000072175226,0.004048985,0.27655917],"genre_scores_gemma":[0.9913584,0.0000061684395,0.007733522,0.00008870889,0.000022936294,0.000046681307,4.7695374e-7,0.000009618319,0.00073350774],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987444,0.000056913737,0.00021529524,0.00038273953,0.00029865553,0.000301981],"domain_scores_gemma":[0.99896204,0.00011105459,0.0001027721,0.0006791425,0.00008833085,0.000056630437],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040485678,0.00013131321,0.0001794566,0.00019174482,0.00008603798,0.00010278754,0.0009974121,0.000109011824,0.0003618279],"category_scores_gemma":[0.00009683634,0.00012573792,0.0000674956,0.00028582112,0.0000533862,0.00067163515,0.000629464,0.00018781093,0.00095505494],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054007646,0.0002301496,0.04123166,0.00003060302,0.00005632792,0.00012600384,0.0043174084,8.993648e-8,0.008190061,0.014543387,0.0040587736,0.92721015],"study_design_scores_gemma":[0.0021978663,0.0017996326,0.36597514,0.0007564872,0.000074257936,0.002266388,0.006189821,0.003503715,0.30974895,0.23363078,0.06952149,0.00433547],"about_ca_topic_score_codex":0.000033566368,"about_ca_topic_score_gemma":0.000036054145,"teacher_disagreement_score":0.9228747,"about_ca_system_score_codex":0.000048200025,"about_ca_system_score_gemma":0.00006831875,"threshold_uncertainty_score":0.9998228},"labels":[],"label_agreement":null},{"id":"W7022465014","doi":"","title":"Research Grant","year":2013,"lang":"en","type":"other","venue":"OpenEdition (OpenEdition)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Closing (real estate); Carry (investment)","score_opus":0.06354586617181678,"score_gpt":0.3104606443534109,"score_spread":0.24691477818159413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7022465014","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008935872,0.00097233715,0.083561376,0.0150924465,0.0052940543,0.0024960863,0.00036754698,0.0012755501,0.89093167],"genre_scores_gemma":[0.007956286,0.0003276426,0.0044271313,0.007869049,0.0029249603,0.0025837615,0.0009995897,0.0005483798,0.9723632],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9928916,0.0011547758,0.00081041706,0.0017137608,0.002370119,0.0010593332],"domain_scores_gemma":[0.995146,0.00055329065,0.0006325317,0.0022315457,0.0009180245,0.0005186513],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017386901,0.00069677195,0.0008824006,0.0016722215,0.0005477649,0.0016263408,0.002907755,0.0007906393,0.024853546],"category_scores_gemma":[0.0001980105,0.00070375815,0.00029255947,0.0014657858,0.00031401636,0.012507058,0.0010609594,0.0011427008,0.0650858],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005482871,0.0001718631,0.000004480786,0.000093641785,0.0000833241,0.000059559596,0.000037679656,9.205308e-7,0.00005707754,0.09150312,0.88775456,0.020228311],"study_design_scores_gemma":[0.000780162,0.0001584469,0.00036530357,0.0008259457,0.000020374013,0.00011690064,0.00007370684,0.0002987896,0.00035663563,0.0021215137,0.99402684,0.00085536763],"about_ca_topic_score_codex":0.0013844335,"about_ca_topic_score_gemma":0.0027439215,"teacher_disagreement_score":0.10627232,"about_ca_system_score_codex":0.00033579438,"about_ca_system_score_gemma":0.0005017671,"threshold_uncertainty_score":0.99954134},"labels":[],"label_agreement":null},{"id":"W7024148998","doi":"","title":"Science et fiction dans trois récits de voyage sur la Lune","year":2012,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Clothing; Context (archaeology); White (mutation); Narrative","score_opus":0.007039240969443914,"score_gpt":0.1638152175231202,"score_spread":0.15677597655367628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024148998","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062283156,0.0007575438,0.015097965,0.004911103,0.0012854255,0.00034349266,0.00036657765,0.00003511862,0.97097445],"genre_scores_gemma":[0.6014326,0.0001451196,0.0013754012,0.00087357906,0.00017264226,0.000013276666,0.000004937238,0.000059006863,0.3959234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951894,0.00038848817,0.0004332523,0.0006388015,0.0026142006,0.0007358207],"domain_scores_gemma":[0.9974766,0.0008479339,0.00047423207,0.00051929627,0.0000030556482,0.00067893293],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000110238754,0.00041703956,0.00048213705,0.00012733588,0.00032068163,0.00022345275,0.0009264977,0.00009504419,0.00035361297],"category_scores_gemma":[0.000022532397,0.00044478444,0.00006654223,0.0003998587,0.00041441404,0.0020526326,0.0005249745,0.00036806805,6.2654536e-8],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001804516,0.00028037492,0.042698987,0.00091156195,0.00024178474,0.000354524,0.0021881915,0.000078241355,0.06567831,0.6630086,0.013070898,0.21130808],"study_design_scores_gemma":[0.00053431425,0.00011379015,0.12145602,0.000867886,0.000052373987,0.00017687511,0.0009066441,0.009128171,0.03342453,0.00031820007,0.8322515,0.00076969847],"about_ca_topic_score_codex":0.02337139,"about_ca_topic_score_gemma":0.10563807,"teacher_disagreement_score":0.8191806,"about_ca_system_score_codex":0.000069171445,"about_ca_system_score_gemma":0.01040463,"threshold_uncertainty_score":0.9998004},"labels":[],"label_agreement":null},{"id":"W7039492575","doi":"","title":"Modèle d'assistances pédagogiques complémentaires : rapport de recherche présenté à l'Université du Québec à Trois-Rivières /","year":2015,"lang":"fr","type":"other","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Subject (documents); Context (archaeology); Identification (biology); Government (linguistics)","score_opus":0.09314513432601915,"score_gpt":0.27894270287446216,"score_spread":0.185797568548443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039492575","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03177275,0.025309863,0.08869295,0.10280442,0.0011111133,0.0023742751,0.0010996435,0.0010883948,0.7457466],"genre_scores_gemma":[0.42250326,0.0016318546,0.0069433697,0.0039225193,0.0012336691,0.00037233086,0.00009437,0.00040349623,0.5628951],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99002945,0.003212327,0.0013183989,0.0018225984,0.0023766716,0.0012405287],"domain_scores_gemma":[0.98054636,0.015175668,0.001536988,0.0012018301,0.0004310549,0.0011080868],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.002596585,0.001365189,0.0013459251,0.0020784535,0.00064936385,0.0015157912,0.003281284,0.0005782271,0.010940958],"category_scores_gemma":[0.0042984425,0.0014176597,0.0007328688,0.0015510954,0.0009490658,0.0021449781,0.0013422237,0.0014305497,0.0017268591],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00031105598,0.00124854,0.08789618,0.00039562676,0.0014875361,0.00025449725,0.033973247,0.0001937598,0.0006299912,0.010838503,0.8458707,0.016900355],"study_design_scores_gemma":[0.0016579404,0.0003929998,0.03588623,0.00078362494,0.00022167228,0.00033233315,0.0013580042,0.0077359416,0.0004389116,0.0009582678,0.94870335,0.0015307015],"about_ca_topic_score_codex":0.91399443,"about_ca_topic_score_gemma":0.9990139,"teacher_disagreement_score":0.39073053,"about_ca_system_score_codex":0.03070195,"about_ca_system_score_gemma":0.23273931,"threshold_uncertainty_score":0.9999099},"labels":[],"label_agreement":null},{"id":"W7042999176","doi":"","title":"Response to the Letter: &amp;amp;ldquo;A Response to: Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review&amp;amp;rdquo; [Response To Letter]","year":2022,"lang":"en","type":"other","venue":"Dove Medical Press (Taylor and Francis Group)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Center (category theory); Autonomy; Research center; University hospital; Columbia university; Medical school","score_opus":0.028523954343494456,"score_gpt":0.2780939000497287,"score_spread":0.24956994570623428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7042999176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006029592,0.0037316873,0.96326625,0.015139561,0.0018880102,0.008256683,0.00037735424,0.00067863904,0.00063222914],"genre_scores_gemma":[0.5641267,0.00011412646,0.024991324,0.27599326,0.0095199905,0.026318187,0.00055060396,0.0038823644,0.09450347],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97940016,0.012028204,0.0017179907,0.002209555,0.0034542053,0.001189908],"domain_scores_gemma":[0.98921967,0.004353439,0.0010315592,0.0037487093,0.0003492958,0.0012973596],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009857775,0.0012006124,0.002256173,0.0011703243,0.0008957898,0.000671178,0.0024689143,0.00062065275,0.0009948685],"category_scores_gemma":[0.009541639,0.0009154553,0.0004275129,0.0018541199,0.00029755017,0.00037774592,0.0015823643,0.0015442367,0.00075922796],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.081827804,0.0018807264,0.00013817516,0.084869616,0.0037409605,0.0026549473,0.04093671,0.0004903369,0.038854707,0.00009072196,0.66635233,0.07816297],"study_design_scores_gemma":[0.0018491813,0.00053156115,0.00012527667,0.007444815,0.00035183795,0.0008613028,0.00009895784,0.0022419754,0.000033895278,0.000015578518,0.9847321,0.0017135321],"about_ca_topic_score_codex":0.0009794692,"about_ca_topic_score_gemma":0.0041014273,"teacher_disagreement_score":0.9382749,"about_ca_system_score_codex":0.00068363047,"about_ca_system_score_gemma":0.00005304987,"threshold_uncertainty_score":0.99991834},"labels":[],"label_agreement":null},{"id":"W7043356161","doi":"","title":"Smart Environments for Collaborative Design, Implementation, and Interpretation of Scientific Experiments","year":2007,"lang":"en","type":"article","venue":"University of Twente Research Information","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Mental Health; Social Sciences and Humanities Research Council of Canada; Bundesministerium für Bildung und Forschung; National Institute of Child Health and Human Development; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Norwegian Biodiversity Information Centre; European Commission; Advanced Research Projects Agency; National Science Foundation","keywords":"Ambient intelligence; Multidisciplinary approach; Focus (optics); Interpretation (philosophy); Position paper; Empirical research; Smart environment","score_opus":0.07837321013787078,"score_gpt":0.3468427066835657,"score_spread":0.2684694965456949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043356161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091964595,0.000014923885,0.9067153,0.000064653716,0.00007954161,0.00071719737,0.000025490206,0.0000090751555,0.00040923667],"genre_scores_gemma":[0.9864759,0.000010445727,0.013379837,0.0000050956687,0.00000277152,0.0000018226189,0.000035631612,0.0000014700165,0.00008703957],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989216,0.00012442833,0.0002193114,0.000116963565,0.0004574251,0.00016027871],"domain_scores_gemma":[0.99866414,0.0003277378,0.00022716528,0.00015373516,0.0005681504,0.00005906781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023900194,0.000051128456,0.00009466014,0.00053182326,0.00022967298,0.000070381495,0.00024795503,0.000031715732,0.000009533464],"category_scores_gemma":[0.000069113885,0.000061181294,0.000024870156,0.00041157173,0.00016464035,0.0027914462,0.00015693126,0.00004725121,0.000007968758],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009523613,0.00023938343,0.004474792,0.00035488003,0.00018858795,0.0000016654402,0.10058034,0.000086463886,0.04599198,0.0082083,0.005454926,0.8334663],"study_design_scores_gemma":[0.009408416,0.0021365157,0.027052004,0.0003714672,0.000033297303,0.0000079658075,0.21110584,0.09499796,0.5793271,0.0016024593,0.07338295,0.000573992],"about_ca_topic_score_codex":0.00008930111,"about_ca_topic_score_gemma":0.000054421856,"teacher_disagreement_score":0.8945113,"about_ca_system_score_codex":0.00012425432,"about_ca_system_score_gemma":0.00012527787,"threshold_uncertainty_score":0.24949008},"labels":[],"label_agreement":null},{"id":"W7084088599","doi":"10.6084/m9.figshare.29955889","title":"Additional file 2 of Prioritizing policy issues for knowledge translation: a critical interpretive synthesis","year":2025,"lang":"en","type":"article","venue":"Figshare","topic":"Context-Aware Activity Recognition Systems","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":"Government (linguistics); Information system; Identification (biology); Key (lock); Process (computing)","score_opus":0.07358792637808802,"score_gpt":0.35433657489117076,"score_spread":0.28074864851308273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084088599","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.9075928e-7,0.00015125722,0.003737545,0.0007500054,0.000049080103,0.00023898887,0.97744936,0.000099138044,0.017524466],"genre_scores_gemma":[0.16468944,0.0000021690623,0.05909274,0.0008440505,0.001470028,0.04029532,0.7277146,0.00006323935,0.0058284337],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991211,0.000077656696,0.00023309015,0.0002796562,0.00011989993,0.0001685576],"domain_scores_gemma":[0.96141315,0.03772461,0.00006940139,0.00023328018,0.00050979777,0.000049793125],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003908959,0.00011032908,0.00020629399,0.00021925439,0.000089131434,0.0000886837,0.0003799081,0.000079045916,0.82048225],"category_scores_gemma":[0.042427,0.00012139766,0.00014795331,0.00041256557,0.00001617836,0.0004055036,0.000110277964,0.00007372941,0.00050229585],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041848557,0.000037592883,8.1974406e-8,0.00023775832,0.000018480494,8.184964e-7,0.0003756239,6.922746e-8,0.000010154147,0.00070702814,0.92235726,0.076250926],"study_design_scores_gemma":[0.000047053123,0.00002917843,0.00008105631,0.014986698,0.0000044538824,0.0000058475407,0.00007722145,0.003983778,0.00065566576,0.0006189771,0.97938466,0.00012540519],"about_ca_topic_score_codex":0.0000042142715,"about_ca_topic_score_gemma":0.000012825458,"teacher_disagreement_score":0.81997997,"about_ca_system_score_codex":0.000064380634,"about_ca_system_score_gemma":0.00042148755,"threshold_uncertainty_score":0.96563905},"labels":[],"label_agreement":null},{"id":"W7099780087","doi":"","title":"REAL SECTOR CONFIDENCE INDEX FROM THE BUSINESS TENDENCY SURVEY OF CBRT Ece Oral, S.Özge Tuncel*","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Survey data collection; Business sector; Quarter (Canadian coin); Consumer confidence index; Survey methodology","score_opus":0.13838641813954047,"score_gpt":0.2948255185813817,"score_spread":0.1564391004418412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099780087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45914853,0.00015178072,0.5278837,0.0013145871,0.0018880351,0.0005541414,0.00012797456,0.00032518947,0.008606018],"genre_scores_gemma":[0.99824214,0.000015329195,0.00077660294,0.00022005937,0.00008953071,0.000017714465,0.000015851228,0.000012111601,0.00061064726],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973318,0.0006722895,0.0004578625,0.0005117324,0.0007506367,0.0002756626],"domain_scores_gemma":[0.99544704,0.0013601626,0.00028400784,0.0010973344,0.0016564969,0.00015497798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017709057,0.00020117359,0.00037548543,0.00007064418,0.00007500582,0.00021508928,0.001617469,0.00010708393,0.000115432726],"category_scores_gemma":[0.0007635953,0.0001382697,0.00005420671,0.001047228,0.0001333802,0.0009389166,0.0004500251,0.00017154367,0.00019535185],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014057687,0.00041909143,0.8941537,0.00003872699,0.00017817346,0.000038688388,0.003908523,0.00005127423,0.0032230788,0.0040335157,0.049584165,0.044230465],"study_design_scores_gemma":[0.00073048775,0.00006781842,0.98687196,0.00007208201,0.000008453235,0.000021958942,0.0002694896,0.0074785026,0.0013035077,0.0015991847,0.0012476166,0.00032893245],"about_ca_topic_score_codex":0.16802728,"about_ca_topic_score_gemma":0.026806258,"teacher_disagreement_score":0.5390936,"about_ca_system_score_codex":0.00006701148,"about_ca_system_score_gemma":0.0006727153,"threshold_uncertainty_score":0.990952},"labels":[],"label_agreement":null},{"id":"W7105829508","doi":"10.1109/jsen.2025.3631314","title":"Interpretable Multisensor-Based Hand Gesture Detection for Fine-Grained Activity Recognition","year":2025,"lang":"","type":"article","venue":"IEEE Sensors Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Interpretability; Wearable computer; Gesture; Accelerometer; Activity recognition; Gesture recognition; Wearable technology; Feature extraction; Photoplethysmogram","score_opus":0.03478568430948424,"score_gpt":0.2879087986890571,"score_spread":0.25312311437957286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7105829508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3792501,0.00017224404,0.6067997,0.0017168877,0.010278117,0.0012185775,0.00011942166,0.0001280838,0.0003168495],"genre_scores_gemma":[0.99051386,0.000043002347,0.0045166127,0.00044583817,0.0013331218,0.00010766543,0.000007831274,0.00006609611,0.0029659898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939471,0.0013899064,0.0013523778,0.0013139029,0.00079649873,0.0012002131],"domain_scores_gemma":[0.9928422,0.0020320574,0.0014024881,0.0008493871,0.0023995473,0.00047429735],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0021781751,0.000848646,0.0011201698,0.00136138,0.0020723073,0.0027355638,0.00082826626,0.0007314902,0.00013124972],"category_scores_gemma":[0.0014455759,0.0009021699,0.0010373172,0.001413681,0.00027420346,0.0019329907,0.00010688739,0.0018070532,0.000103241204],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001612733,0.0005941207,0.00022125454,0.0003183124,0.00045607873,0.000082530474,0.00079752976,0.0016422279,0.19028765,0.000003199387,0.0032848532,0.80069953],"study_design_scores_gemma":[0.0070372024,0.0008576636,0.0016990149,0.0019760674,0.00035364606,0.0007962293,0.00013945258,0.5416667,0.4356046,0.00082710886,0.008005578,0.0010367642],"about_ca_topic_score_codex":0.00013646125,"about_ca_topic_score_gemma":0.0005402459,"teacher_disagreement_score":0.79966277,"about_ca_system_score_codex":0.0008652591,"about_ca_system_score_gemma":0.0009413591,"threshold_uncertainty_score":0.9993429},"labels":[],"label_agreement":null},{"id":"W7105837562","doi":"10.2196/76084","title":"Advancements in Wearable Sensor Technologies for Health Monitoring in Terms of Clinical Applications, Rehabilitation, and Disease Risk Assessment: Systematic Review","year":2025,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wearable computer; mHealth; Wearable technology; Scalability; Focus (optics); Disease; Rehabilitation","score_opus":0.05494547998276667,"score_gpt":0.4693594770365782,"score_spread":0.41441399705381154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7105837562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14014585,0.2695606,0.41151175,0.11611903,0.0010720758,0.060391307,0.00026410157,0.0007134777,0.00022181995],"genre_scores_gemma":[0.92762727,0.04827835,0.018971257,0.0006673642,0.000016447884,0.004402402,0.0000046049704,0.000008109548,0.000024199462],"study_design_codex":"systematic_review","study_design_gemma":"observational","domain_scores_codex":[0.9966965,0.0006354854,0.0016660884,0.00052306865,0.00017261133,0.0003062779],"domain_scores_gemma":[0.9968273,0.0015408121,0.00084097596,0.0005211425,0.000093418916,0.00017634263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004062085,0.00013368124,0.0008930698,0.00031216745,0.00013424392,0.00003025718,0.0002151222,0.00006313671,1.5786205e-7],"category_scores_gemma":[0.0011797856,0.00012284511,0.000045073073,0.0006509021,0.00006010885,0.00029821496,0.00009325843,0.00023101113,5.1374775e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028176879,0.00024995918,0.42061034,0.4591448,0.000008500056,3.6330638e-7,0.00015557924,0.0000016861549,3.9851327e-7,0.0024014753,0.00008286842,0.11731583],"study_design_scores_gemma":[0.003414861,0.0006820192,0.79103726,0.17392412,0.00007092337,0.000003307016,0.0010241495,0.0065882537,0.0000021037447,0.022081377,0.00083355163,0.00033808334],"about_ca_topic_score_codex":0.00009232374,"about_ca_topic_score_gemma":0.00005146891,"teacher_disagreement_score":0.7874814,"about_ca_system_score_codex":0.00016856579,"about_ca_system_score_gemma":0.0005858242,"threshold_uncertainty_score":0.50094783},"labels":[],"label_agreement":null},{"id":"W7106792334","doi":"10.64628/aam.gqxa6j55t","title":"Tracking with care: The ethics of using location tracking technology with people living with dementia","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; University of Toronto","funders":"","keywords":"Tracking (education); Dementia; Tracking system; Location tracking; Activity tracker","score_opus":0.0356617492725806,"score_gpt":0.2878576490783954,"score_spread":0.2521958998058148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106792334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15574801,0.0016507813,0.8377961,0.0025327997,0.00023500956,0.0010059067,0.0000021122935,0.0001780264,0.0008512634],"genre_scores_gemma":[0.9806632,0.000019705823,0.018820971,0.00028583204,0.000041537034,0.000053632666,0.000001213549,0.00003996435,0.00007397124],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959764,0.00056354195,0.0007995099,0.001043513,0.00097129453,0.0006457692],"domain_scores_gemma":[0.99290746,0.0014919454,0.00092302886,0.0013255083,0.0032821961,0.00006983123],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015027662,0.00050439185,0.00068482396,0.00071908813,0.0010741431,0.00068094296,0.0012017749,0.00038902674,0.00003145058],"category_scores_gemma":[0.000255355,0.00032995307,0.00007551924,0.00590117,0.0005868884,0.001434553,0.00039990165,0.0012615939,0.0000034361963],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025156938,0.00063085154,0.44335037,0.0033812858,0.00238931,0.000027861863,0.09950768,0.014469027,0.00374419,0.023388544,0.000014686825,0.40884462],"study_design_scores_gemma":[0.0076798666,0.004391735,0.11956355,0.07073417,0.004854174,0.0021175581,0.2886115,0.396553,0.09928149,0.00081600354,0.00089719635,0.004499741],"about_ca_topic_score_codex":0.0013616001,"about_ca_topic_score_gemma":0.015739817,"teacher_disagreement_score":0.8249152,"about_ca_system_score_codex":0.00025295932,"about_ca_system_score_gemma":0.0025517738,"threshold_uncertainty_score":0.99991524},"labels":[],"label_agreement":null},{"id":"W7110071894","doi":"10.1109/ap-s/cnc-usnc-ursi55537.2025.11266459","title":"Transforming Healthcare with 5G-Driven Digital Twins: A Framework for Integrated Sensing and Communications in Fall Severity Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Scalability; Software deployment; Reliability (semiconductor); Convolutional neural network; Key (lock); Deep learning; Telemedicine","score_opus":0.04226581750030108,"score_gpt":0.3091532195279329,"score_spread":0.26688740202763184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110071894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088499814,0.00033855718,0.9018234,0.00626709,0.0002751247,0.0020603791,0.000061342354,0.00017165512,0.00050262525],"genre_scores_gemma":[0.9682475,0.00008724587,0.030959357,0.00047757907,0.00002358431,0.000082228195,0.000014346946,0.000019447063,0.00008874474],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975275,0.00029664964,0.0007235466,0.00075842306,0.00021544438,0.00047846473],"domain_scores_gemma":[0.9967404,0.0013762863,0.00020117256,0.00097557897,0.0005620209,0.0001445449],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004489325,0.00034943176,0.0005540976,0.00047804823,0.00056672667,0.0010337895,0.000539866,0.00031595398,0.0000014306869],"category_scores_gemma":[0.0002202903,0.00034362086,0.00010530049,0.001705719,0.00022063444,0.0018073354,0.00020628485,0.000780571,0.0000028208774],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011958556,0.00012220113,0.0032037913,0.0002622747,0.00007553307,0.0000030915182,0.004871247,0.000006824304,0.00010738293,0.0020873859,0.0000033218919,0.98913735],"study_design_scores_gemma":[0.002570333,0.0006737073,0.0031664376,0.0068287863,0.00006852938,0.0001686912,0.009931849,0.95758665,0.001846061,0.010112426,0.0061204433,0.0009260809],"about_ca_topic_score_codex":0.0044418816,"about_ca_topic_score_gemma":0.07907287,"teacher_disagreement_score":0.9882113,"about_ca_system_score_codex":0.0004063687,"about_ca_system_score_gemma":0.00054531556,"threshold_uncertainty_score":0.9999016},"labels":[],"label_agreement":null},{"id":"W7116955966","doi":"10.1002/alz70863_110570","title":"From sensors to solutions: Integrating wearable sensor data into health research","year":2025,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Wearable computer; Wearable technology; Data quality; Data collection; Activity recognition; External Data Representation; Quality (philosophy); Activity tracker","score_opus":0.22699092701722656,"score_gpt":0.41026486151509656,"score_spread":0.18327393449787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116955966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008178811,0.093784526,0.84681916,0.042002216,0.0026935528,0.0018316155,0.000095466254,0.0006285014,0.003966163],"genre_scores_gemma":[0.9134706,0.00006079075,0.084470056,0.0015519167,0.0001836951,0.000088340814,0.000059597558,0.000017825992,0.00009716014],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958587,0.0010285942,0.00058413274,0.0011449788,0.00065404025,0.00072954607],"domain_scores_gemma":[0.99608934,0.0007845901,0.00012670239,0.0023727138,0.00039399005,0.00023269188],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027711184,0.00020415723,0.0003242202,0.00048018,0.00082603114,0.0005855814,0.0022185268,0.00008032111,0.000060612467],"category_scores_gemma":[0.00041032615,0.00021255217,0.000056687026,0.0015651251,0.00006962802,0.0010351872,0.002958831,0.0004271153,0.0009345399],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013582808,0.0001613709,0.00034940048,0.0000124440385,0.0036006174,0.000011015535,0.0042390344,0.000025150826,0.0021522695,0.0031873877,0.156758,0.8294897],"study_design_scores_gemma":[0.001632644,0.00037077637,0.0025600307,0.0015583886,0.0014017677,0.000018697847,0.010530903,0.14553118,0.014193577,0.015542005,0.80523944,0.0014205978],"about_ca_topic_score_codex":0.013932037,"about_ca_topic_score_gemma":0.0027077494,"teacher_disagreement_score":0.9052918,"about_ca_system_score_codex":0.00005495816,"about_ca_system_score_gemma":0.00054989045,"threshold_uncertainty_score":0.99984336},"labels":[],"label_agreement":null},{"id":"W7117235256","doi":"10.1002/alz70858_100300","title":"Spatio‐temporal digital markers of behavioural and psychological symptoms to support clinical dementia care","year":2025,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","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 Toronto; Toronto Rehabilitation Institute","funders":"","keywords":"Dementia; Disease; Real-time locating system; Cognition; Patient care; Health care","score_opus":0.08199506428223437,"score_gpt":0.3482672069258371,"score_spread":0.26627214264360277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117235256","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.926102,0.0069810073,0.057420686,0.00077648455,0.002262333,0.0011321421,0.00008144984,0.0002447687,0.0049991235],"genre_scores_gemma":[0.99593276,0.000006747658,0.0034735207,0.0004252099,0.00003448365,0.000047448266,0.000050763876,0.000011062358,0.00001800319],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99760157,0.00014913156,0.00083306566,0.0007309823,0.00034157516,0.00034369229],"domain_scores_gemma":[0.99860156,0.0001403446,0.00022021566,0.0006210583,0.0002197311,0.00019707657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040620557,0.00023406559,0.00039925025,0.00019278238,0.00010184746,0.00025885602,0.0006894496,0.00013511133,0.00008576433],"category_scores_gemma":[0.000042380274,0.0002307315,0.0001809853,0.00038095698,0.00012768322,0.00064419117,0.0006510818,0.00016380394,0.00006518641],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041019706,0.00016234437,0.593355,0.00000526262,0.0020484577,0.000010129078,0.00014278646,3.002829e-7,0.000012821134,0.00026006126,0.0033693768,0.40059248],"study_design_scores_gemma":[0.0022919949,0.0020691962,0.9680665,0.000100296995,0.0042729075,0.000035205027,0.0003128553,0.00024747738,0.0012231944,0.00027016955,0.020381516,0.0007286447],"about_ca_topic_score_codex":0.000094253985,"about_ca_topic_score_gemma":0.000108583205,"teacher_disagreement_score":0.3998638,"about_ca_system_score_codex":0.000007902824,"about_ca_system_score_gemma":0.00007958121,"threshold_uncertainty_score":0.94089574},"labels":[],"label_agreement":null},{"id":"W7117303602","doi":"10.1002/alz70858_101697","title":"Feasibility of Ambient Sensing Technology for Fall Detection in People with Cognitive Changes","year":2025,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Enerkem (Canada); Bombardier (Canada)","funders":"","keywords":"Cognition; Cognitive disabilities; Ambient intelligence; Cognitive load; Cognitive impairment","score_opus":0.03364113654183711,"score_gpt":0.28706454163876244,"score_spread":0.25342340509692535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117303602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38046998,0.0023120989,0.61490935,0.0006643844,0.00026936058,0.0011015336,0.00000754363,0.000107227126,0.00015849258],"genre_scores_gemma":[0.9963092,0.000004106687,0.0034768395,0.00009222655,0.000010148855,0.000093740506,0.0000032407356,0.000006837113,0.0000036271595],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988698,0.00008022887,0.0002457681,0.00043706724,0.00013670158,0.00023042902],"domain_scores_gemma":[0.99900573,0.00018687705,0.00016052593,0.0003043439,0.00031800367,0.000024491874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033452432,0.00013067613,0.00025383345,0.00042026272,0.00007358648,0.000036268608,0.00020782904,0.00008313338,0.0000018616295],"category_scores_gemma":[0.00005444383,0.00012946915,0.00004267717,0.00092654687,0.000060117374,0.00020209902,0.00015147057,0.00009330513,0.0000032180594],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001149413,0.00020026002,0.04459668,0.00003495585,0.0020137862,0.0000031283503,0.00076608063,0.0000032868165,0.0090090865,0.00043748226,0.000023714607,0.9427966],"study_design_scores_gemma":[0.003928021,0.00072898023,0.07717749,0.0004539319,0.002614746,0.000031361596,0.0014865292,0.01142158,0.8983816,0.0024578113,0.0007772763,0.0005407034],"about_ca_topic_score_codex":0.00044522088,"about_ca_topic_score_gemma":0.019813845,"teacher_disagreement_score":0.9422559,"about_ca_system_score_codex":0.000018850209,"about_ca_system_score_gemma":0.00007556042,"threshold_uncertainty_score":0.99807197},"labels":[],"label_agreement":null},{"id":"W7117319367","doi":"10.1002/alz70858_101297","title":"Feasibility of Ambient Home Sensing Technology for People Requiring In‐Home Professional Care","year":2025,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Enerkem (Canada); Bombardier (Canada)","funders":"","keywords":"Usability; Dementia; Ambient intelligence; Confidentiality; Assisted living; Dual (grammatical number); Data collection","score_opus":0.03612290873862642,"score_gpt":0.31441579987222235,"score_spread":0.2782928911335959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117319367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7778054,0.017539881,0.19745511,0.0018633644,0.0027619195,0.0019277522,0.000022684622,0.00026048828,0.00036341845],"genre_scores_gemma":[0.99124813,0.0000031502286,0.008564705,0.00007357366,0.000018614857,0.00006646245,0.000008117063,0.000008790899,0.000008427224],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983066,0.00009913616,0.00050088443,0.0005444187,0.00021809593,0.00033087857],"domain_scores_gemma":[0.99863935,0.00017279926,0.00018959277,0.0006217769,0.0003382533,0.00003823437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040864362,0.0001578845,0.00036265273,0.00045878033,0.00012509737,0.000043992433,0.0004989984,0.00012504523,0.000006529282],"category_scores_gemma":[0.00005146758,0.00016588578,0.00010335187,0.0008957329,0.00006019226,0.00035135474,0.0005272679,0.00014563439,0.000006875296],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001004799,0.00047286463,0.37085706,0.0002985133,0.0029668724,0.000011908908,0.004750338,0.000028408529,0.021601694,0.011231098,0.0006804773,0.5870003],"study_design_scores_gemma":[0.011813347,0.0006788513,0.5192781,0.002517521,0.004531379,0.00006654785,0.009172868,0.02159085,0.37110102,0.049847472,0.006912688,0.0024893614],"about_ca_topic_score_codex":0.00010110125,"about_ca_topic_score_gemma":0.0006120547,"teacher_disagreement_score":0.5845109,"about_ca_system_score_codex":0.00003680998,"about_ca_system_score_gemma":0.00020418531,"threshold_uncertainty_score":0.6764626},"labels":[],"label_agreement":null},{"id":"W7117457302","doi":"10.1145/3714394.3756201","title":"Identifying Routine from Sequences of Activities of Daily Living in Smart-homes","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Context (archaeology); Identification (biology); Task (project management); Embedding; Sequence (biology); Representation (politics); Focus (optics); Context model","score_opus":0.038671545251641126,"score_gpt":0.2913733172462781,"score_spread":0.25270177199463695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117457302","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80374485,0.0023751731,0.18566927,0.00030466917,0.0015662337,0.00034788423,0.00003921939,0.000063532825,0.00588917],"genre_scores_gemma":[0.99542946,0.00014744698,0.0022583304,0.00006896028,0.00004839932,0.000022509925,0.0000022723125,0.00001232061,0.0020103003],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9957068,0.00072814844,0.0015991888,0.00084926747,0.00065355,0.00046303106],"domain_scores_gemma":[0.99430996,0.0035785767,0.00087843195,0.0008733544,0.00028526346,0.00007438349],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013859498,0.00036618728,0.0011610908,0.00097655895,0.000107026644,0.0002992453,0.0012645954,0.00020265426,0.00044919387],"category_scores_gemma":[0.00039624903,0.00038935043,0.00026837806,0.0016965114,0.00032215577,0.0022616836,0.0011158695,0.00032120233,0.000016220785],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007016232,0.000848631,0.5398252,0.0013816117,0.00048808195,0.000024674515,0.03477383,0.00007032209,0.102002315,0.0037634063,0.00019790558,0.31655386],"study_design_scores_gemma":[0.0016713605,0.00023689104,0.41880572,0.016734323,0.00012789629,0.000009992311,0.033447713,0.0402741,0.4828993,0.0046368674,0.00013526207,0.0010205902],"about_ca_topic_score_codex":0.01765492,"about_ca_topic_score_gemma":0.0063521545,"teacher_disagreement_score":0.380897,"about_ca_system_score_codex":0.00016172748,"about_ca_system_score_gemma":0.00065973547,"threshold_uncertainty_score":0.9998558},"labels":[],"label_agreement":null},{"id":"W7117476480","doi":"10.1145/3714394.3750579","title":"HASCA: 13th International Workshop on Human Activity Sensing Corpus and Applications","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University","funders":"","keywords":"Activity recognition; Context (archaeology); Wearable computer; Ubiquitous computing; Wearable technology; Data collection","score_opus":0.057612435310550866,"score_gpt":0.3364357114114679,"score_spread":0.278823276100917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117476480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025737943,0.00011359226,0.8810641,0.0062289196,0.0016502028,0.0009212037,0.000016996462,0.00023993546,0.08402708],"genre_scores_gemma":[0.9822783,0.000034772656,0.0008175305,0.00091519795,0.0003362051,0.000050248786,0.000006644304,0.000015869651,0.015545263],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971505,0.00025448337,0.0004958941,0.001211,0.00049342396,0.00039469887],"domain_scores_gemma":[0.99725866,0.00091150793,0.00030062714,0.00094752817,0.00040737813,0.00017428084],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005417836,0.000373606,0.00042731903,0.0005478314,0.00076967967,0.0013148487,0.00070191803,0.00021698305,0.00009083507],"category_scores_gemma":[0.00009806183,0.00041149798,0.0001418116,0.00090557017,0.00021887528,0.0008315353,0.00082372356,0.0005153181,0.00013906164],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002457899,0.0002857053,0.0006920352,0.00003462602,0.00016238594,0.0000067187902,0.0002443338,0.000012450106,0.002790475,0.024600815,0.0010456925,0.97010016],"study_design_scores_gemma":[0.0075658485,0.00041961187,0.063784525,0.0038521297,0.000336718,0.0003616731,0.0012734608,0.47346014,0.038268823,0.02154917,0.38534567,0.0037822416],"about_ca_topic_score_codex":0.00037178598,"about_ca_topic_score_gemma":0.0005333347,"teacher_disagreement_score":0.96631795,"about_ca_system_score_codex":0.00031488558,"about_ca_system_score_gemma":0.00021630459,"threshold_uncertainty_score":0.9998337},"labels":[],"label_agreement":null},{"id":"W7117478457","doi":"10.1145/3714394.3756211","title":"LSTM vs. DistilBERT: A Comparative Study for 2025 SHL Recognition Challenge","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":true,"ca_institutions":"Transport Canada; Concordia University","funders":"","keywords":"Generalization; Modality (human–computer interaction); Inference; Modalities; Task (project management); Activity recognition; Domain (mathematical analysis)","score_opus":0.13218370749615094,"score_gpt":0.3610271354708418,"score_spread":0.22884342797469087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117478457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022563906,0.0009036863,0.8604753,0.0067753852,0.005901665,0.011415885,0.00024564468,0.00046104792,0.09125748],"genre_scores_gemma":[0.97505265,0.00005794955,0.0015275247,0.00065724633,0.00031053828,0.00201642,0.00004538077,0.0000279107,0.020304402],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936413,0.0010062092,0.0015429783,0.002184093,0.000686756,0.0009386643],"domain_scores_gemma":[0.993498,0.0026796549,0.0005498102,0.0013855931,0.0016026809,0.00028421663],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015200016,0.0008129893,0.0015059769,0.00069944956,0.000838599,0.0010656046,0.0014087091,0.0003020946,0.0005314736],"category_scores_gemma":[0.00031504035,0.0008395299,0.0004996664,0.0016344244,0.00018115244,0.0015942662,0.0008289219,0.0005069161,0.0010204847],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009499864,0.010131486,0.0013803467,0.0006324137,0.0017387357,0.00002802069,0.024636855,0.0000076493925,0.00006357922,0.006925469,0.031576384,0.92192906],"study_design_scores_gemma":[0.045845315,0.021446379,0.025816688,0.0060976697,0.0026616023,0.00007281528,0.061952323,0.5515748,0.0052408073,0.048827685,0.22191502,0.008548874],"about_ca_topic_score_codex":0.00055374537,"about_ca_topic_score_gemma":0.0038917726,"teacher_disagreement_score":0.9524887,"about_ca_system_score_codex":0.00038152965,"about_ca_system_score_gemma":0.0006527346,"threshold_uncertainty_score":0.9999714},"labels":[],"label_agreement":null},{"id":"W7117534739","doi":"10.1109/jiot.2025.3649673","title":"Empowering IIoT With Federated Edge Learning for Human Activity Recognition Problems","year":2025,"lang":"","type":"article","venue":"IEEE Internet of Things Journal","topic":"Context-Aware Activity Recognition Systems","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":"Royal Military College of Canada","funders":"Korea University of Technology and Education","keywords":"Activity recognition; Cloud computing; Software deployment; Upload; Edge computing; Edge device; Enhanced Data Rates for GSM Evolution; Data modeling","score_opus":0.044287412829883945,"score_gpt":0.3108571238291248,"score_spread":0.2665697109992408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117534739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4000253,0.00013263573,0.5943214,0.00045220292,0.0025873634,0.0006856019,0.000003981613,0.00009347726,0.0016979859],"genre_scores_gemma":[0.992926,0.000029729641,0.0021254788,0.00017127348,0.00034558703,0.000060268507,0.000004745012,0.000056466586,0.004280452],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9954519,0.00064522173,0.0014452253,0.0009092691,0.0007462079,0.00080217485],"domain_scores_gemma":[0.9941226,0.00060204894,0.002494608,0.00035294626,0.0021727812,0.0002549861],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0027583323,0.00061075593,0.0010532853,0.0008857377,0.00092797086,0.0024041005,0.0011539783,0.00034643168,0.00009447361],"category_scores_gemma":[0.0003534396,0.0005960132,0.00048244916,0.0007333957,0.0002036457,0.003520833,0.00028545057,0.0021202276,0.000021720225],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013243906,0.0011179572,0.0024592557,0.0016761989,0.0020644085,0.00007829607,0.015894318,0.00062243984,0.17049512,0.00009119352,0.002363809,0.8018126],"study_design_scores_gemma":[0.012106833,0.008221812,0.0017278651,0.03873393,0.00066210516,0.0028874883,0.0014106431,0.35641545,0.56265324,0.0048080445,0.00784014,0.0025324498],"about_ca_topic_score_codex":0.00056422275,"about_ca_topic_score_gemma":0.00014220296,"teacher_disagreement_score":0.79928017,"about_ca_system_score_codex":0.0005241393,"about_ca_system_score_gemma":0.0005725864,"threshold_uncertainty_score":0.9996491},"labels":[],"label_agreement":null},{"id":"W7117791338","doi":"10.1145/3773274.3774697","title":"A Hybrid Graph Convolutional Networks Framework for Context-Aware Multimodal Human Activity Recognition","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Mount Royal University","funders":"","keywords":"Activity recognition; Graph; Convolutional neural network; Context (archaeology); Feature (linguistics); Baseline (sea); Deep learning","score_opus":0.04468851109704399,"score_gpt":0.3089679213188886,"score_spread":0.26427941022184465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117791338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032988917,0.0005696111,0.9498452,0.0025887939,0.0070240004,0.004250495,0.00061431754,0.0007232491,0.0013954383],"genre_scores_gemma":[0.9850696,0.00005140486,0.0077780513,0.002358207,0.0009896018,0.0012574327,0.00021084704,0.000073247924,0.0022116504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99209493,0.0010343599,0.001610866,0.0027074616,0.0008684294,0.0016839247],"domain_scores_gemma":[0.99028665,0.0044081756,0.0010227974,0.0015984537,0.0021690708,0.00051485625],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0018081894,0.0011604148,0.001539677,0.0009423642,0.0021543603,0.0013421786,0.0016576691,0.0009672514,0.0006713436],"category_scores_gemma":[0.00067970966,0.0013443469,0.0012912302,0.0014914566,0.0006285231,0.0022600377,0.00082750036,0.0015772632,0.0001987163],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006456917,0.0018150855,0.0013546743,0.00045423894,0.0011706491,0.000026645013,0.00037320537,0.00019280196,0.00036157513,0.065841414,0.010512733,0.9172513],"study_design_scores_gemma":[0.0071812086,0.0008478306,0.007344719,0.003145156,0.00042751498,0.00010160462,0.00042897297,0.7899609,0.0049741026,0.1774833,0.005495874,0.0026087784],"about_ca_topic_score_codex":0.0011319967,"about_ca_topic_score_gemma":0.00079247373,"teacher_disagreement_score":0.95208067,"about_ca_system_score_codex":0.0007107644,"about_ca_system_score_gemma":0.0009690669,"threshold_uncertainty_score":0.9996945},"labels":[],"label_agreement":null},{"id":"W7124142258","doi":"10.65109/vdpo1688","title":"Smart walkers!: enhancing the mobility of the elderly","year":2011,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Waterloo","funders":"","keywords":"Activity recognition; Linear discriminant analysis; Sigmoid function; Home automation; Intelligent sensor; Discriminant","score_opus":0.052869272679301096,"score_gpt":0.2397471626060403,"score_spread":0.1868778899267392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124142258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49357295,0.0003229166,0.41221175,0.002216977,0.0053598336,0.0020510294,0.000012883721,0.00014025523,0.0841114],"genre_scores_gemma":[0.9957247,0.0000099116705,0.00047042742,0.0003210591,0.00009294828,0.000039190854,1.6235268e-7,0.000014546136,0.003327084],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9959852,0.0010027877,0.0010491565,0.0007274036,0.0007217301,0.00051370775],"domain_scores_gemma":[0.9951318,0.000804652,0.00061837706,0.0027817213,0.0005412002,0.0001222424],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025915047,0.00032546857,0.00045025253,0.000065479886,0.0004471262,0.00016030404,0.0027587553,0.00015426996,0.0012333771],"category_scores_gemma":[0.00028316755,0.00017726018,0.0004705157,0.00093804655,0.00054691324,0.0007495105,0.0011431053,0.0004560517,0.00027346305],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007405495,0.0015022671,0.01318398,0.0002709568,0.00037152128,0.0000061636747,0.04690771,0.0000037111806,0.012484821,0.009038376,0.0018776818,0.91427875],"study_design_scores_gemma":[0.0018410861,0.0017864448,0.15770292,0.0009557868,0.0002715508,0.00023925229,0.008608999,0.023568202,0.7826,0.008911156,0.011896098,0.0016184959],"about_ca_topic_score_codex":0.004194744,"about_ca_topic_score_gemma":0.0024521495,"teacher_disagreement_score":0.91266024,"about_ca_system_score_codex":0.00008146892,"about_ca_system_score_gemma":0.00041813575,"threshold_uncertainty_score":0.9996796},"labels":[],"label_agreement":null},{"id":"W7125580023","doi":"10.1109/cascon66301.2025.00126","title":"Synthetic Data Generation for Alleviating Class Imbalance with Reinforcement Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Western University","funders":"","keywords":"Reinforcement learning; Classifier (UML); Synthetic data; Activity recognition; Generative grammar; Adversarial system; Class (philosophy); Training set; Data modeling","score_opus":0.07690358802464711,"score_gpt":0.3059455331648778,"score_spread":0.2290419451402307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125580023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013679273,0.0003115931,0.97851217,0.0028275202,0.0010640436,0.0016168094,0.000011398252,0.0001897329,0.014098824],"genre_scores_gemma":[0.97018427,0.00004682203,0.011970055,0.0006754971,0.00027301852,0.00019187658,0.00011419547,0.000021915874,0.016522363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961467,0.0002934773,0.00083944167,0.0016180961,0.00051556336,0.0005867599],"domain_scores_gemma":[0.9958556,0.00073796045,0.00052518124,0.0021434273,0.00063010724,0.000107713946],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0018917472,0.00037275246,0.00049251784,0.00020973009,0.00076411356,0.0012383751,0.001568523,0.00013527594,0.00009821483],"category_scores_gemma":[0.0006483276,0.00034585912,0.000084067906,0.0006365165,0.00007146609,0.0020002427,0.0010875945,0.00031198992,0.0000558991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017469958,0.0002670808,0.0020371191,0.0009810053,0.00085253996,0.000010760439,0.001609552,0.03771872,0.010782635,0.02161473,0.015736595,0.90821457],"study_design_scores_gemma":[0.0010776237,0.00028707346,0.000051555227,0.0006333603,0.00007367547,0.00001357372,0.0002471514,0.9638458,0.0022322119,0.000035452493,0.031110749,0.00039180845],"about_ca_topic_score_codex":0.00013527002,"about_ca_topic_score_gemma":0.00034516893,"teacher_disagreement_score":0.96881634,"about_ca_system_score_codex":0.00021015358,"about_ca_system_score_gemma":0.00060506974,"threshold_uncertainty_score":0.9998993},"labels":[],"label_agreement":null},{"id":"W7125642941","doi":"10.1109/cascon66301.2025.00023","title":"Quiet Interaction: Designing a DHH Accessible Home Environment with Mixed Reality AI and IoT","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario College of Art and Design","funders":"","keywords":"QUIET; Mixed reality; Internet of Things; Home automation","score_opus":0.038522362912060834,"score_gpt":0.29159606492601914,"score_spread":0.2530737020139583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125642941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038262457,0.00044104236,0.94340813,0.0073582134,0.0011644843,0.0008548632,0.00001043229,0.0001833731,0.008317004],"genre_scores_gemma":[0.98841107,0.000093662245,0.0056778127,0.0013589619,0.000114875766,0.0001198285,0.0000038506855,0.000019659998,0.004200292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99587196,0.0007005765,0.00079871214,0.001461821,0.00058196817,0.0005849824],"domain_scores_gemma":[0.9975049,0.00058580993,0.00038909444,0.0010898628,0.00016439748,0.00026592318],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010862564,0.000506324,0.00068483764,0.00038285542,0.0005286346,0.0016370241,0.00069820316,0.00019857823,0.00049466576],"category_scores_gemma":[0.000040487714,0.00045670645,0.000113819486,0.00080734014,0.00024180504,0.0018915191,0.0009996415,0.00054353435,0.00014195411],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054533,0.000955441,0.029585123,0.00063951925,0.0008325087,0.00010670785,0.0034509515,0.00027650452,0.0029259617,0.002232918,0.009451464,0.94899756],"study_design_scores_gemma":[0.015977493,0.0038906648,0.1844702,0.008520369,0.0009640408,0.0013229926,0.009089138,0.5348895,0.10850413,0.0048864726,0.12113378,0.006351194],"about_ca_topic_score_codex":0.0011161879,"about_ca_topic_score_gemma":0.00040500262,"teacher_disagreement_score":0.9501486,"about_ca_system_score_codex":0.000342653,"about_ca_system_score_gemma":0.00035346742,"threshold_uncertainty_score":0.99978846},"labels":[],"label_agreement":null},{"id":"W7126467883","doi":"10.21428/594757db.645366b3","title":"SF2D: Semi-supervised Federated Learning for Fall Detection using (Un)labelled Data in Edge-Cloud","year":2025,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 New Brunswick","funders":"","keywords":"Autoencoder; Classifier (UML); Precision and recall; Encoder; Population; Benchmark (surveying); Deep learning; Train","score_opus":0.06386726158764092,"score_gpt":0.3012321805373486,"score_spread":0.2373649189497077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126467883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22815242,0.00006130554,0.7689543,0.0002666278,0.00075116626,0.00052888395,0.0000031448935,0.00026963337,0.0010125372],"genre_scores_gemma":[0.9914668,0.000004096095,0.006993628,0.00024284828,0.00007680962,0.00004199603,0.000025295556,0.00001365091,0.0011348753],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998071,0.0002697965,0.00040737868,0.0007393623,0.0001833461,0.000329092],"domain_scores_gemma":[0.9984403,0.0005313123,0.00010768768,0.0006593348,0.00020500914,0.00005630325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075533835,0.00018170154,0.0002970424,0.00032249436,0.00035915757,0.0004533764,0.0007952398,0.000126585,0.000019483923],"category_scores_gemma":[0.00038973955,0.00019101256,0.00005386988,0.0010612585,0.000017082566,0.0010787421,0.00063533493,0.00023847964,0.000025439513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010230629,0.00026635462,0.0064325477,0.00019106946,0.00016247241,0.00001411211,0.00087985845,0.0006728724,0.09963636,0.0003984569,0.00062045874,0.89062315],"study_design_scores_gemma":[0.0014726977,0.00003778906,0.0004517857,0.000099004836,0.0000115293315,0.000011559206,0.00022874287,0.9820495,0.011474215,0.00021903885,0.0037205836,0.00022352878],"about_ca_topic_score_codex":0.001028598,"about_ca_topic_score_gemma":0.0024546874,"teacher_disagreement_score":0.98137665,"about_ca_system_score_codex":0.00014446961,"about_ca_system_score_gemma":0.000176932,"threshold_uncertainty_score":0.7789266},"labels":[],"label_agreement":null},{"id":"W7127295788","doi":"10.1109/ism66958.2025.00069","title":"Comparison of Multimodal Fall Detection Strategies","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University of Edmonton","funders":"","keywords":"Robustness (evolution); Deep learning; Wearable computer; Recall; Sensor fusion; ALARM; Wearable technology; False alarm","score_opus":0.04325760671341203,"score_gpt":0.34739035555634273,"score_spread":0.30413274884293073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127295788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07933876,0.00061373145,0.8815931,0.00037830335,0.0027528086,0.00051634305,0.000006048256,0.00017577861,0.034625106],"genre_scores_gemma":[0.99612063,0.00001821655,0.0021063122,0.000086142434,0.00007301278,0.000025458137,0.000001298887,0.0000090516,0.0015598933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696875,0.00037425704,0.0010983418,0.0007084749,0.00046294575,0.00038722734],"domain_scores_gemma":[0.9975238,0.0005006763,0.00046335816,0.00078648794,0.000631854,0.000093867005],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000492246,0.0003183217,0.0007423513,0.0004732224,0.00021043561,0.0005395922,0.00082190445,0.000253448,0.00010454675],"category_scores_gemma":[0.00008736292,0.00033613437,0.0002434354,0.0011977913,0.00017351365,0.0014533899,0.000415296,0.0003462226,0.00012338148],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054825763,0.0006317795,0.0060173995,0.00023806278,0.00017936152,0.0000018951378,0.0023550023,0.0002647615,0.020062534,0.0072161197,0.00038409792,0.96259415],"study_design_scores_gemma":[0.0013238933,0.00042101703,0.01811371,0.0004474713,0.000051637187,0.000007707511,0.0053871498,0.79061747,0.17703897,0.0015221002,0.0045664753,0.0005024287],"about_ca_topic_score_codex":0.003597605,"about_ca_topic_score_gemma":0.0045126406,"teacher_disagreement_score":0.96209174,"about_ca_system_score_codex":0.00012827033,"about_ca_system_score_gemma":0.0005257335,"threshold_uncertainty_score":0.99990904},"labels":[],"label_agreement":null},{"id":"W7127450563","doi":"10.1109/ccece64018.2025.11364451","title":"Human Fall Detection using Multimodal Dataset","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Concordia University of Edmonton","funders":"","keywords":"Convolutional neural network; RGB color model; Deep learning; Representation (politics); Feature (linguistics); Motion (physics); Population; Feature extraction","score_opus":0.058174035118055675,"score_gpt":0.3350561696208024,"score_spread":0.27688213450274674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127450563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110724695,0.00010243505,0.88071895,0.0003008754,0.003081333,0.00069426576,0.0003309101,0.00022812479,0.0038183983],"genre_scores_gemma":[0.99521697,0.0000023399514,0.0019729424,0.0006413255,0.00019412761,0.000019999101,0.00007301908,0.000015885913,0.0018633705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965164,0.00046291458,0.0008067656,0.0011859244,0.00044826017,0.0005797037],"domain_scores_gemma":[0.9976833,0.00017396378,0.00028450854,0.0014012336,0.0002889796,0.00016800976],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007599103,0.00039907053,0.00046961743,0.00058384275,0.0008734186,0.0010504853,0.0010708863,0.00026162073,0.00021557478],"category_scores_gemma":[0.00008924899,0.0004474313,0.00017273083,0.0012046736,0.00012471149,0.0018604741,0.0010602648,0.00041985224,0.00037140393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040103492,0.0008815548,0.003163062,0.0003518229,0.0004611186,0.000062045016,0.0007283132,0.00018983436,0.09470081,0.0023219895,0.006841305,0.890258],"study_design_scores_gemma":[0.0017520007,0.00014594416,0.0045945626,0.00037832302,0.000113172326,0.00008085985,0.00020838225,0.9433769,0.023294128,0.00052386004,0.024688123,0.0008437217],"about_ca_topic_score_codex":0.009032466,"about_ca_topic_score_gemma":0.005180919,"teacher_disagreement_score":0.94318706,"about_ca_system_score_codex":0.00036360958,"about_ca_system_score_gemma":0.00027324999,"threshold_uncertainty_score":0.9999865},"labels":[],"label_agreement":null},{"id":"W7128870755","doi":"","title":"Developing and testing a mobile application to observe activities in public spaces: Results of a case study in Montreal, Canada","year":2022,"lang":"","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Mobile device; Data collection; Quality (philosophy); Key (lock)","score_opus":0.04148120888099614,"score_gpt":0.26104965008178516,"score_spread":0.219568441200789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128870755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.903811,0.000666813,0.08686853,0.0019883069,0.00021172274,0.005979689,0.00025256278,0.00014921532,0.000072154915],"genre_scores_gemma":[0.98195755,0.000018157278,0.008508391,0.0004221528,0.000040380484,0.008888435,0.000010657812,0.000058313042,0.0000959396],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9925068,0.0018600909,0.0018779403,0.0015442839,0.0010393058,0.0011715939],"domain_scores_gemma":[0.99475396,0.0019874258,0.0010716533,0.0014311181,0.00032832733,0.00042753606],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004549768,0.0006101435,0.0010694582,0.0018554467,0.0005770943,0.0005243774,0.0011556795,0.00017131733,0.0000047329995],"category_scores_gemma":[0.0011415144,0.0007841432,0.00007892387,0.00559106,0.00007649136,0.0011432754,0.0023572205,0.0009763969,6.6844683e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045556834,0.00216708,0.58206666,0.00025439376,0.00011841397,0.0056098425,0.028897546,0.01748458,0.00162384,0.00038657838,0.00025690816,0.36067858],"study_design_scores_gemma":[0.0048353747,0.0018515317,0.27069283,0.00047922906,0.0000478951,0.006108182,0.090035714,0.6216965,0.0011421384,0.00027734222,0.0010059179,0.0018273625],"about_ca_topic_score_codex":0.9922884,"about_ca_topic_score_gemma":0.9975249,"teacher_disagreement_score":0.6042119,"about_ca_system_score_codex":0.0046543283,"about_ca_system_score_gemma":0.004575531,"threshold_uncertainty_score":0.99946094},"labels":[],"label_agreement":null},{"id":"W7130312948","doi":"10.1109/tencon66050.2025.11374984","title":"ActivityNet-HE: An Encryption-Enabled Deep Learning-Based Framework for Secure Human Activity Monitoring","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Homomorphic encryption; Activity recognition; Encryption; Inference; Principal component analysis; Information privacy; Raw data; Artificial neural network; Curse of dimensionality","score_opus":0.03862477731252369,"score_gpt":0.33850031743234066,"score_spread":0.299875540119817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130312948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1377009,0.00019245247,0.8515031,0.0014314065,0.00431027,0.0023458984,0.000017746637,0.0009825078,0.001515772],"genre_scores_gemma":[0.97670984,0.00001850912,0.017614469,0.00024591936,0.0011599641,0.00074589433,0.000024078972,0.00009540286,0.0033859438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9917217,0.0017158645,0.001080578,0.002743483,0.0010604949,0.0016778614],"domain_scores_gemma":[0.9911589,0.00382928,0.0009600386,0.0022179643,0.0012367765,0.0005970405],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0022954699,0.0011322343,0.001402314,0.000985354,0.0028377,0.0025320991,0.0020813525,0.0012269191,0.0003634624],"category_scores_gemma":[0.0011284993,0.0013051509,0.0007433762,0.0023554906,0.00024143486,0.0040274593,0.00062079716,0.0022652638,0.000085944754],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006312555,0.003567577,0.017758563,0.0014242091,0.0009439176,0.000029360688,0.0030432264,0.0054911706,0.028791832,0.018021783,0.00027562227,0.9200215],"study_design_scores_gemma":[0.0047811633,0.0021177742,0.021839704,0.0023555527,0.00042419328,0.00001310185,0.0013364216,0.80910707,0.123604394,0.020636013,0.010922279,0.0028623592],"about_ca_topic_score_codex":0.0006738532,"about_ca_topic_score_gemma":0.00057263207,"teacher_disagreement_score":0.91715914,"about_ca_system_score_codex":0.0008473183,"about_ca_system_score_gemma":0.00088331866,"threshold_uncertainty_score":0.9989398},"labels":[],"label_agreement":null},{"id":"W7131083678","doi":"10.1109/idicaihei65991.2025.11377804","title":"AI-Based Fall Detection System for Patient Healthcare Using Webcam","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Health care; Healthcare system; Analytics; Patient data; Patient care","score_opus":0.04172239600318696,"score_gpt":0.3018008086049365,"score_spread":0.2600784126017496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131083678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022313835,0.00049556786,0.9594271,0.0034853194,0.009551005,0.003484731,0.000057865524,0.0005156805,0.00066887226],"genre_scores_gemma":[0.9923283,0.0000019724635,0.0042601433,0.0025229203,0.00018643875,0.00033563882,0.0000057915827,0.00003250309,0.0003262876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99520224,0.00071328005,0.0013162417,0.0013562293,0.0005832186,0.0008287624],"domain_scores_gemma":[0.9958959,0.00052250753,0.0005686024,0.0011112945,0.0016335887,0.00026808618],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008639551,0.00050681806,0.0007512243,0.00073555997,0.00093677227,0.0008134777,0.0006337964,0.0003788603,0.000010471451],"category_scores_gemma":[0.00011197315,0.0005463345,0.0004632459,0.0014708429,0.00006422389,0.0008184696,0.00028318,0.00034229865,0.00005344561],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002459203,0.0003362673,0.0012054957,0.005001706,0.00019004582,0.000011522915,0.0006867652,0.0009447092,0.0036930314,0.0027462845,0.00046222776,0.98447603],"study_design_scores_gemma":[0.0015132847,0.00057786854,0.000069799666,0.0020586143,0.000072565614,0.000030211007,0.0008815875,0.9389621,0.047586206,0.00010673666,0.0076151392,0.0005258647],"about_ca_topic_score_codex":0.0034155026,"about_ca_topic_score_gemma":0.0028780964,"teacher_disagreement_score":0.98395014,"about_ca_system_score_codex":0.0018325235,"about_ca_system_score_gemma":0.0015237583,"threshold_uncertainty_score":0.9996988},"labels":[],"label_agreement":null},{"id":"W7131103977","doi":"10.1109/upwiecon67212.2025.11390281","title":"AI-Enhanced Wearable Sensors Connected to Cloud Platforms for Continuous Health Monitoring in Aging Populations","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 British Columbia","funders":"","keywords":"Cloud computing; Wearable computer; Process (computing); Wearable technology; Anomaly detection; Population; Continuous monitoring","score_opus":0.05401135750144125,"score_gpt":0.3504346009641183,"score_spread":0.29642324346267707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131103977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25983268,0.00034559955,0.71718234,0.010727574,0.007037278,0.0033967807,0.000028188537,0.0003358781,0.0011136688],"genre_scores_gemma":[0.9823395,0.000026455677,0.009554821,0.0018720515,0.00035384131,0.00038297664,0.000007558858,0.000036184185,0.005426633],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948681,0.00020781351,0.0017099978,0.001374112,0.0004564201,0.0013835463],"domain_scores_gemma":[0.9965944,0.0009131371,0.00042967885,0.0009377166,0.00072491984,0.00040011993],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015997934,0.00051713723,0.0011741098,0.0011862573,0.00074839283,0.0008342002,0.00079035806,0.00022780946,0.000036963826],"category_scores_gemma":[0.0005812836,0.00057778746,0.00023826296,0.0030784297,0.00004498847,0.0013343836,0.0004088303,0.0005006461,0.00009916978],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044467958,0.00097118114,0.024582217,0.0012581386,0.0003328785,0.000021071062,0.023470333,0.010704362,0.005871735,0.02653209,0.0053080237,0.9005033],"study_design_scores_gemma":[0.020030469,0.0023182475,0.06570108,0.02351256,0.00013221329,0.0000760874,0.028192542,0.6984639,0.114863224,0.022117818,0.0191443,0.0054475567],"about_ca_topic_score_codex":0.0040339595,"about_ca_topic_score_gemma":0.002938176,"teacher_disagreement_score":0.8950557,"about_ca_system_score_codex":0.000962448,"about_ca_system_score_gemma":0.0008750467,"threshold_uncertainty_score":0.99966735},"labels":[],"label_agreement":null},{"id":"W7131988402","doi":"","title":"SF2D: semi-supervised federated learning for fall detection using (un)labelled data in edge-cloud","year":2025,"lang":"en","type":"article","venue":"NPARC","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Autoencoder; Classifier (UML); Precision and recall; Encoder; Population; Benchmark (surveying); Deep learning; Train","score_opus":0.05603759593030436,"score_gpt":0.29406613953955263,"score_spread":0.23802854360924827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131988402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3025551,0.000042382795,0.69436353,0.00031675215,0.000733087,0.000455973,0.0000051768684,0.00018257032,0.0013454282],"genre_scores_gemma":[0.99168825,0.0000043868818,0.007521324,0.00016685546,0.00009570888,0.000048389003,0.000024485254,0.000014265537,0.00043633993],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981964,0.00027663622,0.00035327396,0.0006603477,0.00019213159,0.00032120015],"domain_scores_gemma":[0.99858135,0.0004513843,0.00011302714,0.0006219345,0.00017750425,0.00005477889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007039209,0.00016541901,0.00027942535,0.00027353517,0.00034128275,0.00034177664,0.0007560836,0.00011921201,0.000021019694],"category_scores_gemma":[0.00042483813,0.00018527385,0.000050844577,0.0008898439,0.000019972267,0.0007838296,0.0005274261,0.00026274973,0.000021526701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072769544,0.00014477715,0.0027305204,0.00012617627,0.00008385657,0.00001147704,0.0007202771,0.00022117213,0.30693385,0.00022163709,0.00047017002,0.6882633],"study_design_scores_gemma":[0.0014664753,0.000040542734,0.00044052734,0.00013825348,0.000013321493,0.000012182079,0.00014119859,0.97698456,0.014859956,0.0010200851,0.0046676546,0.00021522066],"about_ca_topic_score_codex":0.00021947654,"about_ca_topic_score_gemma":0.0006511764,"teacher_disagreement_score":0.9767634,"about_ca_system_score_codex":0.00014416694,"about_ca_system_score_gemma":0.00017332775,"threshold_uncertainty_score":0.7555249},"labels":[],"label_agreement":null},{"id":"W7132888438","doi":"","title":"Real-Time Location System Data to Discern Clusters of Rest-Activity in People with Dementia","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Context-Aware Activity Recognition Systems","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":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Actigraphy; Dementia; Telecare; Circadian rhythm; Intervention (counseling); Rhythm; Sample (material)","score_opus":0.04191097799454031,"score_gpt":0.33104262702852033,"score_spread":0.28913164903398003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132888438","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90783364,0.0003368072,0.08030384,0.0010016297,0.0020405583,0.0035532224,0.00015731102,0.00031783007,0.0044551333],"genre_scores_gemma":[0.9931063,0.000037218266,0.0012253803,0.000012163157,0.00012580928,0.00020353467,0.00060222606,0.0001090084,0.004578336],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937534,0.00077658537,0.0010574273,0.0023745107,0.001370892,0.00066720345],"domain_scores_gemma":[0.9939562,0.00046486652,0.0011396878,0.0034660376,0.00067886664,0.00029431382],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016922819,0.0007581636,0.0013252447,0.000978883,0.00015617402,0.0005667081,0.0023763524,0.00036424916,0.000045502893],"category_scores_gemma":[0.00013280893,0.0007726213,0.000113168135,0.0034944932,0.00006264399,0.0014608154,0.0010642647,0.00056250236,0.00067007437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0072447103,0.003457252,0.010760054,0.07725307,0.0049480777,0.00072427356,0.500854,0.008813074,0.19228216,0.0011681604,0.008597809,0.18389735],"study_design_scores_gemma":[0.0026495843,0.0015312078,0.107491754,0.041106567,0.0014115559,0.00019711403,0.05899692,0.7726119,0.00942829,0.000014458113,0.0010794824,0.0034811285],"about_ca_topic_score_codex":0.03394302,"about_ca_topic_score_gemma":0.061949495,"teacher_disagreement_score":0.7637989,"about_ca_system_score_codex":0.0007562451,"about_ca_system_score_gemma":0.0014113289,"threshold_uncertainty_score":0.9994725},"labels":[],"label_agreement":null},{"id":"W7133010003","doi":"","title":"Exploiting social context in personalized web-tasking applications","year":2014,"lang":"en","type":"article","venue":"Biblioteca Digital - Universidad Icesi","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Universidad ICESI","keywords":"Personalization; Exploit; Context (archaeology); Mobile device; Personally identifiable information; Information system; Social environment; Ubiquitous computing","score_opus":0.020770108109746707,"score_gpt":0.24028031288533144,"score_spread":0.21951020477558475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133010003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0479628,0.00023532248,0.70578974,0.0018996734,0.00020936913,0.0007610537,0.000051660718,0.0008018993,0.24228847],"genre_scores_gemma":[0.9986056,0.000009962154,0.00036593794,0.00024586043,0.00014016526,0.000032110565,0.00001359591,0.000018762708,0.0005679697],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99821645,0.00011332184,0.00030032656,0.0005629087,0.00040680048,0.00040017877],"domain_scores_gemma":[0.99872917,0.00040625627,0.00023235513,0.00034074349,0.00016512054,0.0001263717],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00030347623,0.00021501379,0.00029913662,0.002498903,0.00027666212,0.0011149584,0.00090510736,0.00012799675,0.000025867283],"category_scores_gemma":[0.000054859058,0.00025228062,0.00014522884,0.004962605,0.00010474866,0.0049413587,0.0003273585,0.0002196179,0.00021551977],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003306668,0.00024043709,0.0015624673,0.00006738885,0.000055252982,0.00003065254,0.0027813385,0.00001656657,0.00095955195,0.13624631,0.0024647366,0.85554224],"study_design_scores_gemma":[0.0059717954,0.00017710657,0.0035403827,0.00031441505,0.00002866915,0.00010225546,0.00989346,0.050196927,0.0006293803,0.01003835,0.9173707,0.0017365881],"about_ca_topic_score_codex":0.00009202089,"about_ca_topic_score_gemma":0.00007385569,"teacher_disagreement_score":0.9506428,"about_ca_system_score_codex":0.00016412606,"about_ca_system_score_gemma":0.0001265487,"threshold_uncertainty_score":0.99999297},"labels":[],"label_agreement":null},{"id":"W7133040337","doi":"","title":"Multimodal Unsupervised Deep Learning Algorithms for Fall Detection","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Context-Aware Activity Recognition Systems","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 Toronto","funders":"","keywords":"Deep learning; Modalities; Receiver operating characteristic; Anomaly detection; Set (abstract data type); Pattern recognition (psychology); Function (biology)","score_opus":0.03949453441314209,"score_gpt":0.3367478863381419,"score_spread":0.2972533519249998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133040337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12014013,0.0010170096,0.864282,0.00023946019,0.00865909,0.0034713808,0.000028471071,0.00067496096,0.0014874997],"genre_scores_gemma":[0.96360815,0.00011118893,0.00613611,0.0001008369,0.000773847,0.0023600508,0.0007689474,0.00021095145,0.025929898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99331266,0.0010308995,0.0010494472,0.0021955334,0.0013049016,0.0011065463],"domain_scores_gemma":[0.99498504,0.0014046911,0.0012482402,0.00096518535,0.0010390762,0.00035776006],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0014397434,0.000985426,0.0011200693,0.00077903573,0.00211013,0.00086263684,0.0014473997,0.00066078594,0.0008029605],"category_scores_gemma":[0.0006571354,0.0012654265,0.00082388386,0.001295552,0.00006058363,0.0009897538,0.00036505336,0.0017609,0.00026431505],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031223035,0.0002516166,0.00008455597,0.0004734752,0.00025842345,0.000025441195,0.048383452,0.0013177287,0.0080468375,0.000035122222,0.000032982636,0.94077814],"study_design_scores_gemma":[0.0029734138,0.0012526427,0.0010680737,0.0002104104,0.00020474535,0.00009705862,0.04136329,0.897326,0.008413857,0.00008607457,0.04529689,0.0017075628],"about_ca_topic_score_codex":0.0036167742,"about_ca_topic_score_gemma":0.0032322365,"teacher_disagreement_score":0.9390706,"about_ca_system_score_codex":0.0007800816,"about_ca_system_score_gemma":0.0004408495,"threshold_uncertainty_score":0.99918896},"labels":[],"label_agreement":null},{"id":"W7147140136","doi":"10.70675/d098a9edzede3z4290z8dcbzf62c0a318534","title":"Système autonome de sécurité lors de la préparation d'un repas pour les personnes cognitivement déficientes dans un habitat intelligent pour la santé","year":2017,"lang":"","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Urban environment; Population; Elderly people; Cultural environment","score_opus":0.02241961111197183,"score_gpt":0.31018615582585296,"score_spread":0.28776654471388113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7147140136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4872041,0.0011308062,0.48478973,0.0004184407,0.0011397589,0.0019509895,0.00012202766,0.00043675964,0.022807369],"genre_scores_gemma":[0.9709032,0.0004921782,0.0063517685,0.00014547571,0.00067986996,0.0007882539,0.0003622539,0.00015152412,0.020125482],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98831546,0.004131379,0.0020078707,0.0026188886,0.0014250986,0.0015012939],"domain_scores_gemma":[0.98929614,0.00404316,0.0026483482,0.0015902122,0.0018082296,0.00061389955],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.005345522,0.0015787879,0.0015423291,0.000955173,0.0021078147,0.003568675,0.0024754491,0.001178785,0.0007712507],"category_scores_gemma":[0.0017655801,0.0016727564,0.0009399816,0.00053877675,0.0005561237,0.0016666588,0.00041422521,0.0011801848,0.00072920654],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005089365,0.00503882,0.03666323,0.002454149,0.0025243736,0.0005487429,0.27645424,0.00042710357,0.033073753,0.015729154,0.0015529852,0.6250245],"study_design_scores_gemma":[0.004898924,0.0012509556,0.23576759,0.01080462,0.0017638695,0.0023629298,0.16426648,0.17444558,0.3836946,0.00813448,0.00577976,0.006830207],"about_ca_topic_score_codex":0.0018244578,"about_ca_topic_score_gemma":0.001847603,"teacher_disagreement_score":0.61819434,"about_ca_system_score_codex":0.0014317234,"about_ca_system_score_gemma":0.0025577869,"threshold_uncertainty_score":0.999696},"labels":[],"label_agreement":null},{"id":"W7153881224","doi":"10.1145/3772318.3808935","title":"10.1145/3772318.3808935","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Session (web analytics); Automation; Component (thermodynamics); Key (lock); User interface","score_opus":0.010107888268064585,"score_gpt":0.18562985473614907,"score_spread":0.1755219664680845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7153881224","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007367503,0.00003483126,0.0011466261,0.000846847,0.000014584962,0.00025619948,0.000007814473,0.00049279636,0.99646354],"genre_scores_gemma":[0.00710917,1.4195516e-7,0.00065960875,0.0002021942,0.00012960185,0.00003562427,0.0000035428366,0.00001503901,0.9918451],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986469,0.000096637414,0.00022053138,0.00041827362,0.00030151248,0.00031615898],"domain_scores_gemma":[0.9988836,0.000119081844,0.00004403548,0.00067206443,0.00008308831,0.00019811116],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00024567088,0.00015597425,0.00020656716,0.00011726632,0.000097437325,0.0002041617,0.0007768784,0.00006111956,0.94710356],"category_scores_gemma":[0.00003233108,0.00016099698,0.000081178245,0.00046018933,0.000019992376,0.00052187755,0.00013557395,0.00009928328,0.9885581],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009248158,0.000037284786,3.366692e-7,0.000002796977,0.000010623235,0.000009370102,0.00004234825,0.0000108071945,0.00007852938,0.000007019674,0.076144025,0.9236476],"study_design_scores_gemma":[0.00019626855,0.00009456608,0.000055573953,0.000020414396,0.000003726678,0.0000435425,6.146518e-7,0.0022304473,0.00020121869,0.000023803945,0.99691623,0.00021357578],"about_ca_topic_score_codex":0.000043054548,"about_ca_topic_score_gemma":6.098687e-7,"teacher_disagreement_score":0.923434,"about_ca_system_score_codex":0.000050470295,"about_ca_system_score_gemma":0.000050289178,"threshold_uncertainty_score":0.6565266},"labels":[],"label_agreement":null},{"id":"W7163207952","doi":"10.1109/icsm64417.2025.11541562","title":"A Deep Learning-Based Multi-Feature Fall Detection Approach Using Silhouette and Motion Data","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","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 Alberta","funders":"","keywords":"Silhouette; Motion (physics); Feature (linguistics); Pattern recognition (psychology); Noise (video)","score_opus":0.07049159096818569,"score_gpt":0.29540254290963697,"score_spread":0.22491095194145128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7163207952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010349103,0.0010354818,0.98498404,0.00051861577,0.0011154639,0.0010278944,0.000012428871,0.00036463665,0.0005923642],"genre_scores_gemma":[0.95749384,0.000020737658,0.040624272,0.00038533195,0.00013062841,0.000024173329,0.000048928556,0.000028539022,0.0012435234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99505246,0.0012642195,0.00057525333,0.002036597,0.00049744034,0.0005740316],"domain_scores_gemma":[0.99689984,0.00041765996,0.0003962553,0.0016994011,0.00039696635,0.00018987054],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0017119475,0.000511904,0.0005607581,0.0006348298,0.00082122936,0.0013115746,0.0011409819,0.0004880625,0.000013686736],"category_scores_gemma":[0.0005186341,0.00054406706,0.00012007382,0.001553761,0.00015677487,0.0021839887,0.0013385387,0.0009838262,0.000022967934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056955123,0.00043938265,0.00386378,0.0004612415,0.00016667423,0.000009040368,0.0005570959,0.009336847,0.004309897,0.00004400919,0.00006048063,0.9806946],"study_design_scores_gemma":[0.0016643439,0.00007318235,0.0034436535,0.00021920222,0.00013448192,0.000059889786,0.00029403061,0.9904505,0.0013911518,0.000015789237,0.0017604509,0.00049330393],"about_ca_topic_score_codex":0.00094474974,"about_ca_topic_score_gemma":0.0015079823,"teacher_disagreement_score":0.9811137,"about_ca_system_score_codex":0.00024234234,"about_ca_system_score_gemma":0.0002462283,"threshold_uncertainty_score":0.99972516},"labels":[],"label_agreement":null},{"id":"W7199133","doi":"10.1007/978-3-642-21535-3_33","title":"An Intelligent Agent for Determining Home Occupancy Using Power Monitors and Light Sensors","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Occupancy; Thermostat; Computer science; Home automation; Real-time computing; Power consumption; Home security; Range (aeronautics); Embedded system; Power (physics); Telecommunications; Computer security; Electrical engineering; Architectural engineering; Engineering","score_opus":0.0554724926486958,"score_gpt":0.29022424665028246,"score_spread":0.23475175400158665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7199133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011581767,0.000379705,0.98334813,0.00008130281,0.0033742422,0.00078677176,0.000011531349,0.00017466629,0.0002618728],"genre_scores_gemma":[0.81442374,0.000021570735,0.18464835,0.00037677493,0.00038349786,0.000018366447,0.000002304061,0.000055120297,0.00007029993],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995963,0.00007073749,0.0006772187,0.0018867736,0.00066737377,0.000734919],"domain_scores_gemma":[0.9970918,0.00048636986,0.0004665536,0.0012992353,0.00036806543,0.00028795475],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00090896175,0.0006501854,0.0007118238,0.0010376136,0.00036338327,0.00078967714,0.0019383366,0.00036253803,0.000012407947],"category_scores_gemma":[0.00006526497,0.0006207568,0.00016533819,0.00035482494,0.00036522714,0.0011873919,0.00084721344,0.0004941217,0.000014800921],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001692965,0.000056318193,0.00055186875,0.00008211597,0.00003236697,0.000096562966,0.0051416615,0.00092596165,0.0010044877,0.0010473239,0.000007045324,0.99103737],"study_design_scores_gemma":[0.0009458481,0.0016067023,0.00084530946,0.0025462843,0.00006923706,0.0010130861,0.000007809972,0.8942402,0.02225959,0.06839662,0.0043913536,0.0036779807],"about_ca_topic_score_codex":0.00003388889,"about_ca_topic_score_gemma":0.00003855097,"teacher_disagreement_score":0.9873594,"about_ca_system_score_codex":0.00030359253,"about_ca_system_score_gemma":0.0003483115,"threshold_uncertainty_score":0.9996244},"labels":[],"label_agreement":null},{"id":"W77968930","doi":"10.1007/978-3-642-39470-6_4","title":"PhonAge: Adapted SmartPhone for Aging Population","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University; Université de Sherbrooke","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.029017486609838574,"score_gpt":0.2550801597685381,"score_spread":0.22606267315869952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W77968930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018901566,0.0002492136,0.99226785,0.0010065936,0.0032011434,0.0011838884,0.000012194544,0.00032319414,0.0015669318],"genre_scores_gemma":[0.68067104,0.000012321717,0.31511778,0.0014197158,0.000956279,0.00010117206,0.000037585203,0.00006881832,0.0016153231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959543,0.000049421145,0.000671604,0.0016937656,0.0009370304,0.00069388805],"domain_scores_gemma":[0.99653155,0.00095384836,0.00049558486,0.0013136734,0.00051876,0.0001866028],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00094866805,0.0005608053,0.00067206565,0.0009903366,0.00032095966,0.0008993162,0.002298313,0.00034088187,0.000051480853],"category_scores_gemma":[0.00014761636,0.00055495644,0.00020414412,0.00058521656,0.0002156955,0.0014286597,0.0008433351,0.00055913907,0.00016352894],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000408801,0.00001997157,0.000053654167,0.000057904414,0.000014590401,0.0000117178015,0.00042812046,0.0017278449,0.00023016603,0.0037726695,0.000104132945,0.99357516],"study_design_scores_gemma":[0.0008916083,0.0002094658,0.0011766908,0.0009429912,0.000018299224,0.00012930323,4.4170844e-7,0.83981425,0.0016197097,0.14552422,0.008176606,0.0014964167],"about_ca_topic_score_codex":0.00018171627,"about_ca_topic_score_gemma":0.0002231327,"teacher_disagreement_score":0.9920787,"about_ca_system_score_codex":0.00043650385,"about_ca_system_score_gemma":0.0002998787,"threshold_uncertainty_score":0.9996902},"labels":[],"label_agreement":null},{"id":"W801961223","doi":"10.4018/ijmhci.2015070107","title":"Enhancing Self-Reflection with Wearable Sensors Workshop","year":2015,"lang":"en","type":"article","venue":"International Journal of Mobile Human Computer Interaction","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Reflection (computer programming); Wearable computer; Computer science; Data science; Human–computer interaction; Wearable technology; Work (physics); Big data; Engineering","score_opus":0.04367157986297788,"score_gpt":0.3259073405427593,"score_spread":0.28223576067978146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W801961223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3921772,0.000055766082,0.6008124,0.00030773203,0.0056194454,0.00014894268,6.3557076e-7,0.00013222307,0.00074569334],"genre_scores_gemma":[0.9793012,0.000011514014,0.018501576,0.0001644163,0.0017591892,0.00001543322,0.000002044277,0.000018860617,0.00022572301],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975506,0.00021476115,0.00073658273,0.00030425822,0.0009950172,0.00019878172],"domain_scores_gemma":[0.99618405,0.00021639145,0.0009528219,0.000253274,0.0022148052,0.00017865028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075772067,0.00020271726,0.00029302892,0.0006018389,0.00009240131,0.0006392349,0.00084924896,0.000076350065,0.000033335135],"category_scores_gemma":[0.000033412776,0.00017863061,0.00014795917,0.00025087316,0.000023945388,0.0029695223,0.00016794381,0.00044755376,0.00008185185],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017681787,0.004776263,0.0067558587,0.00010794987,0.005328691,0.0023731696,0.039944768,0.10256053,0.038764168,0.0023388104,0.02746015,0.7678215],"study_design_scores_gemma":[0.024874676,0.014565944,0.0053187194,0.009575926,0.00038315798,0.07337904,0.008178785,0.3385536,0.18563129,0.0053055445,0.33000517,0.004228127],"about_ca_topic_score_codex":0.000049603168,"about_ca_topic_score_gemma":0.00006639706,"teacher_disagreement_score":0.7635933,"about_ca_system_score_codex":0.0006566646,"about_ca_system_score_gemma":0.00017246346,"threshold_uncertainty_score":0.72843444},"labels":[],"label_agreement":null},{"id":"W85532711","doi":"10.1007/978-3-642-30779-9_31","title":"A Smart Home 3D Modeling Approach for Spatiotemporal Analysis","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Adaptation (eye); Apartment; Home automation; Object (grammar); Sample (material); Database; Real-time computing; Artificial intelligence; Telecommunications","score_opus":0.04891736990703175,"score_gpt":0.26156920749347895,"score_spread":0.2126518375864472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W85532711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009145383,0.0005700821,0.9955844,0.00018671635,0.0011491032,0.0008978194,0.00002488313,0.00023408046,0.0012614559],"genre_scores_gemma":[0.51340586,0.000008754363,0.48548853,0.00033629584,0.00048711107,0.0000527214,0.000034518773,0.000032946118,0.00015325948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951619,0.000066826025,0.0007846994,0.0019470083,0.0011417632,0.00089781306],"domain_scores_gemma":[0.99645585,0.00053447415,0.0004702234,0.0017222567,0.0005429973,0.0002741957],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019108681,0.00064635085,0.001071664,0.0021131444,0.00032594157,0.0007749174,0.0028793477,0.0004404607,0.000013197567],"category_scores_gemma":[0.00007267742,0.0006241041,0.00048841065,0.0015047635,0.00027554503,0.0013294676,0.0010184527,0.000640274,0.000029217661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011621492,0.00007951908,0.0004219348,0.00009991245,0.00020496936,0.0000080395175,0.0010806641,0.16049817,0.000028937277,0.0026312827,0.000008771284,0.8349262],"study_design_scores_gemma":[0.00026940397,0.00006846718,0.000045219593,0.00008379321,0.0000976217,0.000028422008,2.5531227e-7,0.9870558,0.000070806556,0.011131128,0.00040189322,0.0007471923],"about_ca_topic_score_codex":0.00011749635,"about_ca_topic_score_gemma":0.00014919628,"teacher_disagreement_score":0.834179,"about_ca_system_score_codex":0.0003925698,"about_ca_system_score_gemma":0.00048877177,"threshold_uncertainty_score":0.99962103},"labels":[],"label_agreement":null},{"id":"W90200542","doi":"10.1007/978-3-642-28783-1_21","title":"A Two-Stage Corrective Markov Model for Activities of Daily Living Detection","year":2012,"lang":"en","type":"book-chapter","venue":"Advances in intelligent and soft computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Hidden Markov model; Stage (stratigraphy); Computer science; Markov model; Artificial intelligence; Markov chain; Markov process; Segmentation; Domain (mathematical analysis); Pattern recognition (psychology); Machine learning; Data mining; Mathematics; Statistics","score_opus":0.03150297612552224,"score_gpt":0.2856761116245793,"score_spread":0.25417313549905707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W90200542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011844606,0.005300998,0.9760688,0.000006142416,0.0010907724,0.0006029417,0.000019130397,0.00010232727,0.015624446],"genre_scores_gemma":[0.9762959,0.0006039144,0.012216337,0.00002861054,0.0002179105,0.000030346573,0.0000040833197,0.00004535955,0.01055754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980545,0.00005650841,0.0006480046,0.0006050192,0.00027196648,0.0003639893],"domain_scores_gemma":[0.996158,0.0025241585,0.0007311109,0.00032113676,0.00018992803,0.00007566672],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007192979,0.00037965842,0.0006506543,0.00038633094,0.0001512548,0.00008771089,0.00037834366,0.00017830734,0.000007274147],"category_scores_gemma":[0.00013709988,0.0004160136,0.00017704314,0.00007835439,0.00010957911,0.0010167122,0.0004056646,0.000375871,0.0000028701297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030259782,0.000030498086,0.00021871737,0.00026746976,0.00004347588,0.0000013177546,0.0046676174,0.0022686596,0.000078722245,0.009045074,0.000003132569,0.98334503],"study_design_scores_gemma":[0.0002458599,0.00010628739,0.000017824774,0.0016034379,0.000028552115,0.000023297294,0.00058324274,0.9844352,0.0013465311,0.007589071,0.00339054,0.00063016143],"about_ca_topic_score_codex":0.00003023857,"about_ca_topic_score_gemma":0.00032174157,"teacher_disagreement_score":0.9827149,"about_ca_system_score_codex":0.00016363205,"about_ca_system_score_gemma":0.00006478815,"threshold_uncertainty_score":0.9998292},"labels":[],"label_agreement":null},{"id":"W949896363","doi":"10.1007/978-1-4471-5499-0_9","title":"Enhancing LACOME to Consider Privacy and Security Concerns","year":2013,"lang":"en","type":"book-chapter","venue":"Human-computer interaction series","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Internet privacy; Computer security; Business; Computer science","score_opus":0.06395744805289734,"score_gpt":0.3134798777689585,"score_spread":0.2495224297160612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W949896363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020117288,0.0006323875,0.5313843,0.0055423654,0.01682357,0.0047818953,0.00013001252,0.0032184804,0.41736972],"genre_scores_gemma":[0.6251673,0.00008709464,0.015685515,0.0039542206,0.0039602206,0.0002541587,0.00007490423,0.00022973731,0.35058686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99687433,0.00010617612,0.0008843286,0.0012332277,0.00048832846,0.00041362102],"domain_scores_gemma":[0.99716866,0.00036920572,0.0005989892,0.0010109536,0.00054834026,0.00030385057],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00024984297,0.0006852633,0.00088091654,0.00049311074,0.00033775304,0.0014642423,0.00071926793,0.0003314629,0.0012926655],"category_scores_gemma":[0.000026391943,0.0007319589,0.00021051518,0.00007404292,0.00013015681,0.0028887861,0.0012760261,0.00072636845,0.0019183599],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012459057,0.00023519146,0.00013361247,0.0012044914,0.0015886776,0.00042732683,0.048498083,0.00002127028,0.00536638,0.5803386,0.18441764,0.17764416],"study_design_scores_gemma":[0.00047750887,0.0004009832,0.00017883204,0.0011651816,0.000040439387,0.0007721141,0.00011758046,0.00043908597,0.0026532938,0.019029943,0.9733892,0.001335824],"about_ca_topic_score_codex":0.00014382161,"about_ca_topic_score_gemma":0.00031907184,"teacher_disagreement_score":0.7889716,"about_ca_system_score_codex":0.00023170825,"about_ca_system_score_gemma":0.0000876666,"threshold_uncertainty_score":0.99962026},"labels":[],"label_agreement":null},{"id":"W986746244","doi":"","title":"Cognitive assistance to meal preparation : design, implementation, and assessment in a living lab","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec à Chicoutimi; Group for Research in Decision Analysis; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Autonomy; Assistive technology; Living lab; Assisted living; Independent living; Cognition; Ambient intelligence; Activities of daily living; Meal preparation; Computer science; Quality of life (healthcare); Unit (ring theory); Psychology; Knowledge management; Applied psychology; Human–computer interaction; Gerontology; Medicine","score_opus":0.04760307705661504,"score_gpt":0.32748470239138533,"score_spread":0.2798816253347703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W986746244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07285234,0.00031540004,0.91299695,0.004880606,0.00020781097,0.0014236281,0.000047872712,0.0002045481,0.007070848],"genre_scores_gemma":[0.88385,0.000064798,0.114670426,0.00013256766,0.000018170202,0.0005098125,0.00009047598,0.000025109272,0.0006386823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9879902,0.009090751,0.00072151615,0.0011784221,0.00064578984,0.00037332912],"domain_scores_gemma":[0.9916443,0.0029596405,0.00060641975,0.0012349919,0.0032876425,0.00026696213],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.011766697,0.00034586564,0.000447864,0.00040016606,0.00021858743,0.0011147724,0.0010171909,0.00018965505,0.000031972755],"category_scores_gemma":[0.0012330409,0.00040935024,0.000077302546,0.00064162863,0.00007426544,0.0006498539,0.0022539967,0.0005626277,0.000024347282],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007643151,0.0017411174,0.029611968,0.00048690152,0.00031927484,0.000034889214,0.17775041,0.00033815327,0.0026554102,0.053545527,0.0047525237,0.7286874],"study_design_scores_gemma":[0.008185062,0.00003315004,0.3574774,0.032815147,0.0002942779,0.00017237644,0.014283481,0.4717174,0.053685766,0.038550045,0.01615408,0.00663185],"about_ca_topic_score_codex":0.001854835,"about_ca_topic_score_gemma":0.013154675,"teacher_disagreement_score":0.8109976,"about_ca_system_score_codex":0.0004725796,"about_ca_system_score_gemma":0.0011145294,"threshold_uncertainty_score":0.99992216},"labels":[],"label_agreement":null}]}